diff --git a/.buildinfo b/.buildinfo index 7e4e582..bbafc0f 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 9c7ce5e4f725cdfa4e95f1ad69204be1 +config: c7628aeaa10cf2e8cbdb0ef3d1d5c530 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.doctree index 9628d32..5a69043 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.doctree index 60588cc..94fbe52 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree index 4c8116a..900e0e9 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree index 4a56366..5f21093 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree index f714b36..278b4dd 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree index 4ef16ad..7210e0a 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree index 66c224d..2158fe6 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.doctree index d2bbaa1..127c8ae 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.doctree index 088b708..00f3dbb 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree index d49c7ff..d866285 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.doctree index ad3086c..e6ab547 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.doctree index 5203bcc..fd75bb4 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.doctree index fbb296d..5c6ecc1 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.doctree index 54116fa..44c3d1d 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.doctree new file mode 100644 index 0000000..e4ef8f4 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.doctree new file mode 100644 index 0000000..42026b9 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.doctree index bc8a020..2405fc4 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.doctree new file mode 100644 index 0000000..6c13a21 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree index 6148d4f..8597fe3 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.doctree index 8af4760..2404ae9 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree index 8bf4484..9b91c09 100644 Binary files a/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree and b/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.doctree b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.doctree index 45614a5..e500ad7 100644 Binary files a/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.doctree and b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.figure.doctree b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.figure.doctree new file mode 100644 index 0000000..c41f09e Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.figure.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.table.doctree b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.table.doctree new file mode 100644 index 0000000..cd01a36 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.doc.sdynpy_latex.table.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree index 199b32e..a61245b 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree index b1b2184..7a5f245 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree index 155f67c..e955b90 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree index 06d32e5..744f793 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.doctree index f1982e0..5e04c28 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.doctree index 9adde1d..840b984 100644 Binary files a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.doctree and b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.doctree b/.doctrees/_autosummary/sdynpy.modal.doctree index b660694..6804d21 100644 Binary files a/.doctrees/_autosummary/sdynpy.modal.doctree and b/.doctrees/_autosummary/sdynpy.modal.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.doctree new file mode 100644 index 0000000..bc150c0 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.doctree new file mode 100644 index 0000000..16817c8 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.doctree new file mode 100644 index 0000000..861e991 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.doctree index 10db9d1..f2982f3 100644 Binary files a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.doctree and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.doctree index a5a8bf5..d7416ef 100644 Binary files a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.doctree and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.doctree new file mode 100644 index 0000000..ad86901 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.doctree new file mode 100644 index 0000000..ee630a0 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.doctree new file mode 100644 index 0000000..9c28398 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.doctree index 66446ac..23bcaa8 100644 Binary files a/.doctrees/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.doctree and b/.doctrees/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.doctree index a66f8f1..3d3d6d9 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree index 28d5bf3..249dfdc 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree index 949990f..169f170 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.doctree new file mode 100644 index 0000000..a4211e5 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree index 1952a0b..b144c6f 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.doctree index a7870f2..bf7a006 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.doctree index 2dea26d..14d65a2 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.doctree index d659cd6..2f2b69d 100644 Binary files a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.doctree and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.doctree new file mode 100644 index 0000000..272a17c Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.doctree new file mode 100644 index 0000000..05f27c0 Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.doctree differ diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.doctree new file mode 100644 index 0000000..08bf81a Binary files /dev/null and b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index dac5129..dde8f38 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/example_problems/airplane_modal_test.doctree b/.doctrees/example_problems/airplane_modal_test.doctree index 7b6ba5e..08a03ba 100644 Binary files a/.doctrees/example_problems/airplane_modal_test.doctree and b/.doctrees/example_problems/airplane_modal_test.doctree differ diff --git a/.doctrees/example_problems/rattlesnake_demonstration.doctree b/.doctrees/example_problems/rattlesnake_demonstration.doctree index 06f920a..f23ac0c 100644 Binary files a/.doctrees/example_problems/rattlesnake_demonstration.doctree and b/.doctrees/example_problems/rattlesnake_demonstration.doctree differ diff --git a/.doctrees/modal_tutorials.doctree b/.doctrees/modal_tutorials.doctree index 93e2f31..4be4434 100644 Binary files a/.doctrees/modal_tutorials.doctree and b/.doctrees/modal_tutorials.doctree differ diff --git a/.doctrees/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.doctree b/.doctrees/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.doctree new file mode 100644 index 0000000..5394564 Binary files /dev/null and b/.doctrees/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.doctree differ diff --git a/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb b/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb new file mode 100644 index 0000000..5af974c --- /dev/null +++ b/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb @@ -0,0 +1,3842 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cc4417ad-c73f-4426-8e76-c7a69ae33dd5", + "metadata": {}, + "source": [ + "# Modal Tutorial 06: Complex Modes\n", + "Complex modes can be unintuitive and difficult to understand compared to real normal modes. George Fox Lang wrote an article in Sound and Vibration magazine called \"Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints.\" He states:\n", + "\n", + "> Complex Modes is one of those topics that every vibration practitioner understands -- just not fully.\n", + "\n", + "The author of this document agrees with Lang; in fact, his own lack of knowledge of complex modes is what caused him to write this document, to teach himself what is going on!\n", + "\n", + "To better understand complex modes, we will start from the differential equations of motion and develop the theory of complex modes, discussing:\n", + "\n", + " - Real Modes and their Limitations\n", + " - The state space transformation to derive the complex mode eigenvalue problem\n", + " - Computing natural frequency, damping, and complex mode shapes\n", + " - Computing FRFs from the modal parameters\n", + "\n", + "This document will largely follow the equations from the following sources:\n", + "\n", + " - [An engineering interpretation of the complex modal mass in\n", + "structural dynamics by Lopez and Brinckner](https://doi.org/10.1016/j.ymssp.2023.110621)\n", + " - [Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints by Lang](http://www.sandv.com/downloads/1211lang.pdf)\n", + " - [The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation? by Peeters, et al.](https://content.iospress.com/articles/shock-and-vibration/sav00272)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3fee8375-b72c-474f-bdac-a2ddd842070c", + "metadata": {}, + "source": [ + "## The Second-Order, Linear Differential System of Equations\n", + "Most vibration engineers are familiar with the standard system of second-order, linear differential equations.\n", + "\n", + "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n", + "\n", + "where $\\mathbf{M}$ is the system mass matrix, $\\mathbf{C}$ is the system damping matrix, $\\mathbf{K}$ is the system stiffness matrix, $\\mathbf{F}$ is the forcing applied to the system, and $\\mathbf{x}$ is the system displacement response. $\\dot{\\mathbf{x}}$ and $\\ddot{\\mathbf{x}}$ represent the first and second derivatives of the response, velocity and acceleration, respectively.\n", + "\n", + "For a standard linear, time-invariant (LTI) system, the mass, stiffness matrices are not functions of time (i.e. the mass of the system doesn't change). However, the forces and responses are generally a function of time." + ] + }, + { + "cell_type": "markdown", + "id": "24f927e0-5131-4f23-91a1-7c42a9ec8a37", + "metadata": {}, + "source": [ + "## Real Modes -- Damping is Hard\n", + "\n", + "Many times, when we are just starting out, we will simply ignore the damping in our part because it is \"hard\" or it doesn't fit with our elegant mathematical tools.\n", + "\n", + "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n", + "\n", + "To try to understand the free vibration response of the this system, we can make the following assumptions:\n", + "\n", + " - $\\mathbf{F} = \\mathbf{0}$ -- There is no force on the system.\n", + " - $\\mathbf{x} = \\mathbf{\\phi}\\cos(\\omega t)$ -- The response of the system looks like some shape $\\mathbf{\\phi}$ that will vibrate over time (represented mathematically by multiplying by a cosine function), and the vibration will have a frequency of $\\omega$ radians per second.\n", + "\n", + "Using the chain rule for differentiation, it follows that $\\ddot{\\mathbf{x}} = -\\omega^2\\mathbf{\\phi}\\cos(\\omega t)$\n", + "\n", + "We can substitute these equations into the previous equation to arrive at an equation in the form of the well-known [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem).\n", + "\n", + "First we make the substitutions.\n", + "\n", + "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}\\cos(\\omega t)) + \\mathbf{K}\\mathbf{\\phi}\\cos(\\omega t) = \\mathbf{0}$$\n", + "\n", + "We can then recognize that the term $\\cos(\\omega t))$ is in general nonzero so can be cancelled.\n", + "\n", + "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}) + \\mathbf{K}\\mathbf{\\phi} = \\mathbf{0}$$\n", + "\n", + "We can then add $\\omega^2\\mathbf{M}\\mathbf{\\phi}$ to both sides of the equation.\n", + "\n", + "$$\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{M}\\mathbf{\\phi}$$\n", + "\n", + "This is now the same form of the [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem), which is $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$." + ] + }, + { + "cell_type": "markdown", + "id": "ae951b07-3074-4634-8c80-ab60435a9630", + "metadata": {}, + "source": [ + "Here it can help to further rearrange the equation by premultiplying by the inverse of the mass matrix $\\mathbf{M}^{-1}$. Note that this is generally acceptable to do, because the mass matrix should generally be positive definite and therefore invertable (said another way, every degree of freedom in the system has mass associated with it).\n", + "\n", + "$$\\mathbf{M}^{-1}\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{\\phi}$$\n", + "\n", + "### Geometric Interpretation of the Eigenvalues and Eigenvectors\n", + "\n", + "We can gain intuition here by thinking about what the eigenvalue problem means *geometrically*. We have a vector $\\mathbf{\\phi}$ that represents some shape. Looking at the right side of the equation, if we multiply that vector by a scalar $\\omega^2$, it will simply scale the length of the vector, but not change its direction in which it's pointing. In the context of a vibration shape, this means that it will scale the shape larger or smaller, but it won't change the shape of vibration. You get a bigger or smaller vibration with the same shape.\n", + "\n", + "Now because of the equality, the left-hand side of the equation must be equivalent to the right-hand side of the equation, which means that the left-hand side must also be equivalent to simply scaling the original vector $\\mathbf{\\phi}$. However, we know from linear algebra that multiplying a vector by a matrix will in general change the length of the vector *and* where the vector is pointing. In the context of our vibration shape $\\mathbf{\\phi}$, this means that multiplying a vibration shape by a matrix will in general change not only the size of the vibration shape, but the shape itself.\n", + "\n", + "Let's look at a brief example to illustrate this point." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e58c8729-2539-49d2-a7c7-5913080653e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scalar:\n", + "0.5507979025745755\n", + "Matrix:\n", + "[[0.70814782 0.29090474]\n", + " [0.51082761 0.89294695]]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set random seed so we can get repeatable results for the documentation.\n", + "# Remove this line to allow the matrices and vectors to be random.\n", + "np.random.seed(3)\n", + "\n", + "# Create a vector to test\n", + "vector = np.array([[1],\n", + " [2]])\n", + "\n", + "# Now create a random scalar\n", + "scalar = np.random.random()\n", + "print('Scalar:')\n", + "print(scalar)\n", + "\n", + "# Now create a random matrix\n", + "matrix = np.random.random((2,2))\n", + "print('Matrix:')\n", + "print(matrix)\n", + "\n", + "# Now multiply the vector by the scalar, and multiply the vector by the matrix\n", + "vector_scalar = scalar*vector\n", + "vector_matrix = matrix@vector # Matrix multiplication is @ in numpy.\n", + "\n", + "# Now plot them\n", + "fig, ax = plt.subplots(1,2,figsize=(10,5), sharex=True,sharey=True)\n", + "ax[0].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n", + "ax[0].quiver(*vector_scalar, color='red', angles='xy', scale_units='xy', scale=1)\n", + "ax[0].set_title('Multiply Vector by Scalar')\n", + "ax[1].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n", + "ax[1].quiver(*vector_matrix, color='red', angles='xy', scale_units='xy', scale=1)\n", + "ax[1].legend(['Original Vector','Modified Vector'])\n", + "ax[1].set_title('Multiply Vector by Matrix')\n", + "ax[0].set_ylim([-3,3])\n", + "ax[0].set_xlim([-3,3]);" + ] + }, + { + "cell_type": "markdown", + "id": "d3c7ea4b-d78c-45ca-b5f9-29ecf27aa4ed", + "metadata": {}, + "source": [ + "Clearly, multiplying by a scalar has resulted in a vector with the same direction and a different length, whereas multiplying by a matrix has changed not only the length, but also the direction of the vector.\n", + "\n", + "The resulting direction of the vector will not only be dependent on the matrix that it is multiplied by, but also the original vector itself. Multiplying different vectors by the same matrix will generally result in a different final direction of the resulting vectors. We can investigate this by multiplying many different vectors by the matrix from the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "45bc6338-9cbb-46cd-a241-093ef02801bf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Here we make a 20x20 grid of 2x1 vectors that we can multiply by our matrix\n", + "vectors = np.mgrid[-3:3:20j,-3:3:20j][np.newaxis].transpose()\n", + "\n", + "scaled_vectors = matrix @ vectors\n", + "\n", + "# Reshape for easier plotting\n", + "vectors_for_plotting = vectors.reshape(-1,2).transpose()\n", + "scaled_vectors_for_plotting = scaled_vectors.reshape(-1,2).transpose()\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "ax.quiver(*vectors_for_plotting, *vectors_for_plotting, color='blue', angles='xy', scale_units='xy', scale=10)\n", + "ax.quiver(*vectors_for_plotting, *scaled_vectors_for_plotting, color='red', angles='xy', scale_units='xy', scale=10)\n", + "ax.legend(['Original Vector','Multiplied Vector']);" + ] + }, + { + "cell_type": "markdown", + "id": "7d4844ae-ad85-46cd-b741-2321b59604c9", + "metadata": {}, + "source": [ + "Clearly, we can see that in general, vectors in different directions will be modified by the matrix differently. However, looking closely at the above plot, we can see that there are vectors in particular directions that maintain their original direction after multiplication by the matrix (a change in length is acceptable). And if you recall, we are looking for exactly such a vector that when multiplied by a matrix retains its direction. Therefore, in order to solve the eigenvalue equation listed previously, we need to find these special directions. We can't just use any shape $\\mathbf{\\phi}$. We have to find particular shapes $\\mathbf{\\phi}$ that when multiplied by the system matrices $\\mathbf{M}^{-1}\\mathbf{K}$ do not change shape but perhaps get bigger or smaller. Mathematically, we call these vectors the *eigenvectors* where the prefix *eigen* is German for *particular*. In structural dynamics, we call these particular shapes the *mode shapes*, and they have many special properties.\n", + "\n", + "Similarly, we call the scalar quantity $\\omega^2$ that gets multiplied by the eigenvector the *eigenvalue*. It is the particular value that goes along with the particular vector. We will generally have as many eigenvector/eigenvalue pairs as we have degrees of freedom in the system.\n", + "\n", + "We note that there are linear algebra tools available to solve for the eigenvalues and eigenvectors of a given matrix. For example, in Numpy, we can use `np.linalg.eig`. We can then plot the eigenvector directions on the above figure to show that they indeed align with the directions where the vectors are scaled but the direction does not change. Note also that there are two degrees of freedom in this problem, so we find two eigenvectors, or two directions where the direction of the vectors do not change." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "92c994f7-31b6-4014-ade5-8ce28df278a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eigenvalues, eigenvectors = np.linalg.eig(matrix)\n", + "# Plot the positive and negative eigenvectors so it looks like a line through the origin\n", + "# Also reshape them so they are easier to plot\n", + "# Multiply by 4 so they take up the whole plot\n", + "eigenvectors_to_plot = 4*np.array((eigenvectors,-eigenvectors)).transpose(1,0,2)\n", + "ax.plot(*eigenvectors_to_plot,'k--')\n", + "ax.set_ylim([-3,3])\n", + "ax.set_xlim([-3,3])\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "5e4b233b-ab96-49cf-afe7-21d8ac40ac72", + "metadata": {}, + "source": [ + "We see that vectors that are along the directions represented by the eigenvectors do not change direction, only change scale." + ] + }, + { + "cell_type": "markdown", + "id": "83279b35-f120-4a31-88f8-3d6087c90379", + "metadata": {}, + "source": [ + "Without approximation or loss of generality, we can take *all* of the eigenvectors or mode shapes of a system as column vectors and *stack* them side-by-side to form a matrix. This matrix forms a coordinate transformation: in the same way we can transform between a local and global coordinate system, we can also transform between *physical* and *modal* degrees of freedom. Physical degrees of freedom are generally represented by some physical quantity (e.g., Point 1 moves 2.5 centimeters in the vertical direction). Modal degrees of freedom are generally represented by some *modal degree of freedom* or *modal quantity*. Motions are then represented by how much of each mode shape is present in the motion (e.g., the system moves with a shape that looks like mode shape 1 scaled up by a factor of 2, plus mode shape 2 scaled down by a factor of 1/2). We can represent this mathematically by saying physical motions $\\mathbf{x}$ are represented by a linear combination of shapes $\\mathbf{\\phi}$. We call the coefficient matrix $\\mathbf{q}$ the *modal coefficients* or *modal degrees of freedom*, because they describe how much of each mode shape in $\\mathbf{\\phi}$ is present in $\\mathbf{x}$.\n", + "$$\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$" + ] + }, + { + "cell_type": "markdown", + "id": "f9a4baa0-4a8d-44fc-84a8-14e0885710e3", + "metadata": {}, + "source": [ + "We can substitute this transformation into our original undamped equations of motion to get equations of motion in terms of the modal degrees of freedom $\\mathbf{q}$.\n", + "$$\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{F}$$\n", + "\n", + "We note that the mode shapes $\\mathbf{\\phi}$ are invariant in time, (they are a function of the matrices $\\mathbf{M}$ and $\\mathbf{K}$, which are also invariant in time). Therefore taking the derivative of the physical response $\\mathbf{x}$ is simply\n", + "$$\\ddot{\\mathbf{x}} = \\mathbf{\\phi}\\ddot{\\mathbf{q}}$$" + ] + }, + { + "cell_type": "markdown", + "id": "6c218db3-a489-4015-861e-492ef63345eb", + "metadata": {}, + "source": [ + "While the transformed equation above is technically valid, we generally do not like this form, because it breaks the symmetry of the mass and stiffness matrices. For all real structures, the mass, stiffness, and damping matrices should be symmetric. This is a function of Netwton's third law of equal and opposite reactions: if there is a force developing at degree of freedom 1 due to motion of degree of freedom 2, an equal and opposite force will develop on degree of freedom 2 due to degree of freedom 1 pushing back on it. Currently, however, in our transformed equations of motion, the new effective mass matrix $\\mathbf{M}\\mathbf{\\phi}$ is not symmetric, nor is the equivalent effective stiffness matrix. We therefore premultiply the equations of motion by $\\mathbf{\\phi}^T$.\n", + "\n", + "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "\n", + "In this final format, the effective mass and stiffness matrices $\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$ and $\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$ are symmetric. We will often make a substitution\n", + "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n", + "$$\\tilde{\\mathbf{K}}=\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$$\n", + "where $\\tilde{\\mathbf{M}}$ and $\\tilde{\\mathbf{K}}$ are referred to as the *modal mass* and *modal stiffness* matrices. The modal system of equations is then\n", + "\n", + "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "\n", + "The symmetry of the system matrices is not the only interesting property when we transform into modal coordinates. It turns out that the mode shapes $\\mathbf{\\phi}$ *diagonalize* the mass and stiffness matrices. The modal mass and stiffness matrices, formed by pre- and post-multiplying the mass and stiffness matrices by the mode shapes, are diagonal. This physically means that there is no coupling between modal degrees of freedom; the response of mode 1 does not depend at all on the response of mode 2. They can be treated as entirely separate equations. Compare this to a physical mass and stiffness matrix, where there would be coupling between the degrees of freedom.\n", + "\n", + "We can think about this concept physically. In the physical domain, if I pluck the end of a beam, all of the degrees of freedom on the beam will eventually vibrate. This is because the physical degrees of freedom are all connected to one another through the mass and stiffness of a beam. In the modal domain, however, if I pluck \"mode 1\" (an admittedly hard thing to accomplish in practice, as you would need to deform the beam into its first mode shape and then let it go instantaneously), none of the other modes will be excited at all, because there is no coupling between them. Note that this still means that the entire beam will vibrate, because the mode shape of the beam contains all the beam degrees of freedom, so if the mode shape vibrates, the whole beam vibrates. In fact because the modal representation described above is exact, plucking the end of a beam will result in exactly the same beam motion regardless of whether it is represented by modal or physical degrees of freedom. The modal transformation is simply a different way to represent a dynamic system." + ] + }, + { + "cell_type": "markdown", + "id": "0db175db-4400-49c1-915c-f81bbede2855", + "metadata": {}, + "source": [ + "### An Infinite Number of Mode Shapes\n", + "One last concept to describe before we move on is the concept of mode shape scaling. From the previous figure, we saw that each eigenvector of a matrix mostly represented a particular direction, rather than a specific vector. Any vector in that direction would be only scaled by the matrix instead of having its direction modified. Therefore, eigenvectors are only really unique up to a scale factor. An eigenvector times a scalar is still a valid eigenvector of a matrix, so you could argue there are an infinite number of eigenvectors of any matrix.\n", + "\n", + "Rather than allowing for any arbitrary scaling of eigenvectors, we will often scale them to some preferred normalization. One might, for example, scale an eigenvector such that it has a length of 1, i.e. make it a unit vector. Another might, for example, scale a specific degree of freedom to 1. The most common scaling on an eigenvector, particularly in the context of structural dynamics where an eigenvector represents a mode shape, is to scale the eigenvectors such that the modal mass matrix $\\tilde{\\mathbf{M}}$ is equal to the identity matrix. This is known as *mass normalization*. We have already described how the modal mass matrix is diagonal. To mass-normalize a mode shape, we only need to scale the mode shape such that the diagonal entries obtained when computing the modal mass matrix end up being 1. To do this for the $r$th mode, we can simply compute the modal mass matrix with a single unscaled mode shape $\\tilde{\\mathbf{\\phi}}_r$, then divide the mode shapes by the square-root of the diagonal entry of the modal mass matrix corresponding to that mode shape.\n", + "\n", + "$$\\mathbf{\\phi}_r = \\frac{\\tilde{\\mathbf{\\phi}}_r}{\\sqrt{{\\tilde{\\mathbf{\\phi}}_r}^T\\mathbf{M}\\tilde{\\mathbf{\\phi}}_r}}$$\n", + "\n", + "When we compute the modal stiffness using mass normalized modes, the entries on the diagonal of the modal stiffness matrix will be equal to the natural frequency squared. For example, for the $r$th mode shape:\n", + "\n", + "$${\\mathbf{\\phi}_r}^T\\mathbf{K}\\mathbf{\\phi}_r = {\\omega_r}^2$$\n", + "\n", + "### Units with Scaled Mode Shapes\n", + "Note that when we mass-normalize the mode shapes, the mode shapes will have a unit of $\\frac{1}{\\sqrt{m}}$. The mass matrix has dimensions of $m$ where $m$ is the unit of mass, and to equal the dimensionless identity matrix when the modal mass matrix is assembled, the mode shape units from pre- and post-multiplying must cancel out the units of the mass matrix. The modal coefficient $\\mathbf{q}$ must then have units of ${l}{\\sqrt{m}}$ where $l$ is a length dimension, and one must recall that taking a derivative over time adds a $\\frac{1}{t}$ where $t$ is the unit representing time.\n", + "\n", + "As an example, we will assume we have a meter/kilogram/newton/second unit system. The modal transformation equation must have consistent units.\n", + "$$ \\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$\n", + "$$ \\left[m\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right]$$\n", + "\n", + "Similarly, the equations of motion must have consistent units.\n", + "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{N}{m}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[N\\right]$$\n", + "\n", + "We can substitute $\\left[N\\right] = \\left[\\frac{kg{\\,}m}{s^2}\\right]$\n", + "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{m{\\,}s^2}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{s^2}\\right]$$\n", + "\n", + "Then with a good deal of cancelling units\n", + "$$\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{m\\sqrt{kg}}{s^2}\\right] = \\left[\\frac{m\\sqrt{kg}}{s^2}\\right]$$" + ] + }, + { + "cell_type": "markdown", + "id": "dd03536b-95a6-405b-8249-153b7887f208", + "metadata": {}, + "source": [ + "### Adding Damping\n", + "Eventually, we will need to consider the damping values that we simply disregarded previously. If we bring back our original damped system of equations, and transform to modal space, we end up with the following equations of motion:\n", + "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n", + "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "\n", + "Here the matrix $\\tilde{\\mathbf{C}} = \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$ is referred to as the modal damping matrix.\n", + "\n", + "The important consideration that we must make to determine if the mode shapes that we have computed are still valid mode shapes is to understand if the modal damping matrix is also diagonalized by the mode shape matrix. If it is, then our mode shapes are still valid: they represent independent, uncoupled degrees of freedom in our system. However if the modal damping matrix is not diagonal, that means there is coupling between the degrees of freedom, and the degrees of freedom represented by the equation are not actually modal degrees of freedom.\n", + "\n", + "The mode shapes can diagonalize the damping matrix, but only in a very limited case. Since the mode shapes only decouple the mass and stiffness matrices from which they were computed, the damping matrix will only be decoupled completely if the physical damping matrix is a linear combination of the physical mass and stiffness matrices.\n", + "\n", + "$$\\mathbf{C} = \\alpha\\mathbf{M} + \\beta\\mathbf{K}$$\n", + "\n", + "This is known as *proportional damping*, where the damping is proportional to the mass and stiffness of the system. Transforming to modal coordinates will diagonalize this matrix. If we do have proportional damping, and we use mass normalized modes for the modal transformation, the modal damping matrix will be diagonal, and the entry corresponding to the $r$th mode will be equal to\n", + "\n", + "$$\\tilde{\\mathbf{C}}_{rr} = {\\mathbf{\\phi}_r}^T\\mathbf{C}\\mathbf{\\phi}_r = 2\\zeta_r\\omega_r = \\alpha+\\beta\\omega_r^2$$\n", + "\n", + "where $\\omega_r$ is the natural frequency and $\\zeta_r$ is the fraction of critical damping for mode $r$.\n", + "\n", + "We only get a diagonal modal damping matrix in this case. For any other case, we will still have off-diagonal terms. Luckily for us, these diagonal terms are often small, so using the real mode shapes derived from the mass and stiffness of the system is not a terrible approximation. However, there are cases where the approximation is not good, and in those cases, we need to turn to a different toolset to help us analyze the system." + ] + }, + { + "cell_type": "markdown", + "id": "f3590f2c-04ea-443e-903d-cb05fdeabd13", + "metadata": {}, + "source": [ + "## Complex Modes -- The General Solution\n", + "A more general solution to decoupling damped vibration problems requires us to rearange our equations of motion. Rather than treating the system of equations as $N$ equations with a second order derivative, we will instead treat the system of equations as $2N$ first-order equations using a simple introducton of a new variable $\\mathbf{v} = \\dot{\\mathbf{x}}$ to represent velocity. In this way, we can eliminate the double-derivative in the acceleration term ($\\ddot{\\mathbf{x}} = \\dot{\\mathbf{v}}$)\n", + "\n", + "$$\\mathbf{M}\\dot{\\mathbf{v}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n", + "\n", + "However, we now need to add some relationship between $v$ and $x$. We could simply write $\\mathbf{v} = \\dot{\\mathbf{x}}$ as we showed above. However, it will be useful to keep our matrices symmetric, so we will construct the equations in a slightly more complex way.\n", + "\n", + "$$\\mathbf{M}\\dot{\\mathbf{x}} - \\mathbf{M}\\mathbf{v} = \\mathbf{0}$$\n", + "\n", + "We can combine these two sets of equations into one set of equations of the form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.\n", + "\n", + "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n", + "Here we note that our coefficient matrices are both symmetric, the first term contains all of the derivatives, the second term contains no derivatives, and the right-hand side of the equation contains our input forces." + ] + }, + { + "cell_type": "markdown", + "id": "9e95a290-9795-4fa3-8b31-d71776e2db66", + "metadata": {}, + "source": [ + "We can perform a similar operation as previous to try to understand the free response of the system. We will make the following assumptions.\n", + "\n", + "- $\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$ -- The force into the system is zero.\n", + "- $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$ -- The response is a complex sinusoid.\n", + "\n", + "In this case, the first assumption is essentially identical to the first assumption in the real mode shape case. The second assumption is essentially a complex generalization of the second assumption made in the real mode shape case. In this generalization, instead of a \"real\" shape where all responses are either in-phase or 180$^\\circ$ out-of-phase, the shape can now be complex, meaning there can be any phasing between different values in the shape. The exponential $e$ now represents an oscillatory portion and decaying or growing portion of the response ($\\lambda$ is in general a complex number. The imaginary portion of it will result in sinusoidal motion of the form $e^{j\\theta} = \\cos{\\theta} + j\\sin{\\theta}$ where $j = \\sqrt{-1}$. The real part will result in exponential decay or exponential growth of the form $e^{x}$). The term $\\lambda$ appears on the first row of the second assumption due to the chain rule when differentiating $\\mathbf{v} = \\dot{x}$.\n", + "\n", + "Using the chain rule for differentiation, we can also say $\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} = \\lambda\\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$" + ] + }, + { + "cell_type": "markdown", + "id": "0e824ad9-e3a4-4638-8b43-ace3c75bc707", + "metadata": {}, + "source": [ + "We can then apply these assumptions to our equations of motion:\n", + "\n", + "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n", + "\n", + "We can again recognize that the term $e^{\\lambda t}$ is in general nonzero and therefore can be cancelled.\n", + "\n", + "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n", + "\n", + "We can then subtract $\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$ from both sides of the equation and we end up with\n", + "\n", + "$$ \\lambda \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = - \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "id": "39b05928-6886-4d5a-b714-081a5d10abfd", + "metadata": {}, + "source": [ + "While a little more complex, this equation is also a [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem)." + ] + }, + { + "cell_type": "markdown", + "id": "9aa2bf5c-7063-4df0-97eb-4d0ad1cfb50e", + "metadata": {}, + "source": [ + "Transforming the eigenvalues and eigenvectors into natural frequencies and mode shapes is not as straightforward in the present case with complex modes. In general, we will solve for $2N$ complex eigenvalues; however, due to the fact that the matrices are real, the eigenvalues will be complex conjugates of one another. We will often hear these values called *poles* because they are zeros of the denominator polynomials in the frequency response function formulation that we will discuss later.\n", + "\n", + "The eigenvalues $\\lambda$ are often expressed as a function of the critical damping ratio $\\zeta$ and the natural frequency $\\omega$. For the $r$th mode,\n", + "\n", + "$$\\lambda_r = -\\zeta_r \\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$$\n", + "\n", + "The real part $-\\zeta_r \\omega_r$ drives the exponential decay of the sinusoid. The imaginary part drives the oscillatory behavior. Because of this, the term $\\omega_r\\sqrt{1-\\zeta_r}$ is often referred to as the *damped natural frequency* $\\omega_d$\n", + "\n", + "$$\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$$\n", + "\n", + "To recover the natural frequency $\\omega_r$, we can simply take the magnitude of the eigenvalue.\n", + "\n", + "$$\\left|\\lambda_r\\right| = \\sqrt{\\Re{(\\lambda_r)}^2 + \\Im{(\\lambda_r)}^2} = \\sqrt{(\\zeta_r^2 \\omega_r^2) + (\\omega_r^2 (1-\\zeta_r^2))} = \\sqrt{\\omega_r^2 + \\omega_r^2\\zeta_r^2 - \\omega_r^2\\zeta_r^2} = \\omega_r$$\n", + "\n", + "To recover the damping ratio, we can divide the negative real part of the eigenvalue by the magnitude of the eigenvalue.\n", + "\n", + "$$- \\frac{\\Re{(\\lambda_r)}}{\\left|\\lambda_r\\right|} = \\frac{\\zeta_r\\omega_r}{\\omega_r} = \\zeta_r$$ \n", + "\n", + "Note that for complex modes, the damping is part of the solution to the free vibration problem and appears in the solution, whereas in the real mode case where damping was ignored, it was not.\n", + "\n", + "We will also solve for $2N$ eigenvectors. These will also appear in complex conjugate pairs. They will be of the form\n", + "\n", + "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$\n", + "\n", + "We can extract the mode shape from the eigenvector simply by selecting the bottom partition of the vector.\n", + "\n", + "The equations to compute the modal mass and modal damping matrices are the same as those used in the case of real modes. However, in general, the modal mass and modal damping matrices are not diagonal and have complex elements.\n", + "\n", + "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n", + "$$\\tilde{\\mathbf{C}}=\\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$$\n", + "\n", + "Recall that the eigenvalue problem we solved for was with respect to the generalized form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$. Therefore the eigenvectors will diagonalize these matrices.\n", + "\n", + "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{A}}$$\n", + "\n", + "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{B}}$$\n", + "\n", + "We often refer to these matrices $\\tilde{\\mathbf{A}}$ and $\\tilde{\\mathbf{B}}$ as the *Modal A* and *Modal B* matrices.\n", + "\n", + "Modal mass is defined in different ways for complex modes. Some references will treat the diagonal entries in Modal A $\\tilde{\\mathbf{A}}_{rr}$ directly as the modal mass values. Other references define the modal mass for the $r$th mode $m_r$ as:\n", + "\n", + "$$\\tilde{\\mathbf{A}}_{rr} = 2\\lambda_r m_r + 2\\zeta_r\\omega_r m_r = 2j\\omega_{dr} m_r$$\n", + "$$m_r = \\frac{\\tilde{\\mathbf{A}}_{rr}}{2j\\omega_{dr}}$$\n", + "\n", + "It doesn't really matter which definition is used as long as the definition is consistently used when reconstructing responses from modal parameters. This document will proceed with the modal mass defined as the diagonal terms of $\\tilde{\\mathbf{A}}$.\n", + "\n", + "We can also see that if we premultiply the eigenvalue equation by our eigenvectors, we get a relationship between the Modal A and Modal B matrices.\n", + "\n", + "$$\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f2a6ce4-2692-4839-8826-5ef45900ff85", + "metadata": {}, + "source": [ + "## An Example Problem\n", + "Before we get too far, let's illustrate some of these concepts with a small example problem. We will generate a 3-degree-of-freedom problem that we will simulate. We will explore undamped, proportionally damped, and generally damped cases." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c6eb06f7-2c41-44fc-bf7b-d160b7bd4303", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.linalg as la\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from IPython.display import display, HTML\n", + "\n", + "def pretty_print_table(df):\n", + " return display( HTML( df.to_html().replace(\"\\\\n\",\"
\") ) )\n", + "\n", + "# Set up physical matrices\n", + "K = np.array([[ 6000, -2000, 0],\n", + " [-2000, 4000, -2000],\n", + " [ 0, -2000, 6000]])\n", + "M = np.array([[2, 0, 0],\n", + " [0, 3, 0],\n", + " [0, 0, 2]])\n", + "alpha = 1.36/2\n", + "beta = 0.00154865/2\n", + "C_proportional = alpha*M+beta*K\n", + "\n", + "C_general = np.array([[14, -2, 0],\n", + " [-2, 10, -2],\n", + " [0, -2, 10]])/2" + ] + }, + { + "cell_type": "markdown", + "id": "457c045b-1adb-4923-a5b9-a64cd6294ba4", + "metadata": {}, + "source": [ + "For the complex mode cases, let's construct the $\\mathbf{A}$ and $\\mathbf{B}$ matrices. The $\\mathbf{A}$ matrix will depend on damping, the $\\mathbf{B}$ matrix will not." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0065867a-cd5c-4548-8cf2-dd6580fbf2ea", + "metadata": {}, + "outputs": [], + "source": [ + "# For convenience, construct a matrix of zeros\n", + "Z = np.zeros(M.shape)\n", + "# Assemble State Space Matrices\n", + "A_undamped = np.block([[Z, M],\n", + " [M, Z]])\n", + "A_proportional = np.block([[Z, M],\n", + " [M, C_proportional]])\n", + "A_general = np.block([[Z, M],\n", + " [M, C_general]])\n", + "B = np.block([[-M, Z],\n", + " [ Z, K]])" + ] + }, + { + "cell_type": "markdown", + "id": "d6031364-f521-4438-a584-335ff3e83900", + "metadata": {}, + "source": [ + "Now let's compute the eigenvalues and eigenvectors. For the 2nd-order eigenvalue using the mass and stiffness matrices directly, we can use the `eigh` function which assumes Hermetian, positive definite matrices. For the complex mode cases, we will have to use the more general `eig` function.\n", + "\n", + "When using an eigenvalue solver, be wary of the order that matrices occur in the function call. For example, in the [scipy.linalg.eig documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html), it states the following:\n", + "\n", + "> **scipy.linalg.eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)**\n", + ">\n", + "> Solve an ordinary or generalized eigenvalue problem of a square matrix.\n", + "> \n", + "> Find eigenvalues w and right or left eigenvectors of a general matrix:\n", + ">\n", + "> ```python\n", + "> a vr[:,i] = w[i] b vr[:,i]\n", + "> a.H vl[:,i] = w[i].conj() b.H vl[:,i]\n", + "> ```\n", + ">\n", + "> where `.H` is the Hermitian conjugation.\n", + "\n", + "SciPy uses the definition $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$ for its eigenvalue problem, and our equation we have defined as $- \\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\lambda \\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$. Note that what SciPy calls `a` in its function call is what we have defined as $-\\mathbf{B}$, and what SciPy calls `b` is what we have defined as $\\mathbf{A}$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "954836de-ba9e-4019-8e02-5e730de1fdea", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute Eigenvalues and Eigenvectors\n", + "lam_2ndOrder,E_2ndOrder = la.eigh(K,M)\n", + "lam_undamped, E_undamped = la.eig(-B,A_undamped)\n", + "lam_proportional, E_proportional = la.eig(-B,A_proportional)\n", + "lam_general, E_general = la.eig(-B,A_general)\n", + "\n", + "# Since the half the complex eigenvalues and eigenvectors are complex conjugates of one another,\n", + "# we will only keep those corresponding to the eigenvalues with imaginary part > 0\n", + "keep_undamped = lam_undamped.imag > 0\n", + "keep_proportional = lam_proportional.imag > 0\n", + "keep_general = lam_general.imag > 0\n", + "\n", + "# Now reduce to just those we want to keep\n", + "lam_undamped = lam_undamped[keep_undamped]\n", + "E_undamped = E_undamped[:,keep_undamped]\n", + "lam_proportional = lam_proportional[keep_proportional]\n", + "E_proportional = E_proportional[:,keep_proportional]\n", + "lam_general = lam_general[keep_general]\n", + "E_general = E_general[:,keep_general]\n", + "\n", + "# Let's also sort the eigenvalues so they are all ascending\n", + "for evals,evects in zip([lam_2ndOrder, lam_undamped, lam_proportional, lam_general],\n", + " [E_2ndOrder, E_undamped, E_proportional, E_general]):\n", + " isort = np.argsort(np.abs(evals))\n", + " evals[...] = evals[isort]\n", + " evects[...] = evects[:,isort]" + ] + }, + { + "cell_type": "markdown", + "id": "c785b0f3-4103-44b9-a3f6-99c149557c88", + "metadata": {}, + "source": [ + "Now we can extract the natural frequencies and damping ratios from the eigenvalues." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "99f8881b-af4f-4ac1-9131-a90cd1fb0566", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute natural frequencies\n", + "omega_2ndOrder = np.sqrt(lam_2ndOrder)\n", + "omega_undamped = np.abs(lam_undamped)\n", + "omega_proportional = np.abs(lam_proportional)\n", + "omega_general = np.abs(lam_general)\n", + "\n", + "# Compute damping ratios\n", + "zeta_undamped = -np.real(lam_undamped)/np.abs(lam_undamped)\n", + "zeta_proportional = -np.real(lam_proportional)/np.abs(lam_proportional)\n", + "zeta_general = -np.real(lam_general)/np.abs(lam_general)\n", + "\n", + "# Compute damped natural frequencies\n", + "omega_d_undamped = omega_undamped*np.sqrt(1-zeta_undamped**2)\n", + "omega_d_proportional = omega_proportional*np.sqrt(1-zeta_proportional**2)\n", + "omega_d_general = omega_general*np.sqrt(1-zeta_general**2)" + ] + }, + { + "cell_type": "markdown", + "id": "d5ea9635-f9d9-49fd-b4a2-88f8540f2f24", + "metadata": {}, + "source": [ + "Let's plot these datasets in a table." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8643d599-fbb1-42d8-991f-50e46bc40f3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
UndampedProportional DampingGeneral Damping
Mass Matrix[[2 0 0]
[0 3 0]
[0 0 2]]		[[2 0 0]
[0 3 0]
[0 0 2]]		[[2 0 0]
[0 3 0]
[0 0 2]]
Stiffness Matrix[[ 6000 -2000 0]
[-2000 4000 -2000]
[ 0 -2000 6000]]		[[ 6000 -2000 0]
[-2000 4000 -2000]
[ 0 -2000 6000]]		[[ 6000 -2000 0]
[-2000 4000 -2000]
[ 0 -2000 6000]]
Damping Matrix[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]		[[ 6.00595 -1.54865 0. ]
[-1.54865 5.1373 -1.54865]
[ 0. -1.54865 6.00595]]		[[ 7. -1. 0.]
[-1. 5. -1.]
[ 0. -1. 5.]]
Poles[[0.+27.2518998j ]
[0.+54.77225575j]
[0.+59.92217695j]]		[[-0.62753244+27.24467371j]
[-1.5014875 +54.75167153j]
[-1.73017173+59.89719356j]]		[[-0.73753202+27.24242803j]
[-1.50037636+54.76028673j]
[-1.59542495+59.89042233j]]
Natural Frequencies[[27.2518998 ]
[54.77225575]
[59.92217695]]		[[27.2518998 ]
[54.77225575]
[59.92217695]]		[[27.25240977]
[54.78083727]
[59.91166888]]
Damping Ratios[[-0.]
[-0.]
[-0.]]		[[0.02302711]
[0.02741329]
[0.02887365]]		[[0.027063 ]
[0.02738871]
[0.02662962]]
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(columns = ['Undamped','Proportional Damping','General Damping'],\n", + " index=['Mass Matrix','Stiffness Matrix','Damping Matrix','Poles','Natural Frequencies','Damping Ratios'],\n", + " dtype=object)\n", + "df.at['Mass Matrix', 'Undamped'] = str(M)\n", + "df.at['Mass Matrix', 'Proportional Damping'] = str(M)\n", + "df.at['Mass Matrix', 'General Damping'] = str(M)\n", + "df.at['Stiffness Matrix', 'Undamped'] = str(K)\n", + "df.at['Stiffness Matrix', 'Proportional Damping'] = str(K)\n", + "df.at['Stiffness Matrix', 'General Damping'] = str(K)\n", + "df.at['Damping Matrix', 'Undamped'] = str(np.zeros(C_proportional.shape))\n", + "df.at['Damping Matrix', 'Proportional Damping'] = str(C_proportional)\n", + "df.at['Damping Matrix', 'General Damping'] = str(C_general)\n", + "df.at['Poles', 'Undamped'] = str(lam_undamped[:,np.newaxis])\n", + "df.at['Poles', 'Proportional Damping'] = str(lam_proportional[:,np.newaxis])\n", + "df.at['Poles', 'General Damping'] = str(lam_general[:,np.newaxis])\n", + "df.at['Natural Frequencies', 'Undamped'] = str(omega_undamped[:,np.newaxis])\n", + "df.at['Natural Frequencies', 'Proportional Damping'] = str(omega_proportional[:,np.newaxis])\n", + "df.at['Natural Frequencies', 'General Damping'] = str(omega_general[:,np.newaxis])\n", + "df.at['Damping Ratios', 'Undamped'] = str(zeta_undamped[:,np.newaxis])\n", + "df.at['Damping Ratios', 'Proportional Damping'] = str(zeta_proportional[:,np.newaxis])\n", + "df.at['Damping Ratios', 'General Damping'] = str(zeta_general[:,np.newaxis])\n", + "pretty_print_table(df)" + ] + }, + { + "cell_type": "markdown", + "id": "1c1b1019-07d2-47a9-b59d-834e6fb3e08a", + "metadata": {}, + "source": [ + "We will now look at the eigenvectors. To start out, we will normalize the eigenvectors **to a value that is NOT equal to unity modal mass or unity Modal A**. This is useful while we are learning, because if we have unity modal mass, it allows us to be sloppy and simply drop or ignore effects of the modal mass. This could result in significant errors down the road if we assume that the formulae we developed for the unity modal mass case also apply to the non-unity modal mass case. It is therefore better to develop and check all the formulae with non-unity modal mass to ensure that we understand its effects and have accounted for it successfully. We will deliberately normalize to a value not equal to 1, because many eigenvector solvers will automatically normalize for us." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aa9e0cb3-b626-4b88-9a55-b67e71cfab75", + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize Eigenvectors using einsum to pull out the m_ii terms from the modal normalization matrix\n", + "# We will multiply by 2 to ensure non-unity modal mass.\n", + "E_2ndOrder = 2*E_2ndOrder/np.sqrt(np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder))\n", + "E_undamped = 2*E_undamped/np.sqrt(np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped))\n", + "E_proportional = 2*E_proportional/np.sqrt(np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional))\n", + "E_general = 2*E_general/np.sqrt(np.einsum('ji,jk,ki->i',E_general,A_general,E_general))" + ] + }, + { + "cell_type": "markdown", + "id": "8c8a0830-55f4-48b3-bfee-8d3fff1c6ec1", + "metadata": {}, + "source": [ + "Now that we have the eigenvectors normalized to a non-unity modal mass, we will compute the Modal Mass and Modal A matrices to extract the modal mass.\n", + "\n", + "We will use `np.einsum` in the same way we used previously but here we will describe it a bit better. Numpy's `einsum` function implements a generalized version of Einstein Summation Notation. While we can represent matrix operations like multiplication simply by using symbols for the matrices\n", + "$$\\mathbf{C} = \\mathbf{A}\\mathbf{B}$$\n", + "we can also represent those operations with the indices\n", + "$$C_{ik} = A_{ij}B_{jk}$$\n", + "In the previous equation, we have the index $i$ representing the rows of $\\mathbf{A}$, and the index $j$ representing the columns. Similarly for $\\mathbf{B}$, we have the indices $j$ representing the rows and $k$ representing the columns. When we do matrix multiplication, we sum over the inner dimension, e.g., the $j$ indices, and that index drops out. We are left with rows $i$ and columns $k$ of our output matrix $\\mathbf{C}$. This operation would be represented in Numpy's `einsum` function as\n", + "```python\n", + "C = np.einsum('ij,jk->ik',A,B)\n", + "```\n", + "The first argument `'ij,jk->ik'` is a string that gives the index operations to be performed. We see that there is an arrow `->` that separates input indices and output indices, and the different input indices are separated by a comma, and there is only one set of output indices. The remaining arguments are the actual matrices corresponding to the input indices passed in the first argument. Therefore `ij` will be associated with `A`, and `jk` will be associated with `B`. The output indices on the right side of the arrow `ik` will be associated with the function's output `C`. We note that any index not listed in the output indices will be summed over and drop out.\n", + "\n", + "This notation is very useful for writing complex matrix expressions in a compact way. For example, if instead of $\\mathbf{C} = \\mathbf{A}\\mathbf{B}$ we wanted to do $\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$, we can simply swap the indices on $\\mathbf{A}$, which now means we are summing across the rows of $\\mathbf{A}$, which is equivalent to matrix multiplication by a transpose.\n", + "$$\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$$\n", + "$$C_{ik} = A_{ji}B_{jk}$$\n", + "\n", + "We can therefore represent a more complex product like\n", + "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n", + "as\n", + "$$\\tilde{{M}}_{il}={\\phi}_{ji}{M}_{jk}{\\phi}_{kl}$$\n", + "In the previous equation, we can see that the $j$ index corresponds to the rows of the mass matrix and the rows of the mode shape matrix. Because we sum over $j$, this is equivalent to multiplying the matrix $\\mathbf{M}$ by $\\mathbf{\\phi}^T$. We can indeed improve upon this if, for example, we wanted a 1D array of modal mass values, rather than a 2D matrix that we will then need to extract the diagonals from. We can replace the $l$ index with another $i$ index and have the output specify just a single $i$, and we will get a 1D array of modal mass values. As an aside, this is a syntax that is not acceptable in true Einstein Summation Notation, where a repeated index is automatically summed over. In Numpy's generalized version, it subverts some of these very strict rules to give more flexibility to the user.\n", + "$$\\tilde{{M}}_{i}={\\phi}_{ji}{M}_{jk}{\\phi}_{ki}$$\n", + "\n", + "We can then perform these operations in code to solve for the modal mass and modal A quantities." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0e7c1966-278c-4e88-b4cd-bcb8246b282b", + "metadata": {}, + "outputs": [], + "source": [ + "mm_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder)\n", + "ma_undamped = np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped)\n", + "ma_proportional = np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional)\n", + "ma_general = np.einsum('ji,jk,ki->i',E_general,A_general,E_general)" + ] + }, + { + "cell_type": "markdown", + "id": "7184e6a9-807d-473a-a1d2-5a12854ea046", + "metadata": {}, + "source": [ + "The last thing we will do is to pull out the mode shapes from the eigenvectors. For the 2nd Order case, the mode shapes are the eigenvectors. But for the complex case, the mode shapes are the lower half of the eigenvector matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "806b85b5-ef45-441e-bd98-d0cb087350b7", + "metadata": {}, + "outputs": [], + "source": [ + "phi = E_2ndOrder\n", + "psi_undamped = E_undamped[E_undamped.shape[0]//2:,:]\n", + "psi_proportional = E_proportional[E_proportional.shape[0]//2:,:]\n", + "psi_general = E_general[E_general.shape[0]//2:,:]" + ] + }, + { + "cell_type": "markdown", + "id": "f52d5276-14d0-4ae8-a0f0-924b20ecddb3", + "metadata": {}, + "source": [ + "We should be able to check that the top half of the eigenvector matrix is the bottom half multiplied by the eigenvalue.\n", + "\n", + "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2533c3db-1a36-4fd3-aca6-770889ed201e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.16839552-1.20937963j, 3.7348689 +3.68391729j,\n", + " -3.16635239-3.71568777j],\n", + " [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n", + " 2.00036437+2.07501753j],\n", + " [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n", + " -3.52944456-3.37788878j]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "E_general[:E_general.shape[0]//2,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "87a69d80-b352-43e3-9c7f-55f1fe6c272b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.16839552-1.20937963j, 3.7348689 +3.68391729j,\n", + " -3.16635239-3.71568777j],\n", + " [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n", + " 2.00036437+2.07501753j],\n", + " [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n", + " -3.52944456-3.37788878j]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lam_general*psi_general" + ] + }, + { + "cell_type": "markdown", + "id": "c6db7704-a5c2-4394-8d5d-422e23ae82d6", + "metadata": {}, + "source": [ + "One final thing to note is that in general, the mode shapes extracted from a complex analysis will not diagonalize the orignal mass, stiffness, or damping matrices, as these were not the matrices the eigensolution was performed on. There will be off-diagonal terms, and they will be complex." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3e036cbb-b641-4aac-ac0b-2769f764020d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.13061679e-08-7.34176433e-02j, -1.43810774e-04+9.83528782e-06j,\n", + " 1.15410477e-04+2.13846966e-06j],\n", + " [-1.43810774e-04+9.83528782e-06j, -1.24812005e-07-3.65342888e-02j,\n", + " -1.36140173e-04+1.88235958e-06j],\n", + " [ 1.15410477e-04+2.13846966e-06j, -1.36140173e-04+1.88235958e-06j,\n", + " 1.86118173e-07-3.33825843e-02j]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_general.T@M@psi_general" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6ce545ce-0d31-4d53-a589-4bd891eea2b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.00014982-0.10829239j, 0.00048468+0.01181488j,\n", + " 0.00045558-0.01005105j],\n", + " [ 0.00048468+0.01181488j, -0.00125664-0.1096167j ,\n", + " -0.00020565+0.01561439j],\n", + " [ 0.00045558-0.01005105j, -0.00020565+0.01561439j,\n", + " 0.00140645-0.10654111j]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_general.T@C_general@psi_general" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bed4c0f0-266e-4434-a7e4-65002b1bfeed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-6.49635076e-05-5.45227499e+01j, 2.15177460e-01-2.97517095e-03j,\n", + " -1.87975974e-01-1.36004490e-02j],\n", + " [ 2.15177460e-01-2.97517095e-03j, -1.51087546e-03-1.09568400e+02j,\n", + " 4.46495302e-01+1.79584080e-02j],\n", + " [-1.87975974e-01-1.36004490e-02j, 4.46495302e-01+1.79584080e-02j,\n", + " 1.57583896e-03-1.19907950e+02j]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_general.T@K@psi_general" + ] + }, + { + "cell_type": "markdown", + "id": "bb5b9b23-80f7-4940-bd63-759731824312", + "metadata": {}, + "source": [ + "## Frequency Response Functions and Modal Parameters\n", + "Now that we have familiarized ourselves with the modal parameters for real and complex modes, let's discuss the relationship to the frequency response function. This is of utmost importance, because we eventually would like to fit the modal parameters from the frequency response functions, as well as reconstruct frequency response functions from the modal parameters.\n", + "\n", + "### Real Modes\n", + "\n", + "Starting with the modal system of equations, and assuming proportional damping, we have\n", + "\n", + "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n", + "We can also substitute in the diagonal values for the modal parameters. *Note the inclusion of the modal mass* $m_r$ *in all terms, because we have not assumed mass normalized modes in this case*. We will represent the generalized modal force $\\mathbf{\\phi}^T\\mathbf{F}$ as $\\mathbf{Q}$.\n", + "$$\\left[m_r\\right]\\ddot{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]\\dot{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n", + "\n", + "We can then use the *Laplace Transform* to make this differential equation into an algebraic one.\n", + "$$\\left[m_r\\right]s^2{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]s{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n", + "We can collect terms of $\\mathbf{q}$ to create an equation of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{b}$.\n", + "$$\\left[m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n", + "\n", + "Because the equation is diagonal, inverting the coefficient matrix is trival.\n", + "$${\\mathbf{q}} = \\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{Q}$$\n", + "\n", + "This is now a representation of a *modal* transfer function. If we apply a set of *modal* forces $\\mathbf{Q}$, we get out a set of *modal* responses $\\mathbf{q}$. To transform to physical space, we need to transform our modal quantities to physical quantities. We know the substitution for modal forces $\\mathbf{Q} = \\mathbf{\\phi}^T\\mathbf{F}$, and we know our equation for physical response from modal responses $\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$. Therefore we can make the subsitution for modal force, and pre-multiply the entire equation by the mode shape matrix.\n", + "\n", + "$$\\mathbf{x} = \\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}\\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T\\mathbf{F}$$\n", + "\n", + "If instead of the entire Laplace domain we only consider sinusoidal responses, we can substitute $s=j\\omega$ to get the familiar form for the frequency response function.\n", + "\n", + "$$\\mathbf{H} = \\mathbf{\\phi}\\left[\\frac{1}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T$$\n", + "\n", + "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$\n", + "\n", + "To pull out a specific entry in the FRF matrix, we can index the mode shapes.\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$" + ] + }, + { + "cell_type": "markdown", + "id": "96ab4e96-9288-4414-bf16-82a4534ad509", + "metadata": {}, + "source": [ + "### Complex Modes\n", + "The derivation for the frequency response function in terms of complex modes is similar. Recall that our physical vector for the complex state space formulation includes velocities and displacements. Similarly our eigenvectors consisted of pairs of shapes, the shape and its complex conjugate. Therefore, in the case of complex modes, the projection from modal space to physical space is\n", + "\n", + "$$\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$$\n", + "\n", + "Applying the transformation to the complex state space equation\n", + "\n", + "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n", + "\n", + "Gives\n", + "\n", + "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n", + "\n", + "And then pre-multiplying by the eigenvector matrix transpose completes the transformation into modal space.\n", + "\n", + "$$ \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n", + "$$= \\begin{bmatrix}\\lambda \\mathbf{\\psi}^T & \\mathbf{\\psi}^T \\\\ \\lambda^* {\\mathbf{\\psi}^*}^T & {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$\n", + "\n", + "Transforming into the modal matrices\n", + "$$\\tilde{\\mathbf{A}}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\tilde{\\mathbf{B}}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n", + "We recognize that the diagonal terms of $\\tilde{\\mathbf{A}}$ are simply what we've defined as the modal mass, and the diagonal terms of $\\tilde{\\mathbf{B}}$ are simply the negative modal mass times the eigenvector $\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$.\n", + "\n", + "$$\\begin{bmatrix}\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & \\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "id": "e520d365-73f4-45dc-a5f0-24158348da43", + "metadata": {}, + "source": [ + "Once again, transforming to the Laplace Domain,\n", + "\n", + "$$\\begin{bmatrix}s\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & s\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n", + "\n", + "$$\\begin{bmatrix}(s-\\lambda)\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & (s-\\lambda^*)\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n", + "\n", + "Once again, because this matrix is diagonal, it is easy to invert.\n", + "\n", + "$$\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n", + "\n", + "This is the modal transfer function.\n", + "\n", + "To compute the transfer functions between the force $\\mathbf{F}$ and the response $\\mathbf{x}$ (we ignore $\\mathbf{v}$ here), we can use the identities $\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$, which we defined previously, and the last row of $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$, which is $\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$. \n", + "\n", + "$$\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$" + ] + }, + { + "cell_type": "markdown", + "id": "4afe2217-3136-4cef-a027-a4be65bc7982", + "metadata": {}, + "source": [ + "The transfer function in the $s$-domain is then\n", + "\n", + "$$\\mathbf{H} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}$$\n", + "\n", + "Because it is diagonal, we can take it out of matrix form and put it into summation form, which reveals the usual partial fraction form of the transfer function.\n", + "\n", + "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n", + "\n", + "We can extract a specific transfer function $\\mathbf{H}_{ij}$\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_{ir}\\mathbf{\\psi}_{jr}}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_{ir}^*\\mathbf{\\psi}_{jr}^*}{m_r(s-\\lambda_r^*)}\\right)$$" + ] + }, + { + "cell_type": "markdown", + "id": "17543413-bd30-4a68-80b1-d1b7f117cf34", + "metadata": {}, + "source": [ + "To translate into more intuitive variables, we can make the following substitutions.\n", + "\n", + "- $\\lambda_r = -\\sigma_r + j\\omega_{dr} = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$ - Substitute the poles for the natural frequency and critical damping ratio.\n", + "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I$ - Split the mode shapes into real and imaginary parts.\n", + "\n", + "From these, it follows that the complex conjugate versions simply have the sign flipped on their imaginary components.\n", + "\n", + "- $\\lambda_r^* = -\\sigma_r - j\\omega_{dr} = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$\n", + "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I$\n", + "\n", + "Substituting these into the previous equation, we get\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left(\\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R + j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r + j\\omega_{dr}\\right))} + \\frac{\\left(\\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R - j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r - j\\omega_{dr}\\right))}\\right)$$\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s + \\sigma_r - j\\omega_{dr}\\right)} + \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)}\\right)$$\n", + "\n", + "If we put the two terms over a common denominator, we get\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left(\n", + "\\frac{\n", + " \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n", + " \\left(s+ \\sigma_r + j\\omega_{dr}\\right)\n", + "+ \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n", + " \\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n", + "{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)\\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n", + "\\right)$$\n", + "\n", + "We can expand and reduce this equation\n", + "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n", + "\\frac{\n", + "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n", + "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n", + "}\n", + "{m_r\\left(\\left(s+\\sigma_r\\right)^2 + \\omega_{dr}^2\\right)\n", + "}\n", + "\\right)$$" + ] + }, + { + "cell_type": "markdown", + "id": "97b4be2f-c392-4c79-950e-00d0e32a038f", + "metadata": {}, + "source": [ + "### What are Real Modes in the Complex Mode Framework?\n", + "\n", + "One might naively believe that a real mode is simply a complex mode where the imaginary part is zero. However, it isn't clear whether or not that should be true, because the eigenvalue problem was posed differently. What is clear, however, is that real modes should certainly be some subset of complex modes; in other words, we should be able to represent a real mode as a complex mode. In this section, we will investigate that relationship.\n", + "\n", + "Starting with the previous equation, we can further expand the denominator with natural frequency and damping ratio, where $\\sigma_r = \\zeta_r\\omega_r$ and $\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$.\n", + "\n", + "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n", + "\\frac{\n", + "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n", + "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n", + "}\n", + "{m_r\\left(\\left(s+-\\zeta_r\\omega_r\\right)^2 + \\left(\\omega_r\\sqrt{1-\\zeta_r^2}\\right)^2\\right)\n", + "}\n", + "\\right)$$\n", + "\n", + "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n", + "\\frac{\n", + "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n", + "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n", + "}\n", + "{m_r\\left(\\left(s^2+2s\\zeta_r\\omega_r + \\omega_r^2\\zeta_r^2\\right) + \\left(\\omega_r^2 - \\omega_r^2\\zeta_r^2\\right)\\right)\n", + "}\n", + "\\right)$$\n", + "\n", + "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n", + "\\frac{\n", + "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n", + "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n", + "}\n", + "{m_rs^2+2\\zeta_r\\omega_rm_rs + m_r\\omega_r^2\n", + "}\n", + "\\right)$$\n", + "\n", + "The denominator in the previous equation is identical to that from the real modes equation.\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}$$\n", + "\n", + "The numerators should also be equivalent.\n", + "\n", + "$$2\\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n", + "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\\right) = \\phi_{ir}\\phi_{jr}$$\n", + "\n", + "We note that there is no terms with $s$ on the real modes side of the equation, so any term containing $s$ on the complex modes side of the equation must be zero in the case of real modes.\n", + "\n", + "$$\\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I = 0$$\n", + "\n", + "This then leaves the remainder of the equation\n", + "\n", + "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n", + "\n", + "We must find a solution for these two equations simultaneously. Since the modeshape at location $i$ and the mode shape at location $j$ are generally independent of one another, we can treat them as independent. In other words, $\\psi_{ir}$ cannot be a function $\\psi_{jr}$. This means for the first equation, there are two possibilities; $\\psi_{ir}^I = -\\psi_{ir}^R$ or $\\psi_{ir}^I = \\psi_{ir}^R$.\n", + "\n", + "To figure out which, we will look at the drive point transfer function where $i = j$, as the relationship should hold for that point as much as any other point.\n", + "\n", + "$$-2\\left( \\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R \\right)\\omega_{dr} = \\phi_{ir}^2$$\n", + "$$\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n", + "\n", + "The since term $\\phi_{ir}^2$ cannot be negative and $\\omega_{dr}$ is also non-negative, the term $\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R$ must be negative. This will be the case if $\\psi_{ir}^I = -\\psi_{ir}^R$, but will not be the case if $\\psi_{ir}^I = \\psi_{ir}^R$.\n", + "\n", + "Substituting that expression,\n", + "\n", + "$$-{\\psi_{ir}^R}^2 + -{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n", + "$$-2{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n", + "$${\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{4\\omega_{dr}}$$\n", + "$${\\psi_{ir}^R} = \\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}} = -\\psi_{ir}^I$$\n", + "\n", + "To verify that this is the general expression, let's plug it into the original equation.