From 71f4873bb14a814657bc0914ae72282a937bfdde Mon Sep 17 00:00:00 2001
From: Tom van Woudenberg <t.r.vanwoudenberg@tudelft.nl>
Date: Wed, 27 Nov 2024 16:52:02 +0100
Subject: [PATCH] added files for teacher
---
src/teachers/GA_2_3/Analysis.html | 35 +-
src/teachers/GA_2_3/Analysis_solution.html | 9345 ++++++++++
src/teachers/GA_2_3/CSIRO_Alt_seas_inc.txt | 331 +
src/teachers/GA_2_3/GA_2_3_all_files.zip | Bin 0 -> 758743 bytes
src/teachers/GA_2_3/GA_2_3_solution.pdf | Bin 474642 -> 0 bytes
src/teachers/GA_2_3/README.html | 379 +-
.../GA_2_3/cantileverbeam_acc50Hz.csv | 15002 ++++++++++++++++
7 files changed, 24708 insertions(+), 384 deletions(-)
create mode 100644 src/teachers/GA_2_3/Analysis_solution.html
create mode 100644 src/teachers/GA_2_3/CSIRO_Alt_seas_inc.txt
create mode 100644 src/teachers/GA_2_3/GA_2_3_all_files.zip
delete mode 100644 src/teachers/GA_2_3/GA_2_3_solution.pdf
create mode 100644 src/teachers/GA_2_3/cantileverbeam_acc50Hz.csv
diff --git a/src/teachers/GA_2_3/Analysis.html b/src/teachers/GA_2_3/Analysis.html
index 640cb3ce..45e89a32 100644
--- a/src/teachers/GA_2_3/Analysis.html
+++ b/src/teachers/GA_2_3/Analysis.html
@@ -3,7 +3,7 @@
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>Analysis_solution</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
+<title>Analysis</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
(function() {
function addWidgetsRenderer() {
var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
@@ -7509,7 +7509,7 @@ a.anchor-link {
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="GA-2.3:-Signal-Processing">GA 2.3: Signal Processing<a class="anchor-link" href="#GA-2.3:-Signal-Processing">¶</a></h1><h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
+<h1 id="GA-2.3:-Beam-Beats">GA 2.3: Beam Beats<a class="anchor-link" href="#GA-2.3:-Beam-Beats">¶</a></h1><h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
<style>
.markdown {width:100%; position: relative}
article { position: relative }
@@ -7642,8 +7642,8 @@ $$<p>The result is taken from the last equation in Appendix A. We consider here
<span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t_vec</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'signal'</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'$t \: [s]$'</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'$x(t) \: [V]$'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$t \: [s]$'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$x(t) \: [V]$'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">fr</span><span class="s1">'Sinusoidal signal with $A$=</span><span class="si">{</span><span class="n">A</span><span class="si">}</span><span class="s1"> V, $f_c$=</span><span class="si">{</span><span class="n">f_c</span><span class="si">}</span><span class="s1"> Hz and initial phase $\phi$=</span><span class="si">{</span><span class="n">phi</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> °'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
@@ -7659,6 +7659,17 @@ $$<p>The result is taken from the last equation in Appendix A. We consider here
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h3 id="The-Discrete-Fourier-Transform-(chapter-5)">The Discrete Fourier Transform (chapter 5)<a class="anchor-link" href="#The-Discrete-Fourier-Transform-(chapter-5)">¶</a></h3><p>To discover which frequencies are contained in the signal $x(t)$, we can transform it from the time domain to the frequency domain using the Fourier transform:</p>
$$
X(f) = \int_{-\infty}^{\infty} x(t) e^{-j 2\pi f t} dt
@@ -8136,7 +8147,7 @@ $$<p>Which means that the modulus of the Fourier coefficients is symmetric about
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="Detrending">Detrending<a class="anchor-link" href="#Detrending">¶</a></h3><p>In this project the signal of interest is the damped, harmonic motion of the cantilever-beam. In addition the measurements may contain (unwanted) effects of the sensor (think of an offset, due to imperfect manufacturing and/or prior calibration, or a drift over time, for instance due to temperature changes in the electronics during the experiment). By detrending we mean to remove such effects, prior to our spectral analysis, so that we can start with a nice, zero mean signal. A-priori detrending is good practice and generally yields a cleaner spectrum.</p>
+<h3 id="Detrending">Detrending<a class="anchor-link" href="#Detrending">¶</a></h3><p>In this project the signal of interest is the damped, harmonic motion of the cantilever-beam. In addition the measurements may contain (unwanted) effects of the sensor (think of an offset, due to imperfect manufacturing and/or prior calibration, or a drift over time, for instance due to temperature changes in the electronics during the experiment). By detrending we mean to remove such effects, prior to our spectral analysis, so that we can start with a nice, zero mean signal. A-priori detrending is good practice and generally yields a cleaner spectrum. Next week (week 2.4 on Time Series Analysis) we cover the subject of removing unwanted effects from the signal in more detail, as to obtain a so-called <strong>stationary</strong> signal, meaning that the properties or characteristics of the signal (such as the mean) do not change over time, and, we provide practical means to “stationarize†a given or measured signal.</p>
<p>There are built-in functions in Python to remove such an offset and/or trend (and you're free to use them). But, earlier in the MUDE, in week 1.3, you learned about least-squares parameter estimation, and that's what you can apply to do the detrending (and actually built-in functions just apply the very same principle). Next week, on Time Series Analysis, you will actually be covering (again) the estimation of a trend in a time series of measurements.
As a re-cap for week 1.3: when a series of observations $y_1, …, y_m$ (in our case with $m=N$) is supposed or expected to exhibit a functional linear trend (a straight line in terms of a graph), this can be modelled as</p>
$$
@@ -8332,7 +8343,7 @@ $$<p>with $x_1$ the offset at time $t_1$ (rather than $t=0$), and $x_2$ the slop
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [yr]'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'sea-level height [mm]'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Global Mean Sea-Level (GMSL) rise'</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
</div>
@@ -8378,11 +8389,7 @@ $$<p>with $x_1$ the offset at time $t_1$ (rather than $t=0$), and $x_2$ the slop
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
-<p>
-<b>End of task.</b>
-</p>
-</div>
+<p><strong>Write your answer(s) in this Markdown cell.</strong></p>
</div>
</div>
</div>
@@ -8393,7 +8400,11 @@ $$<p>with $x_1$ the offset at time $t_1$ (rather than $t=0$), and $x_2$ the slop
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</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<p><strong>Write your answer(s) in this Markdown cell.</strong></p>
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>End of task.</b>
+</p>
+</div>
</div>
</div>
</div>
diff --git a/src/teachers/GA_2_3/Analysis_solution.html b/src/teachers/GA_2_3/Analysis_solution.html
new file mode 100644
index 00000000..31dbf404
--- /dev/null
+++ b/src/teachers/GA_2_3/Analysis_solution.html
@@ -0,0 +1,9345 @@
+<!DOCTYPE html>
+
+<html lang="en">
+<head><meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
+<title>Analysis_solution</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
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+| Copyright (c) 2014-2017, PhosphorJS Contributors
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+ flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemContent {
+ flex: 1 1 auto;
+ overflow: hidden;
+}
+
+.lm-CommandPalette-itemShortcut {
+ flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemLabel {
+ overflow: hidden;
+ white-space: nowrap;
+ text-overflow: ellipsis;
+}
+
+.lm-close-icon {
+ border: 1px solid transparent;
+ background-color: transparent;
+ position: absolute;
+ z-index: 1;
+ right: 3%;
+ top: 0;
+ bottom: 0;
+ margin: auto;
+ padding: 7px 0;
+ display: none;
+ vertical-align: middle;
+ outline: 0;
+ cursor: pointer;
+}
+.lm-close-icon:after {
+ content: 'X';
+ display: block;
+ width: 15px;
+ height: 15px;
+ text-align: center;
+ color: #000;
+ font-weight: normal;
+ font-size: 12px;
+ cursor: pointer;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-DockPanel {
+ z-index: 0;
+}
+
+.lm-DockPanel-widget {
+ z-index: 0;
+}
+
+.lm-DockPanel-tabBar {
+ z-index: 1;
+}
+
+.lm-DockPanel-handle {
+ z-index: 2;
+}
+
+.lm-DockPanel-handle.lm-mod-hidden {
+ display: none !important;
+}
+
+.lm-DockPanel-handle:after {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ content: '';
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+ cursor: ew-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='vertical'] {
+ cursor: ns-resize;
+}
+
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+ left: 50%;
+ min-width: 8px;
+ transform: translateX(-50%);
+}
+
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+ top: 50%;
+ min-height: 8px;
+ transform: translateY(-50%);
+}
+
+.lm-DockPanel-overlay {
+ z-index: 3;
+ box-sizing: border-box;
+ pointer-events: none;
+}
+
+.lm-DockPanel-overlay.lm-mod-hidden {
+ display: none !important;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-Menu {
+ z-index: 10000;
+ position: absolute;
+ white-space: nowrap;
+ overflow-x: hidden;
+ overflow-y: auto;
+ outline: none;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.lm-Menu-content {
+ margin: 0;
+ padding: 0;
+ display: table;
+ list-style-type: none;
+}
+
+.lm-Menu-item {
+ display: table-row;
+}
+
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+ display: none !important;
+}
+
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+ display: table-cell;
+ text-align: center;
+}
+
+.lm-Menu-itemLabel {
+ display: table-cell;
+ text-align: left;
+}
+
+.lm-Menu-itemShortcut {
+ display: table-cell;
+ text-align: right;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-MenuBar {
+ outline: none;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.lm-MenuBar-content {
+ margin: 0;
+ padding: 0;
+ display: flex;
+ flex-direction: row;
+ list-style-type: none;
+}
+
+.lm-MenuBar-item {
+ box-sizing: border-box;
+}
+
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+ display: inline-block;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-ScrollBar {
+ display: flex;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+ flex-direction: row;
+}
+
+.lm-ScrollBar[data-orientation='vertical'] {
+ flex-direction: column;
+}
+
+.lm-ScrollBar-button {
+ box-sizing: border-box;
+ flex: 0 0 auto;
+}
+
+.lm-ScrollBar-track {
+ box-sizing: border-box;
+ position: relative;
+ overflow: hidden;
+ flex: 1 1 auto;
+}
+
+.lm-ScrollBar-thumb {
+ box-sizing: border-box;
+ position: absolute;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-SplitPanel-child {
+ z-index: 0;
+}
+
+.lm-SplitPanel-handle {
+ z-index: 1;
+}
+
+.lm-SplitPanel-handle.lm-mod-hidden {
+ display: none !important;
+}
+
+.lm-SplitPanel-handle:after {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ content: '';
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+ cursor: ew-resize;
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+ cursor: ns-resize;
+}
+
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+ left: 50%;
+ min-width: 8px;
+ transform: translateX(-50%);
+}
+
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+ top: 50%;
+ min-height: 8px;
+ transform: translateY(-50%);
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar {
+ display: flex;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] {
+ flex-direction: row;
+ align-items: flex-end;
+}
+
+.lm-TabBar[data-orientation='vertical'] {
+ flex-direction: column;
+ align-items: flex-end;
+}
+
+.lm-TabBar-content {
+ margin: 0;
+ padding: 0;
+ display: flex;
+ flex: 1 1 auto;
+ list-style-type: none;
+}
+
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+ flex-direction: row;
+}
+
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+ flex-direction: column;
+}
+
+.lm-TabBar-tab {
+ display: flex;
+ flex-direction: row;
+ box-sizing: border-box;
+ overflow: hidden;
+ touch-action: none; /* Disable native Drag/Drop */
+}
+
+.lm-TabBar-tabIcon,
+.lm-TabBar-tabCloseIcon {
+ flex: 0 0 auto;
+}
+
+.lm-TabBar-tabLabel {
+ flex: 1 1 auto;
+ overflow: hidden;
+ white-space: nowrap;
+}
+
+.lm-TabBar-tabInput {
+ user-select: all;
+ width: 100%;
+ box-sizing: border-box;
+}
+
+.lm-TabBar-tab.lm-mod-hidden {
+ display: none !important;
+}
+
+.lm-TabBar-addButton.lm-mod-hidden {
+ display: none !important;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
+ position: relative;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
+ left: 0;
+ transition: left 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
+ top: 0;
+ transition: top 150ms ease;
+}
+
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
+ transition: none;
+}
+
+.lm-TabBar-tabLabel .lm-TabBar-tabInput {
+ user-select: all;
+ width: 100%;
+ box-sizing: border-box;
+ background: inherit;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-TabPanel-tabBar {
+ z-index: 1;
+}
+
+.lm-TabPanel-stackedPanel {
+ z-index: 0;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+ display: flex;
+ flex-direction: column;
+ align-items: stretch;
+}
+
+.jp-Collapse-header {
+ padding: 1px 12px;
+ background-color: var(--jp-layout-color1);
+ border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+ color: var(--jp-ui-font-color1);
+ cursor: pointer;
+ display: flex;
+ align-items: center;
+ font-size: var(--jp-ui-font-size0);
+ font-weight: 600;
+ text-transform: uppercase;
+ user-select: none;
+}
+
+.jp-Collapser-icon {
+ height: 16px;
+}
+
+.jp-Collapse-header-collapsed .jp-Collapser-icon {
+ transform: rotate(-90deg);
+ margin: auto 0;
+}
+
+.jp-Collapser-title {
+ line-height: 25px;
+}
+
+.jp-Collapse-contents {
+ padding: 0 12px;
+ background-color: var(--jp-layout-color1);
+ color: var(--jp-ui-font-color1);
+ overflow: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+ --jp-icon-add-above: url(data:image/svg+xml;base64,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);
+ --jp-icon-add-below: url(data:image/svg+xml;base64,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);
+ --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-bell: url(data:image/svg+xml;base64,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);
+ --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
+ --jp-icon-bug: url(data:image/svg+xml;base64,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);
+ --jp-icon-build: url(data:image/svg+xml;base64,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);