\n", + "\n", + "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n", + "$$-2\\left( \n", + "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n", + "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}} + \n", + "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n", + "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}}\n", + "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n", + "$$-2\\left( \n", + "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n", + "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n", + "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n", + "$$-2\\left( \n", + "-2\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n", + "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n", + "$$\\phi_{ir}\\phi_{jr} = \\phi_{ir}\\phi_{jr}$$\n", + "\n", + "Therefore for a real mode represented in a complex mode, we should expect the imaginary part to be equal in magnitude to the real part, with phase angle of -45$^\\circ$ instead of zero degrees, which may have been expected." + ] + }, + { + "cell_type": "markdown", + "id": "b59fa20c-70e7-44b3-ab9b-b584f3b15400", + "metadata": {}, + "source": [ + "### Back to our Example Problem\n", + "\n", + "Let's return to our example problem to compute frequency response functions from the matrices as well as from modal parameters. We will start with computing the frequency response functions from dynamic stiffness matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ae0359c0-8d92-45f2-ab35-1815657a7f89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get frequency lines\n", + "omegas = np.linspace(0,np.max(omega_2ndOrder)*1.5,1000)\n", + "# Set up omega for broadcasting the FRF matrix, the first dimension will be frequency line.\n", + "# The second two dimensions will be i,j degrees of freedom.\n", + "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n", + "# Construct Dynamic Stiffness\n", + "Z_undamped = M*(1j*omegas_bc)**2 + K\n", + "Z_proportional = M*(1j*omegas_bc)**2 + C_proportional*1j*omegas_bc + K\n", + "Z_general = M*(1j*omegas_bc)**2 + C_general*1j*omegas_bc + K\n", + "# Invert to get frequency response functions\n", + "H_undamped = np.linalg.inv(Z_undamped)\n", + "H_proportional = np.linalg.inv(Z_proportional)\n", + "H_general = np.linalg.inv(Z_general)\n", + "# Plot the FRFs\n", + "fig,axes = plt.subplots(H_undamped.shape[1]*2,H_undamped.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_u,f_p,f_g in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_undamped.reshape(omegas.size,-1).T,\n", + " H_proportional.reshape(omegas.size,-1).T,\n", + " H_general.reshape(omegas.size,-1).T):\n", + " ax[0].plot(omegas,np.angle(f_u)*180/np.pi)\n", + " ax[0].plot(omegas,np.angle(f_p)*180/np.pi)\n", + " ax[0].plot(omegas,np.angle(f_g)*180/np.pi)\n", + " ax[1].plot(omegas,np.abs(f_u))\n", + " ax[1].plot(omegas,np.abs(f_p))\n", + " ax[1].plot(omegas,np.abs(f_g))\n", + " ax[1].set_yscale('log')\n", + "ax[1].legend(['Undamped','Proportional Damping','General Damping'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')" + ] + }, + { + "cell_type": "markdown", + "id": "8819a537-c842-495b-b0c3-484a69f959e8", + "metadata": {}, + "source": [ + "Now let's reconstruct the frequency response functions from modal parameters and see if they match those constructed from the dynamic stiffness matrices.\n", + "\n", + "For the second-order case,\n", + "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "25f2ed27-2e2d-4d85-9092-8da2c49e1a95", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2nd Order Case\n", + "# First we need to find the modal damping ratios from the damping matrix\n", + "zeta_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,C_proportional,E_2ndOrder)/(2*mm_2ndOrder*omega_2ndOrder)\n", + "\n", + "# Now Construct the FRF matrix from modal parameters\n", + "num_modes = omega_2ndOrder.size\n", + "H_2ndOrder_fromModes = np.sum([\n", + " phi[:,r,np.newaxis]@phi[:,r,np.newaxis].T/ # The newaxis turns the array from a 1D array back into a 2D array that can be transposed\n", + " (-mm_2ndOrder[r]*omegas_bc**2 + 2j*zeta_2ndOrder[r]*omega_2ndOrder[r]*mm_2ndOrder[r]*omegas_bc + omega_2ndOrder[r]**2*mm_2ndOrder)\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "# Now plot them\n", + "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_proportional.reshape(omegas.size,-1).T,\n", + " H_2ndOrder_fromModes.reshape(omegas.size,-1).T):\n", + " ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n", + " ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n", + "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "031edfd9-54c1-44e0-9ddb-851744aca873", + "metadata": {}, + "source": [ + "We see we get identical FRFs.\n", + "\n", + "For the complex mode cases:\n", + "\n", + "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n", + "\n", + "Note that for a frequency response function, which assumes a single frequency line excitation, the Laplace variable $s = j\\omega$. The pole $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n", + "\n", + "Here is the proportional damped case:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "43e01c80-adc6-41e4-b882-09c24e203d50", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAAMzCAYAAAC2ssN2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wT9f/A8dfdJU33XhRKKXvvvacIDpyIE3HwU0FU/Kq4cOIWt6K4914gAooIgmwoe5dR6KBQuleSu98fKSmV1ULaJO37+XjE9nO5XN6R9tP7rPdHMQzDQAghhBBCCCFEtVHdHYAQQgghhBBC1HbS8BJCCCGEEEKIaiYNLyGEEEIIIYSoZtLwEkIIIYQQQohqJg0vIYQQQgghhKhm0vASQgghhBBCiGomDS8hhBBCCCGEqGbS8BJCCCGEEEKIaiYNLyGEEEIIIYSoZtLwEkIIIYQQQohq5lUNr8WLF3PRRRcRFxeHoij8/PPPFZ7Pz89n4sSJNGjQAD8/P1q3bs2MGTMqnFNcXMyECROIiIggMDCQyy+/nIyMjBr8FEIIIYQQQoi6xqsaXgUFBXTo0IG33nrrpM9PnjyZuXPn8vnnn7N161buvvtuJk6cyK+//uo855577mHWrFl89913LFq0iNTUVC677LKa+ghCCCGEEEKIOkgxDMNwdxBnQ1EUfvrpJy655BLnsbZt23LVVVfx6KOPOo916dKFESNG8PTTT5OTk0NUVBRffvklV1xxBQDbtm2jVatWLFu2jJ49e9b0xxBCCCGEEELUASZ3B+BKvXv35tdff+Wmm24iLi6Ov//+mx07dvDKK68AsGbNGqxWK0OHDnW+pmXLljRs2PCUDa+SkhJKSkqcZV3XycrKIiIiAkVRqv9DCSFOyTAM8vLyiIuLQ1U9dwBf6hEhPJc31CNShwjhuapSh9Sqhtcbb7zB+PHjadCgASaTCVVVmTlzJv379wcgPT0dHx8fQkNDK7wuJiaG9PT0k17z2Wef5Yknnqju0IUQ5yAlJYUGDRq4O4xTknpECM/nyfWI1CFCeL7K1CG1ruG1fPlyfv31VxISEli8eDETJkwgLi6uwihXVTz44INMnjzZWc7JyaFhw4akpKQQHBzsqtCFEGchNzeX+Ph4goKC3B3KaUk94r3eW7ybHv+Mo4OaDMBWPZ5gpZAQ8glUSs7w6jPbrdcjSW/CeqMJB0K70bZVa4a0jKJ9g1AZyagh3lCPSB3iWe546iXeNr0KQLbhj4pBsFJ0VtcqMUxsNxqwWU9ko55IRmAr6jduS7/mUXRvHEGIn9mFkYvqUJU6pNY0vIqKinjooYf46aefuOCCCwBo3749SUlJvPTSSwwdOpTY2FhKS0vJzs6uMOqVkZFBbGzsSa9rsViwWCwnHA8ODpbKTggP4ek3qFKPeC+/gCCCLCrBquNn7IGANxnQPIrsIit5BYXYCrKg6Chq0VHM1hxCySOSXGKULGKVo8QqWcQoR4nmKJpy4pLqTqTTiXRgKdg+ZdP6Rvy+tjtvh1/ABX06cUWXBviatRr+1HWTJ9cjUod4Fh8fX4LNjp+X960XsL7J/6GU5qHnpKLlpxKpHyaWLOopWcQqWdRTjlBPySJUKTjJ1exEsY++7AP+BiA1OZxFOzvwjNGR0sQhXNKtCcNax0hd4OEqU4fUmoaX1WrFarWeMLdS0zR0XQcciTbMZjMLFizg8ssvB2D79u3s37+fXr161XjMQgghvEuzmCCeGNX2pM/ZdYPcIiuZ+SWk5xSTnlvMzpxi0nKLOZRdQPHhPfhm76YRqTRRUmml7qONsg+zYndeo626l7bqXqy5PzDntx6M/etarhk5iIs7xHl0w0CIuuz+81vQJi4EcKz3yS60kpZTTFpOEak5xazNKSYtp5jDhzPhyA7Ci/bTRHXUA82VAzRR0ypcL07J4mrTQq5mIbkp7zJrby/GmYfRq+9QxvZuJKNgXsyrGl75+fns2rXLWd6zZw9JSUmEh4fTsGFDBgwYwH333Yefnx8JCQksWrSITz/9lOnTpwMQEhLCzTffzOTJkwkPDyc4OJg777yTXr16SUZDIYQQJ3Ak/nWMVOnG6Rs+mqoQFuBDWIAPzWNONuWkJ1a7zv6sQnYdymdhai6v703HdmAdHW0bGKqtob26BwCzYmeU9i8jSlbw4fcjmbBhAs9c0YVQfx8Xf0IhRFUplI9e/3ccW1HK64HWcScbjRxKdmEpuzMLSM7M58v0PHbtP4ialkQrfSc91K30VLdiUawABCuFXGtawLXGAv7+uwP/988YLh5xIWO6xaOq0hnjbbyq4bV69WoGDRrkLB+b7zx27Fg+/vhjvv76ax588EGuvfZasrKySEhIYNq0adx2223O17zyyiuoqsrll19OSUkJw4cP5+23367xzyKEEML7nOttjllTaRIVSJOoQIa3iQWao+v9WH8gm7lbMnh6/Xr65/3GGG0hkUouPoqd20yz2LBrE+PffJhXxl9I/VA/V3wUIYSLKFWsGUL9feiS4EOXhLCyI62x2YewIyOff3cf5tNtKSj7lzLcWMZIbQUBZetJB2rr6W9s4NNZC7l53e28eF0fIgNPnIIqPJdXNbwGDhzI6bYdi42N5aOPPjrtNXx9fXnrrbdOuQmzEEIIcYxhlDe2qmvTS1VV6NQwjE4Nw9DPa8E/uwYzecFGuhz8jNu0X7EoNtqre5heMIV73i7l9QmXExviW03RCCHO7PgRL9eMOpk0ldZxwbSOC+aWfo0pLO3D3E3pTFi5k/opv3K76VcaKIdRFYMbTfPpnbqZO15/mKduupgWsZ6bGEZU5JkbVgghhBAeqLqXWamqwoDmUXxy2yBajHmWG7Vn2KvHANBAOcwLxU8w+ZOFFFvtZ7iSEKKmVEe94O9j4rLODfj4tkFcN/EJnm3yBdOs11BkOKYbN1cPMqPkfqbO/IbkzHzXByCqhTS8hBBCiFMwqLieo6YoisL5bWN55a6xPBDyAtt1x94wjdQMbsp8nlf/2FHjMQkhjqnZOqFVvWDeuqEnfcc+yQ0+09mp1wcgXMnnTduTPPD+LHIKrTUakzg70vASQgghKsFAqfJajnMVG+LLO7eN5NGAqRw2HAv1h2rryPj3Mzan5tRoLEKIExko1T4SfsyA5lG8PWk0j0e/SpLeBIAoJYdHil7gwe9WnXY5jvAM0vASQgghTsGxxsu9NzPhAT48NXYkU+23Oo9N0b7g9bkb3RiVEHWYGxs4UUEWZtwyiBcip5GsO/ag7aAm02Lne8zfkuG2uETlSMNLCCGEqISa7Nn+rxaxQTQfOIY/7F0AiFGyqbf7G5JSst0TkBACODYduWYrhiBfMy+PHchDpnspNRybKt+mzeb9XxdSYpP1n55MGl5CCCHEKRguy1l27sb3b8wH5qvLy6bZfLxk12leIYSoDp5QJ9QL8ePqiy/gQ/sIACyKldEFX/LLulQ3RyZORxpeQgghRCUYVH9Ww9Px9zExcMBgFtg7ARCnZJG/Zb4sqhfCrdw3En5xhziW1ruRHMMfgFHaUn5YtFrWenkwaXgJIYQQp+Bp9y+ju8bznTHYWb6cv/hl/UE3RiRE3WIYhtvXfR6jKAo3DenAF/ahAPgodvpk/8yy5CNujkycijS8hBBCiNM4dpPljqyG/xUe4IO55fkcMkIBGKyu46/1e9wakxB1meHmWmFA8ygWhV6K3XBEcam6lF/XSWeMpzJV5WRd11m0aBH//PMP+/bto7CwkKioKDp16sTQoUOJj4+vrjiFEEKIGucZ/doVXdSpIXO3deMG0x9YFBt+KYvILuxDqL+Pu0MTotbzhEynx1NVhUHdOrD0z7b01zYSr2ZyYNNiSi9ph49Jxlc8TaX+RYqKinj66aeJj49n5MiR/P7772RnZ6NpGrt27eKxxx4jMTGRkSNHsnz58uqOWQghhKgxyvHfecCq+r7NIlmkdHWWh6hr+Ht7phsjEqLu8oQm2EUd4vhV7+0s97GtYM2+o26MSJxKpRpezZs3Z8OGDcycOZPc3FyWLVvGDz/8wOeff86cOXPYv38/u3fvpl+/fowZM4aZM2dWd9xCCCFE9fO0RV44kmyYGvcj3/AFYJC6joVb090clRB1w8lqBHcm3QGoH+pHWvQA9LLphgPUDSzaIZ0xnqhSDa/58+fz7bffMnLkSMxm80nPSUhI4MEHH2Tnzp0MHjz4pOcIIYQQ3qZ8jZfn6NeqAf/qbQCIUPLITE6STGZC1JDj21mesuFEp5ZN2WgkAtBa3ceGrVvdHJE4mUo1vFq1alXpC5rNZpo0aXLWAQkhhBCe4r9NGc+4xYJeTSJYrrd2lpsVrWfvkUI3RiRE3XDyDg731wwDWkSxWG/vLEccXk1OkWw14WmqlFwDYMOGDSc9rigKvr6+NGzYEIvFcs6BCSGEEJ6hPKuhp2gcGcB2vw5g+wyAnuoWViQfITEywM2RCVEXGCf5zr3aNwhhhtLSWe6s7iQpJZsBzaPcGJX4ryo3vDp27IhymsmsZrOZq666infffRdfX99zCk4IIYTwJKf7+1eTFEUhPLET2TsCCFUK6KFu5andmYzp3tDdoQlR53hCtWAxaVhjO0PZ0q7O6k4W7DsqDS8PU+U8kz/99BPNmjXjvffeIykpiaSkJN577z1atGjBl19+yQcffMBff/3FI488Uh3xnlFeXh533303CQkJ+Pn50bt3b1atWuV83jAMpk6dSr169fDz82Po0KHs3LnTLbEKIYTwbI7U0WXfe9CIF0CPJlGs1B093OFKPpm7ZZ2XENXNwDPXeAG0TGzIDr0+AK2VfWzem+bmiMR/VbnhNW3aNF577TVuvvlm2rVrR7t27bj55pt55ZVXePnll7n22mt54403+Omnn6oj3jO65ZZb+OOPP/jss8/YuHEj5513HkOHDuXgQcdmci+88AKvv/46M2bMYMWKFQQEBDB8+HCKi4vdEq8QQgjv4Tm3WNCzcQQr9PI12IlFGzhwtMiNEQlRN3lKvdC5YShr9WYAmBU7tgNr0XXpjPEkVW54bdy4kYSEhBOOJyQksHHjRsAxHTEtreZb2UVFRfzwww+88MIL9O/fn6ZNm/L444/TtGlT3nnnHQzD4NVXX+WRRx5h1KhRtG/fnk8//ZTU1FR+/vnnGo9XCCGEZzMwPDKrIUCTqAC2W9o6yz3UbazYk+XGiISo/f67gbInjXh1jA9jvVGe4K6xbTcpRyXpjiepcsOrZcuWPPfcc5SWljqPWa1WnnvuOVq2dEx5OHjwIDExMa6LspJsNht2u/2EtWV+fn4sWbKEPXv2kJ6eztChQ53PhYSE0KNHD5YtW1bT4QohhPAynrCW4xhFUQhu1MW5n1c3dRsrkw+7OSoh6h5PWfsZE2zhoKWps9xK2c+W1Fw3RiT+q8rJNd566y0uvvhiGjRoQPv2jrSVGzduxG63M3v2bACSk5O54447XBtpJQQFBdGrVy+eeuopWrVqRUxMDF999RXLli2jadOmpKc7Npj8b6MwJibG+dx/lZSUUFJS4izn5soPsBCiaqQe8V7H9257Us/2MV0bR7FmZ3MGaBuIUbI5uGcr0NHdYQkXkzrEcxw/Cu4oew5FUTDXa4P9gIKmGLRW9zEvLZcR7eq5OzRRpsojXr1792bPnj08+eSTtG/fnvbt2/Pkk0+yZ88eevbsCcD111/Pfffd5/JgK+Ozzz7DMAzq16+PxWLh9ddf5+qrr0ZVq/xRAXj22WcJCQlxPuLj410csRCitpN6pPbwtKZX98RwVujlKaTjctZyKFfWLNc2Uod4Nk+qF5rWjybZiAOgmXKA7QePuDkicbyzao0EBQVx2223MX36dKZPn87//d//ERQU5OrYzkqTJk1YtGgR+fn5pKSksHLlSqxWK40bNyY2NhaAjIyMCq/JyMhwPvdfDz74IDk5Oc5HSkpKtX8GIUTtIvWI9zo+g5knjni1qhfMJlMbZ7m7so2Ve2WdV20jdYjnOD7TqYNn1Qut6gWzxXDkYrAoNgpTt7o5InG8s2p4ffbZZ/Tt25e4uDj27dsHwCuvvMIvv/zi0uDORUBAAPXq1ePo0aPMmzePUaNGkZiYSGxsLAsWLHCel5uby4oVK+jVq9dJr2OxWAgODq7wEEKIqpB6pPbwlLUcx2iqgiWhKyWGGYDu6jZWSYKNWkfqEM/mSdVC67hgtujlSfCiC3ZwtKD0NK8QNanKDa933nmHyZMnM2LECI4ePYrdbgcgLCyMV1991dXxVdm8efOYO3cue/bs4Y8//mDQoEG0bNmScePGoSgKd999N08//TS//vorGzdu5IYbbiAuLo5LLrnE3aELIYTwMBXXeHmmjomxJJVlMktQD7Frt+xNKUR18tSshgCNIwPYoTZyllur+9iaJmsCPUWVG15vvPEGM2fO5OGHH8ZkKs/N0bVrV2c6eXfKyclhwoQJtGzZkhtuuIG+ffsyb948zGZHb+D999/PnXfeyfjx4+nWrRv5+fnMnTv3hEyIQgghxH951i2WQ4/EcOdGygDhR1aTXSg93ELUFMWDagaTpmKPKp9+3FrZxxZpeHmMKje89uzZQ6dOnU44brFYKCgocElQ52L06NHs3r2bkpIS0tLSePPNNwkJCXE+rygKTz75JOnp6RQXF/Pnn3/SvHlzN0YshBDCUxkVxrk85+bqeO0ahLBOKd9IubuyjZUy3VCIavHffbw8UVz9BA4ZoYBjxGvzwRz3BiScqtzwSkxMJCkp6YTjc+fOpVWrVie+QAghhPBiFW6yPLDtZTFp2Ot3p9TQABikJfHX1owzvEoI4QoGnrXGC6BNXDCb9EYAhCoFZB7Y4d6AhFOV9/GaPHkyEyZMoLi4GMMwWLlyJV999RXPPvss77//fnXEKIQQQriHZ3dsO/Vt3YhlB9swQNtAA+Uw+7csx663R1M97I5QCC9neNyqrhO1rR/CYiORwSQBEHx0MwUlNgIsVb7tFy5W5X+BW265BT8/Px555BEKCwu55ppriIuL47XXXmPMmDHVEaMQQgjhdo7U8p55y3Vemxjem9eVAdoGAHqULmPNvivpnhju5siEqN08sRnWql4wM4zGznI7JZnNqblSH3iAs0onf+2117Jz507y8/NJT0/nwIED3Hzzza6OTQghhHArLxnwIiEigD0R/Z3lS9Sl/LB6nxsjEqJ2Mgzw9JrB16xRGNnOWW6r7GGjrPPyCGfV8DrG39+f6OhoV8UihBBCeJzydPKKx63lOF6fTu1YbHfcbCWoh8jc8Ae5xVY3RyVE7eap9UK9BolkGo7kcu3UPWw6kO3egARQyamGnTp1qvSmkWvXrj2ngIQQQghPYRie3bN9vCu7NODJP4fQX3Ns7TKGuXy54nJuG9DEzZEJUXs4phx7fr3QrkEoGzckMlhLIlQp4FDKDuDErOSiZlVqxOuSSy5h1KhRjBo1iuHDh7N7924sFgsDBw5k4MCB+Pr6snv3boYPH17d8QohhBA1qsKIl5tjOZ3oYF+MFiPIKEsjfZ62hr///oM8GfUSoto4shp6Xs3Qtn4IG41EZzk0e5PUBR6gUiNejz32mPP7W265hUmTJvHUU0+dcE5KSoproxNCCCHcyIsGvAAYP6glb26/hKfMHwMwyfYJL83twxOXtDv9C4UQlWIYnphO40St6gXzDs2c5a7KdlbvO8qgFrJEyJ2qvMbru+++44Ybbjjh+HXXXccPP/zgkqCEEEIIT3HsJssT9+v5rw7xoRxpfhUpehQAvbUt2Fd9wALZ10uIauGpI+G+Zo2SuG7YDUd0PdWtLE8+4uaoRJUbXn5+fixduvSE40uXLsXX19clQQkhhBCewMsGvAC474IOPG7c4ixPNX3KZ199xpp9R90YlRC1gzfVCe2bJrDZaARAK3U/m3ftdWs84iz28br77ru5/fbbWbt2Ld27dwdgxYoVfPjhhzz66KMuD1AIIYRwp4prvDyxb7uixMgA+gy/io/mrmWcaR4+ip03eZFJ79sZdfl1jOpY390hCuHVjk+u4alZDQF6No5g+eLWtFf3ABCUvpK84qEE+ZrdHFndVeURrylTpvDJJ5+wZs0aJk2axKRJk1i7di0fffQRU6ZMqY4YhRBCCLfwtjVex4zr04jNbe9ngd2RxSxQKeY99Tn2fv8It3+yjF2H8t0coRDeyZvqhM4Nw1hNG2e5v7KOxTsOuzEiUeURL4DRo0czevRoV8cihBBCeBxv2cfreIqi8MwVnZlQ8BS2vY8zXFuNSdG5y/Qj+3Yv4fXXLqOg2cVc0b0p/ZpHYjFp7g5ZCK/x33TynjoS7uejYW/Un6KUV/FTShmqreWZzalc0L6eu0OrsyrV8DIMwyNTZQohhBDVyfCqFR0V+ZhU3r6xD4/++BJbkt7gTtNPmBSdBPUQL6szOLLnC37e1ZfP1W74NelDj6YxdIgPpVW9YHzN0hAT4qS8rEoY2Caef/a14zxtDVFKDpnb/8Vq74RZq/KkN+EClWp4tWnThqlTp3LZZZfh4+NzyvN27tzJ9OnTSUhIkGmHQgghagVvymr4X2ZN5bkrOzG7xdNc/1N37rB9Rj9tEwARSh43m37nZn4nN9mPtbuas9howts0oTi8JYGRCTSKDiIxMoAGoX5EB/sSE2wh0GKSzlhRpx3/0+/pyeWHto5h+uwunKetAaC3bSUr91xNn6aRbo6sbqpUw+uNN97ggQce4I477mDYsGF07dqVuLg4fH19OXr0KFu2bGHJkiVs3ryZiRMncvvtt1d33EIIIUS186b1HKdzYfs4+jW7mZmLBzFz6W9cbvzB+epKLIoNgGCliIHaegay3vGCXCjOMbNnVyx7jVi2GpH8bYSSYYSRa4rAHhiLEhCJyT+UYH9fgn1NhPiZCfYzE+JnJsBiws9Hw9+sOb76aPiaNfx9TPiZNXzNqjTehFc62Si4J/8o1wvxIzW6P/asmWiKwUXqMt5clyINLzepVMNryJAhrF69miVLlvDNN9/wxRdfsG/fPoqKioiMjKRTp07ccMMNXHvttYSFhVV3zEIIIUSNUZRjN1oefHdVCSF+Zv43vAX5A5vwa9IV3LJqC+FpixmkrqOvuolIJbfC+b6KlVZKCq1IOfFihWUPINfwI5cAcowAco0AcgigAF+yDB8K8aUIH4oNC4VYKMKHIsNCERZ0zYKh+aCrPqCawWQGzQyqD2g+KCbHV9Xk+N5kMmHWVEyqgqaqaCrlX5WTHFNVNEWhTVwwQ1vHVP//YFFnlNcJnj/iBdC/Y2uW/NmOAdoG4tVM0jf9TdGo9vj5yJTimlal5Bp9+/alb9++1RVLjXrrrbd48cUXSU9Pp0OHDrzxxhvO9PhCCCHEyXn+TdaZBFpMXNOjIdf0aMjh/EEs3pHJszsPk5Gyg9CjG2mrJNNESSNRSaOhkoGPYj/t9YKVIoIpooFyltnS9LKH7fSn2Q0FKyZsaNhRyx6O721o6Ibi+HqsjMoRI4jZHd6RhpeoVp5eK4zqWJ/n5vdlgLYBgPPti/hj61Vc3CHOzZHVPWeV1dDbffPNN0yePJkZM2bQo0cPXn31VYYPH8727duJjo52d3hCCCE8kDf0bFdVZKCFyzo34LLODYCO5BVfyubUXPYcLmD14QL2ZuZSmLkXNT+dwNLDxChHiVaOEqMcJYx8QpQCgikgWCkghIIzNtLOhaYYaFgB68lPOMk/zyEjlDlq7ft3E+5jGP/dx8vzxYb4kt9oOPkHPiRQKeYCbTn3rt4tDS83qJMNr+nTp3Prrbcybtw4AGbMmMFvv/3Ghx9+6JKkIMt2H2H6z//ip5aiKCqoGqgmVFXDUDQU1YSiaaBoKKqGqmll0yQUVFVBU46fKqGgHntOUcqmWBw7z/HVeUxR0FROeZ6PpmIxqfiaNSwmFUvZPHuLqeJXWTgthPvd89VqjqQmo6gm0Eyg+TjqEc2EovmgqCZMJhVT2dQrk1r2VVMwayrase9Vx/dmzTEVy6yV1Q+aillVyl9fdtykqphNKr4mFT8frWw9juNxrKzV4RvZ2l41Bvma6dk4gp6NI4472gOAYqudQ7klZOQVk5FbTEqhlc1FVnKKrOQUWskpLKW4MB+9+ChKaQFGaRGqrRDFVoSPXowvJfgrJfhRii8l+CmlmLFjxuZ8+Ci2imVsmLBjLjtuKRvv0tDLH8p/ymXPm9ApxYSpjv68bjqYw9SvFhOolqBo5dM4Fc2EYvJBPa4O8XHWA8fqiLKvFb53nGPWVMeUT02peK6qOusfX7PjXsPvuHrj2L1Hrby/8IKPNLJLU+bu784V2mKClSL89sznUF43ooN83R1anVLnGl6lpaWsWbOGBx980HlMVVWGDh3KsmXLTji/pKSEkpISZzk3N/eEc/4rt9jK6KPvcaVpcaVi0g0FOyq6c+pE+eP4Y7qhYvvvseOmVJzpPCsmCjGTZfhQgplifCgxyr6WlYsNH/KVAGyWUHS/MPALxxIYRlxYIAkRASRGBtA6LpiYYPlFFaKyzqYeyTqcwad5t572HJvh+P0/6cNwfLWW3YYe/5zVcByzlpWPfV/kfI2JYnyca3OKMVOEhSLDh2Is2FQLdpMvhuaH4RuE4heGOSCcYH9fQvzNhPr5EB5gJi7Uj/phftQP9SPI13zO/x/dwTC8q2e7OvmaNRpG+NMwwr/Kr7XZdYptOoWlNopLdQqtNoqtOja7Tqldx2o3sNp0rGXlAruB1V5WtunYdMfzpXbH97puYNMN7McehoHdXvZVr/gYEBdcDf83atbZ1CF5xTZGZn/BLabfT3mO9bh6wlE/mBzfH1cXHKs3SjGdcPzY60owOa9lQ3PUH0bZmj4sznKJ4oOu+aGb/dBN/mD2Q/ELxewfSnCAL6F+PoT6mwn1NxMRYCEu1Jf6oX5EBlpQPaQB/d96wFtGwoe3iWXizwO4Ase96UXKEmavT+OmvolujqxuqXMNr8OHD2O324mJqTjfOyYmhm3btp1w/rPPPssTTzxRpffQdQNV0St9vqoYqNiBM0zRqMnfbR0ocDzsmQqpyZHsNWLYZ8Twu9GYgyFdaNq8LaO7N6RNXEi1h5NVUMq2tFwOZheRmV9CZl4J+cU2im06xaU2bNZiFMBQVBRFRVE0FNUxwufvoxFgMRFgcWTUCvDRCA+0EBnoQ1SghaggCyF+5trZCyc8wtnUI9hPMZ3qOCbF0at/0qlXNfHjbMdZT3AYcg1/so0Asgkkywgm1YhgjRHJQSOSLEsDTPXa0KxBDJ0bhtKnaaTXNMaOn1YktcTZMWkqgZpjRoWourOpQ2y6jukM9xVmxY4ZO37/faK6f9CP3fKUAPmOQzmGP9lGINkEkmMEkEkIS4xIUo0IMpRoSoMbElyvGa3rh9K2fgg9EiM8JjmEp26gfLwAi4mw1oNI2/oW9ZQsBqrruXHtFml41TCpAc/gwQcfZPLkyc5ybm4u8fHxp32N3TBI0ptiweqc+qBWmAahoykVj6ll0yLUk51fNo2i4vkGprKxLA0dUxUaelWlKQbxSibxZNKPTcACKJzJzrX1+XLVYF5rfBWPXNLlrHpBT0XXDRbtyGTuxjQO7FhDvcJttFT2U185TGMlmyiyCVSK8MWKL6Woysn7oksMMwVYKMSXAsOXgrKvWQSzxQjhsBFCphFKlhqGLTQRv6hGJEYF0yQ6kI7xoTSNCvSYXjbhvc6mHinSNX6190LDcWNkOv6hHF/WMZVNxzr2nLks5YCJ8u+rc+3NMcFKIcFKIQ3JPPFJA+wHFZIPxLHq3+b8j87YGw3k6j4tGNQi2mN/z4wK33tmjKL2O5s6xGY32Gw0Yra9h7MOMR+rK5Rj9YrNWU+UH7c5z3W+rgbqjxClkBClkAQOnfyEQijYZWHrzgQ26I35TOmImtiPS7o24bw2MTW2IbBhGP9Z4+U99cKozg35ZVNvbjPNxqzYScz4g92ZA2gSFeju0OqMs2p47d69m48++ojdu3fz2muvER0dze+//07Dhg1p06aNq2N0qcjISDRNIyMjo8LxjIwMYmNjTzjfYrFgsViq9B6dGoZhv/J+rIZBse5oRPx3+oNulE+PODZlovw8yp/XK553wmuc54Gh28seNgy7HcOwo+iOr+g2DLsVxVoEthKwFaPaS1BsxZiNUiyUYlGs+FNCCAWEKXmEKAWEkUeEkkuCkkGIUljhczZTD/KY+hn7983lwTfuYsL1V9O7ybnvC7Fw+yFm/PoPg3J+ZJK2jPrKETjLjnGLYsWClXDyz9yDlw8leSb2745hlxHH93pTtppb4Z/QlSFtGzKsdQxhAafeQFyIUzmbemTihT05WtiZYrtBgW5g1XXsuoHVbmArm25lsxvY9GPfO6Zr2fWyY3ZHHWG1l73OpmPXbSi6Dd1WWlYn2MBeiqHbQbc6Rtl0O9hLUWzFqPYizPZiRweHc21OKX5KieMrJQQqRYRSQKiST2hZsoVQ8k/aGaQpBs2UgzRTD3INC8lJeYevvhjMB1HX8tAVfWhbv/pHz89GhREv77nHErXI2dQhbeKCKbjqHmx2gyJ7xXrCWla22h11hfVYnWHXsZad5zjuqDtsdju63YZhL0UvqzeO3Vc46g2b46vdiqJby+4vCrEYjnrCTylb11dWfxxb5xeglBBMAaFKAaHkEVqWpOVUnakBSgldlR10VXdwE3PJ3/cqPyb349qAy7j54sEMb3PifVxN8JZ6oU+TCGZYBnGbfTYAl2hL+WXdQSaf18LNkdUdVW54LVq0iBEjRtCnTx8WL17MtGnTiI6OZv369XzwwQd8//331RGny/j4+NClSxcWLFjAJZdcAoCu6yxYsICJEye65D3qh/pRv2N9l1yrJtjsOiU2nWKrnSKrnZwiK9mFVo4WlnKw0Mr6/BL2HS7gyOF0lMzttLFtZpCWRDd1BwAN1Uw+NJ7g1k+shN9xCy1jz24+vWEYPPPbFvRlb/GJ6Vt8TaeeanXUcExFKMaHYsyU4INuqKhlI4kqBho6Ptjwp5gApYiAsoXdp2NRbI4bQw4yQlsFQP5eX/5K7sRDP/fC0mYktwxo7rE3iKL26N88yt0hAGDXDYqtdmf94Phep8hqp7DUTn6xjfSiUrYVWsvqjlKOFpRSkpsJ2fvxL0qjvpJJM+UgbdW9NFdSnKNvIUoht5lmc0XWYh5+51YuvPIWLvKwLFvHb6DsTT3bQkQH+3Jhe/f9PhmGo6PoWL1RVOqoQ4qsdopLHfVHQamNA8U2NheWkl1oJbvISk5BCdaCo9jz0jHnHSSWw9RXMmmspNFG2UtDtXxEPVAp5gbTH1xV/Devf3kp/3SZyOOj2mGqxtEvg4r9uN609tOkqbTs0JNtq+NpqabQVd3BC2vXYAxrLsstakiVG15Tpkzh6aefZvLkyQQFBTmPDx48mDfffNOlwVWXyZMnM3bsWLp27Ur37t159dVXKSgocGY5rGtMmiOrUUDZ3PsGp9kD266fz9a0XOZvTuflFYu51/oe3dQdWBQrr/Iyt3/RmC/uvvisKr3n5myl0fJHuda8wHmsxDCxTG/Dv3prdqqJ2MKa4BMSS1hwECF+ZkfmJJMjW5KiOEYKdaPsq25QatMpKLVTUGKjoNROUXEJ1uJ8jPxDKAWZ+JceIVLJoZ6SRaKSRqKSTqKSjkUpb/QFKsVcrC3jYm0ZB7Z/yjubL6a0w/U8eEFbwmUETNRymqqUrZE8u5npxVY7qdlF7MjIY15qLtP3pmPa/w/DleVcqC7DotiIVHJ5W5vO/d8W4GuezDAP23NJqfC93JwIURmKouBjUvAxqYT4nd20FbtucDi/hANHi9h9KJ8PUnNITjlAYNpyBitruEBbgb9SgkWxcp/5W2av28+U0mm8eFWXGm1IeFOtcGnnBvyyog8t1a8B6Jy3iC1pF9TIen1xFg2vjRs38uWXX55wPDo6msOHz3LzxBp21VVXkZmZydSpU0lPT6djx47MnTv3hIQb4kSaqtC2fght64eQ178x93/TloJdDzBQW0+4ks9lRz/iuzWdubp7wypdd+H2QxT/O8PZ6NINhU/s5zEv/FqGdmvHFc2jaBIV6PI01iU2O4fzS0nLLiL5cAG/HC5gz6Ec8lM206BwM93UbQxR1xGqFADQQDnMNPOHrN/0N7ftvIdHx46iXQOprIQ4FV+zRuOoQBpHBXJ+23pAC/JL+jBnYxrXL1zJzblvM1xbjaYYPGuayY3fNqTdPTcTG+IZmVMNr+rPFqJ20VSFmGBfYoJ96ZIQBsQDbckpHMJf2zO4dclm+mV8yq3ab2iKwYXactI3vcRnCc9xQ69G1RLTf/fx8jbt6ofwTFB/KHY0vAZr61iw9ZA0vGpIlRteoaGhpKWlkZhYMQvKunXrqF/fe6bXTZw40WVTC+uqIF8zr17bnbFv3E/n7FsJVgq5VPuHsUuTGNMtvtK9TbpuMGPWEj4xlTfoJ9sn0PXC8XzVo2G19lpZTJpjamioH10bhR/3TA/Sc4pZlnyYhzYexLpzAdcwl0HaegA6qMm8X/oAd76bwYSbbqZ7YvjJ30AIcYJAi4nRXeMZ1TGOabOakbHmSW4w/YGPYudR/R2mz+vNC6M7uTtMp2M3WQbes5ZDiNosxN/MpZ0acEnH+ny/pjW3/9KaN9WX8VHs3GL6nevn9mJku9uIDKzaurizYaB41TQ9RVFo2boju1bH0VRNpYuyg9e37IIhzdwdWp1Q5flgY8aM4YEHHiA9PR1FUdB1naVLl/K///2PG264oTpiFB7MYtL4vxHd+cI+BAAfxU6bw3PZnpFX6WvM35LO0Ozv8C2b3veJbRidL7iV63omuLUyiw3x5dJODXj7hh68+OD/2DzoA643nmSn7uhgCFYKeUd9kVc/+ZLdmflui1MIb2UxaTxxSTtWt7qfDbqjM6+VmkLh+p/IyC12c3QOhvd2bAtR6ymKwpVd4xl99S08a7vGeXyC8RWf/ru3Wt7TwPDqES+Aoa1i+FPvDDiSHkWmLeZQnmfUubVdlRtezzzzDC1btiQ+Pp78/Hxat25N//796d27N4888kh1xCg83IDmUSz2H+4sD1STWLzjJOmkT+Hn1clcpS0EoNgw83v49VzXI8HlcZ6LUH8fJg5uxkv3/h9P13uDBXZHb7y/UsJ04yUe/XoJNnv1pfQXorZSFIVHL+7Aa8bVzmOj1b/4ce1BN0ZVUfmIl6zwEsITDW0dQ067cezW6wHQU93KihVLsOs100Dytnqhe2I4y7SuzvIgLYl/dnjHciFvV+WGl4+PDzNnziQ5OZnZs2fz+eefs23bNj777DM0zTM2shM1S1UVElt2IEV3ZGHrpm5nxfYDlXptXrEVfddCgpUiAH7Te3DFgC4eu6dPTLAvM28dxHeNp7FCbwlArHKUyw+9wcfV1LsmRG0XFWQhot15zjqkr7qJfzfucHNUDt7dry1E3TF+YDM+tZ/nLPcuWUJSylHXv5HhfQ2t//Ixqfg17k2+4VhL21PdwopkaXjVhLPOtxkfH8/IkSO5/PLLKSgo4OjRavjhFl6jb7Mo/tUde7hZFBvW1I0YlZijs2pvFn1JcpbnGz04r41nJznxMam8cm1PXg68jxzDsWn05doSFvw1n7ziU6fAF0Kc2kWdGjBX7waAqhgEpS8n10N+n47dZHnbWg4h6pKWscHsDB/kLA9W17Jg6yk2Y3YhR71Q7W/jct2bRLNGbw5AjJLNgeTNbo6obqhyw+vuu+/mgw8+AMButzNgwAA6d+5MfHw8f//9t6vjE16ibVwIW4zy6YFxpXs4lHf6PbMA1uw7Spey/cDshkJxXG+Cfc9yt+Qa5Oej8cBVg5luu9J5bJztW75ZleLGqITwXl0TwllJO2e5p7KZ1Xuz3BiRg6zxEsJ7dGjTis26416knbqXzXsqN/umKmpLldCjcTjL9dbOcsPctaTlFLkxorqhyg2v77//ng4dOgAwa9YskpOT2bZtG/fccw8PP/ywywMU3qFBmB97tUbOcktlP9vSz5xgY/OeNFoq+wHYYcTTunGD6grR5bokhHOo+RjSDEdGwyHqWv5Ytga9huaUC1Gb+PlolMR1c5bbqHvZmlb5JD3VydsX0gtRV3RNCGOd3tRZVtI3Vsvf5OPrBMNQvHL1Z8vYYDaa2zrLPdStrEh2f2dXbVflhtfhw4eJjY0FYM6cOYwePZrmzZtz0003sXHjRpcHKLyDqioY0eU9J82VA+ysRGZDPXM7muKowNbrjekYH1pdIVaLcf1b8qVtMODIDNQ7dw6rPKCXXghvlBBXjwNGJADNlANsT8t1c0RQe/q3haj92tUPYaPR2FluYtvF3iMFLn2P2jIKrqkKAY26UWg4Uu53V7exYo/cv1S3Kje8YmJi2LJlC3a7nblz5zJs2DAACgsLJblGHRcZHUu2EQBAfeUwB7NPP2RdYrPjV5TmLO83okmI8K/WGF2tW6MwVoVdgG44ertGqiv4fVO6m6MSwjs1jw1iux4PQLBSRFb6HjdHdExZVkPDO9dyCFFXRAf7kupXvh9VK3U/2ysx+6aqKox4OQ54pa6No50jhPWVI6Ts8YykRrVZlRte48aNY/To0bRt2xZFURg6dCgAK1asoGXLli4PUHiP+qF+pJb1VtdTjpB29PS9TOk5xcQpR5zlNCOCuFC/ao3R1RRFoVfHdqw2HAtUm6kH2b5xVaUSiwghKmoRE8ROo3y6sU/WTrdP3ZVfZSG8ixbRxPl9vHKIlKOFLr2+UYtGwTsnhDnvXwBCs9aRU+QZSY1qqyo3vB5//HHef/99xo8fz9KlS7FYHEOUmqYxZcoUlwcovEdcqB+pZeudfBQ7hUdPP/KTml2x4ZVtjvGKxBr/NaJdLHPt3Z3l9oXLq7SBtBDCISHCn/1GtLMcbWRyuODMSXpqigFeuZZDiLokMjKKLCMQgAZKJilZ1ZswwluzGgK0rR/MBqWFs9xF2cG6/ZKlvDqZzuZFV1xxxQnHxo4de87BCO8WF+rHvrIRLwAl5/QZ/lKzi4hTyveNsAfHVVts1alZdCBbA3tAyWcA9FY38++uI7SMDXZzZEJ4l8hACxlEOMtxymFSs4uJDvJ1W0xGLdizR4i6JD7MnxQjmnAln3pkkZqV49Lr16ZRcItJwxrbBf2QgqoYdFF38Me+owxsEX3mF4uzclYNr4KCAhYtWsT+/fspLS2t8NykSZNcEpjwPvVDfVlmlN80BRanU1Rqx8/n5Gv/UrOL6Fc24qUbCuYw78loeDxFUYhv2o70TWHEKkfppm7ny11p3NQ30d2hCeFVNFWhNDAOyga54pQs0rKLPCbpjjf3bAtRV8SH+5FiRNGBZFTFoPTIfqCPS9+j4hov7x4Hb5UYz46MBrRUUmit7OOVPWlAizO+TpydKje81q1bx8iRIyksLKSgoIDw8HAOHz6Mv78/0dHR0vCqw2JD/Jyp1QFilKMcyismISLgpOen5hQ5pxoeIpSYMO8dIerTLIqlG9pwubYEf6WE4j0rsdl7YtLOeo9yIeokNSQeyvY8rccRtuUUuzUeA0PSyQvhReLD/VljRDnLWt5Bl16/ttUGXRLCWPtvM1qqKZgUHePgamz2/nL/Uk2q/H/1nnvu4aKLLuLo0aP4+fmxfPly9u3bR5cuXXjppZeqI0bhJQItJnJN5SNe0crR026inHE0l2glG3Ak1qjvZYk1jtercQTL9DbOcgfbRjanekIqbCG8S2h4BHmGoy6opxwh9QzZUWuSt/dsC1EXRAVaOGyEOMtBtqMUltpc+h7/zWqoePFQeOeEMFbr5Qk22ti3V2ofVnF2qtzwSkpK4t5770VVVTRNo6SkhPj4eF544QUeeuih6ohReBF7YKzz+2glm4zcU/dWW4+W90IdNCKIC3XfOo5zFR3sy8HQLs5yN3UbK2U/DCGqLDbEl/SykfNY5SgZOe5teDnWeNW2Pm4haq/IoIoNr0glh8N5pad5RdXUtqzFkYEWMkI6OMtd1e2s2ScJNqpLlRteZrMZVXW8LDo6mv379wMQEhJCSsrpkymI2k8JOq7hxVEO5Z58xMswDLTcA85yqhFJvRDvHfECaNS4FQfL1rh1UXeyOvmQmyMSwvtEB/lyyAgFwF8pIS83263xHM/Rs+3uKIQQpxPgo5GrhTrLkUoOmfmuzY56fDVQG0bCYxu15rDhWO7RWd3Jmr1HzvAKcbaq3PDq1KkTq1atAmDAgAFMnTqVL774grvvvpu2bdu6PEDhXYKCwygyfACIUbJPOdUwt8hGuK28YZLq5VMNAXo0jmCl7tjLzl8poWDvarfvQSSEt4kKspBJeW+1kefeDckdKeSFEN5CURRsfuVrvCLJ5bALG161bMALgC6NwllTNt0wRCnkyN5Nbo6o9qpyw+uZZ56hXr16AEybNo2wsDBuv/12MjMzee+991weYFWsXbuWYcOGERoaSkREBOPHjyc/P7/COfv37+eCCy5wJgO57777sNlcO/e3LosJ8SPDCAPK1nidYqrhweyiipsnE0FMsPdONQTonhjOCr2Vs9zGukn28xKiiqKDLBwqq0MAtALPGTl2ZDWUZpgQHi+oPB16pJLj0oYX/Hf6sffXCV0bhbFGb+YsxxdsIM3N07xrqyo3vLp27cqgQYMAx1TDuXPnkpuby5o1a+jQocMZXl19UlNTGTp0KE2bNmXFihXMnTuXzZs3c+ONNzrPsdvtXHDBBZSWlvLvv//yySef8PHHHzN16lS3xV3bRAdZOEQo4Og1OZp78gQTaTkV9/Aq9quHj8m7M+jEhfqxP6ijs9xD3SrrvISoouggC5nHrc8IsB6hqNTuxohkjZcQ3sYSFIndcDSIXL3G62S8vT+maVQgW8ytneUuyg5Z51VNvPtO9zizZ8/GbDbz1ltv0aJFC7p168aMGTP44Ycf2LVrFwDz589ny5YtfP7553Ts2JERI0bw1FNP8dZbb52wH5k4O9HBFuf6DAB7btpJz3NsnlzeKDFC4qs7tBpRL7EdmWXzpLuq21l5knVe2YWlzN2UxrzN6RRb3XtDKYSniQqqWIdEK9lkniY7anU7flqRUQt6toWoCyKC/MkiCKj+Ea/a0C2jqgq+8Z0pMRy7THVRpeFVXarc8MrIyOD6668nLi4Ok8mEpmkVHu5SUlKCj4+PM/EHgJ+fY83QkiVLAFi2bBnt2rUjJibGec7w4cPJzc1l8+bNp7xubm5uhYc4NcfC+PJpQkpexknPO5hd7BzxKjbMBIXFnPQ8b9OjSfk6r2CliOw96ypkQFqzL4vbX/qIgm9uJeur27jhxa/ZdSj/VJcTtYTUI5UXaDGRo5VvSxGlZHMoz717eR1/kyVNL+EOUodUTeRxKeUjyeFwNdch3p9eAzo0imGj0RiAxmo6O/fsdW9AtVSVG1433ngja9eu5dFHH+X777/nxx9/rPBwl8GDB5Oens6LL75IaWkpR48eZcqUKQCkpTlGXdLT0ys0ugBnOT395Au4n332WUJCQpyP+PjaMTJTXWL+M+LlX3r4pKM6qUcLnWu8DhqRxIV5d2KNY3r8Z51X8+KNJB8uAOBQXjEvffwN79sf5XLtH642LeTtkik89MlcSm26u0IWNUDqkcpTFAU9sHx9RvRpkvTUBKNW9GcLbyd1SNVEBvqQWXYvYlFsFOW5LkufY4uJ48q1oNEFjo2Uj1/n5Z+xxuX7n4mzaHgtWbKEL774gttvv51LLrmEUaNGVXi42pQpU1AU5bSPbdu20aZNGz755BNefvll/P39iY2NJTExkZiYmAqjYFX14IMPkpOT43xIyvzTizouFTRAjHL0pNOEco5mEqg4eqDSjHDivDyj4TENw/3Z7dfeWe6hbmNFsmNK5avzt/OQfQYBSvn/j0gll9G5n/DNavm5qs2kHqkaJbC8gywK9041hP+MctWOeyzhZaQOqZqoIF8OH5cdVc/PrNb38/Y1XgAd4kNJMlo4y52VHaxPyXFjRLWTqaoviI+Pr9HN4+69994KCTJOpnFjx9DoNddcwzXXXENGRgYBAQEoisL06dOdz8fGxrJy5coKr83IyHA+dzIWiwWLxXKOn6LuCPY1kXXcNKHosmlC8eH+FU/MqbiHV21peCmKQmTjTuRs9ydEKaSXupn7t6TQJSGMorVf0868F4AMI5QQCvBVrFyq/sM1S9ZwXY+GkjGtlpJ6pGoCQiIoOWTGoliJUrJZ4c6phjLgJTyA1CFVExnoQ9JxSXrUAtc1vAyMWplwJ8BiIi+6E5Qt7eqmbuOfPUfo1STi9C8UVVLloaBXX32VKVOmsHfv3moI50RRUVG0bNnytA8fH58Kr4mJiSEwMJBvvvkGX19fhg0bBkCvXr3YuHEjhw6VJzz4448/CA4OpnXr1ohzpygKekB5b3W0kn3CJso2u465oDzpRioRxHn55snH69s8mgV6Z8CR2VHbNZ8pXy/nf6ZvnOfcY72Dd+0XAqApBl2OziUpJdsd4QrhcaKCfJ17ebk7uYaDUfZfpVas5RCitosKKl/jBRBYjdlRa8tUQ4BmjZuwS48DoJOyi5Ubt7k5otqnUg2vsLAwwsPDCQ8PZ8yYMfz99980adKEoKAg5/FjD3d68803Wbt2LTt27OCtt95i4sSJPPvss4SGhgJw3nnn0bp1a66//nrWr1/PvHnzeOSRR5gwYYL0JLmQGlQ+ehjN0RPWZ2TklVCP8t6nVCOCuFDv3sPreOe3jWU2/Z3l69X5DDj8JfXL1rQttHeg66BL+TfofOc5l2uL+WGNTB0RAiA6uHzKcoSSx5HcArfFUvv6tYWo/aKCLM41XuDazIaOSV+1s2Y4r3Usv+vdAVAVg8aH/2LPYffVv7VRpaYavvrqq9UchmusXLmSxx57jPz8fFq2bMm7777L9ddf73xe0zRmz57N7bffTq9evQgICGDs2LE8+eSTboy69vE/bppQtJLN8v9sorz/SCENlfJRx0NqLOEBPv+9jNcK8jUT1mYoe7d8QCM1g17aFnqxBQCrofGO70181L8xJlVh+aJW9FS30kRNI3njMmwXt8Wk1ZpdHoQ4K/+9aSrNOXl21JpyrD/bsYGyW0MRQlSCIztqqLMcVZak54RlDy5gUDvWeAF0TwzndUs/7rT/DMCF2nJ+TUrlrqHNTv9CUWmVaniNHTu2uuNwiU8//fSM5yQkJDBnzpwaiKbuig7245ARSrySSbRy4ojX/qyCCg0ve2ijWre2aeLQljy/6VreUadXOP6O/SKuGTmMAIuJizvEMfOvXvRUtwLQr/QflidfTt9mke4IWQiPERVk4cBxDS81330Nr5pc0yyEcA1FUbD7R0PZ7UcULhzxovbm2NFUhSZtu7N7XT2aqGn0VLfy6op/uWNQE8zSKewSlf6/qOs6zz//PH369KFbt25MmTKFoqKi6oxNeKnoYAsZOPbyClfyycrNq/D8viOFNFQcN1KlhoZfZO1Li5sYGUDvC8byqPVGCgwLpYbG+7YRHO5yL6M6OuZPN4oMYG/0UGyG49fwIm0Zs5IOujNsITxC9H9GvMzFh7Dr7m8A1eYbLiFqGzWofFuKSCWn2taK1ra1n1d1S+BL+xBneUTRLOZtPvmWS6LqKt3wmjZtGg899BCBgYHUr1+f1157jQkTJlRnbMJLRf8npbwtp+Iv7L4j5SNeKUY0DSOCajK8GnN9r0acd+Mj3NdkNhMazSbs0pd44pJ2FUb3BnRqyVK9LQANlMMc3LxY9vQSdV5UkIVDhJaXyeZIgXsSbDgaW+5v9AkhqsYnKNLZsRnlwiQ9tX0UvF2DELbXu5hCw5H7YLS2iM/mL8dml3sTV6h0w+vTTz/l7bffZt68efz888/MmjWLL774Al2XfwhRUXRQxU2UyU2rUFHlpO9x7mW1z4ghIcL1c649Rb9mUbx9Q3dmjuvN5V0anDCl8oL2cczSeznLg21L+Gdn9e43IoSniwiwcLhCwyvnhOyo7iBrvITwHpHB/hwhGIAYJZuMXNdtS3F8Z0xtrBfG9G/HV/bBAPgppYzK/pQvV+53c1S1Q6UbXvv372fkyJHO8tChQ1EUhdTU1GoJTHivhAh/9hrlmQ0bWPeQXlbhFZXaCcza5Hxui5FAq3rBNR6jp6gf6sehuKGUGI7llhdoy/lt/YEzvEqI2k1TFay+Uc5ytHKUTBetz6gqw5ARLyG8Uf1QPw4YjnokWskm/XCWS65bF2qDkW3r8WfkdeQZjq1+rjEt5K8537NXMhyes0o3vGw2G76+FVN+m81mrFary4MS3q1huD/J5qbOchtlL5sO5gKwJS2XNsoe53ObjURax9XdhhfA4I7NWKx3ABy9cke2LKLYWj37jQjhLYygivsBZnrKiFctWsshRG2WEOHPPqO8HrEd2XOas6um4ohX7Vv7qaoKEy/sySu2K5zHnlHf4oFP/iCnSO77z0WlshqCY07rjTfeWGG/q+LiYm677TYCAgKcx3788UfXRii8jqIoqLFt0dMUVMWgnZrM/JRshrWOYXnyEboqO5zn5oW3xd+n0j+GtdLI9vWYNqcXw7Q1AAzTl7Bw29WMaFfPzZEJ4T6WkFjsRxU0xSBOOcKmo4VuiaM23lQJURckhAewwChPsOFfsJ9Sm46P6dyy89XyJV5OfZpG8nuXW/l33Rp6a1uIU7J4NOcx7njfj9fHDSQiUPa/PRuV/ukbO3Ys0dHRhISEOB/XXXcdcXFxFY4JAdCiYRzbjQYAtFX2sm7LdgCWb95FN9WxE/oePYbExi3dFqOniA7yJbfhUIoMx15mI7SVzFonc6lF3ZYQFeKcstxMOciOtGz3BkTt2q9HiNquYYQ/+/TyEa+GZHDARR04yn9KtW1LnGMeurANr4Xcx0EjAoC26l6mZk7mtjd/Ys2+o26OzjtVeqjho48+qs44RC0zqGU0f/7bhVZqStnu5wv4YU1nEtLmYjI7ErIs0DsztE3sGa5UN5zXqQkLDnTiQm0FEUoevtt/YndmG5pEBbo7NCHcomW9YLYaDWlCGhbFSkHadqBHjcdhGIas8RLCC4X4mTni2xDKcsC1UvezJS2Xxuf8d7Xu1Af+PiZeuul8Jr+dw9vWR4lQ8mihHuCDont48r0b+KXbdUwc0ozoIN8zX0wAVRjxEqIquiaEsdSnr7N8m2kWb34/lwmmX5zHFvgMpmfjcHeE53FGtq3H98pwZ/lO7UdenbfVjREJ4V6t6gWxTW/oLIflbiOn0N1rC2SFlxDexKd+e6yGBkB7JZkNB3Jcct3avsbrePHh/kwbfwW3W54nWXd0lgcrhbxknsFla8fy0Auv8uAPG9joov+3tZ00vES1MGkqvfsMZKHdkTSivnKEhZZ7qac4sgotsHeiV59BWEyaO8P0GCH+Zlr3voBl9tYAJKoZ1N/6Pn9uyXBzZEK4R9PoQDar5Ul6eqpbWJZ8uMbjkH28hPBereOj2Go4OnCaKKls33vuWYPryhqv4zWNDuLNiZfzaNRr/GAv71TvqO7mfe1Zblk/ml/fmcLVL3zLM3O2sjz5iCQJOwVpeIlqc1PfRN7xv40jRsUNkjONEN4Nmsgt/RLdFJlnurVfY97QrsNuOPrO7jH9wIff/sDuzHw3RyZEzbOYNNSGvSgxzAD01zaycOsht8ZkoMgiLyG8SKeGYazRmwOgKgaBB5dwtKD0nK97fC1QG/fxOpnoYF8+ueM80ge/xq22B9iqxzufa6Km8bD5S74qvJVLl49m3Yd3MemJZ7n+jd95ctYWvludQlJKNgUlNjd+As9Qt9PJiWoVaDEx7aaLuP0Dg2uLPqeFksJmoxHfBN7A8zeNqPPZDP8rLMCHSy+8mA9+Xsp4029YFCuvGc8xYYYvj4y7jPYNQt0dohA1qmeLeFbsb0l/bSMNlMOkbVpIwcVtCLDUYN1h1O5pRELUZj0bR/C50plxzAPgPHUlczenc3X3hmd45anVwQEvJ5OmMmFQUw52uoc3/xxJ3rofuUb9g97aFuc5rdT9tFL3A7PgCCRnxrLdiGeR0YD39QbkBCSihicQHh5J/VA/6of5US/El8hACxGBPoQH+NTq2VBy5yuqVbOYID669yrmbOzPb1mFJEYG8Gm7eviaa+8v1bm4oksDJm6ZyIpdu+mhbiNKyeF928M8NGMfjQdcy/j+jQmsyZtOIdxoZPt6vDSvH/21jQDcoP/MzH9GcvfQ5m6Jp7av5RCitvHz0TA3HUB28uuEKgWMUFdw/eI1XNmlASbt7Cd9VVzjVXuzGp5K/VA/nr2iI2nDWvDtqut5bcVyuhYuZpi2ho7q7grnNlbTaUw6I1jlOGAFMiA33Z+DRiQHjEj2GxGsM4I5QjBZRhCF5nB0/wiUgEhM/qH4+foS5Gsi0GIi0GIm0NdEkMVEoK+JAIsJX5OKr1nDYlbxNWn4mjV8zSoWk4bFpKKqnvPvI3dwotoFWExc2TX+zCcKFEXhxTHduXXGY9x/+GE6qMkEK4W8aXqVBYsXMX7paFp3HcglnerTJi64zlX2om6pH+pHUdMLSd/7FbHKUYZq6/jx769Z1XQi3RrVTGIeA4O63ccthHe7qkcTvto5mNtNs/BR7IzOeZ8Zi9oycXCzs7peXVzjdSr1Qvy4a2gz7hzclI0HL+LPrRm8tGU7wZlr6KTsoIu6gzbKPizKiYmRgpVCgpX9tOIU2+cUlT2AIsOHfPzIM/zIx498w488/MnHj8OGL8X4UIyZEqPsKz6UYKbY8KEYH+yaBUOzoGu+oJrBZAbV5PheczxUzex8TtV80DQNs6Zi1hRMmsr9w1sQHXzu2Rul4SWEh/H3MfH2rcOY9IkvYw4+wwjN0Us0RFvHENaxeWUCvyzrzbN+3Ylu0oEuiZG0qhdE85gggnzNbo5eCNe66/x2vPDmGKab3wHgBe0t7vrAh20XXM013Rui1WBPZl1ZyyFEbTKgeRTvhl/JNTkLCFEKuVxbwiML3uJ98yRu7pt4lh2Yxkm+q7tUVaFDfCgd4kPhvBbkFY9gfUoO/+w7yrsHsyjM2I1/zg6acJDGajpxHKa+cph6yhF8lDMn4fBTSvGjlCjlHDMn2sselUiQazcUbJiwovG8bQwFg545t/cuIw0vITxQiJ+ZmbcM5PU/45i35DPu174kriwjZBt1H23UfWD7iiPbgli7pRnLjHg+1uPJCWyCEtbQOXc6LtSPyLI502EBPoT7+xDiZ/aoYXchTqdVvWAie9/A7OXruFBbTqBSzEz1Ob6evYKr/x5N325dGdwymtb1gqvl59owJKuhEN5MVRXuu7w/T793HS+a3wPgafNHfDLvANetv5XrB7ZnSKsYzJWcemhIfXBGQb5m+jaLpG+zyLIjPSi22knOLGDfkQK2ZBfxR3YRqVkFFGWnouSmYSo+Qhi5hJNLhJJLhJJHBDkEKkUEUkSQUkQQhQRShKZU/7+BphhoWLFgRUPH5KK/L17T8Jo2bRq//fYbSUlJ+Pj4kJ2dfcI5q1atYsqUKaxZswZFUejevTsvvPACHTp0cJ6zYcMGJkyYwKpVq4iKiuLOO+/k/vvvr8FPIkTl+JhU/nd+S7a0f4Cn/7iA4B0/MEZbWGH+dISSxzBtLcNY6zhQSoW50weNCI4YIewmiCwjiKMEkU0QVp9QdJ9AFEsQiiUQ1TcIX4svARbHHGp/H8ccaYtJxWJS8SmbJ+1TVraYNXw0FYtZdZ5j1lQ0VXF+NamO4XmTqjjLMjVSnI37z2/JhIxH8dkzlfO0NaiKwTWmvxhTtJBVi1ow568OvGBugxLTliYN6pEY6U+DMH/iwx2dD65K5ONY4yU/w0J4my4JYSwf+n+8syCN202zABhr+oNLM5fyw9f9uMnUj5BmPenRJJq2ccG0qhd82rXoFWoB+btWKb5mjdZxwbSOCz7p87pukFts5XB+KVkFpRzJL+FgQSn5JTbyi23kl9jILbaSX2TFWlyAXpILxbkYpQWotmIUezGqvQRfrPhSiq9SiqXse8tx35uxY8KOSbE5vzdjx4wNEzbMStnzZcdN2MgygvExuSYRvNc0vEpLS7nyyivp1asXH3zwwQnP5+fnc/7553PxxRfz9ttvY7PZeOyxxxg+fDgpKSmYzWZyc3M577zzGDp0KDNmzGDjxo3cdNNNhIaGMn78eDd8KiHOrHVcMG+P7c2uQ+34ce2tPLt2NW0KltNL3UJ3dSshSuEJrznj3GkDKCl7lCkxTBTiSwG+5Bt+FGOmFDMlRtnXskeh4fhaislxzPChFBNWTNjQnA87KjajYllXTOiqhqKYMFQNQzVjKCYUVUPXTCiqGVQNFA1UjQIthA9uGUCIv0yhrMtMmsqbY3vz+C/TWbnmPe42/UCgUoyqGPRQttFD3QaAnq6wLy2afUYsKUYUy40oUo0I8k1h2PwiUAKjsARGEhroR1iAD8G+JoJ8zQT7mQj2NTu/D/I1E+xrIsDHVDbiJYTwdncMbMLrtqk88nckj5g+x1exEqwUMs40j3HMI3tHAGu2NWex0Yh3jUQKghtjimhEg8gwEiIcnTn1QnzJK5aU6NVBVRVC/X0I9fc562sYhkGJTafEqlNsszu/FlvtFFt1Smx2bHYDq13Hpju+FtkN8nQdq93AZnd8teo6tmNl3SDGrrssm67XNLyeeOIJAD7++OOTPr9t2zaysrJ48skniY93JHJ47LHHaN++Pfv27aNp06Z88cUXlJaW8uGHH+Lj40ObNm1ISkpi+vTp0vASHq9pdBD3n98SY3gLdmdewvLkIzyafISs1F34Hd1Oc/aTqKZTn8PEVWHu9DEWxYaFfMLIr7k7Tb3s6ynmXN9nHY9u9K+hYIQnM2sq0y7ryL/tn2T8byPpcGgWV2iLaKKmOc9RFYNEJYNETrLxeFlHg35YIZsAjhpB5OFPruFPLv5kGP7swp9cI4Bc/Mkz/MlT/MnV/TFbHL9HssZLCO+lKAp3DWvO3w0f4oafe3N53hdcrP2Ln+LY1ytUKXCupQagCPQUhbSUcPbrMaQYUewgjHQjgh7q8RsxS6XgKRRFKctoqBGCZ3bYek3D60xatGhBREQEH3zwAQ899BB2u50PPviAVq1a0ahRIwCWLVtG//798fEpb00PHz6c559/nqNHjxIWFnbCdUtKSigpKR8WyMlxLOzLzc2t3g8kxGlE+8LFrcO4uHUY0JQS2zCSM/M5kFXE6pwiUnOKST9aQHHuIYyCLLTiLPxsuYQpeYSRT4hSgD/FBCgl+FOEv1JCAMXHHSvGFytqDcyjPp2iUiv5+Xlo9pNXoMd+Dw0PTzMl9YjrtI324Z0b+5GU0o73N93Eru0baZi3jlbKXlqpKTRVDuJ3kgxa5QxM5BFFHlFnerOyH6vcsn+6fN1OcUG+/LvVMt5Qj0gd4jqd6/ny9q3nMWdjO25cuZWYjL/pq26ih7qVMKXgP2cbBHGYNhymzfGHdcgtG/gqtRbLv0MdV6U6xPAyH330kRESEnLS5zZu3Gg0adLEUFXVUFXVaNGihbF3717n88OGDTPGjx9f4TWbN282AGPLli0nveZjjz12LJewPOQhDw99pKSkuKyOqQ5Sj8hDHp7/8OR6ROoQecjD8x+VqUMUw3BfF8+UKVN4/vnnT3vO1q1badmypbP88ccfc/fdd5+QXKOoqIiBAwfSsmVLJk6ciN1u56WXXmLbtm2sWrUKPz8/zjvvPBITE3n33Xedr9uyZQtt2rRhy5YttGrV6oT3/28vk67rZGVlERERccZEAbm5ucTHx5OSkkJw8MkXE3oiibtmSdxnzzAM8vLyiIuLQ1Vds/C1OpxtPeIJ/4/PhrfGDd4bu8R99ryhHpF7EYm7ukncZ68qdYhbpxree++93Hjjjac9p3HjxpW61pdffsnevXtZtmyZ80N/+eWXhIWF8csvvzBmzBhiY2PJyKg49/9YOTY29qTXtVgsWCyWCsdCQ0MrFdMxwcHBXvVDfIzEXbMk7rMTEhLitveurHOtR9z9//hseWvc4L2xS9xnx9PrEbkXkbhrisR9dipbh7i14RUVFUVU1Bln2VdKYWEhqqpW6Pk5VtZ1xwr+Xr168fDDD2O1WjGbHWtG/vjjD1q0aHHS9V1CCCGEEEII4QqeOaZ+Evv37ycpKYn9+/djt9tJSkoiKSmJ/Px8AIYNG8bRo0eZMGECW7duZfPmzYwbNw6TycSgQYMAuOaaa/Dx8eHmm29m8+bNfPPNN7z22mtMnjzZnR9NCCGEEEIIUct5TVbDqVOn8sknnzjLnTp1AmDhwoXOtV2zZs3iiSeeoFevXqiqSqdOnZg7dy716tUDHMOA8+fPZ8KECXTp0oXIyEimTp1abankLRYLjz322AnTAzydxF2zJG5xKt76/9hb4wbvjV3iFqfirf+PJe6aJXHXDLcm1xBCCCGEEEKIusBrphoKIYQQQgghhLeShpcQQgghhBBCVDNpeAkhhBBCCCFENZOGlxBCCCGEEEJUM2l4CSGEEEIIIUQ1k4aXEEIIIYQQQlQzaXgJIYQQQgghRDWThpcQQgghhBBCVDNpeAkhhBBCCCFENZOGlxBCCCGEEEJUM2l4CSGEEEIIIUQ1k4aXEEIIIYQQQlQzaXgJIYQQQgghRDWThpcQQgghhBBCVDOTqy9YUlKCxWJx9WU9hq7rpKamEhQUhKIo7g5HiDrNMAzy8vKIi4tDVb2nH0nqESE8hzfWI1KHCOE5qlKHnHPD6/fff+frr7/mn3/+ISUlBV3XCQgIoFOnTpx33nmMGzeOuLi4c30bj5Gamkp8fLy7wxBCHCclJYUGDRq4O4xKk3pECM/jTfWI1CFCeJ7K1CGKYRjG2Vz8p59+4oEHHiAvL4+RI0fSvXt34uLi8PPzIysri02bNvHPP/+wbNkybrzxRp566imioqLO6oN4kpycHEJDQ0lJSSE4ONjd4QhRp+Xm5hIfH092djYhISHuDqfSpB4RwnN4Yz0idYgQnqMqdchZj3i98MILvPLKK4wYMeKkw2qjR48G4ODBg7zxxht8/vnn3HPPPWf7dh7j2JB+cHCwVHZCeAhvm2oj9YgQnseb6hGpQ4TwPJWpQ8664bVs2bJKnVe/fn2ee+65s30bIYQQQgghhPB63rGKVAghhBBCCCG8mEuyGk6ePPmkxxVFwdfXl6ZNmzJq1CjCw8Nd8XZCCCGEEEII4VVc0vBat24da9euxW6306JFCwB27NiBpmm0bNmSt99+m3vvvZclS5bQunVrV7ylEEIIIYQQQngNl0w1HDVqFEOHDiU1NZU1a9awZs0aDhw4wLBhw7j66qs5ePAg/fv3rxXJNYQQQgghhBCiqlzS8HrxxRd56qmnKmTWCQkJ4fHHH+eFF17A39+fqVOnsmbNGle8nRBCCCGEEEJ4FZc0vHJycjh06NAJxzMzM8nNzQUgNDSU0tJSV7ydEEIIIYQQQngVl001vOmmm/jpp584cOAABw4c4KeffuLmm2/mkksuAWDlypU0b97cFW8nhBBCCCGEEF7FJck13n33Xe655x7GjBmDzWZzXNhkYuzYsbzyyisAtGzZkvfff98VbyeEEEIIIYQQXsUlDa/AwEBmzpzJK6+8QnJyMgCNGzcmMDDQeU7Hjh1d8VZCCCGEEEII4XVcuoFyeno6aWlpNGvWjMDAQAzDcOXlhRBCCCGEEMIruaThdeTIEYYMGULz5s0ZOXIkaWlpANx8883ce++9rngLIYQQQgghhPBaLml43XPPPZjNZvbv34+/v7/z+FVXXcXcuXNd8RZCCCHqiEO5xTw7ZytTftjAzow8d4cjhBBCuIRL1njNnz+fefPm0aBBgwrHmzVrxr59+1zxFkIIIeoAu24w6b3fmJr7BOFKLvetv4vnJ99GXKifu0MTQgghzolLRrwKCgoqjHQdk5WVhcViccVbCCGEqAM2Hcyh+9E5tFb3Easc5T4+4auV+90dlhBCCHHOXNLw6tevH59++qmzrCgKuq7zwgsvMGjQIFe8hRBCiDpg/YFsOqk7neX26h7Wbt7mxoiEEEII13DJVMMXXniBIUOGsHr1akpLS7n//vvZvHkzWVlZLF261BVvIYQQog5I2neUC9XdFY4FHN5AsfVCfM2am6ISQgghzp1LRrzatm3Ljh076Nu3L6NGjaKgoIDLLruMdevW0aRJE1e8hRBCiDrg0P5thCv5FY61Yg/b0yXJhhBCCO/mkhEvgJCQEB5++GFXXU4IIUQdk1NoJTx7I/hUPN5W3cum1Bw6xIe6JS4hhBDCFc664bVhw4ZKn9u+ffuzfRshhBB1hGN9164TjrdV9/D6wRw3RCSEEEK4zlk3vDp27IiiKBiGgaIozuOGYQBUOGa3288hRCGEEHVBUko2/Y5b37VFT6C1uo96ShYHDuwHpBNPCCGE9zrrNV579uwhOTmZPXv28MMPP5CYmMjbb79NUlISSUlJvP322zRp0oQffvjBlfEKIYSopTbtP0RrZS8AyXosS/S2zud8Dm2k1Ka7KTIhhBDi3J31iFdCQoLz+yuvvJLXX3+dkSNHOo+1b9+e+Ph4Hn30US655JJzClIIIUTtZhgGhSkbsCg2AJKMpmzSGzmfb2Eks/NQHm3iQtwUoRBCCHFuXJLVcOPGjSQmJp5wPDExkS1btrjiLYTwCiU2O/uOFFBUKtNrhaiKA0eLaFyy1VlO0puw2WjkLLdT97D5YK4bIhNCCCFcwyUNr1atWvHss89SWlrqPFZaWsqzzz5Lq1atXPEWwsvUxSlBvyQd5ManZ/DF9Hu5bdobfLZ8n7tDEsJrrEvJpuNx67v2WFrSonUn8g1fANoqe9koCTaEEEJ4MZekk58xYwYXXXQRDRo0cGYw3LBhA4qiMGvWLFe8hfAS+44U8MhnfxCQuR5T8yE8N6YngRaX7Vrgsf7cksGy76bzlfl9MDuOPT5rN/ODHua8NrHuDU4IL7BmbxY3KTsAKDHM+MV3oE2DMLbsTKC7sp14NZM9KQeAtqe/kBBCAL9vTOP3jQdpXT+MG3ol4O9T++9Fjldis7MzI5/oYAvRQb7uDkeUcclPYffu3UlOTuaLL75g27ZtAFx11VVcc801BAQEuOIthBcwDIMpn/7F69l3EmXOZdXu2bw87yMeu7h23ygVW+2898sCPjN9XOH4Q6YvuO7n7gxscS0+JpcMLgtRa+3ek0yCegiA9UZjOjSKoV39EDbpiXRXtwPge2gtxdZh+Jo1d4YqhPBwczelsfrrp3nO9B1Lt7bh8YyXeGF0F3eHVWPmbU7n4+9/pod1Bfv0GMwdr+TxSzrUucanJ3LZv0BAQADjx4931eWEF9qdWUCrw/OIMjvWYXRTd/Dq+qUYF7WpsL1AbfPh0j2MLfwIi2arcNxHsXNJ0Y/8tnEQl3Zq4KbohPB8+SU2gjLXOEeL1+jN6ZIQRqt6wXxjNOcm5gLQ2dhKUko2PRtHuDFaIYSn+3LRBt43fYWPYmeYtpZZ638kc0RbooIs7g6t2q3bf5Sfv3qXz7VX0EyOLZ6Wb/qbidnPMeOm/tIR7GZn/X9/+fLllT63sLCQzZs3n+1buVR2djZdu3alY8eOtG3blpkzZ7o7pFpj2e7D9FIr/js3Lt7CgaNFboqo+mXmlbB84Wwu0FY6ykYwPYvfIM/wA+By7R9+WLLRnSEK4fGS9mfTuWyaIcA6owUdGoQS4mcmO6qb83gPdSsrkrPcEaIQwktY7Tpa2jp8lPIkV8PU1SzakenGqGqGYRi89PMyntdmoCmG83hPdSvXpTzGA9+tQdeN01xBVLezbnhdf/31DB8+nO+++46CgoKTnrNlyxYeeughmjRpwpo1a846SFcKCgpi8eLFJCUlsWLFCp555hmOHDni7rBqhRW7D9FD3VrhWAc1uVYviH9l/lb+Z3zsLL9su4rRg3vwrX0gABbFSkL6fPYcPvnviF03mLspnWfnbOWLFfvIK7bWQNRCeJZlyYfprm5zlgtju+Ln45hO2KJJU3bpcQC0V5JZn3zQLTEKIbzDrkP5tDKSKxzroOxm44Fs9wRUg/7ekUmPQ98QrBQCsFlPILesI3iwlkS3zdN4cd62011CVLOzbnht2bKFCy64gEceeYTQ0FDatGnDsGHDuOiii+jbty+RkZF07tyZPXv2MH/+fG644QZXxn3WNE3D398fgJKSEgzDwDCk9X+udN0ge/cqgpWKo1uJStopGx3ebmtaLiVrv6K9ugeALXoCtvbXMGlIMxb4DHKeN0pbyi9JJ94sFpXaGf/+QtK+nsTo5ZcSPvtmbn75CzbV4oaqECeTtG0nHVTHjdJmPYE2TRs5n+vROJyVeksAzIodNWUZhaW2k11GCCHYdDCHNmV/l49pqGaSnHLATRHVnG/+3ckN2h8AlBoa40snc4f+P0oMx8qia0wL8V36Ai/8vrVOZp/2BGfd8DKbzUyaNInt27ezbNkybr31Vtq2bUv9+vUZOHAg7777LqmpqXz11Ve0a9eu0tddvHgxF110EXFxcSiKws8//3zCOW+99RaNGjXC19eXHj16sHLlyirFnp2dTYcOHWjQoAH33XcfkZGRVXq9ONG29Dzalm444XhjJY3kzNrX8DIMgxdnreU+0zfOYy8YN3DfiNaYNJVm7XuzU68PQHd1O8vXJp3QwH/ilyRuSnmEcaZ5NFHTGKGt4v3SKTz1wbekZBXW6OcRwl0O5RUTnbHUWV6kd2Bg82hnuWdiBP8YHZzlAcZq/tl5uEZjFEJ4j00Hc2ir7D3huCljPVZ77W1sZOaVYNo1j1DFcc/1m96TOy8bzM3X3cD99tud591l+pHW/97FuGdnMv6TFTwzZyvfrkohq6D0VJcWLuSS5Bpdu3ala9eurrgUBQUFdOjQgZtuuonLLrvshOe/+eYbJk+ezIwZM+jRowevvvoqw4cPZ/v27URHO/5Yd+zYEZvtxB7R+fPnExcXR2hoKOvXrycjI4PLLruMK664gpiYGJfEX1f9u/swvY9b33XQiKC+coQwJZ/MQ6lAh1O/2AvN35JBh/2fEGs6CsAf9i50GjCKmGBHytZRnRvw86o+3Kd+C0DnnD9Zf+A8OsaHArAzIw896Wv6mCuuiQtWinjZ/hz3f1aPjyeMlEWwotZbtD2TQVqSs7xC68ytjcKc5RB/M0XxAyhJexOLYmWotpbpm9MZLts0CCFOYs+Bg84Mqcdrpe9mR0YebeJC3BBV9fsl6SCXqYuc5VnKIN7oEEeAxcShUeN56uejPGr+HIALtRVcaF9BSbKJlN3R7DAaMGn2RVx35WjObyt1a3XyuLu6ESNG8PTTT3PppZee9Pnp06dz6623Mm7cOFq3bs2MGTPw9/fnww8/dJ6TlJTEpk2bTnjExcVVuFZMTAwdOnTgn3/+OWU8JSUl5ObmVniIE63Ymepco3HQiGChvaPzOeXILjdFVT2KSu188MsCbtMce9RZDY2ZvuMY37+x85xO8aGsDh7iLF+qLeHHNSnO8tt/beMO7Rdn+ebSe0nSmwDQQDnMtYdf482/dlb3RxE1ROqRU5u/fi+D1CQAcg0//Jv0wqxV/NPUr20jluptAKinZJG6bTl2WSAu6hCpQyrHrhuo6eUJrf6xl29n005NrrVT+Q3DYMHKDQxQ1wOO+7CwNkMIKNtH9apuDWl00f1Msk0i2yjf5smi2GiqpjJSW8knTGXWV++wco8kMKpOHtfwOp3S0lLWrFnD0KFDncdUVWXo0KEsW7asUtfIyMggLy8PgJycHBYvXkyLFi1Oef6zzz5LSEiI8xEfH39uH6IWKrXpWPcux1dxJIZYam9LslHeyI0u3V+rhrDf/GsHtxW9h6Xs835gH8m1Fwx2JgMAUBSFXl06s6JsbUoz9SC71/9DqU13rHnb9CON1AwAltjb0LDX5TwT9DBHjUAALtBWsm/xZ2yoA4uB6wKpR07uSH4J5uQ/CSpbGzrP3o3h7U78fzOsVQx/6uV78PQpXcq/u2W6oag7pA6pnOTMfJrpu53lWXov8g3HTJR26h42HKidDa/Nqbm0y5rrzGT4g70fl3dtWOGc63smcOP4yUyK+YSp1rH8Zu/ONj3euf5LUwyeN83gpe/+kvVf1cirGl6HDx/GbrefMC0wJiaG9PT0Sl1j37599OvXjw4dOtCvXz/uvPPO065Be/DBB8nJyXE+UlJSTnluXZWUkk1XvXx911K9DclGPWe5kZJeaxJsbEnNZe+SbxhcNjUqzQhnWYObuLhD3AnnXtapAT/Y+znLw6wLWbj9EO/8tYMJ2s/O4zOVK7hrSDMevXooU+03OY8/oX3ItK8XUmy1I7yb1CMnN2dTOhep5eu75ip9GNb6xGnfDSP82RM5CKvh6Ny4TPuH71ftq7E4hXA3qUMqZ1NqDu2OS6yxQW/CJiMRcMwm2Z9SO+uNH9akcLlWPntrqf8weiaeuN9h54ZhfHrHMMbd8ywFoz7kl97fc030T/xi7w1AoFLMJbmf89vG1BqLva6pc1tYd+/enaSkpEqfb7FYsFhq/4Z752LupnSuUsu3C8iM6oVqK4CytlZiWcOrS0LYKa7gHYqtdp746i/e1t53HnvGdj2PXNrtpBtEN4zwJ6P++RRnfIyvYmWU9i9jfltHi9x/aWpyVGor9Ja06jWSUH8fQv19aDzgemYvXsmF2nJClQL+L+dVnv+9GY9d3PaE6wvvIfXIiQzDYO7KzXxYNs0w0wgmoGX51Jj/GtatLX/P68gwbQ2xylEKt84np6gDIX7mGoxaCPeQOqRyNhzI4XrF0fAqMcykmePZoDemZ9lWNz6HNlBiG4HFpJ3uMl7FatfZkbSUFqoja+NqvTk9unZDVU+8LzkmMTKAxEjHlEO73oLr3ixg8JF1BClFXKYtYdyiJC7pWP+k9zbi3Lh8xKu4uNjVl3SKjIxE0zQyMjIqHM/IyCA2VhYDuoOuG2xdv8L5C79Kb07Xti3xj2pMaVnvtCOlfL47w3SJ5+Zs5bbs6UQojqmq8+1daND3aprFBJ3yNSO7NWeO3gOAMCWfQTk/c3dZwg2A94xLuaVforM8cXBTvoi4k0zDsfh3sJaEseJd/tpW8WdeCG+XlJJN24xfnVN2f7X34aJODU95/iWd6vODMdBZvoIFfL+m9qeHFkJU3u79B2msOmZAbTESGN2jMRv18r+xrY3d7Ej3/vuR4/29PZOhpQuc5R/s/bi0c4NKv15TFW4c0omv7Y5tcHwVK20y57A1Lc/lsQoXNbx0Xeepp56ifv36BAYGkpzs2I/l0Ucf5YMPPnDFWwDg4+NDly5dWLCg/AdM13UWLFhAr169XPY+ovJW7s2iZ3F5Fp3f7D25qH09GkUHs99wTBlKVNLZm+ndv8A/rTuAZeWbDNIcC1cPGaF8GD6Zu4c1P+3rRnWszzc+lzvLU8xfO/8oLNdb0aTnxUQGlvdimjWVJ64ewIN6eerXB01f8t63v3Aot/o6NYSoaZ8u3c31pj+c5fn+FzKoRdQpzw8P8EFrMZxDRigAw9Q1zF+8tFanhxZCVJ7NrqOmJznL6/XG9G4aydHQ8hkjHdTdJNWytdM/r9nLxdq/gGOU70Dc+c7RrMoa2iqGP/0vcJYv1v7l1/Uy3bA6uKTh9fTTT/Pxxx/zwgsv4OPj4zzetm1b3n///dO88kT5+fkkJSU5pwPu2bOHpKQk9u/fD8DkyZOZOXMmn3zyCVu3buX222+noKCAcePGueKjiCr6YtluRmuOhpduKGwPH0yzmCASIwPYU7bOy1exknvIe+dVr0/JZs6Pn/KA6WvnsYf1/+OpawaccbqCr1njvEGD+N7ev8Jxq6HxqjqWCYOanfCa5jFBDLjgGt63jQAcWYem2abzv8//kQWvolbYd6QA2+ZfaKA4EmQstHdgYO9emLTT/0m6sV8zPrKdD4CqGIwq/IFZcnMghAB2ZebT6rjEGhv0xrSvH0JUw5YcMRwzU7qr21mx+8RU897qSH4J+vb5zpk4f+hdOL/rqRPGnYqmKnTs1IUNZaOD7dU9rFu3+oT9R8W5c0nD69NPP+W9997j2muvRdPKb0Q7dOjAtm3bqnSt1atX06lTJzp16gQ4GlqdOnVi6tSpAFx11VW89NJLTJ06lY4dO5KUlMTcuXNlHy43SM0ugq2zqKc4Uo8u0DszuJtjv67EyACSjfLpn6ajyehemP557+ECpn3yEy+rr6OWZQt6xXo5gy+67rRTDI83tlcCc+LvZbHdkcSlyPDhf7bbuOnKSwnxP/n6lOt6NGRts0ls0hsB0ERNY2zaNB758cSNmIXwNm/8uZ1J6g/O8mdcwJhuZ87S1q1ROJviLifP8APgcu0fvl6wos6PeuUVW8kttro7DCHcakNKDu3V8oZXRmBrIgIt9GoSyXK9FQDBSiHZu1d75f3IyXy9KoUxynxn+WcGcGG7E5N9VcbFHeKYZS+fPdalYDGbDsq2Ba7mkobXwYMHadq06QnHdV3Haq3aH4OBAwdiGMYJj48//th5zsSJE9m3bx8lJSWsWLGCHj16nOtHEGfhzQXb+T/1V2f5K2UEo8tunhofN+IFUF9PJd3LpsqlZhdx33u/8Ib1cYLL0l3PsXcnp9vdXN391GtR/sukqbx1Yz9W9H2f60M+4q6G3zN63GTOO80GsIqi8MyVXXnC9z7nnhtDtHU03fASMxYln9sHE8KNdh3Kx77hO5qpBwHHutCmPS8iLMDnDK90uGFQB76wO/bIsyhWrsz5mK9X7q+2eD1Zek4xt3+0lClPPskjTz7GLTP/dnSICVEHrd57hM6qY//LfMOXkHhHY6tXkwiWle0DCNC6ZD07Dnn38gdwTK38599/GaA5skrv06MJaTv8lB26Z9K6XjCbQgY6y0O0tfy5VdaXu5pLGl6tW7c+6SbE33//vXPkStQu29PzKFr7DW3VvQBs0hvRoPP5zgxjUUEW0rT6zvMbK2lelVL+wNFC7np3Fi8XTyVGyQZgg57Idw0e4uGLqp5h0M9H477hLfnsnst47+YB9GkaecbXhPr78PiNF3G3Phmb4fhVHW/6jSN/vMxny7136qaouwzD4Nlf1nCvqTzBzFtcxf8NaFLpawxpGc0/0deSY/gDjlGv3/6YT05h3Rrxycwr4c53fua+PTfxls/rvO7zFo+l3MJdM37laC3aN1GIyjqwe5Pz7/VqvQVdEx1rRuPD/UkO7Ow8r6+6iSU7vX8fwD+3HmJ40Wxn+TP7MG7oXfm69L8URaFN63Zs0x0d6J2UXazesv2c4xQVuaThNXXqVCZOnMjzzz+Pruv8+OOP3HrrrUybNs05RVDUHja7zpPf/sODpi+dx6Yb1zBhcPl6JUVR0CPKK4BEJY1kL2l47czIY/Lb3/NK4YM0VDMB2KHX57mIp3ltbF/MZ1iH4kpt4kIYc9V1PGa70XnsEfMXbJ/1Ct+tln1chHeZuymdjvs+dK7tWmxvR5s+FxIRWPk02aqqcOeFPXjDdqmjrBjcb3uXabM3VkvMnsgwDO774l+eK3zcmawHIF7N5NHCZ3nmt01ujE6Imncwu4hGeWud5eV6K3o2Lt/HKr5pB1KNcAB6qFv5Z6N3zxwxDIMvF23kcm0x4FjCsC32YjrEh57TdYe0imGB7hgwURWDuEOLScuRUXRXcskd5KhRo5g1axZ//vknAQEBTJ06la1btzJr1iyGDRvmircQHuSledsZk/ka0WU9SwvsnWjT7xJign0rnBcaFe/cMT5RSWdPpuc3vFbuyWLqjM95u/Rh583hHj2GR4Of5o2bhxHsW/N7Bp3fNpbE8+/kZesVzmNPmz8i6afpfLR0z2leKYTnOJxfwsc/z2G85uihLTU03vQdzx0DT5ymfiY9G0eQ2vw6duuO6cyd1V0Er3+fhdtrz6L50/lmVQoDDrxDEzUNgP16FAcNx01me3UPlvWfsj4l240RClGzlu8+Qk91i7O8yac9LWPL12Gf1zaWefZugCNhVcjBhWTmldR4nK6ybPcR2qd+41wG8ZO9D5f1Off9Prs2CmO5qbuzPFRdy4KtdaNerSku67rv168ff/zxB4cOHaKwsJAlS5Zw3nnnuerywkN8uyoFlr7KRdpyALKNAN4NvpMJg068eUqMCiS5bJ1XvHKIfZlHazTWqvp8+T4+ff81PtAfI1JxLCjdoifwYMiLvD5+ZJV65V3tln6NMQ96gHdsFzmPTTN/yNE5T/Pi3K2ScEN4NMMweOS7NTxufQWLYgNgpv0Cxl407JQbJp/JI6M68bhyO7rh2ODzf6ZvmfHd7Fq/7cKh3GJmzfmFsZpjQX2R4cMN1incVTrBec4E0y+8t7Bqia2E8GaLtqfTR3WM9OYbvgQnVtxAuE/TSBap5fkAzldXMm9z+gnX8RYz/ljPLaY5ANgMlZ/9r+TC9meXVON4Zk0lokUvMo1gAPqpG1m0RWbXuFLNzZkSXu+71Sms+vkNppiPS6tuu5WHxgzB13xiWvUmUeUJNjTFoDRjR43FWhXFVjsP/bCerNlTedP8Kv6Koxdspd6CZ6JfZMbtI04YzXOHO4c0I6f3w8w4rvE12fw99ZY8zKQvVlFQYnNjdEKc2gdL9tBt9+u0Uh1/wLfq8WxofBsj2539xvdxoX6MvOBSPrYPBxzbVjxT+gL/++yfWp3l8Mlfkpiqz3BmWZ1uu4LHb7yYIcMv4Q97FwDqKVn4b/+BXYdq10axQpxMqU0nZ/sSZ0r1f/R29GlRMdO1r1kjqEV/Dpc1KIaoa5m70jun5C7ddZi2B74hVHHMIvpF78OoIX3xMbnmln5w6zgW2h3TDf2VEow9iykslfsLVznrf6WwsDDCw8Mr9RDeza4bvDR3G1t/ep4Xze86j79kvZK+o26m4ynmFDeLDmK7Xr57ekjebvI9rHGwPT2PG9+YzbCkO5lk+tl5/Ed7X95PeJn3xg8h1L9y2daqm6IoPDCiJfYhj/O09Vrn8etMC7hux53c/NZv7Dvi+dM5Rd2yaEcm2+a+y82m3wEoMUw8br6baaO7oijKGV59emO6xbM8cQJb9ATAse3CtenP8djPG2rlKPDcTek03jaTFuoBwJHwJ6vdzQxsEc11PRvyiXap89z/02bz/qJd7gpViBqzPPkIfewrneX59q4Ma3XiFkOXdG7Ij/Z+gGO6YYuMOWw6mFNjcbqCza7zxq9Luc3kyChtNxS+8b2KK7o0OMMrK29A8ygWGl2c5YHGav6pBclIPMVZN7xeffVVXnnlFV555RUeeeQRAIYPH87jjz/O448/zvDhjl7IRx991DWRCrdIzsxn3LsLabz0XqaaP3Me/8g2HGufyadNq944KoBdlO/N01xNYWeGZ6RwNQyDT/7dy0tvvcEbORMZpK0HHJXYU9Zr2dHrJd4Z1xd/n7ObBlVdFEVhwqCmNB01hXusd1BiOOLroW7jlZy7efj1D/h53UE3RymEw6aDOcz88iummd53HnvSdgP/d+VFRLpg6q6iKDw/pieP+U5xZjkcrq2m+bppvDyvdmXjyimy8tHPvzPR9BPgmF70jOkOHr6oPQBBvmY69BrGCr0lAE3VVLLWz/bqdSxCVMb8zWmcp64GHL8Xh+MGEn2SWSoDmkfxl99wZ3mMtpAvvCxD8NerUhiV9ZFzbde39oGMGtofi+nEWUdnK8TPTHHD/pQYjjXtQ7R1/Lk5zWXXr+vOuuE1duxY52Pp0qU8+eSTfPXVV0yaNIlJkybx1Vdf8eSTT7Jo0SJXxitqSG6xlenzt/Ps66/zTPqtXKYtcT73hu0SDvV5gikjWp32Gr5mjcLQ5s5yC+UAOzyg4bXncAG3zFyEPud+ZmrPE1W2nivTCOEW/SE6jH6EKSNboann1htfncZ0b8jFN9zDTcoTpBthgGN60SdMZd8Pj3D3l6vIKapb6bWFZ9mRkce0D77mLeNZ57quz2xDiRl8B4Nbum7D+7AAHx4bewGT9UnObRduNM3HtOR5Xl+ws9aMfD3160buL30LH8UOwHv2C7n64gsIP27/s7G9G/GhXj4V+UblN9l6QtRqpTadPRv/pZHq2G9qpd6SXm1PnrDHpKl0797L2TnRTD3IkaRfyfCSdaGH8oqZM28OV2l/A5Br+PFL+DjGdKv8vqKV1a9NI5bojmQd9ZQs0rb+i60WT+GuSS6ZEDpv3jzOP//8E46ff/75/Pnnn654C1FDMvNKeH3BTm5/7j26/HMLM7Xnndn9CgwLk2yTCBzxOPef37JS04RC6jWh0HD0bDdXUtie7r41ByU2O6/9uZNnX3uFJw7ezDjTPOdzC+ydmBjyJg9OvJ2LO5z7AtWaMKhFNNMmjuOu4Fedf0g0xeAu04+M3XYbt07/irmb0mvNjafwHltSc3l05ne8aX+KEKUQgCX2Nqxs+QB3Dq56FsMzaVs/hCuvupEHbOOdx+42/YjPwieYNnuL1/8O/LDmAA02vkGXss1hk/VYkhLHn1BXRQf5EtL+Ame2x97aFlYtW0ix1V7jMQtRE/7adojzSsvvM2fpvRjZtt4pz7+me0M+1i90lu9QfuTdv3dXa4yuYBgGj/24lsfsbzrXd75uu4xJo/pUSyfxeW1imKt3c5b7WJexck+Wy9+nLnJJwysiIoJffvnlhOO//PILERERJ3mF8CRWu87f2w9x15ereOz55+j09418wUPO3dDBcdM0zvIK199yD+P6JFZ6bUaz2BB2GI6NlBOUQ+xNy6yWz3Am/+46zPXTf6L5ott5T3vR2ZgsNsw8ar2Rpd3e4pM7L6B5TNAZruRZGkUG8MHEC/m29Vu8ZL3S2ePfSd3FZ6X3sPWrh7j9k385mC37cIiasXTXYZ599yPetT7iXOy+Wm/O+/Wn8eJVXc55XdepnN+2Hl1HTeBJ6/XOY7eZZtFkxcNM+mKF1yaf2Zaey+xfvmaSVj7F8DHlDh67/ORr5G7u35QP7SOc5dHWX/hh7YEai1eImvTzqt2M0v4FHBk+U+uPoFFkwCnPjw3xJazzKLbqjlGijupuUlf96PHro79fc4C2O99xru/crCdwqPWN9G4SWS3v1yDMn9TogdjLssaer65k7iaZbugKLlnA8sQTT3DLLbfw999/06OHI13nihUrmDt3LjNnznTFWwgXyym0siz5MAu2ZJC2ZSn9bUuZoi2jnqlij0aKHsV0+5WE97yOj85rUeXUzy1igtihx9NRTUZVDKyHtgEDXPhJTu9wfgnPzt5IyMaP+dD0HYFa+ZSCJfY2vO57O3dcM5yBLaJrLCZXC7SYeHlMV2a1epIbfurE0/rrNFbTsSg27jH/wO7kf3nk5ZvpMGAU4/s39rh1a6J2MAyDL1bsZ9nsj5ipvYmv4pjqul5vzGsxzzBjXL+TZj91pau7N+RT+/94eLaZp0wfoSoGV5sW0mRHKuPefIjnbhhK46jAao3BldJyipj6wc+8q7xyXBbDK7ly9JXUD/U76WtaxAaRkXgpWSnfEq7kc6G6nGsXreLqbg0rpNcWwtulZBXiu3sOoSZHo2mu3o0LurU84+tuH9iU59ZcxlvqqwA8qHzKtF8G895Nfaoz3LO2OTWH33/5gve1WYBjD8QnzZN455KO1fq+vdu3YOVfreilbSFRzWDnplXoF7eVeuQcueQO7MYbb6RVq1a8/vrr/PjjjwC0atWKJUuWOBtiwn0MwyA9t5j1KTkkpWSzaeduQjOW01vZxF3aBsfoz39+Evbp0bxnv5DsFlcx6bw2tIg9u5Gg5rFBfGmUZ9uJLkwmq6C0wrqE6qDrBt+sTmHWnNk8pL9LW/Ne53OZRjDP2K4jvOd1fHheCwLPch8hT3NRhzi6NrqJR75tS9d9M7lFm4NZsdNETeMjnubPRb9x4/IbuXz4YK7oEu/Ra9iEdykosfHoj+totvk13jLNch5fbG/HB/Ue562bBpz1fl1VdUOvRvxouZd7fgzgBe0dLIqN7up2Xsu9hwdfv51BI0Zzfc8Ej795OJRbzF0z5/JSyZOEqY4p2n/bO5DdecIZp0OPHdCKzz4Zxl2mnzArdgbn/szC7f0YcpJMb0J4q4+W7OFmdbaz/LMyhLfan3qa4THx4f7E9hzNspXz6aVtIUE9ROvk95m1PpGLPGypwdGCUh7/9HfeU99wdr68YruScVdeUO33UcPbxPLZn93opTk2pu5WtJSkA5fSuWFYtb5vbeeyv4Q9evTgiy++cNXlxFnKL7GRnJnP7sx8dh8qYEfqEQoObCKheBvtlGRGqbuZou4H84mvtRoaf+sd+ZrziGg3nLF9G9MmLuSc4kkI9ydZLV/42VxNYUdGHj0bV98U1G3puUz7YQVD097lc+1PVLV8fccXtiH8FjOehy7rRdv65/bZPFG9ED/ev6U/v65vynWzBnK/dYZzXchQbR0Drev5+pdBXLPoeq4d1oML2tWTBpg4Jyv3ZPHydwuYnP8SPUzlm/b+aO/LwhaP8u5V3ap9pOu/LuvcgIjAuxj3ZT1eNl6inpJFPSWLj7VpfDJnJTdtmMiDl3Q56w6l6rb/SCH3vj+bFwoeoaHqmJ69VW/IB7GPMvPitmd8fd+mkbwdfim35czCoti4RlvApEWbpOElao2cIivJq+fSTt0LwEa9EY27Dq90R+pdw5pza9KtdLPdi0nRmaj9zNifOtMx/mbiw/2rMfLKKyixMfHDhTxZOM3Z+fKHvTMFXSdw/mnWsblK0+hAdoT1h/xPADhfW8Uvm9Kl4XWOXNLw2r9//2mfb9jQ9RlX6iK7bpBTZCUjt5i0nCJSs4tJzS4iLaeYrKwj6Ef2EFq4hyZqKk2UVEYqadyppGJRrCdtaIFjT51/9TbM0XuQFjuEQZ1a8HLn+i7bu8qkqVjDW0LZVhnHMhtWR8OrsNTGa3/uIPXfr3hJ+5QYU7bzua16Q6ap4xl+4cV81r1hrW5sKIrCqI71Gdh8LC/M7cZXa77kXtO31FOyMCk615kWcEXeYr76bjDXzL+Kq4f25ML29TBpsp+6qLzcYisvz92KddXHvG/6kiDVsY7Qamg8Z7sarfcEXh3hvuygA5pH0ejOG7n74wbcmf08fbXNAIw1/cGwtDW8+ObVBHW9mruGNifCBantXWXxjkze+fJ7XjNeIE51TP0+YETyWNDjvDduQKUasYqicFn/zvz8c1+uMv1NsFJIYspPbDzQlXYNal+Hk6h7Plq6h3HGz87y+/YL+V/fxpV+fbCvmWsuGs5b3y/mLtNPmBSdZ/XXuP+zxrx/2/AaG6E/lWKrnUmfLuXuzEdopTrusffoMXwSM4UPLzpz54urdGnXjqSlTeio7qa1uo8n16/BGFG55Gri5Fzyk9WoUaPT/iPY7XUro1JBiY1DeSXYdR27DjZdx64b2HQD+3GPUptOYamdglIbhSU2Cq12Cksc5YISG9kFJZTkH8UoPIxamIW59Cih5BFNNnHKEeKUI3Qt+xpcljmMM7SX7IbCFiOBpXo7ViptIb4XvVvFc3e7eqdcM3CuouolkJPtT4hSSAs1hfnprk8pv2hHJm/+8CcTCt9hoGm983ihYeEV2+UcanMT0y9qR3TQiXt71FYh/mamXdaBbb0TmTr7Aprt+YzbTbMIUorwVayMM83jmvwFfPPDIK6ZP5qL+nfn8i4NZA2YOC2bXeerVSnMnj+fe2wf0NO81flcmhHOfdzDdVdfWSM9smeSEBHAR3dewPNzEpm/6n0eNH2Fn1JKnJLFK6a3WLt2Hv9bM4Ym3Udy64AmxJxk75+aUlhq48W52yhZ8SEfmz51dJjhyGA4JfBpXr11ZJU6xC7uGMcNc0dxle1vAG4xzeGpP69hxo29qyN8IWrM4fwS1i2ezd1lCcAOGJHorUdVeaTq4g5xTN46gTVbN9FF3UlDNZPJRx7n7i9CeHtsb8xu6ozMLbZy50eLGZ/6KN20HQAcNoJ5wPIor18/AB9TzcV1fttYfl3cnY6qI/Njj/y/WL1vJN0ahddYDLWNS+6w1q1bV6FstVpZt24d06dPZ9q0aa54C6+yYNshfvrmQ1ooKWjojodix4TdWTZhxwcbfkoJIRQTSwkBSjH+lOBPMQFKMaHkY1KO2zfhFKNWp2IzVPYasWw0EtmoN2anuRmm+h1onRBLn6aR3NgwrEamADWPDWb7lni6K9upp2RxIDUNaOeSax/OL2HarA1EbvqQT03f46eVOp/7w96F9wJv485LB9G/eZRL3s8btYwNZuYt/flnZytunX0xg458xfXan/grJVgUGzeY/uCawgXMmdOD8fMuom2PIYztnUC9kOppiAvvZNcNftuYxhd/rODS7I/5SltUYRrv17aBzIq9g2lj+p42q1hN8/cx8cQl7VnSZipjv+3G/xV/wBDN8Ters7qLj3iadau+5okVo/BpPYKrejSmZ+PwGuvRtdl1flx3kO/mL2JS0Tv0M29yPrdab86LIY/w5vjhVe40spg0+vfpz4K/OjFEW0d95QixO79i7f5WMlVIeLU3F+zkLsqXtrxiu4KJQ06/r+jJKIrCk5d25Ob99/Nm4X1EK9l0U3eQvWcqd372NK9e26PGp0nvP1LIfZ8s4MHsqXTUkgHIM/y4U32YZ269hNiQmu0cahMXzFOhQ9Hzv0ZVDC7VlvDe2gPS8DoHLml4dejQ4YRjXbt2JS4ujhdffJHLLrvMFW/jNUyqwoXaMi4/btPh6lJimEg3wkk1IjlIJLv1OJKpR1FwE3yjm5AQHUrb+iFc3yCUhHB/tywobxEbyHY9nu7qdseBzK0YxrBzvrGZszGNT3/8hUftb9PGXL5JaKoRzlP2G2nWfwyfDWpa4xWnp+rXLIo+d13I/C3duPmPVfQ/8g03aPMJUEowKToXa8u4mGWsWdaMZ5aMRGl9EWN6JNKzcYTHJyIQ1afYaueXpIP8uHAFI3K/5RNtIb6m8s259+nRPGHcQp/zR/Np70YeO423b7NIOt93NTP+7s5X//zIA8pnNFMPAo7tF95WXyZt+0d8u2UgrwYNp1P7jpzfNpYODUKqpRGWU2jluzUpzP93NaPyvuZL7W/MWvnskE9tw1jR/H98cFXXs04ANLZ3I27952qG6I6G5kTTz/xvzmV89H8DZaqQ8EqbU3PIXvUVnU27ANiuN0Bpf9VZr9cM8jXz3E0jufvtI8zUHyNAKWGYtgbL7vu5debjvHx93xqbKfPH/7N33+FNlW8Dx7/nJGm6F12UllIotOy9NzJcCC5UHIAoiigqKoIL98CFA8WJe/1UXK+CigKCLIFC2atAaWkpdO8k57x/pAQKiLSkTdLen+vK1T7nnCR3C3n63OdZW7OY/9XXvKS/SBP1KAB5uh+3KzOZcePVJETU/ZxURVEY0K0TK/9oQ1/DFpqpWRzYtIzyS9piNkrbqiZqdUxRYmIia9eurc23cEsGVUHTa9YVXKabKMabEt2bXPzJ1QPIIcD+VQ8glwCO6IGU+TRGD2qCX3AUjUP8iA72pkmwD5dG+BPXyNetPhCJUYH8occ6yrGWfWQWlNW4R6Wo3Mpj328ifOObfGz8BpNqb6xousKHtuH82eQWHrm8JwkRnrNsdF1RVYXz20Uxou3F/L6tB5N++4ce2V9zreF3wpQCALqqu+iqvkLmzo/4attAXgm4gAE9unFlt5gGNVSzoduTXcRnq/azY90SRtsW8om6ApPxeGJQoPvyqvVS0ltdx6yLOxLXyH16uf6Nr5eRacMTOdj9Tl7+dTjWlAVMVr8jSU0DoLGSw53Gb7mz9FtSVjZj4fIezPbpSXjzznRvHka3ZiG0CPev0RAkTdNJPVrMqr1HWbL5AGrqUq5Q/uBzdT0G4/Gew3S9EY9qN9Fj+NW83v/s90w8HX+zkfMGD+PHX3sx0rCKMKWAdmmf8teudg16FIDwTDZN56lvVvKK4SPHsRe1sTwy/L+XkD+T5uH+3DP+Kqa8W8gb6gv4KuUMMKQQnXkHt78ynalXjaRfy9rZLwsgp7iCp37cRKOUd/nA+BVeir2ezdRDuNP4CLMmXkGb6MBae///MqpTNHN+70ffynmyw6xL+HP75W4xnNwTOSXxKigoqFLWdZ1Dhw7x6KOP0rJlS2e8hUcxKPYE4DetKzZUdMWIrhpAMaCrRlCN6IoBVBO6ly+K2R/F5IvB2x8fsxe+XgZ8vYyE+HoR6mcixM+LJF8vQvy8CPXzIsTXq07H+J6r6CBv0oxxjnIrJY0dmYU1Sry2ZxbwyIe/cG/xi/Qw7XAc36Y15Qn1VkaNGskHXWOlh+Y/KIrCsDaRDG19IX/v6ckDy3YQvGcBNxoWOhqhUUouU43fcXvJ9yz/ox2P/X4eWqvzGdU1nkGJ4dKTWA9lFZTxc8oh/kjeRWzGQq41/E5bdT+c8E9drJv51DaUPxqNZeolvWptA8/aFBPiy4tXdeXA0Na8tfRKDm/4P8bwO0PU9Rgql2xur+6zr5hm/Yq8HX6s3ZbEAq0Ve5RYykMTCYyMJyrIh8hAM438zJhNKiaDikFR7PN2K2wcLSrnYG4pmdk5aJkbaWnZQXd1J3PUTfgZy6vEVKj78K71QjbFXsfDl/dw2n5j1/WK44Zl13FBxRqMisYtxp+46YeL6H33KJfNYRGiJuavSOXirHmEG+1tzoW27rQZdCUxIee+CmHXuFCmTLyJSfO9mas/Q5BSQoKawQeW+3nxg39Y0H4C95zflmgnzoUvqbDywd/7+HvJIqZr79DBlOo4t0ZL5MXAGcyeMMLlN7ViQnzJjhlOWeb7eCsWRhpW8uC6fZJ41ZCi67r+35edmaqqp9yV03Wd2NhYvvjiC3r37n2ub+E2CgoKCAoKIj8/n8DA09+BsC+koWFUVVQFGdIB3PD6L3x05GoAVmmt2TT0UyYNaFGt11i4+RBffPUpLykvE6rYl1a16QpzbaPYmXQbj4zqKD0y52BPdhHzl+8lff0iruEXhqgbqs4xBI7qAfyfrRe/GgfQpO1ARnVuQs/mjVw2vOxsPo/uyF3i1nWdbYcKWbYrm5VbUwk9uJiL1JUMUDc57roek6/78oFtBKvCr2TceV0Y3iaq3tzgyC2u4NsN6fy6ch1d8xZxvmEtHdTUMz6nVPciSw8hixBy9ADKMVGhm7Ch4qeU4UcZoUohMcphwpWCf32dQ3oon1uH8E/EpUw6vwcDW4U7/W/Gl2sPYP3+Lq41LgZgga0vR4a9zs0Dzn4VuPrMXT6P1eGJMZ+Lzen5vP7mK8wzvghAke7NjX5z+XjapU4d4bM5PZ/HP/iBx8pnO1YTBPvN3Ve1MYR2GcX1fZqRFFXz33laTgmfrTnAxjVLGW/5kuGGdY5zmq7wju1CtrS+i2eu6OLy1RWP+WLNAXx/nMQlhpUA3Ga5m8dmzCQ8wH1WhHWl6nwenZJ4LV26tEpZVVXCw8NJSEjAaHSP/zTO0tAqO2eZ+e0m7to4kkgljxzdnyfb/MRLV3U+6+e/vWwPBxa9xqPGDx3JQJoWzgPKHVw66nIu7dxEElwnySup4Nv16fy6egNdcn7hasOfjr2ETpSmhfO91oflPoNp17EnF7RvTOfY4DptjHvq59FVcVttGjuyCtlwII/1+46Stesf2petY4C6ia7qTsyK9ZTnJGst+NR2HjnNLub6Aa1rJTFwF7qus+lgPou2ZLJh0yYS8/+it7qVHup2Qipv9jjDUT2AxbYuLKY75tbnc23v5vSIr70FPWyaznWv/cwbOZMcP8cN2iyev/c2l67k6C48sR7xxJhrqqjcyo2vfMdbxXc6/v/OtEzkkhsfpHcL529Nk1NcwX2frWTg/te4zvC7Y+NigO1aLF/aBrEj4ny6tW5Jv5bhtI0OPGOCVFxuJSU9n7WpOazespPGWUu4xvAHXdTdVa7bqTXhMXUyIy8cxVXdY92qns0vtTDt6Rd5z/AMAMts7UkZ8gFTBie4ODL3UOeJ17Jly+jTp88pSZbVauXvv/9mwIAB5/oWbqMhVXbONH9FKi0WXs8AQwoA14V8zCd3XvKfz9N1nVd+34m2ZDbTTF87ji+xdeSdiAd59tr+brPZYX2j6zrrD+Tx5ep9HEn5jUtZzDB1Hd6K5ZRrt2lN+cXWgzU+fUlo243z20XTs3lorQ9l8tTPY13EnV9iYefhQnZmFbIrq4i96Vmoh9bTxraDzupuuqi7aKScfmuHQ3ooP9t6sthrMO27D+Ca7k3daqXCuqDrOqlHilmdmsOaPdlkp6YQVrSDRDWNRCWNWCWbSCX3+FYe/yJTDyFND2ePFs1GvQUHfFrTqHlnhraNZlBiOIHe1VyutobWH8jl67ee4GnTe4B9UYJXWrzD3Bt6u1UDzxU8sR7xxJhrwqbpTP7gb27Zdydd1V0A/GzrwaberzLjwuqvZHi2NE3n87UH+OWXH5iuvXdKD7hNV0jRm7NCa8tWrRkF/s1Rgxpj8g1GNRixWi1YS/IgP52Akv20V/bSVd1JF2WXYzjzMVl6MK9aL6OozdU8MLKj294Mmf7VBqZsHkOcehiAMeY3+Pz+sW67oFJdqvPEy2AwcOjQISIiIqocP3r0KBEREfVqH6+GUtk529+7j7D1g9u5yfgLAONtD/LeY/f95wd27p+70Rc/zu3G7x3H3rJexJ4O9/LEZR3dahGR+qywzMJPmw6xaN1OQtN+Y7RhBX3Vzaf8AQH7Jo+LtB4sN/YisnUfhreLpl9CWK0MmfDUz+PZxm3TdCw2jQqbhsWqYbHZy8UVVvJKLOSVWMgvrSC3xEJmfhkZuUWU5mSgFhwktDydVupBWioHaaUcJEY5UuXO7ckOaOH8oXXmD0M/wlv356JOTeiXEO5R80lr27FkdntmIQdzSzhcUE5efh5acQ66rQJsFaDZwMsXxRyAwSeQyJAgYkN9aB7mR8fYYJdu03D//9YzNmUiHVX7MtWvW0fR5PKnubRzjMticgeeWI94Ysw18eSPW2i35l5GG/4G7IvP3Bf2Bh/eNrxO5igeLixj7uJdZP7zPbeoC07ppTodm66c9m/jybZpsXxqG0pW88u58/wOtGvi3pubJ6fl8cu8Gcw0fQ7APOvFJF7/MoMTI/7jmfVfdT6PTmkJ6bp+2jtmR48exc+vYd0lFafXKiqABSesbNhcO8CBnBLiz3AX/fvkdA7//iqPmY4nXU9YriV8+D08N6B5g79LW5cCvE1c06Mp1/RoSkZeX37YmMG89VtodeQ3Rhn+pvMJf4zi1SxuVX/kVn4kc2sIv6d0YRqdsTTtT++kpgxKDCchwl/+/f7DJS/9StSRvzFhw4QVk2LFCysmrPhRRrBSRDBFhCrFNFcKiSKXKCUH07G5Wf+x126e7sdaLZG/tPbsDuhJ88T2DEyM5O2WYbJwyr8I8jXRvVmox+5hM/2Ctty27TY+tt2Pl2JjsuEHrv++J72bT6zz/YGE+C9vLd1DwOoXGG20J10lupl71PuZPXZAnS0MExHgzWOj25M5uCUfrryC2etWMqh0MYPUZMdCVCc7U9K1R2vMb1o3/jL2pGXXwYzrFUfLyLpfJr4mOsYE8Vz4xVTk2ldeHGNYwgMrd0niVU3nlHgd259LURTGjx+P2Xx8kp3NZmPTpk306dPn3CIU9UKYv5lMc3OoXKvh2MqG/5Z4bU7P5/++ns+bxuPLxj5sGU+rkdO4vlfcaZ8j6kZ0sA+3DmzBrQNbsDNrMD8kZzA7JYVWucsYoa6lp7rN8YcnSsnlOuNirmMx5elzWJ3Wmi8WdWSrX0+aJ3VicFIkvVs0cpsJxO4kWCniba+XnfJaRbo3u/UmbNGasV5rSVZQe8KatqFrfCNuTAhrcMMIG6pG/mbGX3oxr36xmntN/8Og6Dyuvc6DX7XjnYkD6s1iKcLzvbc8ldxfZzPD9C1gX3TiHtsUpo2/kqaN6n56QVSQN/efn4RteCIr94zk213ZbNmxg4AjySRwkObqIUIorJyDpmPFSIluJkNvRIYexg7iKA7vSELzBIa1ieTGZqEeN5pAURRG9u7ALz/1ZJThb0KVIvx2/cD+o51cvvKiJzmn1k5QkL1bVNd1AgIC8PE5PoTCy8uLXr16cfPNN59bhKLeMEa2hkP27xPVg/yVVcj57aJOua6kwsrjn/3G24Y3HA34162jiDv/Tkm63EyryADuHZEIIxLZffh8Fm3JZG7KDqKzljBCXUt/NcWxYINZsTLAkGKf51fxCQeSw1myvhP36B0obdKbTi3j6JsQRqfYYI/7g1QrjP/RZXWSXN3f8Uc+XW9Euh5GKk0oD21FcFRzWkYF0bpxIDObBhPmLytRNVQXtG/Mr5tvYeO2dXRU95KgZjB8/0vM/TOaO85reNu/CPei6zrv/LWXrEUv8rDpC8fxJ6zXcd5lE+kR79reZoOq0K9lmH1frwtbU2YZyd7sYlKPFJNeUsHWUguapmM0qPh6GYgK8qZdsA+3RfjXi5EEozpFc+vP5zMKey/kTYb/491l1/HEpe1dHJnnOKfEa/78+QA0a9aMe++9V4YVijOKaxzO/vQI4tTDtFLSeO9Q/mmvm/3zVqYWvESwoRiw79VxpNu9zOoXX5fhimpKiPAnISKBKYMTSM8bwq9bMrklZR/mg3/Rn2QGGTYSoxxxXN9UzeYG9Tdu4DdsWQqbMluwYmlb3lQ7YGzWix4J0fRJaETrqMAGeSfeZvTjWcvVWDBSgRELRiy6/WuZYqLCFILmE4LiE4zBL5TgAH9ign2IDvahebAP/YJ9iGvkK3s1iVPMGtWRSXvu5APLdHyVcq4yLmH6H2+zIu5++iZ43r5son7QNJ0nftpC2JrneNj0g+P4s5araXL+NK7o6n5zEb1NBtpEB7p0g+O65Gc20rrHMNav/ogu6m5aq2lkrP8/coYnEupXvZuFDZVTxvfMmjXLGS8j6rnEqAB26rHEcRg/pZy8zD1AtyrXbDtUQP7az+lnsu+QfkgP5aPwe/hwZDuZE+RBmgT7MKFvPBP6xlNU3p8Vu48wd3sW+7atp13pGgapG+mubnfsFWVQdDoruyvnin1P2X4Ta1MTeW3hUPpdciPXNcCezmfG9KTC1h0vg4rJqGAy2Dfo9TKoeBlVWUlK1FiwrxfTxo7kwfd28LLpDQAeN85n/OetiL/jOqduEivE2Sgos3D/l2sZuvtpLjf+5Tj+ouUKgodP56b+suecuxjfN56n/r6YLuocAMbxE5+supyp0mN+VmqceHXp0oXFixcTEhJC586dz9goXr9+fU3fRtQjrSIDWKnHMAz7ZoE+uTspt9ocKxPqus7sH9bxjPFzx3Me1ibxVB1OpBXO5282MqJtFCPaRqHrHdiRdTl/bs/m7W378D64gl7KZvqqm2mlpjue461Y6G/YzJ9aZ3o1d/4+LZ7AFfMYRMPRq3kj1g+byKe/7+Ba42K8FQvPW57j7vcjeW/KhfjLvEtRR7YdKmDWRwt5oPgZOhnsK25qusKj1huIGXEnkwa0cHGE4kTRwT54t7+EfVs/p5maxQBDCm+u+JNJA5rXi+GUta3GNeuoUaMci2mMHj3aWfGIeqxVpD8fasdXNmxJGnuzi2nd2N5F/8/+XJIOfEGUKReA32xdaT/wijOufCg8i6IoJEUFkhQVyORBLSguH8ia1By+2n2Erbt2EZ69ir7qFvoYthCjHGG7TxdahMu/vxC14dYBLZi89x7a79tLBzWVWDWb+/MeY9onIbwxvi9GueElapGm6Xy0ch/LF37JPPU1QlX75sjluolpttsZfuXNjOrUxMVRitOZOKAl76ZcyJOqfcrRWMs3fL5mGBP6ypSQ/1LjxOvE4YUy1FCcjQBvE7l+zaFy/92W6kF2ZhU6Eq/3/tzG48aFgP1u17ve4/lggAwvqM/8zEYGJ0UwOCkCaMPRovNZufcoc3cdYf/uzUQ1ay1DTIWoJaqqMPvqHtw892FeKbqXxkoOXdTdjNz3JI//8DyPjW4vnz9RKw4cLeHhr1YyLH0u7xoXHz+uhXO/4V7uuP4K+sh8Q7fVJjqQzPjLyE77hnClgIvU1Yz980+u7t4UHy/p9ToTp44lqKio4PDhw2iaVuV406ZNnfk2woN5RSViPaBiVDRaKQf5Jct+h2v34SKCdi8gwpQHwC9ad0YM7C8f4Aamkb+ZiztEc3GHaKAD5db6s/m6EO4oyNfE8zeez92vH+E97WH8lHJGGlZxeN3TvOT7KPeMSHJ1iKIeKa2w8dayPaQs/ZYnlXeJNWY7zv1u68z7ETN44fqBNJF5hm5v8rB2vPX2SB4yfYqq6Fxf/jmfrh4k8/H+g1PGEezcuZP+/fvj4+NDXFwc8fHxxMfH06xZM+LjpdtRHNc8KpT9eiQALZQMdmXmAfD1uoOMNRy/6/WZYTRXdY893UuIBuTY/D8hRO2Ja+THPeOuZJptKjbd3sM10fgLxr+e5c0le1wcnagPbJrO98npjHvhC9ouvYX3DM8Qq9qTrhLdzMOW8Szv+irzJw+TpMtDdI0LZV/81WTr9q2lLjKs4bc/F1NSYXVxZO7NKYnXhAkTUFWVn376iXXr1rF+/XrWr1/Phg0bZGENUUXLyAB26vYlYb0VC8VZe7BpOsnrV9FRtU+q3aTF067HENlUVwgh6kj3ZqFcfOWNzLQe33vzTuMCcn97ng9WpLowMuHJNE3nx40ZXPvSAnK+vpuPy6cyzHC8XbhaS2K8+WUumPAwj47uIDfbPMyU4e15w3qJozzB8iUf/r3fhRG5P6e0bJOTk1m3bh1JSTIkQZxZq8gAluoxXMBaAPzyd7J052EGlC52/G/81tafa7q4334dQghRn43sGE1x+R08+n0Zj5o+AuAB0+c8/TPMs97HrQNldTlxdorKrXy7/iA/Lf+H8/P/xweGxXgbLY7zWXowz1rHEtB9LO9f0FpW0fRQnZuG8Gbzq8g88BNRSi7nG9Zy7ZKfuar7LbKv179wSo9XmzZtOHLkyH9fKBq8lhH+7NKOJ1UJpDNvyV5GqPZEzKqr7AwfTmJUgKtCFEKIBuvqHk2JvWAaz1vGOI49YPqc8t+e5MVF29F13YXRCXem6zpbMvJ59PvN3Pb0XEJ/nsRnRTdzo3Eh3oo96SrRzcy1XsL9jd9n0u0zeXx0e0m6PNztI9rzmvVSR3ma9iGv/r7ThRG5N6ckXs899xzTp09nyZIlHD16lIKCgioPIY7xMxvJ8z9+1zRRTePw/i20UA8BsE5vRZ8O0nMqhBCuMrFfPL7nTa+SfN1p/JbAvx7j8R+3oGmSfInj0nJKeHPJHq55aQHfzp3JmHVj+Uh5hIsNqzEq9sXWSnUv3rJexISgd0m45nnm3zLEsaKx8GwdYoIpaTeWXZp96f+u6i6Orv2S3YeLXByZe3LKbYahQ4cCcN5551U5rus6iqJgs8nKZOI476hWWPYbMCk2WioHGaImO84ttnVmdFKk64ITQgjBlCEtedc0k8cWmpll+hiAm40/8781RUzNf5AXru4um6U2UBabRnJaHou3HWbT1i00y1nB+eoaJqmbMZiqJuXZeiCf2oayKnQ044b14PO2UaiqbFFQ39x7QTse3Xod7/AcANPVz3nq/85n3oS+Lo7M/Tgl8frzzz+d8TKigWgRFcq+fVG0VNJprhxiuOEfx7kU317MbCzDDIUQwtVu6t+cz83Tuf97b54xvouq6FxpXEb0zqNMnDeLVyYMIszf7OowRS3LK6lgS0YBa/flsHFPBsrBNXTWtzBS3cAMdT+YTn3OBi2Bj2zDKWs1kuv7tuLOFo1kT7h6rEmwD636XsqyFT8zwJBCrJpNs90fsWxnKwa0Cnd1eG7FKYnXwIEDnfEyooGwr2zYhJakY1as9FK2AfaNE+Nbd5HKWQgh3MQ1PZri63U3U//nxwvGuXgrFvoathCRfTeTX3+EpydcRMtIuVnm6aw2jUP5ZaTllnAwp5R9R4vZdSiH0oxthBfvoo26n0HqDqYo+zAZTj+K6YAWzgKtHyt9z6NLl+5M69GU2FDfOv5JhKtMHtySiWvH09d6LwZFZ6pxAeMXnEePaZdL7/gJnJJ4bdq06bTHFUXB29ubpk2bYjbLXTFh1yrSnz/0GGBNleN/aJ0ZIsMMhRDCrYzq1IQw/zu46eMw5uizCVMKaKmm82bpfdwzN50xV4zlog6NXR2mOMne7CK+T86gwqZhsWr2rzaNcqtGSZmV0uI8tOKjqKU5eJUdIZpsYpXDxCrZjFSyaKFk4KXY4AyL023UmvOHrTP/mLsT264Po7vEcEezUBlO2AD5m41cOmI4n/4wlBuMv+GrlHNz0Tze+LMj04Ynujo8t+GUxKtTp05n7KUwmUxcddVVvPXWW3h7ezvjLYUHS4jwZ5526nLxy5UuvJYQ5oKIhBBCnEnfhDDCbxvP5Pca8WzZ47RQDxGmFPAeT/LMl3vYmHY7089Pwmhwyppdwgn2Hy3BuuR5LjWswIQVL8WKF1a8sOBDuT2pOuY0wwVPZ5fWhDVaEmu0RLLDetC5bWuGJEUyNTYYgyRbDd6YbrHcsGYS52evJULJY5hhHd8u+4Ldne4hIcLf1eG5BackXgsWLOD+++/nvvvuo0ePHgCsWbOGF198kVmzZmG1WpkxYwYPPfQQL7zwgjPeUngwXy8jeaEd4YQFb4p1M0p8f3y8pDtaCCHcUavIAObecRl3zw/hluynGGBIwahoPGz6lB9X7WFi2v08f20fIgLkBqs7MBlUwpR8Wqnp1X6uRTeQqkexTY9jm9aUXUozKiI6kNg8nh7xoTzaLJQQ2adJnERVFR68vBdPzb2eV4yvAfCw4QNmftOPD24dIlNJcFLi9dRTT/HKK68wYsQIx7H27dsTExPDww8/zJo1a/Dz8+Oee+6RxEsA0LVDe9Yub0V31b7Xw9e2AVzStbmLoxJCCHEmEQHevDd5OI8siGTTpjncbvwegJGGVSRl3M7UOXdzy5WjGJwU4eJIhZdRpQwvinRvKjBiwUiFbqICI2V4kaMHkEuA/aseQAaNyDZEoQXH4R0aS3xkIG0aB3JZ40Diw/wwSW+mOAutGwcS1Xssy1YtYYAhhWglh0Hpb/H5mpaM7dnU1eG5nFMSr5SUFOLi4k45HhcXR0pKCmAfjnjo0CFnvJ2oB8b1acZta6ZwR/k75BDIH9GTeK9dlKvDEkII8R+8TQaeu7Izn8U9wa0/JjDb8AaBSikt1XQ+tM7kmY83sbTHLcy4sLVMqnehMH8vUlrfzYOGe/AyqJiMKl4GFS+jirfJQKiviRA/L1r4eRHi60V0sA8hvibplRDn7M5hrRi/cTI9yu/EW7EwwbiIcf/Xk34Jk2naqGEvuKLoTtiGvnPnznTs2JG3334bLy9717PFYuHmm29m48aNbNiwgRUrVnDdddeRmpp6zkG7UkFBAUFBQeTn5xMYKJv/nYvDBWX8tOkQ/mYjozpHYzbKH2hRPZ76efTUuIU42YYDuTz98U88Vv48bdT9juN/2DrxVsg9PD52MIlR7r3qoSd+Hj0xZtGw/LnjMMs/epSHTZ8CkKaF82Djt/jgliH1bvGV6nwendJvPHfuXH766SdiYmIYOnQoQ4cOJSYmhp9++ok333wTgL1793Lbbbc54+1EPRER6M2N/eIZ0z1Wki4hhPBAnZuG8NZdV/Fq/Dzes17gOD7EkMzr+bcz5/U5vP7HLiw2zYVRCiHq2uDECEo638xqLQmAWDWb89Nf4/0Vnt0Bc66c0uMFUFhYyKeffsrOnfY5O4mJiYwdO5aAAPe+01VdcpdJCPfhqZ9HT41biH+j6zqfrD7A0v/7jGfUNwhXChznvrf14YtGU3hoTH/aRge5MMrT88TPoyfGLBqeonIrN770FfPL7sJPKQfgZtv93Hf7HbSqR/v/Vefz6LTEq6GQyk4I9+Gpn0dPjVuI/7Izq5BHPv2Dm3Nf5jzDBsfxI3ogj1knED/wWqYMSXCrUQ6e+Hn0xJhFw7Ryz1F+fP8pnja9B0C2HsTtga/ywR0j681K1nU+1PCYrVu3snDhQn744YcqDyGEEELUf60iA/jgjotZ0/sNplkmk6f7ARCmFPCa6RVa/zWF619awPJdR1wcqRCiLvRu0QjvnhP509YRgHAln6l5s3n8h00ujsw1nLKq4d69e7n00ktJSUlBURSOdaIdWxnHZrOd6elCCCGEqCe8TQZmXtiG5PYzuPWrnkzIe50Rhn8AuMCwlgHFm5jzweV81WYCD1zcgagg2fdLiPps+gVJjNt9H63z7yBKyaWvYQurNrzO9wmPM6pTE1eHV6ec0uN15513Eh8fz+HDh/H19WXLli0sW7aMbt26sWTJEme8hRBCCCE8SKfYYD688xI295vLVOtUjuj2ITh+SjkPmj5jyo4JTH/xDd79a68sviFEPeZtMvD09YOZrk/Fpts7Ze4yfsO3337J3uwiF0dXt5ySeK1cuZLHH3+csLAwVFVFVVX69evHM888w9SpU53xFkIIIYTwMGajgXtGJDFp8r1MDXuHj61D0SobXonqQT5SHyP01zu47qUFLN6WhUw7F6J+ahHuz2WXXsUc6+UAGBSd2corzPjwd4rKrS6Oru44JfGy2WyO1QvDwsLIyMgA7Bso79ixwxlvIYQQQggP1a5JEJ9MGYFh5MtcpzzNRq2549xlhuXML5pMyqczuPHtpWzJyHdhpEKI2jK6cxMOd5rCCltbACKVPKYXPMX0L9agaQ3jpotTEq927dqxceNGAHr27Mns2bNZsWIFjz/+OM2bN/+PZwshhBCivlNVhbE9m/L6fTfxefv3edByo2PxDV+lnLuM3/Jsxjg+nPsk079aT1ZBmYsjFkI426OjOvJayP1k6KEAdFN30m/XbF5bvMvFkdUNpyReDz30EJpmH5/9+OOPk5qaSv/+/fn555959dVXnfEWQgghhKgHQv28ePbKzlw26RFuC32X+dYRWHT7stKRSh6zTW8zfvN4Zj7/Cs/8vJXc4goXRyyEcBYfLwOzxw3jHnU6ZboJgLHGP8le8ga/bsl0cXS1r9b28crJySEkJMSxsmF9IXtnCOE+PPXz6KlxC+Fsmqbz/cZ0Pv/5T24qm89ww7oq51dprXlDuYYu/S9gYr94ArxNTo/BEz+PnhizECdavusI33zwIi+b3gDAohu4SX+I6bdOdMuN1s/EZft4nSg0NLTeJV1CCCGEcB5VVbi0cwwfTR/LriFvM157hBStmeN8L3UbHymP0GnpTdzy3LvMW7qHkoqGMxFfiPqqX8sw2p5/M29bLwLApNh4RXmBx+YvICOv1MXR1Z5z6vG68cYbz+q6999/v6Zv4XbkLpMQ7sNTP4+eGrcQte1wYRlzft1ByYavmKp+TXO16tCjhbbuzPe6hqEDBzO2Z1P8zOe+Haknfh49MWYhTqbrOvd9uY6Lt0xjkMG+VkSaFs69QS/yzpQLCayFHu7aUJ3P4zklXqqqEhcXR+fOnc+4BOyCBQtq+ha1olmzZgQGBqKqKiEhIfz5559n/Vyp7IRwH576efTUuIWoK6lHinn1t62YNn/FVOO3xChHqpxfaOvOh8bL6dV3KOP7NCPIt+YNNE/8PHpizEKcTpnFxqR3lnB/5t20VfcDsFFrzisxLzPvxgF4GWttcJ7T1FniNWXKFD7//HPi4uKYMGEC1113HaGhoTV9uTrTrFkzNm/ejL+/f7WfK5WdEO7DUz+Pnhq3EHVtR2Yhr/6aQuiOL7nD+B0RSl6V80ttHXhfvYzWvexzwMIDzNV+D0/8PHpizEL8m7ySCibN/ZGXi+6jiXIUgN9tnfml7Qs8P6YrqureU5fqbI7X3LlzOXToENOnT+fHH38kNjaWMWPGsGjRItkEUQghhBDnJDEqgLk39OHKyY/xYOzHPGG5liw92HF+oGETHyqPMmTlDcyc/RKzvkvhwNES1wUshKi2YF8vXpx4AdOMD1Gg+wAw1LCBXpsf47EfUupVTnHO/Xdms5lrrrmG3377ja1bt9K2bVtuu+02mjVrRlFRUbVfb9myZYwcOZLo6GgUReG777475Zq5c+fSrFkzvL296dmzJ2vWrKnWeyiKwsCBA+nevTuffvpptWMUQgghRN3pEBPMOzcN4KJbnmJWs8940HIjaVq443wPdQfvGp5lzPprefmlJ7jjkzUkp+W5LmAhRLXEhvry4ITLmKrdS0Xl9hJXGpfR7J8neX7hdhdH5zxOHTipqiqKoqDrOjabrUavUVxcTMeOHZk7d+5pz3/55ZdMmzaNWbNmsX79ejp27MiIESM4fPiw45pOnTrRrl27Ux4ZGRkALF++nHXr1vHDDz/w9NNPs2nTphrFKoQQQoi606VpCPMm9OW62x/j+cTPmGaZzC6tieN8W3U/L5veYOauq/hp3gOMe+N3ftuahabVnzvmQtRXHWKCGXfdDUyz3YFNtw8vnGBchM+KZ5n7524XR+cc57yPV3l5Od9++y3vv/8+y5cv5+KLL2bChAmcf/75qOq55XWKorBgwQJGjx7tONazZ0+6d+/O66+/DoCmacTGxnLHHXcwY8aMar/HfffdR9u2bRk/fvxpz5eXl1NeXu4o5+fn07RpU9LS0mRctRAuVlBQQGxsLHl5eQQFue++H1KPCFE7Uo8UMf+vPRSn/B/jDD/TQd1X5XyB7s0E68PMu/sqwgO8T/sanlCPSB0iGpJFmzNZ9u0bPGU6vir6S5bLiRoxjbE941wY2elVqw7Rz8HkyZP1kJAQvUOHDvqcOXP07Ozsc3m5UwD6ggULHOXy8nLdYDBUOabrun7DDTfol1xyyVm9ZlFRkV5QUKDruq4XFhbqXbp00desWfOv18+aNUsH5CEPebjxIy0trdr1S12SekQe8nD/hzvXI1KHyEMe7v84mzrknJeTb9q0KZ07dz7jZsnffvttjV7/5B6vjIwMmjRpwt9//03v3r0d102fPp2lS5eyevXq/3zNvXv3cumllwJgs9m4+eabufPOO//1+pPvMmmaRk5ODo0aNfrPDaKPZcCedkdK4q5bEnfN6bpOYWEh0dHR59zDXptqWo+4w++4Jjw1bvDc2CXumvOEekTaIhJ3bZO4a646dcg57Tx4ww03/OcH3t00b96cjRs3nvX1ZrMZs7nq8rTBwcHVes/AwECP+k98jMRdtyTumnHXoUEnOtd6xNW/45ry1LjBc2OXuGvG3esRaYtI3HVF4q6Zs61Dzinx+uCDD87l6dUWFhaGwWAgKyuryvGsrCyioqLqNBYhhBBCCCGEOFvu2af+L7y8vOjatSuLFy92HNM0jcWLF1cZeiiEEEIIIYQQ7uScerxqQ1FREbt3H18yMjU1leTkZEJDQ2natCnTpk1j3LhxdOvWjR49ejBnzhyKi4uZMGGCC6M+PbPZzKxZs04ZHuDuJO66JXGLf+Opv2NPjRs8N3aJW/wbT/0dS9x1S+KuG+e8nLyzLVmyhMGDB59yfNy4cY6hja+//jrPP/88mZmZdOrUiVdffZWePXvWcaRCCCGEEEIIcXbcLvESQgghhBBCiPrGo+Z4CSGEEEIIIYQnksRLCCGEEEIIIWqZJF5CCCGEEEIIUcsk8RJCCCGEEEKIWiaJlxBCCCGEEELUMkm8hBBCCCGEEKKWSeIlhBBCCCGEELVMEi8hhBBCCCGEqGWSeAkhhBBCCCFELZPESwghhBBCCCFqmUclXsuWLWPkyJFER0ejKArfffddlfNFRUXcfvvtxMTE4OPjQ5s2bZg3b16Va8rKypgyZQqNGjXC39+fyy+/nKysrDr8KYQQQgghhBANjUclXsXFxXTs2JG5c+ee9vy0adNYuHAhn3zyCdu2beOuu+7i9ttv54cffnBcc/fdd/Pjjz/yv//9j6VLl5KRkcFll11WVz+CEEIIIYQQogFSdF3XXR1ETSiKwoIFCxg9erTjWLt27bjqqqt4+OGHHce6du3KBRdcwJNPPkl+fj7h4eF89tlnXHHFFQBs376d1q1bs3LlSnr16lXXP4YQQgghhBCiATC6OgBn6tOnDz/88AM33ngj0dHRLFmyhJ07d/Lyyy8DsG7dOiwWC0OHDnU8JykpiaZNm/5r4lVeXk55ebmjrGkaOTk5NGrUCEVRav+HEkL8K13XKSwsJDo6GlV13w58qUeEcF+eUI9IHSKE+6pOHVKvEq/XXnuNSZMmERMTg9FoRFVV3nnnHQYMGABAZmYmXl5eBAcHV3leZGQkmZmZp33NZ555hscee6y2QxdCnIO0tDRiYmJcHca/knpECPfnzvWI1CFCuL+zqUPqXeK1atUqfvjhB+Li4li2bBlTpkwhOjq6Si9XdcycOZNp06Y5yvn5+TRt2pS0tDQCAwOdFbqoBbquM/XxZ3nNyz4nMFMPoUT3IlgpJohiDMq5jbLN131I0ZqzUWtBiiEJ3+Z9GJjUmEFJEQT5mJzxI4j/UFBQQGxsLAEBAa4O5YykHhHCfXlCPSJ1iGeb/PTrvKk8C0C2HkSB7kOQUkwQJZgU2zm9dqHuzWatGRv1FmxUkvCK78OApGiGJEUS4ufljPDFf6hOHVJvEq/S0lIeeOABFixYwEUXXQRAhw4dSE5O5oUXXmDo0KFERUVRUVFBXl5elV6vrKwsoqKiTvu6ZrMZs9l8yvHAwECp7NycpumYzWYCvezDMN6wnE92x9swqJBfXE5ZSQF6SQ5KaS5KeR7+tkJClQKilFwilRyiyCVKySFSycVfKTvl9QMpI5atXMhW4EfyMvz482AnZv0+kMiOIxjfrwWJUe77h7w+cfehNlKPCOH+3LkekTrEs3l5eRGo2v9/fWwdxI620/AxGcgrKae8OB+9JBdKc1HL8/CtbItEKrn29gg5jnZJoFJ6ymsHUk4TdjCCHcDPFGa+ydKMjjz1xwAC2p3PhH4JtGsSVMc/ccN0NnVIvUm8LBYLFovllLGVBoMBTdMA+0IbJpOJxYsXc/nllwOwY8cODhw4QO/eves8ZlH3Jg9qTkLE6ZOh0gobR4vLySooJ6ugjO35ZSwtKCMzv4y8nMPoR3YRWXGAFkoGCUo6ndQ9hCv5jucHK8VcaljBpaxgz6b5zNswEqXjNdx7fhsiAr3r6kcUQgghhBsb37cZXZqGnPZcmcVGTnEFmQVlZOWXsbugjOWV3+fmHEE/sotGZQdooWbQUkmno7qHKCXX8fwApZSLDau4mFUc2DKftzddTGHba5l+YTuaBPvU1Y8o/oVHJV5FRUXs3r3bUU5NTSU5OZnQ0FCaNm3KwIEDue+++/Dx8SEuLo6lS5fy0Ucf8dJLLwEQFBTExIkTmTZtGqGhoQQGBnLHHXfQu3dvWdGwHtIBpUr5zHcifLwMxHj5EhPi+y9XjCCnuII92UXsyCzkuQO5HNq/g/DcZIYYNjBITXbcjWqhHuJ59W22bl7Endtu46Yxl3Je60in/FxCCCGE8Ez/1RbxNhmIDvYh+l+TpGHkFlew90gRu7KKmHMgl7T9u2iUs4GBajJD1GRClCIAmqrZPKnOZ/f2Rdy/YzJXXXYFIztGO/knEtXhUYnXP//8w+DBgx3lY+Odx40bxwcffMAXX3zBzJkzufbaa8nJySEuLo6nnnqKW2+91fGcl19+GVVVufzyyykvL2fEiBG88cYbdf6zCFc5t6EkoX5ehPqF0r1ZKNf1igM6caRoNH9sP8x9mw9i3LWQa9Vf6WPYCkAbdT8faA/yxCfbST1/Kjf1b+6En0EIIYQQnupcB7WG+HnR1S+UrnGhXN2jKdCR3OJLWLLzMA9tzsC641fGKosYaNgEQIKawQf6LJ7/aiu7s+7mrmGt3HpobX3mUYnXoEGDONO2Y1FRUcyfP/+Mr+Ht7c3cuXP/dRNmUX/Y/68c//9SWxvWhfmbGdMtljHdYskq6Mwnq8Yyb/n/cT8f0Fbdj1mx8qRpPi8sLOJtfSaTBrSopUiEEEII4Xb0E9sitZPwhPh5cWnnGC7tHMORoo58seYq3l62iHu1+XRWd2NUNGaaPuetZQW8pD/CPSOSaiUOcWbuuWGFELWktm/wRAZ6c8/wRF647zbeSHibd60XOM7da/ofqYve4PetWbUbhBBCCCHcVm33NoX5m7l9SEtev28Sn7Z5i1eslzrO3WL8P8qWvcL3yem1GoM4PUm8RL118hyvuhQR4M3r1/fEMvQpnrKMdRx/3Dif9/63gCNF5Wd4thBCCCHqj9oac3NmIX5evHB1N0IvfowHLBMdx2caP+ebb78kPe/UVRJF7ZLESzQYOkqdJmKKojB5UAtCh93Le5U9XybFxkO2N3jqx011GIkQQggh3IErbgpf3yuONiPvZI71MgBURedR3uLxb9fVcSRCEi9Rb+k6KC66y3SiWwc2Z32ru9imxQLQVt2P9+bP2ZpR4OLIhBBCCFH7XN8Wua5XHBkd7mCd1hKA5momcXs+Ye2+HBdH1rBI4iUaDB3FJav4KIrCrEs78ZR6i+PYFOP3vP77ljqPRQghhBCuY2+LuOa9HxzZntmm29B0ewC3GH/ijUUbXRNMAyWJl6i3dHS36PEC+5yvHv3P5w9bJwBilCP47PiOtJwS1wYmhBBCiFrmHm2RIB8T5w8ZzI9abwAaKYU0PbCA7ZkyAqeuSOIlGgxXLrYBMKFvM+YbLnOUxxiW8L91B10XkBBCCCHqmFLHM86ruqZHUz43X+koX234ky/XHHBZPA2NJF6i3rLP8XIfAd4mmnUcwm7Nvmt8T3U7a9asQtPc406YEEIIIZxPcaM/894mA1269WGDlgBAa/UAOzcso9xqc3FkDYMkXqIBcd246mOu6tGUL2yDHeXuJUvZeDDPdQEJIYQQos7o1P6eov9lTLfYKm2RgZblrN4ri2zUBWN1LtY0jaVLl/LXX3+xf/9+SkpKCA8Pp3PnzgwdOpTY2NjailOIGnGXOV7HtGsSxFPBg6H4UwCGGtbz67YsOjcNcXFkQgghhKgdmqsDqKJZmB/pUUOxHnkPo6IxTP2H+VszGdAq3NWh1Xtn1eNVWlrKk08+SWxsLBdeeCG//PILeXl5GAwGdu/ezaxZs4iPj+fCCy9k1apVtR2zEDWiu3hc9TEd2rVjixYHQEd1Lxs2b3NxREIIIYSoC7ru+nYIQO92CfyjJwIQr2axa8t6dN29blbXR2eVeLVq1YpNmzbxzjvvUFBQwMqVK/nmm2/45JNP+Pnnnzlw4AB79uyhf//+XH311bzzzju1HbcQ/8ld9vE62fA2kfyudXWU43L+kt3jhRBCiPrK/ZoiDGsTyW+2Lo5y+5KV7MgqdGFEDcNZJV6//vorX331FRdeeCEmk+m018TFxTFz5kx27drFkCFDnBqkEM7gLvVep9gQ/jEdT7x6qdtYvfeoCyMSQgghRF3Q3WDkDUDLCH+2+fV0lO1tEZnnVdvOKvFq3br1Wb+gyWSiRYsWNQ5ICGfRT1O9uXpCK4BBVfCP70axbgagl7qVVXuOuDgqIYQQQtSGk0ffuENbRFEUohM6kq0HAdBd3cGaPYddHFX9V63FNQA2bdp02uOKouDt7U3Tpk0xm83nHJgQzuYud5kAerSI5J9diQw0bCJSyePgns1AJ1eHJYQQQoha5C6jbwB6tQhjdUprLjasIkApJT91HZrWHVV1n/ZSfVPtxKtTp04oZ0jVTSYTV111FW+99Rbe3t7nFJwQ9VXP+Eb8qLVhoMF+IyOucD3peZfTJNjHxZEJIYQQoja5w0JfAD3jQ3lLsydeAG3KN7LrcBGJUQEujqz+qvY+XgsWLKBly5a8/fbbJCcnk5yczNtvv01iYiKfffYZ7733Hn/88QcPPfRQbcT7nwoLC7nrrruIi4vDx8eHPn36sHbtWsd5Xdd55JFHaNy4MT4+PgwdOpRdu3a5JFZRu+yL8xy/t+ROPV5JUQGkmNo7yjLPSwghhKiv3LMtEhvqy16/zo5yL3Ubq6QtUquqnXg99dRTvPLKK0ycOJH27dvTvn17Jk6cyMsvv8yLL77Itddey2uvvcaCBQtqI97/dNNNN/Hbb7/x8ccfk5KSwvDhwxk6dCjp6ekAzJ49m1dffZV58+axevVq/Pz8GDFiBGVlZS6JV9QtdxhXDaCqCgHx3SipnOfVQ93O2lSp7IQQQoj6zl3aIgCNW3TkiB4IQDd1J2tTs10cUf1W7cQrJSWFuLi4U47HxcWRkpIC2IcjHjp06Nyjq6bS0lK++eYbZs+ezYABA0hISODRRx8lISGBN998E13XmTNnDg899BCjRo2iQ4cOfPTRR2RkZPDdd9/VebyidulUndDqTuOqAbo2j2C9lgBAYyWH/Xu3uzgiIYQQQjife/Z4AfRoHspazb6fV6BSQm5qsuznVYuqnXglJSXx7LPPUlFR4ThmsVh49tlnSUpKAiA9PZ3IyEjnRXmWrFYrNpvtlLllPj4+LF++nNTUVDIzMxk6dKjjXFBQED179mTlypV1Ha5wgTPNT6xrPeJDWaMdXzE0Knc9R4rKXRiREEIIIWqbGzVF6BHfiDVakqOcUJrC/qMlLoyofqv24hpz587lkksuISYmhg4dOgD2XjCbzcZPP/0EwN69e7ntttucG+lZCAgIoHfv3jzxxBO0bt2ayMhIPv/8c1auXElCQgKZmZkApySFkZGRjnMnKy8vp7z8eGO4oKCg9n4A4VS6XvW+krvdZWrTOJCXDG0cZftwwxwuaN/YhVGJ2iD1iBDiXEgd4uF09x1906yRLzt9OoDVXu6hbmNNag7NwvxcG1g9Ve0erz59+pCamsrjjz9Ohw4d6NChA48//jipqan06tULgOuvv5777rvP6cGejY8//hhd12nSpAlms5lXX32Va665BlWt9o8KwDPPPENQUJDjERsb6+SIRV1yp9TLaFBRY7tToRsAe+K1OlU2L6yPpB4RQpwLqUPqF3dZ1RDsI4FCmnWmQLevqtxD3S6LfdWiGmUjAQEB3Hrrrbz00ku89NJL3HLLLQQEuMfSky1atGDp0qUUFRWRlpbGmjVrsFgsNG/enKioKACysrKqPCcrK8tx7mQzZ84kPz/f8UhLS6v1n0E4x6lzvNynojumS4vGbNLtG443VzPZvXePiyMStUHqESHEuZA6xLMpZyi5g+7Nw1mntQIgXCngUOpmF0dUf9Uo8fr444/p168f0dHR7N+/H4CXX36Z77//3qnBnQs/Pz8aN25Mbm4uixYtYtSoUcTHxxMVFcXixYsd1xUUFLB69Wp69+592tcxm80EBgZWeQjP5U7jquHUsdVB2WspKLO4MCJRG6QeEUKcC6lD6hf3a4tUnXMeW5hMZr6s9l0bqp14vfnmm0ybNo0LLriA3NxcbDYbACEhIcyZM8fZ8VXbokWLWLhwIampqfz2228MHjyYpKQkJkyYgKIo3HXXXTz55JP88MMPpKSkcMMNNxAdHc3o0aNdHbpwMl2v2uPljjrEBLGe45Vdd2U76/bnujAiIYQQQjiLfb65e4++SYwMYLOpraPcQ93Gmn0y9aE2VDvxeu2113jnnXd48MEHMRqPr83RrVs3x3LyrpSfn8+UKVNISkrihhtuoF+/fixatAiTyQTA9OnTueOOO5g0aRLdu3enqKiIhQsXnrISoqif3GlcNYC3yYAlugeabo+rp7qdNTLPSwghhKi33KslYt9b1CeuG2W6va1sb4vIPK/aUO1VDVNTU+ncufMpx81mM8XFxU4J6lyMGTOGMWPG/Ot5RVF4/PHHefzxx+swKuESOm69quEx7VvEsjUzjnbKPhKVNDbv3gck/dfThBBCCOHmTh59467jcLo2j2TD3pb0NmwlRjnC3t07gPauDqveqXaPV3x8PMnJyaccX7hwIa1btz71CUK4EXcbVw32sdWrKsdWq4pO6KFlsp+XEEIIUU+5e1sEoHnucvYfdX2HSn1T7cRr2rRpTJkyhS+//BJd11mzZg1PPfUUM2fOZPr06bURoxA1oqOjKO56b+m4HvGhLFe7O8rD1H/4fWvWGZ4hhBBCCE9gX2HZ/XWICeYf756O8nD1H37dIm0RZ6t24nXTTTfx3HPP8dBDD1FSUsLYsWN58803eeWVV7j66qtrI0YhnEJ3uxledt4mAwGJA8jV/QEYpG5k8eYDLo5KCCGEEM5mn/bgfq0Rg6rQtE0vDuphAPRWt/JXym4XR1X/1Gg5+WuvvZZdu3ZRVFREZmYmBw8eZOLEic6OTYhz4gmrGh4ztG00i7UuAPgrZXjvXURWgSzlKoQQQngyXddx35ldVQ1v25jfbF0BMCk2mmQsZG92kYujql9qlHgd4+vrS0REhLNiEaJW6eCON5kAOK91JP+nDHSUr1IW89Va2SBTCCGEqE903HOOF0CfhEb8ZhrsKF9j+IMvpC3iVGe1qmHnzp1RzvJ/yfr1688pICGcxTPuL9n5m41EdhhG6qa3iVez6GfYwqsrV3DzgOZ4mwyuDk8IIYQQNeApc7wAzEYDbbsNZNPqeDqoqXRQU5m9ZjEFQxII9Da5Orx64ax6vEaPHs2oUaMYNWoUI0aMYM+ePZjNZgYNGsSgQYPw9vZmz549jBgxorbjFaLG7HO83Lf6G9srjk9sQx3lG8o/55NV+10YkRBCCCGcyV3nmx9zTY+mVdoik2xf8O5fqS6MqH45qx6vWbNmOb6/6aabmDp1Kk888cQp16SlSXekcB8n7xbv7jrEBDMnbgzZ6T8RruRzsWEV1y3+Py7qcBONg3xcHZ4QQgghqsmT5psDNA/3pzjxCtJ2f0esms0AQwofLfuW1M53EB/m5+rwPF6153j973//44Ybbjjl+HXXXcc333zjlKCEqA06ituOqz7mjvM78pp1tKP8mDaXmZ+vpsKquS4oIYQQQjiFvS3i3o2RO0e05WXbFY7yY+rbPPjZX5RZbC6Mqn6oduLl4+PDihUrTjm+YsUKvL29nRKUEM7gSeOqj+ncNISS9jeQrLUAoIV6iGvTH2fm1xuw2iT5EkIIITyJjmeNvgFoFRlAQPdrWWFrC0AT5ShTsh/n7s/WyI3gc1TtxOuuu+5i8uTJTJ06lU8++YRPPvmEO+64gylTpnD33XfXRoxCOIWnJGIPXdKBp81TKdDtwwuHGdYxeMsDTP7wb3KKK1wcnRBCCCFqylPaItMvaM0cvzs5qgcA0NewhSt3z2DSu8s4LNvd1Fi1E68ZM2bw4Ycfsm7dOqZOncrUqVNZv3498+fPZ8aMGbURoxA14mnjqo8J9vXigRtGc6c2DYtuX9HwYsMq7tw3hckvfcKXaw9I75cQQgjhAextEc/jZzby5PgLuZP7KdPtKxoOMSTzYMZt3PXyfD5auU96v2qgRvt4jRkzhhUrVpCTk0NOTg4rVqxgzJgxzo5NCKfyhHHVx3SKDea6seO4TbuXEt0MQDt1H59a70H7fipjn/uc1xbvIj2v1MWRCiGEEOLsuf9882MSowK47YZruEWfSYHuC0BLNZ1PtJn4/zyFG577mJd+3cH+o8UujtRznNWqhrque0yDVYhjPHFc9YnOax1JwI23MuGjRjxhe4VWajpGReMa459cU/EnK5a05ZU/+nCwUV/aJyXRJS6EjjHBRAXJXEshhBDCXXhyW6RPizD8J03kpg8a8XDFy7RX96EqOpcZlnOZZTlrlify9tK+7A3uQ9vWbekaF0KH2GCig7wldziNs0q82rZtyyOPPMJll12Gl5fXv163a9cuXnrpJeLi4mTYoXBLnlYF9IgP5bVp43jk63Yk7J7PJONPBCr2Xq6+hi30NWyBgnfYviqWDX8nMEdvwUGfRIxhrYiObER8Iz/iGvkSFeRNZKA3jfy8MBpq1NEthBBCiHPk7nuKnk6HmGDeunsss75rQ+TW+dxm/IEQpQiAHuoOeqg7oPh9dq1pwoZVCbyht+CAdyvUsFY0jggnPsyPuEZ+lW0RM2H+ZkwNtC1yVonXa6+9xv33389tt93GsGHD6NatG9HR0Xh7e5Obm8vWrVtZvnw5W7Zs4fbbb2fy5Mm1HbcQ/81Dx1WfLCLAmzfH92H57lbc9stltMv6nqsMfxKvZjmuSVLTSFLTuIY/wQpkwqFDoaRqURzQI9hGKFl6CNkEU+EdgR4QicE3BB9ff4J8vQj0NhHoYyLIx0SAtxFfLyO+Xga8TQZ8vQz4VH719jLgazJI8iaEEEKcBU+db36yED8vXr22B6v3tuCuhZeTkL6Aqw1/0lJNd1zTUk2npZrOGJaCDciCrMxg9ulR7NOi2Ekwh/UQDhNCmXck+Edg8A3F+1hbpLIdEuhjItDbWNn2MOLjpeJjsrdLfI49TAaPTN7OKvE677zz+Oeff1i+fDlffvkln376Kfv376e0tJSwsDA6d+7MDTfcwLXXXktISEhtxyxEjegenIYpikL/luH0S7iAjQf78Nbq/RzcvJzu1n8YrG6grbIPg1K1Ym+s5NDYkEMftlZ9MRuQZ3+U60YK8CNf9yMfPwp0Xwrw46juxUHMlGKmRDdTihdlju/NVKhmdIMZXTWhq15gMKEbTCgGExi8wOiFavACgxdK5fdeJhWjqmJUFVRVqfpVsX81nPxQFCb2j8fX66yqKiGEEMKteXoK1rN5I3reNpQtGd35aPUB9qasoEv5WoYYkmmv7MWoVF1wI1LJI1LJo6e6veoL2YB8+6NCN1S2QfwcXwvwJUc3k17ZFinVvexfK78vwRuLakZTvdAN9raIYrB/jxPbIseOXdcrjhC/fx/1d7aq1Zrp168f/fr1O+c3dQdz587l+eefJzMzk44dO/Laa6/Ro0cPV4clapmnDzdWFIVOscF0ig3Geml7Nh7M47cd2czZdwhr+kZaWXeQqKQRr2YSrxyikVJ4xtczK1bCySdcya9ZQDr2yvMs9lSs0A1YMWJFxYYBG+rxh35SufL8K9bLKO05QxIvIYQQ9Yant0UA2kYH8cSl7dFGtWNT+tUs2XGY11IzqUjfSEvLDlorB2hW2RYJVwrO+Fpeio1wCv7zun9Vw7aIhooVQ+XXyvJp2iPvWC8kv+NDdZ941Rdffvkl06ZNY968efTs2ZM5c+YwYsQIduzYQUREhKvDE05i3yvj+L0lXa8HNd0JjAaVrnGhdI0LBRLRtIHsPVLMrqxC1hwt5svsYg4fzoS8NIwlWYSRSyS5RCi5hCv5BCnFBFJCoFJMEMUEKLW7QqKXYsPr32rFf/mnCaQEo+p5QwmEEEIIOLbQ14nl+tUWUdXjN4ShFbren31HS9iZVcj6I8V8k11M5uEsyDuAoSSLRrq9LRJ5rC1Csb0dohQTSLFjHnttOWNbBE7bHglRijCozvl3a5CJ10svvcTNN9/MhAkTAJg3bx7/93//x/vvv++URUFW7T3KC9+uwFetqBx6ZUJRDaB6oRqNoBoxGE0YVQWjQcVU2Z1pNKiYDPbvTQbV/rXyuP2YYu8ePfZVVezfGyq/r3ye2ahiNtnHvx4bB+tjMmA22c815FVmPG1Ca3WoqkJChD8JEf4nHO0IgKbp5JRUkFVQxuGCcrKLykkrtZB/wqOwpAxbSS5aWSFYSlCtJSiWUgy2Urz1CnyVMrypwJdyfJRyTFgxYcOEFS+s9rJirVrGikmxOb73ctxb0jBiQ1V0jNgwoGFwfNUcZSsqDTXvuvvzf8jJSEU3GCvrDyOKagLVhGI0oKhGVNVorx8MSmUdYK8LjpUNlfXCsTriWNmgHi/b65wTzp9QB3mbDHgb7V99vAx4Gw14m+z1i7dJxcvQsOsTIdzZ5vR8Hvl8Gf5qub0tohpRDMaqbRGDvQ45sR3h+N5QtS1iqGx/HKszHO2VyuPH6o1jzzUb7fOEfSrri2PtEW+TocG3ReozRVGID/MjPszvlHOappNbUsHhwnKyCsrILiznYKmFgpPaItbSfPSyfLDY2yGqpQTFVoaPbm9/2NsiZfj+R1vk2HETVryUE9olJ7c7FO0/2iIGSbxqqqKignXr1jFz5kzHMVVVGTp0KCtXrjzl+vLycsrLyx3lgoL/7gYtKLVwZd57XGVc8q/XaLqCFbWyu9Pg+N6CAZuuYrE3S48fw37sWBfosfMW7F2mpahYdftrlWOiDC/HeNgyvCrLZsoUL3SDN5rRB93ki+4djME3FB8/f4J8zQT7mgj2MREV5E10sA9Ngn2IDPR22n+4unTyhFZPH1d9LlRVIczfvpJQ2+jqPVfXdcqtGqUVNkotNkoqbJRW2KiwaVhtGhabjsWmUWHTsFQ+Sqx6lbLjGquGTdfRNB2rdsJXXcdq06ueqzzmiZNnT1aTeiTnSBYfFt70n9dZTqoTrJX1hbWyLrHXL/Y/JceGeR67xv5cQ9XnoFKBkRLdQNaxugMvynWvE+oS+9cKxQubwRvNKxB8QlB8QzD7BhHs50WwrxcRAWZiQnyJCfEhJsSHIB+TNLaEqIGa1CGFZVYuzPuUm4y/nPG6E4deWTnhe93gqD9OPG/B6KhbTmyL2DBQigGrbj9WjskxH8dej1S2R3QvyhQvbKoPuskb3eiL7hOM6hOKj18AwX5eBPl4EexrIjLQTJNgX6KDvYkK9PbIhZ10HajSFmm4daCqKjTyN9PI30zrxoHVeu6xtkjZsXaI5XhbxGL997aIRTt+/sRzNg1s2glfdR2bZn+crn3i62Vwyu+gwSVeR44cwWazERkZWeV4ZGQk27dvP+X6Z555hscee6xa72HTdIyK9YzXqIqOF//S3VkXn0kNKK985NsXWcjHnzzdjzz8ydRDWaeH8b0exiHCqQhNJLxJC9rGBNO/ZRgtI/w9swHlgSG7mqIo9p4PkwFZOqdmalKPoJ25DjnG3qNowxvLqSfr6v+7FSi0P6y6Sh7+5Ot+ZOkhpOth/KaHc1APJ8unOT4x7WkXG07/VmF0iglG9cCbOkLUtRq3Rc5i0su/Dr2qq7ZIReXDsciCP3m6P3n4kaWHsl4P4ye9ERlEUBbSkkZNWtKuSTB9E8Jo3TjAI9siHhiyy53YFgn2dXU0NdfgEq/qmjlzJtOmTXOUCwoKiI2NPeNzLJpOspaAGWtl16W9YWTEirGyO9OIFWPlcCtT5fAro2KrPHf82LHnnbxinbOZFSsR5BGh5J3+gkLI3eZPypZ4vvylA1sD+tC9S3eu6x1HRIB7btirn9TH1ZDvMgnXqkk9UqoZ+cHWGwM2R11xrD4xKprj+PHzlfUIthOGTRy/xlRZ9lLOYvbxOTAqGmEUEKYU0IJDVU/aoHyfka2pzfjzz07M9u1Pzx59mNC3GcG+5z5pWYj6qmZtEY0tejN+svWq0hYxYMN0QnvjlMdpzplOqHtqk32RhTMs+FQE+dt92bw1nh8WtecJ39507NKLcX3iaBzkU6ux1dQp881dF4pwAzVKvPbs2cP8+fPZs2cPr7zyChEREfzyyy80bdqUtm3bOjtGpwoLC8NgMJCVlVXleFZWFlFRUadcbzabMZvN1XqPrnEhKGPux6rplNo0rJXdlVabhtV2wveajlWresyi6dhs9q5Rm2bv3rTYNDSbDZtmBasFTbOCrQJds4LNCprFfnfcZkWxWcBWjmItRbWV4a3bx8L6KBV4U443FnyUcnwqx8cGKcUEU0SwUkyQUkQwRfgp5af9uUKUIgYYUhhgSIGyT1m1vDUz/hpN1yFXMGlAc48YEiZ3mYQr1KQemXJRD/JK3qNc0yk+Nvyhst44NhTiWB1h0yrrjhPKJ19nH1pROXTCZqusRyzoNiu6ZkPXLCg2C7pmq6xPKlAspSjWMhRbGUabfY6ft1KBGQs+HK9bAighWCkiiGKCK+uREKXotAu2mBUrnZXddFZ3g+Vrli1rz20rrubSi0dxRdcYj7x7LURtq0kd0rZxIEVj7na0RWyajqWyHrHXCSe1RSrrD8exk9oiVk3DZq1si9gsaDZ7PYFmRT9dW0QrR7XY2yJeegU+lFe2RSoq647jc3XsbRF7/XGsXeKvlJ325wpSSuhr2EJfwxawfMG6lS15dMUltOo/hilDWuJtcs6QsNok9VzDVe3Ea+nSpVxwwQX07duXZcuW8dRTTxEREcHGjRt57733+Prrr2sjTqfx8vKia9euLF68mNGjRwOgaRqLFy/m9ttvd8p7NKmcG+Vqum4fz1pWoVFmtTnm6JRabJRZbBSX28gvtZBRUkFBqYW8Ugt5JRYKiwqx5aahFqYToR0mTsmijbKfduq+Kneheqnb6MU2vvtjKZN2Tue18f3xN7tPJ+qpc7ykohOeY2CrcFeHUIWmHR9ff6wOKbNolFqsFJRZyS+xsLekwlGP5JZUkJdzFC13P94l6TQli3ZqKh2UvbRQj/eEDTCk0FffzOvf/cPjGffyyMi20igRwgkiAr0Z2bGak3prgaMtYqmsPypsVdokJZVtkR2V7ZD8ynqksKgIS+5BDAVphGvZNFUO07qyLRKl5Dpev6u6i7fUF1m0fCkTd01n7o2D3aoHXdfr96qGonqq3UqeMWMGTz75JNOmTSMgIMBxfMiQIbz++utODa62TJs2jXHjxtGtWzd69OjBnDlzKC4udqxyWF8oin1VIbPRQBCmaj9f13WOFFWw/2gxWzIK+PlgHgd3baRDyUrGGJY4Gk+jDX/TNP1eprz/HO9NGuLWk1+luhOiZlRVsa9K5lX9uX4Wm0ZaTgkp6fl8kpbH9u1baZO3hHGGRTRVszEoOncaFzB/TQlvBDzNlMEJtfIzCCHqXpW2iE/N2iI5xRXsO1rC1kMF/J6ez/5dm2hTuJIrDUtJUtMAGGH4h9jD9zHlnWd477bz3brnS9oiDVe1E6+UlBQ+++yzU45HRERw5MgRpwRV26666iqys7N55JFHyMzMpFOnTixcuPCUBTcaOkVRCA8wEx5gpluzUAB0vSNbMkbz1oq96Bs/52HjxwQqJXRRd3NFxvO8+nsM00YkuThyu5PHVQshXMNkUGke7k/zcH9GdWoCI9uyJWM4z/8xntht73CP8SsMis4E4yLuWtySlJbTaR8T5OqwhRBuQFGOr4TXNc5+20fX27Mj6xI+/nsfhev/xyzDfBophbRR93PTked47uemzBrV3sWR20lbRJyo2l0TwcHBHDp06JTjGzZsoEmTJk4Jqi7cfvvt7N+/n/LyclavXk3Pnj1dHZJHUBSFdk2CmD2mM1dPmskkw+MU6PblZUYaVrHjr69Jz6vdze9qSkfGVQvhLtpGB/Hadd1pecUjPGg9vmz+g4aPefnn9S6MTAjh7hRFISkqkKcu68Ckyfcy2etpsnX78uSDDRvJW/MZuw8XujjK09NRZL55A1btxOvqq6/m/vvvJzMzE0VR0DSNFStWcO+993LDDTfURozCTXWNC2HmhCt52DbRcex29X+8vzzVhVEdd/K4aiGE+7m0cwyRgybxi607AOFKPk33f8v2zP/ep0gIIdo1CeLJmy7lAW2y49hUwze8s3S3C6M67uT55qJhq3bi9fTTT5OUlERsbCxFRUW0adOGAQMG0KdPHx566KHaiFG4sY6xwQR3u4oUrRkA7dV9bFv/FxXW2l1ytiZ0FEnEhHBDkwe14EPzWEd5jGEpX/9z0IURCSE8SavIABL7Xc5KWxsA4tUsslL+oLj87PZDrGvSGmm4qp14eXl58c4777B3715++uknPvnkE7Zv387HH3+MweC+ExlF7RnXN57Pbec5yv0r/mLd/twzPKNuyF0mITyDt8lAl259SNZaANBG3c/WbSkujkoI4Umu7x3H59oQR3motoIVu91g7QFdFtMQx9V4+bnY2FguvPBCLr/8coqLi8nNdX1DW7hG83B/9kcMdpT7qFv4e48bVHankHHVQrir89tFsdjW2VGOyVtLZv7p9/ERQoiTRQZ6UxA7DItu7wSwt0WOujiqU8kcr4at2onXXXfdxXvvvQeAzWZj4MCBdOnShdjYWJYsWeLs+ISHaNcygW1aLADtlVQ27d7v4ojspMdLCM/QNjqIZFNHR7mXuo3Vqe7XaBJCuK/uiTFs0O3bUTRXM9m9e4eLIwIdHUWRtoiwq3bi9fXXX9Oxo/2P448//sjevXvZvn07d999Nw8++KDTAxSeoWfzUNZq9mXkVUWHrC3ountVNPYlXeU2kxDuyKAqeDXt6rhbnaSksTPLPVclE0K4p57xoazVEh1l36NbKLfaXBjRqWS+ecNW7cTryJEjREVFAfDzzz8zZswYWrVqxY033khKiozJb6iSogLZocc6yrHW/WS4eJiQLuOqhfAoCVGhpOr2vy8tlHR2Hcp3cURCCE+SGBXADq2po5xAGqlHil0Ykcw3F1VVO/GKjIxk69at2Gw2Fi5cyLBhwwAoKSmRxTUasMZB3qQZmznKiUoaOzPd6261jKsWwr21igxgZ+UNHLNipSRzp4sjEkJ4kgBvE7n+CY5yKzWNHW7XFkHuCjdg1U68JkyYwJgxY2jXrh2KojB06FAAVq9eTVJSktMDFJ5BURSIOP7v31JNZ/fhIhdGVDmuWu4yCeExWkUGsEtr4ij7Fu7BYnO/rSmEEO7LK6qVY8hySyWdPS5viwhxnLG6T3j00Udp164daWlpXHnllZjNZgAMBgMzZsxweoDCc4SFRZJ/2JcgpYRojrAwr9TVIVUh2ykL4d5iQ304qIc7yo3JITO/jNhQXxdGJYTwJDGNgsjcF0qskk20cpT0PPdaHdU+x0vaIw1VtRMvgCuuuOKUY+PGjTvnYIRnaxzsTYbeiCClhCglh8w814+rlntNQniOIB8TRw0nJF7KUQ5J4gXAhgO5fL3uIDlF5bSJDuLaXnGE+nm5Oiwh3E50sA8ZNCKWbEKUInJcvN2RrsvoG3FcjRKv4uJili5dyoEDB6ioqKhyburUqU4JTHie6GAfMvQwWpOGl2KjNC/T1SFVoYPM8RLCjSmKgi0wGirv2UQrRzmU7149564w949dpC2exxTD90QpOazdmcjkFTfywIQr6Rgb7OrwhHArjYN9OKSHOsq2/IMujOZUMt+8Yat24rVhwwYuvPBCSkpKKC4uJjQ0lCNHjuDr60tERIQkXg1YdJAP6SdUduSnuy4Yji0fL4TwJMbgmCqJ1xo3G7Jc137YmEHB4hd51vS541gfw1ba2R7m5vc1Xr17HJGB3i6MUAj3Eh3kzVq9kaNsKEy39zq5KNuRtog4UbUX17j77rsZOXIkubm5+Pj4sGrVKvbv30/Xrl154YUXaiNG4SGODTU8xqc00632z5Bx1UK4v7DgII7ogUDlUEM3m59Rl4rLrXzy4yLuM37pOJav24ddBiqlPGWbw5M/bHJVeEK4Jfvom+NtkTDtCDnFFWd4Rt2SRKxhq3bilZyczD333IOqqhgMBsrLy4mNjWX27Nk88MADtRGj8BCNA33IPKHHK0rJ4XBBucvikXHVQniexkHeHNZDAAgnj8MFDbfH66OV+7m5/COMin1lx9etoxiqvMUmLR6ABDWDgG1fsDld9jsT4piIADNZnNAWIYcsl7ZFZB8vcVy1Ey+TyYSq2p8WERHBgQMHAAgKCiItLc250QmPEuhjJMdwvLILV/LILnJdZXcyGVcthPsLDzBzWA8GsM8VLch2bUAuous6S1av5Tx1AwAZeigb42/mt+nn85x6s+O624zf8/5fu10VphBux2hQKfeOcJTDlXw3bItIY6Shqnbi1blzZ9auXQvAwIEDeeSRR/j000+56667aNeundMDFJ5DURRsPscruwjyXNvjhdxlEsLThAd4OxIvAL0wy3XBuNCa1Bz6FC5CVex12CfWodw6tC3Bvl70HjCcJbaOAMQoR8jf8hsFZRZXhiuEW9H9T2iLKHkcLnDdkGXZU1ScqNqJ19NPP03jxo0BeOqppwgJCWHy5MlkZ2fz9ttvOz3A6li/fj3Dhg0jODiYRo0aMWnSJIqKqm6cd+DAAS666CLHYiD33XcfVqvVRRHXP0pgpOP7cCWf7EL3mZ8h46qFcH/hAWayCXKUDSWH0fWG12j5dWsW56trANB0hXUhF9ClqX0I5tU9mvKVNsRx7SiW8EvKIZfEKYQ7MlVpi+RxuNB9erxA2iINWbUTr27dujF48GDAPtRw4cKFFBQUsG7dOjp27Oj0AM9WRkYGQ4cOJSEhgdWrV7Nw4UK2bNnC+PHjHdfYbDYuuugiKioq+Pvvv/nwww/54IMPeOSRR1wWd33jGxjm2DE+Qskj280qOyGEe4sIMDvmeAGE2nIoKGt4N8e2bttMompfBjtZb0Gvju0cw5PC/M3orUaQq/sDMFhN5o8trl1FVgh3EhIY4Ph8RCBtEeE+qp14uauffvoJk8nE3LlzSUxMpHv37sybN49vvvmG3bvt499//fVXtm7dyieffEKnTp244IILeOKJJ5g7d+4p+5GJmgkL9HHcrXb1XSb7hNYTyT0mIdxdeICZbP14j5e79ZzXhbScElrk/e0o/2HrzJCkiCrXDG0XyxLNfrMzQCnFsucvyizus4qsEK4UccJc0XAlj2wXDjXkpLaILm2RBq3aiVdWVhbXX3890dHRGI1GDAZDlYerlJeX4+Xl5Vj4A8DHxweA5cuXA7By5Urat29PZOTxLugRI0ZQUFDAli1b/vV1CwoKqjzEv4s4YX5GIwo4WlDi2oBOIhNahStIPXL2vE0Gir3CHGX7/IyGdbd6dWoOvdRtjnKyuSvtmwRVuWZwUgR/aF0c5X76OlbuOVpnMYq6JXVI9ZyYeHkrFooLclwb0EmkKdJwVTvxGj9+POvXr+fhhx/m66+/5ttvv63ycJUhQ4aQmZnJ888/T0VFBbm5ucyYMQOAQ4fsY98zMzOrJF2Ao5yZmXna133mmWcICgpyPGJjY2vxp/B8EQFmsisrO1XRqShw5cT4qhNaG94sEeEupB6pHs3/eD0doeS61YpkdWHd/ly6qDsBKNbNBDTrgqpWbamF+nmRHz3QMbR7sLqBpTsb5gqQDYHUIdUTHuBNNsGOslboujmQJy/0peuSdTVk1U68li9fzqeffsrkyZMZPXo0o0aNqvJwthkzZqAoyhkf27dvp23btnz44Ye8+OKL+Pr6EhUVRXx8PJGRkVV6wapr5syZ5OfnOx6yZP6ZhZ+QeIH7rUgm1Z1wBalHqscYEOX4PlzJb3A9XgdSdxCt2O/QJ2sJdGkWftrreraJZ73eEoB4NYudOzbXWYyibkkdUj0RgeYqq6OqxYddF8xpKNIaabCM1X1CbGxsna4wdc8991RZION0mjdvDsDYsWMZO3YsWVlZ+Pn5oSgKL730kuN8VFQUa9asqfLcrKwsx7nTMZvNmM3mc/wpGo6IAG82n3CXyVR6GE3TT7lbWxdOneMlhGtIPVI9gUFBFGb4EKCUEo577QdY2/JLLYQe3QBe9vI/eiv6Nws57bX9W4bx6+/t6aluByAubzUHc0cSE+JbV+GKOiJ1SPWE+1edKxpozaG43IqfudrN3nMmGyiLE1W7K2jOnDnMmDGDffv21UI4pwoPDycpKemMDy8vryrPiYyMxN/fny+//BJvb2+GDRsGQO/evUlJSeHw4eN3Pn777TcCAwNp06ZNnfw89d2Jm58CNCKPnBL3WLhENlAWwjOcPDHelXvw1LX1B3LpWjnMEGCTkkjb6MDTXts2Oohkr86Ocn81heW7jtR6jEK4O/vom+M3LFy92NeJdJC7wg3YWaX+ISEhVRYlKC4upkWLFvj6+mIymapcm5PjugmMr7/+On369MHf35/ffvuN++67j2effZbg4GAAhg8fTps2bbj++uuZPXs2mZmZPPTQQ0yZMkXuJDlJmL9XlXHVxzZRDvOv+9+vbKAshGeKCPDmCEG04BCBSil5+fmuDqnOrN+fy/DKxEvTFayNu2I2nn7hKoOqEJLQg7ydfgQrxfRVN/Pgziyu7tG0LkMWwu34mY0UGEMd5WObKMeH+dV5LLq0Q8QJzirxmjNnTi2H4Rxr1qxh1qxZFBUVkZSUxFtvvcX111/vOG8wGPjpp5+YPHkyvXv3xs/Pj3HjxvH444+7MOr6xWhQKfcOg8pVjcMV9xkmZO/xkttMQri7k3vOtcLTL35UH6XszeBO5QAAO/UYkuLPvIhCv1aRLN/ejosNqwlSSsjbvQab1g2DC4Z3C+FONP9IqFxYOVzJd7O2iKujEK5yVonXuHHjajsOp/joo4/+85q4uDh+/vnnOoim4dL8IqFypdsIFw4Tsk9FlDtNQniaiAAz209IvJQi95oYX1ssNg0l/R+MBg2AdVorujY9/fyuY/q1DOdVrQMXG1YD0Nmyns3p+XSMDa7tcIVwa0pAlCPxiiCXnS5apEfmeIkTnfUcL03TeO655+jbty/du3dnxowZlJaW1mZswkOZgqquSOY+d5mEEJ7g5BXJfCuOUG6t/5sDb80ooL223VFeqyXSrVnoGZ4BTYJ9OBDcw1Hub0jhr12yrLwQ/oEhlOn26TDu1RaRNQ0bsrNOvJ566ikeeOAB/P39adKkCa+88gpTpkypzdiEhwoJCCBX9wfsd5lctRS0LvvDC+GRwv29qyReEUoe2W4yMb42/bM/l27qDkf5cEgnQv28zvAMu8TEtuzRGgPQRdnF2h37ay1GITxFRODxesSVG7HLfHNxorNOvD766CPeeOMNFi1axHfffcePP/7Ip59+iqZptRmf8EAn3q2OUPLIdpcVyWRQtRAeIdDHSK7BPVckq03rU7Ppou4CIFMPoWl80lk9b0CrMJZpHQAwKho+B/+mqNxaa3EK4QnCA8wcxl6PhChF5BQUuDgiOx1kvnkDdtaJ14EDB7jwwgsd5aFDh6IoChkZGbUSmPBcEQHH98/wViwUFeS6JA4ZVy2EZ1IUBZtvpKMcQf3v8dJ1nbx9yfgr9htV/2iJdItvdFbP7RnfiJV0cJR7K5tYtedorcQphKeICPCuspeXtSDLJXHouoy+EceddeJltVrx9vaucsxkMmGxWJwelPBsJ95lAtALD7kwmhNJ1SeEp1ACTki8GkCP157sIpLKNjrKa7VEuv/Lxskn8zMbqYjpjUW3LzvfX01h+W7Zz0s0bCevjqoWuSbxOpnM8WrYznoLb13XGT9+fJX9rsrKyrj11lvx8zu+L8K3337r3AiFx4kM9GbdCZWdoSgdq03DaKj2ft3nRHq8hPBcfkHhlGWb8FYsNFWy+OtIsatDqlVLdmQzUD2eeO306UzTUN+zfn73xDjWZ7Skp7Kd5momO7ZvRh/ZRoY0iQYrMtDMmhM2UfYry6S43Iqf+aybvk4hbRFxorNuCY8bN46IiAiCgoIcj+uuu47o6Ogqx4RoHubHHj3aUW6hHWDf0RIXRnSMNECE8BQto4LYqccA0EzJIjWjfi8p//e2A/RUtwFwUA+jWesu1Uqa+rcMY5nt+HDDTvl/sCEtz9lhCuExmjXyYy/H2yKJaho7swpdGNExso9XQ3bWaf/8+fNrMw5RjzTyN5Pp0xIq53a3Vg+wPbOAhAj/Oo1DVjUUwnO1bhzANi2ODmoqqqJjydyKrg+slz042YXl+O7/A7PJXmkutXVkUFLkfzyrqnbRQTwZMBjKvgLgasMfzF21jy7/sQ+YEPWVt8lAYXASVHaWt1YOsD2zkM4u+ExIj5c4pm7HfokGw6txG6y6/b9XG2Ufm9Ndv5qQpGFCeI7WjQPZpjd1lOPKd3Iwt37uHfnjxgxGqX85youVXvRLCKvWa6iqwsCePVhmaw9AnHqYvJSfyXKXVWWFcIHg6JYU6fb1Cdqo+9mcnu/iiI4tLy/tkYZKEi9RK5Jiwthe2WhKVA6yedeeOo9BxlUL4bliQ3zZZW7rKPdXU/hrV/1bMELTdBatXM+gyvldh/RQgtoMqdE8lCu7xfCZNsxRvkP5ild/3+m0WIXwNO1iQkjRmgMQoxwhddeWOo/B3hYRwk4SL1Er+iaEsVyz33lVFZ1GWStcvxy01HxCeAxVVQhv2Z2cys3Y+6hb+HNz/dsYeNGWTM7L/waTYgPga9sALusWV6PXigjwJrTLaLZo9ue3V/dRvu5T1u13zZYeQrhav4Qw/tLaOcrN81exz8UL9egyx6tBk8RL1IqucSGsUjs5yqPV5Xyz/mCdx6Eo0uMlhKcamBTJH1oXAAKUUgL2/h/7j9af1Q1LKqzM/+lPbjD8CkC5bmJF6GXVHmZ4ojuGtuJl/WpH+RHjhzz72UIOF8qQQ9HwtGkcyEZzN0d5tGEF/1uXVqcx2OebS1tE2EniJWqF2WigUdvBHNTtDYiB6iaWLPuDwjJX7vsm/92F8CQj2kbxgzrUUb7dsIBnftqErnt+I0bXdWZ+vYFppa/irdjrxY9sw7j2vO7ntIBI4yAfOg8Zw7e2fgAEKqU8W/oYU95ayMFcd1hdVoi6o6oKLTv2ZYdmXyG1m7qTTX8v4kiR60bgyHzzhk1aoqLWjO3VnA+twwH7cMN7LG/zwNcbsGl102iScdVCeDZfLyOtug1ljZYIQHM1k+67XuGFRds9Ovkqs9i476sN9N36BL0ql5A/pIfyV5ObuLhD43N+/VsGNOfbyKns0eyv1UI9xMuF93DfKx/y5doDdVYHC+EOrukZx3zb+Y7yDP09ZnyxhgqrVifvb6+q5DMn7CTxErWma1wIaQnXsV+LAKC7upMROx7m5veXk5ZT93dedcnChPA4d5zXipcME7HoBgAmGn8hevmDjH/rT9ak5nhUAmbTdH5JOcQNc75j5OY7GWNcCkCFbuA+pvHYlb2csly+0aDy0riBzPB9lHS9EWBfWOATfSbl309j7PNf8e5fezmUXz9XiRTiRIlRAVjaX8M2LRaAtup+rtn/MBPfXsKe7KI6j0cHmePVgNXt9t2iwXngks488OpU3tMfxaxYudiwitYHbubZF6/C0OZiRrSPoV/LMIJ8TE5/bx0ducskhGcL8jVx4xWjeOSznTxjeheAa42LGXZoHR+/O5SXAoaQkNSBrnEhJEUF0iLcHy+je9xT1HWdw4XlrNufy+o9R9ixeQ3Dyn7jQ8NifAwVAFh0A/fYpjDxuquID/Nz2ntHBHjz0qRLuPt9Mw8UPE4ndS8GRecG42+MLVnMsl878OLCnmSG9SY+viWdmwbTMiKA5uF+NVpRUQh3Nv3Cdty5807esz2In1LOeYYNtMqcxIuvXEVFq4sZ0aEpA1qGE+Ln5fT3ti8fL4Sdx9SuTz31FP/3f/9HcnIyXl5e5OXlnXLN2rVrmTFjBuvWrUNRFHr06MHs2bPp2LGj45pNmzYxZcoU1q5dS3h4OHfccQfTp0+vw5+kYWnayJebr72aOz7K52XDq/gp5bRQDzFXnUPWzg/4c1snHtQ7kBfcltAmrUhsHEhMiA+xob7EhPgQ5mdGVZ1VZUnVJ4QnGt42isMjp3LPj0aeNr6HWbEQoeRxj+lrKPuaA+vDSV6XwI9aU3YTTZlPYwiKwS84kmA/M0E+JgJ9jPav3ia8TQbMRhUvo3rCVwNeBvW0d6KPHbNpOuVWjQqrRrnVRrlFo9yqUVRuJa+kgpxiC7klFRwpLCP/SDpKbiqNLWl0UXYxTt1OczWzyl/dLD2Y+7UpXDd2HIOTIpz+e4sN9eWdKRfz8LdNaLL1PaYav8VHqcCoaAwxJDPEkAz5b5G2PpyN61qwWI/mbS2afN9mENQE36AwIoJ8iQgwE+LnRYC3iUBvI4E+lV+9TXh7GfA2GjAZlHq5ubWoHyIDvbn3hsuZ8l4+r6ovEqiUEKtmM0d9nSN7PmLJrk7M0jpwNKgdIU1akhgVRGyoL7GhPsSE+BLmb8bgpLaIjiL7eDVgHpN4VVRUcOWVV9K7d2/ee++9U84XFRVx/vnnc8kll/DGG29gtVqZNWsWI0aMIC0tDZPJREFBAcOHD2fo0KHMmzePlJQUbrzxRoKDg5k0aZILfqqGYWCrcHxvuo0JH0dzr2UePdQdAEQqeVxtXMLVLIFiKNjhy67tTUjTw/lbD+egHs4RgrF4N0L3DUP1i8A/MJBGfl4E+ZjsjQAfY2VjwERAZYMgoLJBIPt4CVF/XNcrjqah93L91x0YX/oBI9S1GCpXLW2qZtOUbC4xrLRfbAWOQvkRI/n4k6/7UYAv+bofhfiSo3tRjolyTFRgpLyybMHoqDFOnAB/7HsDGmYsmJUK+9fKh69SRlMK6aAUEUohjZQCfJXKyfun6cwv0018ahvKHxE38NjV/UmICKil3xoE+Zh49doe/Lm9GRMXjaRH9jeMMSwhWslxXBOrZhNL9vEnVf7+LEcMHCaYw3oIubo/hfiSpvtSgC8Flb/LEt1s/z0qXmgGb3SjN7rBG0zeYPRB8fIGgxeqwQQGI6pqQjUYMBlUDKqCUVUwGhQMqorJoGBQlSrnVMX+MKigKPbzqoLjuKqAQVWqnFMUBYOioKr26+Ia+dEpNrjWfsfCM3RrFso9t9zMxA8jmVo2j/6GzQCEKQVcYVjGFYZlUAKFO33YvcPeFlmth/O1Hk42wVjMjdB8wzD4h+EXEEwjf3NlW8RY2QY5sU1yvD2i67KqoThO0T1pgDzwwQcfcNddd53S4/XPP//QvXt3Dhw4QGysfRxvSkoKHTp0YNeuXSQkJPDmm2/y4IMPkpmZiZeXvTt5xowZfPfdd2zfvv2s3r+goICgoCDy8/MJDAx06s9W3+WXWJjz+w4OrP2JMfzGQHWjYzWvs1Wqe3GUQApP+ONfgE/lV1/H8WNfbzb8zCCDfWPSCbYHmf+E9G7WJ576efTUuN1BmcXGJ6v28/vKdXTK/50B6ia6qjsxK1ZXh3ZGFbqBDXpLfrL1Yk/EMMYO7sKF7Ro7sUf/v+m6zso9R1mwPo1DW5fT27qGbupOOip7ql0XnwtNV7BgwIYBKwbH9xYM2HQVC0ZsqNgwYENFQ0Gr/Govq+go2PSq57TKc8evUyjQ/djU+TGevbzDv8bjiZ9HT4zZXRSVW3l98S62r/qZK/RFDFGTj98oOUtluomjBDraHgW6L4WVX4+3Qfwcx682/MnFhlUA3FpxF689MQuTwT2GRItzV53Po8f0eP2XxMREGjVqxHvvvccDDzyAzWbjvffeo3Xr1jRr1gyAlStXMmDAAEfSBTBixAiee+45cnNzCQkJOeV1y8vLKS8//oHMz88H7L9kUT0KcPegpuT3uJmfNl3Ix9vTUQ+upJ2+iyQljSQ1jSjlvzb6LCeQbAKBJmfzplYoqGyPVdjK5N+tnjn27+nu94+kHnGuMR3DuLLDcLZn9mLF7qO8vf8IpZk7CSneTaySTaSSQ2Mllwgll0ClhABK8K9mw6ombLpCHv7k6X5k6GHs1yNJ0yPYZ2yGIboTHeOjuKxNJC3C7ZtCFxUV1npMJ2sX4UW781tQMTSelPSRrDiQx7wDRyjJ2oNv0T7iOUSscphwJY9wJZ9wJY8wxdlx6oAG2JM9U+XD28nvApCtB/FPafEZP2ueUI9IHeJct/VrQmG38fy8eQS3bcvAun8l7fSdJClpJKppNFGO/scrVBDAEQI4Uv22SEU5BQUFknjVI9WqQ3QPM3/+fD0oKOi051JSUvQWLVroqqrqqqrqiYmJ+r59+xznhw0bpk+aNKnKc7Zs2aID+tatW0/7mrNmzTq2QoM85CEPN32kpaU5rY6pDVKPyEMe7v9w53pE6hB5yMP9H2dTh7h0qOGMGTN47rnnznjNtm3bSEpKcpT/bahhaWkpgwYNIikpidtvvx2bzcYLL7zA9u3bWbt2LT4+PgwfPpz4+Hjeeustx/O2bt1K27Zt2bp1K61btz7l/U++y6RpGjk5OTRq1Og/JxIXFBQQGxtLWlqaRw0FkLjrlsRdc7quU1hYSHR0NKrqvncPa1qPuMPvuCY8NW7w3Ngl7przhHpE2iISd22TuGuuOnWIS4ca3nPPPYwfP/6M1zRv3vysXuuzzz5j3759rFy50vFDf/bZZ4SEhPD9999z9dVXExUVRVZWVpXnHStHRUWd9nXNZjNms7nKseDg4LOK6ZjAwECP+k98jMRdtyTumgkKCnLZe5+tc61HXP07rilPjRs8N3aJu2bcvR6RtojEXVck7po52zrEpYlXeHg44eHhTnmtkpISVFWtcufnWFnT7LuT9+7dmwcffBCLxYLJZF9q6rfffiMxMfG087uEEEIIIYQQwhncs0/9NA4cOEBycjIHDhzAZrORnJxMcnIyRUX2XceHDRtGbm4uU6ZMYdu2bWzZsoUJEyZgNBoZPHgwAGPHjsXLy4uJEyeyZcsWvvzyS1555RWmTZvmyh9NCCGEEEIIUc95zKqGjzzyCB9++KGj3LlzZwD+/PNPx9yuH3/8kccee4zevXujqiqdO3dm4cKFNG7cGLB3A/76669MmTKFrl27EhYWxiOPPFJre3iZzWZmzZp1yvAAdydx1y2JW/wbT/0de2rc4LmxS9zi33jq71jirlsSd93wuH28hBBCCCGEEMLTeMxQQyGEEEIIIYTwVJJ4CSGEEEIIIUQtk8RLCCGEEEIIIWqZJF5CCCGEEEIIUcsk8RJCCCGEEEKIWiaJlxBCCCGEEELUMkm8hBBCCCGEEKKWSeIlhBBCCCGEELVMEi8hhBBCCCGEqGWSeAkhhBBCCCFELZPESwghhBBCCCFqmSReQgghhBBCCFHLJPESQgghhBBCiFomiZcQQgghhBBC1DKjs1+wvLwcs9ns7Jd1G5qmkZGRQUBAAIqiuDocIRo0XdcpLCwkOjoaVfWc+0hSjwjhPjyxHpE6RAj3UZ065JwTr19++YUvvviCv/76i7S0NDRNw8/Pj86dOzN8+HAmTJhAdHT0ub6N28jIyCA2NtbVYQghTpCWlkZMTIyrwzhrUo8I4X48qR6ROkQI93M2dYii67pekxdfsGAB999/P4WFhVx44YX06NGD6OhofHx8yMnJYfPmzfz111+sXLmS8ePH88QTTxAeHl6jH8Sd5OfnExwcTFpaGoGBga4OR4gGraCggNjYWPLy8ggKCnJ1OGdN6hEh3Icn1iNShwjhPqpTh9S4x2v27Nm8/PLLXHDBBaftVhszZgwA6enpvPbaa3zyySfcfffdNX07t3GsSz8wMFAqOyHchKcNtZF65MyyCsp4f3kqpRYbE/vFE9fIz9UhiQbAk+oRqUOEcD9nU4fUOPFauXLlWV3XpEkTnn322Zq+jRBCiAakwqpx51s/8kjhE/hRyoz1d/HKPROJCPR2dWhCCCHEOfGMWaRCCCEahN+3ZXFh/pe0UfcTpx7mLv0jvlyb5uqwhBBCiHPmlFUNp02bdtrjiqLg7e1NQkICo0aNIjQ01BlvJ4QQop5auiObaYa1jnJPdTuvb9sD57V0YVRCCCHEuXNK4rVhwwbWr1+PzWYjMTERgJ07d2IwGEhKSuKNN97gnnvuYfny5bRp08YZbymEEKIeSk/bS6SSV+WYMWsjVttwjAYZpCGEEMJzOeWv2KhRoxg6dCgZGRmsW7eOdevWcfDgQYYNG8Y111xDeno6AwYMqBeLawghhKgdZRYbvkdSTjneSktl75FiF0QkhBBCOI9TEq/nn3+eJ554osrKOkFBQTz66KPMnj0bX19fHnnkEdatW+eMtxNCCFEPbc8spK2y95TjbdT9bM0ocEFEQgghhPM4JfHKz8/n8OHDpxzPzs6moMD+xzI4OJiKigpnvJ0QQoh6KOVgHu2U1FOOt1X2sfWQJF5CCCE8m9OGGt54440sWLCAgwcPcvDgQRYsWMDEiRMZPXo0AGvWrKFVq1bOeDshhBD1UMrBPNqr9sQrT/cjRWsGQHPlELsPZrkwMiGEEOLcOWVxjbfeeou7776bq6++GqvVan9ho5Fx48bx8ssvA5CUlMS7777rjLcTQghRD6WnpRJRubBGihZPuh5Ge3UfqqJjObQZXR/oUZvcCiGEECdySuLl7+/PO++8w8svv8zevfbx+c2bN8ff399xTadOnZzxVkIIIeqhkgorAUc3gsle3qi34LAe7DgfV7GbjPwymgT7uCZAIYQQ4hw5dW3ezMxMDh06RMuWLfH390fXdWe+vBBCiHpqc3oBHZQ9jvImvQU7leaOchtlnyywIYQQwqM5JfE6evQo5513Hq1ateLCCy/k0KFDAEycOJF77rnHGW8hhBCiHtuYlkfHExKvorBOmJu0R9PtQwvbqPvZkpHvqvCEEEKIc+aUxOvuu+/GZDJx4MABfH19HcevuuoqFi5c6Iy3EB5C13UWbDjIvf/byMer9mO1aa4OSQjhATam5dBBtQ9Vz9BDiYuLp0VMY/bpkQAkKWlsS891ZYhCCA/yS8ohpn+9kXf/2ku51ebqcIQAnJR4/frrrzz33HPExMRUOd6yZUv279/vjLcQHuKb9emkfj2LWZtH4PXTVJ5fuN3VIQkh3Jyu6+SkbiJAKQVgk9aCjjHBtI0OZKveDABvxULxwS0ujFII4SkWbs7kny+e5KGUEUT9OpnHvj91Y3YhXMEpiVdxcXGVnq5jcnJyMJvNzngL4SF+XrebO4wLCFBKucq4hG2rfqHMIneahBD/LvVIMYml6x3ltVoi3ZqF0D4miE1avON4bMlmMvJKXRGiEMKD/LD+AHcbvyZQKeViwypy1v9AfonF1WEJ4ZzEq3///nz00UeOsqIoaJrG7NmzGTx4sDPeQngATdMpT1uPSTmeaA3S17Bq71EXRiWEcHcr9hylj3q8N2ubT2dahPuTEO7PNlNbx/Fu6g7W7stxRYhCCA+Sd2AT/kqZo3yespZlu7JdGJEQdk5JvGbPns3bb7/NBRdcQEVFBdOnT6ddu3YsW7aM5557zhlvITzA3iNFtNF2VTnWUd3D5nSZEC+E+HerdmXRU90GwBE9kKiELiiKgqoq+MR1pUy3rzHfXdnBP/tknpcQ4t9l5pfRtHRblWPSFhHuwimJV7t27di5cyf9+vVj1KhRFBcXc9lll7FhwwZatGjhjLcQHmDDgTw6qburHGuj7GfLQWkoCSFOr8KqUbh7JYGV87tWam3onRDuON85PpJkPQGApmo2e/fuOu3rCCEEQHJaHp2Uqm2RFkoGuw5muSgiIY5zygbKAEFBQTz44IPOejnhgZLT8rhN3VPlmI9SQWH6NqCHa4ISQri1FXuOMNC20vHX6A9bZ2a0Op54dW8Wwt9aIr0qe8Qij6zicMEFRAR6uyJcIYSbS07LY/RJbRGDomPJSEHXB6IoiosiE+IcEq9Nmzad9bUdOnSo6dsID7J//16aKKfO5wov3EZeSQXBvl4uiEoI4c4WbjrEHYa1AFh0A0eaDCHyhKSqY2wwrxq6AN8BMNiQzJKd2YzpFuuCaIUQ7m77/nRaKQdPOR5v2cXB3FJiQ09dDE6IulLjxKtTp04oioKu61XuHui6DlDlmM0mq9rVd6UVNvyyN4J9KgZbtDjaqvatBNqp+9iSUUDfhDAXRiiEcDdlFhvpW/4iRjkCwN9aWwa0T6hyjcmgEtiyN/m7fAlSShigbuLB7Yck8RJCnMKm6ZCRjKra26JV2iLKPrZk5EviJVyqxnO8UlNT2bt3L6mpqXzzzTfEx8fzxhtvkJycTHJyMm+88QYtWrTgm2++cWa8wk1tzsing3J87sUntqGO79upqaTIpFYhxEkWbs7kYutvjvJPWi/Obxd1ynUDkhqzTLOPnAhSSijc9TcW2ZxdCHGSXYcLSbLtdJQ/tw3Bqtubuu3UfdIWES5X4x6vuLg4x/dXXnklr776KhdeeKHjWIcOHYiNjeXhhx9m9OjR5xSkcH/JB/LopBwfU/2HrTPpxkY0UY7SRtnPJ7LAhhDiJAtWbecNw0oACnQfjsZddNq70YNahfOMrTMjDasA6GdbzfLdRxicGFGn8Qoh3FvygTw6nTC/a5XWmt16E5KUNFoqB9lx3IID3wABAABJREFU8IgLoxPCSasapqSkEB8ff8rx+Ph4tm7d6oy3EG5uY1oOHdS9AGTqIWQRyhatGQABSil56TtcGJ0Qwt1szSgg9uCP+CnlAPxg68NlvVqd9tqIQG8ONx5EhW4AYKRhJT9uSKuzWIUQnmHjwTw6ViZehboPe/VotujNADApNsrTUxxTYoRwBackXq1bt+aZZ56hoqLCcayiooJnnnmG1q1bO+MthJvL3b+FgMrloJO1BK7r1ZQU7XgyHpy3lYIy2TVeCGH35p/budX4o6P8k9f5DGsT+a/Xn9c5kaVaRwCilFxytv5JSYW11uMUQniOA/v20Fixb7K+SWvO2F7NqrRFYst3kVlQ9m9PF6LWOSXxmjdvHosWLSImJoahQ4cydOhQYmJiWLRoEfPmzXPGWwg3lplfRkxxiqOcrLXgqm5N2a4cr+zaqvvYkl7givCEEG5m9+FCvLZ+41hUY4mtI337DsZsNPzrcy7u2JgftL6O8gXaXyzcnFnrsQohPENBmYXgo+sd5WS9BZd0bMJe0/EFe9opqaQclHlewnWcknj16NGDvXv38uSTT9KhQwc6dOjAU089xd69e+nRQ/Zvqu/W7c+lm3J8KOFWUxvaRAdSFt7ecayzupsNaTLPSwgBz/2YzN3Grx3l99XLuaFPszM+JyLAm9L44RTqPoB9uOE3KzbXZphCCA+SfCCPrsrxhTU2kkSHmCDUqA5oun2lbXtbJM9FEQrhxA2U/fz8mDRpkrNeTniQf/bncL1qr+zKdSNeTbtiUBXim7Vg/9EI4tTDdFL28M6eTBiU8B+vJoSoz/7cfpjEvR8SY7L3di2ztadj//MJ8jH953Mv79mSb/b1Z7zxV3yVclpn/sDGtF50jA2u5aiFEO7un/25nKceT7zKorribTLQNr4xWzPiaKfsI1FJY/PufUCSy+IUDVuNe7xWrVp11teWlJSwZcuWmr6VU+Xl5dGtWzc6depEu3bteOedd1wdksfbnZpKc9U+5GezHk/HZvZ5Gj3jG7Fas8/xMysWLPvXYJUloIVosArLLLzx7e/cZvwBAKuu8rp5IrcMbHFWzx/WJpJffEY6yjcYfuWjFXvO8AwhREOxOTWDtso+AHZqTUhqbl99+8S2iKro+B5aI/NDhcvUOPG6/vrrGTFiBP/73/8oLi4+7TVbt27lgQceoEWLFqxbt67GQTpTQEAAy5YtIzk5mdWrV/P0009z9OhRV4flsfJLLQRkrXWU/9Fa0bVZCAA94kMdlR1AR9sWth6SeV5CNFSP/5DC9LKX8a1cyfBj2zDGXDAMf/PZDb4wGlQG9OnDMpt9GHNTNRvL5u/Yf/T0f4OEEA1DmcUGaWswKvabu/9oregaZ2+LdI0LYa1+vC3STdnGhgN5rghTiJonXlu3buWiiy7ioYceIjg4mLZt2zJs2DBGjhxJv379CAsLo0uXLqSmpvLrr79yww03ODPuGjMYDPj62veJKS8vR9d1WVr0HCzfdYQBSrKjvEZpT+dYe2UXHmAmM6SL41xfw2b+2iV7aAjREH23IZ3wjW/SvXIo0AEtnCVNbuGyzk2q9TpXd4/lA473et2hfsPri2W7CiEasjWpOfTWNzjKK/V29GgWCoCf2UhxVHfHub7qFpbtyq7zGIWAc0i8TCYTU6dOZceOHaxcuZKbb76Zdu3a0aRJEwYNGsRbb71FRkYGn3/+Oe3bt//vF6y0bNkyRo4cSXR0NIqi8N13351yzdy5c2nWrBne3t707NmTNWvWVCv2vLw8OnbsSExMDPfddx9hYWHVer44bsn2LAYaNgFQppsgrh8+XsdXJkto2Ya9WhQAXZWdrNq8yyVxCiFcZ2NaHv/37Yfca/wfAJqu8KByB09d3RtVVar1Wo38zbToeTFrNfueXy3VdCybviH1iPR6CdFQLdmRzSB1IwA2XSGvcT9C/Lwc5zu2asFGrTkAbdT9bNosC/MI13DK4hrdunWjW7duzngpiouL6dixIzfeeCOXXXbZKee//PJLpk2bxrx58+jZsydz5sxhxIgR7Nixg4iICAA6deqE1Xrq+N1ff/2V6OhogoOD2bhxI1lZWVx22WVcccUVREb++/4x4vQ0TSd95z+OPTNWam3omxRT5ZqhbaP4/Z+uTFL/D6OiEXZoKYcLBxMR4O2KkIUQdWxvdhFPffAt76qvoir20QWv2i5l1KWXExPiW6PXvGVQAvetHsN8ngTgLvV//8/efUdHUbUBHP7NzG56T0hCIAm99967NAsoCgjSuzTFBhYQBPFDpYgoghQRsAJ2QYqAdCmh95ZASEhI77s78/2xYSHSEkiyu8l9ztmTndnZmTfAXOa29/K/3x/n8wHN8i1uQRDsg6ZpnDx5jIryVQAOaRVpUCXnvNEO1QLYtL0eteULAFRM+IcLMV0oV8Kt0OMVird8SSefn7p06cL06dN5+umn7/r57NmzGTZsGIMGDaJatWosXLgQFxcXli5dajkmLCyMY8eO3fEKCgrKca6AgABq167NP//8c894MjMzSUpKyvESzP69FEfjjJ2W7a1qHVpXLpHjmMZlfdmp3FpSoINygM0nrxdajIJgC4prORIRl8bERetYYHwXj+wF1v80NSSu/ss8W7/0A759b35ujlRq+ji7TdUAKCNHU/bMcv4+LcoWoWgqrmVIbpy8lky1xK2W7a2m2nc8i9Qq5ckB56aW7Q7yQTadjC6sEAXBwuYqXveTlZXFgQMH6NChg2WfLMt06NCB3bt35+oc0dHRJCcnA5CYmMj27dupXLnyPY+fOXMmnp6elldwcPCj/RJFyC9hV3lS3gWYhw6d9mlD+f+0HjnoZDwrNSdOM+9vIx/mr0MiC5lQvBTHcuR4ZCKvfPY9c7MmU0IyL1gappbju1Jv8c5TNR75/C+2rchc/WBM2evzjNH9xOc/byPTaHrkcwuCrSmOZUhu/XI4kqeUW8+A+11aUauUZ45jZFkitGojrmjmqSVN5RNsO3SqUOMUBLCzildsbCwmk+mOYYEBAQFERUXl6hyXL1+mZcuW1K5dm5YtWzJ27Nj7zkGbNGkSiYmJlldERMQj/Q5FhcGkcunoTksa+T1qVVrWq3XXYzvWLMV6k3liq6uUiW/4n0TEpRVarIJgbcWtHPn79HU+WLiURYY3CcoeinxCDWWW3/t8MrAFeuXR/+vxdNbTo2tnVpg6AuAiZTI0+XPmbjzzgG8Kgv0pbmVIbqmqxqFD+y1DCI+qZahdr+Fd5452rlmS302NAdBLJipd/5NTUaLnUChcdlXxyg+NGjUiLCyMw4cPc+TIEUaMGHHf4x0dHfHw8MjxEmDzyWg6GP62bP+sNufJWkF3PbZD1QD+0LW3bD+nbGPNwSsFHqMg2IriUo4YTSofrj/JxhUf8KU0HS/JnPDimFqGad4z+GxoezycHrxQcm49W680mwKGEKOZ/zw7KgeI2bGc/Zfi8u0agmALiksZkle7L9ygedpGy/bPpuY8VfvuzyItKvjxt3NHy3ZPZRs//CsqsELhyveKV0ZGRn6f0sLPzw9FUYiOzjkuNzo6msDAwAK7rnCnb3ec4FllOwDpmgORJTsQ4nv3ifJOeoWytdtwTjUXho3lU+zeu5sso1hMWRCKirPRyQz4fDOVd77M+/olOErmBEdbTbWZVXI2X4zojJeLwwPOkjeyLPFWj6ZMMQ217Jus+4pZ320kNVMskCoIRd3KnWd4XtkCmBdkP+bdgWol714pVWSJevWbcEitAEBVOZzTB7eKxZSFQpUvFS9VVXnvvfcoVaoUbm5uXLhg7vJ95513WLJkSX5cAgAHBwfq16/P5s2bc1x78+bNNG3a9D7fFPLT6ahkSkf8inv2ZPmfTM15pvn952z0bBjCt6a2lu3u6ev49XBkgcYpCELByzCYmLvpDB/Pn8NHMcNzzLVYZuzE2iofsXhYGzxd8q+n63bVgjyo0aEvP5paAeAhpfNa6kdM+uGAWKNREIqwiLg0nM78SgnJPFxwvdqQrs3rIUn3XqLiuQbBOZ5Feht/5nvR6yUUonypeE2fPp3ly5cza9YsHBxutWjWqFGDL7/8Mk/nSklJISwsjLCwMAAuXrxIWFgY4eHhAEyYMIHFixfz1VdfcfLkSUaNGkVqaiqDBg3Kj19FyIWFf59iiPKHZftnx8fpWrPkfb9Ts7QnZ4KeJklzBuAZ5R9+2PovqioejATBHplUjR/2R9B/1mqqbhvFQt3HlqUlkjQXXjS8jNr5f8zr0xBHnfKAsz2aEa3K83PgOMvE+YbyGeqe+phF2y8U6HUFQbCeL7ady/Es8r3clWfq3T9balk/VxLKd7cMT+4i7+OPbbswmMQIHKFw5EvFa8WKFSxatIi+ffuiKLf+g61duzanTuUta8z+/fupW7cudevWBcwVrbp16zJ58mQAevXqxUcffcTkyZOpU6cOYWFhrF+/XqzDVUjORCejHPuBsrJ5uOdOU3UaNWmdqwer/m1rsdpkzkjpKBlpF/8Dfx7LXVKUosqkasSmZGIUhb5gJzKNJr7fH8Hzs38i/aeXWZU1nk7Kfsvnf5tqM8R5LsNGjGdIi7L3bX3OL4osMaN3c16XJpCpmZenHKTbwOm/FrPtTEyBX18QhMJ1JT6NuAPrqCFfAuCIWpZy9Trg5vjg5WmHtK3GMmNnABRJ4+m0H1hzoHjPO7/5LCIqoAUvXxZQvnr1KhUqVLhjv6qqGAyGPJ2rTZs2DxweMmbMGMaMGZOn8wr5Y95fJ3hDWWvZXij3Yn7zMrn6brsq/iz26sGg5PU4SgYGKBvo98dTdKj2XIG3iNsaTdNYtTecPzZuIDTjFJccq9Ctc2d6NwqxdmiCcFfXkzL44cAVtu/awTPp6/ha2YGj7tbciBjNkxmmfgQ07cuKxyrj7FC493SIrwvDnn+OKSsu8oF+MQAzdV8ydGUAviMGU+M/6aUFQbBf8zedZqz8g2X7U+05prYpf59v3NKwjDeflHyW5BjzlImeylb6/LWJJ2v3xzUXFbeiZu3BK3z/50bKpB0jXF+e9h26MLh5mUJpNCuO8qXHq1q1anddhPjHH3+09FwJ9m/XuVgCT60gRDa3IG831aRuiy65njAvyxL9OzZhmcnc0uQoGXkhdTlf7bpUUCHbrE+3nCPut8msNr3GTP0SvlFf49LPM1i557K1QxMEiwyDifXHrjFi2U7emzWDmlsG8p1hPL10Wy3JM1I0J2YbnuXVkssZOup1Jj1erdArXTe1rexPqXYjWG1sB4CjZOBTaRbvLV1D+A2xhIUgFAWHIxLQwlZTVTbPzTqkViCoQTdKejrn6vuSJDGiUz0+Nz4JmHu9RmYuZ+G24rfG6LKdFzm9Zjqrsl7mA/2XrGYS6eunsHCbGKZdUPKlaj958mQGDBjA1atXUVWVtWvXcvr0aVasWMFvv/2WH5cQrCzLqDLvp+18qVsDmBdM/lzpy8IWZfN0nq41A+n/zwCeu74VXymZp5TdDN64hk7VRxPq61oQoducbWdiiN6ygOn6n3Lsn6j/lqG/l6F1pfEE+9w9Q6QgFLTENAN/n77OxmNXSDuznbbaXj5QduOtS8lxXJLmwkpTB7b7PMuIx5vycqUSNtFCOrptBcZHTaLUqVhaK0fwlNKYa3yPcV+68cnIJ3P9cCYIgu0xqRqz1u3mE903ln1ztef5sO2do67up2XFEqwo15+r4ZspJd2grXKYH7d/w6laE6gSWDxS9R+4HMeRPxYxR/9Njv1jdD8zdlMop6u8TuVAdytFV3TlS49Xt27d+PXXX9m0aROurq5MnjyZkydP8uuvv/LYY4/lxyUEK5u36TQDEj+3ZDL8xtSOxzt3xdM5b5nKJEni5Scb8LGxp2XfVGkR73y/t1gk2kjNNLLgx794S7fKsm+3qZrl/dvSMv73+xFrhCYUUymZRraevs7MP07ywrzfeGvGNExrRzDj3NMsU6bTX7cRb+lWpStCLcF7hr6MDVxJlRc+YvVLT9G2sr9NVLrA3LP+Ua/6LC01lcNqOQBKSnF8nPom4xb+yrXEdCtHKAjCw1r8zwWeur4QXykZgF9MTWnS/mn8PZzyfK7XnqjDLFMfy/a7ylKmfbejWMy5zjSamPXDVqbqllv23f4s8pbyNbN+OWCFyIq+fBvM2rJlSzZu3PjgAwW7s+9iHFH/LOc1/T4A4jQ3/vAfyoqHnI9UL8SbdQ0GsPvgbpoqJwiWY+h89RM+3xbC6Dy2WtmbjzecZlz6ApyVLAC+Mj7GFONAvpPeo7F8ijJyNM6n1nDuenUq+IuWJiF/JaYZOHM9maNXEjl2JYHoK2fxjD9GQ+kUT8snmCRHwF3aUjI0PX+qjfhFaod/zfY836QsdYK9Cj3+3HLUKcwf2JJhn01lZsJrlJOjCJFj+Dj1TcYv1Jg74imCvETPlyDYk2NXEzmycSWf6bYC5mHOKz2GsTKPI29uqhTgTlDzvmzctZPHlIOUkBLpEzuPjzaUZWLXqvkYue357O/zDEz8DA/FPAR7rakFEwwv8iUf0kE5RKAUT5nLPxAWUcemy3p7VPxmEQp5EpOcyUerf2eJ7ivLvndMw5jUozmK/PAt3G90rc7gk2OonfkyLlImfXR/89qmRewo/TotKvrlR+g251B4PEl7v6KF/jgAVzQ//i49isMDWjH6fxdpzNsAjFR+5YutzzOrZz1rhivYobQsI1GJGUQlZRCdlMG1xAwuxaYSFR2NduMcfhnhVJCvUlO6yNPyRXNP1j06rVM0J/5W6/CX2pCU4DZ0aVCZT2uWtJvJ5x5OeuYP68jYhQZmJr95W+XrLcZ/ZmLm0Keo4O9m7TAFQciFxDQDU1dt5AtlkWXfVGN/Xn22LQ66hx+8Nb5DJQYcH0uj5NF4Smk8oexh/87PWB/yFp1rBOZH6DbnTHQyZ7d9w8u6fwGI1Tz40W80x0Z2ZOiH0XQwHgJgqO4Ppv/djwX9m1gz3CLnof8H9fb2zvXQkri4uIe9jGBFmUYTL3+1jfcz38ddNg/P+d7Ymhod+j1yhjA3Rx0v9ezE5GUD+Uj/BQDv6ZYyeHVZSr7Yj/IlitYDUZZR5YMftvOFbqVl31R1CFOebYKns546TTuyZ+cqmsgnKS9fI/XIL0R1qkagZ96HTwhFw8o9l7melIFB1TAYVYyqRpZJxWhSMZg0UtKzyEpPQktPQMpIRM5MxM1wgwApnkApjkApnkZSHM9JUZYFRrlPHhyTJnFUK8setTr7pBroy7WkfY0Q3q3qj6+bY+H80vnM392J+SMfZ8xCmJn8FuXlawTLMXyW+QbjPk/k1YG9qB/qY+0wBUG4D6NJZcKqXUxOeQ8f2Tzs+U9TQ0q0GEyjso92/zrpFSb1asebX4xggW4OAG/pVjH0h3IE+wymelDRyoZqUjWmfr+L2cpSy773TAN4+7kWuDnqaNuyNRs31ecx5QAlpThcTq/hQkwNyhWxZzJreuiK19y5cy3vb9y4wfTp0+nUqRNNmzYFYPfu3WzYsIF33nnnkYMUCp9J1Xjj+4MMiZ5OBSUSgDNqKf4oPZ4lrcrlyzWaVfDjUIfhrNp8jr66zThJBuap7zN6sQfzRj9TpCbBf771PP0SFuClpAKwztSceu17UdbPnFBkYPMyTNrxFE04ad6Wf+er3b15o3MVq8UsWNfvu8J4J+EddBjRY0QvmdBhMr/HiBsZyNJt8yIl7lux+q9YzYOjalmOaOU4rpUnI6gxdSqG0rS8HwNDvHDSF40lHvzdnfh05OOM+UJmetJbVJKvUkJK4kt1CuO+TKRX74F0rG7fLdvnrqewYvclridmUK+MN30bh9pNz6Qg3I+maUz5+ShPh79PTeUSAOFqCVaWmMCyxyrnyzXqBHvR+PGBLPz9HCN1v6KXTHyizWL4Eg9mvdi7SCX++mrXJZ6I/pwAXQIAm0x1KdW8L9WCzAlF+jQOYdTf3XkM8/yuIcqfLN3Rj+lP17JWyEXOQ5fMAwYMsLzv0aMH06ZNy7G21rhx4/j000/ZtGkTL7/88qNFKRQqVdV4a00YbU5Opq1yGIB4zY23nN5m4QstHmmI4X+Nal2e0eGvUvl8BA3kM5SQkvgoYwrjFzkxd1jXIjEP40RkEqe3fsN43V4AbmjufO/7Il+3vDUu3c/NEf+6T3A6bBWV5Ss0lM8wd88mUttWEA9QxZSjAtXkR19eIEbz5IJWkvNqSS5oQVwiiAzfqpQsXZ6awV60KuXJiJIeRaaidTf+7k58NrIr45c6MT52Co3k07hKmSyUZ/H26lgudBzFiFblbCZBSF7sOhfLoq+WMkX6ktJSLFvP1mbQntF8OKRLkXpgFIofTdOY/tsJqh6cyhO6PYB5CPSr+jeZ2//Rhhj+V78mobx2+SW2H79EK+UonlIanxinMfYLJ2YN704ZP/u/ly7EpPDPX2tYpvsbgGTNmUXuY1jRoZLlGHcnPTUad+TA7hXUl89SRY7g2sE/iO9YBW/XPLTsCfckaQ9arTgX3NzcCAsLu2MR5XPnzlGnTh1SUlLu8U37k5SUhKenJ4mJiXh4FL2Uo0aTyuSfjlDr0BR6Z09gzdR0DNfeZOKoYVQtmf+/c0qmkRFfbGRy7KtUls2rx0eoJXjFaSr/G97d0itkj1IyjQye9xMLU8fjk50Z7iXjGIaOev2O4ZrnY1JYOHcqH+rNY9h/MzXmRpdFDGhWprDDthv2ej/mJu4Bn/7Bopj+GFAwoMOIYn6vmd8n40yS5koSLiRpLiThyg3Ng2jNhyS9Hyb3kug8gwjw8aJcCVfK+blRroQrwT4u6JX8e2CxJ6mZRl5euZunL02li/KvZf8yYycOV3uNmc/Ws9oaZA8jPjWLkR8tY4X6Fo6SwbL/mubDOOeZfDH2GXzEw9ID2WM5Yo8x54Wqarz/+wkC977HUN2fABg1mRdNrzBi2Gjqh3rn+zUzDCaGf7mNCddeoY5sXscqWvNirG4KU4c+WyDPP4Ulw2Bi4Kd/MjdhDIFSPABvGwbx5JB3aFzON8ex1xLTmT7rAxbo5wLmNVuPtF3GmHYVCztsu5GX+zFfmtJ9fX35+eefeeWVV3Ls//nnn/H19b3HtwRbk55l4uVVe3niwq3WJYOmMM70MoP79y+wQsfNUcf8Ie0Z8dk0PkqeSKh8nWA5hk8z32T8gmTG9H2W5hXsL+GGqmq8+eMhXk2ZZRmXvsHUgMBmL9x1jlz5Em4kV+hOzKVvKSEl0UXex/Pb9/BCk9B87WUU7EPNiuWZ4P0XOkVCr8jos3/qZPN7N0cdHs56PJx1BDrrqeSkx9vVgUAPJ9FLeg+ujjo+G9ict9bOIurwBwzSbQBgkG4Du05GMPizt/hwQDtKe9vHOnqf/X2W10xf4igbcuwvKcUxLW06L68owZfD2xbbirZgnzKNJt74/iBNT0ynV3YDsKpJvGp6kad7DSmQSheY53t9Oqglwz9/j6nxb1BZvkKAlMAXxncYtzCZAb1606FaQIFcuyBpmsa0X44yPO5DAhVzpWunqTpa/UF3VLoASno641jzKcJPrCJEjqGVcpTPdv3DsFblcNTZT8OUrcqXHq/ly5czdOhQunTpQuPGjQHYu3cv69evZ/HixQwcOPBRL2Ezimor05X4NF7/ehvjYqfSRDbPMzJqMhNMY3iyzxgeK4TCJioxg7GL/uC95MlUyV6RPkPTM8k4jKqdhjK0RTlkO6qA/O/Pk4TsnMTz2d36VzQ/3vD9lGUvdrrnEIk9F26wc8nrvKL/EYAlxi6U6j2HzjVKFlrc9sRe70d7jbuo0DSNz7edJ3zjQqbpluIgmQBzT/vryisM6/UM7arY9gNWepaJYe9/xsrsbKjn1CD6Zr3JNw7TKSdHAfCHqRH/NpzDlKdqWDNUm2eP96M9xpwb15MyeHXVTgZGTqOdEgaYK12TjENp9tzLdKtTqsBjuJGSyegvNzEx7m1Lz1eWpvCucSAl2oxkXPuKdtUY+tnWcyibJjNC9zsAMZoHo93ns3zcE7g43L2R7nhkIj8seJt39SsAc2I1ui+gZ4PgQovbnuTlfsyXZrCBAweyc+dOPDw8WLt2LWvXrsXDw4MdO3YUqUpXUfX36eu8/slX/C92rKXSlaY58qL6Kl17jy6UShdAoKcTC0c9zlTfD9mvmsccO0kG5ug/w/mv1xn65TauJtjH4qcLt51Hv+MjS6UrU9MxUXqJD15ofd9x6Y3L+nDQ/xkyNHOO717K36zedrRQYhaE4kKSJF5sU4GO/V5jMO9yXfMCIFiOYbn6Nlu/nsH7v5/AYMMLqf4cdpUepj8t2wtNTzJ/xONMdHyLJM3cY9dV2YfT3vn8dOiqtcIUhFzbff4GY+atZsq10ZZKV6am4yXTWBr3eKlQKl0Avm6OLBrZkY8DP+Qfk7nRwkEy8b5+CSHbJzDw801cjE0tlFge1co9l4nZONdS6TJpEq+rY5ner/09K10A1YM8uRzyDInZZUk3ZSdrtx0kH/pqir18G3/QuHFjVq1axcGDBzl48CCrVq2y9H4Jtikl08iUn46wZcUMlqlvEyzHAObkD0OlKQwdPKrQ17HwdXNk0YgOfFluHquNbS37++k2MfnKcCbOWcTynRdt9oFIVTU+Wn+KjI3TmZDdawXwumkUQ5/vRbDP/YcwSZJEz9Z1WGNqBYCblEHlyHUcCo8v0LgFoThqVyWAqWMGM8ZtNodU8xxlR8nINP1X1N0zjoGfb+RKfJqVo7y79QfP0lU2J+yJ19xIKvckjcr68PaAp3hVvZXo6lXdd/y69muORyZaK1RBuK8Mg4mZv59g3dIPWG6cSHn5GgBJmgvDtLfo0X8cz9QrXagxeTjpWTS0LWuqzuVLYxfL/h7KP3xwfQTT5i1g4bbzZBpNhRpXbmmaxsJt57n46/+Yov/asv8d42Cefa4flQLcH3iOfq2rsdrUHjCXi83i1/LP2dgCi7m4yJeKV3h4+H1fgu3ZdiaGwR9/R+cDI3hPvxxHyQjAQbUCI11mM3VU/0deH+NhuTvp+ax/U2LafMhEw1DSNfPk8DJyNF9Lk3H9cxx95/zM1tPXbar1JTnDwOiv9xC4401e0q217J9u6EuzbsNpU9k/V+fpWrMkvzh3t2wP0q1n6faz+R2uIAiY51Z+OfYpllRcwGJjV8v+Lsq/zLw+mtfnLWfD8SgrRninxHQDzhH/WMrtX01N6dHEPPG9VmkvOnTrzxxDDwAUSeMjeT5TV/xBfGqW1WIWhLvZc+EG/eespcmekczSL8JFygTghBrKEMcPeX3EEFpXKmGV2JwdFOY83wA6vc9LhtEka+Ysy6WkGyxTZlB604v0+/hHNhyPsqlnkfQsE69+dwB54zu8o19l2T/P+DTlOo/h8Vq5m7rQppI/Wz2fxqCZ53W9oGziq20nCiTm4iRfKl5lypShbNmy93wJtuPc9WSGL/mHvV+9xVeZL9FUuXUTLTN2YmnFBSwZ/zQVc9EaUpBkWWJ8h4o8MXASAxxmc0C9lU3nOd12liWP4NCKifRbsIHNJ6OtXujtPBfL4LlrGHp+LC/oNlv2TzP0I7Dzq/RqGJLrc+kVmfYtm7PRVA+AICkOp5M/cDoqOd/jFgTB3Lo9/4XGOD4+k5HGV0nQzJlUQ+QYlmtvs2/1NKb8dIQMg220bv9zNoY20iHL9napHi0r3kpA1LNBMHENxrPJVBcAbymFqWnvM2bZ36RmGgs9XkH4r8s3Uhnz9R7+WjKFJaljLUvXAHxrbMOc0AUsGt/zromoCpMkSQxtWY4Xhr3KEJd57DRVt3z2hLKHFWmjOf/Na/SZ9zt/Hr2Gqlr3WeTA5TgGzvuJZ46PY3j28EKAjw3PorWexJAWuX8ml2WJ7q0b8otqXp/XR0oh9NIPHLgsRuA8inxJrnH48OEc2waDgUOHDjF79mxmzJjBM88886iXsBn2OqH18o1Uvth6lrRDP/Cq8i2lpVvdxVc0Pyarw2nVuScDmpWxubVskjIMTPv5KG5HlvGy7kc8pVtDf5I1Z742PcZ2r+481qQez9QtVahrTVyISWHOXyfwO7GCV3Xf45rdWpep6ZlkHEqDp16kT+PcV7puSsowMHrmZ3wtTQbMf0czyn3N5wOa5Wv89s5e70d7jbs4OHY1kWkr1zMxdRb15HOW/X+barPQ+1VmvNCWCv7WbZh65buDvHHiafylBNI1B8YGr+XLoS1zHJNlVBm8cCPTro+zJNvYr1ZiQakP+XxwyyK9blte2eP9aI8xA0QmpLNo23mi/13D6/IqysrRls+iNS+mmIZSq8PzjGxV3uaSaaVlGfng9xMY9i/nVd33+Eq3GkPTNEe+MbVjo3t32jRpyLP1S+Pn5lhosUXEpTF340kcjqxmkm41HtnPSQZNYaqxP6GdxjGsVbk8nzfDYGLgB0v51vQqALGaB6+WXM6yEe1s7lnRmvJyP+ZLxetefv/9dz788EO2bt1aUJcodPZW2J28lsTiradRj61jpPKLJVsgmCdZrjB1ZGvpkUx7rrHNL7a598IN5v6ymy6xy+mjbEYn3ZrnZdIktqj1+IF2KBU68FjN0rSvEoCniz7f49A0jb0X41i1+zzKyZ8ZL/+Q4z+PK5ofr8mv8mKfZ2lZ8eGHSMzddIY624bSJrsl8F1Df54aMY16IQWTStce2dv9eJO9xl1cJGcYeHPNIaqfnMdI3W+W/dc1L95Qx9Dlqd4816C0VR48VFWj3/QvWKW+AcAmU12udF7GwOZ3tmRHJ2Uwdv63fJ71tuUhcZepGl8ETeeTga3wdM7/8tEe2eP9aG8xn7uewpfbzpF8+CdGyD9RS76Y4/NvjW34NfBF3n2umdVH3DxIWEQCH/28l9ZRyxmgbLBkRQVzBsbtai2+19pjLN+BDjVC6FAtoEDW09M0jYPhCazafYHMY78yTv7BshYqmMurV5jACz170an6w8/XX7LjIiU2jOIpZTdg7j1rOOADWllpCKgtspmK17lz56hduzapqfaR/SU37KGwyzCY+OPoNdbtPkH5yF8ZoGzIUTEA2GaqxWcOg3imcweeqx9scy1L92JSNdYcuMKPm/6he+oPPKtsy1HoASRqLmxS67FJbURqUFNqVgihUVlfapXyfOjesKQMA2HhCWw5dZ3Txw7SKPVvntdtsSxEeNNKY3v+ChrFjN7NH5hI40GSMwwM/98SvtHMD1ixmgevlPyK5SPaipambPZwP96NvcZdnGiaxrf/RrD519XMlBdQQkoCzA9Wn5me4my1sbz3TB08nAq38nIoPJ6/v3jVkrznLcNgRkyYQYjv3cubs9HJTP5iNV+Y3rW0gh9Ry/K+11RmD+5IkJdzocVuq+zxfrSHmLOMKhuOR7Fm90lKRfzGQGUDFeWcGTb3qFX5RBlAp8e62NWalZqm8cvhSFb/tYvOST/wvLIFJynnenrJmjN/q3XYqDUkIbA51cqH0risD7VKez10b1hKppEjEQn8ffo6x44dpm7S3/TRbc4xiglgjakla0qMZlrvllTwd3vo3xPMz5T9Z61mddY4dJJKkubMSJ+lrBzbxW6eHQtaoVe8kpKScmxrmsa1a9d49913OXXqFGFhYY96CZthq4Wd0aSy+8INfguLJPL4dp40buRJZTfOUs7J1AfVCizQnqNqi6cZ2aY8bna60KrBpPJLWCQ//L2Xpgm/0VPZSkkp7o7jVE3ihBbKPrUKx9QyXHcpjz6gCoG+Xvi7O+Hv4Yibow4HRcZBJ5NlVEnNMpGcYSAyIZ0rN1JJjj6Pd/xR6sjnaS0fpoIcecd1dpmqsUDpQ6dOT/JC49B8K4y+/OcCgX+N5AnFnL1svrE7FXp9QJeaYl0vsN378UHsNe7i6Gx0Mu+s3MKLCR/SSrm1tMMBtSIfuLzKW307UyfYq9Dimf3XadrteJ468nkAnnf9km9ee+6+3zl2NZGZi1fwmfa+Zah2uFqC13Wv82Lvp4t9y7U93o+2GrNJ1dh3MY7fj1zl4tFddM36i27KTtykjBzHHVPL8Knag+AmPRjTrlKBjE4pDCZV44+j11i9ZT/1Yn/led2WOypBN51Ug9mrVuW4VoZop3Io/lXx9/UhwMMRfw8n3J1uPYsYTCopmSZSMgxcS8wgIi6VpOiLeMYdobZ0npbyEareNoLppoNqBT6hD03bd2dIi7Lo8mnx9O/+Dcf08zj6ZC+Rs9zYEZdus+nZUKzrBVaoeMmyfEcLvKZpBAcH8+2339K0adNHvYTNsKXCLjHNwI5zsWw/fZ0rJ3fRMmsHTyh77nrT7zBV50vpGSo26sKwVuXx93CyQsT5T9M09lyI49u9F8g8sZ7O0i7ayYfwkO693peqSdzAg2jNm+uaF6k4kYWeTE2Ho2TElQzcSKOkFEdpKRbH/7Ri3WTSJDap9fmGLlRt9jgjW1fI9/88brY0rcwaj4NkIlPT0c9hLstfe/6+a3AUF7Z0P+aFvcZdXGUYTEz/9RiuBz7nVd336LN72ZM0F940Dqdmx/4Ma1k4C7z3nfcrX8f1Q5Y0TqrBrG38PW89Xu2B3zt2NZH3lq1hjuE9grIbqTI1Pe8aB+DVYijjO1QqtvO+7PF+tKWYkzIM7Dp3g+1nrnPxxD6aZ2zncXnPHSNtAP5VK7FIe5qg+k8yvE0FShWRHlfzsL94Vu+5SOKxv+jMLjrIB/CS7j/iK0bzIEbzJlrzIhVnMtGRpenRSyZcSceN9OxnkZg7etRuUjWJbWotVtOZ4IbdGN2uAr75PL/MaFLpM3sty1NexEXKxKRJ9FVm8cWrg+y20pyfCr3itW3bthzbsixTokQJKlSogE5XtB4OrVnYxaZkEhaewMHweI6eu4TntZ20lI7QUjli+Y80R6yaC2tMLVnv1JnGjVswqFmZQk08UdjiU7PYdDKazcciMJ7fSnMtjMbyKapI4chS/oyoNWkSB7RKbDTV56hnOx5r1oBn65Uu0ILn18ORXPnhdUbpfgXMk/z3N/+C1zpXLbBr2gtbevjIC3uNu7j78+g1Vq5ZwwfqXMu6hwCrjO3ZUuZlPujViBLuBTeh/npSBh98MIXZDgsB+Nz4JLUHzaVZeb8HfNMsIi6NV778gzdTZlBHvmDZ/5epPks8x/BGz7bFcg6pPd6P1ow5PjWLsAjzs8iRc+G4XN1JC+kIreQjOe6Lm1I1R34yteA3h07UadSawc3LFuh9Ym1JGQa2nLzOX0evkHF2G820gzSST1FduoSST88iAIfUCmw01We/exvaNW3Mc/VL53uF63ZbT19n34q3eV3/HWCuRP9abynTutcssGvai0KveG3fvp1mzZrdUckyGo3s2rWLVq1aPeolbEZhFHZGk8qlG2mcjU7mTHQKZ6KTiI84Scmko9STz1JXPkdlKfyuN7BRk9mp1uBXtSkJZbrybNPKtK8agD6fupvtRWqmkX8vxbHvYhzHzl9Gd+0Q5bRwKksRVJSv4i/F409CjgQd/5WmORKhlSBc8+eYWpbjUgUyA+vRsGp5HqsWQJVA90KZa6VpGoMXbWVG5GBLBXu08WXGjJ5A1ZL28ZBQUOzxgQnsN24BrsSnMWn1TnpGfcSTyh7L/tNqad7Rv8LY3k8+UlKd+/nu33BcfxnGE9nXHaBN5cvJ4/JUviemG5j4/b80OTubAbqNlv1JmgvvG/tCnReY0KlKkRkVkRv2eD8WRswmVSM8Lo0z0cnm55GoZOKunMYv4Qh15bPUk89SVQq/6/+jJk1ij1qN39QmXCvdlWeaVaNT9QAcdcWrVzU9y8SBy/HsvXiDI+cjkCIPUk4Np5IUQSX5CgHZzyJ66d5LVaRrDlzJfhY5roVyVKtIekBd6lWpwGPVAqlRyqPQ5n2/+NUuXj0/yJIp9XXDcHoNf5P6ocWvweZ2hV7xUhSFa9eu4e+fc4HYGzdu4O/vj8lkG2uf5Ifc/uFqmobBpGEwqRhMKlkmFYNJI9NgIjHdQEK6gcQ0AwlpWcSmZBGZkM6NuFjUhAgcU65ShkgqSVeoJF+hgnT1jvHRt8vQ9OxVq/Kn2ogrAe1oVacKXWuWpLT3oyV3KEpuVmbPRCdz7noK0UkZxCSlkZkUC1lpYMpEM2aBzgHJwRXJ0R03D2+CfV0J9nahWpAHVUu6W+0/jbPRycyfP4tPdJ8A5uEJY70+Z8XYrjjoilel+nb2+MAE9hu3YGY0qczdeIaYf77kXd1Xlrm0GZqeacb+eDQfxiudKud7g9eoFXv43/mn8ZDSSNBcebviT3z6QqM8n0fTNBb/c4GwDV8zVbfEkjgEzHNvPqIf9Vp3Y1DzMrgXcvIQa7DH+zEvzyJGNftZxKiRlf08kmkwkZRhJCEti8R0A4npBmKTM7makEFs3A3UhCs4pFyhjHaVirc9i9xvGH+mpmO/Wpn1akMu+LWnWZ1qPFGrpM1nTC5MNyuzp6OSOXc9meikTK4nppGZHIOWmQZqJhiz0BQHuPks4u5NaV8Xgr1dqFrSg+pBHlYbFhwRl8a7cz5hiTITMDfYDHGdz4qXnsbZoXhVqm9nlTle0dHRlCiRs5XvzJkzNGjQ4I7kG/YsN3+4P4dd5cvv1lJaikGPEQfJiB7zyxEDHlIaXqTgJaXgSSolpESCpNgc61Pdj0mTOK2FsFOtzh6pNlJoc5pULkXHaoH3zGwl2L///XmSertG85hyAIDfTI050+pTJjxWycqRWY89PjCB/cYt5LTrXCzzvv2Vd7M+zjHR/XdTI1b7v8oHfVs+cnbTmzKNJoZPm8dX8lQAfjE1JbPbYp5r8PCT209eS+K973fQI3YBPZQdOT7bYqrDEuU5GjTvyODmZYv0PA57vB9zE/Pmk9F8tGINoVI0DtnPIHrp9meRVLxIxUtKwYsUfKUkgqQbeEspuYpB1STOaqXYpVZnF3VQQ5rRqHIwHaoFUL7Eo2XSE2zXZ1vPEbB5vKXM2G6qyd8NFzLlqRpWjsx68lKGPNIErJsLI0uSxMCBA3F0vDW21GQyceTIEZo1K34LviqyxGDdnzyt7MyX84WrJTithXBILc8JpTJSUD2qlgmiaXlf+pXxKbYTooublx6rRP8T42iQNBpvKYUnlL2M37aCg5VfKZbzMgTB2ppV8KPyS32Y9F0FWlycR//soXuPK/uoHfsiE+e9xKDePWlfNeCRr/XvxXiaqgcguxNti6kub1X2v/+XHqBqSQ9WjOnMkh2VGbh5Ha+xguryZQDaKWG0I4zt279l3I4eVGzQkX7NyojeCzuiU2SeV7ZY/l0+qiuaH6fVYMLU8hyTK6OWrEeVMqVoUt6X3mV9i3WPR3EyvGU5+h8dS/PY4wRK8bRSjrJ+71L+qTqpwIZZFyWPVPHy9PQEzF3Z7u7uODvfyk7j4OBAkyZNGDZs2KNFaIf0ikyalvs/2ixN4ZrmS6TmRyS+XNV8uawGct25LDr/KoSWLEHlQHeeKO3FhAC3fEsPKtgXR53C273aMmXhYMuQw/eULxmysgqLxvcs0olTBMFW+bo5snBQC5buLMWLG75mprIQTymN0lIsS7UpTFx5lcgnR9GvSegjXWfLqev0lg8B5p6G2MAW+ZKgQKfIjGhdnqfrjmH2hvYYw77hZd2PlJJuANBKOUorjnLk36+Yv6cjSeW70ad5RVpVLCHW8LFxekXCkIfHPIOmEKX5ZD+H+BGp+XJZCyDKsSyKf2VCSgZQKcCdTsFejAt0L3ZzxwUznSIzrVcL3p4/gi+VDwCYrFvBwG+qUnF8PwI9i8/80IfxSBWvZcuWAVCmTBleffVVXF1FSxiAgyLzh9qYs4ZSGNBhQEcWOgya+X2q4obm6IXm7I3k4oOTqydB3i6U8nImyMuZVl7OhPi4FMhK54J9q1nak3Jt+rFu2wGeVnbiIaUzNWMWr30TyqLBLcSDkCBYgSxLDG1ZjqNlX2Loqqq8nvohDeUzOEgmZus/5+NfY5gZ9wpvdK760PfoiZNHqZS9+OwhrQINqlXMz18Bfw8nPniuLieal2fGpqdxP72GF5WfCZWvA1BLvshH8hfEXV7FdxfastClEw3qN+KZeqUoJ4aV2SQHRWaTWo8og/ddn0XSZFdURy80F29kZx8cXT0p6e1KkJcTQV7ONPdyprePy0Mv9isUXRX83WjaqTcr1h+gv24jTpKB940f8fqqMiwZ0V5Uyu8jX+Z4FSe5GceZkmkkMiEdvSKjVyQcFNn8XmfeLm5ZfYT8ZTSpDF60lcnXRlsWc/7W2IaoNh/yUofiNd/LHudmgP3GLTxYSqaRt9Ycov6J/+UY4vWNsS1htSczs0fdPFe+LsamsnTOm7ynXw7Ah4aedBr1IbVKe+Vj5Dmdjkrmsy2nUI6vYZDyJzXlS3ccc1CtwBpTKy6X7ESnBlXpUiPQLh/S7fF+zE3MaVlGrsTnfBbRZb/XKzKOujvXYBWE3FJVjVHLdzHm0ouW8uF3UyMONprLO09Wt25whaxQkmvUq1ePzZs34+3tTd26de978x48ePBhLmGT7LGAFoqe68kZjJ23mmWGibhImQBMNgygYc+JPFk7yMrRFR57vR/tNW4hdzRNY87GM6Rvm8tb+tWW/WtMLdhb8z0+eDZvla8lOy5SbsMA2iqHAeijfMTKt4YWSg/35RupfL3rEqcObOFZ0x90lffi8J/U15majs1qPdarjUgKbke72uXpVD2QADtJSW+P96M9xiwUPQlpWQyf9yNfZryCR3aCuA8NPSnd7R2ebxRi5egKT6Ek1+jWrZslmUb37t0f9jSCIDwEf3cnXnq+G28tPcsc/WcATNGtYOSPgQR5jSr2a2oIgjVJksSEjpX53vtNxv3sx8fKAvSSiR7KDqSj7/CmNJ2Zz9bJdW/DzhPh9JVPAHBN86FU5YaFNqw41NeVt5+sTlqnyvx0qBv9dx6i+o0NPKv8Q1U5HABHyUhXZR9dlX1kXvuC7Vdr8b/fGhEb1I4WNcvTtrI/FfzdRO+KIBQxXi4OvN2vK68vPM9C3UcAvKb/nnG/BBLsPZ4WFXO3uHtxIoYa5pFoZRJsyZIdF8lYP5nRul8ASNGcGKpM53+j+xSL7GP2ej/aa9xC3m06Ec2a1Qv5RJlnWSR1mbETSa2nMz4XS0GkZBqZ8N4HLNJ9CMBqY1u8e31Ol5olCzTue9E0jRPXklhz4ConD+2gQ9YWuik78ZPuXDYmS1PYq1Zlq1qHE65NKF+1Nm2rBNC0vC8uDo80xTxf2eP9aI8xC0XX9/sjuLBuOhP13wKQqekZIk3hnZEDqRzobuXoCl6hr+N1U1ZWFtevX0dVc65iHhJSdLobRWEn2BJN05jy01EaHXyVJ5S9AERp3ox1fJ95Lz5NkJfzA85g3+z1frTXuIWHs/lkND+uWsQnyhxL5es9wwtUfWYSz9Yvfd/vrj8WRey3L/KCbjMAI42v8OHbk2xiYWODSWXb6Rh+PhRO8ulttFP30Fn5F38p4a7HX1b9+Vutw06pLmpIcxpWKk2z8r5UD/JEsWJiIHu8H+0xZqFom/XnSUJ2TqS3bisAcZobo/Tv8b+RvSjjV7Qbggu94nXmzBmGDBnCrl27cuzXNA1JkjCZTPf4pv0RhZ1ga4wmlZHLdjI6/CXqyucAiFBL8IrbBywY9WS+pJy2VfZ6P9pr3MLD++t4FBtXz+ZD/ReAOSX8SNOrjBs1lhqlPO/5vYk/Hmbs0acpJd0gU9MxMuhHlo1oW1hh51qGwcS2MzFsOBpJzMl/aGPaTUd5P8FyzF2Pz9T0HFArskutzmF9LVzLNqJxhQCalfejUkDhDku0x/vRHmMWijZV1Ri/+l96n36J5spxAKI1L8Y6vs+cF5+hVBFuCC70ilfz5s3R6XRMnDiRkiVL3lFg1q5d+1EvYTNEYSfYouQMA8MXbmBq3OuWlNPn1ZK86TWLz0d0LrJLE9jr/WivcQuP5qtdl4j/Yyov6dYCkKC5MsxlLkvHP33XHixN0+g7YymrjRMA2G6qyenHVjCsVblCjTuvMo0mdp27wcYTUVw8eYjqaXtpK4fRUD51R2KOm1I1R/apVdilVueEUx28y9ajXhk/GpTxplpJjwJdv9Ie70d7jFko+jIMJoYv2sIr0a9TW74AmBfeftllJvNHPlVk1/gq9IqXq6srBw4coEqVKo96KpsnCjvBVt1IyWTUwj+YlfQGZeRoAE6rpZnsMZ1PhnexmwxjeWGv96O9xi08uqm/HKPRvy/RRfkXMKdk/6rKQub1aXjHsceuJvL7Z6/zRva8iXcN/Xlh/Ewq+NvPulmapnEmOoW/T19nz4mLOF/ZSSvpEM3lY4TcozcMIElz5pBakf1qJY4pVaFUA2qWLUn9Mj7UC/HK16GW9ng/2mPMQvGQlGFgxBd/MeXGG1SRIwC4pAbwmst7zB7+JME+LlaOMP8VSlbD21WrVo3Y2Nj8OJUgCA/J182RT4Z1YcznmXySPokgKY7K8hU+SHqDsZ9nMXv4k5T2LnoFniDYkzcfr8bA8Neofn00IXIM9eRzbDn+BRuOB9OpemCOY7ecuk5b5ZBl+7R7U8qXsK+5EpIkUTnQncqB7oxsXZ7E9NbsPBfLovOxXDh7glIJ+2kmH6e5fDzH3DAPKZ3WyhFaK0cAMEbKnLgayv7tlfleq0SCX33KlClP7WAvapf2ooK/m1XniQmCYObhpGfB0McY+bmB/yW9Tlk5mjJyNPPSJzL+8yw+GP405Yvxouv50uO1ZcsW3n77bd5//31q1qyJXp+zJaootcaIVibB1kXEpfHKwnXMzpxMacncIHJV8+Ulh6nMHPa0XbWWP4i93o/2GreQP64mpPPa3GWs0N5CJ6lkajr66T9m8St98XS+9f9n33m/syKuL4qkcV4tydcN1vDuU0VrYdKoxAx2X4hl19lYrp47TPnUgzSVT9BAPnPPJB03XdV8OayW57BanlNKRShZh8qhQdQu7UXtYE9KeTnnaq6YPd6P9hizULxEJ2UwduFvzEx5m/LyNQBiNE/G6CYzechzVA+699xWe1PoQw1l2Tz2+r8FnEiuIQjWcTUhnZcX/cbMlHduK/A8GC9NYlz/XjQp52vlCPOHvd6P9hq3kH/WHbpC1JqJjNL9CsABtSJran/J+z3qAHAtMZ2PZ03ho+xkHAuNT1Jz4FyaVyi66+JomkZEXDp7Ltxg/6UbRF46iX98GA3kM9SXz1BZvnLf76uaxDktyFwZ08pzybEyzsG1qVvWnxfbVLjn9+zxfrTHmIXiJyY5kzGLN/BuwluWdf8SNFfGaa8yqO8LtK3sb+UI80ehDzX8+++/8+M0giDkk1Jeznw66knGLnLg3cS3qCpHUEJKYok2hQlLY7jWYwhP171/GmtBEApO9zqlGH5wBBcu/0s5OYr68lnWHVzOvnqTaVTWh00nouko77ccv0PXmCFlfawYccGTJIkQXxdCfF3o2TAYqMONlGc4cDmetZfjOXUxHMdr+6nNKerLZ6kpXcBVyrR8X5Y0KklXqSRf5Tm2gwqZl/QMTF5534qXIAgFo4S7I1+M7MzoJQ68FvMmdeTzeEmpLGYGE7+O5eoTI3ihSai1wyxUYgHlPBKtTII9iU/NYsySzYyLfZfG8inA3Co8w9gXt9bjGN+hErIdz4uw1/vRXuMW8tfVhHTemv0Zy+VpgDmhxDD3z1jxUjdGLvmHzyKfw1nKIkbz5P0q65jzfH0rR2x9GQYTR68mcuByPEcjbpAYfoKSqcepLZ2nlnyBqlK4Za00MC+tsajeT7zXvcY9z2mP96M9xiwUXymZRkYv3cagyKm0UQ5b9s82PEtakwlM7Fq1QDOXFrRC7/E6cuTIXfdLkoSTkxMhISE4OhbdtYQEwVZ5uzqweORjvPKNJ5HnpvO0shNZ0nhHv5Jvtl/lxcjX+F+vRjnmlQiCUDhKeTnTquMzfL9+Gz112/CQ0umf+DlvrSuPX/jvOOuzANhoqs9jNYKsHK1tcNIrNCzjQ8MyPkB5oBExyZkcuZLApogEPomIIfNKGOWzTlNLvsANzYPawV5WjloQijc3Rx2LhrVh4g8eXD0+k77ZC8JP0P/Iz/siGX7tDT7s0xRft6JfV8i3OV73m8Cq1+vp1asXX3zxBU5O9p3SWrQyCfbIpGrM+O0Envs+Ynz2GkIAYWo5ZrhOYvqALlQOdLdihA/HXu9He41byH8mVaPf/D+ZHzccXykZgMFZrzJet9ayDs4LvM+Xb43ESa9YM1S7oWka4XFpHL6SyOGIBAY0LUOI770zutrj/WiPMQuCpmnM+es06dvn8ZZ+tWX/STWYyU4Teaf/E9Qq7WW9AB9SXu7HfOnXW7duHRUrVmTRokWEhYURFhbGokWLqFy5MqtXr2bJkiWWzIeCIBQ+RZaY/FR1vLpO4WXDi6Rr5gWV68gXWJg2gZkLvuDnsKtWjlIQih9Flnjz2ebMML5g2bfU4SNLpeukGkKFuq1FpSsPJEki1NeVp2oH8c4T1e5b6RIEofBIksSETlWo9PSbvGicQIpm7oypKkewJPM1Pv3iU77ZF05RngWVLxWvGTNmMG/ePIYMGULNmjWpWbMmQ4YMYc6cOXz88cf07duX+fPns27duvy4nCAID2lAszI8O+gVBikzCFdLAOArJbNEns7ZH6bw2vcHSck0WjlKQSheapTyxLtJP7abat7x2RytN0NalrNCVIIgCAXjuQbBDBk2lkH6/3FONQ+j9pDSWKR8SPwvbzFu9b8kphmsHGXByJeK19GjRwkNvTMrSWhoKEePHgWgTp06XLt2LT8uJwjCI2hewY+Px/Vjot98tppqA6BIGq/qf+DZY6MYPHcthyMSrBukIBQzr3aqwhf+77BPrWzZt8D4FHXb9ybYR/TYCIJQtNQP9WHB+N5MC/yUP0yNLPtf1P3CkNMjGDr3O/ZdjLNihAUjXypeVapU4YMPPiArK8uyz2Aw8MEHH1ClShUArl69SkBAQH5cThCER1TKy5mlozqyofY85hh6YNLMczQby6dYnDaexQvn8NnWc5jUotvdLwi2xNlB4Yvh7fmj/lL66WfTz2MJnk9MZ2Rr0dslCELR5O/uxJIRbdnfcC4zDH0waOYh1XXkCyzLfIXvv/wfH284hcGkWjnS/JMvyTV27drFU089hSzL1KpVCzD3gplMJn777TeaNGnC119/TVRUFK+99tojB21NYkKrUNSsO3SFtT+tYSafUFqKtez/wdiKnwLG8G7PZlQMsM3EG/Z6P9pr3IJQFNnj/WiPMQvC/aw/FsWKNeuYYZpDWTnasv83UxNW+ozl7Z4tqVHK04oR3lte7sd8W8crOTmZVatWcebMGQAqV65Mnz59cHe3zQe2hyUKO6EoCr+RxqTV//D89dk8oeyx7I/SvJlsGkrt9r0Z0aqcza2zYa/3o73GLQhFkT3ej/YYsyA8yLXEdCZ9s4cuV+bSS7fVsj9W82CaaSChLfsypn1FHHW2lWzIKhWv4kIUdkJRZTCpfLLpDNe2L2WKbgXuUrrls3Wm5nzvN5rJPVtStaTt/Lu31/vRXuMWhKLIHu9He4xZEHLDpGos2n6Bk5uWM1VZireUYvlsvakhy7zGMKlnG+rY0Pp8Vqt4nThxgvDw8BxzvQCeeuqp/LqE1YnCTijq/r0Ux4ffb+bF5Pk5VpiP0TyYbhqAf5PnGf9YZdwc82X99Udir/ejvcYtCEWRPd6P9hizIOTF0SuJzPhuKwMS5tNF+deyP0FzZYaxL84N+vFKp6p4uuitGKVZoVe8Lly4wNNPP83Ro0eRJMmSf//mosomk+lRL2EzRGEnFAfpWSbmbDxN3K6veEe3Ak8pzfLZTlN15jkNp/+THXm8Zsn7Lp5e0Oz1frTXuAWhKLLH+9EeYxaEvMoyqny65SwXt61iim4ZflKS5bP9aiU+0g3n2ce70KNeKbt5FsmXCRvjx4+nbNmyXL9+HRcXF44fP8727dtp0KABW7duzY9LCIJQiJwdFN58vBp9R0xkuPsC/jLVt3zWXDnOyqwJhH//BsO+3Mb5mJT7nEkQBEEQBCHvHHQyEzpWZuToVxjr/Tk/m5pZPmsgn2GV6TWS1r3CgM83cSoq6T5nsh350uPl5+fHli1bqFWrFp6enuzbt4/KlSuzZcsWXnnlFQ4dOpQfsdoE0cokFDeZRhMLtpzjzPbveVtZniPz4RXNj/+ZXqBEo56M61ARLxeHQo3NXu9He41bEIoie7wf7TFmQXgUBpPK4n8usH/zGt6WllJOjrJ8dl3z4gNTH1zq9ealjlXwc3Ms1NgKvcfLZDJZshf6+fkRGRkJmBdQPn36dH5cQhAEK3HUKUzoWJk3XprAu8HLmG/sTqZmnt9VWoplvm4unfcPYvSsxXz5zwWyjEVnvQ1BEARBEKxPr8i82KYC0yaM5aPyy/jQ0JN0zdzY6y8lMFv3Gc+FDWTCh5+z4O9zZBhsc5pTvlS8atSoweHD5kn4jRs3ZtasWezcuZNp06ZRrpxY/FEQioKyfq4sHtKSCr0+oJ/DXLabalo+aySfZhVv4vfXaF74+EfWH7uGSJgqCIIgCEJ+Ku3twmcDmlG/33QGOM9n421TIWrLF1ghTaHclpH0//Bbfg67iqra1rNIvgw13LBhA6mpqTzzzDOcO3eOJ554gjNnzuDr68t3331Hu3bt8iNWmyC69wUBUjONzN98lku7fuQ1eRXl5WuWzzI1PUtMXdgV+AIvdq5Pswp+BRaHvd6P9hq3IBRF9ng/2mPMgpDfMgwmFm47z5Ft63hN+pqqcoTlsyxN4WtTRzb5vcDwzo1oU7lEgSXgsIl1vOLi4vD29rZqlpGCIAo7QbglIi6ND/88hteJlbykW4PPbettJGoufGF8gpMhfRjTuQ71Q73z/fr2ej/aa9yCUBTZ4/1ojzELQkG5lpjOx+tPojuymgm6H/CXEiyfJWvOLDV1YX/JPozqXJdm5fO/MdgmKl5FlSjsBOFOB8Pjmf3LPlpGfcVAZQOOktHyWazmwefGpwgv/zzjO9WkRinPfLuuvd6P9hq3IBRF9ng/2mPMglDQjl1N5KNfD1L3ylcMV37HWbq1rnCC5soi4xOcCu3DmM61qReSf43BhVbxGjx4cK6OW7p06cNewuaIwk4Q7k7TNP44GsVXf2zj2ZTV9FC2o0i3ipdrmg+fGrsTX7kno9pVo2bpR6+A2ev9aK9xC0JRZI/3oz3GLAiFQdM0Np+8zuLfd/Bk4ip6KVvRS7cSbcRoHnxu7EZE+d6MbF+N+qE+j3zNQqt4ybJMaGgodevWve9E+nXr1j3sJQpEmTJl8PDwQJZlvL29+fvvv3P9XVHYCcL9GUwqP+y/wk+bttIvYxVPKntyfH5V82WR8Qmuln2O4e2r06jswxd69no/2mvcglAU2eP9aI8xC0JhMppUfgqL5Lu//qFX2mqelv/J0Rh8XfNisbErZ4KfY1j7WjSv4PvQ06MKreI1evRovvnmG0JDQxk0aBAvvPACPj6PXnMsaGXKlOHYsWO4ubnl+buisBOE3MkwmFi1N5y/tmxiqOEbHlMO5Pg8RvNgibErJ0s9x+AOtWlV0S/PhZ693o/2GrcgFEX2eD/aY8yCYA1ZRpXv90fwy+a/6Z/xDU/8pzE4XnNjmbEzBwKfY2D7unSo6l+gzyKPPMcrMzOTtWvXsnTpUnbt2sXjjz/OkCFD6Nixo80m1hAVL0EoPKmZRpbvusTObRsYbPqBDkrOBdUTNReWmzqxz78nfdvWpVP1QBQ5d2WHvd6P9hq3IBRF9ng/2mPMgmBNGQYTK/dcZtPfm+lv+J7O8r/It/WApWhOrDQ9xlaf5+jdtgGP1yqJXsndqluFuoCyo6Mjzz//PBs3buTEiRNUr16dF198kTJlypCSkvLgE/zH9u3befLJJwkKCkKSJH766ac7jlmwYAFlypTBycmJxo0bs2/fvjxdQ5IkWrduTcOGDVm1alWeYxQEIfdcHXWMbluBLyYO50z7L+ktf8Rvpiaomrly5SmlMV63jkU3BhL93Xien/UtX+26RFqW8QFnFgRBEARBeDAnvcLQluVYOnEQkR2/4HmHuawxtcSomatCblIGI3W/sjxxCKlrxtL3g1Us3n6B5AxDvsahy8+TybKMJElomobJ9HArRqemplK7dm0GDx7MM888c8fn3333HRMmTGDhwoU0btyYuXPn0qlTJ06fPo2/vz8AderUwWi886Htr7/+IigoiB07dlCqVCmuXbtGhw4dqFmzJrVq1XqoeAVByB13Jz0vtqnAoGZl+fbfDvTeuoNn03/gaWUHesmEq5TJIN0G+qf/xZ9/NmboX92o17Q9/ZuF4u/uZO3wBUEQBEGwcy4OOoa2LEe/pqGsOdCOPn/v5KmUH3hO2YajZMRJMtBXt5nns7awaWM9Rm/uRpVGjzGweVmCvJwf+fr5OtRwx44dPPHEEwwaNIjOnTsjy4/WoSZJEuvWraN79+6WfY0bN6Zhw4Z8+umnAKiqSnBwMGPHjmXixIl5vsZrr71G9erVGThw4F0/z8zMJDMz07KdmJhISEgIERERontfEB5BllHl18NX+XX7Ph5L+ZmnlR243Jb6FWClsT3RTd7klccq3/UcSUlJBAcHk5CQgKdn/qWpz2+iHBEE22UP5YgoQwShYBhNKn8eu8babQdolfgzzyrbcJcychzzk6kZh2tPYcqT1e96jjyVIdojGDVqlObt7a3VqlVLmzt3rhYTE/Mop7sDoK1bt86ynZmZqSmKkmOfpmla//79taeeeipX50xJSdGSkpI0TdO05ORkrV69etq+ffvuefyUKVM0QLzES7xs+BUREZHn8qUwiXJEvMTL9l+2XI6IMkS8xMv2X7kpQx45nXxISAh169a9byKNtWvXPtT5/9vjFRkZSalSpdi1axdNmza1HPf666+zbds29u7d+8BzXrhwgaeffhoAk8nEsGHDGD9+/D2P/28rk6qqxMXF4ev74LSTN2vA9tYiJeIuXCLuh6dpGsnJyQQFBT1yD3tBethyxBb+jB+GvcYN9hu7iPvh2UM5Ip5FRNwFTcT98PJShjzSHK/+/fvbbObCeylXrhyHDx/O9fGOjo44Ojrm2Ofl5ZWna3p4eNjVP+KbRNyFS8T9cGx1aNDtHrUcsfaf8cOy17jBfmMXcT8cWy9HxLOIiLuwiLgfTm7LkEeqeC1fvvxRvp5nfn5+KIpCdHR0jv3R0dEEBgYWaiyCIAiCIAiCIAi5ZZt96vfg4OBA/fr12bx5s2Wfqqps3rw5x9BDQRAEQRAEQRAEW5Kv6eTzQ0pKCufOnbNsX7x4kbCwMHx8fAgJCWHChAkMGDCABg0a0KhRI+bOnUtqaiqDBg2yYtR35+joyJQpU+4YHmDrRNyFS8Qt3Iu9/hnba9xgv7GLuIV7sdc/YxF34RJxF45HTief37Zu3Urbtm3v2D9gwADL0MZPP/2UDz/8kKioKOrUqcMnn3xC48aNCzlSQRAEQRAEQRCE3LG5ipcgCIIgCIIgCEJRY1dzvARBEARBEARBEOyRqHgJgiAIgiAIgiAUMFHxEgRBEARBEARBKGCi4iUIgiAIgiAIglDARMVLEARBEARBEAShgImKlyAIgiAIgiAIQgETFS9BEARBEARBEIQCJipegiAIgiAIgiAIBUxUvARBEARBEARBEAqYqHgJgiAIgiAIgiAUMLuqeG3fvp0nn3ySoKAgJEnip59+yvF5SkoKY8aMoXTp0jg7O1OtWjUWLlyY45iMjAxGjx6Nr68vbm5u9OjRg+jo6EL8LQRBEARBEARBKG7squKVmppK7dq1WbBgwV0/nzBhAuvXr2flypWcPHmSl156iTFjxvDLL79Yjnn55Zf59ddf+eGHH9i2bRuRkZE888wzhfUrCIIgCIIgCIJQDEmapmnWDuJhSJLEunXr6N69u2VfjRo16NWrF++8845lX/369enSpQvTp08nMTGREiVKsHr1ap599lkATp06RdWqVdm9ezdNmjQp7F9DEARBEARBEIRiQGftAPJTs2bN+OWXXxg8eDBBQUFs3bqVM2fOMGfOHAAOHDiAwWCgQ4cOlu9UqVKFkJCQe1a8MjMzyczMtGyrqkpcXBy+vr5IklTwv5QgCPekaRrJyckEBQUhy7bbgS/KEUGwXfZQjogyRBBsV17KkCJV8Zo/fz7Dhw+ndOnS6HQ6ZFlm8eLFtGrVCoCoqCgcHBzw8vLK8b2AgACioqLues6ZM2cyderUgg5dEIRHEBERQenSpa0dxj2JckQQbJ8tlyOiDBEE25ebMqTIVbz27NnDL7/8QmhoKNu3b2f06NEEBQXl6OXKi0mTJjFhwgTLdmJiIiEhIURERODh4ZFfoQu5kGEw8fb77/ORwxcAxGmu6DHhLmU83Pk0Hae0EI6pZTmqluWGRzVCylejecUSNC7ni5tjkbo9iqSkpCSCg4Nxd3e3dij3JcoR+7Vo+3ka/zOI2vIFAE6qwXhIaXiSgpuU+YBvP9h5tSRhankOa+W54tWQGlWr0b5KCWqV9hI9GYXEHsoRUYYIgu3KSxlSZJ4s09PTefPNN1m3bh2PP/44ALVq1SIsLIyPPvqIDh06EBgYSFZWFgkJCTl6vaKjowkMDLzreR0dHXF0dLxjv4eHhyjsCpk+y4SjowMeDuaHkTmGHkRUGoAxLRE1KRIlJRI/9QZB3CBQiqOkFEegFEeQdAMPKe2O83lgwp+LtOKieYcG4WdKsO1UbaZSG6lCe7o3KE+7Kv446Gxz+IlgZusPqKIcsV/Oru64O8p4yOZ/Y2+4fkrrSiVISDeQnJqGMTUO0uOR0+PRGxLxIhk/kgiQ4giU4gmU4giQ4vEnHkW6c0p1XaKoSxSwE4wrOHa4DH8ebMRnPo/zePO6PFu/NE56pZB/6+LJlssRUYYIgu3LTRlSZCpeBoMBg8Fwx9hKRVFQVRUwJ9rQ6/Vs3ryZHj16AHD69GnCw8Np2rRpoccsPLopT1Yn2McFMI+xjUvN4lpiBlGJGUQkprMv+31sbDTEnsU/K5xy0jXKS5FUkiIoK+dcSiBEjqGfvIl+bCL+4hf8fK4Z/Rw70bZVW/o1CcVV9IIJQrFWMcCdqd1q3PUzk6qRlG4gJiWTqMQMopIyOJuYwbWkDK4npJIRexGnhPOUIZLyUiRV5ctUly6jl0yWc9SQL1FDvoQhaQ1//N6YAVv60qdrW56qHWTTFQNBEAThwezqKTIlJYVz585Zti9evEhYWBg+Pj6EhITQunVrXnvtNZydnQkNDWXbtm2sWLGC2bNnA+Dp6cmQIUOYMGECPj4+eHh4MHbsWJo2bSoyGtoBDQ0px3bOhxBJkvB1c8TXzZEapTzvcoZOxKVmcSEmhfMxKXx1LZkL4RHoosOopp6jiXyCRvIpHLIfgrylFAbq/mKg6S82bGrA4G296f1kF7rXKSUegAShmDAn/jX3VKna/e97RZbwdnXA29WBSgF3G3LSBINJJTwujXPXU/g7MolPLkVhvHKIOsYjdFAOUEs298DrJRPdlF10ydzL0h+7MvrIaN5/tj5eLg75/BsKgiAIhcWuKl779++nbdu2lu2b450HDBjA8uXL+fbbb5k0aRJ9+/YlLi6O0NBQZsyYwciRIy3fmTNnDrIs06NHDzIzM+nUqROfffZZof8ugnX4uDrg4+pDgzI+2XuqYzB15HRUMjvPxbLkVDgOEf/QRdpNZ/lfnCQDAJ2U/bQ3HeTLNdsYeWgks3o3xtNFb71fRBAEq3jUJhe9IlO+hBvlS7jRqXogUAlVbcnhKwmsPxHN9MOHaZX8O72Vv/GTknCQTIzU/cqRc8cY/ulbzBn+BKW8nPPjVxEEQRAKmV1VvNq0acP9lh0LDAxk2bJl9z2Hk5MTCxYsuOcizILtMv/V3/r7/2+P18PSKzI1SnlSo5QnI1qXJzmjBX8cvcbwfacoH/kLI3S/ESjFo5NURup+5eilowz/9G3+N7grZfxc8yUGQRBsk6bdqmwV1KKXsixRN8SbuiHeqB0r88+5dkzYfJT6V79mpPILjpKRWvJFZqdO5OXPsvhkdA8CPZ0KKBpBEAShoIiMAYJdK4gRf+5Oeno1DGHF6I50Hzmdd0JX8rHhWTI1cztFTfkSn6S+yutfrOVK/J1JOwRBKLoKepSxLEu0rlSCr0a2pXLvmQxU3ueSGgBAaSmWWRlTmfDV32QYTA84kyAIgmBrRMVLsBsaIBVYm/Pd1Q72YvHgFtToM4P+uv8RrpYAIEBKYHbWu0xYsoHUTGOhxiQIQuGxRrkD5jmrnWsEMmf8AN7wnMVp1bw2TBk5msEx/2PuxjOFHpMgCILwaETFS7BbGoWX/rdT9UDmjO3La95zOaUGA+bW5wlJ/+Pdnw4XSgyCIFiXhoSUT0OccyvQ04nPR3blHdfJxGrmtOEdlENE7/qa45GJhRqLIAiC8GhExUuwG5qWX7O6Hk6QlzMLh3dkssd0IjVzco4m8kk8jyxh17lYK0YmCEJBMc/xKvwer9v5uDrw3oCuTDYNs+ybqKzik/VHrRiVIAiCkFei4iXYLa3Q257B29WBWQMf4w1esqSWHq9by7yfd2BSrftwJghCwdKQCnyO171UDnSnUpvebDTVB8zDnUue/46wiATrBCQIgiDkmah4CXbDWnMt/quMnyudunTnW5N5aQN3KZ1O8d/w1/EoK0cmCEJ+++/6gdY0vFU5luifv7Wt+43lO87d5xuCIAiCLREVL8FuWbP1+flGIfzsM5h0zbyYaU9lK6u2HbFOMIIgFArzvFLrXd/FQUeb1u3YbKoLQJAUR8qJv0hMM1gvKEEQBCHXRMVLsBu2MNfiJkWW6NOuHj+aWgHgJmVQ5drPHLsqJrsLQlFyn6UjraJng2B+0NpZtnuwhZ8PX7ViRIIgCEJuiYqXYNcKf5bXLV1rluR3526W7aeVHfwcJh6ABKGoudngY42shv/l4+qAvkpnrmteALSTD7Hl8EWrxiQIgiDkji4vB6uqyrZt2/jnn3+4fPkyaWlplChRgrp169KhQweCg4MLKk5BAA2bmWsBoFdk6jdoRNjO8tSRz1Ndvsx7YftQu1RFlm0pUkEQHpaNdXgB8GTdENafakh/3UYcJSPOEdtISGuOl4uDtUMTBEEQ7iNXPV7p6elMnz6d4OBgunbtyp9//klCQgKKonDu3DmmTJlC2bJl6dq1K3v27CnomAUBwCamvHerU4qfTc0s2/XSdnHiWpIVIxIEIb9Jt7+zfrFDi4p+bJMaWLbbywfYejrGihEJgiAIuZGrilelSpU4cuQIixcvJikpid27d7NmzRpWrlzJH3/8QXh4OOfPn6dly5b07t2bxYsXF3TcQjGk3aXt2ZoT3QEqBbhzyqO5Zbu1cphtZ8QDkCAUGbY2yQtzkg1duZakaE4AtJUP8fdJkVVVEATB1uWq4vXXX3/x/fff07VrV/R6/V2PCQ0NZdKkSZw9e5Z27drd9RhBeFSSdOshyBZ6vAAqVanJBTUQgPrSGfadvGTdgARByFe35njZjpZVS7NLrQ6Ar5RMzIUwNBusJAqCIAi35KriVbVq1VyfUK/XU758+YcOSBDu5W7PFLZQ9WpduQTb1VoA6CQVfeQ+Mo0mK0clCEJ++G+xYwtlDkDT8r7sUatZtiumH+bSjTQrRiQIgiA8SJ6SawAcOXL3tYokScLJyYmQkBAcHR0fOTBBuJvb08nbSttu/VAf1mqVGchfANTkDMcjk6gX4m3lyARByB+3shrainJ+rpx2rg3GrwFoIp9g74UblPVztXJkgiAIwr3kueJVp04dpPtMrNHr9fTq1YsvvvgCJyenRwpOEB7IBp6DPJ31xPvUgWTzdn3pDAcvx4uKlyAUQff7/68wSZKET9m6JJxxxUtKpbF8kvfOx9C7UYi1QxMEQRDuIc/reK1bt46KFSuyaNEiwsLCCAsLY9GiRVSuXJnVq1ezZMkStmzZwttvv10Q8T5QcnIyL730EqGhoTg7O9OsWTP+/fdfy+eapjF58mRKliyJs7MzHTp04OzZs1aJVcib//Zw2VLrc0iZilzTfACoI5/n0OVYK0ckCEJ+0G5bxsKWyhyAxuVLsE+tAoCPlELMeTHPSxAEwZblueI1Y8YM5s2bx5AhQ6hZsyY1a9ZkyJAhzJkzh48//pi+ffsyf/581q1bVxDxPtDQoUPZuHEjX3/9NUePHqVjx4506NCBq1fNC9vOmjWLTz75hIULF7J3715cXV3p1KkTGRkZVolXeDTWXsz0prqhPhxUKwDgJmWQeOnuQ3IFQbBvtlHimDUp58te9dYc7LLpR7gSn27FiARBEIT7yXPF6+jRo4SGht6xPzQ0lKNHjwLm4YjXrl179OjyKD09nTVr1jBr1ixatWpFhQoVePfdd6lQoQKff/45mqYxd+5c3n77bbp160atWrVYsWIFkZGR/PTTT4Uer5A3mqb9Z46X7TwC1Q324rB6K6lMQNpZbqRkWjEiQRDyg4Zmk1kNAcqXcOW0Yw3LdmP5FHsvxlkxIkEQBOF+8lzxqlKlCh988AFZWVmWfQaDgQ8++IAqVcxDHq5evUpAQED+RZlLRqMRk8l0x9wyZ2dnduzYwcWLF4mKiqJDhw6Wzzw9PWncuDG7d+8u7HCFfGAj0y0oV8KNc3JZy3Y1+TInryVbMSJBEAqCrZQ5YJ7n5VGmvmU9r4byKfZdEMOcBUEQbFWek2ssWLCAp556itKlS1OrljmF9tGjRzGZTPz2228AXLhwgRdffDF/I80Fd3d3mjZtynvvvUfVqlUJCAjgm2++Yffu3VSoUIGoKPMCk/+tFAYEBFg++6/MzEwyM2/1XCQlJRXcLyDcl0bOYT621PqsyBJG/xpww7xdTbrM0WuJtKjoZ93ABJsgyhH7ZZ7jZXtZDW9qUK4EB85WorVyhAApgasXTwJ1rB2WkM9EGSIIRUOee7yaNWvGxYsXmTZtGrVq1aJWrVpMmzaNixcv0qRJEwD69evHa6+9lu/B5sbXX3+NpmmUKlUKR0dHPvnkE55//nlkOc+/KgAzZ87E09PT8goODs7niIVHYUuPQaVKhRClmTMZVpMvceJqopUjEmyFKEeKDlsqcwAalfVhb3aCDYCgxINcTxJzlosaUYYIQtHwULURd3d3Ro4cyezZs5k9ezYjRozA3d09v2N7KOXLl2fbtm2kpKQQERHBvn37MBgMlCtXjsDAQACio6NzfCc6Otry2X9NmjSJxMREyysiIqLAfwfh7m5veQbba32uVtKdE6p5/qOnlEZs5HkrRyTYClGO2K/be9ptrcwBqFrSg2O66pbtRtIp9l0S87yKGlGGCELR8FAVr6+//poWLVoQFBTE5cuXAZgzZw4///xzvgb3KFxdXSlZsiTx8fFs2LCBbt26UbZsWQIDA9m8ebPluKSkJPbu3UvTpk3veh5HR0c8PDxyvATbYStr6gBUC/LghHYr8YxL3EkyDCYrRiTYClGOFB22VOaAeZizY2gDMjU9AI3kU/wrEmwUOaIMEYSiIc8Vr88//5wJEybQpUsX4uPjMZnMD5be3t7MnTs3v+PLsw0bNrB+/XouXrzIxo0badu2LVWqVGHQoEFIksRLL73E9OnT+eWXXzh69Cj9+/cnKCiI7t27Wzt04QFuzy5miyoHenDytopXFS5zNjrFihEJgvCocs7xsk11ygYSppmzqobK1zl3XqxNKQiCYIvyXPGaP38+ixcv5q233kKnu5Wbo0GDBpZ08taUmJjI6NGjqVKlCv3796dFixZs2LABvd7cGvj6668zduxYhg8fTsOGDUlJSWH9+vV3ZEIU7IMttT27OepI8rw116KafJkT18Q8L0EoSmypzLmpcVkfy0LKAD439pOQlnWfbwiCIAjWkOeK18WLF6lbt+4d+x0dHUlNTc2XoB5Fz549OX/+PJmZmVy7do1PP/0UT09Py+eSJDFt2jSioqLIyMhg06ZNVKpUyYoRC7mm2eZDz+08gyqRqjkCUF26xPFIkXlKEOyZlqOfyzZLoJqlPTkk3VpIuZF0in1iuKEgCILNyXPFq2zZsoSFhd2xf/369VStWvXOLwhCAdGQbGpNHYCqpbwt87yC5RguRlyxckSCIDyqHEOcbazMAXDUKZhKNSJLUwBoq4Sx5WT0A74lCIIgFLY8r+M1YcIERo8eTUZGBpqmsW/fPr755htmzpzJl19+WRAxCgJwM7uYrc6yMKtZypNjalkaymcA0EUfxmjqiE55uOUMBEGwMtsucixaVCvD7qvVaa0cobQUS/iJPZjUWiiyDdYUBUEQiqk8V7yGDh2Ks7Mzb7/9NmlpafTp04egoCDmzZtH7969CyJGQbgrTZOQbKz5uWYpT35Sy1q2q6gXOHs9haolRQYqQbB35sYf2ypzbupYPYBFGxrQWjkCQOOs3Ry4/ByNyvpYOTJBEAThpodqhu/bty9nz54lJSWFqKgorly5wpAhQ/I7NkHI4b/reNkib1cHYtxvDbmtIV/gqFhIWRDslm2XOLeE+rpy0beVZbu7vJM1+y9bMSJBEAThvx5p/JOLiwv+/v75FYsg5IkGNjnfwr10NdKyE2zUki5yTFS8BMGu3Uonb3vzSm/XvG5NtptqAua08jFHNpKUYbByVIIgCMJNuRpqWLdu3VwvGnnw4MFHCkgQ7kWzk7bn6qV9OH42lIbSGXOCjfAIoIa1wxIE4SFomn2UOwDP1S/NtE3taaWYl3bpzXpW7+3ByNblrRyZIAiCALns8erevTvdunWjW7dudOrUifPnz+Po6EibNm1o06YNTk5OnD9/nk6dOhV0vIJgYautzzcTbNykv36YLKNqxYgEQXgUOXq8rBzL/fh7OKFV7kK05gVAR+UAW7duJFn0egmCINiEXPV4TZkyxfJ+6NChjBs3jvfee++OYyIiIvI3OkG4jT3M8QKoVdqTH9UKDGIDALW1Uxy9mkD9UDHJXRDsjR11eAEwvG0VPj3dnff0ywEYZ/yKj9Y3Z2r3mtYNTBAEQcj7HK8ffviB/v3737H/hRdeYM2aNfkSlCDkhq22Pnu5OBDn18Cy3UQ+yZ4LYjFTQbBXN8sZDWyyl/12tYO9uFGpFxFqCQCaKScw/buEzWJdL0EQBKvLc8XL2dmZnTt33rF/586dODk55UtQgnA39tTwXKFCZS6qAQDUkc5x4FyklSMSBOFh2FO5c9Nrj9fmXW2oZXuybgVff/M1By7HWzEqQRAEIc/reL300kuMGjWKgwcP0qhRIwD27t3L0qVLeeedd/I9QEG43e1DDc2tz7bZ/NyknA979lWjrByNo2REDd9HprE5jjrF2qEJgpBHOed42WaZc7uyfq4079SLZesPMki3AQfJxKd8yLgvTXTr8QLd6pSydoiCIAjFUp57vCZOnMhXX33FgQMHGDduHOPGjePgwYMsW7aMiRMnFkSMggDYV3axRmV92aNVs2w30w6yVww3FAS7Y0fFTg6DmpfheI3X2WyqC4CblMEi+QMu/fg2o77azbnrKVaOUBAEofjJc48XQM+ePenZs2d+xyIID3R7W7OtzvEC8HF1IK5kK4wxn6OTVB6T97P0eBStKpWwdmiCIOSRvazjdTtJknj/2XqMTn0P46V36aTsRyepjNet5fL5HXwy7xlSKz7Fs40q0LKSn+iNFwRBKAS56vGyp54Goeiyt3+GTapX4F+1CgBl5WjOnjgo7iVBsDP2sn7g3TjoZD4b2Jy/a33EHEMPjJr5v/xQ+Tof6xcy42IvLq8ez4hpcxn11R6W77zIofB4MgwmK0cuCIJQNOWqx6t69epMnjyZZ555BgcHh3sed/bsWWbPnk1oaKgYdigUiJzp5G276bljtQC+2VSfpsoJAOqm7eLo1W7UKu1l3cAEQcgTe8pq+F96ReaD5+ryW+Xp9FvXiBeNX9NSOQaAr5TMEN2fDOFPki44c/BcJbZr5fmM8mT4VMHNL5Qy/u6U9XOltJcz/h5OBHg44uaos9n5tYIgCLYsVxWv+fPn88Ybb/Diiy/y2GOP0aBBA4KCgnByciI+Pp4TJ06wY8cOjh8/zpgxYxg1alRBxy0IgG0/BFXwd+OER3NI/xqAp5Rd/BgWKSpegmBHikon9RO1gmhZcQiLt7dl8c7f6aFtpLO8D0fJCICHlE4b5TBtOGz+QhJkJOq5eC6QS1ogJzU/tmpeRGveJOl8MbkFIrn6oXPxwsPFCQ8nHZ7Oejyc9Xg663F11OHsoOCiV8w/HRSc9AouDjqc9QpOellU3gRBKHZyVfFq3749+/fvZ8eOHXz33XesWrWKy5cvk56ejp+fH3Xr1qV///707dsXb2/vgo5ZKMb+m9XQlkmSRIM69Ti4swL15HNUlcM5cWgXxi5V0Cl5zmsjCIKVSNLN0sa+Kwqeznpe7VSZlDbl+SXsWYb+ewKfa9tpKx+ihXwMPykpx/FOkoGqUgRVibjzZGnZLyBJcyYJVxI1V5I0VxJxJRUn4jQH0nAiHQcyNEfScCQdB9I1R9JxRFUc0RQHVNkBZD3o9KDoQXYAxQFJZ/4p68zvdTodekVGJ0sosowic+undJd9sowiSVQP8qBDtYCC/wMWBEF4gDwl12jRogUtWrQoqFgK1YIFC/jwww+Jioqidu3azJ8/35IeX7Aftptew+zpeqVYtr0l9eRzALTN3MI/556ibWV/K0cmCMLDse0yJzfcHHX0aRxCn8YhxKa0ZfuZGGaejSU64gxe8UepIV2gvHSNstI1QqRoHKT7z/nykNLxIJ3SUuzDBaRmv4z3P8ykSRjQYUTBhJz9Mr83oqBqkvnnzW1kbmju/Fb7c1HxEgTBJjxUVkN799133zFhwgQWLlxI48aNmTt3Lp06deL06dP4+4sHYlulaXdmNbR15Uu4cSmwE1mxK3CQTHRTdjLjQLioeAmCHbKHMiev/NwceaZeaZ6pVxqoQ3LG0xyPTOJibCr7Y1O5FJNEWswl5JQo3LJiCZDi8ZfiCZDi8SYFTykVD1LxkFLxJPWBlbRHoUgaCgbAcPcD7vLXc13z4g+56P29CYJgn4plxWv27NkMGzaMQYMGAbBw4UJ+//13li5dmi9JQfZcuMGHa3fiImchKQ6g6JBkByRFh6RzQFF06BQFnSJZhk3oFBm9IqGTs3/e9l6vyDk+N3/vtmOz9zkoMo56Occ4eme9gpODjINSNMfT28Ov9Fj9qvz9Z106KfsJkBJIPbmJ5IzauDvprR2aYMNe/mY/NyIvIMk6UHSgOICsQ1Z0SIoDkqxDpzOXDTo5uxyQJUu5otx8L5vf6xXzUCxzmSGhKDL67LLn5vdunkevk3HSyTg7ZJch2a+b20oxfpC1hzLnUbg76WlSzpcm5Xxv29sYgAyDietJmUQnZxCdlEFEmoHj6QYS0w0kphlITMsiIy0FNSMeKSsVLSsd2ZiGZEzHQc3AiUxcpEycycKJTJylLPSY0GO0vBwkY85tjOgwoc/e75jd36Wg3npJ/9nO/lyHShY6dMX03+uRKwlM/W4HblImyDLICsg6JEkBWUGSdUiKApKCJJtf5mGcErIsoUggy9KtfVLOnzrLcbd+6pSbn3PP4/SKhJNOMT+v6BQc9QqOOvOzi+WnXsbNQYdcTP/uhKKr2FW8srKyOHDgAJMmTbLsk2WZDh06sHv37juOz8zMJDMz07KdlJR0xzH/lZRuoGfCEnrptt7zGIOmYETBgPmnEZ35vaZguPn+P/vNx+tyfC8T3W3n0pGJnrTs8fMZlrH0DmRKjqiKE6reGfQuoHNGcvZG7+qJh4szXi56vJz1eLno8XVzpJSXM0Fezvi5OdhMhU1D+88cL9uI60GerB3EW7+3opOyH4Cu/MP6Y315rkGwlSMTCsvDlCNxsdGsSB5232OMmpxdTtzldVsZY/rPZwbNvO9mOXLzffptZUkGDpa5ORnoSceRdM2BDBwxyo6YdE5oijOak3t2WeKDh4sTni56vJwd8HHVE+TlTClvZ0p5OdttQ8PtS0DY+rzSguakVwjxdSHE1yXP3zWaVDKMKmlZRjKyVNIMRjIMKkaTSpZJxWDSMBhVDNnbqSYNgyl726hiVM2fZ5nM71VVw6hqmG6+NA2TKfunmvPVOsijAP40CtfDlCEpmUYej/+awbr1ub6OQTMP0bw5lFPl1vBNyz5Nvm2o583jcg79vHWclHMfMgZ0pKEnXtOTgQOZ6MnQsn/etp0suWB08EZz9kZz8UHv6kOQtxuhvi6U9XOlakkPgrycH+rPUxCspdhVvGJjYzGZTAQE5BzvHRAQwKlTp+44fubMmUydOjVP1zCqGjrp/oPV9ZIJPSbuKDIKui5hyn4BpIB6XSIJFxI0NxJwJVFzIxZPtmp+RGp+RMslMHqE4BVUkWqlvKhVyosGZbxx0ovFNnPLx9UBrWIHEi8uxFNKo7P8L2MPnhcVr2LkYcoRTPcYTnUbnWRu1b/r0KvCaJcwAanZr1hI0lxI0FxJwI04zYNIzZcDmh9XNT/iHEujK1mdiqUDqBfiRfMKfnZTGbu9wcc+mntsj06RcVNk3ByL3WNHvniYMkRVQUbN03f00u0PCfdQmDeBhiWRixojce2SD5fUQC5rAWzWynLJvS5lKtWmZ8MQapf2LPCG4oS0LE5eSyYyIZ3ryZnEJGeSkmkgw6CSnmXEYMhERkOTZCRJRpIUJFnGQSfh4qDDzVGHi4OCa/ZPH1cHSrg54ufuiJ+bI17OetHLV8SJEvABJk2axIQJEyzbSUlJBAff/4HZYFI5rJbHEQN6TOZhEtnDJXTZFS7dzeETNz+7y37z8IqCXchSljS8SMVLSr33QamQfMaZk6dDOKyWZ6lUF335lvRoWJb2VQMKbdiRueHZPlufn6hXlt/PNaGPbguuUibul/8iOqkJAR5O1g5NKAQPU46kqwq/mJqi3FYeWF7S7duqpdy4vYxRLGWP+X1Bzr25yUNKw0NKI4SYOz/UwHRV4sKVIP7dVYlXqYepTBueb16ZtpX9bfZhQ8vx3jZjFIq+hylDTJrGca0Mv5maWIZfyrcNyZRR0Ul37lPusS1LKrrsfqtbn2s5h3dKeavo5YUsaZTiBqWUGzTnuHlnBlwMC+CbA+2YF9KLt55uQAV/93y7pqZp7Dx3gz+PRnLp9CH8k09SRQ4nWIqhoZRACRJwl9JwwoAjWSjS3Z9MMjUdaTiRihOpmpP5veZIHB6c0jyJ0byIxYMbkjcGjzK4+JclpIQn5Uu4UTvYi0oB7sV6iHdR8lAVr/Pnz7Ns2TLOnz/PvHnz8Pf3588//yQkJITq1avnd4z5ys/PD0VRiI6OzrE/OjqawMDAO453dHTE0dExT9doUMYHpddEjCaNNJOK0aRhVM1DKYzZwyQM2fsNavbnJhVD9lCK2z83mkyoJiOaKQvVZARTFqhGNJPB/N5kAJMRSTWAakQyZiCb0nHUsnDOHkPvTGaO905k4Spl4EEaXlIKXqTgJaXgQRryPQoNdymdRtJpGsmnGcYfJF104ftzrenj0YPR3dvQqlKJPP0Z5RcbGQX5QB2qBjBCaUUftgDQXd7BL2GRDGtVzsqRCYXhYcqRMU80IT6tHhkmjVTVXFaYVC1HOXKzbDG/N5cxJjV7n0mzlCWm7LLFpBqRVCOq8WY5Yi5TNNUEqsFcnqgmMGVZyhK9KQMnDDhZ5uZk4SyZyxFnMnGT0rMbb8xliaeUihcpd30AUySNitJVKspX6cPfJEZ8zjer2rGkRF/efLY5NUp55tcfeb7K0eNlJ2WOULQ8TBlSNdCdxJ4vY1BVMlRQ1VtDMVXNXH6o2q2hmqqqYVLBpKrZx3Hr85vfUW8ed9t3tOzvZH9fU03mMkUzoRqNaJoJSTWhaaZbzy/GDCRDBpgykI2ZSKYM9GoWjhhwzH5W8cxuFPaSkvEmBV8piRDpOt5SSo7fs6wczZvyN1yL3MCkT0fT7/l+tK/66Fksd52P5ZOfd9I8bi0j5J2EyDHg8HDncpSMOJKCNykP7jFMA8NFhfAL/pzXgvhFrcgJXRX0wQ1oWzOEjtUCKeGet38Lgu3Ic8Vr27ZtdOnShebNm7N9+3ZmzJiBv78/hw8fZsmSJfz4448FEWe+cXBwoH79+mzevJnu3bsDoKoqmzdvZsyYMflyjVJe5jkN1qJpGlkmlYwslXSDyfzKMv/MyH6fmmUkIt3A0TQDCekGEtIMJKWlk5WagJp0DYeUSEoSQykplvJSJNXkyzlSBXtIaQzV/Umf1C18/NVz7Gw2mje6VC3QVmsN+8tqeJOTXsG/ehuuHPejtBRLS/koiw4cFxUv4Z6s1ZjxXyZVI+Nm2ZH9M8NgLlvSskykZBiJSs/iVJo5yUJCWhbxqVlkJsVAQjgu6dcoJcVQUbpKDfkSlaQIS++bp5TGSN1vPBu3nbc+H8YTzw3lydpBVv6Nc7p9AWV7KnMEwd/Diads7H66H6NJJdNofqUbTCSmZZcnaQaupWdxNDmL8Lg0YmOiUGNOU91wnNbyEZoqJwAoKcWxmPcZvToTn2EvUjfk4daV1TSNeZvOcH3rFyzRrcRVl3nPYxM08/p1GThYXqomI2f3JMpoyGg4YsCFDFyljOyf9z4nmId8lpeuUZ5rdFQOAJAW4cjWy7WZ9ktj1MpPMKRNZeo95O8oWE+eK14TJ05k+vTpTJgwAXf3W9257dq149NPP83X4ArKhAkTGDBgAA0aNKBRo0bMnTuX1NRUS5ZDeydJEo46BUedgicPN4fCpGpcT87ganw6Z6+nsDAykUvh4XhH76adfJAu8j6cJAMuUibv6Ffy7e4rTDW8x9TutfL5t7k/W1/H63bd6wXzy5FmvKj7BZ2kUi52C+E32j/URHVBKCyKLOHqqMP1IefmZBhMRCakcyY6mQ2RScy+FIUu/B86SXt4Qt6No2TET0riM2U2r3+fipN+Ao/Z2JpLUo739lPmCII90WVncHbN7sy5dwN2bVS1I6ejk9l0Ipq5e3YxJmMRLZVj6CUTH8vzGbKqLCte7fVQ89EXbDmL07ZpvK//zbLPoCnsVauwU63JaaksRu/yKJ6B+Hh44OWix8mSoVFGliQ0zTzUU9U0NA0yDSZSs0ykZRlJzTSRnpmFIT0ZU2osSup1HDNvUEJKJFCKo4wURTkpirLSNZylLEsMLlImXZV9dFX2EXV+JYtOP8Gyav1558la+ItpC3Yjz/+THj16lNWrV9+x39/fn9jYh1w8sZD16tWLmJgYJk+eTFRUFHXq1GH9+vV3JNwozhRZoqSnMyU9nWlQxid7b03iUh9j08loBv5zmC43ljNAtxGA3rqtzPl3LutCp/F03dIFEpOm5cxqaG+alPPlM4fmvKj+AkA7+RCbT0UzqHlZK0cmCAXHSa9QroQb5Uq40blGSaAyKZnN+ePoNfr9vY8hSZ/RSdmPImnM1C1m4Pch1Hx5CIGetvEgodlxmSMIRZUsS1Qt6UHVkh4MbVmOt9bWIOn4RB5X9uEupTMwbRkrdjdmeKvyeTrv/ktxXNyyhI9vq3StNrblJ89+tGtUh6cqlaCivxs6Rc7X3yfLqHIjNZPIhAwuxabyR2wqF2OSSLhykqDkozSUTtNeOYivlAxAoBTPZP3XnDq9lXHnx/NK/x40tDyrCbYszxUvLy8vrl27RtmyOR8WDx06RKlSpfItsII2ZsyYfBtaWJz4uDrQs0Ewz9Yrzaq9NRj3+xLmKPNRJI1xurX0/bUp7asOxKMQspVpSHY130KRJQIrNyHqhDeBUjzN5WOsPhEuKl5CsePmqKNng2C61Qlixq8ViT4wjf66jThIJt5RP2f2hmbM6lnX2mFa3Gzw0RBzvATB1jg7KMx6ri6DbrxOo+tDKCEl0ln+l+d37WNoi3K5ngKhaRpzft3H57qvLPveMgymTKexfNuibIFOpXDQyZbG7vqhtw8fbMj15Az2Xojj3WNXST29lV7aeh6TDyBLGlXkCJaY3mLCl1Gk9htBm8r+BRajkD/yXGXv3bs3b7zxBlFRUUiShKqq7Ny5k1dffZX+/fsXRIyCDZJliX5Ny9Cux0jmGXsA5onzQwzf8N2+iAK5pnmOl323PneoFsAWk/mB0kkyoLu8neSMB6cNF4SiyFGnMLV7TfZXfZ0jqrkBoqocQdrhdUQnZVg5OjPNvoscQSgWdIrM+Mfrs8zYGTBnQGyWsoED4fG5Pseu8zeoH/UDHlI6AGtNLQjqMJphrXJfeSsI/u5OPFk7iPl9GzLvzZcIf2wxfaWZHFdDAXCVMvlEmcvylSs4HplotTiF3Mlzxev999+nSpUqBAcHk5KSQrVq1WjVqhXNmjXj7bffLogYBRvWvW4pzlQcTKRm7uJuLx9k8+59ORYdLSj/TbZhD1pU9GMb9SzbLTnE7vM3rBiRIFiXJEm881Rt5mnPW/b1lLew9uBVK0aV060eLzHDSxBsVf1Qbw76dLFst5EPs/3MXZa2uIc1+8Ppq9sEmBen/9b1BUbYWAIsdyc9w1qV49NXBzM3dAG/mZoA5qyJs+U5TFu9hQxDwS8dIjy8PFe8HBwcWLx4MRcuXOC3335j5cqVnDp1iq+//hpFEYvqFkdD2lRltbE9YG5lqp60nbPXUx7wrbzTNPuraP2Xu5OezNItMWjme6WJfJK9F+OsHJUgWFcJd0d8a3YkQjVncmwhH2PX0TNWjspMdHgJgn2QJIna1apwIrsnqKZ0kUOnz+fqu5lGEzdObidASgDgb7UuXVo2yfe5XPnF182RhYNasLnadP421QbAR0phaOInfL41d7+zYB0P/S8qODiYrl270qNHD1JTU4mPz313rlC0NAj15oBLS8t2e/kQm09eL4QrSwW+Sn1BqFuhFEc187CqCnIkp8+fs3JEgmB9T9YtzXq1IWBuwHGP2kOSjQzDvVnKaHZa5ghCcdGyQgl2qdUAcznicP0IRtODF3U+ciWRRqYDlu0/1UZ0rVmywOLMD4os8b/n6rPI93ViNPMaiI8pBzn8z6/Eptw/Xb1gPXmueL300kssWbIEAJPJROvWralXrx7BwcFs3bo1v+MT7IAkSZSvVpfw7Nbq+vIZwi4VRMWraLQ9Ny7nw57s/xgAvK7/S2K6bTxgCoK1NAj1YR81LdtNpOPsv2T93mAxx0sQ7Ef1IA9LjxdAefUyl26kPvB7By7HU18+a9mO8m1MgB2kaHfQyUzp3YoPjLeGao/QfmTF7stWjEq4nzxXvH788Udq1zZ3a/76669cuHCBU6dO8fLLL/PWW2/le4CCfWhQxpdDWkXAPNY47crxArnO7ck17HGOF0CdYC8OSLcqXo2kkzbxgCkI1uTsoJAZ1NCyXV2+xMlryVaM6BZ7T+ojCMWFt6sD110qWLaryOG5KkcOX4qhtmQeondF86NcuQoP+IbtqBLoAbV6cl4199A1VU6wb+8ODLno6RMKX54rXrGxsQQGBgLwxx9/0LNnTypVqsTgwYM5evRovgco2IcapTw5qt5Kix6YfpqY5Pzt6i4qLc9OegVjUCOMmvn2aySfYp+Y5yUIhAaV5IrmB0BF6QqnryVZOSIoKj3tglBcOJasavn/tbJ0hbPRD654pVy/YFms+LBajjrB3g/4hm0Z0rIiX5ses2x3zNjAttO5TywiFJ48V7wCAgI4ceIEJpOJ9evX89hj5r/otLQ0kVyjGCvr58p55Vb2n2rSZU5H5X9r9e09XPa2jtftapcvzQnNPByiknSFkxcLJgW/INiTSoHunFaDAfCQ0omLumjliG7Kzmqo2W+ZIwjFRXAJb65pvgCUkmK5mnD/pSk0TUNJumLZjtACCPV1KdAY81u1IA/OlXyCTM28PG8XZR/rj0ZaOSrhbvJc8Ro0aBA9e/akRo0aSJJEhw4dANi7dy9VqlTJ9wAF+6DIEibvWxWv0lIMEfFp+XqNotTuXC/UmwNqJcA8AVgfdYBMo0gBKxRvlQPcOauVtmw7xJ1FVa175xeVnnZBKC6CvJyIxFzx8pZSuBF3/xEl8WkG/NRYy3ak5kOQl3OBxlgQ2tWpxA7VPE+2pBRH9Ekx3NAW5bni9e677/Lll18yfPhwdu7ciaOjIwCKojBx4sR8D1CwHy6+pSytLcFSDBFx+Vvxgv/O8bLfDGN1Q7w5oFWybNfWTnHsqi0MqxIE6wn1dSFc87ds+2sxxKbaTnYu87xS+yxzBKG4CPJyJjK7xwtATbz/iJLIhHSCuLWe5jX8CHB3LLD4CkrnGoGWzLAAjY3/EhaRYL2AhLvSPcyXnn322Tv2DRgw4JGDEexbaR93rp7zo5wURbB0Pd8rXkWp5dnTWU+CT13IHo1ZXzrLwcvx1A+1r3HlgpCf/NwciebWA1OQFEtkQgb+7tbLLlYU1g8UhOIkyMuZ3bdVvJTkSFRVQ5bvfidHJqQTJN3q8cp0CbLZ9bvuJ8jLmat+LSBxEQDN5OP8c+4GDcv4WDky4XYPVfFKTU1l27ZthIeHk5WVleOzcePG5Utggv0J9nHhilaCckThKmWSeCOqAK6i3eWdfQouW4mrh30pJd2gjnyOlZdioFW5B39REIooRZbIcguC7E6uICmOawnp1An2smpcN9nzvFJBKC5KeTkTmZ2kByAgu+f8Xg04kQnplJNu9XhJXsEFHmNBqVqxImf3laKifJVa0gXmnb0MHSpaOyzhNnmueB06dIiuXbuSlpZGamoqPj4+xMbG4uLigr+/v6h4FWPBPs5c0Urc2pF45d4HPwTN7qtaOTUI9ebgoYqUUm7gKmWSdPkwmtbIbodPCkJ+kD2DIXsZwJLc4FTi/SfGFzQNTaSTFwQ78t+e8wDiuZ50n4pXYgYts3u8UjVHPLxL3PU4e9C8gi8791SnonwVnaTieHUPaVltcHF4qH4WoQDkuS/15Zdf5sknnyQ+Ph5nZ2f27NnD5cuXqV+/Ph999FFBxCjYiRJuTsTgadnWpcfm+8T4HFUSO6+g1L8twQZA+YxjRMSlWzEiQbA+Lx9fkjXzxPaS0g0iE2znntDEDC9BsHmKLGFwvlV5CpDiuZ587wacq/FplJTMCTiuab4EedtfYo2bGpX1Za9W49Y2xzh4OcF6AQl3yHPFKywsjFdeeQVZllEUhczMTIKDg5k1axZvvvlmQcQo2Ak/dwditVsVLx8SSEw35Nv5i9IcLzAnEjjrWN2y3UA+w/7LYj0voXgL9HQiSjPPSQiU4olOtG7FyzzHq4gVPoJQ1HmUtLwtISUQnXTvJD3J8ddxkcyfR2q+lLLDjIY3uTnqSAlsZNluKJ9m38Ub9/mGUNjyXPHS6/XIsvlr/v7+hIeHA+Dp6UlEhFiLqDjzdXXMUfHyI5HYlPzNSJbzAci+254lScIjtA5pmjl7Uj35LAcux1s5KkGwLn93J65rXgC4SJkkJyVYNZ7badh9R7sgFAsOHv6YNPPN6i8lcP0+FS8Sbk2LuKr5UdLTfiteANUqlOFU9nqINaSLHD6fv9M+hEeT54pX3bp1+ffffwFo3bo1kydPZtWqVbz00kvUqFHjAd8WijIHnUy6w63sOX5SIjH5WPEqaj1eAHXKlOCwWh6A0lIsly+etXJEgmBdJdwdcwxZ1pILIklP7plTyAuCYE/8PFy5kV2O3G+oocGk4pR+a6HhSM2XIC/rZVHND43L+rBPNa+rq0gaush9ZBjEOqG2Is8Vr/fff5+SJc1duDNmzMDb25tRo0YRExPDokWL8j3AvDh48CCPPfYYXl5e+Pr6Mnz4cFJSUnIcEx4ezuOPP25JBvLaa69hNBqtFHHRo7reGlftJyUSm5J1n6PzTipCWQ0BGpTxZv9t63l53ThEUkb+Dc8UBHvj7+7Ide3WsgpK6nUrRpOTPa8dKAjFSYCHI9HZPed+JHI98e7L20QlZlAyxxpe9j3UEKB+qA/7tKqW7XraCY5cSbRiRMLt8lzxatCgAW3btgXMQw3Xr19PUlISBw4coHbt2vkeYG5FRkbSoUMHKlSowN69e1m/fj3Hjx9n4MCBlmNMJhOPP/44WVlZ7Nq1i6+++orly5czefJkq8Vd1EhuAZb3fiQSm1xwi58WhWnu1YM8OUJly3Y96Qxh4QnWC0gQrMzf3ZGY24YsuxpukJ5l3dZaMcdLEOyLeciyuQFHJ6lkJt29AedaYkaONbxuKP54OusLJcaC4umsJ96vgWW7kXxKzPOyIfa3Qtw9/Pbbb+j1ehYsWEDlypVp2LAhCxcuZM2aNZw7dw6Av/76ixMnTrBy5Urq1KlDly5deO+991iwYMEd65EJD8fN3ZtMzVxo+UlJ+TrHy5zW+fZt+694OekVMgLrWbbry2fYL+Z5CcVYCXdHyxwvMM/PiCnABpwHuX2Ic1EocwShOPD/Tzki3WPIsnnx5FuVEqN7qSLRq12pQgUuqIEA1JbOc/D8tTuOSc4w8NfxKP48eo3UTDHyq7DkueIVHR1Nv379CAoKQqfToShKjpe1ZGZm4uDgYEn8AeDsbO4u3rFjBwC7d++mZs2aBATc6pXp1KkTSUlJHD9+/J7nTUpKyvES7s3vtvkZ5qGG1ntgshdVy4VyRi0FQHXpEscvRj7gG4K9EeVI7rk56khUbq3BU0JKuG8q6MJwe4+X/T+SCfZIlCF54+/hyHVuDVnWpV2/6/I2V/9T8dJ7ly6U+Ara7fO8HCQTpoh/MZhUy+fHriYy9KOVJHwznJTvhjNo1gqOXRXDEQtDniteAwcO5ODBg7zzzjv8+OOPrF27NsfLWtq1a0dUVBQffvghWVlZxMfHM3HiRACuXTPX9KOionJUugDLdlTU3VtDZs6ciaenp+UVHGy/K5oXBj+3W5kNfUgmLjn/UkEX1bTO9UJureelk1SMVw5iyuf1zwTrEuVI7kmShOrmb9n2lxK4bs0eryJY5gj2R5QheRPg4ZSjx8uXeOLS7hzZdHuPV4zmib+35x3H2KOGZXzYq96a51XHdJzjkebKemK6gWnL1rHI8CY9ddt4TredL41vMfWrX0nLEj1fBS3PFa8dO3awatUqRo0aRffu3enWrVuOV36bOHEikiTd93Xq1CmqV6/OV199xccff4yLiwuBgYGULVuWgICAHL1geTVp0iQSExMtL5Ey//5K3DY/Q5Y0DMkFODG+iDQ91w/15sBtCTaqm05yKkq0ZhYlohzJm9vnipbAukMN4b8Lt1srCqE4E2VI3vi6OhCDl2U7gPi7ppSPjk8mAPPwfnNGQ/tOrHGTr5sj133qW7YbyafYe8Fcwfxsy1lezvwCT+lWwhEPKY2h6ctYvutSYYda7Ojy+oXg4GC0Qszr/corr+RIkHE35cqVA6BPnz706dOH6OhoXF1d/8/efYdHUXUBHP7NbEvvnTQIJPTQe68BBUSlSkeUJoIoYANEAcECIigKAvqJWOkiRaRLh9A7ARISkpCQXnd3vj8WFiI9bEi77/MsYWennE2ykzlz7z0XSZL4/PPPza97eXmxb9++PNvGxsaaX7sXnU6HTqd7wndRerjZ6YhT7iwFbdnES5JK3t1nd3sdMQ7V4WbjYD35DHsvJlLFp2TceRPEeeRx2Tq6kh2nQSfl4i4lsbcwuxqWvFOOUAyJc8jjUatksqw84WZdHg8pidjULCrjkGe93BtXkW9eV5SkxAsgMKgiUeFu+ErXqS2f5dsTEXSs5s2l3ctpqD4JwHXFAStysJOyaK86QM9du3i1WRAqWdxhKiiP3RQ0e/ZsJkyYwKVLlwognLu5u7tTsWLFBz60Wm2ebTw9PbGzs+OXX37BysqKtm3bAtCwYUOOHTtGXNztZGDTpk04ODhQuXLlp/J+Sjp3ex3X75iDR50ZV4CJeompDYN32apcu1mBqaF8ku3HLhRyRIJQeNztrcxjRQu7uIaJcvNfqURUUxWEUsH+dsu5h5RE/L0mUU69av5vtOKGj2PxnsPrTk0ruLPZUBMAKykXj6sbGLV0H2/JS83rvJc7iNn6F8zPm2duYtf563ftS7CcR7pydXZ2xsXFBRcXF3r27MnWrVsJCgrC3t7evPzWozDNnTuXQ4cOcfbsWebNm8fIkSOZPn06Tk5OALRr147KlSvTt29fjhw5woYNG3jvvfcYMWKEuJNkIaauhk7m587GZJIzLTMvlWmMV8nUrooX6w11AdBJuThFbS70ggKCUFg87hif4SqlkpCSXmixiAYvQSietA6eGBXTVcO9JlFOycrFOSfW/DxacSlRLV4tK7qzUd3S/LyvahO1rv1KedlUwOuAMRifBt056d6BXMVUHO951Q6WH7xcKPGWFo/U1XD27NkFHIZl7Nu3j0mTJpGWlkbFihX55ptv6Nu3r/l1lUrF2rVrGTZsGA0bNsTW1pb+/fszZcqUQoy6ZHG105qLa4CpIll8ajZONtoHbJU/SgnKwpoFu/OqqhED2AjAM/Je1h6JYVCTsoUcmSA8ff+9gZOTHHv/lZ+CW6ca0wTKhRqKIAiPyNXBjgTscScFdymJ2P+0eF1JyMBfut0DKgoPvJ1KTouXTq2ifI1mnDgUQBX5MtXlCKrLEebXP5cHMq91BVa62rB1fQ3aqg7iKSWRdGobWbk1sdIUXqXykuyREq/+/fsXdBwW8cMPPzx0nYCAANatW/cUoimddGoVmdo7S0EnE5+WTQVP+yfet6m6WMm8/2ylUeFcsSnXTjvjJd2gpXyYl3YfYECjQGTR11ooZdztdUTdkXjJaYWXeD3NMc2CIFiOp4OOeMUZdynFVKQnJSPP61cSM/CXbydeGTZ+6NQlK9kY2rI8bx/sxxI+zLN8sb49HZ7piLOtlmeqezN1XUPaqg4C0Ma4iy2ne9OhmndhhFziPfIgGaPRyIwZM2jcuDF169ZlwoQJZGZarlS4UHIotrdLQbtJyQU4PqNkJSTd6wbys8HULUAtGWmavIYdoq+1UAp5/KfFS5MVVySmWFAoaWcdQSi53B2siL15HtFKBjJT8v49vZyQgb90+6aO7Fryeph4O1rTuWtPxuYOJUWxQa/ILNW35njVcfRpEACAh70VKf5tyFRMPZM6qPbx55ErhRl2ifbIidfUqVN55513sLOzo0yZMnzxxReMGDGiIGMTiinJ/nbiZclS0CX9xnPDIFf2OHUy97Xuq9rEtxsOijvuQqnjbq8j7o5S0O4kkZBeOAU2TMmW+AwKQnHjYa8jTrk9ibKSkne+1iuJ6QTcTLziFUe83NyeanxPy/O1fOnx8njeqbCaYQFrkDvPZmb32kh39JtuVzOIzcZagGlcbeaZLaRlizm9CsIjJ14//PADX331FRs2bGDlypWsWbOGpUuXYjQaH76xUKrYOziToZiKlXhKN4hNsVyRiLwXQCXr3rMkSXRpVocVhiYAOEoZNIv9gZXhVx+ypSCULK62Oq7nSbyS7zkHz9MmxngJQvHh8Z8bOJqMa+gNt69Zr8Zcw1NKAuCS4om/q81TjvDpqVfWhbl96rJgUBN61fO/q1x8WBUv/jQ2uv1c2cXfJwt3bG1J9ciJ15UrV+jYsaP5eZs2bZAkiejo6AIJTCi+yjjbEKWY7hz5SteJTEizyH5Lwz3nF2v78odDX7IVDQCDVX/x66pVXEsWFQ6F0kMlS+RauZufe0g3iE8rpBYvRbR4CUJxFOBqyyXj7TlayytXuHjdVCHVYFSQrx01v3bSGEBlb4e79lFaONtq0Qe1JlUxVXVsrzrA+iOXCjeoEuqREy+9Xo+VVd5qLxqNhtxcy5QKF0qOAFcbLium+TN0Ui5p1yMttm/pAc9KAo1KZmDHpnyl7wyYxnpNNc5h7PdbyMgRzf5C6aE8yhw8T5mYx0sQig8XWy2xtiHm51XkSxy/mgzAxfg0go3nza8dV8pStYzjXfsoTcJCA9lorA2Ag5SBcn4zyRniGt/SHqmqIZgqOw0YMCDPfFdZWVkMHToUW1tb87Lly5dbNkKh2AlwseGUcvuiSZ10CUVR8vQnzo/SMtYprKoX66sMI/z0YWrIFyknX+P165MYtsiKOf2b4GitKewQBaHA6Ry9MNyQUEkKPlICx29kPHyjAiAKaghC8WXrW4WsixqspFyqSRdZEpnE87V82XMxgTryWfN6MTYVcbcv3fO5tq3iydiVjXmBnQB0lHaz4cQ1utf1K+TISpZHbvHq378/Hh4eODo6mh99+vTBx8cnzzJB8He14bJyu8CGpyGG62k5Ftl3ni4/JXiwxQfP1WCq1VvEK6auD/XkM4yNHsOQeWs5EZ1cyNEJQsELcHfkkmLqJlRBusrZmKTCDYibSVjJPe0IQolTzc+Fo0o5AMrKsZw9FY6iKGw7GUUT+TgA8YoD7uVqFGKURYODlQZNhZYkKabGlLbyQdYfvlDIUZU8j9zitXjx4oKMQyhBvB2tiZTLmJ9XlK5wMiaF5vbuD9jq4UpHe5eJo42G6S93ZtTXyXxjnIKDlEF1OYL5qSOZNG8w3o16MqxFeZxtLT8xtSAUBRW9HTil+BNEDDopl/SYM0D9px6HoihijJcgFFMtK3qwanMt6slnAKiZuo3fDzbH/uKf2GhM3Zf/MdSiVWUxZxVAxxoB/HW2Hr3UW7CRsvG9vJxjUTWp5isaVizlkVu8BOFRqWQJg2eo+XmofIGjkUkW2XdpugAq72HHuy/35mX1VK4qpkmpXaQ0vtR8Qeu9A3lj5lwmrTzG6WsphRypIFheJW97Thv9zc+dU04XgfEGYoSXIBQnlb0dOGLfFKNi+uQOUq/nqz82MEr1h3mdP+XmtAh5shvDJUXrip6s1oSZnw9Xr2bWX0dKzVCPp0EkXkKBKOfvR4TRNM6rinSZo1fin3ifpfFzX7WMI5+N6MlYpzmsM9QzL68vn2ax9AG9DvXi5y/fpddny5m5/jQHLiWSrTcUYsSCYBnlPew4IZc3P28gn2T3xac/obiYx0sQii9Jkghr2oi1xgYAuEkpbNGNpaxsKpW+3xhMSL322FuJsdMA1loVzZq3ZYOhDgBe0g1qX14gprWxIJF4CQWipr8Th5RgwFTZ0HBxB5k5T54Q5LnbLJWOX18/Fxt+eK0jJxt/yfDcMVww3u4SUVGOZLLmB5alDqTDvz3YvXAMIybPoN+8DUz98yR/HIziWFSyqIgoFDs6tQrZv6F5aoVmqmNsORVXqDEpSGKQlyAUM73q+bPU8RViFJc8y5MUWz7RjWJkq+BCiqxo6tcwgO90fchRVAC8qlrLqhW/iPHlFvLIY7wE4XE0q+DO+8ZavKDaAUArZS+bT8fybHWfJ9hr6b3rrFXLvBlWkct132LOpmcxHvuDl1R/56nKVE2+RDX5kulJPJyP9eG04sffRl/mU4Zk23LIzgG4ubrg62SNj5M13k7WuNlpcbXV4WKrRasuHcmsUDw0CPFj75WKNFMdw1e6TszxLaR3roKt7in+6VJEVUNBKM6sNCo+GRjG6O8MdEv9gSpSBOcUX3607ssHAzvhaCNau+5kq1Mz6LkOfLlsJ2M1v6OWjMzmM4Z9a8OY/j2oV9bl4TsR7kskXkKBcLbVkubXnMyYr7GWcnhOtYtXth2jY1VvZDn/lzF5u/yUvsuhAFdbPutZh8h2lfll/wA+3b+LBlk7aCMfouqtpOum8nI05YkG1V7TghwgFpKu2XJVceOq4sYlxYWDiiOJ2HNdcSRL64TR2hXJ1g21jTM21lbY6dTYW6mx0918WKmx16mx0amxUstYaVQ3HzI6temrlUaFTi0/8RQCQunWsbo3n25oSjPVMQD6GVeyYEdHRrcpnDvUorS8IBRP/q42fD/mef463pB18en4udiwpLo3NlpxGXwvYVW9+Cd0OFuOnaOl6ghOUjoLlUm8v/Aymxv1Y3jLCiJhzSfxGycUmK4NKvLH703po96MnZRFu9iF/G9PCP0bBeZrf6VxjNf9+LnY8Gb7EAxtgwmPfI61J+OYfuoUzgkHqSWdo5Z8lsrSZbTS3d07naR0nKR0qnD53jvPvPkAMhQdaViTqliThjVpN7+mYkOcoiMLLdloyFK0ZN/6P9qbzzUYVDqMKisUlRWSSoOiUoOsBlkDKg2otEgqNchaJLUalUqLSq1CrZLRyBJqlcz7z1YWc5eVUmWcrMks/yzXLi3DS7pBG9Vhlm/9mf3lR1I38OncdVVQKM2t7YJQUlhpVHSt6VvYYRQbHz5fg1cSJmET8w715dPYStl8rvmanXu289q+7pSr1YYuNctQw89J3GR9DCLxEgpMx2re9F7fgxcyd2At5TBAvZEx6yqgVQ+lZ12/fH1Q79xCEZ9zVLJE7QAXage4QIeKJGc+S3hkEv9cvsFXV2+QEXsOu5RzVCCKQPkaZaQEynAdLykRzT2Ssv+ykbKxIRsPKenJAjXcfDzKqoqEHjW5qHg/dyD6Dh882bGFYu31sGrMnNuTzzVfAzBTNY/Xv9Ny+ple9K7nj+oJWtAfl4IkhngJglAq6NQqvh7cnNE/fk5UxHTz0JEmqhM0YRJnDi5g1b5GfGpVF5dytahbzo1K3g4Ee9qLm6UPIBIvocBoVDIjn2/FJ9/3YKLmfwDMUs9j/urL9D84mP4tqtAixOORL5zEPeeHc7TW0DzYnebBt0rj1icr18D5uDQuJ2RwLCmT9UmZRN9IIzMxGjktGk1WIs6k4EoKLlIqrlIyrqRiJ2ViRyZ2Uib2ZGBHJiqp4H8KKklBRS46cpFRUKvEuLPSrJK3A26N+rF2z2GeVe3BTspigfwxP6/dS6+t3WlStw6tKnpQ2dvhibox34+iiKqGgiCUTjZaNV/1b8j8bbMY/s8yJsg/4C+bqlSHyFGMk38Fw68knbXl0OkKHFD8+NHoR6JNELKLP04u7pRxsqaMszVudqax5M42WlxstThaa57qjbOiotgkXlOnTuXPP/8kPDwcrVZLUlLSXevs37+fCRMmcPDgQSRJol69esycOZPQ0NtzSh09epQRI0awf/9+3N3dee211xg3btxTfCelS/Ngd3Y3HsGP/0bTR70ZgKHqtXS/tpXfljZnkbYp7hXqUa+cO1XLmO6UWGlUD9hj6R7jlR9WGhVVyzhStcy9J0A0GhWSMnNJTM8mIS2HhPQcItNzSMvSk5adS1qWntRsPamZueRmpqNkJ6NkpyJlp4EhG9mQhcqQjRU56MjFSsox/193x/81GFBjQC3p0WBAgx41hpvL9Wikm6/fXKZBTxK2aFTi51zajQuryIjY99FGTKSd6iCypNBb/Q89M7ewf1sI6/4JZaamCpJnVYJ8vSnrZoOvsw1+LqYiMpYax2Ea4yV+HwVBKD3UKpmRrSpwvuobfLopDM3JP+ip+oe6dxT3cpLSaaUKpxXhpgV6IA5SYq2JVtyIVly5rjgSgT2Jij03MD1ytc4oWjvQ2oHODpWVHVZWNtho1djpVNjq1OYx41q1aRy56at8z2VWGhmNSkYlS6hlGbVKQi1LqGTpjuVSoXaNLDaJV05ODt26daNhw4Z89913d72elpZGWFgYnTt35quvvkKv1zNp0iTat29PZGQkGo2GlJQU2rVrR5s2bZg/fz7Hjh1j0KBBODk58corrxTCuyodxoVV5GPlI6b968Gb6l/RSgZcpDReVf/Jq8Y/SThtz6GTwWw2BjKXsmQ6lkPjGoifmyP+rrb4OVvj5WhFQlpOYb+VEkmWJVxsTXegynvkbx9Go0KOwUhWroFsvelrVq6RbP3tr3qDQq7BiN5o+ppuUNAbjOQaTV/1BoVco+nrreXlDEa0osWr1FOrZOb2b8TkVZ+z7+C3jFb/gZ2UhSwp1JdOU18+DYDxmsTlGA8uK15EKu7sUdyJVlxJUzujt3ZFsnNHZ+eGk501zrZaHKzU2FtpcLBW42ClMf/f3kqDg5UaW636ZouXIAhC6Vbew445L9Xj0vUqrDg8mM8OHSIk5V8ayCepL5/CRUq7axsHKRMHKZKKRN57pwqQffORalqUo6hIx5p0rEhXrMxjyXMUtXkseQ5qbihaclCTjebm66avuajRo8KATC5qDMjolbzPjZIKo6wGSQ2yCkVWo0gaJFmFolKjyKb/I6lAVpGpsmdWv6aUcbJ+4u9jsUm8PvjANM5jyZIl93z99OnTJCYmMmXKFPz8/ACYNGkS1atX5/Lly5QvX56lS5eSk5PDokWL0Gq1VKlShfDwcD7//HOReBUgWZZ455nKrA+YTJ/VTemVuZSO8l50kmluKVcplbaqg7RVHTRtkAGGdImYK65cNnoSpbhzChdiFBdayTF37FlcDhUVsixhJase0lopCPmnUclMfb4G/1afwit/diQ0bg0vqrYRdMc5QZYUykqxlCX27h3c/ONuvC6RhC03FHtSsSFFsSEFG2IVG85jQ4piSwo2pCo2pEo2pBht0OhMAxTFGC9BEEq7QDdbxrQNRmlTgcsJndhzMYEPLyYQH30R68RTlFciKSdF4yMlUEa6jreUYL7eexRayYCWNJxJe3qXecabXw1A7t0vf5jbB72hsUUOVWwSr4cJCQnB1dWV7777jnfeeQeDwcB3331HpUqVCAwMBGD37t00a9YMrVZr3q59+/bMmDGDGzdu4OzsfNd+s7Ozyc7ONj9PTjZNIJeSklKwb6gEauRvQ61XO7LmSA367j+JX/xWGsknqC+fwkHK/M/aCg7EU414qt252AgpNz+/OWSJn0Mpd+vnrxTxkpfiPGI5VT20fD2gKeGR1Vh4fBDnzxzDP/UwlaRLVJIjKS9dxVq6x19OMwU1qbiTivsD1rq5KgApN390aUYDWelp4udWwhSH84g4hwhFkYsWOlZ0omNFJyCIHH1rLiWYxpQfTs7iz6QsYpPSSU+Og4wEVFlJWOUm4yyl4kwqjlIGNmRiJ2VjTRa2UjY2ZOVZZkXuUxlf/iAZuXrSU1NJ0dy7SthjnUOUYmbx4sWKo6PjPV87duyYEhQUpMiyrMiyrISEhCiXLl0yv962bVvllVdeybPNiRMnFEA5efLkPfc5adKkW7WExUM8xKOIPiIjIy12jikI4jwiHuJR9B9F+TwiziHiIR5F//Eo5xBJUQrvFs+ECROYMWPGA9c5deoUFStWND9fsmQJo0ePvqu4RmZmJi1atKBixYqMHDkSg8HAp59+yunTp9m/fz/W1ta0a9eOsmXL8s0335i3O3nyJFWqVOHkyZNUqlTpruP/9y6T0WgkMTERV1fXhw7OS0lJwc/Pj8jISBwcHB64blEi4n66RNz5pygKqamp+Pj4IMtFdyxYfs8jReF7nB/FNW4ovrGLuPOvOJxHxLWIiLugibjz73HOIYXa1XDs2LEMGDDggeuUK1fukfb1008/cenSJXbv3m1+0z/99BPOzs6sWrWKnj174uXlRWxs3r7/t557eXndc786nQ6dTpdnmZOT0yPFdIuDg0Ox+iW+RcT9dIm488fR0bHQjv2onvQ8Utjf4/wqrnFD8Y1dxJ0/Rf08Iq5FRNxPi4g7fx71HFKoiZe7uzvu7g/tZf9IMjIykGU5z52fW8+NRtOouYYNG/Luu++Sm5uLRmOa3G3Tpk2EhITcc3yXIAiCIAiCIAiCJRTNNvV7uHLlCuHh4Vy5cgWDwUB4eDjh4eGkpZnKV7Zt25YbN24wYsQITp06xYkTJxg4cCBqtZqWLVsC0Lt3b7RaLYMHD+bEiRP88ssvfPHFF7zxxhuF+dYEQRAEQRAEQSjhik1Vw4kTJ/L999+bn9esWROALVu2mMd2rVmzhg8++ICGDRsiyzI1a9Zk/fr1eHt7A6ZmwI0bNzJixAhq166Nm5sbEydOLLBS8jqdjkmTJt3VPaCoE3E/XSJu4X6K6/e4uMYNxTd2EbdwP8X1eyzifrpE3E9HoRbXEARBEARBEARBKA2KTVdDQRAEQRAEQRCE4kokXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMbekdZmdno9PpLL3bIsNoNBIdHY29vT2SJBV2OIJQqimKQmpqKj4+Pshy8bmPJM4jglB0FMfziDiHCELR8TjnkCdOvP766y9+/vlnduzYQWRkJEajEVtbW2rWrEm7du0YOHAgPj4+T3qYIiM6Oho/P7/CDkMQhDtERkbi6+tb2GE8MnEeEYSipzidR8Q5RBCKnkc5h0iKoij52fmKFSsYP348qampdOzYkXr16uHj44O1tTWJiYkcP36cHTt2sHv3bgYMGMCHH36Iu7t7vt5IUZKcnIyTkxORkZE4ODgUdjiCUKqlpKTg5+dHUlISjo6OhR3OIxPnEUEoOorjeUScQwSh6Hicc0i+W7xmzpzJrFmz6NChwz2b1bp37w7A1atX+fLLL/nxxx8ZM2ZMfg9XZNxq0ndwcBAnO0EoIopbVxtxHhGEoqc4nUfEOUQQip5HOYfkO/HavXv3I61XpkwZPv744/weRhAEQRAEQRAEodgrHqNIBUEQBEEQBEEQijGLVDV844037rlckiSsrKwoX748Xbp0wcXFxRKHEwRBEARBEARBKFYskngdPnyYQ4cOYTAYCAkJAeDs2bOoVCoqVqzIV199xdixY9m5cyeVK1e2xCEFQRAEQRCE+zAajeTk5BR2GIJQImi1WotMN2GRxOtWa9bixYvNgzyTk5N5+eWXadKkCUOGDKF3796MGTOGDRs2WOKQgiAIgiAIwj3k5OQQERGB0Wgs7FAEoUSQZZmyZcui1WqfaD8WSbw++eQTNm3alKeyjqOjI5MnT6Zdu3a8/vrrTJw4kXbt2lnicIIgCIIgCMI9KIpCTEwMKpUKPz+/YjMptCAUVbcmLI+JicHf3/+JKqBaJPFKTk4mLi7urm6E8fHxpKSkAODk5CSavAVBEARBEAqQXq8nIyMDHx8fbGxsCjscQSgR3N3diY6ORq/Xo9Fo8r0fi9wG6dKlC4MGDWLFihVERUURFRXFihUrGDx4MM899xwA+/btIzg42BKHEwRBEARBEO7BYDAAPHGXKEEQbrv1ebr1+covi7R4ffPNN4wZM4aePXui1+tNO1ar6d+/P7NmzQKgYsWKLFy40BKHEwRBEARBEB6gOE0ILQhFnaU+TxZJvOzs7FiwYAGzZs3i4sWLAJQrVw47OzvzOjVq1LDEoQRBEARBEARBEIodi464vHbtGjExMVSoUAE7OzsURbHk7gVBEARBEARByAdJkli5cuVTP25gYCCzZ882P7927Rpt27bF1tYWJyen+y4riSySeCUkJNC6dWuCg4Pp2LEjMTExAAwePJixY8da4hCCIAiCIAhCCTVgwAAkSbrrcf78+cIOjRYtWpjj0el0lClThk6dOrF8+fLCDu2xxMTE0KFDh3xvv23bNlq1aoWLiws2NjZUqFCB/v37m4vnLVmy5J5J0/79+3nllVfMz2fNmkVMTAzh4eGcPXv2vstKIoskXmPGjEGj0XDlypU8FXR69OjB+vXrLXEIQRAEoZSIS8li+rpTTPjjKOdiUws7HEEQnpKwsDBiYmLyPMqWLXvXeoVRJXvIkCHExMRw4cIF/vjjDypXrkzPnj3zJBRFnZeXFzqdLl/bnjx5krCwMOrUqcP27ds5duwYX375JVqt9qEFJ9zd3fPkBxcuXKB27dpUqFABDw+P+y4riSySeG3cuJEZM2bg6+ubZ3mFChW4fPmyJQ4hCIIglAIGo8Kob/+ky54ejD7amSnzFhKdlFnYYQlCsWQ0KiSkZRfqw2h89GEnOp0OLy+vPA+VSkWLFi0YOXIko0ePxs3Njfbt2wOmFph69eqh0+nw9vZmwoQJ5iJvYGqpeu211xg9ejTOzs54enqyYMEC0tPTGThwIPb29pQvX56//vrrobHZ2Njg5eWFr68vDRo0YMaMGXzzzTcsWLCAv//+G4BWrVoxcuTIPNvFx8ej1WrZvHkzYOp2N23aNAYNGoS9vT3+/v58++23ebYZP348wcHB2NjYUK5cOd5//31yc3PNr0+ePJkaNWqwaNEi/P39sbOzY/jw4RgMBmbOnImXlxceHh5MnTo1z37/29UwKiqKXr164eLigq2tLXXq1GHv3r33fP8bN27Ey8uLmTNnUrVqVYKCgggLC2PBggVYW1uzdetWBg4cSHJysrl1cPLkyeb3fKurYWBgIH/88Qc//PADkiQxYMCAey67Fe/ChQvp2rWruYVt9erVeeI6fvw4HTp0wM7ODk9PT/r27cv169fNr//+++9Uq1YNa2trXF1dadOmDenp6QBs3bqVevXqmbs3Nm7cuMDzFosU10hPT7/nXBGJiYn5zqwFQRCE0uf41WTq3VhHZY3pj99byvcs29eRse1CCjkyQSh+bmTkUPujvws1hoPvtcHV7smvBb///nuGDRvGrl27ALh69SodO3ZkwIAB/PDDD5w+fZohQ4ZgZWVlvuC/td24cePYt28fv/zyC8OGDWPFihV07dqVd955h1mzZtG3b9+7em09iv79+zN27FiWL19OmzZtePnllxk5ciSfffaZ+fr3xx9/pEyZMrRq1cq83WeffcaHH37IO++8w++//86wYcNo3rw5ISGm85y9vT1LlizBx8eHY8eOMWTIEOzt7Rk3bpx5HxcuXOCvv/5i/fr1XLhwgRdffJGLFy8SHBzMtm3b+Pfffxk0aBBt2rShfv36d8WelpZG8+bNKVOmDKtXr8bLy4tDhw5hNBrv+V69vLyIiYlh+/btNGvW7K7XGzVqxOzZs5k4cSJnzpwByFNk75b9+/fTr18/HBwc+OKLL7C2tiYnJ+euZbd88MEHzJw5k08++YQvv/ySl156icuXL+Pi4kJSUhKtWrXi5ZdfZtasWWRmZjJ+/Hi6d+/OP//8Q0xMDL169WLmzJl07dqV1NRUduzYgaIo6PV6nnvuOYYMGcKyZcvIyclh3759BV4N1CKJV9OmTfnhhx/48MMPAVOGajQamTlzJi1btrTEIQRBEIRS4EhUEjXlc+bn1eUIZpw4DSLxEoQSb+3atXku1jt06MBvv/0GmHpRzZw50/zau+++i5+fH3PnzkWSJCpWrEh0dDTjx49n4sSJyLKpU1doaCjvvfceAG+//TYff/wxbm5uDBkyBICJEyfy9ddfc/ToURo0aPBY8cqyTHBwMJcuXQLg+eefZ+TIkaxatYru3bsDpnFPt8av3dKxY0eGDx8OmFq3Zs2axZYtW8yJ1614wdRC9Oabb/Lzzz/nSbyMRiOLFi3C3t6eypUr07JlS86cOcO6deuQZZmQkBBmzJjBli1b7pl4/fTTT8THx7N//35cXFwAKF++/H3fa7du3diwYQPNmzfHy8uLBg0a0Lp1a3PCpNVqcXR0RJIkvLy87rsfd3d3dDod1tbWeda71zIwjf3r1asXANOmTWPOnDns27ePsLAw5s6dS82aNZk2bZp5/UWLFuHn58fZs2dJS0tDr9fz/PPPExAQAEC1atUAU+NQcnIyzz77LEFBQQBUqlTpvnFbikUSr5kzZ9K6dWsOHDhATk4O48aN48SJEyQmJprvTAiCIAjCw4RfvsGz8oU8y2yvHyUr91msNKpCikoQhKehZcuWfP311+bntra25v/Xrl07z7qnTp2iYcOGeRKaxo0bk5aWRlRUFP7+/gBUr17d/LpKpcLV1dV88Q3g6ekJQFxcXL5iVhTFHIOVlRV9+/Zl0aJFdO/enUOHDnH8+PG7usfdGdOtROXO4//yyy/MmTOHCxcumJMHBweHPPsIDAzE3t4+z/tQqVTmhPPWsvu9r/DwcGrWrGlOuh5GpVKxePFiPvroI/755x/27t3LtGnTmDFjBvv27cPb2/uR9vO47vxe2dra4uDgYH5PR44cYcuWLfdsWbtw4QLt2rWjdevWVKtWjfbt29OuXTtefPFFnJ2dcXFxYcCAAbRv3562bdvSpk0bunfvXmDv4xaLjPGqWrUqZ8+epUmTJnTp0oX09HSef/55Dh8+bM4iBUEQBOFh4q6cxkVKy7OsEhGcuSaKbAhCSWdra0v58uXNjzsvgu9Mwh6HRqPJ81ySpDzLbiVN9+ti9yAGg4Fz587lKQDy8ssvs2nTJqKioli8eDGtWrUyt7Y8KKZbx9+9ezcvvfQSHTt2ZO3atRw+fJh33333roIiD3tf/93vf93Zne9xlClThr59+zJ37lxOnDhBVlYW8+fPz9e+HsWD3lNaWhqdOnUiPDw8z+PcuXM0a9YMlUrFpk2b+Ouvv6hcuTJffvklISEhREREALB48WJ2795No0aN+OWXXwgODmbPnj0F9l7AQi1eAI6Ojrz77ruW2p0gCIJQyiRn5OKSdAy0eZdXlS9xPDqZUD+nQolLEIorZxstB99rU+gxFIRKlSrxxx9/5Glx2rVrF/b29ncVeyso33//PTdu3OCFF14wL6tWrRp16tRhwYIF/PTTT8ydO/ex9vnvv/8SEBCQ55q6IAo+VK9enYULF5KYmPjIrV7/5ezsjLe3t7lYxaNUOLSkWrVq8ccffxAYGIhafe+URpIkGjduTOPGjZk4cSIBAQGsWLGCN954A4CaNWtSs2ZN3n77bRo2bMhPP/302F1OH0e+E6+jR48+8rp3NhMKgiAIwr2YxnfdPWdPVTmCOVeTCyEiQSjeZFmySGGLomj48OHMnj2b1157jZEjR3LmzBkmTZrEG2+8kae7naVkZGRw7do19Ho9UVFRrFixglmzZjFs2LC76hncKrJha2tL165dH+s4FSpU4MqVK/z888/UrVuXP//8kxUrVljyrQDQq1cvpk2bxnPPPcf06dPx9vbm8OHD+Pj40LBhw7vW/+abbwgPD6dr164EBQWRlZXFDz/8wIkTJ/jyyy8BU/fHtLQ0Nm/eTGhoKDY2No9dsORxjBgxggULFtCrVy/GjRuHi4sL58+f5+eff2bhwoUcOHCAzZs3065dOzw8PNi7dy/x8fFUqlSJiIgIvv32Wzp37oyPjw9nzpzh3Llz9OvXr8DihSdIvGrUqIEkSXnuNICpryuQZ9nTzH4FQRCE4ik8Mommd4zvOmkMoLJ8GW8pkaioK4C4iScIgkmZMmVYt24db731FqGhobi4uDB48OA8hSksacGCBSxYsACtVourqyu1a9fml19+uWdi1atXL0aPHk2vXr2wsrJ6rON07tyZMWPGMHLkSLKzs3nmmWd4//3381RqtAStVsvGjRsZO3YsHTt2RK/XU7lyZebNm3fP9evVq8fOnTsZOnQo0dHR2NnZUaVKFVauXEnz5s0BU2XDoUOH0qNHDxISEpg0aZLF476Tj48Pu3btYvz48bRr147s7GwCAgIICwtDlmUcHBzYvn07s2fPJiUlhYCAAD777DM6dOhAbGwsp0+f5vvvvychIQFvb29GjBjBq6++WmDxAkjKrUzpMd3Z7Hn48GHefPNN3nrrLXOWvHv3bj777DNmzpzJc889Z5Fgi4KUlBQcHR1JTk6+a6CjIAhPV3H9PBbXuAvaK4t38eWlzugkPReNXvxtrM0r6j8BGKyfwNeTx6NVW/5OtlC6FcfP44NizsrKIiIigrJlyz72Rb9gGZcuXSIoKIj9+/dTq1atwg5HsIAHfa4e5xyS7xavOwcKduvWjTlz5tCxY0fzsurVq+Pn58f7779fohIvQRAEwfIURSEj8ig6yTT5abhSnuPGQPPrIcpFzsWlUsXHsZAiFARBeLDc3FwSEhJ47733aNCggUi6hLtY5NbhsWPH8lR0uaVs2bKcPHnSEocQBEEQSrCoG5mUyz5lfh5uDOKEEmh+Xk2O4MTVlEKITBAE4dHs2rULb29v9u/fX6CV/h7GqChk6w0YjPnq1CYUIIskXpUqVWL69Ol5Sl3m5OQwffr0pzIZmSAIglC8HY5MosYd47sidBUJqVyTNMXUpaOqdIljosCGIAiPwZi/0TT51qJFCxRF4cyZM3nmCnuakjNziYi5TmJsFJExscSnZhVKHMK9WaSc/Pz58+nUqRO+vr7mCoZHjx5FkiTWrFljiUMIxciKw1FsPhlDzQA3XqrvX+omPc3KNXA+Lg0vRyvcSmg1KUGwtIOXEhkknQUgW9Fg7RdKFV9nTp4LoJ50Bj85nojIKKBq4QYqCEKRl6M3cjUxBTk3A7T2lHGxRa0q+eND07L0JCfGUY44btW4u5aSxQ3ZB2fbginrLzweiyRe9erV4+LFiyxdupTTp08D0KNHD3r37p3vCe+E4um3A5FErpjMJ+rVbDpdmxmJnzCpc+Hc9SkMKw9f5fdVy6mrP8R5pQzOdbrzbqcq6NSlK/kUhMd1IeIiAXIcAEeUcoQGelKtjCPHjWWpJ58BwCruEFm5bUvdzRxBEB7P1cQ0yuRGopX0ZObcICY5ED+Xkn09qigKsUmpBBLPHYXF8eQGF5NtcbB2QyVL99+B8FRYbAJlW1tbXnnlFUvtTiimVm7by//UfyBLCp1Vu1mzfw1p7Sthp7PYr1qRtev8dbb//iU/ar42f7I2H9rJ6JRpfNmnQam42yYI+ZGWrcc+/iBoTM8PGoOpHeBMJW8HflGCGcR6AGoppwiPTKJBOddCjFYQhKIs12BEnZuM9mahHmspB31mKopik2eqo5ImMT0HZ0MCKilv90pJAlflBjcyHEQvnCIg31eCe/bseeR1MzIyOHHiRH4PZVFJSUnUqVOHGjVqULVqVRYsWFDYIZUYadl6HBOOIN/xoW+p7GfX+euFGNXTYTAqzFmxjWnqhXmWt1YdpuP5D5i86hj5nLlBEEq88CtJ1LrZzRDgsBJCqK8TjtYaktzrmpfXl0+x92JiYYQoCEIxkZatx46845qsySIrt+TOKas3GklKScGZVAAMiswpox+5iql3gCPpJKeli+uQIiDfiVffvn1p3749v/32G+np6fdc5+TJk7zzzjsEBQVx8ODBfAdpSfb29mzfvp3w8HD27t3LtGnTSEhIKOywSoST0SlUkyPyLAuVL3AsquQPiP/zWAxhyb9gJeUCEG4sR6Zi6k/dSbUHv0Mz+HrbhQftQhBKrd0Xr1NPPm1+nuFVB2ut6YIhJKg8540+AFSXLnLk4tVCiVEQhOIhPUuPLZl5llmTTWYJTrziU7LxVK6buxjG4YSLgx2J2AOmVi8bQ+p9vweKopCSmUtMcibX07LRG4xPK/RSJ9+J18mTJ3nmmWd47733cHJyokqVKrRt25ZOnTrRpEkT3NzcqFWrFhEREWzcuJF+/fpZMu58U6lU2NjYAJCdnY2iKOIOgIUcv5pMFelSnmXBUhSnouILJ6Cn6I9/T9JTtQWADEXHoJxxvG54HYNiOgu+qv6ThE2fM2/LeXFCE4T/CD99jlD5IgAnjAFUKR9ofq1+ORf2GSsCoJEMyJG7ycjRF0aYgiAUAznZmWilvAmGjlyy9SXzb2+23kBO+g3sJFMrX7aiJkfnioe9jjT59mS+TqSRlJF71/ZGReFKQjpZiZE4p11Am3yJy7EJpGeL82xByHfipdFoGDVqFGfOnGH37t0MGTKEqlWrUqZMGVq0aME333xDdHQ0y5Yte6ySmtu3b6dTp074+PggSRIrV668a5158+YRGBiIlZUV9evXZ9++fY8Ve1JSEqGhofj6+vLWW2/h5ub2WNsL93Y8Komq/2nx0kgGsqOOlOjk9nJCOl5R67GWTNMp/G5oxoe9W9Cl+2De1w8yr/e+Zime/4yh3/TFDP1hH9P/OsXvB6NIvseJUBBKi7jULDxid5mfbzOG0iLYw/y8QVlXdiih5ufNlQPsOFfyuy8LgvD4cvQGdMaMu5Zr0ZNTxFu8lixZgpOT02NtExgYyNSPP8WL2z23ruGKl6M1kiRha2NLumIa12Ut5ZCVkXbX9di15Cycs6/iISVjJeXiIGUQqFwl9npCie6eWVgsMtq/Tp06jB49mlmzZjF//nw++ugjXnjhBVxcXB57X+np6YSGhjJv3rx7vv7LL7/wxhtvMGnSJA4dOkRoaCjt27cnLi7OvM6t8Vv/fURHRwPg5OTEkSNHiIiI4KeffiI2NjZ/b1zII+7qeVyktLuWB+acI+pG5j22KBn+OHSVF1XbzM83aFrTtrInz1T3plzYSGbrnze/9qJqOz/p3+CLCx15YfcL6FYOZugnC9l+tuS3CgrCvWw7E09LVbj5+V5VLeoEOpufO9poyPRrTrZiqrzRRnWIv09ce9phCoJQwAYMGIAkSXc9zp8//8j7SMvO280wRzFVupIlBYM+536bPVSLFi2QJImPP/74rteeeeYZJEli8uTJ+d5/fikKqAzp6G4WEklTrNDYOqO7Wfl17qfTsPOtglSmFmr/urSoX5ORo0aTlma6VsvRG8hNT8RBypusqiQFP2K5mpCCsYhNwhwYGMjs2bMLO4x8K3Jl1jp06MBHH31E165d7/n6559/zpAhQxg4cCCVK1dm/vz52NjYsGjRIvM64eHhHD9+/K6Hj49Pnn15enoSGhrKjh077htPdnY2KSkpeR7C3TJy9Ngl3i6gstNQxfz/6tLFEjvxqdGosHf/PurKpsIAZ4y+VKjRFK3a9NF6uWk5rNu+x1u5r5CqWJu300m5BMtX6aTaw/fGd/n+f99xvIR+jwRxHnmQjUcu0VIOByBFscYmqCGa/1QAbVo1kF1G0znFW0ok+vQeDEXsYkAQClJpOYeEhYURExOT51G2bNm71svJuXcSlZalx+5m4qVXZJK4XUJeNmQ/Ue8bPz8/lixZkmfZ1atX2bx5M97e3vneb34ZFQWD0Yj9zferKBAnueLhcLtyoVolUyGkItGHNnJpz1qmvzOKxYsWMnbsWADiU7PxIMm8/hWjh3nSeo1kwMMQy7WU24VKFEVBry8ZXRDv9ztU0Ipc4vUgOTk5HDx4kDZt2piXybJMmzZt2L179yPtIzY2ltRUU9WX5ORktm/fTkhIyH3Xnz59Oo6OjuaHn5/fk72JEupUTCqVpdvdDH8zNEevmH69qskXOVpCC2zsiUigScYm8/PfDc14sU7e35FXmwfxwuAJDHVdzIe5fVhvqMtZYxlztSGtZOAT+Uum/b6jyN1ZEixDnEfuLSEtG83Fv7GXTBcOGwx1aV/t7u9N20qe/G2sbX7eOGcX/14Q3Q2F0qO0nEN0Oh1eXl55HiqVihYtWjBy5EhGjx6Nm5sb7du3B2Dbtm3Uq1cPnU6Ht7c30ya9DQbTBXU6VnR58SVee28Goyd+Qo0qFfHy8mLBggWkp6czcOBA7O3tKV++PH/99ddDY3v22We5fv06u3bd7hr9/fff065dOzw8PPKse+PGDfr164ezszM2NjZ06NCBc+fO5VlnyZIl+Pv7Y2NjQ9euXe8q9HbhwgW6dOmCp6cndnZ21K1bl7///tv8+vW0bGSMyJiuGxKxx9HBEbWc99Jeq9Vi6+GPr48nL3VpS5fnurJ69Wpy9UZ+/WEhTTt2wz64CZ412jJq9BhO3lCbr08O7t5BGWcblq9aS+3atdHpdOzcufOhsYGpZeqjjz6iX79+2NnZERAQwOrVq4mPj6dLly7Y2dlRvXp1Dhw4kGe7nTt30rRpU6ytrfHz82PUqFHmIn4tWrTg8uXLjBkzxtwi+ijb3Yrnww8/pF+/fjg4OPDKK6+Qk5PDyJEj8fb2xsrKioCAAKZPn/6A34InV6wSr+vXr2MwGPD09Myz3NPTk2vXHq3ryeXLl2natCmhoaE0bdqU11577YFj0N5++22Sk5PNj8jIyCd6DyXViehkqt2ReB0whnBWMf1hCJaiOBNZMrsGrTgYSVfVTgAMisRxl/ZU8XG4a70G5Vz58bUwer3+MUmdFvFHwz/o5vo7mwy1AHCR0mge/xP/nI67a1uh+BPnkXtbd/waneTbFzHrpca0rex513r+rjZEuLU0Xww8r9rB7/svP7U4BaGwiXOIKcnRarXs2rWL+fPnc/XqVTp27EjdunU5cuQIs+fM47dlS/noC9O0LmlYY0Ti+9/W4ubizIY1y3ll2HCGDRtGt27daNSoEYcOHaJdu3b07duXjIy7x4bdSavV8tJLL7F48WLzsiVLljBo0KC71h0wYAAHDhxg9erV7N69G0VR6NixI7m5pjHde/fuZfDgwYwcOZLw8HBatmzJRx99lGcfaWlpdOzYkc2bN3P48GHCwsLo1KkTV65cITvXQEZKEjKmgiF6RSZZ7Y6LrfauWFSyxA3szM8drFRk5+QQn5aNlT6FD98azpFNPzN/4WJir0Yx8a1RXMU9zz7efWcCU6dN59SpU1SvXv2Bsd1p1qxZNG7cmMOHD/PMM8/Qt29f+vXrR58+fTh06BBBQUH069fP3BJ54cIFwsLCeOGFFzh69Ci//PILO3fuZOTIkQAsX74cX19fpkyZYm4RfZTtbvn0008JDQ3l8OHDvP/++8yZM4fVq1fz66+/cubMGZYuXUpgYOD9fwksoOTPavsf9erVIzw8/JHX1+l06HRiwrmHORqZRIebhTVuKHYkaT05YixHZfkyKkkhN/ooitK8RE1emJGj5/rxzfjKpjvv243VaVW3+n3foyRJlPewp7yHqbxrTtsQ+sy6QbO0V9FJufRSbWHU9uO0uceFp1C8ifPI3RRFYf2+Eyy62c0wXnHAtmJrbO8z2XrbulXZuqEGbVUH8ZJukHFqI8mZoThaa55i1IJQOJ74HPJNc0grhBt7dh7w6raHr3fT2rVrsbO7nSR06NCB3377DYAKFSowc+ZM82vvvvsufn5+zJ07F0mScPYJ5L2xw5k47XMmjnmFbJUtkiQTWrkC741+mVTFmsDqbzHr009wc3NjyJAhAEycOJGvv/6ao0eP0qBBgwfGN2jQIJo2bcoXX3zBwYMHSU5O5tlnn80zvuvcuXOsXr2aXbt20ahRIwCWLl2Kn58fK1eupFu3bnzxxReEhYUxbtw4AIKDg/n3339Zv369eT+hoaGEht4uLPThhx+yYsUKVq1axTPd++HL7Z/nNVzwcra95/WHBKBzRJ99nSPHjrN85WrqNWpOTnoSw3o9A0CmokVXsSxf+nhTt25dFFlLgtHevI9JY4dSpW5j/FxMXTddXFzuGdvq1avzJDsdO3bk1VdfzfN9rlu3Lt26dQNg/PjxNGzYkNjYWLy8vJg+fTovvfQSo0ePNv/M58yZQ/Pmzfn6669xcXFBpVJhb2+Pl5eX+TgP287KytR9slWrVuZulgBXrlyhQoUKNGnSBEmSCAgIuNeP3aIsnnhlZWWZ36Clubm5oVKp7iqGcesHJhSea5HncZdMfc6PGcvSp2Egx3aVoxemEuvlc89xOSGDQDfbB+2mWFl//BrPKLf/oCw3NuP9Gj4P2CIvrVqme6v6rFjRmJ7qrThIGXhG/kVkYkP8XGwKImRBKDLCI5OoGrsancZ0B3i1oTGdavrfd/3napbhnfUtaItpTsgX2czvB3sxuMnd4z8EQfiPtDhIjS7sKB6qZcuWfP311+bntra3rxlq166dZ91Tp07RsGFDJElCURTSMjJoVbcqb6RncD76BnbBlZFkmarBwYCppHyKIuHq6pqnp9OtXlR3Fmm7n9DQUCpUqMDvv//Oli1b6Nu3L2p13kvpU6dOoVarqV+/vnmZq6srISEhnDp1yrzOf2sZNGzYME/ilZaWxuTJk/nzzz+JiYlBr9eTmZnJ6fMR9NXHmkvmZ6NBsnHFRnvvS/pjx45RJdALg15PTm4uz7RuwrQP38GTRA4ePcnkz77h0MkLpKSkYDSaWtAyEq+B++0JmJuGVkCVGU9ShgYnG+19Y/tvi1f16tXv+j7f73vv5eXFkSNHOHr0KEuXLjWvoygKRqORiIgIKlWqdM/3+Kjb1alTJ892AwYMoG3btoSEhBAWFsazzz5Lu3bt7nkMS7FI4mU0Gpk6dSrz588nNjaWs2fPUq5cOd5//30CAwMZPHiwJQ6DVquldu3abN68meeee8587M2bN9/VnCg8PRk5euwTj8HNG89HlCDaVfFk1sEQuFmJNFS+wJGopBKVeP154Dxz5L0ApCg2ZJVrj4fD49106BTqzaC1YfRUtgLQWf6X1UeiGdGyvKXDFYQi5YddFxirvj0+cqPNsywNcb/v+i62WlQh7Yk7/x0eUhJt5YP02r6Lfg0D7irGIQjCf9h5PHydInBcW1tbype/99+/O5Ow/8rKNWBtSOVWXeUUrCljrUGSQFabut9pJT25uXokSUKjud1SfquV6FbS8TCDBg1i3rx5nDx58rGnM3ocb775Jps2beLTTz+lfPnyWFtb8/zzL5CdnoyTdHvsUqpkj5ej9X33ExISwspVq4i5kU599xysdKb3np6RTPveI2nTvDFzFiyhSjk/rly5Qvv27cnNzcXHxZbdOAFga2ONIzeIuGGNtdbtnrG9+OKLdxWsuNf3+UHf+7S0NF599VVGjRp11/vw97//jblH3e6/v0O35hv+66+/+Pvvv+nevTtt2rTh999/v++xnpRFEq+PPvqI77//npkzZ5qbbgGqVq3K7NmzHyvxSktLy1M6NCIigvDwcFxcXPD39+eNN96gf//+1KlTh3r16jF79mzzIEmhcJyITqGadNH8/LgSxMvejtj6VSMzQou1lEN9+RRfXrhOlxplCjFSy4m6kYHT5b+w1WQDsNZQn051gh57Pzq1ioBqjYkI96SsHEtD+STzDh8XiZdQol1OSEd/YhW+alM33S2GUFo0aoj6IQnUgKYVWHw6jPGan5ElhS4Zf7DmSAuer+X7FKIWhGLsMbr7FReVKlXijz/+QFEUEtJzcCOVlfvDsbezxalMMDZaFbIkYUBl3saoz37i4/bu3Zs333yT0NBQKleufM+49Ho9e/fuNXc1TEhI4MyZM+b1K1WqxN69e/Nst2fPnjzPd+3axYABA8wtY9dvJHPp8iVa1rtdNdqACgcbHSr5/sM4tFotwRUq4JaeQ1pSJFaYeiedPh9Bwo0kXnvnA+rVqolGJecpdKFTq3Cwv93dUJLAV4kjKkF3V2xpaWlcunTpod+7h6lVqxYnT568b/J96/0YDHnnF3uU7e7HwcGBHj160KNHD1588UXCwsJITEzM15RYj8Iitwl/+OEHvv32W1566SVUqtu/4KGhoZw+ffqx9nXgwAFq1qxJzZo1AXjjjTeoWbMmEydOBKBHjx58+umnTJw4kRo1ahAeHs769evvKrghPD1HIpOoLl0wP09zrYa1VkXdIC/2G00VI32kRC6fP15YIVrcj3uu8JLqdgWfdapWtMvn2KzONcqwxtgQMM01Ui5hK5eupz9kK0Eovr78+wyj5D/Mz//HM/Ss+/AqbXUDXTju84J5aoYXVDv4efNecg2Pdqe6pErNyiUlS0zELpQuw4cPJzIykhEjR3Lm6AE2bNzEpM/mM3JIf+zs7JEkCVkCwx2Xuirjkydezs7OxMTEsHnz5nu+XqFCBbp06cKQIUPYuXMnR44coU+fPpQpU4YuXboAMGrUKNavX8+nn37KuXPnmDt3bp5uhrf2s3z5csLDwzlw8DA9evQAo4FbKVa84oAky2jVKh6Fs42GdJ0nyYppKIOvjzdarZZffvqJyMuXWL16NR9++GGebeysTK1TaTcnYdZKejz0MZQJKGuO7ciRI/Tu3fuRWwwfZPz48fz777/moiPnzp1j1apVeXq1BQYGsn37dq5evcr169cfebt7+fzzz1m2bBmnT5/m7Nmz/Pbbb3h5eT32RNaPwyKJ19WrV++ZZRqNRnMFl0fVokULFEW563Hn3AkjR47k8uXLZGdns3fv3jz9aIWn7/Cl64TKphavWMWJMv7lAGgY5Mpu4+07MwEpB4m68eCqQcVBVq6BY/u2UEs2tcyeNAZQvlYrrDSPdvL7r3qBLuzRNTE/byMf4u9TYlJvoWQ6H5eG4ehvVJCvArDfGEz5Bp1wvkc1rnvp1zKUpYbWgGk+vG7JS/h535WHbFUyXUvOYtjiXUyYMoX3pkzi5QVbiU4quZPVC8KdypQpw7p169i9Zy8d27Vh6IRpDO71HINfH4+rnel8IklSnsRLRy6WmLTFycnpgV0fFy9eTO3atXn22Wdp2LAhiqKwbt06cze7Bg0asGDBAr744gtCQ0PZuHEj7733Xp59fP755zg7O9OoUSM6d+5E5+a1qVWtIgCpijVZ1l7Ij1GwTJIk/FxtybIPIEIVSKZ3Hb7+diFrVi6ncuXKfPzxx3z66af33DZe5WEe72UnZTH9vTewtXekUaNGdOrUifbt21OrVq1HjuV+qlevzrZt2zh79ixNmzY1N7zcOQ/vlClTuHTpEkFBQbi7uz/ydvdib2/PzJkzqVOnDnXr1uXSpUusW7cOWS647uuS8iSzyd1Uu3ZtxowZQ58+fbC3t+fIkSOUK1eOKVOmsGnTpgdOUFzcpKSk4OjoSHJyMg4Od5cNL22MRoV+H83nR+MEANYYGpDVZSHd6viZXvvwK35U3gFM3fHSOy+kR93799MtDn47EIm0ajgvqrYDMD53CK+MnkyQu91Dtry/8b8dYdTxrpSREshW1Az1/o3FQ1tZKuQSq7h+Hotr3E9KURReXridD6IG4SuZ7lQOML7PZ+New9Xu0Sq2GY0Kfef+xVcJg3GUMjAqEr1VM/nmzYE42pSeCofxqdkMn7eCGRmTKCebpuuINLrzhs1Uvn2t6yMnskLx/Dw+KOasrCwiIiIoW7ZsgRU7Kypy9AaiY2MJlEw3K7MVDXHWZc3V9wAuRsdTjigAEhU7VC4BOFoXj89HrsHIlfgUyhiisJJMDRlZioZotR+B7g7ID+hiaElZuQai4xMIVKKRJVPaEKs4ITl442Ffsn/HbnnQ5+pxziEWSekmTpzIyJEjmTFjBkajkeXLlzNkyBCmTp1q7iIolExn41Kpkn3E/HyPsTINyrkCIMsSTkF1SVJMJ8Dm8lH+OV685x4xGhX+2H6ITrJpwu5kxYbrgZ2eKOkCaF3Zk8035/TSSXqsIreTnCG6Dgkly/rj16hxeZE56dpuqEaVxs8+ctIFpvPKa8/W50u9aWyBLCmM03/D1LXHCiTmokhRFN5a+i8fZ0w2J10AfnI872dMZ9qfJadbtyDcj6IoXE1Mx5vbEw/H4nxXIiCpb59fdOSSrS8eXZOz9QauxCXhe0fSla2ouaoqg7+b/VNLugCsNCrcXZyJumN+L08pCUNKLNfTnrz7ZmlikcSrS5curFmzhr///htbW1smTpzIqVOnWLNmDW3btrXEIYQias+FBBrIJ83PL9rVylMKvU2VMmw2mhIKeykT44VtpGXrn3qclrLxZCwtE39Bd/Mk+LOhJT0bV3zi/Tap4MZ26Xap3NbyQbaeFZMpCyXH9bRslqxcxyuqtQDkKCrmWr3C8BaPPxi6QTlXooP7cMHoDUAt+TwORxay5Uzp+Mz8sj+S5lFfEySbJg+9YnTnqmK64VVdjkB35AeORCYVYoSCUPDiU7NxyI1DJ5muKdIUK9S2Luj+0+1fo1GTc7ObnI5csnOLfuKVkaMnKu4GfsYo8/VGjqIiSvbB383xoYWICoK9lQY7Jzeib55rALylRHKTrxGbkoUFOtCVChb7yTVt2pRNmzYRFxdHRkYGO3fuLPBa+ELh23U6inqyqYBKnOKEb1C1PK+3rOjBJmM98/M27GVzMR2/pCgKSzYdoO/NohrZioZ/nLrRuuKTl+m10aqRyzUjTTHdqWspH+afkzFPvF9BKAoUReG93w4yOXeW+SJpgeEZ+ndqe98Jkx/mvS41mSwNw6iY7vq+qf6V+b+tJS4ly2JxF0VxKVmsWbeK/qqNgGni0365E3g9Z4R5nRHqVXy75fEKWwlCcZKckUNuajyuUioABkUiVvbA0+Hu1nOdWkXOzflu1JIRvb5o9ya5kZ5DXHw8gcpV81xdWYqGSNkXP3cnNOrCmz7DxVaH1sGTa4qzeZm3lIicGk1MUqZIvh6BmPxEyLe0bD1SxDZsJVMz81ZDKE1D8iYhjtYacsu2NCcUHVV7Wb3v3FOP1RI2nLhG84Sfsbn5fn8ytOKltvUt1tzfvLIvO4ymxNVVSiXx7L+lvlqbUDJ8tzOCuhfmUEk2dTU+ZfTjaLmhdKyW/4nvfZys6fhMV5YY2gNgJeUyLWcmb/5vR4n+3ExZFc5E43zzOIvP9S8yeUBnWrd/jk0GU6u5t5SIzZk/OB+X9qBdCUKxlJyRQ9KNeHy4bl4WjSuezg6o7lEUQaeWyeaO8Z/6otk6Y1QUopMyyUqKIYBrqKSbkxkrWq6qffH3cHrkCoYFyc1eh2zvlSf5cpeSsc64yuWENPQWqG5YkuU78XJ2dsbFxeWRHkLJtO1MPC25PefDZurQ4h4ToHaqXY41BlO5dHspE5fLf3I5oXiVS8/WG1i0dhsDVaZyr9mKmr8cu/NMNW+LHaN1RU9zt0yARvp97L+UaLH9C0Jh2HY2ntPrv2Gw+i/A9NmZrBnN1O51zJNn5lfPun7sKTuCk8YAAILkGF669jGTVh4tkhdWT2r98WuUO72AENlUKOCosSyJ1QbTIsSDPg38+V7V1bzuq6q1LNx2/n67EoRiR1EU4lKzSL0Riz9x3Dp9xCuOWDm4m0uf/5eVRiaL28U0NEoOemPROj9k5xq4FJeCbXok3lKi+b0lKzbEavwIcHcoUhPFezhYoXb0Jkpx49ap1llKwyM7kstxSWTlGh68g1Is3z/F2bNnM2vWLGbNmmUugdm+fXsmT57M5MmTad/edBfy/ffft0ykQpGz6cRV2qgOApCh6NAHtsThHie+sKperFXfHuvXW/UPPxWz8s+Ldl6iX/oi8wDX7w3t6dOu0QMnLXxcXo5WxHg0N3edaiMfYtPJ4tktUxAAjl9NZsFPy5iqXmheNkXfj1e7dcLtMQpq3I8kSczo2YBJVhPMc9O0Vx0g+PBUPttw5on3X5QkZ+ayeOVfjFSvAECvyExTD+fdTtUB0/iL0IZt2Ws0jTktL0eTeGQt8ali4HtpVZJuPuToDVy+noYqJQpf6bo5MUlU7Mix8Xrg+USjksm5I/GyIqfIJAaKopCYnkN0XDy++ss4SrdvSl9TnEmz8SPA3R51AZY3zy83Ox12zp5cwRPDzesWGykbf0MUMXHxJKRnl6jfQUu9l/x1rgf69+9v/v8LL7zAlClT8kxUNmrUKObOncvff//NmDFjnixKochJz9aTfGor7rJpBvQdxmq0qHrvMvFWGhXBtZpz8kAAleXL1JTPM2vPBpKbly8W5Z8jEzPY+88KhqlMs8xfVxzY6T2Q76tbrrXrlnpVgjmwPZh60hkqyFc5fewgyrOVn7hlQBCetrOxqUz97mfmK9PN47r+p2+DZ6vhtKpouQnvnW21TOr/DG/Mv8w38kzUkpEB6o3M3mnHHM07vNaqfIn4/Hy4+hjjcuahlU0XjN8anqVX12dwuaNsfP9Ggby/oxP1b467HSD9yf/2dOeNtsGFErNQODQaDZIkER8fj7u7e7H+/TcYFZIycshMT8GDG2glPbdGcd5Q7MixcsPDSiI7+8E3GPSKiiyD6cJZUrJITc9EQ+EmXzl6A/GpWVjlJOJFKkYJsjDdVInFBVt7J1ytZXIe8t4Kk5UMBnsHLiSBl3IdraQH9HgTTeL1VC5qXfB0sC6UYiCWpCgK8fHxSJJknostv/KdeN1pw4YNzJgx467lYWFhTJgwwRKHEIqYdcdi6KRsMT//U2nEe5XvfzHVt2Eg8/Z24DN5PgAvG39n8b8dGd2maF8QGI0K7/+6lynMNy/7VN+Dt7rUK5A/Zu2revLblrrUk01362um7+T41S5U83W0+LEEoaCcjE7hg0W/85XhQxwl06TpOw1V2FdxPHNaPX4Vw4epWsaRbj0GMH5ZEp9pTJ/V0erlzN+Sy9SMt3m3mN+8+ONgFL7HvqS22jQ+9qLRi/CyrzAsNO/koB72VjhWf4YLx38kSI6hkeokX+7eQlaLoHxP8C4UPyqVCl9fX6Kiorh06VJhh5MvRqNCeo6erKws7MjAmmyu3nxNQSIJO9RWauytrvEoPfJvZOSQnZOACiMGZFI0epILaa47RVFIy9aTnZWBE2nkYCDl5mvZaEmRHHC0TSUnI50bhRLh4zMYFY6k6bEzJmNFzs2l8eRyhUPYY2tjjY3WIilHoZEkCV9fX1SqJzuXWuS74OrqyqpVqxg7dmye5atWrcLV1fU+WwnF2dr9Z5gv7wNMfZCzyoXh4XD/SfTKuduhr/Iil08vJ0COo5nqGN9tX0tsveF4PmC7wrbk30u0ipqHvzoegH3GEORafQosEQrxtOeEQ1PI/BGA9qr9rD8RIxIvodjYdf468/+3lG+YgdPNbjMHjMEsLDOV+T1qF1gCFFbVmxtdRjBlVToTNf8DYKh6Dcv2pjEqeQIfd6uT7wqKhen0tRTWrvqZ71S3uxhOkobz8Qv3HiM3uFl5Fh3pwFR5EQDdc1fxx6EwXqof8FTjFgqXnZ0dFSpUIDe3aFfwu5PeYORoVDKbTkSTfm4HHfiXhqqTedY5ZgzkO01vXgqrSq2yj15D4ODBKAJ3fkUdlenmxXjHT5jRt6VF438UJ64m8d3Gg3RI+YW2qkPm5bmKikX6MDKq9GRoiwpYF8MkJTvXwFdbzqE78Ssvq9fdbP0yvbc/DM045t2N4e2qUsbZ5iF7Kpo0Gs0TJ11gocTrgw8+4OWXX2br1q3Ur18fgL1797J+/XoWLFhgiUMIRciZa6n4Ra3FWmO6q7HK0Jiudcs9dLvhrSoy98RzfCJ/C8AEFvHxn02Z1atugcabXwcuJXLwryXM02wCTOPYPrV6ne+eqVJgx5QkidDqoRzfHUhV+RKh8kU+PnoM2j/5XGGCUJAURWHp3ivsXruYBaq55vGQR4zl+MJzGvMHNi3wVpde9fz5wfAm767V8KF6MbKk0Eu9haCz0Qyc+w4f92tDuSec7PxpiknOZOJ3K/lGmnVHFcNudOvejTJO1vfcJsTLntiyXUmM/BUXKY1n5T28tG0/ver6P9UJV4XCp1KpLHKhWJBSsnLZezGRf07FcuXEbhrn7GSQvBs/OT7PejGKC1/on0dVux8zOlTG0frxunsFejhxJlVHE7Wpsmpy8gm02rCn9plIyshh5l+nUA59z3T1MnNPADDdmPpE8yoDuz5LWNX8V3otbFZW8HbnGvxd3pshv5flA/0XVJQjsQIGcInIC3/x4VcDCGr0AsNalr9nTYDSwCKJ14ABA6hUqRJz5sxh+fLlAFSqVImdO3eaEzGh5Phu+3mGqv4yP1+vbcviSg+fyyrEyx65Ri+OHttEdTmCSnIkLseXsOVMAC1DnnwuLEuKTcli5o+rWaS+3cVwmr43o/q0x76ATxZhVbxYv7MuVeVLAFRM2sb5uDDKe9gX6HEFIb/Ss/W8v/wwFU58wTz1GvPy7YZqfOc9mXmDmj+11qZ+DQNZrhvLmOW2zFR9jU7SU08+wxcpY3h7zjBaduhO3wYBRT4JiUvJ4vUF6/k0ewrOsqks/FZDKEm1RtD5P10M/6t/80r87/u2vK5egUYy0CplJVvONKV1JcuNrROE/IhLyeJIVDJHIpM4ci4Cu5g9NJSOM1Q+QoAcd9dVaZTixiJ9B6KCevFau6r57v0R7GXHasXX/DzQeJmoG5n4uxZs64uiKKwMv8rPa/5inP4bamtuT6eTpNgyQ98LVe1+fBtWuViMeX8UbSp7EjqmHxOXV6HC2e8YoV6JTtLjJ8fzLZ+w49+/eGV/fzq0aUfv+v5Fqlrj02Cxv4T169dn6dKlltqdUETFpWSRcnQN5dTXAPjXUJk6jVuie8S5Jd7qUIXRJwbzI6ZKmOPUv9Dv15pUHd0Xd/snr3JmCckZuYxauJGPc6ZjJ5uG8a4wNMahyas0qeBW4McP9XVihk0TyP0NgDDVftYfv8bIViLxEoqefRGJfPbbZt5I+5T66tuT9i43NGFLyPt806PuUx9f9HwtX1ztXmfgT958pnyKt5SIt5TIEtVUvl+3j0FHR/L2c7UJ8Sqan6krCRmMXbiWmenv4X/zzv8poz/feb3Pgs5VH7p9k/JufOXSlaHJa9BJenqrNjNq23GReAlPTUaOnovx6VyIT+NCXBpnY26QFnmcMpmnqS5dJEy+wBvSZWTN3ZXiDIrEDmN1lintsKkSRr/GQdT0d77HUR6du52OGG1ZuHm4YCmSM7GpBZp4XYhP46PlB6gfuYClqnWo5dvzW/1haMJvLkMZ90ITaj3heyuK3O11fN2/ERtOlKPvyuaMzvqaRje7jTZVHaexYRzL/1rDSzv60r1NI56r4VPsC3A8KoskXleuPLg0uL//vavdCcXPN9suMERebX6+mE5Mb/joYwfc7HSEhXVm8dodDFRvQCfl8mHu54z9XwDfDmlV6APA07L1jFy8lfeT3qecbEouTxn9Wes/nm+fUnc/WZYIqVKbC4e8CZJjqCudYXb4SUa2qvBUji8IjyIlK5fP1p8id/8SFqp/wl7OBEz9+T/W90LVaASzO1Sy6JQLj6N5sDuBrw1g9BJfXkuaQRPVCQD6qzfRNuYgn8zthX2dXrzeJhhXC5S2t5TtZ+P5+qff+UKZiY9sqhoQpbgxyX4y3w5s/kjnSEmSeL5ZLVaubEIP9VYcpAzKRq7gWFQdMV5UeGJGo0JyZi5xqdlEJ2cSk5RFdFIm0cmZJCbewJBwEYf0SwRJ0QTJ0bSTohkuXTV1P75Po06uomKPsRLrjPW57NGKZjUqMbW2r0WmnQDTZ0L2qAg3Z2gJkaPYE5tK2wcUBcuvrFwDX2+9wOltv/KhajG+6tsTPV8wevMhL9O47fP82DiwxCcb7at40bh8bz7fUJuf9/3Mm/Iy/OV4ZEnhRdV2Omfs4rcVLei9qTsvtG7E87V8S3wLmEUSr8DAwAcOmDYYisZ8CcKTibqRwdW9y82Vtc4YfXGv0fGxT4wv1fdn6MnXaXDpFJXkKwTLVxkcM4Vxvzoyq1fdQrtQS0zPYeR3m3nz+nvmbn7RigsTbd/nm94Nn2pcYdV8WHegPq/JK5ElhUoJmzh9rSUVvRyeWgyCcC96g5Fl+yNZu3EjY/Tf0UBzyvxajOLCW4yhT69uhFW1/HQLjyvA1ZbFrz3DjHVl2bh/IW+rl2Et5eAjJTJLPY9Dhzbw5sGeBNXryJDmQYVa6CcjR88n60+TvXcRS9Q/oLs5Ru6i0YsJdh8xe0hHnGwevQpb5xo+9FvfhR76rQC8rF7Hh3/3Zv6ARgURvlAEZeYYuJaShcGoYDAq6I3Gm18VjDe/GowKOQYjGdkGMnL0ZOQYSM/Rk5ljID3bQHq2nhvp2WSn38CYnoCcmYgmOxEnUvEgCW8pAR8pgZpSAt5SgrmgDg/5VTUqEmcUP3Yaq7KPauT6NqR+RX+GVvMiwNW2QL4fAT6eRF1zw1e6TrAUxZKYlIdv9Jj2XEzg8z+2Mijla8ao95uXZysa5uq7cD54MFO71LzvGM2SyE6nZmLnKlxoOJ6P13XA++yPvKZeiZOUjlYy8JJ6M90zt/LHqqa8tKk77Zs1onsd3wIf1lFYLJJ4HT58OM/z3NxcDh8+zOeff87UqVMtcQihCJi18RRj5J/Nz2cbu/NOy8cvBy9JEtO612HoF+NYkDMeJymd5qqj3Dg9iTd/mcLM7rWf+h2P83GpvLNkI1PSJlFRNg2+TVTsGKOdxOdDns0zV87TUK+sC19Yt+K13JUAdFXtYMXhq7zdQSReQuEwGBX+PBbD0k176Zq0hGWqbcjy7W5CP+tbsMZrOFN7NiHQrWAunPLDRqvmg+eqs7PKRPr/WpdXs76jtcr0N6uWfJ7FfMTh/T/zwd4uaCt3oEf9cjQo5/LUys/rDUaWH77Kbxu3MSrza5pqjptfO2AM5hPH95j7Sns87B8vKdSpVTRr3IzN/9SkteowZaQEvM4t49CVSiWya5Nwtz0RCSz8fhHVpAhkjKgxopIMqDGgQkGF6f8a9FhL2diTjSdZ2EjZ2JKFNdnYSpk4k4Zaut1N7n6tVvdjUCQuK54cV8py1FiOc+oK4B1KpQAfGpd3pU+AC9bagu/tEuxpzxmjH76q69hLmdyIuQjUssi+kzNy+XjdcdSHl/Cd+hfsVZnm17YbqvGl9VCGdG/D2CrFt3jGkwpyt+Or/o3YF1GREWs70yB2GQNUG7CXMtFIBnqqt9ItexubNtRhxKZnqVCnLQMal8XPpXhWQbwfiyReoaGhdy2rU6cOPj4+fPLJJzz//POWOIxQiPZFJGJ99H+EaKIAOGQsj2fdF/L9gXC10/HBwC6M+iaBBcqH6CQ9z6n+xfrkBF5dNInPejfA+SklO6uPRPP7Hz8zT5qNu5wMQLziyGuq95gyOP/v8UmoZInQWvU4urss1eUIqsmXmHpoL4b2FQutRVAonbJyDawKv8ryLXvpkPIr36u2YKW+XaL6stGDD5SXaRzWnR8aBRbZ388mFdyo9VYv5m+tx7Idyxkv/Y8KsmlmoJryeb6SPyPmzGJ+PdmC2fbtqVm9BmFVvQj1dSyQJCw5I5ffDkay8d8DdEn9mZ9UW9GobvcO+UHflr3Bb/JdjzrY5bMwSf9GgQzZ0YvWRlOiOVK9kjfXPc/iV1sU63nNhEejliXaygcZoN5Y4MfKUVRcU1yIwZUoxY0LRh8u4kO6fTl0HkEEeLhQxceBnr5OlHOzLZTiNiFe9uxX/GiN6fNglXiaHL0RrfrJbvRuOR3H17+tZULuPGppzpuXxysOTDX0w6PhSyxpE1wsp7MoCPXKuvDjyPZsPVOLVzf1ol7sLwxSrcdBykAlKYSp9hPGfo7uX8LnezqQHdKZ7vWDaFrBvcj+fXkcBfpbEBISwv79+x++olCk5eiNfPbHNhaob7d2zaIPs1o/2Zijyj4ODOnzEqO+T2GO6gt0kp72qgP4Ro7k1S/GM653B+oEPvo8HY8rLjWLKSvDKXd6AYvUy8139K4Y3XnT6gOmD+lCUCGWnn6+pi8/72xCdTkCgCaZ/7Dn4rM0Ll/wBT4E4UJ8Gj/tucyZg1t5zrCeH+VdaNS3E4MUxYY5+q5cDe7DpGdDC6x7kCXZaNW80S6EqLqvM2tjO/THVjBMXmlu5faWEnldvZzXM5dzbHcg63fWY6Z1fdzL1aRuOTfqBDoT5G6XrxZ5o1EhIiGdPRcT2Hr8CnLENl6U/mGZfAiV+nbL4VXFlcnGl6nXridzm5Z9ogTJTqemdcu2rNnYgE6qPbhJKVSNXMqOc1VpFuye7/0KxYNKljCSv6QiS9GQgY4MrLih2HFDsScRe9NXxZ4b2JOgOJBh7YXi4IuNsxdeTrb4OFlRxsmGTu62lHWzLfRx23cK9rTnR+PtyoblieRSQjrBnvkrspOZY2D6nydQHfiW/6l/RiffviH1k74laz2H8t4LjajsI3qq/JckSbSs6EGLkPZsP1eb4Rt7EhrzO/3UG/GUkgCoLkcwS/6K+As/8sfZ5nxtE0ajevXpXtcXb8fi21XTIolXSkrefrKKohATE8PkyZOpUEEUBCju5m45T9/k+TjcbDr/Td+MFmHPWWTQa9MK7hj7DWPo/6yYK3+KrZRNFfkyC7PHMmPBAZbX6s/odhUfu5vNg6Rm5bJwRwThO//kbeU7Kmoiza9tN1Tja9cJfDGwdaF/sEO87Dnt1g590lLUkpHnVLuYfTBSJF5CgYlNyWLdsRj+CT+HX/R6XlL9TRX5Mtxx7ZSu6FhqaMM/rr0Z1bkBjYKK3++jr7MNn/WozZU2lfhmWzfiDv9Jd/6mlXwI1c35sqrJl6gmXwL9rySdsWX/qYqsMAZzQfIj2yUEB8+yeDla4+mgw9VWh04jo1HJqCSJ9JtjZRLSsom6kcm1+ESM145QIfcMdeWzzJaPYqvOzhNTqmLNQn1Hjvr14f0X6llsvrE+DQLot70PHXL2oZaMvKpey8urn6HhmC4lfhB7aaeSJH41tGCvsRJ6ZBRJjSKrQFJhlNVIkgpFVoOsRtHaIt18qHS2WFnpsNWqsNGqcbLR4GKrxdlGS/DNry62WpxtNY9c0bgocLTWkGgbBDfzo2A5ijPXUvOVeF2MT+Pt7zcxIvkzmmmOmZdfMHrzAa/QqkNX/tew6PYAKCokSaJ5sDvNKrRj/6W6fLBjELozqxmkWmc6/wLuUgpD1WsYmrOG3dsqM2NLS9KDOtK5djnaVPJ8Kt1ULUlSFOXuWp6PSZblu+7KKYqCn58fP//8Mw0bNnzSQxQZKSkpODo6kpycjINDyb+LsS8ikZ8XzuRzzdeAadzTSJdv+GFkR4tW4zl4OZGZP6xkWu5MguQY8/JwYxBzlW741e1En4aBT9QCFXE9naW7L3H64FaGGH6mueqo+TW9IjPX8BzXQkcx+bnqReYu3YLtFym/aQAtVUcAGGR8j9nvjC61Ew/+V3H9PBaVuBVF4VRMKtvPxbP7ZAQuUZt5Rt5NM/koWilvUaRkxYYlhvbsce9G/9a1aFfZq8jPhfWobqTnsPzwVTbuPkjtpA2EqfabW5rvJ1PREqs4E4sziYo92WjIUTQYkLGVsrAlCxcpFV8pDnfp/oP4YxQXlulbccCjK6+E1aN5sLvFuwH+sv8K+lWjeUm9GTBNj3G97VyGNHv4xPelQVH5PD6OR4n5VkENtSwjS4jupcDL3+1k/pVOqCUjJ4wBrG/yG2PbhTzWPraeiWPBsl/4XPnU3DoDsFDfgb3lRvLB87XxKUXFMyztSkIGS3ZFcP7gJrob19FOPnDX36MkxZZ1hnpskJvhVrkFnWv50TjItdCqRD7OOcQiide2bdvyPJdlGXd3d8qXL49aXbL6tBbHE3R+3UjP4dXZv7Ao503sJNN8VqP0oxgy9M0CKUkcl5LFmz/uoFP0HLqpt+d57ZgxkF8MLYnwCqNepSCaVHCjsrfDA+90pGTlcjwqmb0Riew/fprA61vppdpsvoty576nqkfQq/MzdKlRxuLv60nEpWQxZcY05mq+AGCtoT43On5L34aBhRtYEVFcP4+FFbfeYORMbCqHryRx6FICsecOUC3rIM3ko9SWz6KT9HdtE24MYqmhNYmBz9K3WaUCSQyKCkVROBqVzIYT1zh89CghyTtoKJ+knnwaZynNYsdJUOzZbKjFZuqiqxTGSw3LUa9swRX0MBgV+ny5jq8SXzG/j37GSXzy5vBCreRYVBTH80hxjLkomLbuFN33PE95OZpsRcOocn/yTf/6j7z9T3uvcHD1PKapF5rPl9cUZ942Dqf1Mz14qb5/iT0/Pm2pWbmsDI/mrz1HqRK/jp6qLXluzN8Srbiw2tCIbboWhFRvSIdq3tQJdHmqrY1PPfHavn07jRo1uivJ0uv1/PvvvzRr1uxJD1FklJaTXY7eyJAFW5gQM5pKsmmetl/0Lbje+jNGtCxfYMfVG4ws+fcSOzctZwJLzGMvzK8rMuFKeXYZq3BG8SfVPgiVvRdqG0dklRq9Ppfc9BtIyVE4Zl6munSROvJZQqULyFLeX/UoxY3Z+heQqvdgwjNVi9RcPnca8cMeJl/ojruUTK6iop/jYn4a01mc3Cm+n8enEXdyRi5n41I5G5vKudg0Ll6NRY45RGXDGWrK56kln8NVSr3ntjGKC+sM9dmsbUm1us3oVde/SFUqfBoURSHiejp7IxLZdyGe+IhjuKWdIUSOJESKxE+Kx1O6gYOU8cD9XFOciVTcuWD04YgSxBXrSriWq0mbKj60CHF/aq3Xh67c4PdvPmSa5jvANB3IF0ELmNevYak/lxTH80hxjLko+P1gFNYrB/KMah8Afazn8eP4Po+07Xc7I7j612dM1PzPvGyPsRKznN5lap+WlPcovDHhJd2xqGSW7bvM1SOb6Wz8mzB5P7ZS9l3rnTOW4S9jXfZoGxFQpQHtqnrTKMi1wLvEPvXES6VSERMTg4eHR57lCQkJeHh4lKh5vErDyU5RFCb8fpg2R8fSVnUIMPVb/qjMfBYOaf5U7iJEJ2Xy5d9nSD28gldVK+9qpfovoyKhgHl8xoMcNZblR0Mb0oKfZ3RYtXwPrH1adpyL58j3YxmpXgXAJ7ndafXKDGoHFFzhkeKiuH4eHzVug1Eh12Akx2AkV28k12B6np6jJykjl6SMXJIzc7iRkcu15Cyib6SRmRiNnBKFS/ZVguUoKkhRBEtR+ErX77r5cKcrRnf+MdbkH1UT3Cs15ZkaZWhS3v2JK36VJLeS2dPXUom6kUFcSjZJyUkY0xNRDDlgyAGjAbQ2SDp7VNYOeDo74udiTTk3W0L9nAp17Oj43w7R+9hgQuWLAMzVd6HMC9PoWtP3IVuWbMXxPFIcYy4KjkUl8/fXYxij+QOAYbmj+WzS+9hoH9w764fdl7i69mPe1iwzL/tR35q9FScwo3uth24vWEZ6tp51x2JYf+gCdpc30VneRTP5KBrp7jwj0ujOBmMddqga4FyxCW2rlKFpsFuB3Ox6nM+jRX5TFEW55x2zhIQEbG1L113S4k5RFD7dcJqQIzNoqzYlXcmKDW/rJvBlrwZPrenWx8ma6S/WILJVMD/s7sG0Q7tonb2ZFvIRysvRd63/oAtKMN3d3WSszS5NI6rWb8aw+gGULSZ38BsHuTHXviPDM1YjSwq91P/w+e4IkXiVcJ0/34jX9X/R3JxnRyPp0aJHgx5bsnCS0nAiDRcpnXJSKl7cwEtKvP0H6CGzMSQptuw3hrDDWI3z9vUpF1KN5iGefFvBrciMcSxqHG001A10oW4BVlstSOM6VGH4qeH8zzAerWRgmGo1fVfVp2G5wXg5ii6HQslX3sOOr/AzPw8mkvNxaVT3dbrvNn+fjOXo2q/59I6ka7b+eQxNxzOnXUipbzF+mmx1arrV8aNbHT/iUhqw5mgMiw6dpGzsJjqr/qWOdNZ8Pegnx/Oy/Bcv8xfxpx34+0Rtxis1yfBtTINKZWkR4k5FL/un/vN7osTr1vxckiQxYMAAdLrbXbUMBgNHjx6lUaNGTxah8FTN+fscNjunMUi9HjB17RttfIOJA7oWylgAPxcb3n2mMrlhFdl1vgs/nb3OmbMncUg8RgWiKCvH4EwaTlIaoJCLmgzFimjFlauKG6elsmR7hBJSrhxtKnsyNMC50AZf5pcsS7RqUJetf4fSShWOr3SdzOPriEupgocYn1FiOUlpfKudZZF9pSlWnFfKcMIYyCFjBWIdq+HmX5naZV0ZVN6t1HUjLK1c7XQM6Posc37ey5ua31BJClOMc3n316osGNysxBRLEYT7sdaqSHWoADfnNw6WIzlzLfW+ideF+DR+WPYjC9ULzMtm5nbHvu0EhrUIegoRC/fj4WDF4CZlGdykLBfim7PmSDSzj56kbMI22sv7aSifNN+IdJdS6KXeQi+2kHvtCw5Eh7BiUyjHrevhX7E2LSp60Li8G/ZPoev3EyVejo6mAguKomBvb4+19e0uFFqtlgYNGjBkyJAni1B4KhRFYdbGM8g7ZjBCvdq8/B39y/To1YeqZSxfTONxaFQyLUI8aBHiAVQmM6crF+LTiLieTmRmLsczclAU0KhlbLQqvB2tqe5kxWsedsWq3O39vFjblwl/t6cV4QAMlNew5N9ujAurWLiBCQVH/XgTiN9Q7IhWXIlW3Lh688ZDBGXIdgnGyascFbwcqeTtwNv+ThaZCkIonjpU82bj8Vc5cuogofJFysvRtLv8OfO2+PDaE87NKAjFgZ13BbIvaNBJuYRIUfwSe++xrjl6I+//tJVZ8hxzVb3v9W3RNH9TJF1FTJC7HaPbBEObYC5db8uGE9dYcOwcbtFbaa86QHP5CNZSDgAayUBD1Ukaqk6CfhlXj7myLTyU8Up1Ur0bElqhLI3Ku1I7wLlArh+fKPFavHgxAIGBgbz55puiW2ExlWsw8t7ycCodmZ5nhvv3cgdS+7nXCKvqXYjR3Zu1VkXVMo6FnhA+La52OtxrdOTskR8Jlq9STz7DnD2bSG9ZHlud6FteEhnUtnyc25Nc1OSgJhc1uYrpa5akIUfjjNHaGcnaCZWtC072dvg6WePjZE05J2uaOFkT4Goj5moS7jKpSyivXHidJbnjsJGy6aHeyrh/vmVXwHgxT6BQ4gV7OXHufBmqSpcIlK5xISbhnuvN+fss/a7PxlOVBMAOQ1WOVZ3AJ22Dn2K0wuMKdLPl1eZBvNo8iNiUFmw8GcvIY5eQL++iMYdpKYcTIMeZ1y8jJdBb/Q+9+Qdj/Bccjwvk3x1V+U6qCv4NqVPBl8blXani42iR4TYWuWKbNGmSJXYjFILE9Bze+mk3L175kA7q/eblH+T2pWKnMfSo61+I0Ql3GtS0PN8ceoZP5G8B6GVYza8HnmVg47KFHJlQEKZ3r0+OoS5alYxGLaFRmSbo1apktGpZTMwp5JuTjZY3enfi3e/OMEvzFQBT1IsZsCyYsq/1EXMQCSVasJc9ZxRfqnIJtWQk59ppoGmedS4npHNu5++8efO6KFGx40vHN/nhhZpiTFcx4ulgRd8GAfRtEEBGTmN2X0hgwelYzp86QuX0vbSQw6kvn0YnmWbVliWF6lIE1eUIhrKG7Cg1h64Es2hjC0LavWyRls58J161atVi8+bNODs7U7Pmg38RDx06lN/DCAXo8JUbTP/fWj7InkEllalse66i4m39EGp2Hs5L9QMKOULhTuU97Eit0JW4iF/xkJIIk/fTa/u/9G0QUOzGrQkP5+9qU9ghCCVYg3KuHGo7mKV/n+El9WaspFw+yZ3BmEWefDeiI3aiJV0ooUI87fnd6Ac3e5G5ZVwkKSMHJ5vb3btn/nmEt+UfzM8nGwYxqXcrUXioGLPRqmldyZPWlTxRnqvGhfjn2HomniWnrqC68i/1OEYT+bh5CiUAnaSnoeoke4yVaFDOMkWV8n1m7dKli7mYxnPPPWeRYISnQ28wsmBHBMf+/pGFqq9xkE2jTNMVHa8bx9C990DaVfEq5CiFexnUvCJLzrVnnOYXVJLCc+m/szK8OS/WLt3loAVBeHxDmwUx7OJYql26SHU5Aj85nvFJH/DGj858NaCxuKEjlEiBbrZckG5XNgyRIzkbm0a9sqYL61MxKTif+ZVATSxgmqvLo35PqviUjqENpYEkSZT3sKe8hz0vNy1HZk5T9l9KZOWF60w9ewGnuD00ko7TWD5BgBzHIU0or1loaEu+E687uxeKrobFx/m4NCb9spPn4+bylXrH7eVGH97VjmNc3+eoHeBciBEKD1I30Jk53s+TGr8aeymTbqptvLRpJ11qdBdjeQRBeCyyLDGzZz2GzHufL9LexFtKpJZ8nk6XPmLK6k/44LlqoluVUOJoVDLZziFws6ZGBSmKs7Gp5sRrwbZzvK7607z+HFV/vm4jxnWVZNZaFc2C3WkW7A4dKpGU0Y49FxNYeD6B8+dO4OhhuZ5FFu1LkJOTQ1xcHEajMc9yf38xTqiwpWXrmbv5HFf+/Y3PVIvwUt0wv7bG0IDffcbxZZ/GeNiL8uRFmSRJDG5bi0X/C+N19Qo0koHn05ax/FAjMR5PEITH5mij4ZNBYYyZe53vjO9jK2XTSbWHuIPT+NxmMmPbi8qpQsnj6FWWtBQr7KQsgqUodtysbBiTnEnOsVUEaEzFF7YbqlG/eRscrQu+zLhQdDjZaAmr6n2zuFxVsvV3T9CcXxZJ386ePUvTpk2xtrYmICCAsmXLUrZsWQIDAylbVgz8L0x6g5FfD0Qy+JP/0Wj3EL5Sf4aXZEq6UhRr3sp9hRMNZ/PdKy1F0lVMtAh2Z79XT1IU0xigF1Xb+WPTDnL0xodsKQiCcLcAV1vG9u/GG4ZRGBRTC9dg9V+od3zM11svFHJ0gmB5wV4OnFfKAOAvx3M55joAyw9dpaf8t3m97+lMv4ZivHtpZ8my8hZp8Ro4cCBqtZq1a9fi7e0tuiYUAXqDkVXh0fz69y66pP7MUtVW1KrbF+bbDNWZZz+K8T1aUzvAMgMGhadDkiRebV+bhUs68obmd9SSkW4ZP/PrgSb0aSD+QAiC8PjqBrpwrdsg3v41mZkaU+XU19UrmLbJiiWa8QwQ1VOFEiTY046zRl9qyKYbC4b4UyhKc3YcOMww+SQAEUZPPGqE4Wz7eHMqCsKDWCTxCg8P5+DBg1SsKLokFLbkzFx+OxDJpl376Jz2K/9TbUWrvt1EGqW4MV3fhzKNevB92xCstaJCT3HUpLwbC3x6khT3F05SOs+rdtB702aeq9lPVCMTBCFfOoX6kJ79GpNXZTFZY6ro9o5mGdPWwXz9WwxtLiaNFUqGCp72LFNuF6XyyLzEtrPx1Ez6G1mjALDc0JQX64jCVYJlWaSrYeXKlbl+/boldiXkg6IoHIlM4t3lRxk9bTb+G19mWeZQXlJvNs+2nqpY83nui3zgv4Q3Rr/FO89UFklXMSZJEkPDajJf3wkAlaQwLGcJ32wT3YIEQci/nvX88evwBp/kdjcve0ezjOxNH/HZhtMoilKI0QmCZQS42BBxR2XDCnIUX225QHvV7flM9zm0pZa/KDYmWJZFEq8ZM2Ywbtw4tm7dSkJCAikpKXkeQsGIuJ7OnM3n6PXJ76yfP54B4T1ZrPqIdqqDyJLpj2OaYsWX+ucY6rqYWv2ms2BwU4Lc7Qo5csESGgW5cTGoH1GKGwAtVEc4tWM50UmZhRyZIAjF2eAmZbFpPS5P8vW6ejkOOz5gypoTGI0i+RKKN7VKJtvldqXCECmSi5cizF0PTxoDqFEtVAydESzOIn2S2rRpA0Dr1q3zLFcUBUmSMBgsVw2kNMvWGzh0OYl/Tsdy/MQxKiTvoqNqH6PkU/Cfgjsxigv/07fhiGdXBrerzcgQD3ECKYHGPVudT+b04gv1lwC8Jf3IZ+vb8lnPOoUcmSAIxdmIVhVYqHmbD9brmKT5HwBD1Ov4bV8ao5Lf5dOedcVkskKx5uJVlpRkaxykTCrIV2mpOmx+bbOxJq0qehRidEJJZZHEa8uWLZbYjfAfiek5HL+azP5LiRy9EIU6ej91lOO8IIfzrhx5V7IFsNdYkR+N7aBSJ/o1Ls9bAc4i4SrBynvY41inB4cO/UUt+TwhchRWx5ZyqFGQ6CIhCMITeblpOZbpxjF+lRXT1QuRJYVu6u34nE1g8PxJfDGwBW52usIOUxDyJdjLnvOny1BLOo+vdJ1O8m7za3vVdRgm5jQVCoBFEq/mzZtbYjelUq7BSExSFpE3MohMzCAiIZ3z0Qlkx5zEK/M8laXLtJXPMFq6hEp17+4dF4zerDA0Yb99a+rXrs079fzwdrR+yu9EKCyvtwnm9fAB/Mh7ALyl/oWhf7Tmx1EdLTbhnyAIpVOvev7YaMcw6jdbPlXPw0rKpbHqBB7xYxg2dyLTBj5DBU/7wg5TEB5bBU97zhp9qSWfB6CZ6hgACYo9LiENxd9PoUBYJPE6evToPZdLkoSVlRX+/v7odKXnrtipmBT+On6NXIORHL3R/DXHYCQ9K5estGSMGdeRMxLQZifgSxx+Ujx+Ujw1pVjKSTFoJFyi8rEAABcWSURBVMM9W7QAjIpEuBLEZkMtDlvXp1yVenSt5cdYfyfRulUKudrpaN7qGVZs+ouuql04Sel0S5zP4l2VGdKsXGGHJwhCMdelRhnc7F7j5f+5MVuZiZuUQgX5Kl9nvsXYeVfp/mJvnqnuXdhhCsJjCfa050fl7qqFW42htKzkVQgRCaWBRRKvGjVqPPCCX6PR0KNHD7755husrEr+JL1nY1Ox2vYhz8oH0aJHI+nRkosWPTZkm5KqWx5xMvRTRj/2GyuyT6nIDY/61K4cQvtKHoz1cUSWRbJV2g1oHEjfA6/SKvkwjlIGL6h20H/TcjpWH0EZJ9H6KQjCk2lc3g334QMY9p0rH2dNIUiOwU1K4Ts+YvovFzgSOZJxYRVFK4FQbPi72BAh+921fKuxJlOCxfguoWBYJPFasWIF48eP56233qJevXoA7Nu3j88++4xJkyah1+uZMGEC7733Hp9++qklDlmkaVUyntINguWrj71tjqLiguLDKSWAU0Z/zsuBGDxDqRQUQP2yLnTxd8HR5hGzNaHU0Khk3nqhKTO+7cU0zXcATJQW8uHKRswf0KiQoxMEoSQI9rRn3mvPM2axM6/GT6WZ6hhqycj7mqWs2XOBwZHj+eSlRnjYl/wbrELxp5Il9J6h6ONl1JIRgFxFRbJPUzFpslBgLJJ4TZ06lS+++IL27dubl1WrVg1fX1/ef/999u3bh62tLWPHji0diZda5rqiI02xIgc1OWjIVdTkoCYTHYmKPTewN31V7LmquJGg8cLgFICNSxmCPB2p5O1AD28HAl1txB1E4ZHUDnDhj1r9OXhkO7XlcwTJMVQ6v4A1RwLpFOpT2OEJglACeNhb8d2wdkxc4cnRo7MZqV4FQCfVHipGj2TU7DG82q0LLUVFOKEYaFI9mO2bqtNKFQ7AGmNDwupUKtyghBLNIonXsWPHCAgIuGt5QEAAx46ZBivWqFGDmJgYSxyuyPN0sOKPSu9wUCWjUclo1aavOrWMlUaFi60WZ1stFWy0ONtqKONkjaO1RozPEp7Y+A6VeeXEUJYa3kItGRmuWkW/lfWoX3YgHg7iLrQgCE/OSqNiRrea/BTwIUPXlGem6itzSe7v9W8z/X9H2VbvVSZ0rCRKzgtFWs96/rz871Ck9K/JRMfvrkNZXLtMYYcllGCSYoFp6GvWrEloaCjffvstWq2peTY3N5chQ4Zw5MgRDh8+zK5du+jTpw8RERFPHHRhSklJwdHRkeTkZBwcHAo7HEG4y+oj0UT89i6vq5cDcNZYhs8CFzB/YKMSl9wX189jcY1bEP7r8JUbTPvfWj7I/oTK8mXz8n8MNfjGeSxTerckxKtoVz0sjp/H4hhzUZWYnsOaI9FoVDLP1fTBRmuRNgmhFHmcz6NFfrvmzZtH586d8fX1pXr16oCpFcxgMLB27VoALl68yPDhwy1xOEEQHqBTdW9ePzaU42cPUVW+RLB8lVoX5/HbAX+61717ILEgCEJ+1fR35pvRPXjn1yDqXpjDYPVfALRShVMteSQT5w6hSquevNo8CI3oNi8UQS62Wvo3CizsMIRSwiItXgCpqaksXbqUs2fPAhASEkLv3r2xty/ad7oel7jLJBQHiek5DPv8R37Qv4VO0mNUJAYwmamvv4Kfi01hh2cxxfXzWFzjFoT7URSFH/deYdufPzFd/gp3KcX82ipDI352HcF73ZtSxcexEKO8t+L4eSyOMQtCSfU4n0eLJV6lhTjZCcXF3ydj2b90Em9rlgEQpbgxzm0eS4a1Q6suGXeei+vnsbjGLQgPczY2lYlL/2HIjVm0Vh02L7+uOPCBfiBlm7/EiFbl0amLztiv4vh5LI4xC0JJ9TifR4tefZ08eZL169ezevXqPA9BEJ6+NpU9uRH6CvuMIQD4StfpH/cJn6w/VciRCYJQUgV72rPktWfZ1/Ar3sgdRpJiC4CblMKXmi+otGMEfT9fwc5z1ws5UkEQhKfPImO8Ll68SNeuXTl27BiSJHGrEe3WQH6DwfCgzQVBKCATu1RnYMSbfJsxBmcpjfaqA/y7+xs2B71L60qehR2eIAglkJVGxdsdKxNebQJDf63PwKS5tFcdAKCDaj/N0o8ye8kL/Fp5IO88Wx0vR1FxVRCE0sEiLV6vv/46ZcuWJS4uDhsbG06cOMH27dupU6cOW7dutcQhBEHIBzudmkl92jPeMMy87B31Uhb+upzopMxCjEwQhJKuhp8T37/emeNN5jFKP4rriqkLjq2UzbuanxhxZiDjPvuKhTsukmswFnK0giAIBc8iidfu3buZMmUKbm5uyLKMLMs0adKE6dOnM2rUKEscQhCEfKpaxpGmz/ThW/0zAOgkPR8bPuetH7aSlStaowVBKDg6tYqx7SvyyrA3GeW2gP/p22BUTL1hQuQofpA/wGXja/T5fAWbT8Uihp0LglCSWSTxMhgM5uqFbm5uREdHA6YJlM+cOWOJQwiC8AT6NAjgWMVRhBuDAAiQ43g1firvLw8XFzqCIBS4qmUc+XFEe1SdZtFHmsYRYznza8+rdrI4bRjHlk5g0LfbOBGdXIiRCoIgFByLJF5Vq1blyJEjANSvX5+ZM2eya9cupkyZQrly5R6ytSAIBU2SJKa+WJupthPM3X2aqY5R/thnLN51qXCDEwShVJBlid71/Zn71sssq7aId3MHmYtv2EjZjFYv5+Po/nw/7yPG/XqI2JSsQo5YEATBsiySeL333nsYjab+2VOmTCEiIoKmTZuybt065syZY4lDCILwhBysNEwd0IE3lDHkKqZSzq+q/+TY+gXsOi8qjAmC8HS42Gr5uFtNnn9lIsNdFrJY3958TvKUkpip+ZYBxwfw9idfMH3dSW6k5xRyxIIgCJZRYPN4JSYm4uzsbK5sWFKIuTOE4m7DiWvs+OljPtIsBiBL0TBI/pCPhvelnLtdIUf3eIrr57G4xi0IlmY0Kqw6cpVl67bwctZi2qkO5nl9j7ESX0m9qNW0A4OblMXeSmPxGIrj57E4xiwIJVWhzeN1JxcXlxKXdAlCSdC+iheuzYexTN8SACspl9nGj5nw3Vqup2UXcnSCIJQmsizRtaYvP4zrzblW3zLAOJFjxkDz6w3kU/wgTaTGtpd5dcZC5m+7QEaOvvACFgRBeAJP1OI1aNCgR1pv0aJF+T1EkSPuMgklgdGoMPJ/uxlwYTT1ZFMBnHPGMkxy/5zvXm2LtVZVyBE+muL6eSyucQtCQYtLzWL2xjNkHP6VUfLvlJOv5Xl9vaEui7W9aNO8Jb3r+2Ore/LpSIvj57E4xiwIJdXjfB6fKPGSZZmAgABq1qz5wMpoK1asyO8hCkRgYCAODg7IsoyzszNbtmx55G3FyU4oKdKz9bw8fwMfJYwlSI4BTN16vg/6nLl9G6KSi36LdXH9PBbXuAXhaYm4ns6cTSfRHP+VUerl+Ep5x6GuN9Tle/ULNGjchgGNAnG0yX8XxOL4eSyOMQtCSfXUEq8RI0awbNkyAgICGDhwIH369MHFxSW/u3tqAgMDOX78OHZ2jz+eRZzshJIkLjWLEV8u5+vs8bhJKQCsMjTiYK0ZfPBctSLfXbi4fh6La9yC8LSduZbKnI3HcDnzC6+pV+IhJeV5fZuhOovk56nUwDQGzN1e99jHKI6fx+IYsyCUVE9tjNe8efOIiYlh3LhxrFmzBj8/P7p3786GDRvE3ECCUAx42Fsx/eVOjJImkKloAeii+peyBz/i0w2nCzk6QRBKuxAve+b1a0S3YR/wrt//+DD3JWIVJ/PrzVVH+V6aTKvd/Xh75udMWnmMKwkZhRewIAjCAzxxcQ2dTkevXr3YtGkTJ0+epEqVKgwfPpzAwEDS0tIee3/bt2+nU6dO+Pj4IEkSK1euvGudefPmERgYiJWVFfXr12ffvn2PdQxJkmjevDl169Zl6dKljx2jIJQk5T3seb1fT8YYRmFQTC1cA9Ub0O38mK+2ni/k6ARBEKC6rxMLXm7GM69OZVLgT7ybO4hIo7v59XryGRaqPqb7oZeY9fmHvPbjPsIjkwovYEEQhHuwaFXD/7d3/zFRnHkYwB9Y2AWjZRXOXaGsYtMcrfgDQSjapGkk0dbWH216bYJ1pTmNFhUk0XI2ltw1CImXnLXamjYRk6uW1gSstZc03FrJkVIQDCi1Ih705KoLNQYW1KLd+d4fl05dAcsPZ2eGPJ/kTeCdl+XZzeYJLzs7GxoaipCQEIgI/H7/qG7jxo0bmDt3Lvbv3z/o8U8++QT5+fkoLCzEmTNnMHfuXCxZsgRdXV3qmnnz5iEpKWnAuHLlCgCguroaDQ0NOH78OHbt2oWzZ8+OKivReJE+MxrP/+GPKPh5vTq3JewYeir/ir/XfK9fMCKiu8x3TcaB7EVYvenP2P37I8i/sxGtSpx6fFbof/C38Pfwp9aXceLADrjf+ycqz3dCUXgWDhHpb8yf49Xf34/y8nIcPHgQ1dXVeO6555CdnY2lS5ciNHRs+7qQkBBUVFRg5cqV6lx6ejoWLFiAffv2AQAURUF8fDw2b96MgoKCEf+Obdu2YdasWVi7du2gx/v7+9Hf/+sltnt6euByudDR0cHzqmncKW/4Ly58sRc7wo+oc3+5sxpzn9+MFclx9/lJffh8PsTHx6O7uxtRUVF6xxkSe4RIG+3X+lD6r3/jxrkv4Lb8A3NCvw847pMIZP+8Ewe2vozfTYoY9DbM0CPsECLjGlGHyBhs3LhRJk+eLHPmzJE9e/bIjz/+OJabGwCAVFRUqN/39/eLxWIJmBMRWbNmjSxfvnxYt9nX1yc+n09ERHp7e2X+/PlSV1c35PrCwkIBwMHBYeDR0dEx4n4JJvYIB4fxh5F7hB3CwWH8MZwOGfPl5F0uF5KTk+979bPy8vJR3f69r3hduXIFcXFx+Prrr5GRkaGu2759O6qqqlBbW/ubt9nW1oZVq1YBAPx+P9atW4fc3Nwh19/7XyZFUXD9+nVER0f/5hXfftkBm+0/UswdXMw9eiKC3t5exMbGjvkVdi2NtkeM8BiPhllzA+bNztyjZ4Ye4d8izK015h69kXTImD55cM2aNYa/3PS9Zs6ciaampmGvt9lssNkCL09rt9tH9DsfeughUz2Jf8HcwcXco2PUU4PuNtYe0fsxHi2z5gbMm525R8foPcK/RZg7WJh7dIbbIWPaeB06dGgsPz5iMTExsFgs6OzsDJjv7OyE0+kMahYiIiIiIqLhMuZr6kOwWq1ISUmBx+NR5xRFgcfjCTj1kIiIiIiIyEjG9IqXFvr6+nDp0q+fHdTe3o7GxkZMmTIFLpcL+fn5cLvdSE1NRVpaGvbs2YMbN24gOztbx9SDs9lsKCwsHHB6gNExd3AxNw3FrI+xWXMD5s3O3DQUsz7GzB1czB0cY76c/IN26tQpPP300wPm3W63emrjvn37sHv3bni9XsybNw979+5Fenp6kJMSERERERENj+E2XkREREREROONqd7jRUREREREZEbceBEREREREWmMGy8iIiIiIiKNceOlkf3792PGjBmIiIhAeno66urq9I4UoLi4GAsWLMCkSZMwdepUrFy5Ei0tLQFrfvrpJ+Tk5CA6OhoTJ07Eiy++OOAz1PRWUlKCkJAQ5OXlqXNGzf3DDz9g9erViI6ORmRkJGbPno36+nr1uIjgrbfewrRp0xAZGYnMzEy0trbqmBjw+/3YuXMnEhISEBkZiUceeQRvv/027n5rqBFzjxfsEe2ZqUMA9giNDDskOMzUI+wQnQk9cGVlZWK1WuXgwYPy7bffyrp168Rut0tnZ6fe0VRLliyR0tJSaW5ulsbGRnn22WfF5XJJX1+fumbDhg0SHx8vHo9H6uvr5YknnpCFCxfqmDpQXV2dzJgxQ+bMmSO5ubnqvBFzX79+XaZPny5r166V2tpaaWtrky+//FIuXbqkrikpKZGoqCg5duyYNDU1yfLlyyUhIUFu3bqlW+6ioiKJjo6WEydOSHt7uxw9elQmTpwo77zzjqFzjwfsEe2ZqUNE2CM0MuyQ4DBTj7BD9MeNlwbS0tIkJydH/d7v90tsbKwUFxfrmOr+urq6BIBUVVWJiEh3d7eEh4fL0aNH1TXfffedAJCamhq9Yqp6e3vl0UcflcrKSnnqqafUsjNq7jfeeEOefPLJIY8riiJOp1N2796tznV3d4vNZpOPP/44GBEHtWzZMnnttdcC5l544QXJysoSEePmHg/YI9oyW4eIsEdoZNgh2jNbj7BD9MdTDR+w27dvo6GhAZmZmepcaGgoMjMzUVNTo2Oy++vp6QEATJkyBQDQ0NCAO3fuBNyPxMREuFwuQ9yPnJwcLFu2LCAfYNzcx48fR2pqKl566SVMnToVycnJ+PDDD9Xj7e3t8Hq9AbmjoqKQnp6ua+6FCxfC4/Hg4sWLAICmpiZUV1fjmWeeAWDc3GbHHtGe2ToEYI/Q8LFDgsNsPcIO0V+Y3gHGm2vXrsHv98PhcATMOxwOXLhwQadU96coCvLy8rBo0SIkJSUBALxeL6xWK+x2e8Bah8MBr9erQ8pflZWV4cyZMzh9+vSAY0bN3dbWhvfffx/5+fnYsWMHTp8+jS1btsBqtcLtdqvZBnve6Jm7oKAAPp8PiYmJsFgs8Pv9KCoqQlZWFgAYNrfZsUe0ZcYOAdgjNHzsEO2ZsUfYIfrjxouQk5OD5uZmVFdX6x3lN3V0dCA3NxeVlZWIiIjQO86wKYqC1NRU7Nq1CwCQnJyM5uZmHDhwAG63W+d0Q/v0009x+PBhHDlyBLNmzUJjYyPy8vIQGxtr6NwUfGbpEbN2CMAeofHNLB0CmLdH2CH646mGD1hMTAwsFsuAK9d0dnbC6XTqlGpomzZtwokTJ/DVV1/h4YcfVuedTidu376N7u7ugPV634+GhgZ0dXVh/vz5CAsLQ1hYGKqqqrB3716EhYXB4XAYMve0adPw+OOPB8w99thjuHz5MgCo2Yz2vNm2bRsKCgrwyiuvYPbs2Xj11VexdetWFBcXAzBubrNjj2jHrB0CsEdo+Ngh2jJrj7BD9MeN1wNmtVqRkpICj8ejzimKAo/Hg4yMDB2TBRIRbNq0CRUVFTh58iQSEhICjqekpCA8PDzgfrS0tODy5cu63o/Fixfj3LlzaGxsVEdqaiqysrLUr42Ye9GiRQMukXvx4kVMnz4dAJCQkACn0xmQ2+fzoba2VtfcN2/eRGhoYE1YLBYoigLAuLnNjj2iHbN2CMAeoeFjh2jLrD3CDjEAnS/uMS6VlZWJzWaTQ4cOyfnz52X9+vVit9vF6/XqHU21ceNGiYqKklOnTsnVq1fVcfPmTXXNhg0bxOVyycmTJ6W+vl4yMjIkIyNDx9SDu/tKQiLGzF1XVydhYWFSVFQkra2tcvjwYZkwYYJ89NFH6pqSkhKx2+3y2WefydmzZ2XFihW6XwrV7XZLXFycegnX8vJyiYmJke3btxs693jAHgkeM3SICHuERoYdElxm6BF2iP648dLIu+++Ky6XS6xWq6Slpck333yjd6QAAAYdpaWl6ppbt27J66+/LpMnT5YJEybIqlWr5OrVq/qFHsK9ZWfU3J9//rkkJSWJzWaTxMRE+eCDDwKOK4oiO3fuFIfDITabTRYvXiwtLS06pf0/n88nubm54nK5JCIiQmbOnClvvvmm9Pf3q2uMmHu8YI8Eh1k6RIQ9QiPDDgkes/QIO0RfISJ3fewzERERERERPXB8jxcREREREZHGuPEiIiIiIiLSGDdeREREREREGuPGi4iIiIiISGPceBEREREREWmMGy8iIiIiIiKNceNFRERERESkMW68iIiIiIiINMaNFxERERERkca48SIiIiIiItIYN15EREREREQa48aLiIiIiIhIY/8DHpEipM1ba+8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_modes = omega_proportional.size\n", + "# Compute the laplace variables\n", + "s=1j*omegas_bc\n", + "\n", + "# Compute FRFs\n", + "H_proportional_fromModes = np.sum([\n", + " psi_proportional[:,r,np.newaxis]@psi_proportional[:,r,np.newaxis].T/\n", + " (ma_proportional[r]*(s-\n", + " (-zeta_proportional[r]*omega_proportional[r] + 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n", + " + psi_proportional[:,r,np.newaxis].conj()@psi_proportional[:,r,np.newaxis].T.conj()/\n", + " (ma_proportional[r]*(s-\n", + " (-zeta_proportional[r]*omega_proportional[r] - 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "# Now plot them\n", + "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_proportional.reshape(omegas.size,-1).T,\n", + " H_proportional_fromModes.reshape(omegas.size,-1).T):\n", + " ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n", + " ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n", + "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "811671ce-53fa-48a5-8851-bfad6a333687", + "metadata": {}, + "source": [ + "And here is the general damped case:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4c6bbc32-5c2a-4f85-af36-18c8856b976a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_modes = omega_general.size\n", + "s=1j*omegas_bc\n", + "\n", + "H_general_fromModes = np.sum([\n", + " psi_general[:,r,np.newaxis]@psi_general[:,r,np.newaxis].T/\n", + " (ma_general[r]*(s-\n", + " (-zeta_general[r]*omega_general[r] + 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n", + " + psi_general[:,r,np.newaxis].conj()@psi_general[:,r,np.newaxis].T.conj()/\n", + " (ma_general[r]*(s-\n", + " (-zeta_general[r]*omega_general[r] - 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "# Now plot them\n", + "fig,axes = plt.subplots(H_general.shape[1]*2,H_general.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_general.reshape(omegas.size,-1).T,\n", + " H_general_fromModes.reshape(omegas.size,-1).T):\n", + " ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n", + " ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n", + "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "335edf60-cc3b-4bcf-8671-5f36dec4965b", + "metadata": {}, + "source": [ + "We can see that we understand the relationships between the modal parameters and the frequency response functions." + ] + }, + { + "cell_type": "markdown", + "id": "4d0912a4-8590-4b7e-83b4-860e436cd0ac", + "metadata": {}, + "source": [ + "## Solving for Modal Parameters from FRFs\n", + "The process of Experimental Modal Analysis involves measuring FRFs and then fitting modal parameters to those FRFs. Many mode fitting software packages utilize a two-step process. First, the poles (frequency and damping) are identified, then the shape information is found. Some curve fitters identify modal participation factors in the first step, which are essentially the mode shapes at the reference degrees of freedom.\n", + "\n", + "This section of the document will follow the implementation of the paper [Peeters, Bart et al. ‘The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation?’ 1 Jan. 2004 : 395 – 409](https://content.iospress.com/articles/shock-and-vibration/sav00272), which implements the popular PolyMax modal parameter estimator. This is the algorithm implemented in SDynPy's `PolyPy` fitter. Other modal parameter estimators may give similar results." + ] + }, + { + "cell_type": "markdown", + "id": "99330d1c-fe11-4cb4-91c8-339a638b8c7e", + "metadata": {}, + "source": [ + "### Setting up the System of Equations\n", + "\n", + "Let us consider the frequency response function that we may measure during a modal test. We understand that we will only measure a finite bandwidth during the test, therefore there may be contributions to the measured FRFs from modes outside the bandwidth. We will turn our typical FRF matrix equation\n", + "\n", + "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n", + "\n", + "into a \"measured\" version. We will replace the value $s$ with $j\\omega$. We will also introduce lower and upper \"residuals\" ($\\mathbf{R}_l$ and $\\mathbf{R}_u$, respectively), to try to fit portions of the FRFs from out-of-band modes. We will explicitly add the notation to show that the frequency response function matrix is a function of frequency $\\omega$. Additionally, we will call the mode shapes of the references the *modal participation factor* $\\mathbf{l}_r$ and the mode shapes corresponding to the responses $\\mathbf{v}$. Finally, We will remove the modal mass term, which will make the assumption that the modes we fit from the algorithm will be mass normalized. Note that the format of the residual terms implies that the frequency response functions we are investigating here are *admittance* frequency response functions, which relate the force at a location to the displacement at a location.\n", + "\n", + "$$\\mathbf{H}(\\omega) = \\sum_{r=1}^N\\left( \\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}\\right) + \\frac{\\mathbf{R}_l}{(j\\omega)^2} + \\mathbf{R}_u$$\n", + "\n", + "Recall the poles $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$ and $\\lambda_r^* = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n", + "\n", + "The PolyMax approach assumes that the frequency response function can be represented by a right matrix-fraction model.\n", + "\n", + "$$ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$$\n", + "\n", + "Note that these are not the same $\\mathbf{A}$ and $\\mathbf{B}$ matrices from the previous sections, which represent the state space formulation. However, this document attempts to match the notation from the referenced paper, so we will override those previous definitions and apologize for any confusion it may cause.\n", + "\n", + "We can see similarities between the two formulations. The FRF equation has a numerator and a denominator, as well as a summation over a number of modes. Similarly, the right matrix-fraction model has a matrix that is not inverted (representing something like a numerator), a matrix that is inverted (representing something like a denominator), and a summation operation inherent in the matrix multiplication that is performed. Terms in $\\mathbf{B}$ will relate to the shape information in the numerator of the FRF equation, and terms in $\\mathbf{A}$ will relate to the pole information in the denominator of the FRF equation.\n", + "\n", + "The numerator matrix $\\mathbf{B}$ will have a number of rows equal to the number of responses or outputs $N_o$ and number of columns equal to the number of references or inputs $N_i$. The denominator matrix $\\mathbf{A}$ will have a number of rows and columns equal to the number of inputs $N_i$, and is therefore square.\n", + "\n", + "We can split up the above equation and look at an individual row of the FRF matrix, which we will denote $\\mathbf{H}_o$ to represent a single output. This will involve selecting a single row from $\\mathbf{B}_o$.\n", + "\n", + "$$ \\mathbf{H}_o(\\omega) = \\mathbf{B}_o(\\omega)\\mathbf{A}(\\omega)^{-1}$$" + ] + }, + { + "cell_type": "markdown", + "id": "23ba4d5b-112a-42e4-8034-3e2616448aad", + "metadata": {}, + "source": [ + "Each entry in the $\\mathbf{B}_o$ and $\\mathbf{A}$ matrices is represented as a set of coefficients multiplied by a set of basis functions, similar to how we represent displacements in modal analysis by a set of modal coefficients multiplied by a set of mode shapes. The basis functions $\\Omega_r(\\omega)$ are a set of functions that depend on the frequency $\\omega$. The coefficients are constant with respect to $\\mathbf{\\omega}$. We will call the coefficient of the $\\mathbf{B}$ matrix corresponding to the $o$th row and the $r$th basis function $\\beta_{or}$. Similarly, we will call the coefficient of the $\\mathbf{A}$ matrix for the $r$th basis function $\\alpha_r$. The number of coefficients and basis functions will be equal to the polynomial order $p+1$.\n", + "\n", + "$$\\mathbf{B}_o = \\sum_{r=0}^p \\Omega_r(\\omega)\\beta_{or}$$\n", + "$$\\mathbf{A} = \\sum_{r=0}^p \\Omega_r(\\omega)\\alpha_{r}$$\n", + "\n", + "The secret of PolyMax is that instead of using a polynomial basis function for $\\Omega_r(\\omega)$, it instead transforms to the $z$-domain. For larger model orders, polynomials will have large exponents, which when applied to large frequency ranges will result in huge differences between the largest and smallest values, which is a sure-fire way to run into numerical issues. Instead, by using the $z$-domain, the PolyMax approach avoids these numerical issues and generally achieves better results. For a test where the minimum measured frequency is $\\omega_0$ and the maximum measured frequency is $\\omega_{end}$, this frequency range is mapped onto a half-unit circle.\n", + "\n", + "$$\\Omega_r(\\omega) = e^{-j\\omega_z \\Delta t_z r}$$\n", + "where $\\omega_z = \\omega - \\omega_0$ and $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$. If instead of angular frequencies, the frequency range is represented in Hz with minimum measured frequency $f_0$ and maximum measured frequency $f_{end}$, then the values are instead $\\omega_z = 2\\pi(f - f_0)$ and $\\Delta t_z = \\frac{1}{2(f_{end}-f_0)}$ where $f=\\frac{\\omega}{2\\pi}$.\n", + "\n", + "The polynomnial coefficients are stacked into matrices.\n", + "\n", + "$$\\beta_o = \\begin{bmatrix}\\beta_{o0}\\\\\\beta_{o1}\\\\\\beta_{o2}\\\\\\vdots\\\\\\beta_{op}\\end{bmatrix}$$\n", + "\n", + "$$\\alpha = \\begin{bmatrix}\\alpha_{0}\\\\\\alpha_{1}\\\\\\alpha_{2}\\\\\\vdots\\\\\\alpha_{p}\\end{bmatrix}$$\n", + "\n", + "Here $\\beta_o$ has number of rows equal to the polynomial order $p+1$ and number of columns equal to the number of references or inputs $N_i$. Each row of $\\beta_o$ corresponds to the coefficients of one polynomial basis function for all of the references.\n", + "\n", + "$\\alpha$ also has a number of columns equal to the number of references or inputs $N_i$. However, each row partition $\\alpha_r$ of $\\alpha$ has a number of rows equal to the number of inputs $N_i$ as well. This means after stacking $p+1$ of these matrices on top of one another, the final number of rows of $\\alpha$ is equal to $N_i(p+1)$.\n", + "\n", + "We finally stack all of these coefficients into a single coefficient matrix $\\theta$, recalling that there is one matrix $\\beta_o$ for each of the $N_o$ outputs of the system.\n", + "\n", + "$$\\theta = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\beta_{3}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n", + "\n", + "Each row partition in $\\theta$ has columns equal to the number of inputs $N_i$. Each of the $\\beta_o$ row partions has number of rows equal to the polynomial order $p+1$, and there are $N_o$ of those partitions, resulting in the $\\beta$ portion of the $\\theta$ matrix having $N_o(p+1)$ rows. Added to the $N_i(p+1)$ rows of the $\\alpha$ partition, the final $\\theta$ matrix has $(N_i+N_o)(p+1)$ rows.\n", + "\n", + "At this point, we can represent the FRF matrix as a function of frequency $\\omega$ as well as a function of the coefficients $\\theta$, $\\mathbf{H}(\\omega,\\theta)$.\n" + ] + }, + { + "cell_type": "markdown", + "id": "81e2b187-5adc-488c-a62d-9ce63f2ddc6f", + "metadata": {}, + "source": [ + "### Selecting Coefficients that Minimize Errors\n", + "It may be obvious, but the goal is to eventually find the parameters $\\theta$ that best fit some measured frequency response functions. In a real test, we will have measured frequency response functions $\\hat{\\mathbf{H}}$ at $N_f$ discrete frequency values $\\omega_k$. We would like to find the coefficients $\\theta$ that minimize the error between the fit frequency response functions and the measured frequency response functions. These coefficients can be found by minimizing the weighted non-linear least-squares error for the $o$th output $\\varepsilon_o^{NLS}$\n", + "\n", + "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{H}_o(\\omega_k,\\theta) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n", + "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o)\\mathbf{A}^{-1}(\\omega_k,\\alpha) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n", + "\n", + "where $w_o(\\omega_k)$ is a weighting function as a function of the output degree of freedom and the frequency line that allows taking into account data quality in the least-squares solution. The previous equation is a matrix equation, and we would ideally like to transform it into a scalar equation that can be minimized which is only a function of the parameters $\\theta$.\n", + "\n", + "$$l^{NLS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)$$\n", + "\n", + "We multiply the value $\\varepsilon_o^{NLS}(\\omega_k,\\theta)$ complex-conjugate-transpose ($\\bullet^H$) by itself, and compute the trace ($\\mathrm{tr}(\\bullet)$). We then sum over all frequency lines and all outputs.\n", + "\n", + "This equation is solved by setting the derivatives of the equation with respect to the unknown modal coefficients to zero. It is reasonably clear that this leads to nonlinear equations due to the matrix inverse in the error equation. We can construct a linear set of equations that approximates the original equations by simply post-multiplying the equation by the matrix $\\mathbf{A}$.\n", + "\n", + "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o) - \\hat{\\mathbf{H}}_o(\\omega_k)\\mathbf{A}(\\omega_k,\\alpha)\\right)$$\n", + "Substituting for the basis functions and coefficients:\n", + "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\sum_{r=0}^p\\left(\n", + "\\Omega_r(\\omega_k)\\beta_{or}\n", + "- \\Omega_r(\\omega_k)\\hat{\\mathbf{H}}_o(\\omega_k)\\alpha_r\\right)$$\n", + "\n", + "We can stack this equation for all frequency lines ($\\omega_1$, $\\omega_2$, ... $\\omega_{N_f}$)\n", + "\n", + "$$E_o^{LS}(\\theta) = \\begin{bmatrix}\n", + "\\varepsilon_o^{LS}(\\omega_1,\\theta) \\\\\n", + "\\varepsilon_o^{LS}(\\omega_2,\\theta) \\\\\n", + "\\vdots \\\\\n", + "\\varepsilon_o^{LS}(\\omega_{N_f},\\theta) \\\\\n", + "\\end{bmatrix} = \\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o \\end{bmatrix}\\begin{bmatrix}\\beta_o\\\\\\alpha\\end{bmatrix}$$\n", + "\n", + "This will result in $E_o^{LS}(\\theta)$ having a number of rows equal to the number of frequency lines in the test, and the number of columns equal to the number of references or inputs $N_i$.\n", + "\n", + "The $\\mathbf{X}_o$ matrix can be constructed from pulling off the coefficients of the $\\beta$ terms, which are generally the basis functions $\\Omega_r$ evaluated at each frequency line $\\omega_k$. Each row will correspond to a separate frequency line, and each column will correspond to a basis function order.\n", + "\n", + "$$\\mathbf{X}_o = \\begin{bmatrix}\n", + "w_o(\\omega_1)\\Omega_0(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1) & \\cdots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\\\\n", + "w_o(\\omega_2)\\Omega_0(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2) & \\cdots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\\\\n", + "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + "w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f}) & \\cdots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\\\\n", + "\\end{bmatrix} $$\n", + "\n", + "The $\\mathbf{Y}_o$ matrix can be constructed by pulling off the coefficients of the $\\alpha$ terms, which are generally the basis functions somehow multiplied by the FRFs. We will explore this expression a bit more here. The relevant portion of $\\varepsilon_o^{LS}(\\omega_k,\\theta)$ containing the $\\alpha$ terms is \n", + "\n", + "$$\\mathbf{Y}_o \\alpha = -\\begin{bmatrix}\n", + " \\sum_{r=0}^pw_o(\\omega_1)\\Omega_r(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_r \\\\\n", + " \\sum_{r=0}^pw_o(\\omega_2)\\Omega_r(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_r \\\\\n", + " \\vdots \\\\\n", + " \\sum_{r=0}^pw_o(\\omega_{N_f})\\Omega_r(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_r\\end{bmatrix}$$\n", + "\n", + "Expanding the equation out in terms of the summation can help visualize which terms go where in the $\\mathbf{Y}_o$ matrix.\n", + "\n", + "$$\\mathbf{Y}_o \\begin{bmatrix}\n", + " \\alpha_0 \\\\\n", + " \\alpha_1 \\\\\n", + " \\vdots \\\\\n", + " \\alpha_p\n", + " \\end{bmatrix} = -\\begin{bmatrix}\n", + " w_o(\\omega_1) \\left( \\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_0 + \\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_1 + \\dots + \\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_p \\right) \\\\\n", + " w_o(\\omega_2) \\left( \\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_0 + \\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_1 + \\dots + \\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_p \\right) \\\\\n", + " \\vdots \\\\\n", + " w_o(\\omega_{N_f}) \\left( \\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_0 + \\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_1 + \\dots + \\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_p \\right) \\\\\n", + " \\end{bmatrix}$$\n", + "or in matrix form\n", + "$$\\mathbf{Y}_o \\begin{bmatrix}\n", + " \\alpha_0 \\\\\n", + " \\alpha_1 \\\\\n", + " \\vdots \\\\\n", + " \\alpha_p\n", + " \\end{bmatrix} = -\\begin{bmatrix}\n", + " w_o(\\omega_1)\\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & \\dots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) \\\\\n", + " w_o(\\omega_2)\\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & \\dots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n", + " w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & \\dots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) \\\\\n", + " \\end{bmatrix}\\begin{bmatrix}\n", + " \\alpha_0 \\\\\n", + " \\alpha_1 \\\\\n", + " \\vdots \\\\\n", + " \\alpha_p\n", + " \\end{bmatrix}$$\n", + "\n", + "We can recognize the pattern $\\begin{bmatrix}\\Omega_0\\hat{\\mathbf{H}}_o & \\Omega_1\\hat{\\mathbf{H}}_o & \\dots & \\Omega_p\\hat{\\mathbf{H}}_o\\end{bmatrix}$ as the [Kronecker Product](https://en.wikipedia.org/wiki/Kronecker_product) $\\mathbf{\\Omega} \\otimes \\hat{\\mathbf{H}}_o$ where the matrix $\\mathbf{\\Omega}$ has only one row. Therefore, \n", + "\n", + "$$\\mathbf{X}_o = \\begin{bmatrix}\n", + "w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\\\\n", + "w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\\\\n", + "\\vdots \\\\\n", + "w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\\\\n", + "\\end{bmatrix} $$\n", + "$$\\mathbf{Y}_o = -\\begin{bmatrix}\n", + " w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\otimes\\hat{\\mathbf{H}}_o(\\omega_1)\\\\\n", + " w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\otimes\\hat{\\mathbf{H}}_o(\\omega_2)\\\\\n", + " \\vdots\\\\ \n", + " w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\otimes\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\\\\n", + " \\end{bmatrix}$$\n", + "\n", + "where $\\mathbf{\\Omega}(\\omega) = \\begin{bmatrix}\\Omega_0(\\omega)&\\Omega_1(\\omega)&\\dots&\\Omega_p(\\omega)\\end{bmatrix}$" + ] + }, + { + "cell_type": "markdown", + "id": "873cdaf8-293e-46d6-837b-29eedf756c3b", + "metadata": {}, + "source": [ + "Similarly to the nonlinear least squares solution, we can construct an error cost function to minimize based on the linearized equation.\n", + "\n", + "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)$$\n", + "\n", + "or \n", + "\n", + "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\\left(E_o^{LS}(\\omega_k,\\theta)\\right)^HE_o^{LS}(\\omega_k,\\theta)\\right)$$\n", + "\n", + "If we substitute the matrix equations, we get\n", + "\n", + "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\n", + "\\begin{bmatrix}\\beta_o^T & \\alpha^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o^H \\\\ \\mathbf{Y}_o^H\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o\\end{bmatrix}\\begin{bmatrix}\\beta_o \\\\ \\alpha\\end{bmatrix}\\right)$$\n", + "\n", + "Ideally, we would like to remove the summation in the previous equation and end up with an equation of the form $l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$. We recognize that we can stack the equations for $E_o^{LS}$ for all outputs and arrive at an equation\n", + "\n", + "$$\\begin{bmatrix}E_1^{LS} \\\\ E_2^{LS} \\\\ \\vdots \\\\ E_{N_o}^{LS} \\end{bmatrix} = \n", + "\\begin{bmatrix}\n", + " \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{Y}_1\\end{bmatrix}\\begin{bmatrix}\\beta_1 \\\\ \\alpha\\end{bmatrix} \\\\ \n", + " \\begin{bmatrix}\\mathbf{X}_2 & \\mathbf{Y}_2\\end{bmatrix}\\begin{bmatrix}\\beta_2 \\\\ \\alpha\\end{bmatrix} \\\\\n", + " \\vdots \\\\\n", + " \\begin{bmatrix}\\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o}\\end{bmatrix}\\begin{bmatrix}\\beta_{N_o} \\\\ \\alpha\\end{bmatrix}\\end{bmatrix} = \n", + "\\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n", + " \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n", + "\n", + "where\n", + "\n", + "$$\\mathbf{J} = \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n", + " \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}$$\n", + "and as we've seen previously,\n", + "$$\\mathbf{\\theta} = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n", + "\n", + "Therefore our cost function can be simplified to\n", + "\n", + "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$$" + ] + }, + { + "cell_type": "markdown", + "id": "347b489b-ba1c-4564-a8ef-ad96fa26c986", + "metadata": {}, + "source": [ + "In general, the quantity $\\mathbf{J}^H\\mathbf{J}$ will be conjugate-symmetric or [Hermitian](https://en.wikipedia.org/wiki/Hermitian_matrix). This means that its upper triangle will be equal to the complex conjugate of the lower triangle transposed, and the diagonal will be real valued. While the matrix $\\mathbf{J}^H\\mathbf{J}$ is therefore complex, in the previous equation, because it multiplies by real valued coefficients $\\mathbf{\\theta}$ and we take the trace, which should only select the real-valued diagonal, we can replace the quantity $\\mathbf{J}^H\\mathbf{J}$ with its real part $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$\n", + "\n", + "Computing $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$ gives\n", + "\n", + "$$\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right) = \\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n", + " \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n", + " \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n", + " \\end{bmatrix} $$\n", + "where\n", + "$$\\mathbf{R}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{X}_o\\right)$$\n", + "$$\\mathbf{S}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{Y}_o\\right)$$\n", + "$$\\mathbf{T}_o = \\Re\\left(\\mathbf{Y}_o^H\\mathbf{Y}_o\\right)$$\n", + "\n", + "We minimize the cost function by setting the derivatives of the cost function with respect to the coefficients $\\mathbf{\\theta}$ to zero.\n", + "\n", + "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n", + " \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n", + " \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n", + " \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n", + " \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}\\right)$$\n", + "Expanding the multiplication gives\n", + "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\n", + " \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{R}_o\\beta_o\\right) + \n", + " \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{S}_o^T\\beta_o\\right) + \n", + " \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{S}_o\\alpha\\right) + \n", + " \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{T}_o\\alpha\\right)\\right)$$\n", + "$$l^{LS}(\\theta) = \\mathrm{tr}\\sum_{o=1}^{N_o}\\left(\n", + " \\beta_o^T\\mathbf{R}_o\\beta_o + \n", + " \\alpha^T\\mathbf{S}_o^T\\beta_o + \n", + " \\beta_o^T\\mathbf{S}_o\\alpha + \n", + " \\alpha^T\\mathbf{T}_o\\alpha\\right)$$\n", + "\n", + "We can therefore compute the partial derivatives with respect to the unknown coefficients and set them to zero.\n", + "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\beta_o} = \n", + " 2\\mathbf{R}_o\\beta_o + \n", + " 2\\mathbf{S}_o\\alpha = \\mathbf{0}$$\n", + "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\alpha} = \n", + " \\sum_{o=1}^{N_o}\\left( \n", + " 2\\mathbf{S}_o^T\\beta_o + \n", + " 2\\mathbf{T}_o\\alpha\\right) =\\mathbf{0}$$\n", + "\n", + "As a first pass, we only care about the poles, which come from the denominator terms $\\alpha$. We can eliminate the $\\beta_o$ terms using the first of these constraints\n", + "$$\\beta_o = -\\mathbf{R}_o^{-1}\\mathbf{S}_o\\alpha$$\n", + "\n", + "We can then plug this into the second constraint to get an equation for $\\mathbf{\\alpha}$.\n", + "\n", + "$$\\left[2\\sum_{o=1}^{N_o}\\left( \n", + " \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)\\right]\\alpha= \\mathbf{M}\\alpha= \\mathbf{0}$$\n", + "\n", + "This matrix $\\mathbf{M}$ is a square matrix with number of rows and columns equal to the number of inputs times the polynomial order $N_i(p+1) \\times N_i(p+1)$, and depends only on the measured FRFs from the test. Note, however, that this equation is not unique. For example, the trival solution $\\alpha = \\mathbf{0}$ satisfies this equation. Additionally, one may see that in the right matrix-fraction formulation $ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$, one could multiply both $\\mathbf{A}$ and $\\mathbf{B}$ by the same matrix, and the equation would still be satisfied. Therefore, there is not a unique value for $\\alpha$." + ] + }, + { + "cell_type": "markdown", + "id": "dc9b2e09-e498-4663-9717-d1ffa3085ee7", + "metadata": {}, + "source": [ + "### Solving the Constrained Problem\n", + "To solve a problem of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{0}$ for the non-trivial $\\mathbf{x}$, we must apply some constraint to the problem.\n", + "\n", + "To solve the problem, we will partition the matrix $\\mathbf{M}$ as follows\n", + "\n", + "$$\\mathbf{M} = \\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n", + " \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}$$\n", + "\n", + "where the $a$ indices are the first $N_ip$ rows or columns and the $b$ indices are the last $p$ columns, recalling the size of $\\mathbf{M}$ is $N_i(p+1)$. Therefore the matrix $\\mathbf{M}_{aa}$ matrix will have size $N_ip\\times N_ip$, the $\\mathbf{M}_{ab}$ matrix will have size $N_ip\\times p$, the $\\mathbf{M}_{ba}$ matrix will have size $p\\times N_ip$, and the $\\mathbf{M}_{bb}$ matrix will have size $p\\times p$.\n", + "\n", + "Because we must apply constraints to our problem, we will make the assumption that our solution matrix $\\mathbf{\\alpha}$ will have the form\n", + "\n", + "$$\\mathbf{\\alpha} = \\begin{bmatrix}\\hat{\\mathbf{\\alpha}} \\\\ \n", + " \\mathbf{I}\\end{bmatrix}$$\n", + "\n", + "We can see that if we plug this version of $\\mathbf{\\alpha}$ into the equation, we get an expression for the portion of $\\mathbf{\\alpha}$ that was not constrained.\n", + "\n", + "$$\\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n", + " \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}\n", + " \\begin{bmatrix}\\hat{\\mathbf{\\alpha}} \\\\ \n", + " \\mathbf{I}\\end{bmatrix} =\n", + " \\begin{bmatrix}\\mathbf{M}_{aa}\\hat{\\alpha} + \\mathbf{M}_{ab}\\\\\n", + " \\mathbf{M}_{ba}\\hat{\\alpha} + \\mathbf{M}_{bb}\\end{bmatrix} = \n", + " \\begin{bmatrix}\\mathbf{0} \\\\\n", + " \\mathbf{0}\\end{bmatrix}$$\n", + "\n", + "We can then simply take the top partition to solve for $\\hat{\\alpha}$.\n", + "\n", + "$$\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$$" + ] + }, + { + "cell_type": "markdown", + "id": "38d7b7fb-bb03-4710-b443-d5f1989876a8", + "metadata": {}, + "source": [ + "### Extracting Poles and Participation Factors\n", + "Now that we have the polynomial coefficients $\\mathbf{\\alpha}$ for the right matrix-fraction model, we would like to extract the roots from it. The roots of this polynomial are the poles of the frequency response function. We will do this by setting up a [Companion Matrix](https://en.wikipedia.org/wiki/Companion_matrix). A Companion matrix of a polynomial is a matrix whose characteristic equation is equal to that polynomial. Then the eigenvalues of the matrix (which are the roots of the characteristic equation) are also the roots of the polynomial. In our case, the eigenvectors are also useful, as they are related to the participation factors.\n", + "\n", + "For a polynomial $p(x) = \\alpha_p x^p + \\alpha_{p-1} x^{p-1} + \\dots + \\alpha_2 x^2 + \\alpha_1 x + \\alpha_0$, the companion matrix will be \n", + "\n", + "$$ \\mathbf{C} = \\begin{bmatrix} 0 & 1 & 0 & \\dots & 0\\\\\n", + " 0 & 0 & 1 & \\dots & 0\\\\\n", + " \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + " 0 & 0 & 0 & \\dots & 1\\\\\n", + " -\\frac{\\alpha_0}{\\alpha_p} & -\\frac{\\alpha_1}{\\alpha_p} & -\\frac{\\alpha_2}{\\alpha_p} & \\dots & -\\frac{\\alpha_{p-1}}{\\alpha_p} \n", + " \\end{bmatrix}$$\n", + "\n", + "Readers can verify that $\\mathrm{det}(\\mathbf{I}x - \\mathbf{C}) = p(x)$.\n", + "\n", + "Our present case is slightly different, because our coefficients are matrices rather than scalars.\n", + "\n", + "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n", + " \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n", + " -\\alpha_0^T{\\alpha_p^T}^{-1} & -\\alpha_1^T{\\alpha_p^T}^{-1} & -\\alpha_2^T{\\alpha_p^T}^{-1} & \\dots & -\\alpha_{p-1}^T{\\alpha_p^T}^{-1}\n", + " \\end{bmatrix}$$\n", + "However, because we applied the constraint such that $\\alpha_p = \\mathbf{I}$ when solving for $\\mathbf{\\alpha}$, the quantity $\\alpha_p^{-1} = \\mathbf{I}$, and the remaining terms are those in $\\hat{\\mathbf{\\alpha}}$.\n", + "\n", + "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n", + " \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n", + " -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n", + " \\end{bmatrix}$$\n", + "\n", + "The $\\mathbf{C}$ matrix will be a square matrix with size $N_ip \\times N_ip$ where $N_i$ is the number of inputs or references, and $p$ is the order of the polynomial.