+ --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+ --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-case-sensitive: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KICA8ZyBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiM0MTQxNDEiPgogICAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiAgPC9nPgogIDxnIGNsYXNzPSJqcC1pY29uLWFjY2VudDIiIGZpbGw9IiNGRkYiPgogICAgPHBhdGggZD0iTTcuNiw4aDAuOWwzLjUsOGgtMS4xTDEwLDE0SDZsLTAuOSwySDRMNy42LDh6IE04LDkuMUw2LjQsMTNoMy4yTDgsOS4xeiIvPgogICAgPHBhdGggZD0iTTE2LjYsOS44Yy0wLjIsMC4xLTAuNCwwLjEtMC43LDAuMWMtMC4yLDAtMC40LTAuMS0wLjYtMC4yYy0wLjEtMC4xLTAuMi0wLjQtMC4yLTAuNyBjLTAuMywwLjMtMC42LDAuNS0wLjksMC43Yy0wLjMsMC4xLTAuNywwLjItMS4xLDAuMmMtMC4zLDAtMC41LDAtMC43LTAuMWMtMC4yLTAuMS0wLjQtMC4yLTAuNi0wLjNjLTAuMi0wLjEtMC4zLTAuMy0wLjQtMC41IGMtMC4xLTAuMi0wLjEtMC40LTAuMS0wLjdjMC0wLjMsMC4xLTAuNiwwLjItMC44YzAuMS0wLjIsMC4zLTAuNCwwLjQtMC41QzEyLDcsMTIuMiw2LjksMTIuNSw2LjhjMC4yLTAuMSwwLjUtMC4xLDAuNy0wLjIgYzAuMy0wLjEsMC41LTAuMSwwLjctMC4xYzAuMiwwLDAuNC0wLjEsMC42LTAuMWMwLjIsMCwwLjMtMC4xLDAuNC0wLjJjMC4xLTAuMSwwLjItMC4yLDAuMi0wLjRjMC0xLTEuMS0xLTEuMy0xIGMtMC40LDAtMS40LDAtMS40LDEuMmgtMC45YzAtMC40LDAuMS0wLjcsMC4yLTFjMC4xLTAuMiwwLjMtMC40LDAuNS0wLjZjMC4yLTAuMiwwLjUtMC4zLDAuOC0wLjNDMTMuMyw0LDEzLjYsNCwxMy45LDQgYzAuMywwLDAuNSwwLDAuOCwwLjFjMC4zLDAsMC41LDAuMSwwLjcsMC4yYzAuMiwwLjEsMC40LDAuMywwLjUsMC41QzE2LDUsMTYsNS4yLDE2LDUuNnYyLjljMCwwLjIsMCwwLjQsMCwwLjUgYzAsMC4xLDAuMSwwLjIsMC4zLDAuMmMwLjEsMCwwLjIsMCwwLjMsMFY5Ljh6IE0xNS4yLDYuOWMtMS4yLDAuNi0zLjEsMC4yLTMuMSwxLjRjMCwxLjQsMy4xLDEsMy4xLTAuNVY2Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-clear: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8bWFzayBpZD0iZG9udXRIb2xlIj4KICAgIDxyZWN0IHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgZmlsbD0id2hpdGUiIC8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSI4IiBmaWxsPSJibGFjayIvPgogIDwvbWFzaz4KCiAgPGcgY2xhc3M9ImpwLWljb24zIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxyZWN0IGhlaWdodD0iMTgiIHdpZHRoPSIyIiB4PSIxMSIgeT0iMyIgdHJhbnNmb3JtPSJyb3RhdGUoMzE1LCAxMiwgMTIpIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIxMCIgbWFzaz0idXJsKCNkb251dEhvbGUpIi8+CiAgPC9nPgo8L3N2Zz4K);
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+ --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-file: url(data:image/svg+xml;base64,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);
+ --jp-icon-filter-dot: url(data:image/svg+xml;base64,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);
+ --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-filter: url(data:image/svg+xml;base64,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);
+ --jp-icon-folder-favorite: url(data:image/svg+xml;base64,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);
+ --jp-icon-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY4YzAtMS4xLS45LTItMi0yaC04bC0yLTJ6Ii8+Cjwvc3ZnPgo=);
+ --jp-icon-home: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjRweCIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjRweCIgZmlsbD0iIzAwMDAwMCI+CiAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPjxwYXRoIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xMCAyMHYtNmg0djZoNXYtOGgzTDEyIDMgMiAxMmgzdjh6Ii8+Cjwvc3ZnPgo=);
+ --jp-icon-html5: url(data:image/svg+xml;base64,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);
+ --jp-icon-image: url(data:image/svg+xml;base64,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);
+ --jp-icon-info: url(data:image/svg+xml;base64,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);
+ --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaW5zcGVjdG9yLWljb24tY29sb3IganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
+ --jp-icon-json: url(data:image/svg+xml;base64,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);
+ --jp-icon-julia: url(data:image/svg+xml;base64,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);
+ --jp-icon-jupyter-favicon: url(data:image/svg+xml;base64,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);
+ --jp-icon-jupyter: url(data:image/svg+xml;base64,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);
+ --jp-icon-jupyterlab-wordmark: 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+ --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+ --jp-icon-keyboard: url(data:image/svg+xml;base64,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);
+ --jp-icon-launch: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMzIgMzIiIHdpZHRoPSIzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yNiwyOEg2YTIuMDAyNywyLjAwMjcsMCwwLDEtMi0yVjZBMi4wMDI3LDIuMDAyNywwLDAsMSw2LDRIMTZWNkg2VjI2SDI2VjE2aDJWMjZBMi4wMDI3LDIuMDAyNywwLDAsMSwyNiwyOFoiLz4KICAgIDxwb2x5Z29uIHBvaW50cz0iMjAgMiAyMCA0IDI2LjU4NiA0IDE4IDEyLjU4NiAxOS40MTQgMTQgMjggNS40MTQgMjggMTIgMzAgMTIgMzAgMiAyMCAyIi8+CiAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+ --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+ --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4K);
+ --jp-icon-markdown: url(data:image/svg+xml;base64,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);
+ --jp-icon-move-down: url(data:image/svg+xml;base64,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);
+ --jp-icon-move-up: url(data:image/svg+xml;base64,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);
+ --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+ --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+ --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-palette: url(data:image/svg+xml;base64,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);
+ --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-pdf: url(data:image/svg+xml;base64,PHN2ZwogICB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyMiAyMiIgd2lkdGg9IjE2Ij4KICAgIDxwYXRoIHRyYW5zZm9ybT0icm90YXRlKDQ1KSIgY2xhc3M9ImpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iI0ZGMkEyQSIKICAgICAgIGQ9Im0gMjIuMzQ0MzY5LC0zLjAxNjM2NDIgaCA1LjYzODYwNCB2IDEuNTc5MjQzMyBoIC0zLjU0OTIyNyB2IDEuNTA4NjkyOTkgaCAzLjMzNzU3NiBWIDEuNjUwODE1NCBoIC0zLjMzNzU3NiB2IDMuNDM1MjYxMyBoIC0yLjA4OTM3NyB6IG0gLTcuMTM2NDQ0LDEuNTc5MjQzMyB2IDQuOTQzOTU0MyBoIDAuNzQ4OTIgcSAxLjI4MDc2MSwwIDEuOTUzNzAzLC0wLjYzNDk1MzUgMC42NzgzNjksLTAuNjM0OTUzNSAwLjY3ODM2OSwtMS44NDUxNjQxIDAsLTEuMjA0NzgzNTUgLTAuNjcyOTQyLC0xLjgzNDMxMDExIC0wLjY3Mjk0MiwtMC42Mjk1MjY1OSAtMS45NTkxMywtMC42Mjk1MjY1OSB6IG0gLTIuMDg5Mzc3LC0xLjU3OTI0MzMgaCAyLjIwMzM0MyBxIDEuODQ1MTY0LDAgMi43NDYwMzksMC4yNjU5MjA3IDAuOTA2MzAxLDAuMjYwNDkzNyAxLjU1MjEwOCwwLjg5MDAyMDMgMC41Njk4MywwLjU0ODEyMjMgMC44NDY2MDUsMS4yNjQ0ODAwNiAwLjI3Njc3NCwwLjcxNjM1NzgxIDAuMjc2Nzc0LDEuNjIyNjU4OTQgMCwwLjkxNzE1NTEgLTAuMjc2Nzc0LDEuNjM4OTM5OSAtMC4yNzY3NzUsMC43MTYzNTc4IC0wLjg0NjYwNSwxLjI2NDQ4IC0wLjY1MTIzNCwwLjYyOTUyNjYgLTEuNTYyOTYyLDAuODk1NDQ3MyAtMC45MTE3MjgsMC4yNjA0OTM3IC0yLjczNTE4NSwwLjI2MDQ5MzcgaCAtMi4yMDMzNDMgeiBtIC04LjE0NTg1NjUsMCBoIDMuNDY3ODIzIHEgMS41NDY2ODE2LDAgMi4zNzE1Nzg1LDAuNjg5MjIzIDAuODMwMzI0LDAuNjgzNzk2MSAwLjgzMDMyNCwxLjk1MzcwMzE0IDAsMS4yNzUzMzM5NyAtMC44MzAzMjQsMS45NjQ1NTcwNiBRIDkuOTg3MTk2MSwyLjI3NDkxNSA4LjQ0MDUxNDUsMi4yNzQ5MTUgSCA3LjA2MjA2ODQgViA1LjA4NjA3NjcgSCA0Ljk3MjY5MTUgWiBtIDIuMDg5Mzc2OSwxLjUxNDExOTkgdiAyLjI2MzAzOTQzIGggMS4xNTU5NDEgcSAwLjYwNzgxODgsMCAwLjkzODg2MjksLTAuMjkzMDU1NDcgMC4zMzEwNDQxLC0wLjI5ODQ4MjQxIDAuMzMxMDQ0MSwtMC44NDExNzc3MiAwLC0wLjU0MjY5NTMxIC0wLjMzMTA0NDEsLTAuODM1NzUwNzQgLTAuMzMxMDQ0MSwtMC4yOTMwNTU1IC0wLjkzODg2MjksLTAuMjkzMDU1NSB6IgovPgo8L3N2Zz4K);
+ --jp-icon-python: url(data:image/svg+xml;base64,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);
+ --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+ --jp-icon-react: url(data:image/svg+xml;base64,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);
+ --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+ --jp-icon-regex: url(data:image/svg+xml;base64,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);
+ --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-search: url(data:image/svg+xml;base64,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);
+ --jp-icon-settings: url(data:image/svg+xml;base64,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);
+ --jp-icon-share: url(data:image/svg+xml;base64,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);
+ --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+ --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-tag: url(data:image/svg+xml;base64,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);
+ --jp-icon-terminal: url(data:image/svg+xml;base64,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);
+ --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==);
+ --jp-icon-toc: url(data:image/svg+xml;base64,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);
+ --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
+ --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+ --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-users: url(data:image/svg+xml;base64,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);
+ --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+ --jp-icon-word: url(data:image/svg+xml;base64,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);
+ --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddAboveIcon {
+ background-image: var(--jp-icon-add-above);
+}
+
+.jp-AddBelowIcon {
+ background-image: var(--jp-icon-add-below);
+}
+
+.jp-AddIcon {
+ background-image: var(--jp-icon-add);
+}
+
+.jp-BellIcon {
+ background-image: var(--jp-icon-bell);
+}
+
+.jp-BugDotIcon {
+ background-image: var(--jp-icon-bug-dot);
+}
+
+.jp-BugIcon {
+ background-image: var(--jp-icon-bug);
+}
+
+.jp-BuildIcon {
+ background-image: var(--jp-icon-build);
+}
+
+.jp-CaretDownEmptyIcon {
+ background-image: var(--jp-icon-caret-down-empty);
+}
+
+.jp-CaretDownEmptyThinIcon {
+ background-image: var(--jp-icon-caret-down-empty-thin);
+}
+
+.jp-CaretDownIcon {
+ background-image: var(--jp-icon-caret-down);
+}
+
+.jp-CaretLeftIcon {
+ background-image: var(--jp-icon-caret-left);
+}
+
+.jp-CaretRightIcon {
+ background-image: var(--jp-icon-caret-right);
+}
+
+.jp-CaretUpEmptyThinIcon {
+ background-image: var(--jp-icon-caret-up-empty-thin);
+}
+
+.jp-CaretUpIcon {
+ background-image: var(--jp-icon-caret-up);
+}
+
+.jp-CaseSensitiveIcon {
+ background-image: var(--jp-icon-case-sensitive);
+}
+
+.jp-CheckIcon {
+ background-image: var(--jp-icon-check);
+}
+
+.jp-CircleEmptyIcon {
+ background-image: var(--jp-icon-circle-empty);
+}
+
+.jp-CircleIcon {
+ background-image: var(--jp-icon-circle);
+}
+
+.jp-ClearIcon {
+ background-image: var(--jp-icon-clear);
+}
+
+.jp-CloseIcon {
+ background-image: var(--jp-icon-close);
+}
+
+.jp-CodeCheckIcon {
+ background-image: var(--jp-icon-code-check);
+}
+
+.jp-CodeIcon {
+ background-image: var(--jp-icon-code);
+}
+
+.jp-CollapseAllIcon {
+ background-image: var(--jp-icon-collapse-all);
+}
+
+.jp-ConsoleIcon {
+ background-image: var(--jp-icon-console);
+}
+
+.jp-CopyIcon {
+ background-image: var(--jp-icon-copy);
+}
+
+.jp-CopyrightIcon {
+ background-image: var(--jp-icon-copyright);
+}
+
+.jp-CutIcon {
+ background-image: var(--jp-icon-cut);
+}
+
+.jp-DeleteIcon {
+ background-image: var(--jp-icon-delete);
+}
+
+.jp-DownloadIcon {
+ background-image: var(--jp-icon-download);
+}
+
+.jp-DuplicateIcon {
+ background-image: var(--jp-icon-duplicate);
+}
+
+.jp-EditIcon {
+ background-image: var(--jp-icon-edit);
+}
+
+.jp-EllipsesIcon {
+ background-image: var(--jp-icon-ellipses);
+}
+
+.jp-ErrorIcon {
+ background-image: var(--jp-icon-error);
+}
+
+.jp-ExpandAllIcon {
+ background-image: var(--jp-icon-expand-all);
+}
+
+.jp-ExtensionIcon {
+ background-image: var(--jp-icon-extension);
+}
+
+.jp-FastForwardIcon {
+ background-image: var(--jp-icon-fast-forward);
+}
+
+.jp-FileIcon {
+ background-image: var(--jp-icon-file);
+}
+
+.jp-FileUploadIcon {
+ background-image: var(--jp-icon-file-upload);
+}
+
+.jp-FilterDotIcon {
+ background-image: var(--jp-icon-filter-dot);
+}
+
+.jp-FilterIcon {
+ background-image: var(--jp-icon-filter);
+}
+
+.jp-FilterListIcon {
+ background-image: var(--jp-icon-filter-list);
+}
+
+.jp-FolderFavoriteIcon {
+ background-image: var(--jp-icon-folder-favorite);
+}
+
+.jp-FolderIcon {
+ background-image: var(--jp-icon-folder);
+}
+
+.jp-HomeIcon {
+ background-image: var(--jp-icon-home);
+}
+
+.jp-Html5Icon {
+ background-image: var(--jp-icon-html5);
+}
+
+.jp-ImageIcon {
+ background-image: var(--jp-icon-image);
+}
+
+.jp-InfoIcon {
+ background-image: var(--jp-icon-info);
+}
+
+.jp-InspectorIcon {
+ background-image: var(--jp-icon-inspector);
+}
+
+.jp-JsonIcon {
+ background-image: var(--jp-icon-json);
+}
+
+.jp-JuliaIcon {
+ background-image: var(--jp-icon-julia);
+}
+
+.jp-JupyterFaviconIcon {
+ background-image: var(--jp-icon-jupyter-favicon);
+}
+
+.jp-JupyterIcon {
+ background-image: var(--jp-icon-jupyter);
+}
+
+.jp-JupyterlabWordmarkIcon {
+ background-image: var(--jp-icon-jupyterlab-wordmark);
+}
+
+.jp-KernelIcon {
+ background-image: var(--jp-icon-kernel);
+}
+
+.jp-KeyboardIcon {
+ background-image: var(--jp-icon-keyboard);
+}
+
+.jp-LaunchIcon {
+ background-image: var(--jp-icon-launch);
+}
+
+.jp-LauncherIcon {
+ background-image: var(--jp-icon-launcher);
+}
+
+.jp-LineFormIcon {
+ background-image: var(--jp-icon-line-form);
+}
+
+.jp-LinkIcon {
+ background-image: var(--jp-icon-link);
+}
+
+.jp-ListIcon {
+ background-image: var(--jp-icon-list);
+}
+
+.jp-MarkdownIcon {
+ background-image: var(--jp-icon-markdown);
+}
+
+.jp-MoveDownIcon {
+ background-image: var(--jp-icon-move-down);
+}
+
+.jp-MoveUpIcon {
+ background-image: var(--jp-icon-move-up);
+}
+
+.jp-NewFolderIcon {
+ background-image: var(--jp-icon-new-folder);
+}
+
+.jp-NotTrustedIcon {
+ background-image: var(--jp-icon-not-trusted);
+}
+
+.jp-NotebookIcon {
+ background-image: var(--jp-icon-notebook);
+}
+
+.jp-NumberingIcon {
+ background-image: var(--jp-icon-numbering);
+}
+
+.jp-OfflineBoltIcon {
+ background-image: var(--jp-icon-offline-bolt);
+}
+
+.jp-PaletteIcon {
+ background-image: var(--jp-icon-palette);
+}
+
+.jp-PasteIcon {
+ background-image: var(--jp-icon-paste);
+}
+
+.jp-PdfIcon {
+ background-image: var(--jp-icon-pdf);
+}
+
+.jp-PythonIcon {
+ background-image: var(--jp-icon-python);
+}
+
+.jp-RKernelIcon {
+ background-image: var(--jp-icon-r-kernel);
+}
+
+.jp-ReactIcon {
+ background-image: var(--jp-icon-react);
+}
+
+.jp-RedoIcon {
+ background-image: var(--jp-icon-redo);
+}
+
+.jp-RefreshIcon {
+ background-image: var(--jp-icon-refresh);
+}
+
+.jp-RegexIcon {
+ background-image: var(--jp-icon-regex);
+}
+
+.jp-RunIcon {
+ background-image: var(--jp-icon-run);
+}
+
+.jp-RunningIcon {
+ background-image: var(--jp-icon-running);
+}
+
+.jp-SaveIcon {
+ background-image: var(--jp-icon-save);
+}
+
+.jp-SearchIcon {
+ background-image: var(--jp-icon-search);
+}
+
+.jp-SettingsIcon {
+ background-image: var(--jp-icon-settings);
+}
+
+.jp-ShareIcon {
+ background-image: var(--jp-icon-share);
+}
+
+.jp-SpreadsheetIcon {
+ background-image: var(--jp-icon-spreadsheet);
+}
+
+.jp-StopIcon {
+ background-image: var(--jp-icon-stop);
+}
+
+.jp-TabIcon {
+ background-image: var(--jp-icon-tab);
+}
+
+.jp-TableRowsIcon {
+ background-image: var(--jp-icon-table-rows);
+}
+
+.jp-TagIcon {
+ background-image: var(--jp-icon-tag);
+}
+
+.jp-TerminalIcon {
+ background-image: var(--jp-icon-terminal);
+}
+
+.jp-TextEditorIcon {
+ background-image: var(--jp-icon-text-editor);
+}
+
+.jp-TocIcon {
+ background-image: var(--jp-icon-toc);
+}
+
+.jp-TreeViewIcon {
+ background-image: var(--jp-icon-tree-view);
+}
+
+.jp-TrustedIcon {