\n", + "\n", + "We can therefore notate the companion matrix compactly as\n", + "$$\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{I}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "id": "b34449b0-790f-426e-9f61-7590aa486efc", + "metadata": {}, + "source": [ + "We can then solve for the eigenvalues and eigenvectors of this matrix. The companion matrix is set up such that its characteristic polynomial is identical to the polynomial it represents. Therefore, its eigenvalues, which are roots of the characteristic polynomial, will be roots of the polynomial it represents.\n", + "\n", + "$$\\mathbf{C}\\mathbf{V} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n", + " \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n", + " \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n", + " -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n", + " \\end{bmatrix}\\mathbf{V}= \\mathbf{\\Lambda}\\mathbf{V}$$\n", + "The diagonals of the eigenvector matrix $\\bar{\\lambda}_r = \\mathbf{\\Lambda}_{rr}$ are the eigenvalues of the companion matrix. These are the poles of the system; however, we must remember they are represented in the $z$-domain, so we must return to the $s$-domain $\\lambda_r$ in order to solve for the natural frequency and damping ratio.\n", + "\n", + "$$\\bar{\\lambda}_r = e^{-\\lambda_r \\Delta t_z}$$\n", + "$$\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$$\n", + "\n", + "where $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.\n", + "\n", + "We can then solve for the natural frequency and damping ratio from the poles. Recall that the frequency was shifted by $\\omega_0$ during the mapping, so we will need to take that into account.\n", + "\n", + "$${\\omega_z}_r = \\left|\\lambda_r\\right|$$\n", + "$${\\omega_r} = {\\omega_z}_r + \\omega_0$$\n", + "$$\\zeta_r = -\\frac{\\Re\\left({\\lambda_r}\\right)}{\\omega_r}$$\n", + "\n", + "Note that there will be in general $N_ip$ eigenvalues. Half of them will be complex conjugates of the other half, so we can ignore those. Similarly, we can ignore non-physical eigenvalues. A criteria that is successfully used is to only keep poles where the real part is less than zero and the imaginary part is greater than zero.\n", + "\n", + "The modal participation factors $\\mathbf{l}_r$ can be determined from the eigenvectors of the companion matrix. They will be the last $N_i$ rows of the eigenvector matrix. One must also be sure that for each eigenvalue discarded for being non-physical or complex conjugate, the corresponding eigenvector should also be discarded." + ] + }, + { + "cell_type": "markdown", + "id": "724700d5-1b64-4f54-8c17-505f115c560f", + "metadata": {}, + "source": [ + "### Stabilization Diagrams\n", + "Solving for a polynomial of a particular order will in general result in a large number of poles. For a typical problem, only a subset of these poles will correspond to true modes of the structure. For a typical modal test, it may not be obvious how many modes are in a given bandwidth anyways. Therefore, we need some kind of metric to determine if a pole is valid or not, and for this we utilize the stabilization diagram.\n", + "\n", + "A stabilization diagram is constructed by computing the poles over a large number of polynomial orders. As we increase the polynomial orders, more poles may be found in the system. We declare a *stable pole* to be one which does not change significantly as the polynomial order changes. Computational poles may bounce around the measurement bandwidth. Real poles of the system, on the other hand, will generally result in a similar frequency, damping ratio, and set of participation factors regardless of the polynomial order. We then construct a final set of poles by selecting from the stable poles in the stabilization diagram." + ] + }, + { + "cell_type": "markdown", + "id": "9e84ee11-8387-4196-8c61-dc3e1c944e31", + "metadata": {}, + "source": [ + "### Back to the Example Problem\n", + "To demonstrate the equations shown here, we will implement the previous equations in code. We will demonstrate solution of one polynomial order to demonstrate the process, and then rely on the implementation in SDynPy to perform the rest of the analysis. We will initially focus only on the general damping case rather than repeating the analysis for all the damping matrices.\n", + "\n", + "To start, we will pull in the measured frequency response functions and corresponding frequency values. We will shape the frequency response function such that it is a 3D array with shape $N_o \\times N_i \\times N_f$. We will also assume that we only peformed measurements with inputs at the first and second degrees of freedom." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0e5df1a5-cdc8-4e07-a7f9-e041e22c4a96", + "metadata": {}, + "outputs": [], + "source": [ + "H = H_general.transpose(1,2,0)[:,:2,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c834190f-3f0d-41fe-866f-e616e36e9cfc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2, 1000)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H.shape" + ] + }, + { + "cell_type": "markdown", + "id": "20e3730a-73f0-4316-9c23-6545c19572aa", + "metadata": {}, + "source": [ + "We will also assume that we only performed measurements over a limited bandwidth from 10 rad/s to 80 rad/s." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a274ff4c-0012-48b5-9e62-48351fac2513", + "metadata": {}, + "outputs": [], + "source": [ + "frequency_lines_to_keep = (omegas > 10) & (omegas < 80)\n", + "H = H[:,:,frequency_lines_to_keep]\n", + "omegas = omegas[frequency_lines_to_keep]" + ] + }, + { + "cell_type": "markdown", + "id": "3b873134-0289-4846-8bcc-824d57174fc3", + "metadata": {}, + "source": [ + "Now that we have truncated the frequency domain, we will define some helper variables to make the code a bit more intuitive." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f7dcec4f-fbf1-4afd-bccb-e9f1b84364b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Outputs: 3\n", + "Num Inputs: 2\n", + "Num Frequencies: 778\n" + ] + } + ], + "source": [ + "num_output, num_input, num_freq = H.shape\n", + "print('Num Outputs: {:}\\nNum Inputs: {:}\\nNum Frequencies: {:}'.format(num_output, num_input, num_freq))" + ] + }, + { + "cell_type": "markdown", + "id": "8f5f1720-01be-4a77-bfd2-074f1d23d420", + "metadata": {}, + "source": [ + "We will also set our polynomial order. In this case, we know that there are 3 modes of the structure in the bandwidth. However, let's select 5 as the polynomial order, as we generally would like to overfit the model. Keep in mind that this will result in 6 polynomial coefficients: 0, 1, 2, 3, 4, and 5. Therefore, you shouldn't be confused when dimensions of arrays associated with the polynomial order have length 6 instead of length 5." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3c9da069-ffcd-4640-a8c7-fa281bd33fa1", + "metadata": {}, + "outputs": [], + "source": [ + "order = 5" + ] + }, + { + "cell_type": "markdown", + "id": "ae930b28-a34b-4a65-a03a-05977c5809d7", + "metadata": {}, + "source": [ + "We will assume no weighting, or rather the weighting is identically 1 across all outputs and across all frequencies." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4df3d9a0-b12a-4ba5-b2e3-11d818d4cca2", + "metadata": {}, + "outputs": [], + "source": [ + "weighting = np.ones((num_freq,num_output))" + ] + }, + { + "cell_type": "markdown", + "id": "8e27b2ab-82df-4618-bae9-7dfb65ec472a", + "metadata": {}, + "source": [ + "The first thing we will do is to set up our $\\mathbf{\\Omega}_r(\\omega) = e^{-j\\omega_z\\Delta t_z r}$ vectors. We will set this up as an array with number of rows equal to number of frequency lines and number of columns equal to the number of polynomial coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4942b941-1e13-4e88-97b1-f03a17335542", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(778, 6)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "omega_z = omegas - omegas[0] # Perform the shift\n", + "deltat = np.pi/(omegas[-1] - omegas[0]) # Compute the scale factor\n", + "Omega = np.exp(-1j * omega_z[:,np.newaxis] * deltat * np.arange(order+1)) # Compute Omega\n", + "Omega.shape" + ] + }, + { + "cell_type": "markdown", + "id": "3ea75991-908b-41c4-b425-29b5e46c286a", + "metadata": {}, + "source": [ + "In the previous code, we use broadcasting to set up our $\\mathbf{\\Omega}_r$ array. We add a `newaxis` to our shifted frequency vector $\\omega_z$ to make it a `num_freq` $\\times$ 1 array. This is then multiplied by an array `[0,1,2,3,4,5]` constructed from the `arange` function. Per [Numpy's Broadcasting Rules](https://numpy.org/doc/stable/user/basics.broadcasting.html), a `num_freq` $\\times$ 1 array and a `order+1` array will be broadcast to a `num_freq` $\\times$ `order+1` array. Multiplying by scalars `deltat` and `-1j` get performed elementwise, as does the exponential function `exp`." + ] + }, + { + "cell_type": "markdown", + "id": "b46fbf38-0e64-49fa-b8c2-4fcb128ef1a3", + "metadata": {}, + "source": [ + "The next step will be to assemble the $\\mathbf{R}_o$, $\\mathbf{S}_o$, and $\\mathbf{T}_o$ matrices. These are built from the $\\mathbf{X}_o$ and $\\mathbf{Y}_o$ matrices.\n", + "\n", + "Before we do this let's think about the dimensions of the arrays. The matrix $\\mathbf{R}_o$ for each output will have dimension `order+1` $\\times$ `order+1`. The matrix $\\mathbf{S}_o$ for each output will have dimension `order + 1` $\\times$ `num_input*(order+1)`. The $\\mathbf{T}_o$ matrix for each output will have dimension `num_input*(order+1)` $\\times$ `num_input*(order+1)`. Because we will have `num_output` of each of these matrices, we will consider assembling them into a 3D array with dimension `num_output` $\\times$ `...` $\\times$ `...` ." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "97c9d6a9-be8a-4e44-a440-7ff0ac71a911", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " R: (3, 6, 6)\n", + " S: (3, 6, 12)\n", + " T: (3, 12, 12)\n" + ] + } + ], + "source": [ + "R = np.zeros([num_output, order + 1, order + 1])\n", + "S = np.zeros([num_output,order + 1, num_input * (order + 1)])\n", + "T = np.zeros([num_output, num_input * (order + 1), num_input * (order + 1)])\n", + "print('Shapes:\\n R: {:}\\n S: {:}\\n T: {:}'.format(R.shape,S.shape,T.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "8f86ddef-0627-4f7d-8554-66900ad82bc1", + "metadata": {}, + "source": [ + "We will then loop through each of the outputs and assemble the corresponding portions of the matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c050e1b7-3872-4bfe-9344-9758c8403463", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating Data: Progress = 0.00%\n", + "Accumulating Data: Progress = 33.33%\n", + "Accumulating Data: Progress = 66.67%\n", + "Accumulating Data: Progress = 100.00%\n", + "Shapes:\n", + " Xo: (778, 6)\n", + " Yo: (778, 12)\n", + " Ro: (6, 6)\n", + " So: (6, 12)\n", + " To: (12, 12)\n" + ] + } + ], + "source": [ + "for o in range(num_output):\n", + " print('Accumulating Data: Progress = {:0.2f}%'.format(o / num_output * 100.0))\n", + " # The Xo matrix depends only on the weighting and Omega. We use\n", + " # broadcasting to extend the weighting across all polynomial coefficients\n", + " Xo = weighting[:, o][..., np.newaxis] * Omega\n", + " # The Yo matrix will need the measured frequency response functions\n", + " # at the output o.\n", + " Ho = H[o, ...]\n", + " # We note that rather than re-multiplying the weighting by Omega in the\n", + " # Yo matrix, we can simply use Xo. Here we must loop over all frequency\n", + " # lines to perform the kronecker product. The frequency lines are the\n", + " # rows of X, and the columns of Ho, so we transpose Ho.\n", + " # Xk is then the polynomial coefficients at the k frequency line and\n", + " # Hk is the FRF for each input at that frequency line.\n", + " Yo = np.array([-np.kron(Xk, Hk) for Xk, Hk in zip(Xo, Ho.transpose())])\n", + " # We then construct the R, S, and T matrices for that output from their\n", + " # definitions\n", + " Ro = np.real(Xo.conjugate().transpose() @ Xo)\n", + " So = np.real(Xo.conjugate().transpose() @ Yo)\n", + " To = np.real(Yo.conjugate().transpose() @ Yo)\n", + " # We stick the matrices into the 3D arrays for future usage.\n", + " R[o, :, :] = Ro\n", + " S[o, :, :] = So\n", + " T[o, :, :] = To\n", + "print('Accumulating Data: Progress = {:0.2f}%'.format((o+1) / num_output * 100.0))\n", + "print('Shapes:\\n Xo: {:}\\n Yo: {:}\\n Ro: {:}\\n So: {:}\\n To: {:}'.format(\n", + " Xo.shape,Yo.shape,Ro.shape,So.shape,To.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "7004a057-06f0-42bc-882c-39cdb34aa075", + "metadata": {}, + "source": [ + "We will now compute the $\\mathbf{M}$ matrix, which is equal to $\\sum_{o=1}^{N_o}\\left(\\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)$. Note that we dropped the factor of 2 in front of the expression because the final answer $\\alpha$ is only defined up to a matrix product. Multiplying the answer by 2 will not result in any meaningful change.\n", + "\n", + "We will compute the contribution $\\mathbf{M}_o = \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o$ for each output and then sum them together to construct $\\mathbf{M}$." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cbee950b-972c-45aa-8ead-6d65fdd1afc8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " M: (12, 12)\n" + ] + } + ], + "source": [ + "# Because S is a 3D array, to transpose it, we will only want to\n", + "# swap the last two dimensions and leave the first output dimension\n", + "# out front.\n", + "# We compute R^-1 using np.linalg.solve, which solves a problem.\n", + "# np.linalg.solve(R,S) is equivalent to R^-1@S, but is faster and\n", + "# better conditioned.\n", + "M = np.sum(T - S.transpose(0,2,1) @ np.linalg.solve(R, S),axis=0)\n", + "print('Shapes:\\n M: {:}'.format(M.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "26545d69-262d-459a-919c-72a6fcfc2dc6", + "metadata": {}, + "source": [ + "Now we will solve for the parameters $\\mathbf{\\alpha}$ and construct the companion matrix. Recall that in our solution we will assume $\\mathbf{\\alpha}_p$ is equal to the identity matrix and drops out of the companion matrix computation, so we really only need to compute $\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "40d56445-b74b-4f9b-af2e-1e98251a22ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " Maa: (10, 10)\n", + " Mbb: (10, 2)\n", + " alpha: (10, 2)\n" + ] + } + ], + "source": [ + "Maa = M[:order * num_input, :order * num_input]\n", + "Mbb = -M[:order * num_input, order * num_input:]\n", + "alpha = np.linalg.solve(Maa, Mbb)\n", + "print('Shapes:\\n Maa: {:}\\n Mbb: {:}\\n alpha: {:}'.format(\n", + " Maa.shape,Mbb.shape,alpha.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "f24809a1-e67f-4446-b842-19f139beadd7", + "metadata": {}, + "source": [ + "We will then construct the companion matrix $\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{1}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$. The partition $\\mathbf{0}$ will have shape `(order-1)*num_input` $\\times$ `num_input`, and the partition $\\mathbf{I}$ will have shape `(order-1)*num_input` $\\times$ `(order-1)*num_input`." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "788bc574-8350-4be2-b8db-acfab146d5b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " C: (10, 10)\n" + ] + } + ], + "source": [ + "C_top_left = np.zeros([(order - 1) * num_input, num_input])\n", + "C_top_right = np.eye((order - 1) * num_input)\n", + "C_bottom = -alpha.transpose()\n", + "C = np.concatenate(\n", + " (np.concatenate((C_top_left, C_top_right), axis=1),\n", + " C_bottom), axis=0)\n", + "print('Shapes:\\n C: {:}'.format(\n", + " C.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "ab985b8c-3070-4041-a128-e24fa02ba8ec", + "metadata": {}, + "source": [ + "Finally, we can compute the eigenvalues and eigenvectors from this matrix, which should be the solutions to the polynomial. Remember that the eigenvalues will be in the $z$-domain, and must be transformed back to the $s$ domain for us to extract natural frequency and damping ratio: $\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5f2a5fec-6a50-4f5e-9286-ab7ab4b95ade", + "metadata": {}, + "outputs": [], + "source": [ + "zpoles, V = np.linalg.eig(C)\n", + "spoles = -np.log(zpoles)/deltat" + ] + }, + { + "cell_type": "markdown", + "id": "be16c045-b456-42dd-acf1-c4704cc460fb", + "metadata": {}, + "source": [ + "We will immediately discard any poles where the imaginary part is less than zero or the real part is greater than zero, ensuring that we also discard the corresponding eigenvectors." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9070751d-05f8-4510-8fbf-41d66fb9942f", + "metadata": {}, + "outputs": [], + "source": [ + "keep_poles = (np.imag(spoles) > 0) & (np.real(spoles) < 0)\n", + "spoles = spoles[keep_poles]\n", + "V = V[:,keep_poles]" + ] + }, + { + "cell_type": "markdown", + "id": "bbc83e04-6f70-44ce-8720-e4cd0bf0a96b", + "metadata": {}, + "source": [ + "We can then extract the (shifted) natural frequency and damping ratios from the poles. We unshift the natural frequency from the pole to recover the natural frequency of the structure." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b06e5f64-ca4f-4ffe-8e8e-56304150103d", + "metadata": {}, + "outputs": [], + "source": [ + "omega_general_fit = abs(spoles) + omegas[0]\n", + "zeta_general_fit = -np.real(spoles)/omega_general_fit" + ] + }, + { + "cell_type": "markdown", + "id": "f11f8a6a-7f07-4e72-bd4f-bbf181f354cf", + "metadata": {}, + "source": [ + "The modal participation factors $\\mathbf{l}_r$ are then the last `num_input` rows of the eigenvectors." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "cd6279de-eb9f-401c-9baf-12ba4ebdc193", + "metadata": {}, + "outputs": [], + "source": [ + "lr = V[-num_input:]" + ] + }, + { + "cell_type": "markdown", + "id": "681c6a98-1297-4e84-b1a2-c787d2853b53", + "metadata": {}, + "source": [ + "Let's compare the fit natural frequencies to those from the test problem. We will sort them ascending so they match those computed directly from the eigenvalues of the state space formulation. Make sure that we also sort the damping and participation factors identically." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "74d9e3ed-63cc-4595-860f-2d2cad63f99c", + "metadata": {}, + "outputs": [], + "source": [ + "sort_indices = np.argsort(omega_general_fit)\n", + "omega_general_fit = omega_general_fit[sort_indices]\n", + "zeta_general_fit = zeta_general_fit[sort_indices]\n", + "lr = lr[:,sort_indices]" + ] + }, + { + "cell_type": "markdown", + "id": "1c5091ad-1e98-4063-a1c7-7cd06a1053b5", + "metadata": {}, + "source": [ + "Let's now set up a table to show the differences. We will compare the participation factors against the same degrees of freedom from the corresponding mode shapes. Note that the participation factors may be scaled differently and rotated differently than the mode shapes, so we must rescale and rotate them to judge how well they fit." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "71fa8ee2-5eab-48e8-a0cb-6933f8b9d67e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Frequency From System MatricesFrequency From FRF FitsFrequency ErrorDamping From System MatricesDamping From FRF FitsDamping ErrorParticipation From System MatricesParticipation From FRF Fits
027.2527.300.19%2.71%2.60%-3.90%[-0.04320045+0.04405838j -0.09857882+0.09838338j][-0.04318693+0.04408044j -0.09857685+0.09838141j]
154.7854.860.15%2.74%2.76%0.67%[0.06535574-0.06999465j 0.00185538+0.00185558j][0.06534612-0.06998435j 0.00118367+0.00286075j]
259.9159.88-0.06%2.66%2.69%1.02%[-0.06059005+0.05448316j 0.03373321-0.03429903j][-0.06070207+0.05458388j 0.03320861-0.0344524j ]
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n", + " 'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n", + " 'Participation From System Matrices','Participation From FRF Fits'],\n", + " dtype=object)\n", + "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n", + " omega_general,omega_general_fit,\n", + " zeta_general, zeta_general_fit,\n", + " psi_general[:2,:].T, lr.T)):\n", + " df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n", + " df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n", + " df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n", + " df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n", + " df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n", + " df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n", + " # Find the maximum participation degree of freedom from the\n", + " # system matrices\n", + " check_index = np.argmax(np.abs(lm))\n", + " # Compute the angle of that index\n", + " check_angle = np.angle(lm[check_index])\n", + " # Compute the same angle from the fit\n", + " this_angle = np.angle(lf[check_index])\n", + " rotation = check_angle - this_angle\n", + " # Compute the magnitudes of the vectors\n", + " check_magnitude = np.linalg.norm(np.abs(lm))\n", + " this_magnitude = np.linalg.norm(np.abs(lf))\n", + " # Correct rotation and scaling\n", + " lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n", + " df.at[i,'Participation From System Matrices'] = str(lm)\n", + " df.at[i,'Participation From FRF Fits'] = str(lf)\n", + " \n", + "pretty_print_table(df)" + ] + }, + { + "cell_type": "markdown", + "id": "5d4b3d62-f721-4cd0-93ad-60ca93746ee3", + "metadata": {}, + "source": [ + "We can verify that we get very good agreement between the quantities derived from the system matrices and those fit by the FRFs. However, we may have simply been lucky: our system of equations actually solved for `order*num_inputs` or 10 poles, and it just so happened that 3 of them ended up being physically realizable with positive damping and frequency. It is possible that more of them would have been physically realizable, in which case we would have had to go through and try to figure out which poles are \"real\" and which are \"computational\", and without *a priori* knowledge of the true pole information, it could be very difficult to do this.\n", + "\n", + "Instead, we use a stabilization diagram. We solve for a range of different polynomial orders and we plot the resulting frequencies on a figure. We then identify poles that appear in many different model orders to be \"stable\" and we accept these as real poles.\n", + "\n", + "Rather than code all this from scratch, we will simply rely on SDynPy's `PolyPy` implementation to help us out. `PolyPy` relies on the FRF being structured as a `TransferFunctionArray`, so we must first create one from our data. Note that `PolyPy` works with frequencies in Hz rather than in radians per second, so we must convert our `omegas` variable by dividing by $2\\pi$." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "547b6ff7-d71a-4f9c-9950-26f4d3cf9066", + "metadata": {}, + "outputs": [], + "source": [ + "import sdynpy as sdpy\n", + "tfarray = sdpy.data_array(sdpy.data.FunctionTypes.FREQUENCY_RESPONSE_FUNCTION,\n", + " abscissa = omegas/(2*np.pi), ordinate = H, \n", + " coordinate = sdpy.coordinate.outer_product(\n", + " sdpy.coordinate_array([1,2,3],'X+'),\n", + " sdpy.coordinate_array([1,2],'X+')))\n", + "pp = sdpy.PolyPy(tfarray, displacement_derivative = 0) # We tell PolyPy these are displacement FRFs." + ] + }, + { + "cell_type": "markdown", + "id": "4c771ea7-f4a6-46ab-a8e9-1e71f32fd5e1", + "metadata": {}, + "source": [ + "We can then tell `PolyPy` which polynomial orders to solve for." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "eefd5ef6-b2a6-4ba8-912b-d8428a3f7925", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating Data: Progress = 0.00%\n", + "Accumulating Data: Progress = 33.33%\n", + "Accumulating Data: Progress = 66.67%\n", + "Solving for 29 roots (1 of 13)\n", + "Solving for 27 roots (2 of 13)\n", + "Solving for 25 roots (3 of 13)\n", + "Solving for 23 roots (4 of 13)\n", + "Solving for 21 roots (5 of 13)\n", + "Solving for 19 roots (6 of 13)\n", + "Solving for 17 roots (7 of 13)\n", + "Solving for 15 roots (8 of 13)\n", + "Solving for 13 roots (9 of 13)\n", + "Solving for 11 roots (10 of 13)\n", + "Solving for 9 roots (11 of 13)\n", + "Solving for 7 roots (12 of 13)\n", + "Solving for 5 roots (13 of 13)\n" + ] + } + ], + "source": [ + "pp.compute_poles(np.arange(5,30,2))" + ] + }, + { + "cell_type": "markdown", + "id": "d394d513-7cd6-4323-8ae9-fa540a477736", + "metadata": {}, + "source": [ + "We can then plot the stabilization diagram. The stabilization diagram will have different markers to denote the stability of a given pole. We generally overlay these poles with a mode indicator function to help us figure out which poles are \"real\". In this case, a red X denotes an unstable pole, a blue triangle denotes that the frequency has stablized, a blue square means that the frequency and damping have stabilized, and a green circle means that the frequency, damping, and participation factor have stabilized." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a5ff5306-cdbf-46db-95cc-b539e9adc423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Polynomial Order')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmif_axis, order_axis = pp.plot_stability()\n", + "cmif_axis.set_ylabel('CMIF')\n", + "cmif_axis.set_xlabel('Frequency (Hz)')\n", + "order_axis.set_ylabel('Polynomial Order')" + ] + }, + { + "cell_type": "markdown", + "id": "a7e544c5-0ad6-4d0d-853e-5f91a09066ae", + "metadata": {}, + "source": [ + "We can see that as we move up to a higher model order, we start to see more computational poles appear, as shown by the red Xs throughout the frequency band. However we see the three main columns of green circles denoting three stable poles in our fits. We would then select these three stable poles.\n", + "\n", + "In `PolyPy` we can select them by index, and we can determine the indices by plotting the stabilization diagram with labels." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "56fd5c29-a2d3-4dd3-84f4-5d21ad3e38ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Polynomial Order')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAApYAAAGzCAYAAACVe1cSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAADNz0lEQVR4nOzdd1zU9R/A8dexlQ3KUqa4d040FHOWmuYqG5qpPyttqYXlTCutLFum5W7gRi0ztzjJlagIblBAQFFBhjLuPr8/vkIioIB33HF+no/HPfS+9x3vu+/3c/fm8/0MlRBCIEmSJEmSJEmPyETfAUiSJEmSJEnGQSaWkiRJkiRJklbIxFKSJEmSJEnSCplYSpIkSZIkSVohE0tJkiRJkiRJK2RiKUmSJEmSJGmFTCwlSZIkSZIkrZCJpSRJkiRJkqQVMrGUJEmSJEmStEImlpIkSZIkSZJWmOk7AEOWl5fHsWPHcHV1xcRE5uCSJEmSVBloNBqSk5Np3rw5ZmYPT3XmzZvHvHnziI2NBaBhw4ZMmTKFp59+GoA7d+4wbtw4VqxYQXZ2Nt27d+fHH3/E1dVVl2+jUlLJucJLdvjwYVq3bq3vMCRJkiRJKodDhw7RqlWrh673559/YmpqSu3atRFCsGzZMr788kuOHTtGw4YNeeONN/jrr79YunQp9vb2jBkzBhMTE/bv318B76JykYllMebOncvcuXO5c+cOMTExHDp0CHd3d32HJUmSJElSKSQmJtK6dWsuXbqEl5dXufbh5OTEl19+yYABA6hevTohISEMGDAAgNOnT1O/fn3Cw8Np27atNkOv9OSt8GKMHj2a0aNHEx8fj6enJ+7u7tSsWVPfYUmSJEmSVAaZmZncunWr4LmlpSWWlpYP3EatVrN69WoyMzMJCAjg6NGj5Obm0qVLl4J16tWrh5eXl0wsiyEbDkqSJEmSZJQaNGiAvb19wWPmzJklrnvy5ElsbGywtLTk9ddfZ926dTRo0ICkpCQsLCxwcHAotL6rqytJSUk6fgeVj6yxlCRJkiTJKEVFRVGjRo2C5w+qraxbty4RERGkpaWxZs0ahg4dyu7duysiTKMiE0tJkiRJkoySra0tdnZ2pVrXwsICf39/AFq0aMHhw4f59ttvef7558nJySE1NbVQrWVycjJubm66CLtSk7fCJUmSJEmS7qPRaMjOzqZFixaYm5uzY8eOgtfOnDnD5cuXCQgI0GOEhknWWBYjv1d4Tk6OvkORJEmSJEnHPvzwQ55++mm8vLxIT08nJCSEsLAwtmzZgr29PcOHD2fs2LE4OTlhZ2fHW2+9RUBAgOy4UwxZY1mM0aNHExUVRVhYmL5DkfRg7ty5+Pj4YGVlRZs2bTh06NAD11+9ejX16tXDysqKxo0bs2nTpjLv59SpU/Tv3x8fHx9UKhXffPONNt+SUSrL57tgwQICAwNxdHTE0dGRLl26PPS8Sor8z9nc3LygR21Jn3doaCgtW7bEwcEBa2trmjVrxujRo1GpVPTt21er8ZTmvC9duhSVSlXoYWVlpZU4JONy9epVhgwZQt26dencuTOHDx9my5YtdO3aFYA5c+bQq1cv+vfvT4cOHXBzcyM0NFTPURsoIZUoLi5OACIuLk7foUgVZMWKFcLCwkIsXrxYnDp1SowcOVI4ODiI5OTkYtffv3+/MDU1FV988YWIiooSkyZNEubm5uLLL78s034OHTokxo8fL5YvXy7c3NzEnDlzdPguK7+ynqcXX3xRzJ07Vxw7dkxER0eLV199Vdjb24v4+PgKjrxyyf+cX3/9dWFubi46dOggbG1txcsvv1zs571r1y4RGhoqoqKixPnz58XkyZMFIBo1aiT69OmjtXhKe96XLFki7OzsRGJiYsEjKSnpkeOQDJ/8/dYfmVg+gLwwHz+tW7cWo0ePLniuVquFh4eHmDlzZrHrDxo0SPTs2bPQsjZt2ggXF5cy7ede3t7eMrF8iLKep/vl5eUJW1tbsWzZMl2FaBTyP+f8f/M/508//fShn3deXp5o166d8PLyEk2aNNFKYlnW875kyRJhb2//yMeVKh/5+60/8la4JN2Vk5PD0aNHCw2Ca2JiQpcuXQgPDy92m/Dw8ELrA3Tp0oWrV6+WaT9S6ZXnPN0vKyuL3NxcnJycdBVmpZf/OXfs2LHg887/nA8ePPjQz/vjjz9GpVKRkpKilfmUy3veMzIy8Pb2xtPTkz59+nDq1KlHjkWSpJLJzjvSY02tUbP38l4S0xMxzzJHrVYX+RF0dXXl9OnTxW6flJRUZP2qVasWbFfa/Uill5KSUubzdL/g4GA8PDyK/FHwuCuuPFhaWhb6vPM/57p16xb7eaelpeHm5sadO3ewsLBg3rx57Nmzh9TUVK3EU5bzXrduXRYvXkyTJk1IS0tj9uzZtGvXjlOnTsnZ1CRJR2RiKT22QqNDGbd1HLGpscqCu7N+hcWGlW0IiTVr4Px5mDy56GszZoBaDdOmPWq4j6dp08DUtPjPdvFi2LKlzJ/trFmzWLFiBWFhYbIjxz2KlIeTTwIQsq3sNXzVqlUjODiY7Oxsxo4dS9u2bcv8WWujfAYEBBRat127dtSvX5+ffvqJGTNmlCkeSZJKR94Klx5LodGhDFg1gMYujQkfHk76h+nsGb0HlYmKjzZ8RGj0f739HjQIrpubG8lZWTBlipJEotxmBUj+/ntluanpQ/cjlcDUtNBnC0rSYmpiQvLChQWfLZTu8509ezazZs1i69atNGnSRGdhVzb3l4dbE9JpcGE9YMrKEDUmpiYkJycD/33OJX3eMTExxMfH8+677xIcHMytW7fYsmULf/zxB2ZmZly4cKHM8ZS3fN7P3Nyc5s2bc/78+VKtL0lS2cnEshhz586lQYMGBAUF6TsUSQfUGjXjto6jV51erH9hPW1rtsXGwoZAv0BatWyFz00fxm8dj1qjRqPRsGPHjhJrSAICAthhZgbTpxckQDt37sTF2pody5cryydPfuh+pBJMnlzoswWw+PxzWmg07GjTpqAmszSf7xdffMGMGTPYvHkzLVu2rJDwK4PiysOBMBuiIpxxdW0BN5IxcXZi+/btBZ9zmzZtSvy869Wrx8mTJ4mIiCAiIoI+ffrg7OxMp06diIiIwNPTs0zxNHFpye1sc570fbLM5bPIvtVqTp48ibu7e7k+K0mSSkHfvYcMmexVZpx2xewSTEOEx4UXeW3FihXCwtJC0BexdNtS8b///U84ODiUOETJ/v37hZmZmZg9e7aIfustMRWEOYgvQViamYmlS5eKqKioh+4nOztbHDt2TBw7dky4u7uL8ePHi2PHjolz585p9b1XWtOnCwFCWFgIAWLFwIHC0tKy1J/vrFmzhIWFhVizZk2hoWfS09Mr+I0YnvvLg0YjRNu2ymP58hVCpbIUWD8nzMzNRFBQkLC1tRWvvPJKsZ/3Z599JrZu3SouXLggoqKixOzZs4WZmZkICAgoda/w/Hg2nd4t3vjtiPCdsFF4B28U7WbuEGNnzi1T+fz444/Fli1bxIULF8TRo0fFCy+8IKysrMSpU6ce6TOTDJ/8/dYfmVg+gLwwjVPIiRDBNER6dvFJxZdzvhTYI8zMzUTr1q3FP//8U/Da0KFDRceOHQutv2rVKlGnTh1hYWEhGqpU4q+7CdD3338vvLy8hIWFxUP3ExMTI4Aij/uP9Vi7m1QKCwshhCjT5+vt7V3s5zt16tQKfhOG597ykJqZI6bPSxEgxOr1OUIIIUaM+kqAl1CZmAgLCwthbm5e8Hnnf875/06cOFH4+/sLKysr4ejoKAICAsSKFSvE0KFDS51Y5sfT4pM/hHfwxiKPZ0a9X+ry+e677xZcI66uruKZZ54R//77rzY/PslAyd9v/ZGdd6THjrutchss8mokbWsWnY6rff/2kAbbhm4jyCeo0GsxMTF06tSp0LKBAwcycOBA5VbtlClgYQE5OYy5eZMxly4VG8P9+/Hx8UEI8YjvzIjNmAE5OQWfLTNmMGbyZMaMGVPs6vd/vrGxsRUUaOWTXx7m7t3Jr7stObeoNRYeN5l85CB3XOsz7KMAFv4VQP3qDYg8ao9K9d+2H3zwAZ06dWLXrl106tSJadOm8cknnxQ5xvPPP1/qeKxMnQFIyDhLQ5eWfPdCc/yqWzMv7ALf7jjHv04u8F7pyuecOXOYM2dOGT4NSZIemb4zW0Mm/+IxTnnqPOHzjY/oHdJbqDVqkZh6W6w8fFlcz8gWao1a9A7pLXy/8RV56rxC26WmpooaNWoUf/s0/1bt9OnFPy/tfqSiyvDZCiE/37LKU+cJl889RZXJrUX1geEChGg+8rjwDt4ovIL/EE1/6CxcRw0RIMTmzf9tl/85x8fHa+3zVqs1YtD8fcJ0iqtwntFO3Mi4U+j1RXvPiyqTWwuzKa5i37nCs+3I8y7dS/5+64+ssZQeO6YmpnzV7SsGrBpA9197czm2G7ezPLCxScTbZytbLmxizaA1mJqYFtrO3t6e+Pj4ojvMr6m821EH+O/fKVMKP3/QfqSiyvjZgvx8y+ryjTuYZQzhtuozxCFbanjfZs6rfsw/kMS6M79x+eZNRjR5i121lJGdunUDlarw56ytzzvk0GUOxqTiZjGSK+pPGfrHQD588kMauTQi8mok6+Nmctv0MNWzP+TD0FNsfc8ZK3OlnMrzLkmGQSaW0mOpX/1+rBm0htfWvUWa2ARVADUkxtVkzaA19Kvfr/Q7U6sLJz758p+r1VqL+7EjP1ud0mgEwWtOYJ4TQDuXzzmY5khCahWC2gN0u/uAhXfXz8lRHpaW2o/lekY2s/5WBjqf3u01HJybM27rONotblewjq+DL789t5LvNzpx+UYW3+04xwc96mk/GEmSyk0lhGzYVZL4+Hg8PT2Ji4uTszQYoYzsPJrP2Ey65iTdGluy9WQ21SyacmRi94JaEEkyZhtPXGFMyDGqWpiy5d0OqNMt2XryGCmZKVSzrkaj6k0Zv+okMdczCfSvxtev1kdXX4XT/jjF0gOxNKphx4bRT2Jqoio08467rTuBXoGYmpiyOTKJ1387ioWpCTvHd6SmY1XdBCVVWvL3W39kjWUx5s6dy9y5c8nJydF3KJIOhZ25Sm6eivrVAlj6fEcCL+0iIfU2YWeu0qORHOdOMm55ag1fbzsLwP86+OHpVBWc4H/ehcf4/LlmLfrM3c+hjFtczq5OTappPZbL17P4/aDS0e3Dp+tjaqL0EDI1MS3SQQege0NXAvycCb94nTnbzvHVoKZaj0mSpPKRA6QXY/To0URFRREWFqbvUCQdCjtzDYBuDVwxMVHRtYEyB/Hecyn6DEuSKsSfJ65w8VomjlXNGf6kb4nrNaphz0ttvACY/mcUao32b3J9te0MuWpBYO1qtPd/eOKqUqn4oEddAEKPxXMmKV3rMUmSVD4ysZQeW/9euglAWz9leJP8H7QDF67rLSZJqghCCBbtiwFgRKAftlbmD1z/vS51sK9izumkdFYejtNqLJEJaWyIuAJAcBnaSzb3cqRHQzeEgNlbz2g1JkmSyk8mltJj6XpGNhdTMgF4wssRgDZ+TpioICYlkyupt/UZniTp1JFLN4lMuIWlmQkvtvZ66PqO1ha826U2AF9tPUP6nVytxfLFFiUpfLapB41q2Jdp2/Hd66JSwbaoZKKu3NJaTJIklZ9MLKXH0r+XUwGo7WKDfVWltsbOypwmNR0A2H9e3g6XjNeS/Upt5XPNa+BobVGqbV5u641fdWuuZ+bwY9gFrcSx/3wKe85ew9xUxfhudcu8vb+LDT0b3x3gPey8VmKSJOnRyMRSeiwdvXsbvIW3Y6Hl7f2V2+LhF+XtcMk4JaTeZsupZABebe9T6u3MTU346On6ACzaF0PcjaxHikOjEQXDC73Uxhsv5/L17B7dyR+ATScTuXAt45FikiTp0cnEUnos5bevfOK+xLKltxMAEXGpFR2SJFWIlYfjUGsEAX7O1HOzK9O2neu70K6WMzl5moJb2OX118lETiakYW1hypin/Mu9n/rudnSp74oQ8OMu7dSkSpJUfjKxlB47Go3g1JU0AJp5OhR6rUlNpY3XxWuZpN3WXjsySTIEao1gzRGl880LrT3LvL1KpWJiz/qoVPDn8SsFNf9ldSdXzeebldrK/3WoRTWbRxtxPT8xXR+R8Mg1qZIkPRqZWEqPnfibt8nMUWNhaoJfNetCrznbWOLlpNySOxGfqofoJEl3DlxI4UraHeyszOje0K1c+2joYc/AFsqA0zM2RlGeOTYW748h/uZt3OysGNmh5KGOSquZpwOBtauh1gjm75a1lpKkTzKxlB47UYlK79HarjaYmRYtAk3v1mIel7fDJSOz6ogyl3afZjUeaXapcd3qUtXClIi4VP44fqVM215Lzy64Zf1Bj7pUtdDOPB1vPaX0Wl99JJ6ktDta2ackSWUnE8tizJ07lwYNGhAUFKTvUCQdOJ2kJJYltS/Lvz0u21lKxiQ1K4ctp5IAGNSy7LfB7+VqZ8XrHWsB8PGfUaRkZJd62+kbo8jIzqNJTXv6NqvxSHHcq7WvE619nchRa/hpj6y1lCR9kYllMeTMO8btdKIyS0d9d9tiX2/mqbSzjIhLK9dtPkkyRH8cv0JOnoZ6brY0qlG2TjvFGdXRj3puttzIzOGj0JOlKitbTyXx5/ErmKjgk76NMLk7daO2vHW3reXyQ5e5ll76ZFeSJO2RiaX02HlYjWVDD3vMTFSkZGSTIAdKl4zEqruddga19ESlevSEztLMlK8HNcPcVMXWqGSWH3rwjDzxN7P4YO0JAEZ28CsYM1abnvSvRlNPB+7kali476LW9y9J0sPJxPIR7Nmzh969e+Ph4YFKpWL9+vUP3ebbb7/F3t4elUqFSqXi7bfffuD6iYmJvPjii9SpUwcTExPeffdd7QT/mMrMzuPS3V6jxdVY7tmzh4H9+hI/dwiXPu/FvGUrHri/0NBQunbtSvXq1bGzsyMgIIAtW7boJPbKrqzlZd++fbRv3x5nZ2eqVKlCvXr1mDNnTsUEa2SirtwiMuEW5qYq+jYv3e3n4s7X/cvOHtrJe13rADBlQyR7z10DYP/+/ZiZmeHv71/wXefpZM3xqd259HkvBjcqX43pw64hlUrF23drLX8Lv8TNzByys7OZOHEi3t7eWFpa4uPjw+LFi8t1fMm4lfU76tVXXy24vu99NGzYsGICfojy5Ci///47TZs2pWrVqri7u/Paa69x/XrZxnWWieUjyMzMpGnTpsydO7dU68fExBAcHEz9+vX57rvvAKU954MSkezsbKpXr86kSZNo2rSpVuJ+nJ1NTkcIqG5riXMxQ5zkn9Neb0wCIOZa5gP3t2fPHrp27cqmTZs4evQonTp1onfv3hw7dkwn8VdmZS0v1tbWjBkzhj179hAdHc2kSZOYNGkSP//8s44jNT6rjyq1iV3qu+JUypl2ijtfxS17o2Mtnm3qQZ5GMHzZEX7fG8WQIUPo3LlzwTpBk36n5uhfafT+Co5GX8Crhnu53kdprqGn6rnQwN2OzBw1S/bHMGjQIHbs2MGiRYs4c+YMy5cvp27dss/yIxm/sn5HffvttyQmJhY84uLicHJyYuDAgTqOtHTK+n7279/PkCFDGD58OKdOnWL16tUcOnSIkSNHlu3AQipRXFycAERcXNxD1wXEunXrHrjOBx98IBo2bFhom/bt24vu3buXKp6OHTuKd955p1TrSsULOXhJeAdvFC8v/OeB660+opz7gFEzy3yMBg0aiI8//ri8IT4WSlNeivPcc8+Jl19+WfsBGbHsXLVoPn2r8A7eKHZGJ5drH8Wdr3uX3c7JEyOWHRbewRtF1XqBosEzr4qnh7wlHFw9BSA831khmk/fKqIT0x7x3Tw4pnybTlwR3sEbhfdLnwh7e3tx/fp1rR1XqhzK8vtdnPJ8R61bt06oVCoRGxtbrmPqUmnez5dffin8/PwKLfvuu+9EjRo1ynQsWWNZgcLDw+nSpUuhZc2bNyc8PFxPET1+Tt8daqi++4NvxeV34Ll0I5M8tabU+9doNKSnp+Pk5FT+IKViHTt2jAMHDtCxY0d9h1Kp7DydzI3MHFxsLQmsXU0nx7AyN2X+yy1onXscdVoyGQ2f48CFFNKzlUkGrv/6LvE/vMJbr/Rn//79OonhXt0bulHbxYbrUQdwrdWAL774gho1alCnTh3Gjx/P7duy7bSkfYsWLaJLly54e3vrO5RyCQgIIC4ujk2bNiGEIDk5mTVr1vDMM8+UaT/aGUBMKpWkpCRcXV0LLXNwcODWrVvcvn2bKlWq6Cmyx0f03R7h9dyU9pVqjZq9l/eSmJ6Iu607gV6BmJqY4lfNBoDcPMGZ5HQaetiXav+zZ88mIyODQYMG6eYNPIZq1qzJtWvXyMvLY9q0aYwYMULfIVUqa44qY1c+90SNYsdtvVdJ5aE0Ll44z+7fvmXLn1uJyrJh2UVbzsdWYeh7X/LC00Hk5uawcOFCgoKCOHjwIE888cRD91neeExMVIx5yp99PyRxLu4kx10dWbduHSkpKbz55ptcv36dJUuWlOp9SVJpXLlyhb///puQkBB9h1Ju7du35/fff+f555/nzp075OXl0bt371LfSs8nE8vSmjYNTE1h8uSir82YUfb11WptRyg9hBCC6Ht6hIdGhzJu6zhiU2ML1vFx8OGrbl/Rr36/gmXH49JKlViGhITw8ccfs2HDBlxcXLQef6Wh5Wt/7969ZGRk8M8//zBhwgT8/f0ZPHiwdmI1clfT77DrjNKhJn+2nJLcWx6m7oIoExjWRykPhRRzDtVqNS+++CIff/wxHVs3pSOQHObO+vN2fDVlfMF67dq148KFC8yZM4dff/211PHkK658lqRnY3eqmKnIRkXXNz+ldetmAHz99dcMGDCAH3/8Uf4xL2nNsmXLcHBwoG/fvvoOpdyioqJ45513mDJlCt27dycxMZH333+f119/nUWLFpV6P/JWeGmZmsKUKUWTyBkzlOWlWN/NzY3kP/5Qlpsqf3WnpqZiZ2cnv+AqwJW0O6TfycPMRMXJG1sZsGoAjV0aEz48nPQP0wkfHk5jl8YMWDWA0OjQgu0i4h4+H/KKFSsYMWIEq1atKtLc4bHzsLJiWrYZX3x9fWncuDEjR47kvffeY9q0adqL1chtOHYFtUbQzNMBf5fix20FJYm7tzx8GDSJGbvgk32WDFg14L8VSziH6enpHDlyhDFjxmBmZoaZmRnTp0/n+PHjmJmZsXPnzoJ1W7duzfnz5x8Y9/3xPKh8lsTM1IQmdXwxtXFm2dGrpN1WbsvXr18fIQTx8fEP3YcklYYQgsWLF/PKK69gYVG6znGGaObMmbRv357333+fJk2a0L17d3788UcWL15MYmJiqfcjayxLK7/2JT+JnDz5vy/Z6dOLJpfFrB8gBJv++UdZf/JkmDKF48ePExAQUDHv4TGX377Sr1oVJuwYTa86vVj/wnpMVMrfV21rtmX9C+vpu6Iv47f+V8tyPC7tgftdvnw5r732GitWrKBnz566ewOVxcPKyt1rvzw0Gg3Z2XLg69IQQhT0Bh/YsuTaSrVGzbit4wqXh2ltwdSCl6ZMAeryMmfQrFwBK1YWew7t7Ow4efJkof3++OOP7Ny5kzVr1uDr+9984BEREbi7l9wrvNh4KLl8PsgLvbqwd+uf3Ey7xYI9FxnfvS5nz57FxMSEmjUfXIMrSaW1e/duzp8/z/Dhw/UdyiPJysrCzKxwWmh6949IUYbJQmRiWRb3/mB+8gkZOTmcf+MN6N0bpkwhJiaGiIgInJyc8PLyKrL+6zk5fG9mxtDz5+m7bh2gjNX3ww8/cPnyZWWbYkRERACQkZHBtWvXiIiIwMLCggYNGuj6HRuV00lK+0o7h/PExsWyvP/ygh+tfFmZWfR37M+fu/8EIC8tmZMnjxN9rib1a/sV2WdISAhDhw7l22+/pU2bNiQlKVPmValSBXv70rXLNEr3Xfvk5JAxcSLne/eGu9dzkfJyn7lz5+Ll5UW9evUAZWin2bNnP3TsV0lxIj6Ns8kZWJqZ0KuJR4nr7b28l9jUouUh4733OJ+YSMN58wC4tGIlB0aMIK1lS9yLOYeNGjUqtF8XFxfS09O5cOECVlZW3Llzh4ULF7Jz5062bt1a5nigaPl82DX08ssvMXnadK5v+oZ5t1+hjojj/fff57XXXpN3iaQiMjIyCtWmP+z6yrdo0SLatGlTpAzoW1nfT+/evRk5ciTz5s0ruBX+7rvv0rp1azw8Sv4OKaJMfcgfMyUOV2BhIQSIXWZmAijyGDp0qBBCiKlTpwpvb++C9YWFhZgzZ07ptrlHcevfv470cKN/Pyq8gzeK11bOEUxDpGenF1ln165dxX7ez/R7QQhR9Px07NjxgefzsXfPtV/SZ1vStf/dd9+Jhg0biqpVqwo7OzvRvHlz8eOPPwq1Wq2f91LJTFp3UngHbxRvhfz7wPVCToQUWx5KOl+lPYdTp04V7u7uolatWsLKyko4OTmJoKAgsXPnznLF86CYHvT9GRUVJarXbSlUZpbCztlVjB07VmRlZT0wBqnyK89wQ+W5vlJTU0WVKlXEzz//rMXotaM87+e7774TDRo0EFWqVBHu7u7ipZdeEvHx8WU6rqyxLMbcuXOZO3cuOTk5RV+cMQNycsDCgqCcHET+baFixMTEEOToCJcugYUF5OTwbno67z6gSjkmJoagoKBCy4Scr1or8mssG7t5QzREXo2kbc22hdYJCgriwOUDtFvcjl1Dd7Fqvy2bTibRq4dSa3b/+ZHzyT/APWWFnByC9u594LV8/2f71ltv8dZbb1VAoMbnTq6aDREJwINvgwO42yq3pe8vD0FBQcr325QpZJuCpZr/boMDQ4cORaVSsXTp0oJt7j2H06ZNK1d72JLiyY/p3vIZ5BNU6PXivj/r16/P+o1/8+LCg5ibqnh3XJCsrZSKFRQUVKbvKAB7e3uysrJ0HFn5lOf9aOV7VwtJsdEq8hfP9OlK7cv06cU/v4dGoxFe9vbicinXL9jGy0tcvnxZF2/nsXY7J0/4TtgovIM3ivgbGcLnGx/RO6S3UGsK136pNWrRO6S38P3GV+Sp88RPu88L7+CNYtQvR+T5KYsylBUh5LWvbX8eTxDewRtF28+2izy15oHr5qnzii8Pd8/ZbwPqCt9vfIX642kF57C486Wtc1hiPKJo+bzXw47/8sJ/hHfwRvHO8gfX4ErG4VEHSL+fsX1H6fL9yMTyAQpdmCX9MGpruaRTx+NuCu/gjaL59K1Co9GItVFrhWqaSvQO6S0OXD4gbt25JQ5cPiB6h/QWqmkqsTZqrRBCiH8upAjv4I2izafb9fwOKhF57evdkEUHhXfwRvHl5tOlWv/+8nBnysSCpPLe8lBR57C05bMsTsanCp+7f1weib2hg6glQ6LtxFIqPXkrvLTU6kK3gQrkP79/bL6yri/pVNQVpUd4A3c7VCoV/er3Y82gNYzbOo52i9sVrOfr4MuaQWsKxslrXNMeUxMVSbfucCX1Nh4O8hbaQ8lrX68SUm+z95wyduWAh4xdme/+8jB1N6g7we9P5rCm23/loaLOYWnLZ1k0qmHPoBaerDwSx8d/nmL9m+0xMVFpM2xJkgCVELIBX0ni4+Px9PQkLi5ODk1RyU3dEMmy8EuMDPRlYs//etOXZmaP3t/v42RCGt8Nbs6zTcvQM06S9ODrrWf4bud5AvycWf6/tg/f4B6PMvOOLmg7nmvp2XSaHUZGdh5fDGjCoJaeWoxWMiTy91t/ZI2l9FiIujuGZQOPwnOEm5qYFukAcL9WPk6cTEjjcMwNmVhKBi1XrWHFYWXsyhfblDw8SklKUx4qkrbjqW5rydud/fls02m+2HyGpxu5YWtlrrX9S5IkZ96RHgMajSiYI7yBe9nHlmzl4wjA4dgbWo1LkrRtR/RVrqZn42xtQfeGbvoOxyC92s4X32rWpGRk8/3OB88AJElS2cnEUjJ6cTezyMjOw8LUBL/q1mXevqWPEwBnktNJy8rVdniSpDUhhy4DMLClJxZm8uu9OBZmJkzuVR+AxftiCtpfS5KkHfKbRzJ60Xdvg9dxs8HctOyXfHVbS/yqWSMEHL0say0lw3T5ehZ7ziqddl5sXfbb4I+Tp+q50qOhG3kawYTQE6g1squBJGmLTCwlo3dvj/Dyann3dvihmJtaiUmStG35YaW2MrB2Nbycq+o5GsP3cZ+G2FqZcSI+jSX7Y/QdjiQZDZlYSkYvv+NO/UdILFvdvR0u21lKhignT8PqI0qnnZfaeOs5msrB1c6Kic8ot8S/2nqWuBuGOXuKJFU2MrGUjN5/HXfKn1i29lUSyxPxqdzOkeMwSoZl08lEUjJycLG1pHN9F32HU2k838qTtn5O3M5V89G6k3L6XEnSAplYSkYtNSuHhNTbANT3KH9i6eVUlRoOVchVC1lrKRkUIQSL797KfaWtd7naET+uVCoVM/s1wdLMhL3nUvj1n0v6DkmSKj35DSQZtfzb4DUdq2D3COPVqVQq2vs7A7DvfIpWYpMkbTh66SYn4tOwMDMp19iVjzvfatZMeLoeAJ/+Fc255HQ9RyRJlZtMLCWjpo3b4Pna+1cDYN85mVhKhiO/tvK5ZjVwtrHUczSV06vtfOhQpzrZeRreWRFBdp5s7iJJ5SUTy2LMnTuXBg0aEBQUpO9QpEd0KiENeLSOO/na1VISy6jEW1zPyH7k/UnSo4q/mcXmyCQAhj3po99gKjGVSsXsAU1wsrYgKvEWX209q++QJKnSkollMUaPHk1UVBRhYWH6DkV6RMfjUwFo6ln2GXfuV93WknputgAcuHD9kfcnSY/ql/BLaAS093emntuj//H0OHOxs2JWv8YA/LznImFnruo5IkmqnGRiKRmt9Du5XEzJBKBJTQet7PPJu7fD98t2lpKeZWbnseLuTDuvtffVczTGoVtDN15uq7RTfWdFhByCSJLKQSaWOjBz5kxatWqFra0tLi4u9O3blzNnzjx0u9WrV1OvXj2srKxo3LgxmzZtqoBojdfJhDSEgBoOVahWirZnpTlv7WsrieXecykIITh16hT9+/fHx8cHlUrFN998o4u3Uq5rasGCBQQGBuLo6IijoyNdunTh0KFDOolPqnirjsRx604ePs5V6VRX+0MMleeaCw0NpWXLljg4OGBtbY27uzt+fn5l/i7Mt2LFClQqFX379i1XPEuXLkWlUhV6WFlZPXCbyb0a0LSmPWm3c3nj96PcyZXtLSWpLGRiqQO7d+9m9OjR/PPPP2zbto3c3Fy6detGZmZmidscOHCAwYMHM3z4cI4dO0bfvn3p27cvkZGRFRi5cTkZr7SvbFKzdLfBS3Pe2vo6Y2lmQkLqbc4kp5OVlYWfnx+zZs3Czc1NJ++jtLHdLywsjMGDB7Nr1y7Cw8Px9PSkW7duJCQk6CxOqWLk5GlYsOciACMC/TAxUWn9GOW55pycnJg4cSLh4eGcOHECBwcHYmNjmT17dqn3kS82Npbx48cTGBhY7ngA7OzsSExMLHhcuvTgIYUszUz58eUWOFlbEJlwiykb5HewJJWJkEoUFxcnABEXF/dI+7l69aoAxO7du0tcZ9CgQaJnz56FlrVp00aMGjXqkY79OHvzt6PCO3ijmLvrXLm2L+m8DVtySHgHbxQ/7Cy8X29vbzFnzpzyhquV2B4kLy9P2NraimXLlukwMqkirDx8WXgHbxQtP9kmbufkVcgxy3PNCSFE8+bNxaRJk8q0j7y8PNGuXTuxcOFCMXToUNGnT59yxbNkyRJhb29fpnjz7T17TfhO2Ci8gzeKZQdiyrUPSX+09fstlZ2ssawAaWlKzZmTk1OJ64SHh9OlS5dCy7p37054eLhOYzNmBR13ytm+sqTzlj+zyfbo5HLH9qhKc03dLysri9zc3DJtIxketUYwP+wCACMDfbEyN62Q45b1mhNCsGPHDs6cOUOHDh3KtI/p06fj4uLC8OHDHzmejIwMvL298fT0pE+fPpw6dapU8T9Zuxrvd1fGt5z2xyl2yc48klQqMrHUMY1Gw7vvvkv79u1p1KhRieslJSXh6upaaJmrqytJSUm6DtEoXc/IJv6mMuNOoxol3wpXa9SExYax/ORywmLDUGuU9lQPOm+d6ynnKSIulRQ9DDtU2mvqfsHBwXh4eBT5A0aqXDZHJnExJRP7Kua8qOV5wctTHu6XlpaGjY0NFhYW9OzZk++//56uXbuWeh/79u1j0aJFLFiwALVGTVJGEvG34ssVT926dVm8eDEbNmzgt99+Q6PR0K5dO+Lj40v1ebze0Y8BLWqiETDm93+JvjvhgiRJJTPTdwCV3rRpYGoKkycXfW3GDEZv2EBkSgr79u2r8NAeZyfujl/pV80a+yrFz7gTGh3KuK3jiE2NLVjm4+DDV92+Ytt324iMjCz2vLnZW9Gohh2RCbfYdfoqA1t6ajd4HVxTs2bNYsWKFYSFhT2084JkuIQQzN11HlAG9bax1N5XeHnLQ4G7163txIlERESQkZHBjh07GDt2LH779rHyxImHXrfp6em88sorLFiwgD3X9jDu93HEno+FO9BpWaeyxQMEBAQQEBBQ8Lxdu3bUr1+fn376iRkzZjz0M1GpVHz2XGMSbt4m/OJ1Xlt6mPWj2+NqJ8uQsZk5cyahoaGcPn2aKlWq0K5dOz7//HPq1q1bsE5QUBC7d+8utN2oUaOYP39+RYdr0GRi+ahMTWHKFOX/9yYCM2YwZsoUNtrZsScigpo1az5wN25ubiQnF761mpycrNMOIcbseFwqUHLHndDoUAasGkCvOr1Y3n85jVwaEXk1ks/2fkb/of1xjnPm8IHDJZ63zvVciUy4xfboZO0nllq6pvLNnj2bWbNmsX37dpo0aaLdWKUKFXb2GlGJt6hqYcqr7Xy0tt9HLQ9AwXVrAvjfvW6bNWtGdEgIQ5YsQZTiur1w4QKxsbH06t0LjUaj9OQWSsckkxkm+M32K308xTA3N6d58+acP3++1NtYmJkw/+UWPDdvPxevZTJ08SFW/i8A+6rlnyJWMjz5ncNatWpFXl4eH330Ed26dSMqKgpra+uC9UaOHMn06dMLnletWlUf4Ro2fTfyNGSlbvw7fboQoPwrhNB8/LEYDcLD1lacPXu2VMcaNGiQ6NWrV6FlAQEBsvNOOb2y6GCJje7z1HnC5xsf0Tukt1Br1AXLNRqNePPNN4WVo5WoObGmyFOX3CniZHyq8A7eKOpO2iQys3OFEFruvKOFa0oIIT7//HNhZ2cnwsPDtROXpDcajUY8+8M+4R28Ucz485TW9quN8lCgmOu2HggLU9NSXbe3b98WEccjhEewh+j4eUdx/MRx0adPH/HUU0+JEydOiFGvjypbPPe/17w8UbduXfHee++VedtLKZmi5SfbhHfwRvHc3H0F5V4yTI/aeae4zmEdO3YU77zzjpYiNF6yjaU2TJ4M06crtUyWloyeOpXfrKwI+fNPbG1tSUpKIikpidu3b5e4i3feeYfNmzfz1Vdfcfr0aaZNm8aRI0cYM2ZMBb4R46DRCI5dugnAE16ORV7fe3kvsamxfBT4ESaq/4rA6NGj+f333/nqp6+IvxPPhqMbSjxvDT3s8HKqyu072Sxcv5OIiAhycnJISEggIiKiTDUixdLCNfX5558zefJkFi9ejI+PT8E2GRkZjxabpBc7oq9yPC6VKuamjOpYS2v71UZ5yDfTzIxtQ4ZwccoUoi0saD91KqeBt997r1TXrZWVFTftbnKlyhVmvTiLJo2b4ODggK2tLfPmzWPF8hVlimf69Ols3bqVixcv8u+///Lyyy9z6dIlRowYUebPycu5Kr8Ob419FXP+vZzKqF+PyjnFK4H09HRu3bpV8MjOLl27+JI6h/3+++9Uq1aNRo0a8eGHH5KVJQfRL0Lfma0hK/NfPBYWQoCghMeSJUsKVh06dKjo2LFjoc1XrVol6tSpIywsLETDhg3FX3/9pb038xiJTkwT3sEbRf3Jf4vcPHWR10NOhAimIdKz0wstL+t5+/zvaFHj9UXFbnP/uS23R7imvL29i91m6tSp2olNqjBqtUZ0n7NbeAdvFLP+jtbqvrVVHoQQYuLEicLf319YgXAs53V7fzz5ww2VZ1/vvvuu8PLyEhYWFsLV1VU888wz4t9//32kz+vfSzdE/cl/C+/gjWLEssMiO7fod4ykf/m/3+X5/lOr1aJnz56iffv2hZb/9NNPYvPmzeLEiRPit99+EzVq1BDPPfecjt5B5SXbWGrLjBmQkwMWFoicHKW2qbjOF3fFxMTQqVOnQssGDhzIwIEDdR2p0Tt6t7ayuZcDZqZFK+Xdbd0BiLwaSduabQuWCyEACI8Lp93iduwauosgn6BC29573no2cefHMFfqTNzE0cldtdqRAnjkayo2Nla78Uh6sykykdNJ6dhamjGqg59W962t8gDwySef8ImlpVLTbmGhXL9lvG7vj2fp0qWF1i9LPHPmzGHOnDkP/xDKoLmXIwuGtGTY0sNsi0rm9d+O8uNLT1TYsE9S2URFRVGjRo2C55aWD5+FbfTo0cV2Dvvf//5X8P/GjRvj7u5O586duXDhArVqae8uQqWn78zWkJW3jWWR5/dJTU0VNWrUEOnp6cW+Lj2a91YeE97BG8VXW04X+3pJbcqEEEKtUYveIb2F7ze+Rdpw3X/eNBqN6PTlLuEdvFGsPxav3Tchrynprtw8teg0W7nO5mw7o/X9a6s8CCG0ct1qNR4d2nP2qqg7aZPwDt4oXl74j8jKrpiB6qXSKW8by9GjR4uaNWuKixcvPnTdjIwMAYjNmzeXN0yjJBPLByjVhVnSF+dDvlAl3enwxU7hHbxR7DqdXOI6a6PWCtU0legd0lscuHxA3LpzSxy4fED0DuktVNNUYm3U2lIda/aW08I7eKMYvvSQtsKX15RUyJojccI7eKNo+vEWcet2jk6OoZXyoMXrVlvlU9cOnE8puC3+wk/hskOPASlrYqnRaMTo0aOFh4dHqTtI7tu3TwDi+PHjjxKq0ZG3wh+VWl38rZ7852rZuLsiXUvP5tL1LFQq5ZZVSfrV78eaQWsYt3Uc7Ra3K1ju6+DLmkFr6Fe/X6mO16eZB9/vPE/YmWukZGRTzebht1keSl5T0l13ctV8ve0sAK93rIWtlW6GuNFKedDidaut8qlrAbWcWfZaa4YtOUz4xesMXnCQxUNb4qyN7wGpQo0ePZqQkBA2bNhQ0NEMwN7enipVqnDhwgVCQkJ45plncHZ25sSJE7z33nt06NBBDuN2H5UQdxvSSEXEx8fj6elJXFxcmcdLk/Rjc2QSr/92lLqutmx5r8ND11dr1Oy9vJfE9ETcbd0J9ArE1KRsbaX6zN3P8bhUJvWsz4hA7bZ/kx5v88Iu8Pnm07jZWbFrfBBVLHTbjk8b5cGY4ynJscs3Gbb0MKlZufg4V2XZa63xdrZ++IaSzpT191ulUhW7fMmSJbz66qvExcXx8ssvExkZSWZmJp6enjz33HNMmjQJOzs7bYdfqckaS8moHI69AUALn5JrK+9lamJapANAWQ1sUZPjcamsORrP8Cd9S/yCkqSyuJ6RzY93Z9l5v3tdnSeVoJ3yoE2GFk9Jmns5sub1dry65BCx17Po9+MBFr/aiqaeDvoOTSqlh9WxeXp6Fpl1RyqeHMdSMir/XLwOQFs/5wo7Zu8mHliYmXA6KZ3IBDmXsKQd32w/R3p2Ho1q2PFc8xoP30DSK38XG0LfbEdDDzuuZ+bwws//sOVUkr7DkqQKJxNLyWikZuUQlagkdm19nR6ytvbYVzWnWwNXAFYdiauw40rG6/zVdEIOXQbgo2fqY2Iia8ErAxdbK1aOCqBDnerczlUz6tejfLP9LBqNbHEmPT5kYikZjUMxNxACalW3xsXOqkKP/XwrZb7wdccSyMjOq9BjS8Zn5qbTqDWCLvVdaFermr7DkcrAxtKMRUNbFszl/s32c7z+21H5vSA9NmRiKRmNfy4q7Ssr8jZ4vva1quFXzZqM7DzWHUuo8ONLxmPn6WR2nL6KmYmKCU/X13c4UjmYm5ow7dmGfDGgCRamJmyNSua5ufs5f1VOpyoZP5lYFmPu3Lk0aNCAoKAgfYcilYE+2lfmMzFR8UqANwC/HIh9aENwSSrOnVw10/6IAuC1J33xd7HRc0TSoxjU0pMVo9riYmvJuasZ9P5+H2uOxus7LEnSKZlYFmP06NFERUURFham71CkUkrNyiE6SWlf2cav4tpX3qt/i5pUtTDl3NWMgtpTSSqLn3Zf5PKNLFztLHm7c219hyNpwRNejmx8+0na+ztzO1fN+NXHGbsygkx5a1wyUjKxlIzCwXvbV9pWbPvKfHZW5gW9dxfti9FLDFLlFXcjix/DlOGFJvVsoP255yW9cbG14pfX2jC+Wx1MVBB6LIHe3+8jMiFN36FJktbJxFIyCnvPXQOgvb9+Ozq89qQvKhVsj07mbHK6XmORKpeP/4wiO09Du1rO9Griru9wJC0zNVEx5qnarPhfAG52VlxMyaTv3P18u/0cuWqNvsOTJK2RiaVkFPacTQGgQ+3qeo2jVnUbejR0A2D+7gt6jUWqPDZHJrI9OhkzExXT+zSUg+wbsda+Tvz9TiA9GrqRpxHM2X6Wfj8e4EyS/ENUMg4ysZQqvdiUTC7fyMLMREXbWhXfced+bwTVAuCPiCvE38zSczSSoUvLymXyhlMAjOroh7+LrZ4jknTN0dqCeS8/wbcvNMO+ijknE9Lo/f0+fgw7L2svpUpPJpZSpbfn7m3wFt6OBtEurUlNB570r0aeRrBwr2xrKT3YZ5uiuZaejV91a956SnbYeVyoVCr6NKvBtvc60LmeCzlqDV9sPkPv7/dx9NJNfYcnSeUmE0up0iu4DV5Hv7fB75Vfa7ni8GWupt/RczSSodp/PoWVd2dr+rx/E6zMdT8fuGRYXOysWDi0JbMHNsWxqjmnk9LpP+8AH4aeIDUrR9/hSVKZycRSqtRy8jSEX1ASy6R//qBJkybY2dlhZ2dHQEAAf//99wO3X716NfXq1cPKygoPDw98fX1Lvf2pU6fo378/Pj4+qFQqvvnmm4LX2tVyppmnA3dyNczdeV4r71Uqat68eWU65wsWLCAwMBBHR0ccHR3p0qULhw4dqsCI/5OVk8eHoScBGBLgTSsf/QyTVZKyfrahoaG0bNkSBwcHrK2tadasGb/++qve4rnXihUrUKlU9O3bV2vxaJNKpWJAi5rsGBfEoJY1AVh+KI7OX+1m1eE41HJKSKkSkYmlVKn9e/kmmTlqnK0taNnAn1mzZnH06FGOHDnCU089RZ8+fTh16lSx2x44cIDBgwczfPhwjh07RmBgIHFxcSxfvrxU22dlZeHn58esWbNwc3Mr9JpKpeKDHnUBCDl0mbgbsq2lLtSsWbNM5zwsLIzBgweza9cuwsPD8fT0pFu3biQkVPxsSV9vPcvlG1l42FvxQY96FX78hynrZ+vk5MTEiRMJDw/nxIkTDBs2jGHDhrFlyxa9xJMvNjaW8ePHExgYqJU4dMnJ2oIvBjRl1agAarvYcD0zhw/WnqD39/sIv3Bd3+FJUukIqURxcXECEHFxcfoORSrB539HC+/gjeKd5f8W+7qjo6NYuHBhsa8NGjRI9OzZs9CyNm3aiFGjRpVq+3t5e3uLOXPmFFn+8sJ/hHfwRvHeimMP3YekHaU9Z0IIkZeXJ2xtbcWyZct0HFVhBy9eFz4TNgrv4I1iZ3RyhR77UZTlsxVCiObNm4tJkybpLZ68vDzRrl07sXDhQjF06FDRp08fncWibdm5avHz7gui0dTNwjtYuVZGLjssYq5l6Du0SkH+fuuPrLGUKrX8jjuB9w0zpFarWbFiBZmZmQQEBBS7bXh4OF26dCm0rHv37oSHh5dq+9L4oLtSE7UuIoHTd2cGknSjPOcsKyuL3NxcnJwq7jZ0+p1c3lsZgRAwoEVNOtVzqbBjl1dZP1shBDt27ODMmTN06NBBb/FMnz4dFxcXhg8frvUYdM3CzISRHfwIGx/EK229MTVRsTUqma5zdjPtj1NcS8/Wd4iSVCz9d6GVpHJKSrtDZMItVKr/Ou6cPHmSgIAA7ty5g42NDevWraNBgwbFb5+URPXq1QmLDSMxPRF3W3dyc3M5efIklpaWD92+NBrXtKdnY3f+OpnIzE2nWTqslRyjUMvKcs7vFxwcjIeHR5E/MHTp4z+jSEi9TU3HKkztXf5rSxfUGjV7L+8tKA8OaQ482f7JUn+2aWlp1KhRg+zsbExNTfnxxx/p2rWrXuLZt28fixYtIiIiotzHNwTONpbM6NuIIQHefLopmrAz11h6IJaVh+N4tb0Pozr44VDVQt9hSlIBmVhKlc+0aWBqyrauQwBo7ulAdVtLAOquWUPEa6+RNnQoa9asYejQoezevbvYHx8hBO9ueZeU8ykFyxzPOGJtb03Y9rCHbl9a47vXZVtUMrvPXmN79FW6NnAt974ea3fPO5MnF1pct25dIkaMIC0rizXOzqU+Z7NmzWLFihWEhYVhZVUx04BujkxkzdF4VCqY83wzbK3MK+S4pREaHcq4reOITY0tWOZt483naz6nbfW2pSoPtra2REREkJGRwY4dOxg7dix+fn4EBQVVaDzp6em88sorLFiwgGrV9Dsbl7bUdrVl6bDW7DuXwpdbz3A8LpV5YRf4LfwSIwL9eO1JH4O6nqTHmL7vxRsy2UbDQE2fLgSINX3/J7yDN4q5u84VWi6mTy9YtXPnzuJ///tfkV2sjVorsEM0fKWhCI8LF+nZ6SI8LlzU6VdH4IpYG7X2gdvfr6Q2lvny24K2n7VD3M7JK9v7lRTFnN/ilpfmnH355ZfC3t5eHD58WFfRFpGcdls0+3iL8A7eKGb9HV1hxy2NtVFrhWqaSvQO6V2oPPQO6S1U01RlLg/5hg8fLrp161bh8Rw7dkwAwtTUtOChUqmESqUSpqam4vz582WOyZBoNBqx7VSS6D5nd0H7y8ZTN4uvtpwW1zOy9R2eQZC/3/ojayylymfyZLLzNPSfPo3YJzPpNnYezJgBU6bA9OmFarQ0Gg3Z2YXbIqk1asZtHYdHAw98bvrQtmZbANrWbItzojPejbwZv3U8fer2KXb78hjzlD/rjiUQf/M283df4N0udR55n4+d/PM6Zcp/z4s57w87Z1988QWffvopW7ZsoWXLlrqO+m5MgvFrTnAzK5cG7na8Z0DnP7889KrTi/UvrMdEpTS9b1uzLetfWE/fFX3LXR7KU360EU+9evU4efJkoWWTJk0iPT2db7/9Fk9PzzLFZGhUKhVdGrjyVD0XNkUmMmfbWS5cy+S7nedZsDeGwa29GNnBF3f7KvoOVXoc6TuzNWTyLx7DtfH4FTH7yZeEAKGxsBACxITAQLF7924RExMjTpw4ISZMmCBUKpXYunVroW13xewSTEP8tO4nYWZmJmbPni2io6NF+/bthampqfj6168FbyBefOPFYrfPl52dLY4dOyaOHTsm3N3dxfjx48WxY8fEuXPnSozZO3ijqDNxk4hNkT07yy2/htLCQkwAsfu11x56zvPNmjVLWFhYiDVr1ojExMSCR3p6uk5D/nHX+YJzfybplk6PVVb55SE8LrzQ8gkTJojdu3eLtfvXlqo8fPbZZ2Lr1q3iwoULIioqSsyePVuYmZmJBQsW6CWe+1W2XuFlkafWiE0nroie3+0pqMH0/+gv8cHq4+Jcsm6vbUMlf7/1RyaWDyAvTMP17opjwjt4o8g1My9IMl577TXh7e0tLCwsRPXq1UXnzp0L/egMHTpUdOzYUYScCBFMQ6Rnp4tVq1aJOnXqCAsLC+Hg4CBcXFyEhYWFoCqiYZuGxW6fLyYmRgBFHveucy+NRlMw/NCg+QeEWq3R1cdj/O7+MfGaiUmpznk+b2/vYs/Z1KlTdRbq4Zjrwu/Dv4R38Eax/OAlnR2nvO4tD/e6tzyVpjxMnDhR+Pv7CysrK+Ho6CgCWj0hVnwzUYizW4VIPCGEunRNQLQVz/2MObHMp9FoxO4zV8Wg+QcKEkzv4I3i5YX/iJ3RyY/Vd478/dYfeStcqnRy1Rp2nr7KW/uXY5aXCxYWkJPDIh8fiI0tcbuYmBg6deqEu607AJFXIxk4cCADBw4stF54XDjtFrfjh6E/EOQTVGT7fD4+PghR+hkxVCoVnz3XmO7f7OFgzA1+O3iJIQE+pd5eumvGDMjJAQsLFuXkwPDhRTr05Lv/nMU+4PrQhZuZOby9/BhqjeDZph4838rwbsHeWx7ym4UALFq0CCh9efjkk0/4ZNpk+PcXOLwIrkXDze/h9++VFazsofEgCHgTnPx0Hs/9li5d+uAPwgioVCo61KlOhzrVOXrpBvN3X2R7dDJ7z6Ww91wKvtWsGRrgzYCWnthYyp9/STdUoiy/jI+Z+Ph4PD09iYuLo2bNmvoOR7rrwIUUwl99l3H7fkfz8ceYTJlSYhvLfGlpaTRs2JDTp09TpWoV/L/3p7FL40JtuAA0QkPfFX2JvBrJubfOYWpiWmR7GxubR4p/2YFYpv5xiqoWpmx5twOeTlUfaX+PlfvP8wPOuzbPWXkIIRj5yxG2R1/Ft5o1f771pEH+mKs1au2Uh8v/wLrX4WaM8tzEHJxrgak53IiFnPT/lrcbA0Efgpml7uKRALh8PYtfwmNZeSSO9Dt5ANhamtG/RU0Gt/airputniPUDfn7rUf6rTA1bLIq3TBtHzxaCBB/D3qz8Asl9Rouxr29Tg9cPiBu3bklDlw+UKTXqS6o1Rox8O6tqkHzD4i8x+j21CMpZa9wQ/Hz7gvCO3ijqD1xk4hMSNV3OA/0yOXhn/lCTHMQYqqdEF/WEeLgz0Jk3fzvdXWeEOd3CrGsj7LOVDsh5gcKca349sj6LJ/GKuNOrvjlQIzoNHtXodvkfefuEysPXRaZ2bn6DlGr5O+3/sgayweQf/EYHrVGsKjLUNJzNTyx8Gs61b1v1pIZM0CtVsY8fIjixsnzdfBldrfZ9KvfT7uB3+fS9Uye/nYvWTlqxnWtw1uda+v0eEahhHEsgTKd94pw4HwKLy86iEbAjL6NeKWtt75DeqhylQeNBrZ8CAfnK88bD4Kes5Xb3sURAk5vhD/egts3wdIenv8V/DpqJx7poTQawd7zKYQcvMSO6KvkaZQUwMbSjGebeTC4lReNathV+okc5O+3/sjE8gHkhWl4wi9cZ/CCf7CvYs7hiV2wMHu0WUnvn9kj0Cuw4Paarq09Gs+41ccxUcHKUQG08qm4aQUl3Ym/mUXv7/dxMyuX/k/UZPbAJpXmR7pM5UEI+GscHFHaPtLlY2j/DpTmvd66AquHQdw/yq3xPnOh6fOPFo9UZlfT77D2aAIrD18m9npWwfI6rjb0bV6DPs1qUMOhcg5ZJH+/9Ucmlg8gL0zDM2n9SX775zKDWtbkiwFN9R3OI3tvZQTrjiXgYW/FpncC5dRsldydXDX95x3g1JVbNK5hz+rXA7AyN8JESAjYOgnCfwBU8NxPxSaGD5R7B9a/AadClX08+x08MUQX0UoPodEI/om5zopDcWw+lUROnqbgtTa+TvR7ogY9GrljX6XyzOwjf7/1x/BakktSCfLUGv4+mQRAryYeeo5GO2b0bcSxyzeJvZ7FW8uPsXRYa0xNKkftllSYEIIPQ09y6sotnKwtmP9KC+NMKgH2fHk3qURJCMuaVAKYW0H/RWBdDQ79rNweV+dCq+HajVV6KBMTFe1qVaNdrWrcupPL3ycTWXcsgX8u3uBgjPKYvOEUXeq70LOxB53qVaeqhUwfpOLJK0OqNA7G3OB6Zg6OVc0JqOWs73C0wsbSjB9fakG/efvZey6FL7ac5sOn6+s7LKkcluyPZd2xBExNVPzwYvNKewvxoSJDYdenyv97fP5otYwmJvD0F8rt8H/mwl9jlVvpLV/TTqxSmdlZmfN8Ky+eb+VFQupt/oi4wrpj8ZxNzmDTySQ2nUzCytyEoDouPN3Yjafqucg5yqVCZGIpVRobTyQC0KORG+amj9a20pA08LDjywFNeWv5MX7afZFGHvb0bmocNbKPi73nrvHppmgAPnqmPu1qVdNzRDpyJQLWv6n8P2AMtH390fepUkH3T8HEFA58BxvHgqUdNB7w6PuWHkkNhyq8EVSL1zv6EZV4iz+OX+Hvk0lcvpHF5lNJbD6VhIWpCR3qVOPpRu50qe+KfVWZZD7uZGIpVQq5ag2bI5XE0lhug9+rd1MPIq+k8dPui4xffRwPBytaeMvOPJXBueR03vztX9QaQb/mNXitvY++Q9KN9CRY8SLk3Qb/LtB1uvb2rVIp+8vNgsMLIfR/YF4V6j2jvWNI5aZSqWjoYU9DD3sm9KhHVOIt/j6ZxKaTiVxMyWR79FW2R1/FzERFKx8nOtd34al6LvhVl2OKPo5k550HkI1/Dcf2qGRG/HKEajaW/PPhU5gZUY1lPrVGMOpXZUBth6rmrH2jHbXkF7NBS8nIpu/c/cTfvE0rH0d+G9EGSzMjbFeZeweW9oSEI1CtDozYXvKQQo9Co1E69JxYAaaW8NIq8AvS/nEkrRBC3L1FnsjfkYmcTc4o9LpvNWs613PhqfoutPJxqtA7TfL3W39kYvkA8sI0HG/+fpRNJ5MY8aQvk3o10Hc4OnM7R83gBf8QEZdKTccqhL7ZDhdbK32HJRXjTq5yro5dTsXbuSrr3myPk7UR9uoXAtaNghMrwcoBRu5UZtTRFXUerB6qjHdpbg1DNoBnK90dT9KaS9cz2Xn6Kjuir3Iw5jq56v/SC1srM2W6ydrVeLJ2dZ23QZa/3/ojE8sHkBemYUjLyqXVp9vJUWvY9HYgDTzs9B2STl3PyKb/vAPEXs+igbsdy//XtlIN8/E4EELw9ooI/jx+BTsrM9aNbm+8tcv7voHtU0FlCq+EVkwNYl42hDwPF3cpNaOv/gVujXV/XElr0u/ksu9cCjtOX2XX6atcz8wp9LpfNWuerF2NJ/2rEVDLWesdgOTvt/7IxPIB5IVpGH4/eImJ6yKp52bL5nc76DucCnHpeib95x0gJSOHpjXt+XVEG+xkz0uD8eWW08zddQEzExW/DG9tvJ11zvwNywcDAp6ZDa1HVtyxczLh1+cg7iBUrQbD/obqdSru+JLWaDSCiPhUws5cY9+5a0TEpaK5J/MwNVHRzNOBJ/2rEVi7Gk1qOjzy5Bfy91t/jD6x3LhxI+PGjUOj0RAcHMyIESNKva28MA1D/3kHOHrpJh89U4//ddDhLTgDE514ixcX/MPNrFyaeznwy2ut5bAeBmDJ/hg+/jMKgC/6N2FQK089R6QjyVGwqCvkZECLYdBrTulm1dGm26mwrDcknQBbD3jtb3D0qdgYJK1Lu51L+IXr7Dt/jX3nUgrN+gNgZW7CE16OtPF1prWvE829HMo8Jqz8/dYfo04s8/LyaNCgAbt27cLe3p4WLVpw4MABnJ1LNwaivDD179L1TDp+GYaJCsI/7Iyr3ePV3vDUlTReXHCQtNu5tPB2ZOmwVjK51KM/jl/hnRXHEALjnuM98zos6ASpl8AnEF5ZB6Z6uu4yr8PSZ+DaaXDwVmou7WvoJxZJJ+JuZLHvfAr7zqVw4EIKN7NyC71uYWpCM08HWvs60cbPiSe8HLG2fPCgNvL3W3+MerihQ4cO0bBhQ2rUUL6Enn76abZu3crgwYP1HJlUWmv/TQCgvX+1xy6pBGjoYc/vI9rw4oJ/OHrpJoMX/MPSYa2pZmOp79AeO/vOpTBuVQRCwNAAb8Y85a/vkHQjLwdWvaIklQ7eMHCZ/pJKAGtnpQPP4h5wMwZ+6aMklzbV9ReTpFWeTlUZ3NqLwa290GgE569lKDP+XLzOwZgbXEvP5lDsDQ7F3uCHXWBmoqKhhx3NvRxp7uXAE16O1HSsgqqia9SlYhn0mC179uyhd+/eeHh4oFKpWL9+fZF15s6di4+PD1ZWVrRp04ZDhw4VvHblypWCpBKgRo0aJCQkVETokhbkqTWsPhIHwIAWj+9fnI1q2BMysi3O1hZEJtxi4Pxw4m5kPXxDSWtOxKcy6tcj5KoFPZu4M6V3Q+P8ERMCNr4Hl/aDhS28uFJJ7PTN1g2G/gF2NeH6Ofi1L2Td0HdUkg6YmKio42rLK229+eHFJzj0UWd2jQ/i8/6N6de8BjUcqpCnERyPT2PpgVjeWRFB4Be7aP3ZDkb9eoT5uy9wKOYG2Xlqfb+Vx5ZBJ5aZmZk0bdqUuXPnFvv6ypUrGTt2LFOnTuXff/+ladOmdO/enatXr1ZwpJIu7D57jcS0OzhWNadHI7cKP/6sWbNQqVS8++67D1xv9erV1KtXDysrKxo3bsymTZu0HkujGvasfj2AGg5ViEnJZMD8A0Qn3tL6caSiYlIyGbbkMJk5atr7O/P1oKbGO5/7/m8h4jdQmcDApeCiTC9amrJw6tQp+vfvj4+PDyqVim+++Ua7sTl4KcmltQskRzLr5VYPjWnBggUEBgbi6OiIo6MjXbp0KVT5IBk+lUqFbzVrnm/lxdfPN2P/hKfYF9yJ7wY359V2PjStaY+ZiYpr6dlsOZXMrL9PM+incDrP3l1kX6W5jkNDQ2nZsiUODg5YW1vTrFkzfv31Vx2+Q+Nj0Inl008/zSeffMJzzz1X7Otff/01I0eOZNiwYTRo0ID58+dTtWpVFi9eDICHh0ehGsqEhAQ8PEqetSU7O5tbt24VPNLT07X7hqQyWX7oMgD9n6hZ4YNOHz58mJ9++okmTZo8cL0DBw4wePBghg8fzrFjx+jbty99+/YlMjJS6zH5Vbdh7RvtqONqQ/ItZUiibVHJWj+O9J/4m1m8vPAg1zNzaFTDjp9eaWmcA6ADRG+E7dOU//eYBbW7AKUvC1lZWfj5+TFr1izc3HT0h6BzLRiygcMp1vy0K4YmNa0h706Jq4eFhTF48GB27dpFeHg4np6edOvWTd65quRqOlbl2aYeTHu2IRvGPEnkx91Z83oAHz1Tjx4N3XCxtSRPU7j7SGmvYycnJyZOnEh4eDgnTpxg2LBhDBs2jC1btujyLRkXUUkAYt26dQXPs7OzhampaaFlQggxZMgQ8eyzzwohhMjNzRX+/v4iPj5epKenizp16oiUlJQSjzF16lQBFHnExcXp4i1JD5CYelv4TtgovIM3inPJ6RV67PT0dFG7dm2xbds20bFjR/HOO++UuO6gQYNEz549Cy1r06aNGDVqlM7iS83MES8uCBfewRuFz4SNYn7YeaHRaHR2vMfVldQsEfj5TuEdvFF0mr1LXEu/o++QdOfKcSE+cRNiqp0Qf74nxN3rqSxl4V7e3t5izpw5Ogk1PT1d1PbzEtuGu4qO3qbinU4eQmReL9W2eXl5wtbWVixbtkwnsUmGQaPRiEMnzxb8fpf3Os7XvHlzMWnSJN0Ea4QMusbyQVJSUlCr1bi6uhZa7urqSlJSEgBmZmZ89dVXdOrUiWbNmjFu3LgH9gj/8MMPSUtLK3hERUXp9D1IJVt1JA6NgNY+Tvi7VOzA06NHj6Znz5506dLloeuGh4cXWa979+6Eh4frKjzsq5qzdFhrXmrjhRAw8+/TvLcygszsPJ0d83Fz9dYdXlpwkMs3svB2rkrIiLbG22Eq9TKEDFLm6fYLgqc/LxhWqCxloaKMHj2ans/2o8unO5RORVkpsLQXZFx76LZZWVnk5ubi5ORUAZFK+qJSqXC/Z2af8l7HQgh27NjBmTNn6NDh8RhDWRuMulc4wLPPPsuzzz5bqnUtLS2xtPzvx+PWLdmGTR/UGsHKw0qnncFtdDtGoFqjZu/lvSSmJ+Ju686VA1f4999/OXz4cKm2T0pKeuAfN7pibmrCJ30bUcfVlukbo1gfcYXIK7f48aUnqONqq9NjG7vrGdm8tPAgF1MyqeFQhZCRbXGzN9IRCTJTlEHI0xOhej3U/RezN24/iemJRIdFl6ks6MIDy6eVlTIbj8k5uHpKmct86B9KR58SBAcH4+HhYVCJsqRbGzZsKPN1nJaWRo0aNcjOzsbU1JQff/yRrl276jBK41JpE8tq1aphampKcnLhNmbJycm6a98jVYi9566RkHob+yrmPN3IXWfHCY0OZdzWccSmxioL0sBkgQlf/volVlaGn0ioVCqGtvOhgYcdY0L+5fzVDPr8sJ8ZfRs91r3oH0VqVg4vLzrEuasZuNlZETKyjc7nNNab7Az4fSBcPw92NQltO4JxC1sq5SEN+Bk8RnuwKWYT/er3q/DwSlU+zatCvb5gdxBSzsDi7vByaLFzmc+aNYsVK1YQFhZWKcq3VEbTpoGpKUyefN/iaezYsaNM59zW1paIiAgyMjLYsWMHY8eOxc/Pj6CgIO3GbKQq7a1wCwsLWrRowY4dOwqWaTQaduzYQUBAgB4jkx5VyEGl085zzWuUebaF0gqNDmXAqgE0dmlM+PBw0j9MZ1aDWWgyNIzrNw5TM1PMzMzYvXs33333HWZmZqjVRYevcHNz0/sfN618nNj0diCBtatxO1fN+NXHeWfFMVKzch6+sVTgZmYOLy86SHTiLarZWPL7yDZ4O1vrOyzdyMuBlS/DlX+hihOh7d5gwMZRBeUhJCAEMiFxdiL9G/bH1Mz0oWVBm8pUPn9ehtkHZ1Hbe8PNWFjUDRKOFtrf7NmzmTVrFlu3bn1o5w2pkjI1hSlTYMaMQotTUlJ44oknMDMzK9V3OoCJiQn+/v4FTegGDBjAzJkzyx1aXl4e06dPJz4+vtz7qEwMOrHMyMggIiKCiIgIAGJiYoiIiODyZSXxGDt2LAsWLGDZsmVER0fzxhtvkJmZybBhw/QYtfQo4m5ksT1aSdReauOlk2OoNWrGbR1Hrzq9WP/CetrWbIuNhQ1vPv8mx08cp+OsjriPd+fov0dp2bIlL730EhEREZiaFk1yAwICCv1xA7Bt27YK/+PG2caSZcNaM7ZrHUxNVGyIuEL3b/aw64wceqs0rqVn88LP/xCZcAtnawtCRrahVvWKbdtbYdS5EDoCLu4Cc2vUg1cy7uBXhcpDrx69OHnyJBEREQXl4WFlQWvhlbd8jtwO7k3/a3N5bjsAX3zxBTNmzGDz5s20bNlSZ3FLejZ5MkyfXiS53LZtW0EeERERUa7rWKPRkJ2dXe7QzMzM+PLLL8nLezzawRv0rfAjR47QqVOngudjx44FYOjQoSxdupTnn3+ea9euMWXKFJKSkmjWrBmbN28u0uatrObOncvcuXPJyZE1PhXtt38uoRHwpH81auuoreDey3uJTY1lef/lmKj++9vK1taWJo2bMNNhJu0Wt+Om3U2sra1xdnamUaNGxe7rnXfeoWPHjnz11Vf07NmTFStWcOTIEX7++WedxP4gJiYq3u5cm8Da1Ri3+jgXrynjLw5u7cWHz9TDTk4FWayktDu8uPAfLl7LxMXWkpCRbfB3MdJ2qupcWDsCojaAqQU8/yt7NVlFyoOtrW3BNZ9fHqqZVntgWcjJySno8JiTk0NCQgIRERHY2Njg71/6WYoeqXy++hesfEVJmpc/z+cpXZjy03pCQkLw8fEpaPtsY2ODjY2R/uHwOMu/DT5lCrZ3k8t69eoVmtLxYd/pM2fOpGXLltSqVYvs7Gw2bdrEr7/+yrx58x4ptKeeeordu3fj4+PzSPupDAw6sQwKCkI8ZCrzMWPGMGbMGK0ed/To0YwePbpgrlGpYmTl5BWMXTmsvY/OjpOYnghAI5fiv1jyl+evd69XX32V2NhYwsLCAGjXrh0hISFMmjSJjz76iNq1a7N+/foSv7QqQnMvR/56K5Avtpxmyf5Ylh+6zPboZKb0akCvJu7GOWNMOcXfzOLFu72/PeytCBnZFp9qRnr7W513N6lcDybmMOhX8O9M4snlwMPLQ4668B/a95eFK1eu0Lx584LXZ8+ezezZs+nYsWPBOqXxSOVz1FvExtwk7J3n4cRK5v2ympwcwYABAwqtN3XqVKZNm1bqmKRKZPJk+OQTVKWsGLr/Os7MzOTNN98kPj6eKlWqUK9ePX777Teef/75Rwrr6aefZsKECZw8eZIWLVpgbV34e6a0nYwrA4NOLKXHy/pjV7h1Jw9v56p0quuis+O42yodgiKvRtK2Ztsir0dejSxY7/4fxJiYmEK16AADBw5k4MCBugm2nKpYmDK1d0O6NXDjo3UniUnJ5K3lx1h1JI4ZfRoZb/JUBpeuZ/LigoMkpN7Gy6kqv49og6dTVX2HpRt52RA6UqmpNDGH53+Duj2A0peHH1f/SJBPUMHy+8uCj4/PQysCSkMr5fO5qeDsTyyfKi/UegoGLIEqDo8cn2TgZsyAnByEuTnk5hZ5+WHf6Z988gmffPKJ1sN68803AWVil/upVCqdt1uuSAbdxlJ6fAghWHogBoAhAT6Y6HDKvECvQHwcfPhs72dohKbQaxqhYea+mfg6+BLoFVjotbS0NC5cuMD48eN1Fpu2BdRy5u93AnmvSx0szEzYey6FbnP2MHNTNGm3i37pPi6irihzriek3savmjUrR7U13qQyO10Zp7Igqfy1IKmE8pUHXZYFrZRPlQo6fgCDflF6jl/YCQs7w9XTWo9XMiAzZihtLKdPJ/3ixYeuXpHf6RqNpsSHMSWVIBNLyUCEX7jO2eQMqlqYMrClbofKMTUx5atuX7Hx7Eb6ruhLeFw46dnphMeF03dFXzae3cjsbrMxNSncsNve3p74+PhK1zbLytyUd7rUZsu7HQisXY0ctYaf9lyk45e7WLI/hpw8zcN3YkTCL1zn+Z/CuZqeTV1XW1aMaou7vZEOKZSZAst6w8UwMLeGl1ZB3acLrVKe8qDLsqDV8tmgD7y2Bew9lWGVFnSCiOVaj1kyAPcklfcPOVQSfX2n37lT8jSkxkAltHHvwkjlt7GMi4sr1PhX0r4Ry46wPTqZIQHeTO9TMe0Ti4yTB/g6+DK722y9jNtXEYQQhJ29xmd/RXPuagYAPs5Vea9rHXo18cBUhzXFhuDvk4m8syKCHLWG1r5OLBjSEvsqRtqpKeU8LH9eSaiqOsNLq6FGixJXN7TyoNV4Mq4pTQEu7lKeN38Znv4SLIy0lvpxdN84lob2+61Wq/nss8+YP38+ycnJnD17Fj8/PyZPnoyPjw/Dhw/Xd4haIxPLYtzbK/zChQsGc2EaqwvXMujy9W6EgO1jO1boFI73z+wR6BVYpCbEGOWpNaw6Es/X286SkqEMo1GrujVvPVWb3k2NM8H89Z9LTNkQiRDQo6Eb37zQTGfjpOrd+R2wehhkp4G9F7wSCtVqP3QzQysPWo1Ho4a9X8GuzwABLg2g3wJw019HO0l3DC2xnD59OsuWLWP69OmMHDmSyMhI/Pz8WLlyJd98841OpwGuaDKxfABDuzCN1YehJ1h+KI4u9V1ZOFSOM1eRMrLzWLIvhoX7YgraXPpVs+bNTv4829QDC7PK31pGrRF8sfk0P+1R2ly91MaL6X0aGWXyjBDwzzzYOhGEBjzbKB11bHTXGa7Subhb6R2feVUZcqnTRGj3FjwGf1A+Tgzt99vf35+ffvqJzp07Y2try/Hjx/Hz8+P06dMEBARw8+ZNfYeoNZX/V0Oq1K6m32HtvwkAvN7RT8/RPH5sLM14q3Nt9gV3Yny3OjhUNediSibjVx/nyc93MnfXeW5mVt7xXDOy8/jfL0cKksqxXevwSV8jTSpvp8LqV2HLh0pS2exlGPqnTCrv59cR3tgPdZ8BdQ5sn6rMM34jRt+RSUYsISGh2PFcNRoNucX0Xq/MZGIp6dWyA7Hk5Glo4e1ISx8nfYfz2LK1MmfMU7XZF/wUwT3q4WJrydX0bL7ccoaAWTuYuO4kp5Nu6TvMMom7kUX/Hw+w4/RVLMxM+G5wc97uXNs4x/GMPwo/Bd4do9IMun8GfX4AM0t9R2aYbFzghRDoMxcsbOFyOMxrB/u/VQaRlyQta9CgAXv37i2yfM2aNYXGfzUGchxLSW8ysvP4NfwSAKM6yNpKQ2BjacYbQbUY/qQvG09cYeHeGKISb/H7wcv8fvAyzTwdGNzak15NPLC2NNyvj73nrvHOighuZOZQ3daSBUNa0szTQd9haZ86F/bNgd2fgyYPHLyU8RpryiYlD6VSKZ14fAJhw2iI3QvbpsCJVdDrG/Bspe8IJSMyZcoUhg4dSkJCAhqNhtDQUM6cOcMvv/zCxo0b9R2eVskaS0lvVhy6zK07efhVt6ZL/UebhlPSLgszE/o9UZO/3n6S5SPb0qOhG2YmKiLiUglee5LWn27nw9ATHIq5gUZjOM201RrB11vPMGTxIW5k5tDQw44/xrQ3zqTySgT83Al2faoklQ36wKi9MqksK0dvpclAnx+hiiMkR8KirrBxLNw2nnZv0oPNnDmTVq1aYWtri4uLC3379uXMmTOF1rlz5w6jR4/G2dkZGxsb+vfvT3Jycqn236dPH/7880+2b9+OtbU1U6ZMITo6mj///JOuXbvq4i3pjey88wCG1vjXmOTkaej45S4S0+7wef/GPN/KS98hSQ9xLT2btf/Gs/JwHDEpmQXL3eyseKaxO72butPM00Fvt5qvpN5m3KrjhF+8DsDg1l5M7d3A+Hp+30mD3V8onXSEGqo4wTNfQqP+Si2cVH6ZKbB1MhwPUZ5XcYSOwdByOJhZ6Dc2qUzK+vvdo0cPXnjhBVq1akVeXh4fffQRkZGRREVFFUy/+MYbb/DXX3+xdOlS7O3tGTNmDCYmJuzfv1/Xb6dSkYnlA8jEUndWHr5M8NqTVLe1ZF9wJyzNjOzH34gJITgUc4NVR+LZeiqJ9Oy8gtdqOFShe0M3OtWrTmtfpwo5rxqNIOTQZWb9fZqM7DyqWpjy2XON6du8hs6PXaE0Goj4HXZ8DJnXlGUN+sIzs8Gmul5DMzoxe2DTB3AtWnnuVAu6Tod6PWXyXkk86u/3tWvXcHFxYffu3XTo0IG0tDSqV69OSEhIwdzzp0+fpn79+oSHh9O2bdHpRx9XhttISo/uHcdS0r5ctYYfdp0HlLaVMqmsXFQqFW38nGnj58yd3EbsOXuNjScS2R6dTELqbRbvj2Hx/hiqWpjS3r8aneq68KR/NTydqmi9NvPopZvM3BTNkUvKLcsnvBz4cmBTalWvXLMjPZAQcOZvZfzF5JPKMmd/6D4T6nTTb2zGyrcDvL4Pjv2qNDW4cQFWvgReAdDpI+V1qVJIT0/n1q3/Oh5aWlpiafnwTm1paWkAODkpnUqPHj1Kbm4uXbp0KVinXr16eHl5lZhYOjo6lvo778aNG6VarzKQNZYPIGssdWPN0XjGrz5ONRsL9n7wFFUsZGJpDG7nqNl99io7T19l15lrXEvPLvS6q50lrXycaO3rRAtvR2q72JZrnEyNRrDn3DWWHogl7IxSc1fF3JQPetRlSICP8QwlpNHAua2wexZcOaYss7RT5sBuPUremq0o2elKB6nwuZB3dyo+7yeh04fg86R+Y5NKlP/7fb+pU6cybdq0B26r0Wh49tlnSU1NZd++fQCEhIQwbNgwsrMLf6+1bt2aTp068fnnnxfZz7Jlywr+f/36dT755BO6d+9OQEAAAOHh4WzZsoXJkyfz3nvvlfUtGixZYylVqDy1hh92ngNgZKCfTCqNSBULU3o0cqdHI3eEEJy6couwM1cJO3ON4/GpJN/KZuOJRDaeSATA3FRFreo2NHC3w9/VhhoOVfBwqIKrrRVVLU2pYm5Knlpw604uKRnZnE1O599Lqew8c7UgaTVRwcAWnrzXtQ5u9lb6fPvak5MJx5fDP/PhulJWMLeGNqOUgbyrymG5KpSlLXSeAq1GwN6v4d9lcGmfMvalTyAEjgW/TvIWuYGKioqiRo3/msWUprZy9OjRREZGFiSV5TV06NCC//fv35/p06czZsyYgmVvv/02P/zwA9u3b5eJpSSV158nrhB7PQvHqua83NZb3+FIOqJSqWhUw55GNewZ81RtbueoiYhL5XDsDQ7H3iAiLpX0O3mcTkrndFJ6mfdva2XGwBaevBLgjW81ax28Az1IOgkRyyHiN6WDDig1lC1ehfbvgHU1vYb32LPzgJ6z4cl3lakh//1VGaIodi+4NoKAMUoHKlmTbFBsbW2xs7Mr9fpjxoxh48aN7Nmzp9CdSjc3N3JyckhNTcXBwaFgeXJyMm5ubg/d75YtW4qt1ezRowcTJkwodXyVgUwspQqj1gi+36m0rRwR6GfQ4yBK2lXFwpSAWs4E1HIGlA5ACam3OZ2YTnTiLWKuZ3Il9TZXUu9wNf0Od3I1BdtamZvgUMUCfxcb6rvb0rGOC619nYxiuklS45RBzY+vUIa5yefoC23fgGYvKjVmkuGwrwm95sCT7ym3x//9VTl3619XOla1GQVPDJU1y5WMEIK33nqLdevWERYWhq+vb6HXW7Rogbm5OTt27KB///4AnDlzhsuXLxfc2n4QZ2dnNmzYwLhx4wot37BhA87Oztp7IwZA/rJLFeavk4lcvJaJfRVzhgTI2srHmUqloqZjVWo6VqVLg6JjmGo0gjt5asxMTIwjgcyn0UDScaUzzplNSi1lPlMLqPs0NH0RaneVc1cbOgcvePpzCJoAR5bAwZ8gPRG2T4NdM5VxRVsOUzr8yNvkBm/06NGEhISwYcMGbG1tSUpKAsDe3p4qVapgb2/P8OHDGTt2LE5OTtjZ2fHWW28REBBQqh7hH3/8MSNGjCAsLIw2bdoAcPDgQTZv3syCBQt0+t4qmhF9Y0uGTK0RfL9DaS82/ElfbK3M9RxRUdOmTUOlUhV61KtX74HbrF69mnr16mFlZUXjxo3ZtGlTBUVr3ExMVFS1MKv8SWVejjKQefiPsPxF+MIXfg5SZspJOgkqEyXx6DUHxp+FQb9A3R4GkVSWtTycOnWK/v374+Pjg0ql4ptvvtFrPAsWLCAwMBBHR0ccHR3p0qULhw4d0mpMgDLWZeBYePck9J0Hbk1AnQ0nV8GSp+HHtsqYo1nG0+vXGM2bN4+0tDSCgoJwd3cveKxcubJgnTlz5tCrVy/69+9Phw4dcHNzIzQ0tFT7f/XVV9m/fz92dnaEhoYSGhqKnZ0d+/bt49VXX9XRu9IPWWMpVYgNEQmcu5qBnZUZQ9v56DucEjVs2JDt27cXPDczK7mIHDhwgMGDBzNz5kx69epFSEgIffv25d9//6VRo0YVEa5kKIRQaqtuXISr0ZB0AhKPK/9X3zdsmYUN1OoEdZ+B2t0Muu1kWcpDVlYWfn5+DBw4UGcdEcoST1hYGIMHD6Zdu3ZYWVnx+eef061bN06dOlWoM4fWmFkoTReaDoYr/yq1mJFr4dpp2DxBGXi9dldoPFCpmTavov0YpHIrzQA5VlZWBcMRlkVubi6jRo1i8uTJ/P777+UNsdKQiWUx5DiW2pWTp2HO9rMAvB5UC/sqhldbmc/MzKxUDbEBvv32W3r06MH7778PwIwZM9i2bRs//PAD8+fP12WYUkXQaCA3E7IzICcDsm9BxjXISP7vkZ4EN2OVhDI3q/j9WNlDzdbK0DQ+geDeFEwrx1dvWcpDq1ataNVKmV9bV50RyhLP/T/gCxcuZO3atezYsYMhQ4boIjyFSgU1WiiP7p8qc4//+4vyx8aZTcrDwhYaPKskmT6BleZ6kMrH3NyctWvXMnnyZH2HUiHk1VyM0aNHM3r06BLHwZLKZuXhy8TduE01G0teNeDaSoBz587h4eGBlZUVAQEBzJw5Ey+v4qebDA8PZ+zYsYWWde/enfXr11dApBIAGVeVmsGbsZB6SZmSLztdeeTdAaEp5iFAo1amQyz4V1P4ee4dJZmkDMP8qkyVdnfVaiu3Q92bKEmkg3elbWNXlvJg6PFkZWWRm5tbMOB1hbCyh9YjlcfV08rt8ROrIe2yMotSxO/KlJx1n4b6vZVhi8yNZNgsqZC+ffuyfv16oxpWqCQysZR0Kisnj+/u9gR/u7M/VS0M65JTa9TsvbyXxPREqvhUYfHixdSvX5/ExEQ+/vhjAgMDiYyMxNa2aM/cpKQkXF0LdzxxdXUtaPQt6YBGo4whGP0nnN2iJJO6pjJRapgsbcC6Oti4gq0r2LiBjYuSODrXUpJKU8OtjS+NRykPhh5PcHAwHh4ehWZOqVAu9ZTxMDtNgriDcGIlRG2A2zf+SzLNraF2F6jXC2o9ZdDNJKSyqV27NtOnT2f//v20aNGiYP7xfG+//baeItM+w/qVl4zO0gOxXEvPxtOpCi+00l9NR3FCo0MZt3UcsamxBct8HHz4qslX9OvejzZt2uDt7c2qVasYPny4/gKVICdLGZj60M/KbecCKqheV5nL2dFbSfQs7ZQhesyrKLWIKpO7D9Xdh4my3MT0vn/vWW5mpezDwubufipnjWNZGFp50GY8s2bNYsWKFYSFhWFlpecaQRMT8A5QHs/MhsvhcHojRG+EW/FKshm1AVCBR3Pw76I8arSQt8wrsUWLFuHg4MDRo0c5evRooddUKpVMLCWpNNKycpkfdgGA97rUMagevqHRoQxYNYBedXqxvP9yGrk0IvJqJJ/t/YwBqwawZtAa+tXvR506dTh//nyx+3BzcyM5ObnQstIOliuVkjoXjv2m9KJOV2bswdIeGvSGer3Bux1YlX7wY6l42igPhhrP7NmzmTVrFtu3b6dJkyY6j71MTM3AN1B59Lg7defpjXB2qzIv/JV/lceeL5Tb6n5ByjzlPoFQrc5j8QePsYiJidF3CBVGJpaSzvy89wK37uRRx9WGPs100AuznNQaNeO2jqNXnV6sf2E9Jiol4W1bsy3rX1hP3xV9Gb91PJ1rdObChQu88sorxe4nICCAHTt28O677xYs27ZtW6kGy5VK4VI4bHxX6VULYO+lzHrS5HnltrSkFdoqD4YYzxdffMGnn37Kli1baNmypU7jfmQqFdR4Qnl0ngK3EuHCTji/Xfn3Tuo9tZkozTK829/tFPYkVK8nE81KICUlBYBq1Yy3mYNMLCWduJp+h8X7YgEY160upiaG84W39/JeYlNjWd5/ecGPFsD48ePp3bs3r3q/Sv/t/en8TGdMTU0ZPHhwsft555136NixI1999RU9e/ZkxYoVHDlyhJ9//rmi3opxun0Ttk1RetICVHWGjsHK1IZmD5/nVyobbZWHnJwcoqKiCv6fkJBAREQENjY2+Pv7V3g8n3/+OVOmTCEkJAQfH5+Cts82NjbY2FSCP0zs3KH5S8pDo4aEf5UE89I+iDsEmdeUWZui1ivrV3VWRh+o2QJqtgKPJ2RtvoFITU1l4sSJrFy5kps3bwLg6OjICy+8wCeffFJoikhjIBNLSSe+3X6O27lqmno60K2YmVX0KfHuLdVGLoXHmoyPj2fw4MFcv34dLMG8vTn//PMP1atXB5QBbmNjYwkLCwOgXbt2hISEMGnSJD766CNq167N+vXr5RiWj+LCLlg3ShnKB+CJIdDlYzk9ng5pqzxcuXKF5s2bF2w/e/ZsZs+eTceOHQvWqch45s2bR05ODgMGDCi0n6lTpzJt2rRSx2MQTEzBs5XyIBjyspVEM3bff4lm1nU4+7fyAJT2x/X+SzRrtFCeV/IOZpXNjRs3CAgIICEhgZdeeon69esDEBUVxdKlS9mxYwcHDhzA0dFRz5Fqj0wsJa07l5zO8kOXAfjo6XqoDOz2jLutOwCRVyNpW/O/qbhWrFgBQHhcOO0Wt2Pm0JnU8qlV8HpMTAydOnUqtK+BAwcycODACojayKlzYddnsG8OIMC5Njz7ndKGUtIpbZUHHx+fUg0yXVHxxMbGPnIsBsvM8r8OQLyvzPCUeBwSjkD8YYg/ooyYcC1aeRz7TdnO1AJc6oNbY3BrevffRnI+eh2aPn06FhYWXLhwocgoItOnT6dbt25Mnz6dOXPm6ClC7VMJbXwTGKn8cSzj4uKoWbOmvsOpNIYtOcSuM9fo1sCVn4cYXrsmtUaN//f+NHZpXKgNF4BGaOi7oi+RVyM599Y5TO9OrZeWlkbDhg05ffp05biNVpncvARrR0D83en2WrwK3WeCRVW9hvW4MLTyYGjxVFoZVyHh6N1E8zBcOQ7ZacWv6+SnJJkuDZRRFqrXU0ZaMLOo2Ji1yFB+v318fPjpp5/o3r17sa9v3ryZ119/3aj+EJI1lsWQM++U375zKew6cw0zExUTnn7wPNv6YmpiylfdvmLAqgH0XdGXD5/8sKDX6cx9M9l4diNrBq0p+NECsLe3Jz4+Xo9RG6lT6+GPt5UfPEt7ePZbaPicvqN6rBhaeTC0eCotGxdl4PW6TyvPhVBqMRNPKPPUJ93991aCMoTXjYv/dQwCZegt51r/JZr5D2d/OYh7GSQmJtKwYcMSX2/UqJHRjX0saywfwFD+4qks1BpBz+/2cjopnVfb+TDt2ZILkyEobpw8XwdfZnebTb/6/fQX2OMg9zZs/hCOLlGe12gJAxaBo49ew3qcGVp5MLR4jFZmipJgJkcqIzBcO6M8sm+VsIEK7GqAs59S0+lU6+6/fuDkazBzoBvK73eNGjVYuXIlTz75ZLGv7927l+eff54rV65UcGS6IxPLBzCUC7OyWHUkjg/WnMDOyozd73fC0drwb6PcO7OHu607gV6BhWpCJB24Gg1rXoOrUYBKGUKo00TZqcAAGFp5MLR4HhtCKOPGXo2+m2jmJ5zRcKeE2+n57Gr8l2Q6eCnDhDl4gr0n2HkoHZEqgKH8fr/22mtcuHCBbdu2YWFR+DcxOzub7t274+fnx+LFi/UUofbJxPIBDOXCrAyycvII+jKMq+nZTHymPiM7+Ok7JMnQCKHMnvP3BMi7DdYu0O8nZeo6SZIMnxBK7/MbF+H6hbu30O/+e/1iyW0485mYKcmlvZeSdOYnnA6eYFdTGWLJwvrB+yglQ/n9jo+Pp2XLllhaWjJ69Gjq1auHEILo6Gh+/PFHsrOzOXLkCJ6ennqLUdtkG0tJK37ec5Gr6dl4OVVlSDtvfYcjGZrbqfDnO/+NuVfrKXjuJ6UdmCRJlYNKpcxfbl0NPFsXfk0IyLpxT7IZA2lxkHpZ+TctHjR5yvPUy3CphGNY2oOtm5Jk2nrc/ffuI3+ZjUuF1Xw+qpo1axIeHs6bb77Jhx9+WDBygkqlomvXrvzwww9GlVSCTCwlLbiSepufdivzNwf3qIelWeUo8FIFiTsMa19TfkxMzJRZRQLeUuZMliTJOKhUYO2sPDxbFX1do4b0pLvJZpzSkSj//2lxcOsK5GQotZ7ZaZBy5gHHMgUbVyXBtHEFm+p3/3VVZiSycYVbGt291zLy9fXl77//5ubNm5w7dw4Af39/nJyMc3xemVhKj+zTTdHczlXT2teJZxrLebKluzQaOPAt7PxEqalw8IYBi6Gm4Q1BJUmSjpmYgn0N5eHVtvh17txS2nbeuqIkoelXlKktC5YlKpMnCLXyWvoDOrwYUGKZz9HRkdatWz98xUpOJpbSIwm/cJ2/TiRiooJpvRsa3GDokp6kJysz6FzcpTxv2A96fwNW9noNS5IkA2Zlpzyq1y15HY1aGaMz/QpkXFMSzYyrkHn1v/9nXIXbV4CMCgtd+o9MLKVyy1Nr+PjPUwC81MabBh5yXloJOL9DSSozr4FZFXjmC2j+inKrTJIk6VGYmCptLe3cH7xefDx8YVxtFysLmVhK5fbbP5c4nZSOY1VzxnWro+9wJH1T58LOGbD/W+W5S0Pl1reLYQ6UL0mSJGmfTCylcrmekc3X284CML57XRyqGv6YlZIO3YiBtcOVKeQAWg6H7p8azGDJkiRJUsUoU2Lp5eXFsWPHcHZ2BuCHH35gyJAh2NnJW6CPmy+3nOHWnTwaetjxQisvfYcj6VPkWvjzXWWmDit7ePYHaPCsvqOSJEnSqz/++KPU6z77rPF8Z5YpsYyPj0etVhc8/+ijj3jmmWeMLrGUc4U/2In4VFYeiQPg42cbYmoi2849lnKyYHMw/PuL8tyzDfRfqAx8LEmS9Jjr27dvqdZTqVSFcqvK7pEGkjPWSXtGjx5NVFQUYWFh+g7F4Gg0gql/nEIIeK55DVr6GOc4XNJDJJ+Cn4PuJpUqCBwPr26SSaUkSdJdGo2mVA9jSipBtrGUymjlkTiOXU7F2sKUCU/LThmPHSHgyCLYMhHy7oCNG/T7Gfw66jsySZIkyQCUucZy4cKFfPfdd3z33Xfk5eWxdOnSguf5D8k4pWRkM+vv0wCM7VYXVzsrPUekfQkJCbz88ss4OztTpUoVGjduzJEjRx64TVhYGE888QSWlpb4+/uzdOnSigm2ot2+Categb/GKUll7W7wxn6ZVBqxspaHxMREXnzxRerUqYOJiQnvvvuuXuMJDQ2la9euVK9eHTs7OwICAtiyZYtWY5IMx73Xh7+/PwDHjx8vcf19+/bRvn37guupXr16zJkzR2fxZWZmsmnTJubPn2/UeVOZO+8sWLCg4Lmbmxu//vproXVUKhVvv/22dqKTDMpnm6JJu51LA3c7hgYY33zgN2/epH379nTq1Im///6b6tWrc+7cORwdHUvcJiYmhp49e/L666/z+++/s2PHDkaMGIG7uzvdu3evwOh17PJBpdd3WhyYmEPXj6HNG3JaRiNWnvKQnZ1N9erVmTRpktZ/oMsTz549e+jatSufffYZDg4OLFmyhN69e3Pw4EGaN2+u1fgk/br/+sjLy6N9+/bY25c8KYO1tTVjxoyhSZMmWFtbs2/fPkaNGoW1tTX/+9//tBrfsWPHeOaZZ8jKyiIzMxMnJydSUlKoWrUqLi4uRpU3qYSxNpTUgvj4eDw9PYmLi6NmzZr6DkevDlxI4cUFB1GpIPSNdjT3KvnLvLKaMGEC+/fvZ+/evaXeJjg4mL/++ovIyMiCZS+88AKpqals3rxZF2FWLI0a9n4NYTOVadQcfZWxKWs8oe/IJB0rT3m4V1BQEM2aNeObb74xiHjyNWzYkOeff54pU6ZoJS7JMNx/fZT397tfv35YW1sXqTR7VEFBQdSpU4f58+djb2/P8ePHMTc35+WXX+add96hX79+Wj2ePsnqBumhsvPUTFqvJE4vtfEyyqQSlKEhWrZsycCBA3FxcaF58+aFauiLEx4eTpcuXQot6969O+Hh4boMtWKkJcCyZ2HXJ0pS2eR5eH2vTCofE+UpD4Yej0ajIT09HScn2enQ2Nx/ffTo0aPM+zh27BgHDhygY0ftN++JiIhg3LhxmJiYYGpqSnZ2Np6ennzxxRd89NFHWj+ePpXpVnhp2wEYU5WuBAv2XOTitUyq2Vjwfnfj7bBz8eJF5s2bx9ixY/noo484fPgwb7/9NhYWFgwdOrTYbZKSknB1dS20zNXVlVu3bnH79m2qVKmkA4Sf/gs2jFbaVVrYQM+voOkL+o5KqkDlKQ+GHs/s2bPJyMhg0KBBOo5Wqmj3Xx9bt25lwoQJrF69mvfee++B29asWZNr166Rl5fHtGnTGDFihNbjMzc3x+Ru0yEXFxcuX75M/fr1sbe3Jy4uTuvH06cyJZalaTMj21gal0vXM/l+53kAJvdqgH0Vcz1HpF1qjZq9l/eSmJ6IWqOmZcuWfPbZZwA0b96cyMhI5s+fr5cfUr3IvQPbJsOhn5Xn7s2UW9/OtfQallQxDK08aDOekJAQPv74YzZs2ICLi4uuQ5cqmEajKXR9VK9enQkTJvDbb789NLHcu3cvGRkZ/PPPP0yYMAF/f38GDx6s1fiaN2/O4cOHqV27Nh07dmTKlCmkpKTw66+/0qhRI60eS9/KlFjGxMToKg7JAAkhmLLhFNl5Gp70r8azTT30HZJWhUaHMm7rOGJTY5UFVSFSRBIaHUq/+kp7l/r167N27doS9+Hm5kZycnKhZcnJydjZ2VW+2sqrp2HNa3D1lPK83Vvw1BQwk9N1Pg60UR4MNZ4VK1YwYsQIVq9eXaTpilQJTZsGpqYweXLBInd3dxo0aAAzZoBaDXdrHRMSEh66O19fXwAaN25McnIy06ZN03pi+dlnn5Geng7Ap59+ypAhQ3jjjTeoXbs2ixcv1uqx9E22sZRKtOlkErvPXsPCzIQZfRuhUhnPDDuh0aEMWDWAxi6NCR8eTvqH6XQN6orFTQsGrBpAaHQoAGfPnsXbu+Qe8AEBAezYsaPQsm3bthEQEKDT+LVKCDi6VBnw/OopsK4OL6+Fbp/IpPIxoa3yYIjxLF++nGHDhrF8+XJ69uyp89ilCmBqClOmKEnkXe3bt+fMtm3KclPTguVl7Xir0WjIzs7WWqj5WrZsSadOnQDlVvjmzZu5desWR48epWnTplo/nj6Vqcbyl19+KdV6Q4YMKVcwkuFIy8pl2p9KzdWbQbXwrWat54i0R61RM27rOHrV6cX6F9ZjolL+vvp04qe0a9eOulF1eef3d8iql8XPP//Mzz//XOK+Xn/9dX744Qc++OADXnvtNXbu3MmqVav466+/KurtPJrbN+HPdyBqg/K81lPQdz7Yuj54O8loaLM8gNJJASAjI4Nr164RERGBhYWFUptUwfGEhIQwdOhQvv32W9q0aUNSUhIAVapUeeAwNJKBy6+pzO/ZP3ky79nZ0e7yZT7r3JlBgwfz97p1AA9sJjF37ly8vLyoV0/pO7Bnzx5mz54tm/M9KlEGKpVK2NraCkdHR+Hg4FDsw9HRsSy7NGhxcXECEHFxcfoOpcJ9sPq48A7eKJ6avUvczsnTdzhatStml2AaIjwuvMhrf/75p/Cr6ycwRXjV8hI///xzodenTp0qvL29C+9v1y7RrFkzYWFhIfz8/MSSJUt0GL0WXfpHiK8bCjHVToiPnYTY960QarW+o5IqmLbLA1Dkcf86FRVPx44di41n6NChpY5HMmDTpwsBQlhYCAHiz5deEo0aNRKWlpbC39+/yO/3/dfHd999Jxo2bCiqVq0q7OzsRPPmzcWPP/4o1Fr6HmzevLm4ceOGEEKIZs2aiebNm5f4MCZlqrGsX78+ycnJvPzyy7z22ms0adJEmzmuZCAOnE9h5RGll9qs/k2wMjd9yBaVS2J6IgCNXIo2mO7Vqxcdu3bEbpYds/rNYnDjwu1sYmJiCAoKKrQsKCiIY8eO6SxerSt2bMpFUKOFviOT9EDb5UE84tDI2ownLCzskWKRDNzkyfDJJ5CTAxYW9PrtN3rdfSl/HMt73X99vPXWW7z11ls6C69Pnz5YWloC0LdvX50dx9CUKbE8deoUBw8eZPHixXTo0AF/f3+GDx/OSy+9hJ2dna5ilCrQ7Rw1E0JPAvBKW29a+RjfeG/utu4ARF6NpG3NtkVej7waWWi9fEIIwsLC2Ldvn+6D1JW0BAj9H1y6+x6aPK8MJWRpq9+4JL0xtPJgaPFIBmzGjIKkkpwc5fk9HXrupY/rY+rUqcX+39iVufNOmzZt+Omnn0hMTOTtt99m1apVuLu789JLL+mkwatUseZsP8vlG1m421vxQY+6+g5HJwK9AvFx8OGzvZ+hEZpCr2mEhpn7ZuLr4EugV2Ch11QqFZcuXSryV3ClcfovmN9eSSotbOC5n6DfzzKpfMwZWnkwtHgkAzVjhtLGcvp0yM5W/r2vQ8+9DOn6yMjI4NatW4UeRuVR76Xv3r1bBAUFCRMTk4K2BJXdDz/8IOrXry9q1ar1WLWxPB53U/hO2Ci8gzeK7VFJ+g5Hp9ZGrRWqaSrRO6S3OHD5gLh155Y4cPmA6B3SW6imqcTaqLX6DlF7cm4L8dd4pS3lVDsh5ncQIuW8vqOSDIihlQdDi0cyMPltK6dPL3G5ofWRuHjxonjmmWdE1apVhYmJScFDpVIJExMTfYenVeWaKzwhIYFly5axZMkSMjMzC9pc5vesMhaP01zhuWoNz/6wn+jEW/Ru6sH3g5vrOySdKzJOHuDr4MvsbrMLxsmr9OTYlFIpGVp5MLR4JANSzDiWBe6OYxk/YoRB/X63b98eIQTvvPMOrq6uRYbv08U0kvpSpsRy1apVLFmyhN27d9O9e3eGDRtGz549MTU1rs4d+R6nxHLurvN8ueUMDlXN2T62I9VsLPUdUoW4d2YPd1t3Ar0CMTUxgutZCDi6BDZ/BHm3lbEpn5sP/nJwaKlkhlYeDC0eqfIwtN9vGxsbjh49St26xtnE7F5l6rzzwgsv4OXlxXvvvYerqyuxsbHMnTu3yHpyDKjK5cK1DL7dcQ6AyT0bPDZJJYCpiSlBPkH6DkO7sm7AH2/B6Y3Kc79OSntKOTal9BCGVh4MLR5JKq9WrVoRFxcnE8v7eXl5oVKpCAkJKXEdOVd45aLRCD5ce5KcPA2BtavR74ka+g5JehQXd8O6UZCeCCbm0GUatH0TTOQkW5IkSfqycOFCXn/9dRISEmjUqBHm5uaFXjem4RvLlFjGxsbqKAxJX0IOXeZQ7A2qWpjy2XONjWraxsdKXg7s+hT2fwsIcK6tjE3pblxThUmSJFVG165d48KFCwwbNqxgmUqlQgiBSqVCrVbrMTrtKlM1xs6dO2nQoEGxXePT0tJo2LAhe/fu1Vpwkm7F38xi5qZoAMZ1q4unU1U9RySVS8p5WNQV9n8DCGjxKozaLZNKSZIkA/Haa6/RvHlzwsPDuXjxIjExMYX+NSZlqrH85ptvGDlyZLGDodvb2zNq1Ci+/vprAgMDi9laMiQajeCDNSfIzFHT0tuRV9v56DskqayEgIjfYdMHkJsJVg7w7PfQ4Fl9RyZJkiTd49KlS/zxxx/4+/vrOxSdK1ON5fHjx+nRo0eJr3fr1o2jR48+clCS7v1+6DIHLlzHytyELwc2xdRE3gKvVG7fhDXDYMNoJan0CYQ3DsikUpIkyQA99dRTHD9+XN9hVIgy1VgmJycXaXBaaGdmZly7du2Rg5J06/L1/26BB/eoh281az1HJJXJpQOwdiTcigcTM+g0Edq/A3IYFkmSJIPUu3dv3nvvPU6ePEnjxo2L5FLPPms8lQJlSixr1KhBZGRkiVW5J06cwN3dvdjXJMOg0QjeX3OcrBw1rX2dGBrgo++QpNJS58Luz2HvVyA04OQH/RdCjRb6jkySJEl6gNdffx2A6dOnF3ntse6888wzzzB58mTu3LlT5LXbt28zdepUevXqpbXgJO37JTyWgzFKL/DZA5piIm+BVw43YmDJ07DnSyWpbPYSjNojk0pJkqRKQKPRlPgwpqQSylhjOWnSJEJDQ6lTpw5jxowpGOjz9OnTzJ07F7VazcSJE3USqPToYlMymbX5NAAfPl0PL2fZC7xSOLEKNo6FnHSwtIdeX0PjAfqOSpIkSZKKKFNi6erqyoEDB3jjjTf48MMPyZ8NUqVS0b17d+bOnYurq5zdwxCpNYLxq49zJ1dDu1rOvNTGW98hSQ9z5xZsGg8nVirPPdtC/wXg4KXfuCRJkqQy2717N7NnzyY6Wunj0KBBA95//32jG0mnTIklgLe3N5s2beLmzZucP38eIQS1a9fG0dFRF/FJWrJkfwxHLt3E2sKUz/s3kbfADV3cIVg7AlIvgcoUOgZD4DgwLXORlSRJkvTst99+Y9iwYfTr169gdsL9+/fTuXNnli5dyosvvqjnCLWn3PO8OTo60qpVK1q3bi2TSgN34VoGX245A8DEng3kQOgl8PHxQaVSFXmMHj26xG1Wr15NvXr1sLKyonHjxmzatOnRgtCoYfcXsLiHklQ6eMGwvyEoWCaVUoUqa3k4deoU/fv3L9jum2++0Ws8CxYsIDAwEEdHRxwdHenSpQuHDh3SakySVFqffvopX3zxBStXruTtt9/m7bffZuXKlcyaNYsZM2boOzytkhMIG7n8W+DZd+cCH9zaU98hGazDhw+TmJhY8Ni2bRsAAwcOLHb9AwcOMHjwYIYPH86xY8fo27cvffv2JTIysnwBpMbB0l7K1IxCDY0Hwuv7wKtNed+SJJVbWctDVlYWfn5+zJo1Czc3N73HExYWxuDBg9m1axfh4eF4enrSrVs3EhIStB6bJD3MxYsX6d27d5Hlzz77LDExMXqISHdkFYiRW7j3Iscup2Jracbn/ZvIucAfoHr16oWez5o1i1q1atGxY8di1//222/p0aMH77//PgAzZsxg27Zt/PDDD8yfP79sB48MhT/fhew0sLCBnl9D0+fL8zYkSSvKWh5atWpFq1atAJgwYYLe4/n9998LPV+4cCFr165lx44dDBkyROvxSdKDeHp6smPHjiLDNW7fvh1PT+Oq8JGJpRE7l5zOV9vOAjC5VwM8HKroOaLKIycnh99++42xY8eWmIyHh4czduzYQsu6d+/O+vXrS3+g7Az4OxgiflOe12ipdNBx8itn5JKkfaUpD4YeT1ZWFrm5uTg5Oek4Okkqaty4cbz99ttERETQrl07QGljuXTpUr799ls9R6ddMrE0UnlqDeNXHycnT0NQ3eoMbFlT3yFVKuvXryc1NZVXX321xHWSkpKKjILg6upKUlJS6Q6S8K/SQefGBUCldM4JmgCmJc9uJUn6UJryUJHKE09wcDAeHh506dJFd4FJUgneeOMN3Nzc+Oqrr1i1ahUA9evXZ+XKlfTp00fP0WmXTCyN1E97LnI8Pg1bKzNm9ZO3wEui1qjZe3kviemJuNu6E+gViKmJKYsWLeLpp5/Gw8ND+wfVaODAt7DzE9DkgV0N6Pcz+Dyp/WNJUhnopTxUQDyzZs1ixYoVhIWFYWVlpeOoJal4zz33HM8995y+w9A5mVgaodNJt/hmu3ILfFrvhrjZyy/S4oRGhzJu6zhiU2MLlvk4+BDcMJjt27cTGhr6wO3d3NxITk4utCw5OfnBHRduXYF1oyBmj/K8QR/o/S1UkSMrSPr1qOXBUOOZPXs2s2bNYvv27TRp0kRH0UrGYM+ePXz55ZccPXqUxMRE1q1bR9++fQtef/XVV1m2bFmhbbp3787mzZtLfYycnByuXr2KRqMptNzLy3jGJ5a9wosxd+5cGjRoQFBQkL5DKbNctYZxq46TqxZ0qe9Cvydq6DskgxQaHcqAVQNo7NKY8OHhpH+YTvjwcBq7NOaNT97AzsmOnj17PnAfAQEB7Nixo9Cybdu2ERAQUPwG0X/CvHZKUmleFZ79AQYuk0mlpHfaKA+GGM8XX3zBjBkz2Lx5My1btqyAyKXKLDMzk6ZNmzJ37twS1+nRo0eh0QmWL19eqn2fO3eOwMBAqlSpgre3N76+vvj6+uLj44Ovr6+23oJBUIn86XOk/7d353FR1fsfx18wyiKyiMqWIqSZirjlkpqJiagphaamWeJ27+3+sOTaYpvbdS8tM02rW3q7hWaleNNyB5dCTZGKNLdQcQEzk01lmfn+/pjr1MQiywxnBj7Px2MezVnnPZNfz8fvOed7ijl//jxNmzYlPT2dJk3s4xrFN3ec5I0dJ/CqV5dt/7gfH3fprfwzvUFPi7daEOoTSvzIeBwdfv/3VZG+CA8/D5zaO/Hrtl/ROepK3c8333xD7969WbBgAYMGDWLt2rXMmzeP5ORk2rZt+/uKBddh60tweJVx2r89PPI+NLrLWl9RiHKzVHsoKCjg6NGjADz44IOMHj2a0aNHU79+/WJ3wlZHnoULFzJ9+nTi4uLo2bOnaX79+vWpX79+ufMI+1TV47eDg0OJPZbXrl2r2A2a/9OzZ0/q1KnDCy+8gL+/f7HL09q3b1/hfdosJUqVnp6uAJWenq51lHJJvXBNNX9xs2o2dZOKP3Je6zg2KyEtQTETlZSeVGzZ1q1bFaCYhEpISzBbFh0drXr37m02b926daply5bKyclJhYSEqM2bN5vv8OJ3Sr3VWakZHsbXtmlKFeZb+BsJUXmWag9paWnGdf/0+nObqa48zZo1KzHPjBkzKpRH2Kdbx++jR4+qrKws0+vmzZvl2h5QGzZsMJsXHR2tPD09VePGjVXLli3Vk08+qa5cuVKu/dWrV08dO3asol/DLsk1ljVEQZHxFHiRQTEgxI+H2lfvRfb25FLOJQDa+rQttiwiIoLsm9l4LPAwrXdLWloaffr0MZs3fPjwkgdoNhjgwArYMRP0BVDfD4ashOZ9iq8rhIYs1R6CgoJQFjgBZqk8Z86cqXIWYf/atGljNj1jxgxmzpxZqX0NGDCAoUOHEhwczOnTp3nppZcYOHAgSUlJ6HSl957fynHlypVKfa69kcKyhli26yQ/ZeTg7ebEnCFt5S7wMvi7+wOQejmVe5vcW2x56uVUs/UAsrKyOH36NJs3b779B+Rehvi/w6kdxum7B8FDb4Fbw6qHF8LCrN4e7DyPsG9Hjx7ljjt+v9fA2dm50vsaOXKk6X1oaCjt2rWjefPmJCYm0rdv3zK3XbhwIc8//zzz5s0jNDSUunXNh5Xz8PCodC5bIzfv1AA/nM9ieeJpAGY/3JZG9SvfcGqDXoG9CPIKYt7eeRiU+Z15BmVg/r75BHsF0yuwl2m+p6cn58+fv/21Wad3wYqexqKyjgsMWgwjP5aiUtgsq7aHGpBH2Dd3d3c8PDxMr6oUln9255130qhRI06dOnXbdcPDw9m/fz99+/bFx8fH9Ax7Ly8vGjSoWTdwSo+lncsv0vPMpynoDYpB7fwZ1M7/9hvVcjpHHYsjFjNs3TCi1kbx4n0v0tanLamXU5m/bz6bTmzisxGflXljQDFFBZAwB77+3xMUGreG4avAp7V1voQQFmKV9lCD8ghRmvPnz/Prr7/i73/74+6uXbtqzZlEuSu8DPZwV/irW37i7cTTNKrvxLZ/9MbbzUnrSHajpHHygr2CWRSxiKGth5Z/R1fT4PMJcOGwcbrzeOg/D+rKIzSF/bBYe6iheYR9qczxOzc319T72LFjR15//XX69OmDt7c33t7ezJo1i0ceeQQ/Pz9Onz7N888/T05ODj/88INFe0LtnRSWZbD1wjIl/RpD3/4ag4KVj9/DgLZlDMwtSlTakz3K7YfP4ItYKMgBF0/j2JRtHrJaXiGsqcrtoYbnEfajMsfvxMTEYjdoAkRHR7NixQqioqI4cuQI165dIyAggIiICGbPnl3s0b4lCQ4OZty4cYwdO7ZGDYZeEiksy2DLheXNQj2Dlu7l9C95PNwhgDdHdtQ6Uu2SnwtfTYWUj4zTgd1h6Hvg1VTbXEIIIWzu+L1kyRJWr15Namoqffr0YcKECQwZMqRG9nTKzTt26o3tJzj9Sx6N3Z2ZGRmidZza5dJ38G6Ysah0cITeUyF6kxSVQgghShQbG0tKSgoHDx6kdevWPPXUU/j7+zNp0iSSk5O1jmdRUljaocNnr/Lu3p8BmDcklAZyXWX1UAr2r4B/hcOvJ8E9AKK/gD4vgU7ugxNCCFG2Tp06sXTpUi5evMiMGTP417/+RZcuXejQoQMffPCBRcaC1ZocDe3MjQI9z376PUrB0E530K/N7a/tEBaQdwXi/w9ObjVO3/0gPLwc6nlrm0sIIYTdKCwsZMOGDaxatYrt27dz7733MmHCBM6fP89LL73Ejh07iIuL0zpmlUhhaWcWbTtO2pU8fD2cmTFYToFXi7Q98PlfIDcDdM7Qfy50mQi1ZOgIIYQQVZOcnMyqVatYs2YNjo6OjBkzhjfeeINWrVqZ1hkyZAhdunTRMKVlSGFpRw6mXeWDr9MAWDC0HZ716t5mC1El+kJIXAB7FwMKGrWEYR+AX6jWyYQQQtiRLl260K9fP9Pd5X9+8g4Y7xz/49N97JUUlnbiekERz332HUrBiM5N6NPKR+tINdu1c/DZBDh/0DjdaQwMWABObtrmEkIIYXd+/vlnmjVrVuY6bm5urFq1qpoSWY8Ulnbi1S3HOfvrdfw9XXhlcBut49RsP202Puv7ZhY4e0DkEmj7iNaphBBC2KnbFZU1iRSWdmD/z7+y+pszACx8pB0eLnIK3CqKCmD7dDiwwjh9xz3GU98NgjSNJYQQwv40aNCg3I9xvHr1qpXTVB8pLG1cXr7xFDjAqK5Nub9lY40T1VC/nYFPx8HF/40n1n0S9J0BdWQoJyGEEBW3ZMkSrSNoQgpLG7dwy0+kX73BHV6uvPRga63j1ExH/wsbJ0F+Frh4wZCVcPdArVMJIYSwY9HR0VpH0IQUljbsm9NX+DDpLGA8Be4up8Atqygftr0CB981Tjfpajz1LU/QEUIIYWF6vZ74+HiOHTsGQEhICA899BA6nU7jZJYlT96xUXn5RTz/2fcAPNYtkPvuaqRxohrm6s/wfsTvRWXPyejHfMG0xe8SHByMq6srzZs3Z/bs2bd9EkJiYiKdOnXC2dmZFi1asHr1auvnF8LK9Ho906ZNq1B7uHTpEo899hgtW7bE0dGR2NhYTfOsX7+efv360bhxYzw8POjevTtbt261WCYhyuvUqVO0bt2aMWPGsH79etavX8/jjz9OSEgIp0+f1jqeRUlhaaPmf3WM87/JKXCr+HEDvNMbLqWAqzc8tg76/ZOFi15nxYoVLFu2jGPHjrFw4UJeffVV3nrrrVJ3lZaWxqBBg+jTpw8pKSnExsYyceJEOXgJu7dw4cIKt4f8/HwaN27MK6+8Qvv27TXPs2fPHvr168eXX37J4cOH6dOnD5GRkRw5csSi2YS4naeffprmzZuTnp5OcnIyycnJnDt3juDgYJ5++mmt41mUg6oJD6a0kvPnz9O0aVPS09Np0qRJtX3uN6eu8Ni/DgDw8cRu9GwhvZUWUXgTtr0M3/7LON30XuOpb887ABg8eDC+vr68//77pk0eeeQRXF1d+eijj0rc5dSpU9m8eTOpqammeSNHjuTatWts2bLFet9FCCurTHv4o7CwMDp06GCxGxiqmueWkJAQHn30UaZPn26RXMI2aXX8Lo2bmxv79+8nNNT8ARvfffcdPXv2JDc3V6Nklic9ljYmN7+I5/53CvzxewOlqLSUX0/D++G/F5X3TYGxm01FJUCPHj3YuXMnJ06cAIwNft++fQwcWPqNPElJSYSHh5vN69+/P0lJSZb/DkJUo8q0B1vPYzAYyMnJwdvb21oxhSiRs7MzOTk5xebn5ubi5FSzRh+Rm3dszLwvj3Hh2g2aNHDlxYFyCtwiftwAG5+Cghyo1xCGvgstwout9sILL5CdnU2rVq3Q6XTo9Xrmzp3L6NGjS911RkYGvr6+ZvN8fX3Jzs7mxo0buLq6WvzrCFEdKtMebD3PokWLyM3NZcSIEVZMKkRxgwcP5q9//Svvv/8+Xbt2BeDAgQM8+eSTPPTQQxqnsywpLG3IvpNXiDtwDoBXh7XDzVn+91TJnwc8b9YTHvkXeASYVtEb9Ow9t5dLOZc4lniMjz/+mLi4OEJCQkzXTAYEBNTaYSNE7WJr7cGSeeLi4pg1axYbN27Ex0ceiSuq19KlS4mOjqZ79+6m54QXFRXx0EMP8eabb2qczrKkcrEROTcLmfq58RT4mO7N6NFcToFXSdYF+HTs78/67hkLD0wD3e9/5NcfW88z257hzLUzxhmvg3c/b5zaOxHaOpTQ0FDOnj3L/PnzSz1w+fn5kZmZaTYvMzMTDw8P6a0UdsUS7cFW86xdu5aJEyfy6aefFrt0RYjq4OXlxcaNGzl16pRpuKHWrVvTokULjZNZnlxjaSPmffkTF67doKm3K1MHtNI6jn07nQDv9DIWlc6eMHIN9JtVrKgctm4YoT6hJE1IIufFHDwcPbjD4w6GrRvG+mPrAdDpdBgMhlI/qnv37uzcudNs3vbt2+nevbt1vpsQVmCp9mCLedasWcO4ceNYs2YNgwYNsnp2If7IYDCwcOFCevbsSZcuXfjXv/5FeHg4kZGRNbKoBOmxtAl7TvzCmoP/OwX+SHs5BV5ZBgPsXQwJcwEFfqEw4kPwvtNsNb1BzzPbnmFwy8HEj4zH0cH476shDw9hx7YddHmsC5PXTEa1V7z++uuMHz++1I988sknWbZsGc8//zzjx49n165drFu3js2bN1vzmwphMZZsDwApKSmA8aaEX375hZSUFJycnGjTpk2154mLiyM6Opo333yTbt26kZGRAYCrqyuenp7lyiNEVcydO5eZM2cSHh6Oq6srb775JpcvX+aDDz7QOpr1KFGq9PR0Baj09HSrfUbWjQLVfd4O1WzqJjVjY6rVPqfGy/tVqY+GKTXDw/iKj1Gq4HqJqyakJShmopLSk8zmZ2dnq8mTJyvfO3wVdVABgQHq5ZdfVvn5+aZ1ZsyYoZo1a2a+v4QE1aFDB+Xk5KTuvPNOtWrVKkt/OyGsxtLtASj2+vM61ZWnd+/eJeaJjo4udx5hn6rj+F0eLVq0UCtXrjRNb9++XTk5OSm9Xq9hKuuSrjGNzf/yGBezbtKsYT2eH3C31nHs04VkWBcNWeegjgsMWgwdHy919Us5lwBo69PWbL67uztLlixh9sLZeCzwYNHQRYwKHWW2TlpaGmFhYWbzwsLCZMBlYbcs3R5UFYdGtmSexMTEKmURoqrOnTvHgw8+aJoODw/HwcGBixcv2sT4mtYghaWGvjl1hTUH0wF49ZF21HOS/x0VohQceh+2vAj6AmgQbDz17d+uzM383f0BSL2cyr1N7i22PPVyqtl6v3+cIjExkX379lnoCwihPVtrD7aWR4iqKCoqwsXFxWxe3bp1KSws1CiR9dWKJ+8MGTKExMRE+vbty2effVbu7aw5cv/1giIGLNnLuavXeeLeZsyOanv7jcTvCvLgi1j4YZ1xutVgiHobXG5/3ZTeoKfFWy0I9Qk1u4YLwKAMRK2NIvVyKiefOonOUWelLyCEbbC19mBreYR9spUn7zg6OjJw4ECcnZ1N87744gseeOAB3NzcTPPWr1+vRTyrqBV3hU+ePJkPP/xQ6xhmXt92gnNXrxPg6SKnwCvq19Pwr3BjUemgg36z4dGPylVUAugcdSyOWMymE5uIWhtFUnoSOfk5JKUnEbU2ik0nNrEoYpEctEStYGvtwdbyCFEV0dHR+Pj44OnpaXo9/vjjBAQEmM2rSWpFjyUYr7VZtmyZTfRYHjn3G4+s+AaDglVju9CnlQzWW24ntsH6iXAzC9x8YPhqCOpZqV0VGycPCPYKZlHEIoa2HmqZvELYCVtrD7aWR9gXW+mxrI00v6hvz549vPbaaxw+fJhLly6xYcMGoqKizNZZvnw5r732GhkZGbRv35633nrL9Egke5NfpGfq599jUDCk4x1SVJbXn4cSatLVeD2lh/9tNy3N0NZDefjuh01P9vB396dXYC/pCRG1kq21B1vLI4QoH80Ly7y8PNq3b8/48eMZOrT4v0I/+eQTpkyZwsqVK+nWrRtLliyhf//+HD9+3PRYrg4dOlBUVFRs223bthEQEFBsvpbeTjjNicxcGro5MX1w+cZ1q/VuZkP83+GnTcbpzuNhwAKo41z2duWgc9QRFhRW5f0IURPYWnuwtTxCiNvTvLAcOHAgAwcOLHX566+/zl/+8hfGjRsHwMqVK9m8eTMffPABL7zwAvD7gLxVlZ+fT35+vmk6JyfHIvu95aeMbN5OPAXArIdDaODmZNH910i/nIBPRsOVE6BzMg4l1GmM1qmEEEIIUQKbvnmnoKCAw4cPmz3b1dHRkfDwcJKSkiz+efPnzze7mLa8T4ooD71BMfWz7ynUK/q18WVQaOVP4dYaxzbBew8Yi0r3ABi3RYpKIYQQwobZdGF55coV9Ho9vr6+ZvN9fX1Nj+Yqj/DwcIYPH86XX35JkyZNSi1KX3zxRbKyskyvo0ePVin/H32wL43vzmfh7lKHOVFtcXBwsNi+axyDHnbNMfZUFuRAs57wt93Q5B6tkwkhhBCiDJqfCq8OO3bsKNd6zs7OZmNNZWdnW+Tzz1zJY/H24wC8/GBrfD1cbrNFLXbjN/j8L3Bqu3G6298hYjbo6mqbSwghhBC3ZdOFZaNGjdDpdGRmZprNz8zMxM/PT6NUFaOU4oX133Oz0ECP5g15tEtTrSPZrl+Ow5qRcPVn46MZI5dC+0e1TiWEEEKIcrLpU+FOTk7cc8897Ny50zTPYDCwc+dOunfvrmGy8vvk23T2/3wVl7qOLBjaTk6Bl+bENnivr7Go9AyECdukqBRCCCHsjOY9lrm5uZw6dco0nZaWRkpKCt7e3gQGBjJlyhSio6Pp3LkzXbt2ZcmSJeTl5ZnuEreG5cuXs3z5cgoKCqq0n19y8pn35TEAnul3N4EN61kiXs2iFHyzFLbPAJTxesoRH4JbI62TCSGEEKKCNH/yTmJiIn369Ck2Pzo6mtWrVwOwbNky0wDpHTp0YOnSpXTr1s3q2ao6cn/s2iPEp1ykjb8H/53Ukzo6m+4grn6FN+GLyfD9WuN0p2h4cBHUkWGYhBBCVJ48eUc7mvdYhoWFcbvadtKkSUyaNKmaElnG3pO/EJ9yEQcHmD80VIrKP8vJgLWj4cIh4/O+B8yHrn8FuVRACCGEsFuaF5Y10c1CPa/EpwIQ3T2I9k29tA1kay4egTWPQc5FcPEyPu+7efFeayGEEELYFyksrWDZrlOc/fU6fh4uPBPRUus4tiX1c4iPgaIb0KgljFoLDZtrnUoIIYQQFiCFpYWdyMzhnT2nAZj5UAjuLjL+IgAGAyTMhb2LjNMt+sGw98HFU9tcQgghhLAYufDPggwGxUvrf6BQrwhv7Uv/EN/bb1QbFFyHT8f8XlT2eAoe+8Qmi8qcnBxiY2Np1qwZrq6u9OjRg2+//bbMbRITE+nUqRPOzs60aNHCdNOZEPauou3h0qVLPPbYY7Rs2RJHR0diY2M1zbN+/Xr69etH48aN8fDwoHv37mzdutWimYQQ5qSwLMHy5ctp06YNYWFhFdruk0PpHDr7G/WcdMx6OETGrATIyYTVg+DYF6BzgqgVEDEHHHVaJyvRxIkT2b59O//5z3/44YcfiIiIIDw8nAsXLpS4flpaGoMGDaJPnz6kpKQQGxvLxIkT5eAlaoSKtof8/HwaN27MK6+8Qvv27TXPs2fPHvr168eXX37J4cOH6dOnD5GRkRw5csTi2YQQRpoPN2TLKjJcwS85+fRdnEj2zSJeGdSaib3urKaUNizzKMSNgKx0cG0AI+OgWQ+tU5Xqxo0buLu7s3HjRgYNGmSaf8899zBw4EDmzJlTbJupU6eyefNmUlNTTfNGjhzJtWvX2LJlS7XkFsIaKtMe/igsLIwOHTqwZMkSm8hzS0hICI8++ijTp0+3SC5hm2S4Ie1Ij6WFzN18lOybRbS9w4OxPYK0jqO9Uzvhg/7GotK7OUzcadNFJUBRURF6vR4XF/Nnubu6urJv374St0lKSiI8PNxsXv/+/UlKSrJaTiGqQ2Xag63nMRgM5OTk4O3tbY2IQgjk5h2L2P/zr6YxK+cNkTErOfQBbH4WlN74JJ1HP4J6tvkXud6gZ++5vVzKuYS/uz/3dr+X2bNn07p1a3x9fVmzZg1JSUm0aNGixO0zMjLw9TW/ltbX15fs7Gxu3LiBq6trdXwNISyiqu3B1vMsWrSI3NxcRowYYeXkQtReUlhWUaHewPSNxtOgj3UNpF0TL20DaclggO3TIGmZcbrdo/DQW1DHWdtcpVh/bD3PbHuGM9fOmObd0fcOPLd4cscdd6DT6ejUqROjRo3i8OHD2gUVohrYWnuwdJ64uDhmzZrFxo0b8fHxsWJyIWq3Wt61VnX//uYMJzJzaVCvLs/1v1vrONopuA7rnvi9qOzzMgx5x6aLymHrhhHqE0rShCRyXswhaUISndp04tjgY3x86GPS09M5ePAghYWF3HlnydfM+vn5kZmZaTYvMzMTDw8P6a0UdsNS7cFW86xdu5aJEyeybt26YpeuCCEsTIlSpaenK0Clp6eXuDwj64ZqM+0r1WzqJrX24NlqTmdDsjOUeqe3UjM8lPpnI6W+/1TrRGUq0hepoCVBKjIuUukNerNleoNeRcZFquAlwapIX6SuXr2qPD091TvvvFPivp5//nnVtm1bs3mjRo1S/fv3t1p+ISzJku3hj3r37q0mT56seZ64uDjl4uKi4uPjK5xF2K/bHb+F9UiPZQnKO9zQ3M3HyCvQ06GpF8PvaVo94WzNlZPwfrjxMY2u3hD9BYQO0zpVmfae28uZa2d4qddLODqYN4Ht27bTp6gPaWlpvPHxG/Tp04dWrVoxbty4Evf15JNP8vPPP/P888/z008/8fbbb7Nu3Tr+8Y9/VMdXEaLKLNkeAFJSUkhJSSE3N5dffvmFlJQUjh49qkmeuLg4xowZw+LFi+nWrRsZGRlkZGSQlZVV7jxCiArSurK1ZWX9i+ebU1dUs6mbVNALm9QP569pkM4GnDuo1IIgY0/lmx2UunJK60TlEvd9nGImKic/p9iyTz75RAUFByl0KK9GXiomJkZdu/b7/98ZM2aoZs2amW2TkJCgOnTooJycnNSdd96pVq1aZeVvIITlWLo9AMVef16nuvL07t27xDzR0dHlziPsk/RYakdu3qmEP96w83i3ZrS9w/aeIGN1P30Jn403PvM7oBM8tg7qN9Y6Vbn4u/sDkHo5lXub3Gu2bMSIETTt3pQeH/RgQ/QGwoLCzJanpaUV68kOCwuTAZeF3bJ0e1BVHBrZknkSExOrlEUIUXFyKrwSVn99hpOXc/F2c+LZiFp4w86hVfDJaGNReVcEjN1kN0UlQK/AXgR5BTFv7zwMymC2zKAMzN83n2CvYHoF9jJbppQiMTGR2bNnV2dcIazK1tqDreURQlSMFJYVlJF1kyU7TgDwwoBWeNarq3GiaqQUJMyDTbGgDNDxcRi5BpzctE5WITpHHYsjFrPpxCai1kaRlJ5ETn4OSelJRK2NYtOJTSyKWITuT4+ddHBw4OzZszRtWkuvpxU1kq21B1vLI4SoGHmkYxlKeiRU7NojxKdcpGOgF58/2QNHx1ryPHB9kbGgPPIf43TvqRD2Itjx89BLGicv2CuYRRGLGNp6qHbBhNCArbUHW8sj7Is80lE7UliW4c9/MJPP/cbQt7/BwQH+G3MfoU1qybWVBXnw6Vg4uQ0cHGHQ69C59LtC7cmfn+zRK7BXsZ4QIWoLW2sPtpZH2A8pLLUjN++Uk8GgmPWFcciMYZ2a1J6i8vpV+Hg4XDgEdVxh2AfQ6kGtU1mMzlFX7AYAIWorW2sPtpZHCHF7UliWU3zKBb5Lv4abk47nBtSSG3ayL8F/hsAvx8C1gfHO76ZdtU4lhBBCCBslhWUJli9fzvLlyykoKADgekERC7ecAiDmgRb4uLtoGa96XP0ZPnwYrp0Dd394YgP4tNY6lRBCCCFsmNwVXoKYmBiOHj1qGgPtP0lnyczOp6m3K+N7BmsbrjpkpMIHA4xFZYNgGL9FikohhBBC3JYUluXw8YFzALz8YGtc6tbwC8fTD8LqByE3E3zbwvit0CBI61RCCCGEVe3Zs4fIyEgCAgJwcHAgPj7ebLlSiunTp+Pv74+rqyvh4eGcPHlSm7A2TArLcigoMnDvnd70D/HTOop1ndppPP19MwuadjMOfO7uq3UqIYQQwury8vJo3749y5cvL3H5q6++ytKlS1m5ciUHDhzAzc2N/v37c/PmzWpOatvkGstycHSA6YNDcLDjMRtv68d4+HwiGAqheV949D92N/C5EEIIUVkDBw5k4MCBJS5TSrFkyRJeeeUVHn74YQA+/PBDfH19iY+PZ+TIkdUZ1aZJj2U5PNThDtoEeGgdw3qOfASfjTMWlSFDYNRaKSqFEELYvZycHLKzs02v/Pz8Su0nLS2NjIwMwsPDTfM8PT3p1q0bSUlJlopbI0hhWQ5/u/9OrSNYz7fvw8YY4yMaO0XDI+9DHSetUwkhhBBV1qZNGzw9PU2v+fPnV2o/GRkZAPj6ml8e5uvra1omjORUeDk0cKuhhdb+FbDlBeP7bn+HAfPt+hGNQgghxB8dPXqUO+64wzTt7OysYZraQXosa6t9S34vKnvGSlEphBCixnF3d8fDw8P0qmxh6ednvHk3MzPTbH5mZqZpmTCSwrI22v0q7JhhfN97KoTPlKJSCCGEKEVwcDB+fn7s3LnTNC87O5sDBw7QvXt3DZPZHiksS7B8+XLatGlDWFiY1lEsSynYORsS5hqnH5gGfV6SovIPrDWO2fLlywkKCsLFxYVu3bpx8OBBK30DISzDGm3hdvvUItP8+fPp0qUL7u7u+Pj4EBUVxfHjxyuUS9QMubm5pKSkkJKSAhhv2ElJSeHcuXM4ODgQGxvLnDlz+O9//8sPP/zAmDFjCAgIICoqStPctkYKyxL8+ck7NYJSsH0a7F1knI6YA/c/q20mG2SNccw++eQTpkyZwowZM0hOTqZ9+/b079+fy5cvW+trCFFl1mgLt9unFpl2795NTEwM+/fvZ/v27RQWFhIREUFeXl6lMgr7dejQITp27EjHjh0BmDJlCh07dmT69OkAPP/88zz11FP89a9/pUuXLuTm5rJlyxZcXGrBY54rQolSpaenK0Clp6drHaVqDAalvnpRqRkexteBd7VOZBcAtWHDBtO0wWBQfn5+6rXXXjPNu3btmnJ2dlZr1qwpdT9du3ZVMTExpmm9Xq8CAgLU/PnzrZJbCEuzVFsoa5+2kEkppS5fvqwAtXv37kpnE9qrMcdvOyQ9ljXdrZ7K/f/7F37km9D1L9pmslOVGcesoKCAw4cPm23j6OhIeHi4jH0m7JYtjulnqUxZWVkAeHt7WzyjELWBFJY1mVKwcxZ885ZxevASuGeslonsWmXGMbty5Qp6vV7GPhM1ii2O6WeJTAaDgdjYWHr27Enbtm0tnlGI2kDGsazJEubBvjeM7x9cBJ3HaZvHBukNevae28ulnEv4u/vTK7AXOked1rGEqFbnzsGVK1qnKK6622dMTAypqans27fPap8hRE0nhWVNlbgQ9rxqfD9ggZz+LsH6Y+t5ZtsznLl2xjQvyCuIxRGLGdp6aLH1/ziOmb+/v2l+ZmYmHTp0KPEzGjVqhE6nk7HPhM06dw5at4br18te75dffn9fmbZQUdXRPv9o0qRJbNq0iT179tCkSZMq5xeitpJT4TXRntcgcZ7xfcRcuPfv2uaxQeuPrWfYumGE+oSSNCGJnBdzSJqQRKhPKMPWDWP9sfXFtqnMOGZOTk7cc889ZtsYDAZ27twpY58Jm3DlirGo/OgjOHy4+Oujj4zrZWf/vo21x/SrrvYJxiGKJk2axIYNG9i1axfBwcFVzi9EbSY9ljXNvjdg1xzj+/BZ0GOStnlskN6g55ltzzC45WDiR8bj6GD899W9Te7lo8EfEbkskqc+eAr4fRwzb29vAgMDTeOY3XXXXQQHBzNt2rTbjmM2ZcoUoqOj6dy5M127dmXJkiXk5eUxbpxcmiBsR+vW0KnT79O5ubmcOnUKx/91P1y8WPW2cGuft/y5fUH1t8+YmBji4uLYuHEj7u7upusxPT09cXV1rcQvKUQtp/Vt6bbM7oYrSHr79yGFdr92+/VrqYS0BMVMVFJ6UvFlCQkKKPaKjo5WShmHNJk2bZry9fVVzs7Oqm/fvur48eNm++jdu7dp/VveeustFRgYqJycnFTXrl3V/v37rfX1hKiQw4eVAuN//8gabeF2+1Sq+ttnSfsD1KpVqyr6UwobYnfH7xpECssy2NUfzMMf/l5U7pqndRqbFvd9nGImKic/p8Tl2TezFTNRcd/HVWr/gYGBclASdqO0wrK8y8tSmbYg7VNYgl0dv2sYucayJvgxHr542vi++yQIe0HTOLbO3914YX/q5dQSl9+af2u9ivjxxx/x9PRkzJgxlQ8oRA1Q2bYg7VMI+yaFZQns6lnhp3bA5xNBGaDTGOOjGuXZ32XqFdiLIK8g5u2dh0EZzJYZlIH5++YT7BVMr8BeFd53SEgI33//PY6O0rRE7VbZtiDtUwj7Jq2rBHbzrPCzSbD2cTAUQsgQ4wDoUlTels5Rx+KIxWw6sYmotVEkpSeRk59DUnoSUWuj2HRiE4siFsl4lqJWOXYMkpOLv44dq94c0j6FsG9yV7i9upgCcSOg6Aa06AdD3gX5i7bchrYeymcjPuOZbc/Q44MepvnBXsF8NuKzEsfJE6ImatQI6tWDxx8vfZ169YzrVRdpn0LYLwellNI6hK06f/48TZs2JT093bYGzP3lBKwaANd/hcAe8Pjn4FRP61R2SZ68I8Ttn7zTqBH8bzSgaiXtU1SWzR6/awHpsbQ3WRfgP0OMRaV/B3jsEykqq0DnqCMsKEzrGEJoKjBQm8LxdqR9CmF/5BpLe3LjGnw8DLLPQ8O7jD2VLh5apxJCCCGEAKSwtB+FN2HtaLh8FOr7wRPrwa0aL3oSQgghhLgNKSztgcEAG/4GZ/eBkzuM/hS8bPC8lRBCCCFqNSksbZ1SsPUlOBoPjnVh5Mfg307rVEIIIYQQxUhhaeu+WQoHVhjfD1kJd/bWNo8QQgghRCmksLRl36+D7dON7yPmQugwbfMIIYQQQpRBCktblbYH4v/P+P7eGOgxSds8QgghhBC3IYWlLbpyEj659ajGocbnfwshhBBC2DgpLG1N3q/w8XC4mQVNukLUCnCU/01CCCGEsH1SsdiSonxjT+VvacbhhEbGQV0XrVMJIYQQQpSLFJa2Qin479Nw7htw9oDHPoX6jbVOJYQQQghRblJYlmD58uW0adOGsLCw6vvQPYvg+7XgoIMR/wafVtX32cJkz549REZGEhAQgIODA/Hx8WbL169fT0REBA0bNsTBwYGUlJRy7ffTTz+lVatWuLi4EBoaypdffmn58EJYkDXawo8//sgjjzxCUFAQDg4OLFmyRPNM7733Hr169aJBgwY0aNCA8PBwDh48WKFcQojfSWFZgpiYGI4ePUpiYmL1fGDq55Dwvxt0Bi2C5g9Uz+eKYvLy8mjfvj3Lly8vdfl9993HwoULy73Pb775hlGjRjFhwgSOHDlCVFQUUVFRpKamWiq2EBZnjbZw/fp17rzzThYsWICfn59NZEpMTGTUqFEkJCSQlJRE06ZNiYiI4MKFCxXOJ4QAlChVenq6AlR6err1PuRCslKzfZSa4aHUlpes9zmiwgC1YcOGEpelpaUpQB05cuS2+xkxYoQaNGiQ2bxu3bqpv/3tbxZIKYT1Waot/FGzZs3UG2+8YVOZlFKqqKhIubu7q3//+9+Vzia0Vy3Hb1Ei6bHUUu5lWDsaim7CXf2h3z+1TiSsICkpifDwcLN5/fv3JykpSaNEQojSXL9+ncLCQry9vbWOIoRdksJSK0UFsG4MZF+AhnfBI++Bo07rVMIKMjIy8PX1NZvn6+tLRkaGRomEEKWZOnUqAQEBxf4xKIQonzpaB6i1trwA55KMd4CPWgMunlonqpX0Bj17z+3lUs4l/N396RXYC50U+KKWsrX2UN15FixYwNq1a0lMTMTFRYZ6E6IypLDUwuHVcOh9wAGGvgeN7tI6Ua20/th6ntn2DGeunTHNC/IKYnHEYoa2Hmqxz/Hz8yMzM9NsXmZmZqVuXhDCWspqD7aWx5Lt85ZFixaxYMECduzYQbt27Sy+fyFqCzkVXt3OHYDNzxrfP/Ay3D1A2zy11Ppj6xm2bhihPqEkTUgi58UckiYkEeoTyrB1w1h/bL3FPqt79+7s3LnTbN727dvp3r27xT5DiKq4XXuwtTyWbJ8Ar776KrNnz2bLli107tzZovsWoraRHsvqlH0R1j1hfAZ464eg17NaJ6qV9AY9z2x7hsEtBxM/Mh5HB+O/r+5tci8fDf6IyGWRPPXBUwCkpaWRkpKCt7c3gYGBXL16lXPnznHx4kUAjh8/Dhh7JUvrgZw8eTK9e/dm8eLFDBo0iLVr13Lo0CHefffdavi2QpSttPbQ1qstM1vNJOtMFnvYw+mfT1e5LRQUFHD06FHT+wsXLpCSkkL9+vVp0aJFmXms1T4XLlzI9OnTiYuLIygoyHTtc/369alfv37VflwhaiOtb0u3ZRYdrqAwX6n3+hqHFVp+r1I3c6q+T1EpCWkJipmopPSk4ssSEhRQ7BUdHa2UUmrVqlUlLp8xY4ZpH9HR0ap3795m+123bp1q2bKlcnJyUiEhIWrz5s1W/IZClF9p7cEabeHWMEB/fv1xnepun82aNbvtNsL+yHBD2pEey+qyfRqc/9Z4k87Ij8FZ/iWslUs5lwBo69O22LKwsDCyb2bjscCDuKFxjAodZbZ87NixjB07tsz9p6Wl0adPH7N5w4cPZ/jw4VULLoQVlNYewsLCUEqRk59TYnuoTFsICgpCKVWpPLcyWbp9njlzpsz1hRAVI9dYVocfN8CBlcb3USvB+05t89Ry/u7+AKReLvnJN7fm31qvIrKysjh9+jTPPiuXOQj7YK32UNm2IO1TCPsmhaW1XTkJGycZ3/eMhVYPahpHQK/AXgR5BTFv7zwMymC2zKAMzN83n2CvYHoF9qrwvj09PTl//rxcmyXshrXaQ2XbgrRPIeybFJbWVHDdOAh6QS406wkPTNM6kQB0jjoWRyxm04lNRK2NIik9iZz8HJLSk4haG8WmE5tYFLFIxrMUtYKttQdbyyOEqBgHdbsLXmqx8+fP07RpU9LT02nSpEnFNlYK4v8O360BNx94ci+4y7iFtqSkcfKCvYJZFLHIKuPkCWHLbK092FoeYV+qdPwWVSKFZRmq9Afz8L/hi6fBwRHG/BeCK37aRlifrT1pRAgt2Vp7sLU8wn5IYakduSvcGjKPwlfPG98/ME2KShumc9QRFhSmdQwhbIKttQdbyyOEuD25xtLSCm/AZ+Oh6Ca0CDfesCOEEEIIUQtIYWlpW1+GX44Zr6uMWgGO8hMLIYQQonaQqseSjm2CQ+8b3w9ZCfV9tM0jhBBCCFGNpLC0lKzzsDHG+L7H09Cir7Z5hBBCCCGqmRSWlmDQw/q/ws1rENBRxqsUQgghRK0khWUJli9fTps2bQgLCyvfBnsXw9mvwak+PPI+1HGyaj4hhBBCCFskhWUJYmJiOHr0KImJibdf+cJhSFxgfD9oMTRsbtVsQgghhBC2SgrLqii8Aev/BkoPbR+B9iO1TiSEEEIIoRkpLKtixyz49STU94MHF2mdRgghhBBCU1JYVlbaHjiwwvj+4WVQz1vbPEIIIYQQGpPCsjJuZkP8/xnf3zMO7uqnbR4hhBBCCBsghWVlbHkRstKhQRBEzNE6jbCgPXv2EBkZSUBAAA4ODsTHx5uWFRYWMnXqVEJDQ3FzcyMgIIAxY8Zw8eLF2+53+fLlBAUF4eLiQrdu3Th48KAVv4UQVWeNtlDWPrXIM3/+fLp06YK7uzs+Pj5ERUVx/PjxcmcSQhQnhWVFHf8KUj4CHIyPbHSur3UiYUF5eXm0b9+e5cuXF1t2/fp1kpOTmTZtGsnJyaxfv57jx4/z0EMPlbnPTz75hClTpjBjxgySk5Np3749/fv35/Lly9b6GkJUmTXaQln71CLP7t27iYmJYf/+/Wzfvp3CwkIiIiLIy8urcD4hxP8oUar09HQFqPT0dOOMvF+VerWFUjM8lNr6irbhhNUBasOGDWWuc/DgQQWos2fPlrpO165dVUxMjGlar9ergIAANX/+fEtFFcKqLNUWKrrP6syjlFKXL19WgNq9e3elcgnbUez4LaqN9FhWxNaXIO8yNG4FfV7WOo2wAVlZWTg4OODl5VXi8oKCAg4fPkx4eLhpnqOjI+Hh4SQlJVVTSiGs73ZtobpVJk9WVhYA3t5yM6YQlSWFZXmd3AHfrQEc4KFlUNdF60RCYzdv3mTq1KmMGjUKDw+PEte5cuUKer0eX19fs/m+vr5kZGRUR0whrK48bcHW8xgMBmJjY+nZsydt27a1ckIhaq46WgewCwW5sCnW+P7ev0PTLprGEdorLCxkxIgRKKVYsWKF1nGE0IyttYXK5omJiSE1NZV9+/ZZMZ0QNZ8UluWxb4nxLnCvQHjgFa3TCEuYORN0Opg2rfiy2bNBry9101sHrrNnz7Jr164ye0QaNWqETqcjMzPTbH5mZiZ+fn6VTS+EZd2uPZSiIm3BYlks1Db/aNKkSWzatIk9e/bQpEmTSgYX9mzmzJnMmjXLbN7dd9/NTz/9pFEi+yWnwssjJc7438g3wclN2yzCMnQ6mD69+EFz9mzjfJ2uxM1uHbhOnjzJjh07aNiwYZkf4+TkxD333MPOnTtN8wwGAzt37qR79+5V/hpCWMTt2kMJKtoWLJbFQm0TQCnFpEmT2LBhA7t27SI4ONgS30DYqZCQEC5dumR6Se915UiPZbko6PA4NH9A6yDCUm71htw6aE6bBrNnkzt9Oqf+/neIjITp00lLSyMlJQVvb2/8/f0ZNmwYycnJbNq0Cb1eb7pO0tvbGycnpxI/asqUKURHR9O5c2e6du3KkiVLyMvLY9y4cdXxTYW4vRLaQ+4rr3Bq7lz4+99hxQqLtIXc3FxOnTplmv7jPgMDA0vNYq22GRMTQ1xcHBs3bsTd3d20jaenJ66urpX9NYWdqlOnjpxJsgRtb0q3babhCl4KMg41JGqef/5TKVDKyUkpUAnjximg2Cs6OlqlpaWVuAxQCQkJpl327t1bRUdHm33MW2+9pQIDA5WTk5Pq2rWr2r9/f/V+TyHK4w/tIaGUP+tVaQsJCQml7rOsLNZqm6Vts2rVKkv/sqKa3Tp+Hz16VGVlZZleN2/eLHH9GTNmqHr16il/f38VHBysHnvssQoNVSV+56CUUtYoWGuC8+fP07RpU9J3vkeTByZqHUdYi7MzFBSAkxPk51d5d82aNWPWrFmMHTu26tmEqG4WbA9VbgvSNkUl3Tp+/9mMGTOYOXNmsflfffUVubm53H333Vy6dIlZs2Zx4cIFUlNTcXd3r4bENYdcY1ked/XXOoGwltmzfz9wFRSUeaNCefz44494enoyZswYCwUUohpZsD1UuS1I2xQWcPToUbKyskyvF198scT1Bg4cyPDhw2nXrh39+/fnyy+/5Nq1a6xbt66aE9s/KSzLw8FB6wTCGm7dDPDPfxp7Q/75z5JvGqiAkJAQvv/+exwdpWkJO2Ph9lCltiBtU1iIu7s7Hh4eppezs3O5tvPy8qJly5Zm1wSL8pGbd0Tt9McD162bBUq6aUCI2sCW2oMtZRG1Vm5uLqdPn+aJJ57QOordkcJS1E56vfmB65Zb02WMlSdEjWNL7cGWsoha49lnnyUyMpJmzZpx8eJFZsyYgU6nY9SoUVpHszty804ZTDfvpKfLoLlCCCGEnajo8XvkyJHs2bOHX3/9lcaNG3Pfffcxd+5cmjdvXg1paxbpsRRCCCFErbZ27VqtI9QYchWzEEIIIYSwCCkshRBCCCGERUhhKYQQQgghLEIKSyGEEEIIYRFSWAohhBBCCIuQwlIIIYQQQliEFJZCCCGEEMIipLAUQgghhBAWIYWlEEIIIYSwCHnyThkMBgMAly5d0jiJEEIIIcrr1nH71nFcVB8pLMuQmZkJQNeuXTVOIoQQQoiKyszMJDAwUOsYtYqDUkppHcJWFRUVceTIEXx9fXF0rPxVAzk5ObRp04ajR4/i7u5uwYTiFvmNrU9+4+ohv7P1yW9sfVr/xgaDgczMTDp27EidOtKHVp2ksKwG2dnZeHp6kpWVhYeHh9ZxaiT5ja1PfuPqIb+z9clvbH3yG9decvOOEEIIIYSwCCkshRBCCCGERUhhWQ2cnZ2ZMWMGzs7OWkepseQ3tj75jauH/M7WJ7+x9clvXHvJNZZCCCGEEMIipMdSCCGEEEJYhBSWQgghhBDCIqSwFEIIIYQQFiGFpRBCCCGEsAgpLK1o/vz5dOnSBXd3d3x8fIiKiuL48eNax6rRFixYgIODA7GxsVpHqVEuXLjA448/TsOGDXF1dSU0NJRDhw5pHavG0Ov1TJs2jeDgYFxdXWnevDmzZ89G7q2smj179hAZGUlAQAAODg7Ex8ebLVdKMX36dPz9/XF1dSU8PJyTJ09qE9ZOlfUbFxYWMnXqVEJDQ3FzcyMgIIAxY8Zw8eJF7QILq5PC0op2795NTEwM+/fvZ/v27RQWFhIREUFeXp7W0Wqkb7/9lnfeeYd27dppHaVG+e233+jZsyd169blq6++4ujRoyxevJgGDRpoHa3GWLhwIStWrGDZsmUcO3aMhQsX8uqrr/LWW29pHc2u5eXl0b59e5YvX17i8ldffZWlS5eycuVKDhw4gJubG/379+fmzZvVnNR+lfUbX79+neTkZKZNm0ZycjLr16/n+PHjPPTQQxokFdVFhhuqRr/88gs+Pj7s3r2b+++/X+s4NUpubi6dOnXi7bffZs6cOXTo0IElS5ZoHatGeOGFF/j666/Zu3ev1lFqrMGDB+Pr68v7779vmvfII4/g6urKRx99pGGymsPBwYENGzYQFRUFGHsrAwICeOaZZ3j22WcByMrKwtfXl9WrVzNy5EgN09qnP//GJfn222/p2rUrZ8+eJTAwsPrCiWojPZbVKCsrCwBvb2+Nk9Q8MTExDBo0iPDwcK2j1Dj//e9/6dy5M8OHD8fHx4eOHTvy3nvvaR2rRunRowc7d+7kxIkTAHz33Xfs27ePgQMHapys5kpLSyMjI8Ps7wxPT0+6detGUlKShslqtqysLBwcHPDy8tI6irCSOloHqC0MBgOxsbH07NmTtm3bah2nRlm7di3Jycl8++23WkepkX7++WdWrFjBlClTeOmll/j22295+umncXJyIjo6Wut4NcILL7xAdnY2rVq1QqfTodfrmTt3LqNHj9Y6Wo2VkZEBgK+vr9l8X19f0zJhWTdv3mTq1KmMGjUKDw8PreMIK5HCsprExMSQmprKvn37tI5So6SnpzN58mS2b9+Oi4uL1nFqJIPBQOfOnZk3bx4AHTt2JDU1lZUrV0phaSHr1q3j448/Ji4ujpCQEFJSUoiNjSUgIEB+Y1EjFBYWMmLECJRSrFixQus4workVHg1mDRpEps2bSIhIYEmTZpoHadGOXz4MJcvX6ZTp07UqVOHOnXqsHv3bpYuXUqdOnXQ6/VaR7R7/v7+tGnTxmxe69atOXfunEaJap7nnnuOF154gZEjRxIaGsoTTzzBP/7xD+bPn691tBrLz88PgMzMTLP5mZmZpmXCMm4VlWfPnmX79u3SW1nDSWFpRUopJk2axIYNG9i1axfBwcFaR6px+vbtyw8//EBKSorp1blzZ0aPHk1KSgo6nU7riHavZ8+exYbJOnHiBM2aNdMoUc1z/fp1HB3N/zrW6XQYDAaNEtV8wcHB+Pn5sXPnTtO87OxsDhw4QPfu3TVMVrPcKipPnjzJjh07aNiwodaRhJXJqXAriomJIS4ujo0bN+Lu7m66bsfT0xNXV1eN09UM7u7uxa5ZdXNzo2HDhnItq4X84x//oEePHsybN48RI0Zw8OBB3n33Xd59912to9UYkZGRzJ07l8DAQEJCQjhy5Aivv/4648eP1zqaXcvNzeXUqVOm6bS0NFJSUvD29iYwMJDY2FjmzJnDXXfdRXBwMNOmTSMgIKDMu5qFubJ+Y39/f4YNG0ZycjKbNm1Cr9ebjoPe3t44OTlpFVtYkxJWA5T4WrVqldbRarTevXuryZMnax2jRvniiy9U27ZtlbOzs2rVqpV69913tY5Uo2RnZ6vJkyerwMBA5eLiou6880718ssvq/z8fK2j2bWEhIQS/w6Ojo5WSillMBjUtGnTlK+vr3J2dlZ9+/ZVx48f1za0nSnrN05LSyv1OJiQkKB1dGElMo6lEEIIIYSwCLnGUgghhBBCWIQUlkIIIYQQwiKksBRCCCGEEBYhhaUQQgghhLAIKSyFEEIIIYRFSGEphBBCCCEsQgpLIYQQQghhEVJYCiGEEEIIi5DCUgghrKygoIAWLVrwzTffWHS/W7ZsoUOHDvJMcSGEzZDCUghRIWPHjsXBwaHY64/PCxbmVq5cSXBwMD169DDNc3BwID4+vti6Y8eOLfezqgcMGEDdunX5+OOPLZRUCCGqRgpLIUSFDRgwgEuXLpm9goODi61XUFCgQTrbopRi2bJlTJgwwSr7Hzt2LEuXLrXKvoUQoqKksBRCVJizszN+fn5mL51OR1hYGJMmTSI2NpZGjRrRv39/AFJTUxk4cCD169fH19eXJ554gitXrpj2l5eXx5gxY6hfvz7+/v4sXryYsLAwYmNjTeuU1MPn5eXF6tWrTdPp6emMGDECLy8vvL29efjhhzlz5oxp+a3ewEWLFuHv70/Dhg2JiYmhsLDQtE5+fj5Tp06ladOmODs706JFC95//32UUrRo0YJFixaZZUhJSSmzx/bw4cOcPn2aQYMGVfBXhjNnzpTYOxwWFmZaJzIykkOHDnH69OkK718IISxNCkshhEX9+9//xsnJia+//pqVK1dy7do1HnjgATp27MihQ4fYsmULmZmZjBgxwrTNc889x+7du9m4cSPbtm0jMTGR5OTkCn1uYWEh/fv3x93dnb179/L1119Tv359BgwYYNZzmpCQwOnTp0lISODf//43q1evNitOx4wZw5o1a1i6dCnHjh3jnXfeoX79+jg4ODB+/HhWrVpl9rmrVq3i/vvvp0WLFiXm2rt3Ly1btsTd3b1C3wegadOmZr3CR44coWHDhtx///2mdQIDA/H19WXv3r0V3r8QQlicEkKICoiOjlY6nU65ubmZXsOGDVNKKdW7d2/VsWNHs/Vnz56tIiIizOalp6crQB0/flzl5OQoJycntW7dOtPyX3/9Vbm6uqrJkyeb5gFqw4YNZvvx9PRUq1atUkop9Z///EfdfffdymAwmJbn5+crV1dXtXXrVlP2Zs2aqaKiItM6w4cPV48++qhSSqnjx48rQG3fvr3E737hwgWl0+nUgQMHlFJKFRQUqEaNGqnVq1eX+ntNnjxZPfDAA8XmA8rFxcXsd3Rzc1N16tRRDz/8cLH1b9y4obp166YGDx6s9Hq92bKOHTuqmTNnlppBCCGqSx1ty1ohhD3q06cPK1asME27ubmZ3t9zzz1m63733XckJCRQv379Yvs5ffo0N27coKCggG7dupnme3t7c/fdd1co03fffcepU6eK9QzevHnT7DRxSEgIOp3ONO3v788PP/wAGE9r63Q6evfuXeJnBAQEMGjQID744AO6du3KF198QX5+PsOHDy81140bN3BxcSlx2RtvvEF4eLjZvKlTp6LX64utO378eHJycti+fTuOjuYnm1xdXbl+/XqpGYQQorpIYSmEqDA3N7dST/3+scgEyM3NJTIykoULFxZb19/fv9x3kzs4OKCUMpv3x2sjc3Nzueeee0q8Q7px48am93Xr1i2231vD9bi6ut42x8SJE3niiSd44403WLVqFY8++ij16tUrdf1GjRqZCtc/8/PzK/Y7uru7c+3aNbN5c+bMYevWrRw8eLDEU+pXr141+45CCKEVKSyFEFbVqVMnPv/8c4KCgqhTp/hfOc2bN6du3bocOHCAwMBAAH777TdOnDhh1nPYuHFjLl26ZJo+efKkWS9dp06d+OSTT/Dx8cHDw6NSWUNDQzEYDOzevbtYT+ItDz74IG5ubqxYsYItW7awZ8+eMvfZsWNHVqxYgVIKBweHCmf6/PPP+ec//8lXX31F8+bNiy2/1SPbsWPHCu9bCCEsTW7eEUJYVUxMDFevXmXUqFF8++23nD59mq1btzJu3Dj0ej3169dnwoQJPPfcc+zatYvU1FTGjh1b7HTvAw88wLJlyzhy5AiHDh3iySefNOt9HD16NI0aNeLhhx9m7969pKWlkZiYyNNPP8358+fLlTUoKIjo6GjGjx9PfHy8aR/r1q0zraPT6Rg7diwvvvgid911F927dy9zn3369CE3N5cff/yxAr+aUWpqKmPGjGHq1KmEhISQkZFBRkYGV69eNa2zf/9+nJ2db5tDCCGqgxSWQgirCggI4Ouvv0av1xMREUFoaCixsbF4eXmZisfXXnuNXr16ERkZSXh4OPfdd1+xazUXL15M06ZN6dWrF4899hjPPvus2SnoevXqsWfPHgIDAxk6dCitW7dmwoQJ3Lx5s0I9mCtWrGDYsGH83//9H61ateIvf/kLeXl5ZutMmDCBgoICxo0bd9v9NWzYkCFDhlRqEPNDhw5x/fp15syZg7+/v+k1dOhQ0zpr1qxh9OjRZZ6OF0KI6uKg/nzRkhBC2ICwsDA6dOjAkiVLtI5SzN69e+nbty/p6en4+vredv3vv/+efv36cfr06RJvYqqsK1eucPfdd3Po0KESB6gXQojqJj2WQghRTvn5+Zw/f56ZM2cyfPjwchWVAO3atWPhwoWkpaVZNM+ZM2d4++23pagUQtgMuXlHCCHKac2aNUyYMIEOHTrw4YcfVmjbsWPHWjxP586d6dy5s8X3K4QQlSWnwoUQQgghhEXIqXAhhBBCCGERUlgKIYQQQgiLkMJSCCGEEEJYhBSWQgghhBDCIqSwFEIIIYQQFiGFpRBCCCGEsAgpLIUQQgghhEVIYSmEEEIIISzi/wHZLR+u/nh+9wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmif_axis, order_axis = pp.plot_stability(label_poles=True)\n", + "cmif_axis.set_ylabel('CMIF')\n", + "cmif_axis.set_xlabel('Frequency (Hz)')\n", + "order_axis.set_ylabel('Polynomial Order')" + ] + }, + { + "cell_type": "markdown", + "id": "96f1fb64-126c-4b63-9cd3-c2ea7bb2bf84", + "metadata": {}, + "source": [ + "We can then extract a final pole list from those indices. For example we may wish to select poles `(4,0)`, `(4,1)`, and `(4,2)`." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "550a58ed-8f09-4a2c-a4d9-bb7d532ffe4a", + "metadata": {}, + "outputs": [], + "source": [ + "pole_indices = [(4,0), (4,1), (4,2)]\n", + "pole_list = pp.pole_list_from_indices(pole_indices)\n", + "omega_general_stab = pole_list['omega']\n", + "zeta_general_stab = pole_list['zeta']\n", + "lr_stab = pole_list['Lr_complex']" + ] + }, + { + "cell_type": "markdown", + "id": "6e01bd62-660d-4575-9f35-3d5295933814", + "metadata": {}, + "source": [ + "The stabilization diagram actually reveals that for our test case of `order=5`, our poles may not have yet stabilized, and therefore our results may be inaccurate! Let's compare the same quantities extracted from the stabilization diagram with those computed from the system matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "43de5b7e-a171-48b8-a89a-94fcddfe309f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Frequency From System MatricesFrequency From FRF FitsFrequency ErrorDamping From System MatricesDamping From FRF FitsDamping ErrorParticipation From System MatricesParticipation From FRF Fits
027.2527.260.02%2.71%2.71%-0.02%[-0.04320045+0.04405838j -0.09857882+0.09838338j][-0.04320045+0.04405838j -0.09857882+0.09838338j]
154.7854.790.01%2.74%2.74%-0.01%[0.06535574-0.06999465j 0.00185538+0.00185558j][0.06535574-0.06999465j 0.00185538+0.00185558j]
259.9159.920.01%2.66%2.66%-0.01%[-0.06059005+0.05448316j 0.03373321-0.03429903j][-0.06059006+0.05448316j 0.03373322-0.03429902j]
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n", + " 'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n", + " 'Participation From System Matrices','Participation From FRF Fits'],\n", + " dtype=object)\n", + "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n", + " omega_general,omega_general_stab,\n", + " zeta_general, zeta_general_stab,\n", + " psi_general[:2,:].T, lr_stab)):\n", + " df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n", + " df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n", + " df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n", + " df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n", + " df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n", + " df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n", + " # Find the maximum participation degree of freedom from the\n", + " # system matrices\n", + " check_index = np.argmax(np.abs(lm))\n", + " # Compute the angle of that index\n", + " check_angle = np.angle(lm[check_index])\n", + " # Compute the same angle from the fit\n", + " this_angle = np.angle(lf[check_index])\n", + " rotation = check_angle - this_angle\n", + " # Compute the magnitudes of the vectors\n", + " check_magnitude = np.linalg.norm(np.abs(lm))\n", + " this_magnitude = np.linalg.norm(np.abs(lf))\n", + " # Correct rotation and scaling\n", + " lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n", + " df.at[i,'Participation From System Matrices'] = str(lm)\n", + " df.at[i,'Participation From FRF Fits'] = str(lf)\n", + " \n", + "pretty_print_table(df)" + ] + }, + { + "cell_type": "markdown", + "id": "ef16ad2f-08bb-458e-bf22-5143e52820b4", + "metadata": {}, + "source": [ + "We now see that we have extracted nearly identical modes to those that were computed directly from the system matrices, meaning the fit has significantly improved by using a higher-order polynomial.\n", + "\n", + "Note that this section has used the code-based `PolyPy` implementation. However, in SDynPy there is also a `PolyPy_GUI` which can be used to interactively select poles." + ] + }, + { + "attachments": { + "188f4ca3-1e6a-465f-8085-438e9f6aac2b.png": { + "image/png": "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" + }, + "628e4999-feea-4f9c-888c-9e86b6bd0f32.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "31e870ab-8990-4de7-8137-d3c2324bb9ed", + "metadata": {}, + "source": [ + "```python\n", + "ppgui = sdpy.PolyPy_GUI(tfarray)\n", + "```\n", + "![image.png](attachment:188f4ca3-1e6a-465f-8085-438e9f6aac2b.png)\n", + "![image.png](attachment:628e4999-feea-4f9c-888c-9e86b6bd0f32.png)" + ] + }, + { + "cell_type": "markdown", + "id": "278d5451-ab7a-4f7e-8a54-c4f12ace5031", + "metadata": {}, + "source": [ + "## Fitting Mode Shapes to FRF Data\n", + "Once the poles and participation factors are known, we can go ahead and fit the mode shapes of the system. Looking again at the frequency response function equation\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n", + "containing the frequency response function $\\mathbf{H}$, the participation factors $\\mathbf{l}_r$, the frequency lines $\\omega$, and the poles $\\lambda_r$. We wish to solve for the mode shapes $\\mathbf{v}_r$ and the residuals $\\mathbf{R}_u$ and $\\mathbf{R}_l$." + ] + }, + { + "cell_type": "markdown", + "id": "913fad3e-aa05-4d5e-a13c-8c08d3f5cc48", + "metadata": {}, + "source": [ + "We want to pose this problem in an entirely real set of equations $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$.\n", + "\n", + "The mode shapes will generally be complex $\\mathbf{v}_r = \\mathbf{v}_r^R + j \\mathbf{v}_r^I$, so we will want to solve simultaneously for the real and imaginary parts. Similarly, the frequency response function is complex, so we will need to split that up into its real and imaginary parts. Ignoring the residuals for now:\n", + "\n", + "$$\\begin{bmatrix}\\mathbf{H}^R\\\\\\mathbf{H}^I\\end{bmatrix} = \\mathbf{P}\\begin{bmatrix}\\mathbf{v}^R\\\\\\mathbf{v}^I\\end{bmatrix}$$\n", + "\n", + "The goal is to then find the coefficient matrix $\\mathbf{P}$ which allows us to solve for the mode shape coefficients.\n", + "\n", + "$$\\mathbf{P} = \\begin{bmatrix}\\mathbf{P}_{RR} & \\mathbf{P}_{RI}\\\\\\mathbf{P}_{IR} & \\mathbf{P}_{II}\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "id": "25037b11-5fbc-4520-83d5-b2ae1d418a15", + "metadata": {}, + "source": [ + "We will start with the term\n", + "\n", + "$$\\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}$$\n", + "\n", + "We will substitute each term with its real and imaginary parts to ensure that all of the variables in the expression are real.\n", + "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right)}{j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right)}{- j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega}$$\n", + "\n", + "As we wish to collect real and imaginary parts, we will want to multiply each term's numerator and denominator by the denominator's complex conjugate.\n", + "\n", + "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(- j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}$$\n", + "\n", + "We would then like to collect real and imaginary terms, as well as collecting terms that have the real and imaginary part of the mode shapes. $\\mathbf{P}_{RR}$ will contain real coefficients of the real part of the mode shapes. $\\mathbf{P}_{RI}$ will contain real coefficients of the imaginary part of the mode shapes. $\\mathbf{P}_{IR}$ will contain imaginary coefficients of the real part of the mode shapes. Finally, $\\mathbf{P}_{II}$ will contain imaginary coefficients of the imaginary part of the mode shapes.\n", + "\n", + "$$j v^{I}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + v^{I}_{ir} \\left(\\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n", + "$$+ v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + j v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n", + "\n", + "We can substitute this into a matrix form where the real equations are on the top row and the imaginary equations are on the bottom row.\n", + "\n", + "$$\\left[\\begin{matrix}H_r^{R}\\\\j H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\j \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) & j \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n", + "\n", + "Cancelling out the $j$ terms in the bottom row reveals the final matrix equation for the contribution to the frequency response function from one mode shape $H_r$.\n", + "\n", + "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n", + "\n", + "We can then solve this equation in a least squares sense by using the pseudoinverse.\n", + "\n", + "$$\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right]^+ \\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$$" + ] + }, + { + "cell_type": "markdown", + "id": "600deb13-308c-4883-b3d3-66147f5f863e", + "metadata": {}, + "source": [ + "Note that this equation must be generalized to solve for all modal coefficients simultaneously in terms of all frequency response functions for all degrees of freedom at all frequency lines. For this we will give some thought as to how to structure the matrices.\n", + "\n", + "At the end of the computation, the matrix $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ should have real and imaginary parts for each degree of freedom for each mode being solved for. We will solve for these terms over all frequency lines simultaneously. However, because there is no frequency line dimension in the mode shape matrix, this reveals that the frequency line must be on the inner dimension of the matrix product. The frequency lines must correspond to the rows of $\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$ and $\\mathbf{P}$ (which will be the columns of $\\mathbf{P}^+$). We also see that the columns of the FRF matrix corresponding to the references at each frequency line do not appear in the final mode shape matrix (they are related to the participation factors, which are known). Therefore this dimension must also be encoded in the inner dimensions of the matrix product.\n", + "\n", + "We can therefore start to envision what the structure of each term in $\\mathbf{P}$ should look like. The columns of $\\mathbf{P}$ (the rows of $\\mathbf{P}^+$) must correspond to the mode shape matrix, and they should correspond to the mode index rather than the response degree of freedom index because we need matrix multiplication of $\\mathbf{P}\\mathbf{v}$ to sum over modes, and summation occurs on the inner dimensions of the matrices in matrix multiplication. The rows of $\\mathbf{P}$ (the columns of $\\mathbf{P}^+$) must correspond to both the mode participation factor (reference degrees of freedom) and frequency lines, separated into real and imaginary parts. If we compute each partition of $\\mathbf{P}$ as a `num_input` $\\times$ `num_freq` $\\times$ `num_modes` array, and then reshape it into a `num_input*num_freq` $\\times$ `num_modes` array, this should produce the correct size matrix.\n", + "\n", + "Each partition of the mode shape matrix $v^{R}_{ir}$ and $v^{I}_{ir}$ will then be revealed as a matrix with number of rows equal to the number of modes and columns equal to the number of response degrees of freedom.\n", + "\n", + "We can similarly construct the dimensions of the partitions $H_r^{R}$ and $H_r^{I}$, as they must now be consistent with the rest of the terms in the equation. The number of rows of these partitions must match the number of rows of partitions in $\\mathbf{P}$, meaning that the FRF must be similarly structured with mode participation factor (reference degrees of freedom) and frequency lines along its rows and response degrees of freedom along its columns.\n", + "\n", + "This generalization into matrix form can be somewhat abstract to visualize, but should become more obvious when applied to real data arrays." + ] + }, + { + "cell_type": "markdown", + "id": "7c129812-1273-49d1-b18f-5d2851a401f0", + "metadata": {}, + "source": [ + "### Fitting Residuals for Out-of-Band Modes\n", + "We note that the terms $v_{ir}$ are not the only unknowns in the frequency response function formulation. In general, we can never fit all of the modes of a structure, so there will always be some contributions from out-of-band modes in our frequency response functions. As we try to solve for the mode shapes of the structure, these out-of-band modes can contaminate the response. Therefore we also include terms to try to match the contributions from these out-of-band modes, and we call these terms residuals.\n", + "\n", + "We will in general have a lower residual $R_{Lij}$ and an upper residual $R_{Uij}$, and as the expression suggests, there can be one term in the residual for each pair of input and output degrees of freedom. We must therefore solve for these terms simultaneously with the mode shapes. We can think of these terms as \"extra modes\" of the structure, which therefore should imply that the $\\mathbf{P}$ matrix should have columns added corresponding to the coefficients of the residual variables, and the $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ matrix should have rows appended corresponding to the real and imaginary parts of these terms.\n", + "\n", + "Indeed, the coefficients are easily picked directly from the frequency response function.\n", + "\n", + "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n", + "\n", + "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]\n", + "= \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & -\\frac{1}{\\omega^2} & 0 & 1 & 0 \\\\\n", + "\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & 0 & -\\frac{1}{\\omega^2} & 0 & 1\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\\\R^R_{Lij}\\\\R^I_{Lij}\\\\R^R_{Uij}\\\\R^I_{Uij}\\end{matrix}\\right]$$\n", + "\n", + "When generalizing to multiple references, responses, and frequency lines, each residual term $R^R_{Lij}$, $R^I_{Lij}$, $R^R_{Uij}$ and $R^I_{Uij}$ will have a number of rows equal to the number of references and a number of columns equal to the number of responses. Similarly the coefficient terms ($0$, $1$, $-\\frac{1}{\\omega}$) will have a number of rows equal to the number of `num_freq*num_input` and number of columns equal to `num_input`. Because of the structure of the matrices, they are easily constructed using Kronecker products of identity matrices multiplied by either $1$ or $-\\frac{1}{\\omega^2}$." + ] + }, + { + "cell_type": "markdown", + "id": "533195b0-3bd2-4e9a-8314-dcf73de1d52d", + "metadata": {}, + "source": [ + "### A Note on Types of FRFs\n", + "Note that the mode shape derivations described in the previous sections are valid for *receptance* FRFs, meaning FRFs between displacement and force. When transforming to from *receptance* to *mobility* (velocity over force) to *accelerance* (acceleration over force) FRFs, we multiply the FRF expression by $j\\omega$ at each step. This will clearly change which terms are real and which terms are imaginary, as well as the coefficients on each term. As this document is already quite long, these will not be derived, but the same approach as described above can be used to find the coefficient matrices for those cases; simply substitute for real and imaginary parts of each variable, collect imaginary and real coefficients for imaginary and real parts of each term of interest, and place them into a matrix form." + ] + }, + { + "cell_type": "markdown", + "id": "22c07fae-f815-4be9-8a10-04fec78dd6c2", + "metadata": {}, + "source": [ + "### Normalizing Mode Shapes\n", + "Once we solve for the mode shapes, our job is not quite done. Due to how the problem is formulated, the particpation factors have some arbitrary scaling and rotation applied to them. This means when the shapes are found, they will also have an arbitrary scaling and rotation applied to them. There is no constraint that the participation factors are identical to the mode shapes at the input locations.\n", + "\n", + "To perform the normalization, we compute the residue matrices for each mode\n", + "\n", + "$$R_{rij} = \\psi_{ir}l_{jr}$$\n", + "\n", + "We recognize the at the drive point, the residue is equal to the mode shape squared or the participation factor squared if the mode shapes are scaled such that the participation factors are the mode shapes.\n", + "$$R_{rii} = \\psi_{ir}l_{ir} = \\psi_{ir}^2 = l_{ir}^2$$\n", + "\n", + "However, we currently do not have such a scenario. In our case, the fit shapes $\\bar{\\psi}_{ir}$ are scaled by some complex coefficient $c$, and the participation factors $\\bar{l}_{jr}$ are scaled by some factor ${\\frac{1}{c}}$.\n", + "\n", + "$$\\psi_{ir} = c\\bar{\\psi}_{ir}$$\n", + "$$l_{jr} = \\frac{1}{c}\\bar{l}_{jr}$$\n", + "\n", + "We note that due to the reciprocal scale factors, we can still compute the residuals. We can therefore set up a set of equations to solve for the scale factor that gets applied to the mode shapes.\n", + "\n", + "$$R_{rii} = \\psi_{ir}l_{ir} = c\\bar{\\psi}_{ir}\\frac{1}{c}\\bar{l}_{ir} = \\bar{\\psi}_{ir}\\bar{l}_{ir}$$\n", + "$$R_{rii} = \\psi_{ir}^2 = c^2\\bar{\\psi}_{ir}^2$$\n", + "$$\\sqrt{R_{rii}} = c \\bar{\\psi}_{ir}$$\n", + "\n", + "Since we have participation factors and shapes at multiple degrees of freedom for a multiple-input solution, we can solve for the scale factor $c$ in a least-squares sense for each mode.\n", + "\n", + "$$\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = \\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}c$$\n", + "\n", + "$$\\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}^+\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = c$$\n", + "\n", + "**BE AWARE:** when taking the square root of the residue matrix terms, there are two values that can be obtained (e.g. $\\sqrt{4} = \\pm2$). In the case of real numbers, most square root algorithms will only return the positive root. However for complex numbers, it is not so obvious which sign the root will have (positive real part? positive imaginary part?). Flipping a sign on one term in the matrix $\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix}$ will make the least squares solution incorrect, therefore we must make sure the signs are consistent between the mode shapes $\\bar{\\psi}_{ir}$ and the residue terms $\\sqrt{R_{rii}}$.\n", + "\n", + "We can do this by dividing the termes $\\sqrt{R_{rii}}$ by $\\bar{\\psi}_{ir}$ and looking at the direction of the complex vectors. If the vectors are all pointing nominally the same direction, then the least squares problem will solve for that direction successfully. If some are pointing opposite due to the sign of the output of the square root, then the scale factor found by the least squares solution will be incorrect." + ] + }, + { + "cell_type": "markdown", + "id": "13f2483d-e733-4d8d-94e5-b4cfbfcfde11", + "metadata": {}, + "source": [ + "### Back to the Example Problem\n", + "While the symbolic mathematics in the previous sections was straightforward (though perhaps a bit tedious), the discussion of the construction of a system of equations to solve for each coefficient of the mode shape matrix was perhaps a bit abstract. We will therefore make it more concrete by applying it to our specific example problem. Note that we will take advantage heavily of broadcasting and dimensional manipulation to help us put these equations together. Readers are encouraged to run these problems themselves to understand exactly what operations are being performed at each step, and the resulting shapes of all of the arrays.\n", + "\n", + "We will first generate the $\\mathbf{P}$ matrix described above. We will start with only the mode shape coefficients, then show how it can be expanded to include residuals. This will involve translating our symbolic code into numeric Numpy code. To help us repeat this process for different sets of modes and frequency ranges when we introduce the residuals later, we will define the process to generate the $\\mathbf{P}$ matrix as a function." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9cc21035-44bd-4128-ac92-0709ab740be6", + "metadata": {}, + "outputs": [], + "source": [ + "def P_modal(omegas, poles, participation_factors):\n", + " '''Construct the mode shape coefficient matrix\n", + "\n", + " Constructs the coefficients from the system poles,\n", + " participation factors, and frequency lines.\n", + "\n", + " Arguments should be passed as arrays and will be broadcast\n", + " together.\n", + " '''\n", + " # We want the output array to be n_input x n_freq*2 x n_modes*2\n", + " # So let's adjust the terms so they have the right shapes\n", + " # We want frequency lines to be the middle dimension\n", + " omegas = omegas[np.newaxis,:,np.newaxis]\n", + " # We want inputs to be the first dimension and modes the\n", + " # last dimension\n", + " participation_factors = participation_factors.T[:,np.newaxis,:]\n", + " # Split up terms into real and imaginary parts\n", + " pr = poles.real\n", + " pi = poles.imag\n", + " lr = participation_factors.real\n", + " li = participation_factors.imag\n", + " P_blocks = np.array([\n", + " [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n", + " + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n", + " (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n", + " + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n", + " [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n", + " + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n", + " (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n", + " + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n", + " return np.block([[P_blocks[0,0],P_blocks[0,1]],\n", + " [P_blocks[1,0],P_blocks[1,1]]])" + ] + }, + { + "cell_type": "markdown", + "id": "cef05146-3d81-4003-86fe-1500592485de", + "metadata": {}, + "source": [ + "We can now create our pole vector from the extracted frequencies and damping ratios." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "0693d32a-2136-4c72-8f6e-fb67515ce6f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " omegas: (778,)\n", + " poles: (3,)\n", + " participation_factors: (3, 2)\n", + " P: (2, 1556, 6)\n" + ] + } + ], + "source": [ + "# Compute poles from the extracted data\n", + "poles = -omega_general_stab*zeta_general_stab + 1j*omega_general_stab*np.sqrt(1-zeta_general_stab**2)\n", + "# Compute P with our function\n", + "P = P_modal(omegas,poles, lr_stab)\n", + "\n", + "# Check shapes\n", + "print('Shapes:\\n omegas: {:}\\n poles: {:}\\n participation_factors: {:}\\n P: {:}'.format(\n", + " omegas.shape, poles.shape, lr_stab.shape, P.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "68336074-9c72-4186-9c32-76c4597712f8", + "metadata": {}, + "source": [ + "We must now set up the frequency response functions to perform the least squares computation." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "b9a0bf64-820f-4786-b4e6-f78f7f209dad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " H_LS: (2, 1556, 3)\n" + ] + } + ], + "source": [ + "H_LS = H.transpose(1,2,0)\n", + "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n", + "\n", + "print('Shapes:\\n H_LS: {:}'.format(\n", + " H_LS.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "f0af6fc0-5e43-4fe6-838d-d30266bfb2f5", + "metadata": {}, + "source": [ + "To construct the least-squares set of equations, we simply squash the first two dimensions into a single dimension, in which case the least-squares solution will solve over all frequency lines and all inputs simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "0ef24c44-cb79-4317-9aae-5a3b99ec51ba", + "metadata": {}, + "outputs": [], + "source": [ + "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n", + " H_LS.reshape(-1,H_LS.shape[-1]))[0]" + ] + }, + { + "cell_type": "markdown", + "id": "818bdad7-6a69-422e-9d1e-f5d2480acd83", + "metadata": {}, + "source": [ + "We can reconstruct the FRF by muliplying the output shapes by the coefficient matrix and extracting real and imaginary parts." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "570bd80c-83d0-4f5e-a6ca-289637763d7f", + "metadata": {}, + "outputs": [], + "source": [ + "reconstructed_frfs = P.reshape(-1,P.shape[-1])@shapes_LS\n", + "H_recon = reconstructed_frfs.reshape(*H_LS.shape).transpose(1,2,0)\n", + "H_recon = H_recon[:H_recon.shape[0]//2] + 1j*H_recon[H_recon.shape[0]//2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "a6e4c568-2976-4d5b-a4cb-76547020ee30", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_modes = omega_general_stab.size\n", + "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n", + "# Compute the laplace variables\n", + "s=1j*omegas_bc\n", + "\n", + "# Now plot them\n", + "fig,axes = plt.subplots(num_output*2,num_input, sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n", + " H_recon.reshape(omegas.size,-1).T,\n", + " ):\n", + " ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n", + " ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n", + "ax[1].legend(['Truth','Reconstructed from\\nLeast-Squares Solution'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "669682c7-558a-4ab6-995c-c24c8dccdea1", + "metadata": {}, + "source": [ + "We have now solved in a least-squares sense for our modal coefficients. We should extract the shape arrays from the matrix. The top partition will be the real part of the shape, and the bottom part will be the imaginary part of the shape. Remember also that the terms are transposed from the typical mode shape matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "aa810667-ba40-42ca-8b2b-fab6ecfcbf3f", + "metadata": {}, + "outputs": [], + "source": [ + "psi_stab = (shapes_LS[:shapes_LS.shape[0]//2]+1j*shapes_LS[shapes_LS.shape[0]//2:]).T" + ] + }, + { + "cell_type": "markdown", + "id": "39a5a890-5f7e-478a-b0b2-985454864c4a", + "metadata": {}, + "source": [ + "Note that because the participation factors were arbitrarily rotated, these shapes will also be arbitrarily rotated. Therefore we need to normalize the mode shapes. We will do this by computing the residue matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "09077e1b-1ba5-4527-8898-cf6539d8c7b8", + "metadata": {}, + "outputs": [], + "source": [ + "residues = np.einsum('ik,jk->kij',psi_stab,lr_stab.T)" + ] + }, + { + "cell_type": "markdown", + "id": "2a8143f5-66d3-41b2-a024-931218b1a1ba", + "metadata": {}, + "source": [ + "We can get the drive point response indices corresponding to the participation factors and pull out the drive point residues. In general, the drive point residues should have a negative imaginary part, so we can use this to discard 'bad' drive point data where the drive point does not excite the mode well." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "d6a3f830-7704-46ef-a7a0-a6388757dfd7", + "metadata": {}, + "outputs": [], + "source": [ + "drive_indices_response = np.array([0,1])\n", + "drive_indices_input = np.array([0,1])\n", + "drive_residues = residues[:,drive_indices_response,drive_indices_input]" + ] + }, + { + "cell_type": "markdown", + "id": "56cd126e-1a06-4234-99ae-8929d52116a7", + "metadata": {}, + "source": [ + "We will now go through each mode and compute the correct shape scaling using only the good drive points where the imaginary part is negative and not close to zero. First, let's go through and make sure that our participation factors and mode shapes that we solved for are actually scaled versions of the true mode shapes. I'm going to mass normalize my mode shapes for better comparisions to the fit data without having to carry around the modal mass term." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "883ccaa0-1f86-41ae-947e-2d0796702b0a", + "metadata": {}, + "outputs": [], + "source": [ + "# Mass normalize the mode shapes\n", + "psi_general /= np.sqrt(ma_general)" + ] + }, + { + "cell_type": "markdown", + "id": "aed6d5e9-2bb7-43a1-957b-b76d1bda87d6", + "metadata": {}, + "source": [ + "Let's find the scale factor between the participation factors and the mode shapes at the participation factors. We will average over all of the degrees of freedom for each mode." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "bcf8a804-ecc4-45d6-8f55-e1e0874bfde2", + "metadata": {}, + "outputs": [], + "source": [ + "lr_scaling = np.mean(psi_general[:2,:]/lr_stab.T,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "a6b122a4-87ab-476a-8d7a-510fb951cd0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.18452662-0.18416078j, 0.08928666-0.0956242j ,\n", + " -0.093857 +0.08439714j])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr_scaling" + ] + }, + { + "cell_type": "markdown", + "id": "83c5bce4-a5cb-4e7d-9214-83b2800f6bf2", + "metadata": {}, + "source": [ + "We can then check to see if we reconstruct the mode shape by applying the scaling to the participation factors." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "aa40a7de-d7e4-47e3-985a-f4f24fe5e3ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.02160023+0.02202919j, 0.03267784-0.03499731j,\n", + " -0.03029502+0.02724158j],\n", + " [-0.04928941+0.04919169j, 0.00092769+0.00092779j,\n", + " 0.01686661-0.01714951j]])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr_stab.T*lr_scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "e887ee89-7ad0-4fae-bee5-be261d25caa6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.02160023+0.02202919j, 0.03267787-0.03499732j,\n", + " -0.03029503+0.02724158j],\n", + " [-0.04928941+0.04919169j, 0.00092769+0.00092779j,\n", + " 0.0168666 -0.01714951j]])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_general[:2,:]" + ] + }, + { + "cell_type": "markdown", + "id": "2245282f-b4cc-4448-8e05-72d35a076f38", + "metadata": {}, + "source": [ + "Now we will do the same thing for the mode shapes." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5458c528-b3dd-449f-87b7-01181b91c076", + "metadata": {}, + "outputs": [], + "source": [ + "psi_scaling = np.mean(psi_general/psi_stab,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "349fc0a1-850a-41f4-aab0-8c1198a09e69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.70380647+2.72025259j, 5.15973139+5.66778918j,\n", + " -5.88158134-5.30751768j])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_scaling" + ] + }, + { + "cell_type": "markdown", + "id": "805218db-fbff-419b-a61f-248fad8c7c63", + "metadata": {}, + "source": [ + "Let's reconstruct the mode shapes by scaling the fit mode shapes." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "ec397820-33e5-4bd5-b968-8d6ea7d63cc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.02159971+0.02202864j, 0.03316166-0.03473379j,\n", + " -0.03033178+0.02722321j],\n", + " [-0.04928856+0.04919178j, 0.00094139+0.00090236j,\n", + " 0.0168663 -0.01716533j],\n", + " [-0.02186329+0.02176496j, -0.03539605+0.03236945j,\n", + " -0.02735936+0.03018341j]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_stab*psi_scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "3e49f237-ee6c-48e2-8854-e52abd728337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.02160023+0.02202919j, 0.03267787-0.03499732j,\n", + " -0.03029503+0.02724158j],\n", + " [-0.04928941+0.04919169j, 0.00092769+0.00092779j,\n", + " 0.0168666 -0.01714951j],\n", + " [-0.02186239+0.02176446j, -0.03496025+0.03263839j,\n", + " -0.02739619+0.03019566j]])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_general" + ] + }, + { + "cell_type": "markdown", + "id": "e0854c79-1cc4-492b-bf1c-c43526067a1a", + "metadata": {}, + "source": [ + "Since the particpation factors times the mode shapes should be equal to the scaled mode shapes squared, we should be able to see that the scale factors are reciprocals of one another." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "7b80d0c2-c92a-479c-97a9-823c368e0cad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.70380647+2.72025259j, 5.15973139+5.66778918j,\n", + " -5.88158134-5.30751768j])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psi_scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "87376ad2-6f5c-4c78-8b9b-f92a5191f2a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.71501354+2.70963081j, 5.2165319 +5.58679996j,\n", + " -5.89109483-5.29733029j])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1/lr_scaling" + ] + }, + { + "cell_type": "markdown", + "id": "050511f9-7ba5-4c09-a5e9-1f92d713f291", + "metadata": {}, + "source": [ + "This was a sanity check to make sure the mode shapes and participation factors were identified consistently. Unfortunately in a real situation we don't have the true mode shapes, so we need to compute them from the residue matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "5676966d-8021-4f4b-8da1-9fdff6a48330", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scale for mode 1: (-2.709418738512416-2.7149722671668157j)\n", + "Scale for mode 2: (5.211812790651124+5.590252990269536j)\n", + "Scale for mode 3: (5.886705065831037+5.299694143958719j)\n" + ] + } + ], + "source": [ + "psi_stab_normalized = []\n", + "for k,drive_residue in enumerate(drive_residues):\n", + " # Throw away non-negative imaginary parts\n", + " bad_indices_positive = drive_residue.imag > 0\n", + " # Throw away small values compared to the average value (only considering negative imaginary parts)\n", + " bad_indices_small = np.abs(drive_residue) < 0.01*np.mean(np.abs(drive_residue[~bad_indices_positive]))\n", + " # Combine into a single criteria\n", + " bad_indices = bad_indices_positive | bad_indices_small\n", + " # Get the good indices that are remaining\n", + " remaining_indices = np.where(~bad_indices)[0]\n", + " # We will then construct the least squares solution\n", + " shape_coefficients = psi_stab[drive_indices_response[remaining_indices],k][:,np.newaxis]\n", + " residue_coefficients = np.sqrt(drive_residue[remaining_indices])[:,np.newaxis]\n", + " # Before we compute the scale, we need to make sure that we have all of the signs the same way.\n", + " # This is because the square root can give you +/- root where root**2 = complex number\n", + " # This will mess up the least squares at it will try to find something between the\n", + " # two vectors.\n", + " scale_vector = (residue_coefficients/shape_coefficients).flatten()\n", + " sign_vector = np.array((scale_vector.real,scale_vector.imag))\n", + " # Get the signs\n", + " signs = np.sign(np.dot(sign_vector[:,0],sign_vector))\n", + " residue_coefficients = residue_coefficients*signs[:,np.newaxis]\n", + " # Now compute the least-squares solution\n", + " scale = np.linalg.lstsq(shape_coefficients,residue_coefficients)[0].squeeze()\n", + " print('Scale for mode {:}: {:}'.format(k+1,scale))\n", + " psi_stab_normalized.append(psi_stab[:,k]*scale)\n", + "psi_stab_normalized = np.array(psi_stab_normalized).T" + ] + }, + { + "cell_type": "markdown", + "id": "ab437cbd-420d-4421-8cff-040a1f1bf4cb", + "metadata": {}, + "source": [ + "Now that we have our mode shapes, we should resynthesize frequency response functions from them to make sure they fit our original data." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "576a0ff3-1f51-4d50-85a6-a9b9985a4a5f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_modes = omega_general_stab.size\n", + "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n", + "# Compute the laplace variables\n", + "s=1j*omegas_bc\n", + "\n", + "H_general_fromUnNormModes = np.sum([\n", + " psi_stab[:,r,np.newaxis]@lr_stab.T[:,r,np.newaxis].T/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " + psi_stab[:,r,np.newaxis].conj()@lr_stab.T[:,r,np.newaxis].T.conj()/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "H_general_fromFitModes = np.sum([\n", + " psi_stab_normalized[:,r,np.newaxis]@psi_stab_normalized[drive_indices_input,r,np.newaxis].T/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " + psi_stab_normalized[:,r,np.newaxis].conj()@psi_stab_normalized[drive_indices_input,r,np.newaxis].T.conj()/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "H_general_fromFitResidues = np.sum([\n", + " residues[r]/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " + residues[r].conj()/\n", + " ((s-\n", + " (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n", + " for r in range(num_modes)],axis=0)\n", + "\n", + "# Now plot them\n", + "fig,axes = plt.subplots(H_general_fromFitResidues.shape[1]*2,H_general_fromFitResidues.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_t,f_m,f_r,f_u in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_general[frequency_lines_to_keep,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n", + " H_general_fromFitModes[:,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n", + " H_general_fromFitResidues.reshape(omegas.size,-1).T,\n", + " H_general_fromUnNormModes.reshape(omegas.size,-1).T\n", + " ):\n", + " ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n", + " ax[0].plot(omegas,np.angle(f_u)*180/np.pi,linewidth=2)\n", + " ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n", + " ax[0].plot(omegas,np.angle(f_r)*180/np.pi,linewidth=1)\n", + " ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_u),linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n", + " ax[1].plot(omegas,np.abs(f_r),linewidth=1)\n", + "ax[1].legend(['Truth','Unnormalized','Normalized','Residues'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "c801ba95-827a-4b4d-9bdd-d1205b84c327", + "metadata": {}, + "source": [ + "The last demonstration will be to understand how residuals can be implemented. To do this, we will truncate the frequency domain to remove the first mode. We will then use residuals to try to fit the effect of this mode while only truly fitting the second two modes. We will assume we have correctly identified the poles and participation factors from the first analysis, and only consider solving the shape problem here." + ] + }, + { + "cell_type": "markdown", + "id": "036eaad3-1f6a-4cb8-a107-5ddc8a079dea", + "metadata": {}, + "source": [ + "We will update our function to solve for the $\\mathbf{P}$ matrix to also solve for residual terms." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "fd95b767-a897-4fa4-b46f-23fb79831fa8", + "metadata": {}, + "outputs": [], + "source": [ + "def P_modal_w_residuals(omegas, poles, participation_factors, lower_residuals = False, upper_residuals = False):\n", + " '''Construct the mode shape coefficient matrix\n", + "\n", + " Constructs the coefficients from the system poles,\n", + " participation factors, and frequency lines.\n", + "\n", + " Arguments should be passed as arrays and will be broadcast\n", + " together.\n", + " '''\n", + " # Get the number of modes and number of inputs from the shape\n", + " # of the participation factors.\n", + " n_modes,n_inputs = participation_factors.shape\n", + " n_freq, = omegas.shape\n", + " # We want the output array to be n_input x n_freq*2 x n_modes*2\n", + " # So let's adjust the terms so they have the right shapes\n", + " # We want frequency lines to be the middle dimension\n", + " omegas = omegas[np.newaxis,:,np.newaxis]\n", + " # We want inputs to be the first dimension and modes the\n", + " # last dimension\n", + " participation_factors = participation_factors.T[:,np.newaxis,:]\n", + " # Split up terms into real and imaginary parts\n", + " pr = poles.real\n", + " pi = poles.imag\n", + " lr = participation_factors.real\n", + " li = participation_factors.imag\n", + " P_blocks = np.array([\n", + " [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n", + " + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n", + " (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n", + " + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n", + " [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n", + " + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n", + " (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n", + " + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n", + " if lower_residuals:\n", + " RL_block = np.concatenate([\n", + " np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n", + " np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n", + " if upper_residuals: \n", + " RU_block = np.concatenate([\n", + " np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n", + " np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n", + " P = np.block([[P_blocks[0,0],P_blocks[0,1]],\n", + " [P_blocks[1,0],P_blocks[1,1]]])\n", + " if lower_residuals:\n", + " P = np.concatenate((P,RL_block),axis=-1)\n", + " if upper_residuals:\n", + " P = np.concatenate((P,RU_block),axis=-1)\n", + " return P" + ] + }, + { + "cell_type": "markdown", + "id": "43af6e4a-d977-4586-af15-07d6fe168447", + "metadata": {}, + "source": [ + "We can then call the function with a subset of the modes that we aim to solve for." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "917fedde-e85a-4b05-8c3e-f253ab9dc214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shapes:\n", + " P: (2, 778, 12)\n", + " H_LS: (2, 778, 3)\n" + ] + } + ], + "source": [ + "min_freq = 45\n", + "mode_indices = slice(1,None)\n", + "P = P_modal_w_residuals(omegas[omegas > min_freq],\n", + " poles[mode_indices],\n", + " lr_stab[mode_indices],\n", + " lower_residuals = True, upper_residuals = True)\n", + "\n", + "H_LS = H[...,omegas>min_freq].transpose(1,2,0)\n", + "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n", + "\n", + "print('Shapes:\\n P: {:}\\n H_LS: {:}'.format(P.shape,H_LS.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "6151d42f-0920-4794-adb0-e66fd8d34f8d", + "metadata": {}, + "source": [ + "We can then solve the least squares problem over all frequency lines and inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "c6ee7e46-ed84-434a-aa91-5b438cc0e8d0", + "metadata": {}, + "outputs": [], + "source": [ + "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n", + " H_LS.reshape(-1,H_LS.shape[-1]))[0]" + ] + }, + { + "cell_type": "markdown", + "id": "08ee6d5a-4f87-464b-a35b-53c94ab7282f", + "metadata": {}, + "source": [ + "Now at this point, we can reconstruct the portions of the FRFs from the modes and those from the residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "6aaeb549-5e36-4667-87e2-8f32afbfe4ef", + "metadata": {}, + "outputs": [], + "source": [ + "P_full = P.reshape(-1,P.shape[-1])\n", + "P_modes = P_full[...,:2*poles[mode_indices].size]\n", + "P_residuals = P_full[...,2*poles[mode_indices].size:]\n", + "\n", + "shapes_modes = shapes_LS[:2*poles[mode_indices].size]\n", + "shapes_residuals = shapes_LS[2*poles[mode_indices].size:]\n", + "\n", + "H_modes = (P_modes@shapes_modes).reshape(*H_LS.shape).transpose(1,2,0)\n", + "H_modes = H_modes[:H_modes.shape[0]//2] + 1j*H_modes[H_modes.shape[0]//2:]\n", + "\n", + "H_residuals = (P_residuals@shapes_residuals).reshape(*H_LS.shape).transpose(1,2,0)\n", + "H_residuals = H_residuals[:H_residuals.shape[0]//2] + 1j*H_residuals[H_residuals.shape[0]//2:]\n", + "\n", + "H_full = (P_full@shapes_LS).reshape(*H_LS.shape).transpose(1,2,0)\n", + "H_full = H_full[:H_full.shape[0]//2] + 1j*H_full[H_full.shape[0]//2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "bba9570d-2dcf-43bf-9c9f-b0ddb08213b1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now plot them\n", + "fig,axes = plt.subplots(H_full.shape[1]*2,H_full.shape[2], sharex=True,sharey='row',figsize=(10,10))\n", + "for ax,f_t in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n", + " ):\n", + " ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n", + " ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n", + "for ax,f_r,f_m,f_f in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n", + " H_residuals.reshape(omegas[omegas>min_freq].size,-1).T,\n", + " H_modes.reshape(omegas[omegas>min_freq].size,-1).T,\n", + " H_full.reshape(omegas[omegas>min_freq].size,-1).T\n", + " ):\n", + " ax[0].plot(omegas[omegas>min_freq],np.angle(f_f)*180/np.pi,linewidth=2)\n", + " ax[0].plot(omegas[omegas>min_freq],np.angle(f_r)*180/np.pi,linewidth=1)\n", + " ax[0].plot(omegas[omegas>min_freq],np.angle(f_m)*180/np.pi,linewidth=1)\n", + " ax[1].plot(omegas[omegas>min_freq],np.abs(f_f),linewidth=2)\n", + " ax[1].plot(omegas[omegas>min_freq],np.abs(f_r),linewidth=1)\n", + " ax[1].plot(omegas[omegas>min_freq],np.abs(f_m),linewidth=1)\n", + "ax[1].legend(['Truth','Fit','Residual Contribution','Mode Contribution'])\n", + "for i,ax in enumerate(axes[:,0]):\n", + " if i % 2 == 0:\n", + " ax.set_ylim([-180,180])\n", + " ax.set_yticks([-180,-90,0,90,180])\n", + " ax.set_ylabel('Phase (deg)')\n", + " else:\n", + " ax.set_ylim([1e-5,1e-2])\n", + " ax.set_ylabel('Magnitude (deg)')\n", + " ax.set_yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "3c6f0d0b-7662-4165-b20b-9e2d1a111635", + "metadata": {}, + "source": [ + "We see that the fit matches the measured data very due to the inclusion of the residual terms. If we were to just include the modal contributions, it would not fit as well, but by adding in residual contributions we can get a much better match." + ] + }, + { + "cell_type": "markdown", + "id": "739c7f46-ba2e-4b8a-be4a-01b7fa034e55", + "metadata": {}, + "source": [ + "## Summary\n", + "In this document, we have extensively studied complex modes. We first explored the real-modes case to understand the limitations of that approach. We then derived the state space system of equations from the typical 2nd order differential equation to construct the eigenvalue equation to solve for the complex modes.\n", + "\n", + "We then related the modal parameters to the frequency response function, which allowed us to compute a frequency response function given the modal parameters.\n", + "\n", + "We finally spent a good deal of time discussing how to fit modal parameters to frequency response functions using the PolyMax approach, which is implemented in SDynPy as PolyPy." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png new file mode 100644 index 0000000..258a434 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_111_1.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_111_1.png new file mode 100644 index 0000000..83baf86 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_111_1.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_135_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_135_0.png new file mode 100644 index 0000000..56909b4 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_135_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_162_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_162_0.png new file mode 100644 index 0000000..da65ab0 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_162_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_172_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_172_0.png new file mode 100644 index 0000000..ec1bcb6 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_172_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png new file mode 100644 index 0000000..f7a34c8 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_50_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_50_0.png new file mode 100644 index 0000000..e18805f Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_50_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png new file mode 100644 index 0000000..7e3bd31 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png new file mode 100644 index 0000000..7e3bd31 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_56_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_56_0.png new file mode 100644 index 0000000..07b7684 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_56_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png new file mode 100644 index 0000000..dfeae3f Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png differ diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_8_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_8_0.png new file mode 100644 index 0000000..de1ed26 Binary files /dev/null and b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_8_0.png differ diff --git a/_autosummary/sdynpy.core.html b/_autosummary/sdynpy.core.html index 6ccb11a..071273a 100644 --- a/_autosummary/sdynpy.core.html +++ b/_autosummary/sdynpy.core.html @@ -4,7 +4,7 @@ - sdynpy.core — SDynPy 0.11.0 documentation + sdynpy.core — SDynPy 0.14.0 documentation @@ -172,7 +172,7 @@
- + Sandia National Laboratories
diff --git a/_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html b/_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html index 529409c..3550104 100644 --- a/_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html +++ b/_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_array.SdynpyArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_array.SdynpyArray — SDynPy 0.14.0 documentation @@ -278,7 +278,7 @@