+ background-image: var(--jp-icon-trusted);
+}
+
+.jp-UndoIcon {
+ background-image: var(--jp-icon-undo);
+}
+
+.jp-UserIcon {
+ background-image: var(--jp-icon-user);
+}
+
+.jp-UsersIcon {
+ background-image: var(--jp-icon-users);
+}
+
+.jp-VegaIcon {
+ background-image: var(--jp-icon-vega);
+}
+
+.jp-WordIcon {
+ background-image: var(--jp-icon-word);
+}
+
+.jp-YamlIcon {
+ background-image: var(--jp-icon-yaml);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+.jp-Icon,
+.jp-MaterialIcon {
+ background-position: center;
+ background-repeat: no-repeat;
+ background-size: 16px;
+ min-width: 16px;
+ min-height: 16px;
+}
+
+.jp-Icon-cover {
+ background-position: center;
+ background-repeat: no-repeat;
+ background-size: cover;
+}
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+ background-size: 16px;
+ min-width: 16px;
+ min-height: 16px;
+}
+
+.jp-Icon-18 {
+ background-size: 18px;
+ min-width: 18px;
+ min-height: 18px;
+}
+
+.jp-Icon-20 {
+ background-size: 20px;
+ min-width: 20px;
+ min-height: 20px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.lm-TabBar .lm-TabBar-addButton {
+ align-items: center;
+ display: flex;
+ padding: 4px;
+ padding-bottom: 5px;
+ margin-right: 1px;
+ background-color: var(--jp-layout-color2);
+}
+
+.lm-TabBar .lm-TabBar-addButton:hover {
+ background-color: var(--jp-layout-color1);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-tab {
+ width: var(--jp-private-horizontal-tab-width);
+}
+
+.lm-DockPanel-tabBar .lm-TabBar-content {
+ flex: unset;
+}
+
+.lm-DockPanel-tabBar[data-orientation='horizontal'] {
+ flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+ fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[fill] {
+ fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[fill] {
+ fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[fill] {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[fill] {
+ fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon0[stroke] {
+ stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon1[stroke] {
+ stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon2[stroke] {
+ stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon3[stroke] {
+ stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon4[stroke] {
+ stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[fill] {
+ fill: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[fill] {
+ fill: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[fill] {
+ fill: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[fill] {
+ fill: var(--jp-layout-color4);
+}
+
+.jp-icon-accent0[stroke] {
+ stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-accent1[stroke] {
+ stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-accent2[stroke] {
+ stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-accent3[stroke] {
+ stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-accent4[stroke] {
+ stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+ fill: none;
+}
+
+.jp-icon-none[stroke] {
+ stroke: none;
+}
+
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+ fill: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[fill] {
+ fill: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[fill] {
+ fill: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[fill] {
+ fill: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[fill] {
+ fill: var(--jp-brand-color4);
+}
+
+.jp-icon-brand0[stroke] {
+ stroke: var(--jp-brand-color0);
+}
+
+.jp-icon-brand1[stroke] {
+ stroke: var(--jp-brand-color1);
+}
+
+.jp-icon-brand2[stroke] {
+ stroke: var(--jp-brand-color2);
+}
+
+.jp-icon-brand3[stroke] {
+ stroke: var(--jp-brand-color3);
+}
+
+.jp-icon-brand4[stroke] {
+ stroke: var(--jp-brand-color4);
+}
+
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+ fill: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[fill] {
+ fill: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[fill] {
+ fill: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[fill] {
+ fill: var(--jp-warn-color3);
+}
+
+.jp-icon-warn0[stroke] {
+ stroke: var(--jp-warn-color0);
+}
+
+.jp-icon-warn1[stroke] {
+ stroke: var(--jp-warn-color1);
+}
+
+.jp-icon-warn2[stroke] {
+ stroke: var(--jp-warn-color2);
+}
+
+.jp-icon-warn3[stroke] {
+ stroke: var(--jp-warn-color3);
+}
+
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+ fill: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[fill] {
+ fill: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[fill] {
+ fill: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[fill] {
+ fill: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-contrast0[stroke] {
+ stroke: var(--jp-icon-contrast-color0);
+}
+
+.jp-icon-contrast1[stroke] {
+ stroke: var(--jp-icon-contrast-color1);
+}
+
+.jp-icon-contrast2[stroke] {
+ stroke: var(--jp-icon-contrast-color2);
+}
+
+.jp-icon-contrast3[stroke] {
+ stroke: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-dot[fill] {
+ fill: var(--jp-warn-color0);
+}
+
+.jp-jupyter-icon-color[fill] {
+ fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
+}
+
+.jp-notebook-icon-color[fill] {
+ fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
+}
+
+.jp-json-icon-color[fill] {
+ fill: var(--jp-json-icon-color, var(--jp-warn-color1));
+}
+
+.jp-console-icon-color[fill] {
+ fill: var(--jp-console-icon-color, white);
+}
+
+.jp-console-icon-background-color[fill] {
+ fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
+}
+
+.jp-terminal-icon-color[fill] {
+ fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
+}
+
+.jp-terminal-icon-background-color[fill] {
+ fill: var(
+ --jp-terminal-icon-background-color,
+ var(--jp-inverse-layout-color2)
+ );
+}
+
+.jp-text-editor-icon-color[fill] {
+ fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
+}
+
+.jp-inspector-icon-color[fill] {
+ fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
+}
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+ fill: #fff;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+ fill: var(--jp-brand-color1);
+}
+
+/* stylelint-disable selector-max-class, selector-max-compound-selectors */
+
+/**
+* TODO: come up with non css-hack solution for showing the busy icon on top
+* of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+ .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+ > .lm-TabBar-tabCloseIcon
+ > :not(:hover)
+ > .jp-icon3[fill] {
+ fill: none;
+}
+
+.lm-DockPanel-tabBar
+ .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+ > .lm-TabBar-tabCloseIcon
+ > :not(:hover)
+ > .jp-icon-busy[fill] {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+/* stylelint-enable selector-max-class, selector-max-compound-selectors */
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+ fill: #fff;
+}
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+ fill: var(--jp-brand-color1);
+}
+
+/* special handling for splash icon CSS. While the theme CSS reloads during
+ splash, the splash icon can loose theming. To prevent that, we set a
+ default for its color variable */
+:root {
+ --jp-warn-color0: var(--md-orange-700);
+}
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+ margin-right: 4px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[fill] {
+ fill: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[fill] {
+ fill: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[fill] {
+ fill: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[fill] {
+ fill: var(--jp-layout-color4);
+}
+
+.jp-icon-alt .jp-icon0[stroke] {
+ stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-alt .jp-icon1[stroke] {
+ stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-alt .jp-icon2[stroke] {
+ stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-alt .jp-icon3[stroke] {
+ stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-alt .jp-icon4[stroke] {
+ stroke: var(--jp-layout-color4);
+}
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+ fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[fill] {
+ fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[fill] {
+ fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[fill] {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[fill] {
+ fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+ stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-alt .jp-icon-accent1[stroke] {
+ stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-alt .jp-icon-accent2[stroke] {
+ stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-alt .jp-icon-accent3[stroke] {
+ stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-alt .jp-icon-accent4[stroke] {
+ stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
+ display: none !important;
+}
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+ fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+ fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+ fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+ fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+ fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+ fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+ fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+ fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+ stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+ stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+ stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+ stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+ stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+ fill: none;
+}
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+ stroke: none;
+}
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+ fill: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+ fill: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+ fill: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+ fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+ stroke: var(--jp-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+ stroke: var(--jp-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+ stroke: var(--jp-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+ stroke: var(--jp-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+ stroke: var(--jp-layout-color4);
+}
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+ fill: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+ fill: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+ fill: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+ fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color0);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color1);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color2);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color3);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+ stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+ width: 100%;
+ height: 100%;
+}
+
+.jp-IFrame > iframe {
+ border: none;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+ position: relative;
+}
+
+body.lm-mod-override-cursor .jp-IFrame::before {
+ content: '';
+ position: absolute;
+ top: 0;
+ left: 0;
+ right: 0;
+ bottom: 0;
+ background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+ position: fixed;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FormGroup-content fieldset {
+ border: none;
+ padding: 0;
+ min-width: 0;
+ width: 100%;
+}
+
+/* stylelint-disable selector-max-type */
+
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
+ font-size: var(--jp-content-font-size2);
+ border-color: var(--jp-input-border-color);
+ border-style: solid;
+ border-radius: var(--jp-border-radius);
+ border-width: 1px;
+ padding: 6px 8px;
+ background: none;
+ color: var(--jp-ui-font-color0);
+ height: inherit;
+}
+
+.jp-FormGroup-content fieldset input[type='checkbox'] {
+ position: relative;
+ top: 2px;
+ margin-left: 0;
+}
+
+.jp-FormGroup-content button.jp-mod-styled {
+ cursor: pointer;
+}
+
+.jp-FormGroup-content .checkbox label {
+ cursor: pointer;
+ font-size: var(--jp-content-font-size1);
+}
+
+.jp-FormGroup-content .jp-root > fieldset > legend {
+ display: none;
+}
+
+.jp-FormGroup-content .jp-root > fieldset > p {
+ display: none;
+}
+
+/** copy of `input.jp-mod-styled:focus` style */
+.jp-FormGroup-content fieldset input:focus,
+.jp-FormGroup-content fieldset select:focus {
+ -moz-outline-radius: unset;
+ outline: var(--jp-border-width) solid var(--md-blue-500);
+ outline-offset: -1px;
+ box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-FormGroup-content fieldset input:hover:not(:focus),
+.jp-FormGroup-content fieldset select:hover:not(:focus) {
+ background-color: var(--jp-border-color2);
+}
+
+/* stylelint-enable selector-max-type */
+
+.jp-FormGroup-content .checkbox .field-description {
+ /* Disable default description field for checkbox:
+ because other widgets do not have description fields,
+ we add descriptions to each widget on the field level.
+ */
+ display: none;
+}
+
+.jp-FormGroup-content #root__description {
+ display: none;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator {
+ width: 5px;
+ background-color: var(--jp-brand-color2);
+ margin-top: 0;
+ margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
+ flex-shrink: 0;
+}
+
+.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
+ background-color: var(--jp-error-color0);
+ margin-right: 0.5em;
+}
+
+/* RJSF ARRAY style */
+
+.jp-arrayFieldWrapper legend {
+ font-size: var(--jp-content-font-size2);
+ color: var(--jp-ui-font-color0);
+ flex-basis: 100%;
+ padding: 4px 0;
+ font-weight: var(--jp-content-heading-font-weight);
+ border-bottom: 1px solid var(--jp-border-color2);
+}
+
+.jp-arrayFieldWrapper .field-description {
+ padding: 4px 0;
+ white-space: pre-wrap;
+}
+
+.jp-arrayFieldWrapper .array-item {
+ width: 100%;
+ border: 1px solid var(--jp-border-color2);
+ border-radius: 4px;
+ margin: 4px;
+}
+
+.jp-ArrayOperations {
+ display: flex;
+ margin-left: 8px;
+}
+
+.jp-ArrayOperationsButton {
+ margin: 2px;
+}
+
+.jp-ArrayOperationsButton .jp-icon3[fill] {
+ fill: var(--jp-ui-font-color0);
+}
+
+button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
+ cursor: not-allowed;
+ opacity: 0.5;
+}
+
+/* RJSF form validation error */
+
+.jp-FormGroup-content .validationErrors {
+ color: var(--jp-error-color0);
+}
+
+/* Hide panel level error as duplicated the field level error */
+.jp-FormGroup-content .panel.errors {
+ display: none;
+}
+
+/* RJSF normal content (settings-editor) */
+
+.jp-FormGroup-contentNormal {
+ display: flex;
+ align-items: center;
+ flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
+ margin-left: 7px;
+ color: var(--jp-ui-font-color0);
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-description {
+ flex-basis: 100%;
+ padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-default {
+ flex-basis: 100%;
+ padding: 4px 7px;
+}
+
+.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
+ font-size: var(--jp-content-font-size1);
+ font-weight: normal;
+ min-width: 120px;
+}
+
+.jp-FormGroup-contentNormal fieldset:not(:first-child) {
+ margin-left: 7px;
+}
+
+.jp-FormGroup-contentNormal .field-array-of-string .array-item {
+ /* Display `jp-ArrayOperations` buttons side-by-side with content except
+ for small screens where flex-wrap will place them one below the other.
+ */
+ display: flex;
+ align-items: center;
+ flex-wrap: wrap;
+}
+
+.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
+ padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+ margin-top: 2px;
+}
+
+/* RJSF compact content (metadata-form) */
+
+.jp-FormGroup-content.jp-FormGroup-contentCompact {
+ width: 100%;
+}
+
+.jp-FormGroup-contentCompact .form-group {
+ display: flex;
+ padding: 0.5em 0.2em 0.5em 0;
+}
+
+.jp-FormGroup-contentCompact
+ .jp-FormGroup-compactTitle
+ .jp-FormGroup-description {
+ font-size: var(--jp-ui-font-size1);
+ color: var(--jp-ui-font-color2);
+}
+
+.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
+ padding-bottom: 0.3em;
+}
+
+.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
+ width: 100%;
+ box-sizing: border-box;
+}
+
+.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
+ padding-bottom: 7px;
+}
+
+.jp-FormGroup-contentCompact
+ .jp-objectFieldWrapper
+ .jp-objectFieldWrapper
+ .form-group {
+ padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+ margin-top: 2px;
+}
+
+.jp-FormGroup-contentCompact ul.error-detail {
+ margin-block-start: 0.5em;
+ margin-block-end: 0.5em;
+ padding-inline-start: 1em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-SidePanel {
+ display: flex;
+ flex-direction: column;
+ min-width: var(--jp-sidebar-min-width);
+ overflow-y: auto;
+ color: var(--jp-ui-font-color1);
+ background: var(--jp-layout-color1);
+ font-size: var(--jp-ui-font-size1);
+}
+
+.jp-SidePanel-header {
+ flex: 0 0 auto;
+ display: flex;
+ border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
+ font-size: var(--jp-ui-font-size0);
+ font-weight: 600;
+ letter-spacing: 1px;
+ margin: 0;
+ padding: 2px;
+ text-transform: uppercase;
+}
+
+.jp-SidePanel-toolbar {
+ flex: 0 0 auto;
+}
+
+.jp-SidePanel-content {
+ flex: 1 1 auto;
+}
+
+.jp-SidePanel-toolbar,
+.jp-AccordionPanel-toolbar {
+ height: var(--jp-private-toolbar-height);
+}
+
+.jp-SidePanel-toolbar.jp-Toolbar-micro {
+ display: none;
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-title {
+ box-sizing: border-box;
+ line-height: 25px;
+ margin: 0;
+ display: flex;
+ align-items: center;
+ background: var(--jp-layout-color1);
+ color: var(--jp-ui-font-color1);
+ border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+ box-shadow: var(--jp-toolbar-box-shadow);
+ font-size: var(--jp-ui-font-size0);
+}
+
+.jp-AccordionPanel-title {
+ cursor: pointer;
+ user-select: none;
+ -moz-user-select: none;
+ -webkit-user-select: none;
+ text-transform: uppercase;
+}
+
+.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
+ /* Title is rotated for horizontal accordion panel using CSS */
+ display: block;
+ transform-origin: top left;
+ transform: rotate(-90deg) translate(-100%);
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
+ user-select: none;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+ overflow: hidden;
+}
+
+.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
+ transform: rotate(-90deg);
+ margin: auto 0;
+ height: 16px;
+}
+
+.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
+ transform: rotate(0deg);
+}
+
+.lm-AccordionPanel .jp-AccordionPanel-toolbar {
+ background: none;
+ box-shadow: none;
+ border: none;
+ margin-left: auto;
+}
+
+.lm-AccordionPanel .lm-SplitPanel-handle:hover {
+ background: var(--jp-layout-color3);
+}
+
+.jp-text-truncated {
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+ position: absolute;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ z-index: 10;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ background: var(--jp-layout-color0);
+ outline: none;
+}
+
+.jp-SpinnerContent {
+ font-size: 10px;
+ margin: 50px auto;
+ text-indent: -9999em;
+ width: 3em;
+ height: 3em;
+ border-radius: 50%;
+ background: var(--jp-brand-color3);
+ background: linear-gradient(
+ to right,
+ #f37626 10%,
+ rgba(255, 255, 255, 0) 42%
+ );
+ position: relative;
+ animation: load3 1s infinite linear, fadeIn 1s;
+}
+
+.jp-SpinnerContent::before {
+ width: 50%;
+ height: 50%;
+ background: #f37626;
+ border-radius: 100% 0 0;
+ position: absolute;
+ top: 0;
+ left: 0;
+ content: '';
+}
+
+.jp-SpinnerContent::after {
+ background: var(--jp-layout-color0);
+ width: 75%;
+ height: 75%;
+ border-radius: 50%;
+ content: '';
+ margin: auto;
+ position: absolute;
+ top: 0;
+ left: 0;
+ bottom: 0;
+ right: 0;
+}
+
+@keyframes fadeIn {
+ 0% {
+ opacity: 0;
+ }
+
+ 100% {
+ opacity: 1;
+ }
+}
+
+@keyframes load3 {
+ 0% {
+ transform: rotate(0deg);
+ }
+
+ 100% {
+ transform: rotate(360deg);
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+ font-size: var(--jp-ui-font-size1);
+ color: var(--jp-ui-font-color0);
+ border: none;
+ box-sizing: border-box;
+ text-align: center;
+ line-height: 32px;
+ height: 32px;
+ padding: 0 12px;
+ letter-spacing: 0.8px;
+ outline: none;
+ appearance: none;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+}
+
+input.jp-mod-styled {
+ background: var(--jp-input-background);
+ height: 28px;
+ box-sizing: border-box;
+ border: var(--jp-border-width) solid var(--jp-border-color1);
+ padding-left: 7px;
+ padding-right: 7px;
+ font-size: var(--jp-ui-font-size2);
+ color: var(--jp-ui-font-color0);
+ outline: none;
+ appearance: none;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+}
+
+input[type='checkbox'].jp-mod-styled {
+ appearance: checkbox;
+ -webkit-appearance: checkbox;
+ -moz-appearance: checkbox;
+ height: auto;
+}
+
+input.jp-mod-styled:focus {
+ border: var(--jp-border-width) solid var(--md-blue-500);
+ box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-select-wrapper {
+ display: flex;
+ position: relative;
+ flex-direction: column;
+ padding: 1px;
+ background-color: var(--jp-layout-color1);
+ box-sizing: border-box;
+ margin-bottom: 12px;
+}
+
+.jp-select-wrapper:not(.multiple) {
+ height: 28px;
+}
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+ border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+ box-shadow: var(--jp-input-box-shadow);
+ background-color: var(--jp-input-active-background);
+}
+
+select.jp-mod-styled:hover {
+ cursor: pointer;
+ color: var(--jp-ui-font-color0);
+ background-color: var(--jp-input-hover-background);
+ box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
+}
+
+select.jp-mod-styled {
+ flex: 1 1 auto;
+ width: 100%;
+ font-size: var(--jp-ui-font-size2);
+ background: var(--jp-input-background);
+ color: var(--jp-ui-font-color0);
+ padding: 0 25px 0 8px;
+ border: var(--jp-border-width) solid var(--jp-input-border-color);
+ border-radius: 0;
+ outline: none;
+ appearance: none;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+}
+
+select.jp-mod-styled:not([multiple]) {
+ height: 32px;
+}
+
+select.jp-mod-styled[multiple] {
+ max-height: 200px;
+ overflow-y: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-switch {
+ display: flex;
+ align-items: center;
+ padding-left: 4px;
+ padding-right: 4px;
+ font-size: var(--jp-ui-font-size1);
+ background-color: transparent;
+ color: var(--jp-ui-font-color1);
+ border: none;
+ height: 20px;
+}
+
+.jp-switch:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-switch-label {
+ margin-right: 5px;
+ font-family: var(--jp-ui-font-family);
+}
+
+.jp-switch-track {
+ cursor: pointer;
+ background-color: var(--jp-switch-color, var(--jp-border-color1));
+ -webkit-transition: 0.4s;
+ transition: 0.4s;
+ border-radius: 34px;
+ height: 16px;
+ width: 35px;
+ position: relative;
+}
+
+.jp-switch-track::before {
+ content: '';
+ position: absolute;
+ height: 10px;
+ width: 10px;
+ margin: 3px;
+ left: 0;
+ background-color: var(--jp-ui-inverse-font-color1);