sdynpy.core.sdynpy_array.SdynpyArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_array.html b/_autosummary/sdynpy.core.sdynpy_array.html index 7604b37..a85dea8 100644 --- a/_autosummary/sdynpy.core.sdynpy_array.html +++ b/_autosummary/sdynpy.core.sdynpy_array.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_array — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_array — SDynPy 0.14.0 documentation @@ -163,7 +163,7 @@
- + Sandia National Laboratories
diff --git a/_autosummary/sdynpy.core.sdynpy_colors.html b/_autosummary/sdynpy.core.sdynpy_colors.html index 42409ec..33bc81c 100644 --- a/_autosummary/sdynpy.core.sdynpy_colors.html +++ b/_autosummary/sdynpy.core.sdynpy_colors.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_colors — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_colors — SDynPy 0.14.0 documentation @@ -140,7 +140,7 @@
- + Sandia National Laboratories
diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html b/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html index 70a2372..68c6367 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.CoordinateArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.CoordinateArray — SDynPy 0.14.0 documentation @@ -79,6 +79,7 @@
  • CoordinateArray.from_matlab_cellstr()
  • CoordinateArray.from_nodelist()
  • CoordinateArray.local_direction()
    • +
  • CoordinateArray.offset_node_ids()
  • CoordinateArray.sign()
  • CoordinateArray.string_array()
    • @@ -170,10 +171,13 @@

    sdynpy.core.sdynpy_coordinate.CoordinateArray

    local_direction()

    Returns a local direction array

    -

    sign()

    +

    offset_node_ids(offset_value)

    +

    Returns a copy of the CoordinateArray with the node IDs offset

    + +

    sign()

    Returns the sign on the directions of the CoordinateArray

    -

    string_array()

    +

    string_array()

    Returns a string array representation of the coordinate array

    @@ -281,6 +285,20 @@

    sdynpy.core.sdynpy_coordinate.CoordinateArray +
    +offset_node_ids(offset_value)[source]
    +

    Returns a copy of the CoordinateArray with the node IDs offset

    +
    +
    Parameters
    +

    offset_value (int) – The value to offset the node IDs by.

    +
    +
    Return type
    +

    CoordinateArray

    +
    +
    +
    +
    sign()[source]
    @@ -316,7 +334,7 @@

    sdynpy.core.sdynpy_coordinate.CoordinateArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.coordinate_array.html b/_autosummary/sdynpy.core.sdynpy_coordinate.coordinate_array.html index e08bbeb..7381118 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.coordinate_array.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.coordinate_array.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.coordinate_array — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.coordinate_array — SDynPy 0.14.0 documentation @@ -174,7 +174,7 @@

    sdynpy.core.sdynpy_coordinate.coordinate_array - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.create_coordinate_string.html b/_autosummary/sdynpy.core.sdynpy_coordinate.create_coordinate_string.html index 03bfa93..9de9adb 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.create_coordinate_string.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.create_coordinate_string.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.create_coordinate_string — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.create_coordinate_string — SDynPy 0.14.0 documentation @@ -160,7 +160,7 @@

    sdynpy.core.sdynpy_coordinate.create_coordinate_string
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.from_matlab_cellstr.html b/_autosummary/sdynpy.core.sdynpy_coordinate.from_matlab_cellstr.html index 85347d0..42c590c 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.from_matlab_cellstr.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.from_matlab_cellstr.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.from_matlab_cellstr — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.from_matlab_cellstr — SDynPy 0.14.0 documentation @@ -158,7 +158,7 @@

    sdynpy.core.sdynpy_coordinate.from_matlab_cellstr
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.from_nodelist.html b/_autosummary/sdynpy.core.sdynpy_coordinate.from_nodelist.html index a6e1ef2..ff497de 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.from_nodelist.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.from_nodelist.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.from_nodelist — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.from_nodelist — SDynPy 0.14.0 documentation @@ -167,7 +167,7 @@

    sdynpy.core.sdynpy_coordinate.from_nodelist - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.html b/_autosummary/sdynpy.core.sdynpy_coordinate.html index ce2090f..d6371e7 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate — SDynPy 0.14.0 documentation @@ -194,7 +194,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.outer_product.html b/_autosummary/sdynpy.core.sdynpy_coordinate.outer_product.html index cf49e42..914dc5f 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.outer_product.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.outer_product.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.outer_product — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.outer_product — SDynPy 0.14.0 documentation @@ -157,7 +157,7 @@

    sdynpy.core.sdynpy_coordinate.outer_product - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.parse_coordinate_string.html b/_autosummary/sdynpy.core.sdynpy_coordinate.parse_coordinate_string.html index 1345e47..65b6e16 100644 --- a/_autosummary/sdynpy.core.sdynpy_coordinate.parse_coordinate_string.html +++ b/_autosummary/sdynpy.core.sdynpy_coordinate.parse_coordinate_string.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_coordinate.parse_coordinate_string — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_coordinate.parse_coordinate_string — SDynPy 0.14.0 documentation @@ -158,7 +158,7 @@

    sdynpy.core.sdynpy_coordinate.parse_coordinate_string
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html b/_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html index fe4b0da..680344c 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html +++ b/_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.AbscissaIndexExtractor — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.AbscissaIndexExtractor — SDynPy 0.14.0 documentation @@ -190,7 +190,7 @@

    sdynpy.core.sdynpy_data.AbscissaIndexExtractor - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html b/_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html index 2fb2f21..3320bef 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html +++ b/_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.AbscissaValueExtractor — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.AbscissaValueExtractor — SDynPy 0.14.0 documentation @@ -190,7 +190,7 @@

    sdynpy.core.sdynpy_data.AbscissaValueExtractor - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html b/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html index 60e9a56..c2c334e 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html +++ b/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.CPSDPlot — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.CPSDPlot — SDynPy 0.14.0 documentation @@ -473,7 +473,7 @@

    sdynpy.core.sdynpy_data.CPSDPlot - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html b/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html index 0156f7f..d123623 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.CoherenceArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.CoherenceArray — SDynPy 0.14.0 documentation @@ -79,6 +79,7 @@
  • sdynpy.core.sdynpy_data.CoherenceArray
    • CoherenceArray
      • @@ -172,6 +173,9 @@

      sdynpy.core.sdynpy_data.CoherenceArray +

      from_time_data(response_data[, ...])

      +

      Computes coherence from reference and response time histories

      +

      Attributes

      @@ -186,6 +190,40 @@

      sdynpy.core.sdynpy_data.CoherenceArray +
      +static from_time_data(response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, window=array([1.]), reference_data: Optional[TimeHistoryArray] = None)[source]
      +

      Computes coherence from reference and response time histories

      +
      +
      Parameters
      +
        +

      • response_data (TimeHistoryArray) – Time data to be used as responses

        • +

      • samples_per_average (int, optional) – Number of samples used to split up the signals into averages. The +default is None, meaning the data is treated as a single measurement +frame.

        • +

      • overlap (float, optional) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap). +The default is 0.0, meaning no overlap is used.

        • +

      • window (np.ndarray or str, optional) – A 1D ndarray with length samples_per_average that specifies the +coefficients of the window. A Hann window is applied if not specified. +If a string is specified, then the window will be obtained from scipy.

        • +

      • reference_data (TimeHistoryArray) – Time data to be used as reference. If not specified, the response +data will be used as references, resulting in a square coherence matrix.

        • +
      +
      +
      Raises
      +

      ValueError – Raised if reference and response functions do not have consistent + abscissa

      +
      +
      Returns
      +

      A PSD array computed from the specified reference and +response signals.

      +
      +
      Return type
      +

      PowerSpectralDensityArray

      +
      +
      +

    • +
    property function_type
    @@ -207,7 +245,7 @@

    sdynpy.core.sdynpy_data.CoherenceArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html b/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html index 0c79ee1..3babaf9 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html +++ b/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.ComplexType — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.ComplexType — SDynPy 0.14.0 documentation @@ -242,7 +242,7 @@

    sdynpy.core.sdynpy_data.ComplexType - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html b/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html index fc03168..ee93c47 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.CorrelationArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.CorrelationArray — SDynPy 0.14.0 documentation @@ -207,7 +207,7 @@

    sdynpy.core.sdynpy_data.CorrelationArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html b/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html index 296439d..c84c844 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html +++ b/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.DecayedSineTable — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.DecayedSineTable — SDynPy 0.14.0 documentation @@ -224,7 +224,7 @@

    sdynpy.core.sdynpy_data.DecayedSineTable - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html b/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html index 911db2c..c70fe53 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html +++ b/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.FunctionTypes — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.FunctionTypes — SDynPy 0.14.0 documentation @@ -458,7 +458,7 @@

    sdynpy.core.sdynpy_data.FunctionTypes - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html b/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html index f1e86c3..c155559 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html +++ b/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.GUIPlot — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.GUIPlot — SDynPy 0.14.0 documentation @@ -181,9 +181,6 @@

    sdynpy.core.sdynpy_data.GUIPlot`.

    -

    Note that all datasets will be reshaped so that the coordinates match -the first dataset. If not all coordinates exist in subsequent datasets -an error will be thrown.

    Parameters
      @@ -191,6 +188,32 @@

      sdynpy.core.sdynpy_data.GUIPlotNDDataArray) – Data to visualize. Data passed by keyword argument will be labeled with its keyword

      +

    • abscissa_markers (np.ndarray) – Abscissa values at which markers will be placed. If not specified, +no markers will be added. Markers will be added to all plotted +curves if this argument is passed. To add markers to just a +specific plotted data, pass the argument abscissa_markers_* where +* is replaced with either the index of the data that was passed +via a positional argument, or the keyword of the data that was +passed via a keyword argument. Must be passed as a keyword argument.

      • +

    • abscissa_marker_labels (iterable) – Labels that will be applied to the markers. If not specified, no +label will be applied. If a single string is passed, it will be +passed to the .format method with keyword arguments index and +abscissa. This marker label will be used for all plotted +curves if this argument is passed. To add markers to just a +specific plotted data, pass the argument abscissa_marker_labels_* where +* is replaced with either the index of the data that was passed +via a positional argument, or the keyword of the data that was +passed via a keyword argument. Must be passed as a keyword argument.

      • +

    • abscissa_marker_type (str:) – The type of marker that will be applied. Can be ‘vline’ for a +vertical line across the axis, or it can be a pyqtgraph symbol specifier +(e.g. ‘x’, ‘o’, ‘star’, etc.) which will be placed on the plotted curves. +If not specified, a vertical line will be used. +This marker type will be used for all plotted +curves if this argument is passed. To add markers to just a +specific plotted data, pass the argument abscissa_marker_type_* where +* is replaced with either the index of the data that was passed +via a positional argument, or the keyword of the data that was +passed via a keyword argument. Must be passed as a keyword argument.

    Return type
    @@ -379,7 +402,7 @@

    sdynpy.core.sdynpy_data.GUIPlot - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html index 41fbc0a..8281b0a 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray — SDynPy 0.14.0 documentation @@ -336,7 +336,7 @@

    Paramters - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html b/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html index 5651d4f..614f123 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html +++ b/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.MPLCanvas — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.MPLCanvas — SDynPy 0.14.0 documentation @@ -199,7 +199,7 @@

    sdynpy.core.sdynpy_data.MPLCanvas - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html b/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html index a86eea6..7777285 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html +++ b/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.MPLMultiCanvas — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.MPLMultiCanvas — SDynPy 0.14.0 documentation @@ -199,7 +199,7 @@

    sdynpy.core.sdynpy_data.MPLMultiCanvas - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html index 794de20..80953ad 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray — SDynPy 0.14.0 documentation @@ -207,7 +207,7 @@

    sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html b/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html index 8aaf794..f1cbc53 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.MultipleCoherenceArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.MultipleCoherenceArray — SDynPy 0.14.0 documentation @@ -89,6 +89,7 @@
  • sdynpy.core.sdynpy_data.MultipleCoherenceArray
    • MultipleCoherenceArray
      • @@ -172,6 +173,9 @@

      sdynpy.core.sdynpy_data.MultipleCoherenceArray +

      from_time_data(response_data[, ...])

      +

      Computes coherence from reference and response time histories

      +

      Attributes

      @@ -186,6 +190,40 @@

      sdynpy.core.sdynpy_data.MultipleCoherenceArray +
      +static from_time_data(response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, window=array([1.]), reference_data: Optional[TimeHistoryArray] = None)[source]
      +

      Computes coherence from reference and response time histories

      +
      +
      Parameters
      +
        +

      • response_data (TimeHistoryArray) – Time data to be used as responses

        • +

      • samples_per_average (int, optional) – Number of samples used to split up the signals into averages. The +default is None, meaning the data is treated as a single measurement +frame.

        • +

      • overlap (float, optional) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap). +The default is 0.0, meaning no overlap is used.

        • +

      • window (np.ndarray or str, optional) – A 1D ndarray with length samples_per_average that specifies the +coefficients of the window. A Hann window is applied if not specified. +If a string is specified, then the window will be obtained from scipy.

        • +

      • reference_data (TimeHistoryArray) – Time data to be used as reference. If not specified, the response +data will be used as references, resulting in a square coherence matrix.

        • +
      +
      +
      Raises
      +

      ValueError – Raised if reference and response functions do not have consistent + abscissa

      +
      +
      Returns
      +

      A PSD array computed from the specified reference and +response signals.

      +
      +
      Return type
      +

      PowerSpectralDensityArray

      +
      +
      +

    • +
    property function_type
    @@ -207,7 +245,7 @@

    sdynpy.core.sdynpy_data.MultipleCoherenceArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html b/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html index de8aeb0..d600e32 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.NDDataArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.NDDataArray — SDynPy 0.14.0 documentation @@ -100,6 +100,8 @@
  • NDDataArray.from_uff()
  • NDDataArray.from_unv()
  • NDDataArray.function_type
    • +
  • NDDataArray.get_drive_points()
    • +
  • NDDataArray.get_reciprocal_data()
  • NDDataArray.idx_by_ab
  • NDDataArray.idx_by_el
  • NDDataArray.interpolate()
    • @@ -110,6 +112,7 @@
  • NDDataArray.num_coordinates
  • NDDataArray.num_elements
  • NDDataArray.plot()
    • +
  • NDDataArray.plot_image()
  • NDDataArray.reference_coordinate
  • NDDataArray.reshape_to_matrix()
  • NDDataArray.response_coordinate
    • @@ -224,6 +227,12 @@

    sdynpy.core.sdynpy_data.NDDataArray

    from_unv(unv_data_dict[, squeeze])

    Create a data array from a unv dictionary from read_unv

    +

    get_drive_points([return_indices])

    +

    Returns data arrays where the reference is equal to the response

    + +

    get_reciprocal_data([return_indices])

    +

    Gets reciprocal pairs of data from an NDDataArray.

    +

    interpolate(interpolated_abscissa[, kind])

    Interpolates the NDDataArray using SciPy's interp1d.

    @@ -239,31 +248,34 @@

    sdynpy.core.sdynpy_data.NDDataArray

    min([reduction])

    Returns the minimum ordinate in the data array

    -

    plot([one_axis, subplots_kwargs, plot_kwargs])

    +

    plot([one_axis, subplots_kwargs, ...])

    Plot the data array

    -

    reshape_to_matrix()

    +

    plot_image([ax, reduction_function, ...])

    +

    + +

    reshape_to_matrix([error_if_missing])

    Reshapes a data array to a matrix with response coordinates along the rows and reference coordinates along the columns

    -

    save(filename)

    +

    save(filename)

    Save the array to a numpy file

    -

    to_imat_struct([Version, SetRecord, ...])

    +

    to_imat_struct([Version, SetRecord, ...])

    Creates a Matlab structure that can be read the IMAT toolbox.

    -

    to_imat_struct_array([Version, SetRecord, ...])

    +

    to_imat_struct_array([Version, SetRecord, ...])

    Creates a Matlab structure that can be read the IMAT toolbox.

    -

    to_shape_array([abscissa_values])

    +

    to_shape_array([abscissa_values])

    Converts an NDDataArray to a ShapeArray

    -

    transform_coordinate_system(...[, ...])

    +

    transform_coordinate_system(...[, ...])

    Performs coordinate system transformations on the data

    -

    validate_common_abscissa(**allclose_kwargs)

    +

    validate_common_abscissa(**allclose_kwargs)

    Returns True if all functions have the same abscissa

    -

    zero_pad([num_samples, update_abscissa, ...])

    +

    zero_pad([num_samples, update_abscissa, ...])

    Add zeros to the beginning or end of a signal

    @@ -467,6 +479,56 @@

    sdynpy.core.sdynpy_data.NDDataArray +
    +get_drive_points(return_indices=False)[source]
    +

    Returns data arrays where the reference is equal to the response

    +
    +
    Parameters
    +

    return_indices (bool, optional) – If True, it will return a set of indices into the original array +that extract the drive point functions. If False, then the +drive point functions are returned directly. The default is False.

    +
    +
    Raises
    +

    ValueError – If the data does not have reference and response coordinates, the + method will raise a ValueError.

    +
    +
    Returns
    +

    If return_indices is True, this will return the indices into the +original array that extract the drive point data. If return_indices +is False, this will return the drive point NDDataArrays directly.

    +
    +
    Return type
    +

    np.ndarray or NDDataArray subclass

    +
    +
    +

    + +
    +
    +get_reciprocal_data(return_indices=False)[source]
    +

    Gets reciprocal pairs of data from an NDDataArray.

    +
    +
    Parameters
    +

    return_indices (bool, optional) – If True, it will return a set of indices into the original array +that extract the reciprocal functions. If False, then the +reciprocal functions are returned directly. The default is False.

    +
    +
    Raises
    +

    ValueError – If the data does not have reference and response coordinates, the + method will raise a ValueError.

    +
    +
    Returns
    +

    If return_indices is True, this will return the indices into the +original array that extract the reciprocal data. If return_indices +is False, this will return the reciprocal NDDataArrays directly.

    +
    +
    Return type
    +

    np.ndarray or NDDataArray subclass

    +
    +
    +
    +
    property idx_by_ab
    @@ -610,7 +672,7 @@

    sdynpy.core.sdynpy_data.NDDataArray
    -plot(one_axis: bool = True, subplots_kwargs: dict = {}, plot_kwargs: dict = {})[source]
    +plot(one_axis: bool = True, subplots_kwargs: dict = {}, plot_kwargs: dict = {}, abscissa_markers=None, abscissa_marker_labels=None, abscissa_marker_type='vline', abscissa_marker_plot_kwargs={})[source]

    Plot the data array

    Parameters
    @@ -622,6 +684,16 @@

    sdynpy.core.sdynpy_data.NDDataArrayReturns @@ -633,6 +705,11 @@

    sdynpy.core.sdynpy_data.NDDataArray +
    +plot_image(ax=None, reduction_function=None, colorbar_scale='linear', colorbar_min=None, colorbar_max=None)[source]
    +

    +
    property reference_coordinate
    @@ -641,15 +718,22 @@

    sdynpy.core.sdynpy_data.NDDataArray
    -reshape_to_matrix()[source]
    +reshape_to_matrix(error_if_missing=True)[source]

    Reshapes a data array to a matrix with response coordinates along the rows and reference coordinates along the columns

    -
    Returns
    -

    output_array – 2D Array of NDDataArray

    +
    Parameters
    +

    error_if_missing (bool) – If True, an error will be thrown if there are missing data objects +when trying to make a matrix of functions (i.e. if a response +degree of freedom is missing from one reference). If False, +response coordinates will simply be discarded if they do not exist +for all references. Default is True.

    -
    Return type
    -

    Data Aarray

    +
    Returns
    +

    output_array – 2D Array of NDDataArray

    +
    +
    Return type
    +

    Data Aarray

    @@ -911,7 +995,7 @@

    sdynpy.core.sdynpy_data.NDDataArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html index 40ed68d..092811a 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.PowerSpectralDensityArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.PowerSpectralDensityArray — SDynPy 0.14.0 documentation @@ -92,10 +92,12 @@
  • PowerSpectralDensityArray

    • +
    +
    +bandwidth_average(band_lb, band_ub)[source]
    +

    Integrates the PSD over frequency to get the power spectrum for each +frequency bin (line)

    +
    +
    Parameters
    +
      +

    • band_lb (ndarray) – (n_bands,1) array of bandwidth lower bounds

      • +

    • band_ub (ndarray) – (n_bands,1) array of bandwidth upper bounds

      • +
    +
    +
    Returns
    +

      +

    • PowerSpectralDensityArray with abscissa given by the mean of band_lb

      • +

    • and band_ub

      • +
    +

    +
    +
    +

    Notes

    +

    Determines which freq bins (lines) contribute to each band. Contribute +means the freq bin is at least partially within the band limits

    +

    The portion of the bin which contributes to the band is computed based +multiplied by the fraction of the contributing frequency to get how +much bin PS adds to the band PS

    +
    +
    coherence()[source]
    @@ -366,6 +402,40 @@

    sdynpy.core.sdynpy_data.PowerSpectralDensityArray

    +
    +
    +static from_time_data(response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, window=array([1.]), reference_data: Optional[TimeHistoryArray] = None, only_asds=False)[source]
    +

    Computes a PSD matrix from reference and response time histories

    +
    +
    Parameters
    +
      +

    • response_data (TimeHistoryArray) – Time data to be used as responses

      • +

    • samples_per_average (int, optional) – Number of samples used to split up the signals into averages. The +default is None, meaning the data is treated as a single measurement +frame.

      • +

    • overlap (float, optional) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap). +The default is 0.0, meaning no overlap is used.

      • +

    • window (np.ndarray or str, optional) – A 1D ndarray with length samples_per_average that specifies the +coefficients of the window. A Hann window is applied if not specified. +If a string is specified, then the window will be obtained from scipy.

      • +

    • reference_data (TimeHistoryArray) – Time data to be used as reference. If not specified, the response +data will be used as references, resulting in a square CPSD matrix.

      • +
    +
    +
    Raises
    +

    ValueError – Raised if reference and response functions do not have consistent + abscissa

    +
    +
    Returns
    +

    A PSD array computed from the specified reference and +response signals.

    +
    +
    Return type
    +

    PowerSpectralDensityArray

    +
    +
    +
    +
    property function_type
    @@ -614,7 +684,7 @@

    sdynpy.core.sdynpy_data.PowerSpectralDensityArray
    svd(full_matrices=True, compute_uv=True, as_matrix=True)[source]
    -

    Compute the SVD of the provided FRF matrix

    +

    Compute the SVD of the provided CPSD matrix

    Parameters
    Returns
    @@ -365,7 +375,7 @@

    sdynpy.core.sdynpy_data.ShockResponseSpectrumArray
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html b/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html index f675d5f..33326dc 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html +++ b/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.SpecificDataType — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.SpecificDataType — SDynPy 0.14.0 documentation @@ -368,7 +368,7 @@

    sdynpy.core.sdynpy_data.SpecificDataType - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html b/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html index 2985628..3462829 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.SpectrumArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.SpectrumArray — SDynPy 0.14.0 documentation @@ -182,8 +182,8 @@

    sdynpy.core.sdynpy_data.SpectrumArray

    interpolate_by_zero_pad(...[, ...])

    Interpolates a spectrum by zero padding or truncating its time response

    -

    plot([one_axis, subplots_kwargs, plot_kwargs])

    -

    Plot the transfer functions

    +

    plot([one_axis, subplots_kwargs, ...])

    +

    Plot the spectra

    plot_spectrogram([abscissa, axis, ...])

    Plots a spectrogram

    @@ -270,8 +270,8 @@

    sdynpy.core.sdynpy_data.SpectrumArray
    -plot(one_axis=True, subplots_kwargs={}, plot_kwargs={})[source]
    -

    Plot the transfer functions

    +plot(one_axis=True, subplots_kwargs={}, plot_kwargs={}, abscissa_markers=None, abscissa_marker_labels=None, abscissa_marker_type='vline', abscissa_marker_plot_kwargs={})[source] +

    Plot the spectra

    Parameters
    +
    split_into_frames(samples_per_frame=None, frame_length=None, overlap=None, overlap_samples=None, window=None, check_cola=False, allow_fractional_frames=False)[source]
    @@ -1068,7 +1077,7 @@

    sdynpy.core.sdynpy_data.TimeHistoryArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html index 71959ab..f55c116 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.TransferFunctionArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.TransferFunctionArray — SDynPy 0.14.0 documentation @@ -110,6 +110,7 @@
  • TransferFunctionArray.plot()
  • TransferFunctionArray.plot_cond_num()
  • TransferFunctionArray.plot_singular_values()
    • +
  • TransferFunctionArray.plot_with_coherence()
  • TransferFunctionArray.substructure_by_constraint_matrix()
  • TransferFunctionArray.substructure_by_coordinate()
  • TransferFunctionArray.svd()
    • @@ -214,7 +215,7 @@

    sdynpy.core.sdynpy_data.TransferFunctionArray

    interpolate_by_zero_pad(irf_padded_length[, ...])

    Interpolates a transfer function by zero padding or truncating its impulse response

    -

    plot([one_axis, subplots_kwargs, plot_kwargs])

    +

    plot([one_axis, part, subplots_kwargs, ...])

    Plot the transfer functions

    plot_cond_num([number_retained_values, ...])

    @@ -223,13 +224,16 @@

    sdynpy.core.sdynpy_data.TransferFunctionArray

    plot_singular_values([rcond, ...])

    Plot the singular values of an FRF matrix with a visualization of the rcond tolerance

    -

    substructure_by_constraint_matrix(dofs, ...)

    +

    plot_with_coherence(coherence[, part, ...])

    +

    + +

    substructure_by_constraint_matrix(dofs, ...)

    Performs frequency based substructuring using the

    -

    substructure_by_coordinate(dof_pairs)

    +

    substructure_by_coordinate(dof_pairs)

    Performs frequency based substructuring by constraining pairs of degrees of freedom

    -

    svd([full_matrices, compute_uv, as_matrix])

    +

    svd([full_matrices, compute_uv, as_matrix])

    Compute the SVD of the provided FRF matrix

    @@ -377,15 +381,15 @@

    sdynpy.core.sdynpy_data.TransferFunctionArray
    -static from_time_data(reference_data: TimeHistoryArray, response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, method: str = 'H1', window=array([1.]), **timedata2frf_kwargs)[source]
    +static from_time_data(reference_data: TimeHistoryArray, response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, method: str = 'H1', window=array([1.]), return_model_data=False, **timedata2frf_kwargs)[source]

    Computes a transfer function from reference and response time histories

    Parameters
    @@ -455,12 +459,14 @@

    sdynpy.core.sdynpy_data.TransferFunctionArrayRaises @@ -548,7 +554,7 @@

    Paramters
    -plot(one_axis=True, subplots_kwargs={}, plot_kwargs={})[source]
    +plot(one_axis=True, part=None, subplots_kwargs={}, plot_kwargs={}, abscissa_markers=None, abscissa_marker_labels=None, abscissa_marker_type='vline', abscissa_marker_plot_kwargs={})[source]

    Plot the transfer functions

    Parameters
    @@ -557,9 +563,23 @@

    ParamtersReturns @@ -614,6 +634,11 @@

    Paramters +
    +plot_with_coherence(coherence, part=None, subplots_kwargs={}, plot_kwargs={})[source]
    +

    +
    substructure_by_constraint_matrix(dofs, constraint_matrix)[source]
    @@ -700,7 +725,7 @@

    Paramters - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html b/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html index 2be7104..86c7a25 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html +++ b/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.TransmissibilityArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.TransmissibilityArray — SDynPy 0.14.0 documentation @@ -207,7 +207,7 @@

    sdynpy.core.sdynpy_data.TransmissibilityArray - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html b/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html index 11c232a..626cea0 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html +++ b/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.TypeQual — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.TypeQual — SDynPy 0.14.0 documentation @@ -206,7 +206,7 @@

    sdynpy.core.sdynpy_data.TypeQual - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.data_array.html b/_autosummary/sdynpy.core.sdynpy_data.data_array.html index 260735f..e93e416 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.data_array.html +++ b/_autosummary/sdynpy.core.sdynpy_data.data_array.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.data_array — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.data_array — SDynPy 0.14.0 documentation @@ -193,7 +193,7 @@

    sdynpy.core.sdynpy_data.data_array - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html b/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html index a255d42..c4822fc 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html +++ b/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.decayed_sine_table — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.decayed_sine_table — SDynPy 0.14.0 documentation @@ -194,7 +194,7 @@

    sdynpy.core.sdynpy_data.decayed_sine_table - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html b/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html index 74f43ff..6b466c7 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html +++ b/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.frf_from_time_data — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.frf_from_time_data — SDynPy 0.14.0 documentation @@ -153,7 +153,7 @@

    sdynpy.core.sdynpy_data.frf_from_time_data

    -frf_from_time_data(reference_data: TimeHistoryArray, response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, method: str = 'H1', window=array([1.]), **timedata2frf_kwargs)
    +frf_from_time_data(reference_data: TimeHistoryArray, response_data: TimeHistoryArray, samples_per_average: Optional[int] = None, overlap: float = 0.0, method: str = 'H1', window=array([1.]), return_model_data=False, **timedata2frf_kwargs)

    Computes a transfer function from reference and response time histories

    Parameters
    @@ -166,12 +166,14 @@

    sdynpy.core.sdynpy_data.frf_from_time_dataRaises @@ -201,7 +203,7 @@

    sdynpy.core.sdynpy_data.frf_from_time_data - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html b/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html index ebb343f..6f66b38 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html +++ b/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.from_imat_struct — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.from_imat_struct — SDynPy 0.14.0 documentation @@ -193,7 +193,7 @@

    sdynpy.core.sdynpy_data.from_imat_struct - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.from_uff.html b/_autosummary/sdynpy.core.sdynpy_data.from_uff.html index 93533bf..aca4699 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.from_uff.html +++ b/_autosummary/sdynpy.core.sdynpy_data.from_uff.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.from_uff — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.from_uff — SDynPy 0.14.0 documentation @@ -185,7 +185,7 @@

    sdynpy.core.sdynpy_data.from_uff - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.from_unv.html b/_autosummary/sdynpy.core.sdynpy_data.from_unv.html index 31412f3..858dc82 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.from_unv.html +++ b/_autosummary/sdynpy.core.sdynpy_data.from_unv.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.from_unv — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.from_unv — SDynPy 0.14.0 documentation @@ -185,7 +185,7 @@

    sdynpy.core.sdynpy_data.from_unv - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.html b/_autosummary/sdynpy.core.sdynpy_data.html index 31a2fbb..932fcfa 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.html +++ b/_autosummary/sdynpy.core.sdynpy_data.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data — SDynPy 0.14.0 documentation @@ -292,7 +292,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_data.join.html b/_autosummary/sdynpy.core.sdynpy_data.join.html index 007f7c9..c91bf4d 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.join.html +++ b/_autosummary/sdynpy.core.sdynpy_data.join.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.join — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.join — SDynPy 0.14.0 documentation @@ -183,7 +183,7 @@

    sdynpy.core.sdynpy_data.join - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_data.load.html b/_autosummary/sdynpy.core.sdynpy_data.load.html index 557bd55..cee6ba5 100644 --- a/_autosummary/sdynpy.core.sdynpy_data.load.html +++ b/_autosummary/sdynpy.core.sdynpy_data.load.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_data.load — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_data.load — SDynPy 0.14.0 documentation @@ -187,7 +187,7 @@

    sdynpy.core.sdynpy_data.load - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html b/_autosummary/sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html index 6a3aa81..2b2de9d 100644 --- a/_autosummary/sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html +++ b/_autosummary/sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_geometry.CoordinateSystemArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_geometry.CoordinateSystemArray — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -68,6 +68,7 @@
  • sdynpy.core.sdynpy_geometry

    • +
    classmethod from_exodus(exo: Exodus, blocks=None, local=False, preferred_local_orientation=array([0., 0., 1.]), secondary_preferred_local_orientation=array([1., 0., 0.]), local_nodes=None)[source]
    @@ -917,6 +990,23 @@

    sdynpy.core.sdynpy_geometry.Geometry +
    +static write_excel_template(path_to_xlsx)[source]
    +

    Writes an Excel File Template for Creating Geometry

    +
    +
    Parameters
    +

    path_to_xlsx (string) – Path to write xlsx Excel file

    +
    +
    Return type
    +

    Nothing

    +
    +
    +

    Notes

    +

    See documentation for from_excel_template for instructions on filling +out the template to create a geometry.

    +

    +
    write_to_unv(filename, write_nodes=True, write_coordinate_systems=True, write_tracelines=True, write_elements=True, dataset_2412_kwargs={}, dataset_2420_kwargs={})[source]
    @@ -966,7 +1056,7 @@

    sdynpy.core.sdynpy_geometry.Geometry - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html b/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html index 32009f1..eca98e4 100644 --- a/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html +++ b/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_geometry.GeometryPlotter — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_geometry.GeometryPlotter — SDynPy 0.14.0 documentation @@ -68,6 +68,7 @@
  • sdynpy.core.sdynpy_geometry

    • +
    save_animation(filename=None, frames=20, frame_rate=20, individual_images=False)[source]
    @@ -519,7 +531,7 @@

    sdynpy.core.sdynpy_geometry.ShapePlotter - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html b/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html index 08c6ee6..1864e49 100644 --- a/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html +++ b/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_geometry.TracelineArray — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_geometry.TracelineArray — SDynPy 0.14.0 documentation @@ -68,6 +68,7 @@
  • sdynpy.core.sdynpy_geometry
    • +
  • sdynpy.core.sdynpy_geometry.from_excel_template
  • sdynpy.core.sdynpy_geometry.from_exodus
  • sdynpy.core.sdynpy_geometry.from_imat_struct
  • sdynpy.core.sdynpy_geometry.from_uff
    • @@ -82,6 +83,7 @@
  • sdynpy.core.sdynpy_geometry.node_array
  • sdynpy.core.sdynpy_geometry.split_list
  • sdynpy.core.sdynpy_geometry.traceline_array
    • +
  • sdynpy.core.sdynpy_geometry.write_excel_template
  • sdynpy.core.sdynpy_geometry.CoordinateSystemArray
  • sdynpy.core.sdynpy_geometry.DeflectionShapePlotter
  • sdynpy.core.sdynpy_geometry.ElementArray
    • @@ -269,13 +271,13 @@

    sdynpy.core.sdynpy_geometry.element_array - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html new file mode 100644 index 0000000..396e435 --- /dev/null +++ b/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html @@ -0,0 +1,245 @@ + + + + + + + sdynpy.core.sdynpy_geometry.from_excel_template — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.core.sdynpy_geometry.from_excel_template

    +
    +
    +from_excel_template(path_to_xlsx)
    +

    Create a geometry from Excel file template

    +
    +
    Parameters
    +

    path_to_xlsx (string) – Path to xlsx Excel file containing geometry information

    +
    +
    Returns
    +

    Geometry object created from the Excel file

    +
    +
    Return type
    +

    Geometry

    +
    +
    +

    Notes

    +

    To use this function, first save out an excel template file using the +write_excel_template function. This will construct an excel +workbook with four worksheets on which the different portions of the +Geometry are defined.

    +

    On the Coordinate Systems tab, users will define the various global and +local coordinate systems in their geometry. Each geometry requires +an ID number. A Name can optionally be given. The Color should be +specified as an integer corresponding to the Ideas color map. The +Type of the coordinate system should be an integer:

    +
    +

    0 - Cartesian +1 - Polar +2 - Spherical

    +
    +

    The origin of the coordinate system can be specified with the X Location, +Y Location, Z Location columns. Then rotations of the coordinate system +can be specified using rotations about axes. Up to three axes and angles +can be specified to create arbitrary compound rotations. The rotation +axes should be X, Y, or Z, and the rotation angles are in degrees.

    +

    On the Nodes tab, all tabs must be filled out. ID numbers must be unique. +Colors should be an integer corresponding to the Ideas color map. The +position of the node is specified using the X Location, Y Location, Z +Location columns. Each node has a displacement coordinate system in which +its position is defined, and a a definition coordinate system in which +its displacements are defined. These columns should consist of integers +corresponding to ID numbers from the Coordinate Systems tab.

    +

    On the Elements tab, elements connecting nodes are defined. The ID +column must consist of unique integer identifiers. The Color tab should +be specified as an integer corresponding to the Ideas color map. The +type can be a string (hex, quad, tet, tri, beam, etc.) or an integer +consisting of a universal file format element type. If the type column +is empty, an element type based on the number of connections given will +be used. Defult element types for connection length of 2 is “Type 21 - Linear Beam”, +for a connection length of 3 is “Type 41 - Plane Stress Linear Triangle”, +and for a connection length of 4 is “Type 44 - Plane Stress Linear Quadrilateral” +The columns Node 1 through Node 20 contain the nodes in each element. Only the required +number of nodes must be filled out (e.g. a tri element would only contain +3 nodes, so only columns Node 1, Node 2, and Node 3 would be filled).

    +

    On the Trace Lines tab, lines connecting the nodes are defined. The +ID column must consist of unique integer identifiers. The Description +column contains a string description of the line. The Color column +should consist of an integer corresponding to the Ideas color map. The +Node 1 through Node 20 columns should contain the nodes for each line. +Only the number of nodes in the line must be filled out, so if a line +only connects 5 nodes, only Node 1 though Node 5 must be filled.

    +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html index 985f873..35d2288 100644 --- a/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html +++ b/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_geometry.from_exodus — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_geometry.from_exodus — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -68,6 +68,7 @@
  • sdynpy.core.sdynpy_geometry

    • +
    expand(initial_geometry, expansion_geometry, expansion_shapes, node_id_map=None, expansion_coordinates=None, return_coefficients=False)[source]
    @@ -521,6 +562,28 @@

    sdynpy.core.sdynpy_shape.ShapeArray +
    +normalize(system_or_matrix, return_modal_matrix=False)[source]
    +

    Computes A-normalized or mass-normalized shapes

    +
    +
    Parameters
    +
      +

    • system_or_matrix (System or np.ndarray) – A System object or a mass matrix for real modes or A-matrix for +complex modes.

      • +

    • return_modal_matrix (bool, optional) – If true, it will return the modal mass or modal-A matrix computed +from the normalized mode shapes. The default is False.

      • +
    +
    +
    Returns
    +

    A copy of the original shape array with normalized shape coefficients

    +
    +
    Return type
    +

    ShapeArray

    +
    +
    +

    +
    optimize_degrees_of_freedom(sensors_to_keep, group_by_node=False, method='ei')[source]
    @@ -729,14 +792,28 @@

    sdynpy.core.sdynpy_shape.ShapeArrayReturns

      -

    • response_array (TimeHistoryArray) – Input forces assembled into a TimeHistoryArray.

      • -

    • reference_array (TimeHistoryArray) – Responses assembled into a TimeHistoryArray.

      • +

    • response_array (TimeHistoryArray) – Responses assembled into a TimeHistoryArray.

      • +

    • reference_array (TimeHistoryArray) – Input forces assembled into a TimeHistoryArray.

    +
    +
    +to_complex()[source]
    +

    Creates complex shapes from real shapes

    +
    +
    Returns
    +

    Complex shapes compute from the real shape coefficients

    +
    +
    Return type
    +

    ShapeArray

    +
    +
    +
    +
    to_real(force_angle=-0.7853981633974483, **kwargs)[source]
    @@ -825,13 +902,13 @@

    sdynpy.core.sdynpy_shape.ShapeArray - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html b/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html new file mode 100644 index 0000000..ad8a903 --- /dev/null +++ b/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html @@ -0,0 +1,242 @@ + + + + + + + sdynpy.core.sdynpy_shape.ShapeCommentTable — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.core.sdynpy_shape.ShapeCommentTable

    +
    +
    +class ShapeCommentTable(shapes, plotter=None, parent=None)[source]
    +

    Bases: QDialog

    +
    +
    +__init__(shapes, plotter=None, parent=None)[source]
    +

    Creates a table window that allows editing of comments on the mode +shapes.

    +
    +
    Parameters
    +
      +

    • shapes (ShapeArray) – The shapes for which the comments need to be modified.

      • +

    • plotter (ShapePlotter, optional) – A shape plotter that is to be linked to the table. It should have +the same modes used for the table plotted on it. The plotter will +automatically update the mode being displayed as different rows of +the table are selected. If not specified, there will be no mode +shape display linked to the table.

      • +

    • parent (QWidget, optional) – Parent widget for the window. The default is No parent.

      • +
    +
    +
    Return type
    +

    None.

    +
    +
    +
    + +

    Methods

    + ++++ + + + + + + + + + + + +

    __init__(shapes[, plotter, parent])

    Creates a table window that allows editing of comments on the mode shapes.

    accept(self)

    update_mode()

    +

    Attributes

    + ++++ + + +
    +
    +
    +accept(self)[source]
    +
    + +
    +
    +update_mode()[source]
    +
    + +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html b/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html index 876a6cb..e2ceb91 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.concatenate_dofs — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.concatenate_dofs — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -163,7 +164,7 @@

    sdynpy.core.sdynpy_shape.concatenate_dofs - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html b/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html index 4f0d420..e8f89e2 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.from_exodus — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.from_exodus — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -187,7 +188,7 @@

    sdynpy.core.sdynpy_shape.from_exodus - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html b/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html index 05c2822..34a4145 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.from_imat_struct — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.from_imat_struct — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -165,7 +166,7 @@

    sdynpy.core.sdynpy_shape.from_imat_struct - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html b/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html index 0151f82..b392d68 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.from_unv — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.from_unv — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -168,7 +169,7 @@

    sdynpy.core.sdynpy_shape.from_unv - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.html b/_autosummary/sdynpy.core.sdynpy_shape.html index 8c3f3c6..0c4a03a 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape — SDynPy 0.14.0 documentation @@ -81,6 +81,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -199,6 +200,9 @@

    ShapeArray(shape, ndof[, shape_type, ...])

    Shape information specifying displacements at nodes.

    +

    ShapeCommentTable(shapes[, plotter, parent])

    +

    + @@ -214,7 +218,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.core.sdynpy_shape.mac.html b/_autosummary/sdynpy.core.sdynpy_shape.mac.html index f2bace1..4c997fc 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.mac.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.mac.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.mac — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.mac — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -166,7 +167,7 @@

    sdynpy.core.sdynpy_shape.mac - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.overlay_shapes.html b/_autosummary/sdynpy.core.sdynpy_shape.overlay_shapes.html index e359d0b..63b28fd 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.overlay_shapes.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.overlay_shapes.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.overlay_shapes — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.overlay_shapes — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -172,7 +173,7 @@

    sdynpy.core.sdynpy_shape.overlay_shapes - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html index 87388be..a959e18 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.rigid_body_check — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.rigid_body_check — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -134,7 +135,7 @@

    sdynpy.core.sdynpy_shape.rigid_body_check

    -rigid_body_check(geometry, rigid_shapes, distance=0.25, distance_number=5, distance_yscale=20, residuals_to_label=5, return_shape_diagnostics=False, plot=True, **rigid_shape_kwargs)[source]
    +rigid_body_check(geometry, rigid_shapes, distance=0.25, distance_number=5, distance_yscale=20, residuals_to_label=5, return_shape_diagnostics=False, plot=True, return_figures=False, **rigid_shape_kwargs)[source]

    Performs rigid body checks, both looking at the complex plane and residuals

    Parameters
    @@ -184,7 +185,7 @@

    sdynpy.core.sdynpy_shape.rigid_body_check - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_error.html b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_error.html index 374b626..f27ca61 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_error.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_error.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.rigid_body_error — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.rigid_body_error — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -175,7 +176,7 @@

    sdynpy.core.sdynpy_shape.rigid_body_error - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation.html b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation.html index 35ce2cb..82328f7 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -180,7 +181,7 @@

    sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.shape_alignment.html b/_autosummary/sdynpy.core.sdynpy_shape.shape_alignment.html index 49f8a66..b729fd4 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.shape_alignment.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.shape_alignment.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.shape_alignment — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.shape_alignment — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_array
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -166,7 +167,7 @@

    sdynpy.core.sdynpy_shape.shape_alignment - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html b/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html index 3505188..b98a848 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.shape_array — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.shape_array — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.shape_comparison_table
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -182,7 +183,7 @@

    sdynpy.core.sdynpy_shape.shape_array - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html b/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html index d080adb..2f18e2b 100644 --- a/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html +++ b/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_shape.shape_comparison_table — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_shape.shape_comparison_table — SDynPy 0.14.0 documentation @@ -84,6 +84,7 @@
  • sdynpy.core.sdynpy_shape.ShapeArray
    • +
  • sdynpy.core.sdynpy_shape.ShapeCommentTable
  • sdynpy.core.sdynpy_system
    • @@ -177,7 +178,7 @@

    sdynpy.core.sdynpy_shape.shape_comparison_table - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_system.System.html b/_autosummary/sdynpy.core.sdynpy_system.System.html index b884d5c..505205a 100644 --- a/_autosummary/sdynpy.core.sdynpy_system.System.html +++ b/_autosummary/sdynpy.core.sdynpy_system.System.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_system.System — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_system.System — SDynPy 0.14.0 documentation @@ -95,6 +95,7 @@
  • System.reduce_craig_bampton()
  • System.reduce_dynamic()
  • System.reduce_guyan()
    • +
  • System.remove_transformation()
  • System.save()
  • System.set_proportional_damping()
  • System.simulate_test()
    • @@ -243,37 +244,40 @@

    sdynpy.core.sdynpy_system.System

    reduce_guyan(coordinates)

    Perform Guyan reduction on the system

    -

    save(filename)

    +

    remove_transformation()

    +

    + +

    save(filename)

    Saves the system to a file

    -

    set_proportional_damping(mass_fraction, ...)

    +

    set_proportional_damping(mass_fraction, ...)

    Sets the damping matrix to a proportion of the mass and stiffness matrices.

    -

    simulate_test(bandwidth, frame_length, ...)

    +

    simulate_test(bandwidth, frame_length, ...)

    -

    spy([subplots_kwargs, spy_kwargs])

    +

    spy([subplots_kwargs, spy_kwargs])

    Plot the structure of the system's matrices

    -

    substructure_by_coordinate(dof_pairs[, ...])

    +

    substructure_by_coordinate(dof_pairs[, ...])

    Constrain the system by connecting the specified degree of freedom pairs

    -

    substructure_by_position(systems, geometries)

    +

    substructure_by_position(systems, geometries)

    Applies constraints to systems by constraining colocated nodes together

    -

    substructure_by_shape(constraint_shapes, ...)

    +

    substructure_by_shape(constraint_shapes, ...)

    Constrain the system using a set of shapes in a least-squares sense.

    -

    time_integrate(forces[, dt, responses, ...])

    +

    time_integrate(forces[, dt, responses, ...])

    Integrate a system to produce responses to an excitation

    -

    to_state_space([output_displacement, ...])

    +

    to_state_space([output_displacement, ...])

    Compute the state space representation of the system

    -

    transformation_matrix_at_coordinates(coordinates)

    +

    transformation_matrix_at_coordinates(coordinates)

    Return the transformation matrix at the specified coordinates

    -

    transformation_shapes([shape_indices])

    +

    transformation_shapes([shape_indices])

    @@ -699,6 +703,11 @@

    sdynpy.core.sdynpy_system.System +
    +remove_transformation()[source]
    +

    +
    save(filename)[source]
    @@ -964,7 +973,7 @@

    sdynpy.core.sdynpy_system.System - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.core.sdynpy_system.html b/_autosummary/sdynpy.core.sdynpy_system.html index 8344f07..fe9f1fb 100644 --- a/_autosummary/sdynpy.core.sdynpy_system.html +++ b/_autosummary/sdynpy.core.sdynpy_system.html @@ -4,7 +4,7 @@ - sdynpy.core.sdynpy_system — SDynPy 0.11.0 documentation + sdynpy.core.sdynpy_system — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -165,14 +165,14 @@

    @@ -175,13 +182,13 @@

    sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html new file mode 100644 index 0000000..23ff252 --- /dev/null +++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html @@ -0,0 +1,203 @@ + + + + + + + sdynpy.modal.sdynpy_modeshape.generate_kernel_complex — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.modal.sdynpy_modeshape.generate_kernel_complex

    +
    +
    +generate_kernel_complex(omegas, poles, participation_factors, lower_residuals=False, upper_residuals=False, displacement_derivative=0)[source]
    +
    +
    Parameters
    +
      +

    • omegas (np.ndarray) – The angular frequencies (in radians/s) at which the kernel matrix will +be computed. This should be a 1D array with length num_freqs.

      • +

    • poles (np.ndarray) – An array of poles corresponding to the modes of the structure. This +should be a 1D array with length num_modes.

      • +

    • participation_factors (np.ndarray) – A 2D array of participation factors corresponding to the reference +degrees of freedom. This should have shape (num_modes, num_inputs).

      • +

    • lower_residuals (bool, optional) – If True, construct the kernel matrix such that lower residuals will be +computed in the least-squares operation. The default is False.

      • +

    • upper_residuals (bool, optional) – If True, construct the kernel matrix such that upper residuals will be +computed in the least-squares operation. The default is False.

      • +

    • displacement_derivative (int, optional) – The derivative of displacement used to construct the frequency response +functions. Should be 0 for a receptance (displacement/force) frf, 1 +for a mobility (velocity/force) frf, or 2 for an accelerance +(acceleration/force) frf.

      • +
    +
    +
    Returns
    +

    kernel_matrix – A 3D matrix that represents the kernel that can be inverted to solve +for mode shapes. The size of the output array will be num_inputs x +num_freq*2 x num_modes*2. The top partition of the num_freq dimension +corresponds to the real part of the frf, and the +bottom portion corresponds to the imaginary part of the frf. The top +partition of the num_modes dimension corresponds to the real part of +the mode shape matrix, and the bottom partition corresponds to the +imaginary part. If residuals are included, there will be an extra two +entries along the num_modes dimension corresponding to real and +imaginary parts of the residual matrix for each of the residuals +included.

    +
    +
    Return type
    +

    np.ndarray

    +
    +
    +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html new file mode 100644 index 0000000..fcc2812 --- /dev/null +++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html @@ -0,0 +1,201 @@ + + + + + + + sdynpy.modal.sdynpy_modeshape.generate_kernel_real — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.modal.sdynpy_modeshape.generate_kernel_real

    +
    +
    +generate_kernel_real(omegas, poles, participation_factors, lower_residuals=False, upper_residuals=False, displacement_derivative=0)[source]
    +
    +
    Parameters
    +
      +

    • omegas (np.ndarray) – The angular frequencies (in radians/s) at which the kernel matrix will +be computed. This should be a 1D array with length num_freqs.

      • +

    • poles (np.ndarray) – An array of poles corresponding to the modes of the structure. This +should be a 1D array with length num_modes.

      • +

    • participation_factors (np.ndarray) – A 2D array of participation factors corresponding to the reference +degrees of freedom. This should have shape (num_modes, num_inputs).

      • +

    • lower_residuals (bool, optional) – If True, construct the kernel matrix such that lower residuals will be +computed in the least-squares operation. The default is False.

      • +

    • upper_residuals (bool, optional) – If True, construct the kernel matrix such that upper residuals will be +computed in the least-squares operation. The default is False.

      • +

    • displacement_derivative (int, optional) – The derivative of displacement used to construct the frequency response +functions. Should be 0 for a receptance (displacement/force) frf, 1 +for a mobility (velocity/force) frf, or 2 for an accelerance +(acceleration/force) frf.

      • +
    +
    +
    Returns
    +

    kernel_matrix – A 3D matrix that represents the kernel that can be inverted to solve +for mode shapes. The size of the output array will be num_inputs x +num_freq*2 x num_modes. The top partition of the num_freq dimension +corresponds to the real part of the frf, and the +bottom portion corresponds to the imaginary part of the frf. +If residuals are included, there will be an extra two +entries along the num_modes dimension corresponding to real and +imaginary parts of the residual matrix for each of the residuals +included.

    +
    +
    Return type
    +

    np.ndarray

    +
    +
    +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.html index 69f156f..0ab1956 100644 --- a/_autosummary/sdynpy.modal.sdynpy_modeshape.html +++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.html @@ -4,7 +4,7 @@ - sdynpy.modal.sdynpy_modeshape — SDynPy 0.11.0 documentation + sdynpy.modal.sdynpy_modeshape — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -67,10 +67,14 @@
  • sdynpy.fileio
  • sdynpy.modal

    • @@ -239,7 +240,7 @@

    sdynpy.modal.sdynpy_polypy.PolyPy - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html b/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html index d1ab0f3..6a1c4fd 100644 --- a/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html +++ b/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html @@ -4,7 +4,7 @@ - sdynpy.modal.sdynpy_polypy.PolyPy_GUI — SDynPy 0.11.0 documentation + sdynpy.modal.sdynpy_polypy.PolyPy_GUI — SDynPy 0.14.0 documentation @@ -67,6 +67,7 @@
  • sdynpy.fileio
  • sdynpy.modal @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.frac - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.html index f119ef5..ee70d2e 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation — SDynPy 0.14.0 documentation @@ -85,6 +85,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -179,7 +180,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.mac.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.mac.html index 57c0d7f..77f5312 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.mac.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.mac.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation.mac — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation.mac — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.mac - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html index 50a90ac..ae2928b 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation.matrix_plot — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation.matrix_plot — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.matrix_plot
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html index 7a101d6..2844c1e 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation.msf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation.msf — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.msf - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html index 8a1bba9..8cdf611 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation.orthog — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation.orthog — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.orthog
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.trac.html b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.trac.html index 1563c93..de0445a 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_correlation.trac.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_correlation.trac.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_correlation.trac — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_correlation.trac — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_correlation.trac - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html index caf7930..0cdaddb 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -140,7 +142,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd

    -cpsd(signals: ndarray, sample_rate: int, samples_per_frame: int, overlap: float, window: str, averages_to_keep: Optional[int] = None, only_asds: bool = False)[source]
    +cpsd(signals: ndarray, sample_rate: int, samples_per_frame: int, overlap: float, window: str, averages_to_keep: Optional[int] = None, only_asds: bool = False, reference_signals=None)[source]

    Compute cpsd from signals

    Parameters
    @@ -150,10 +152,15 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsdReturns @@ -180,7 +187,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html index 469eedd..1b4faa3 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html index 5c24032..5172a92 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html index 14261f7..7f886f8 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase.html index 99e2e7e..9a11bce 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd_phase - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history.html index 63a8ad2..b85b9d4 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -183,7 +185,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.cpsd_to_time_history - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html index 393bee7..09d682a 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.dB_pow — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.dB_pow — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@

  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.dB_pow - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html index 61f7311..0338097 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.db2scale — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.db2scale — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -168,7 +170,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.db2scale - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html index cd213c4..05bb663 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -94,6 +95,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -182,22 +184,25 @@

    match_coherence_phase(cpsd_original, ...)

    -

    plot_asds(cpsd[, freq, ax, subplots_kwargs, ...])

    +

    nth_octave_freqs(freq[, oct_order])

    +

    Get N-th octave band frequencies

    + +

    plot_asds(cpsd[, freq, ax, subplots_kwargs, ...])

    -

    plot_cpsd_error(frequencies, spec[, ...])

    +

    plot_cpsd_error(frequencies, spec[, ...])

    -

    rms(x[, axis])

    +

    rms(x[, axis])

    -

    rms_csd(csd, df)

    +

    rms_csd(csd, df)

    Computes RMS of a CPSD matrix

    -

    shaped_psd(frequency_spacing, bandwidth[, ...])

    +

    shaped_psd(frequency_spacing, bandwidth[, ...])

    -

    trace(cpsd)

    +

    trace(cpsd)

    @@ -215,7 +220,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html index 755a8ee..e79defa 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase — SDynPy 0.14.0 documentation @@ -28,7 +28,7 @@ - + @@ -83,6 +83,7 @@
  • match_coherence_phase()
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -150,13 +152,13 @@

    sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html new file mode 100644 index 0000000..82d9cd2 --- /dev/null +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html @@ -0,0 +1,218 @@ + + + + + + + sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs

    +
    +
    +nth_octave_freqs(freq, oct_order=1)[source]
    +

    Get N-th octave band frequencies

    +
    +
    Parameters
    +
      +

    • freq (ndarray) – array of frequency values, either including all freqs or only min and max

      • +

    • oct_order (int, optional) – octave type, 1/octave order. 3 represents 1/3 octave bands. default is 1

      • +
    +
    +
    Returns
    +

      +

    • nominal_band_centers (ndarray) – rounded band center frequencies using ANSI standers. Used to report +results, not to compute band limits

      • +

    • band_lb (ndarray) – lower band frequencies in Hz

      • +

    • band_ub (ndarray) – upper band frequencies in Hz

      • +

    • band_centers (ndarray) – exact computed octave center band frequencies

      • +
    +

    +
    +
    +

    Notes

    +

    Uses equations in ANSI S1.11-2014 “Electroacoustics – Octave-band and +Fractionaloctave-band Filters – Part 1: Specifications”

    +

    Uses the min and max freq lines in the provided freq to determine the upper +and lower bands

    +

    Uses 1000 Hz as the reference frequency (as per the ANSI standard)

    +

    Computes the 1/Nth band center frequencies, using different equations +depending on the octave order. Orders with odd numbers (1, 1/3, etc.) +use ANSI eqn 2. Order with even numbers (1/6, 1/12, etc.) use eqn 3. +This causes any sub-bands to fit entirely within the full octave band

    +

    Note: even-numbered sub-bands will not share center frequencies with +the full octave band, but will share the upper and lower limits. +ANSI S1.11 Annex A gives instructions for rounding to make the nominal +band center freqs. If left-most digit is 1-4, round to 3 significant +digits. If left-most digit is 5-9, round to 2 significant digits. For +example, 41.567 rounds to 41.6. 8785.2 rounds to 8800.

    +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html index 5e615ac..c933dc8 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.plot_asds — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.plot_asds — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -149,14 +151,14 @@

    sdynpy.signal_processing.sdynpy_cpsd.plot_asds - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html index 923d59c..db04155 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html index 131c79f..a40903b 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.rms — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.rms — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.rms - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html index 706678c..6f39918 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.rms_csd — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.rms_csd — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -173,7 +175,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.rms_csd - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html index 5fa8006..5ddf022 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.shaped_psd — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.shaped_psd — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.shaped_psd - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html index 6c7c576..a2a5a79 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_cpsd.trace — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_cpsd.trace — SDynPy 0.14.0 documentation @@ -80,6 +80,7 @@
  • sdynpy.signal_processing.sdynpy_cpsd.dB_pow
  • sdynpy.signal_processing.sdynpy_cpsd.db2scale
  • sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase
    • +
  • sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs
  • sdynpy.signal_processing.sdynpy_cpsd.plot_asds
  • sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error
  • sdynpy.signal_processing.sdynpy_cpsd.rms
    • @@ -97,6 +98,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -156,7 +158,7 @@

    sdynpy.signal_processing.sdynpy_cpsd.trace - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html index 33e8679..fff29ad 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.delay_signal — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.delay_signal — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -167,7 +168,7 @@

    sdynpy.signal_processing.sdynpy_frf.delay_signal - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html index 48ab1eb..03f28da 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.fft2frf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.fft2frf — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -131,7 +132,7 @@

    sdynpy.signal_processing.sdynpy_frf.fft2frf

    -fft2frf(references, responses, method='H1')[source]
    +fft2frf(references, responses, method='H1', freqs_in=None, **kwargs)[source]

    Creates an FRF matrix given ffts of responses and references

    This function creates a nf x no x ni FRF matrix from the ffts provided.

    @@ -142,16 +143,23 @@

    sdynpy.signal_processing.sdynpy_frf.fft2frfReturns -

    H – A nf x no x ni array where nf is the number of frequency lines, no is +

      +

    • H (ndarray) – A nf x no x ni array where nf is the number of frequency lines, no is the number of outputs, and ni is the number of inputs. The output -frequency lines will correspond to the frequency lines in the ffts.

      -
    -
    Return type
    -

    ndarray

    +frequency lines will correspond to the frequency lines in the ffts.

    • +

  • freqs_out (None or ndarray) – None unless method == ‘LRM’. See sdynpy_lrm.frf_local_model.

    • +

  • model_data (None dict) – None unless method == ‘LRM’. See sdynpy_lrm.frf_local_model.

    • + +

    • Notes

    @@ -172,7 +180,7 @@

    sdynpy.signal_processing.sdynpy_frf.fft2frf - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.html index bd56498..2658085 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf — SDynPy 0.14.0 documentation @@ -85,6 +85,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -149,7 +150,7 @@

    delay_signal(times, signal, dt)

    Delay a time signal by the specified amount

    -

    fft2frf(references, responses[, method])

    +

    fft2frf(references, responses[, method, ...])

    Creates an FRF matrix given ffts of responses and references

    modes2frf(frequencies, natural_frequencies, ...)

    @@ -179,7 +180,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html index e2e4543..55b67d2 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.modes2frf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.modes2frf — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -191,7 +192,7 @@

    sdynpy.signal_processing.sdynpy_frf.modes2frf - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html index acecc28..6086998 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.plot — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.plot — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -147,7 +148,7 @@

    sdynpy.signal_processing.sdynpy_frf.plot - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html index a10a0cc..869e508 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.sysmat2frf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.sysmat2frf — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -189,7 +190,7 @@

    sdynpy.signal_processing.sdynpy_frf.sysmat2frf - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html index 460ad0e..23008db 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf.timedata2frf — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf.timedata2frf — SDynPy 0.14.0 documentation @@ -88,6 +88,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -131,7 +132,7 @@

    sdynpy.signal_processing.sdynpy_frf.timedata2frf

    -timedata2frf(references, responses, dt=1, samples_per_average=None, overlap=0.0, method='H1', window=array([1.]), response_fft=<function <lambda>>, reference_fft=<function <lambda>>, response_fft_array=None, reference_fft_array=None)[source]
    +timedata2frf(references, responses, dt=1, samples_per_average=None, overlap=0.0, method='H1', window=array([1.]), response_fft=<function <lambda>>, reference_fft=<function <lambda>>, response_fft_array=None, reference_fft_array=None, return_model_data=False, **lrm_kwargs)[source]

    Creates an FRF matrix given time histories of responses and references

    This function creates a nf x no x ni FRF matrix from the time histories provided.

    @@ -150,7 +151,7 @@

    sdynpy.signal_processing.sdynpy_frf.timedata2frfReturns @@ -167,6 +175,7 @@

    sdynpy.signal_processing.sdynpy_frf.timedata2frf - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html index 8825ae4..e58eae6 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values — SDynPy 0.14.0 documentation @@ -86,6 +86,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -168,7 +169,7 @@

    sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singul
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html index 34580c8..c79e578 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse — SDynPy 0.14.0 documentation @@ -86,6 +86,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -239,7 +240,7 @@

    sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html index 0de8341..ce221f9 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf_inverse — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf_inverse — SDynPy 0.14.0 documentation @@ -83,6 +83,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -171,7 +172,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html index 03b1b8a..0fdced2 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov — SDynPy 0.14.0 documentation @@ -86,6 +86,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -192,7 +193,7 @@

    sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html index 697a247..6022b10 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation — SDynPy 0.14.0 documentation @@ -86,6 +86,7 @@
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -173,7 +174,7 @@

    sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.burst_random.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.burst_random.html index 250e733..b0fa24a 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.burst_random.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.burst_random.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.burst_random — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.burst_random — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.ramp_envelope
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -148,7 +150,7 @@

    sdynpy.signal_processing.sdynpy_generator.burst_random
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.chirp.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.chirp.html index 7b7cbeb..5272acc 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.chirp.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.chirp.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.chirp — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.chirp — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.ramp_envelope
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -148,7 +150,7 @@

    sdynpy.signal_processing.sdynpy_generator.chirp - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.html index 5e2a906..6ece86f 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator — SDynPy 0.14.0 documentation @@ -81,11 +81,13 @@
  • sdynpy.signal_processing.sdynpy_generator.ramp_envelope
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -168,6 +170,9 @@

    sine(frequencies, dt, num_samples[, ...])

    +

    sine_sweep(dt, frequencies, sweep_rates, ...)

    +

    Generates a sweeping sine wave with linear or logarithmic sweep rate

    + @@ -183,7 +188,7 @@
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.pseudorandom.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.pseudorandom.html index 87af25c..9737142 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.pseudorandom.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.pseudorandom.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.pseudorandom — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.pseudorandom — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.ramp_envelope
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -186,7 +188,7 @@

    sdynpy.signal_processing.sdynpy_generator.pseudorandom
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.pulse.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.pulse.html index 519f4d0..a1ba067 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.pulse.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.pulse.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.pulse — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.pulse — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.ramp_envelope
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -148,7 +150,7 @@

    sdynpy.signal_processing.sdynpy_generator.pulse - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.ramp_envelope.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.ramp_envelope.html index b1d17f4..19c38a1 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.ramp_envelope.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.ramp_envelope.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.ramp_envelope — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.ramp_envelope — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.random
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -148,7 +150,7 @@

    sdynpy.signal_processing.sdynpy_generator.ramp_envelope
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.random.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.random.html index a985b83..60da8d3 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.random.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.random.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.random — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.random — SDynPy 0.14.0 documentation @@ -84,11 +84,13 @@
  • sdynpy.signal_processing.sdynpy_generator.sine
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -148,7 +150,7 @@

    sdynpy.signal_processing.sdynpy_generator.random - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html index 1ad9671..896e470 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_generator.sine — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_generator.sine — SDynPy 0.14.0 documentation @@ -28,7 +28,7 @@ - + @@ -84,11 +84,13 @@
  • sine()
    • +
  • sdynpy.signal_processing.sdynpy_generator.sine_sweep
  • sdynpy.signal_processing.sdynpy_geometry_fitting
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -142,13 +144,13 @@

    sdynpy.signal_processing.sdynpy_generator.sine - +
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.html new file mode 100644 index 0000000..93c4f66 --- /dev/null +++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.html @@ -0,0 +1,215 @@ + + + + + + + sdynpy.signal_processing.sdynpy_generator.sine_sweep — SDynPy 0.14.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    sdynpy.signal_processing.sdynpy_generator.sine_sweep

    +
    +
    +sine_sweep(dt, frequencies, sweep_rates, sweep_types, amplitudes=1, phases=0)[source]
    +

    Generates a sweeping sine wave with linear or logarithmic sweep rate

    +
    +
    Parameters
    +
      +

    • dt (float) – The time step of the output signal

      • +

    • frequencies (iterable) – A list of frequency breakpoints for the sweep. Can be ascending or +decending or both. Frequencies are specified in Hz, not rad/s.

      • +

    • sweep_rates (iterable) – A list of sweep rates between the breakpoints. This array should have +one fewer element than the frequencies array. The ith element of this +array specifies the sweep rate between frequencies[i] and +frequencies[i+1]. For a linear sweep, +the rate is in Hz/s. For a logarithmic sweep, the rate is in octave/s.

      • +

    • sweep_types (iterable or str) – The type of sweep to perform between each frequency breakpoint. Can be +‘lin’ or ‘log’. If a string is specified, it will be used for all +breakpoints. Otherwise it should be an array containing strings with +one fewer element than that of the frequencies array.

      • +

    • amplitudes (iterable or float, optional) – Amplitude of the sine wave at each of the frequency breakpoints. Can +be specified as a single floating point value, or as an array with a +value specified for each breakpoint. The default is 1.

      • +

    • phases (iterable or float, optional) – Phases of the sine wave at each of the frequency breakpoints. Can +be specified as a single floating point value, or as an array with a +value specified for each breakpoint. Be aware that modifying the phase +between breakpoints will effectively change the frequency of the signal, +because the phase will change over time. The default is 0.

      • +
    +
    +
    Raises
    +

    ValueError – If the sweep rate and start and end frequency would result in a negative + sweep time, for example if the start frequency is above the end frequency + and a positive sweep rate is specified.

    +
    +
    Returns
    +

    ordinate – A numpy array consisting of the generated sine sweep signal. The length +of the signal will be determined by the frequency breakpoints and sweep +rates.

    +
    +
    Return type
    +

    np.ndarray

    +
    +
    +
    + +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html index 655c6b2..3e2e1c8 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle.html index cf38d75..fed4fa3 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html index a8ffda7..65419ed 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html index 64835c1..b6b1864 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html index 2aa6aff..91b29ed 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html index 76e5a1f..d3482f3 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html index c0447a7..9351c22 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html index 5d7cec4..f16d322 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html index ac0ee9f..5cc0e11 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder - + Sandia National Laboratories diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html index 051f6cb..f9b774b 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud — SDynPy 0.14.0 documentation @@ -92,6 +92,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -151,7 +152,7 @@

    sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud
    - + Sandia National Laboratories
    diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.html index c440aff..beb8c34 100644 --- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.html +++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.html @@ -4,7 +4,7 @@ - sdynpy.signal_processing.sdynpy_geometry_fitting — SDynPy 0.11.0 documentation + sdynpy.signal_processing.sdynpy_geometry_fitting — SDynPy 0.14.0 documentation @@ -29,7 +29,7 @@ - + @@ -89,6 +89,7 @@
  • sdynpy.signal_processing.sdynpy_harmonic
  • sdynpy.signal_processing.sdynpy_integration
    • +
  • sdynpy.signal_processing.sdynpy_lrm
  • sdynpy.signal_processing.sdynpy_rotation
  • sdynpy.signal_processing.sdynpy_srs
    • @@ -191,14 +192,14 @@