+ -webkit-transition: 0.4s;
+ transition: 0.4s;
+ border-radius: 50%;
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track {
+ background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+ /* track width (35) - margins (3 + 3) - thumb width (10) */
+ left: 19px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-private-toolbar-height: calc(
+ 28px + var(--jp-border-width)
+ ); /* leave 28px for content */
+}
+
+.jp-Toolbar {
+ color: var(--jp-ui-font-color1);
+ flex: 0 0 auto;
+ display: flex;
+ flex-direction: row;
+ border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+ box-shadow: var(--jp-toolbar-box-shadow);
+ background: var(--jp-toolbar-background);
+ min-height: var(--jp-toolbar-micro-height);
+ padding: 2px;
+ z-index: 8;
+ overflow-x: hidden;
+}
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+ flex-grow: 1;
+ flex-shrink: 1;
+}
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+ display: inline-block;
+ width: 32px;
+ background-repeat: no-repeat;
+ background-position: center;
+ background-size: 16px;
+}
+
+.jp-Toolbar > .jp-Toolbar-item {
+ flex: 0 0 auto;
+ display: flex;
+ padding-left: 1px;
+ padding-right: 1px;
+ font-size: var(--jp-ui-font-size1);
+ line-height: var(--jp-private-toolbar-height);
+ height: 100%;
+}
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+ color: transparent;
+ border: none;
+ box-sizing: border-box;
+ outline: none;
+ appearance: none;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+ padding: 0;
+ margin: 0;
+}
+
+button.jp-ToolbarButtonComponent {
+ background: var(--jp-layout-color1);
+ border: none;
+ box-sizing: border-box;
+ outline: none;
+ appearance: none;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+ padding: 0 6px;
+ margin: 0;
+ height: 24px;
+ border-radius: var(--jp-border-radius);
+ display: flex;
+ align-items: center;
+ text-align: center;
+ font-size: 14px;
+ min-width: unset;
+ min-height: unset;
+}
+
+button.jp-ToolbarButtonComponent:disabled {
+ opacity: 0.4;
+}
+
+button.jp-ToolbarButtonComponent > span {
+ padding: 0;
+ flex: 0 0 auto;
+}
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+ font-size: var(--jp-ui-font-size1);
+ line-height: 100%;
+ padding-left: 2px;
+ color: var(--jp-ui-font-color1);
+ font-family: var(--jp-ui-font-family);
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+ .jp-MainAreaWidget
+ > .jp-Toolbar.jp-Toolbar-micro {
+ padding: 0;
+ min-height: 0;
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+ .jp-MainAreaWidget
+ > .jp-Toolbar {
+ border: none;
+ box-shadow: none;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-WindowedPanel-outer {
+ position: relative;
+ overflow-y: auto;
+}
+
+.jp-WindowedPanel-inner {
+ position: relative;
+}
+
+.jp-WindowedPanel-window {
+ position: absolute;
+ left: 0;
+ right: 0;
+ overflow: visible;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+body {
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+}
+
+/* Disable native link decoration styles everywhere outside of dialog boxes */
+a {
+ text-decoration: unset;
+ color: unset;
+}
+
+a:hover {
+ text-decoration: unset;
+ color: unset;
+}
+
+/* Accessibility for links inside dialog box text */
+.jp-Dialog-content a {
+ text-decoration: revert;
+ color: var(--jp-content-link-color);
+}
+
+.jp-Dialog-content a:hover {
+ text-decoration: revert;
+}
+
+/* Styles for ui-components */
+.jp-Button {
+ color: var(--jp-ui-font-color2);
+ border-radius: var(--jp-border-radius);
+ padding: 0 12px;
+ font-size: var(--jp-ui-font-size1);
+
+ /* Copy from blueprint 3 */
+ display: inline-flex;
+ flex-direction: row;
+ border: none;
+ cursor: pointer;
+ align-items: center;
+ justify-content: center;
+ text-align: left;
+ vertical-align: middle;
+ min-height: 30px;
+ min-width: 30px;
+}
+
+.jp-Button:disabled {
+ cursor: not-allowed;
+}
+
+.jp-Button:empty {
+ padding: 0 !important;
+}
+
+.jp-Button.jp-mod-small {
+ min-height: 24px;
+ min-width: 24px;
+ font-size: 12px;
+ padding: 0 7px;
+}
+
+/* Use our own theme for hover styles */
+.jp-Button.jp-mod-minimal:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-Button.jp-mod-minimal {
+ background: none;
+}
+
+.jp-InputGroup {
+ display: block;
+ position: relative;
+}
+
+.jp-InputGroup input {
+ box-sizing: border-box;
+ border: none;
+ border-radius: 0;
+ background-color: transparent;
+ color: var(--jp-ui-font-color0);
+ box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+ padding-bottom: 0;
+ padding-top: 0;
+ padding-left: 10px;
+ padding-right: 28px;
+ position: relative;
+ width: 100%;
+ -webkit-appearance: none;
+ -moz-appearance: none;
+ appearance: none;
+ font-size: 14px;
+ font-weight: 400;
+ height: 30px;
+ line-height: 30px;
+ outline: none;
+ vertical-align: middle;
+}
+
+.jp-InputGroup input:focus {
+ box-shadow: inset 0 0 0 var(--jp-border-width)
+ var(--jp-input-active-box-shadow-color),
+ inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-InputGroup input:disabled {
+ cursor: not-allowed;
+ resize: block;
+ background-color: var(--jp-layout-color2);
+ color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input:disabled ~ span {
+ cursor: not-allowed;
+ color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+ color: var(--jp-ui-font-color2);
+}
+
+.jp-InputGroupAction {
+ position: absolute;
+ bottom: 1px;
+ right: 0;
+ padding: 6px;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+ background-color: initial;
+ border: none;
+ border-radius: 0;
+ box-shadow: none;
+ color: var(--jp-ui-font-color0);
+ display: block;
+ font-size: var(--jp-ui-font-size1);
+ font-family: var(--jp-ui-font-family);
+ height: 24px;
+ line-height: 14px;
+ padding: 0 25px 0 10px;
+ text-align: left;
+ -moz-appearance: none;
+ -webkit-appearance: none;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled {
+ background-color: var(--jp-layout-color2);
+ color: var(--jp-ui-font-color2);
+ cursor: not-allowed;
+ resize: block;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
+ cursor: not-allowed;
+}
+
+/* Use our own theme for hover and option styles */
+/* stylelint-disable-next-line selector-max-type */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+ background-color: var(--jp-layout-color2);
+ color: var(--jp-ui-font-color0);
+}
+
+select {
+ box-sizing: border-box;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-StatusBar-Widget {
+ display: flex;
+ align-items: center;
+ background: var(--jp-layout-color2);
+ min-height: var(--jp-statusbar-height);
+ justify-content: space-between;
+ padding: 0 10px;
+}
+
+.jp-StatusBar-Left {
+ display: flex;
+ align-items: center;
+ flex-direction: row;
+}
+
+.jp-StatusBar-Middle {
+ display: flex;
+ align-items: center;
+}
+
+.jp-StatusBar-Right {
+ display: flex;
+ align-items: center;
+ flex-direction: row-reverse;
+}
+
+.jp-StatusBar-Item {
+ max-height: var(--jp-statusbar-height);
+ margin: 0 2px;
+ height: var(--jp-statusbar-height);
+ white-space: nowrap;
+ text-overflow: ellipsis;
+ color: var(--jp-ui-font-color1);
+ padding: 0 6px;
+}
+
+.jp-mod-highlighted:hover {
+ background-color: var(--jp-layout-color3);
+}
+
+.jp-mod-clicked {
+ background-color: var(--jp-brand-color1);
+}
+
+.jp-mod-clicked:hover {
+ background-color: var(--jp-brand-color0);
+}
+
+.jp-mod-clicked .jp-StatusBar-TextItem {
+ color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-StatusBar-HoverItem {
+ box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
+}
+
+.jp-StatusBar-TextItem {
+ font-size: var(--jp-ui-font-size1);
+ font-family: var(--jp-ui-font-family);
+ line-height: 24px;
+ color: var(--jp-ui-font-color1);
+}
+
+.jp-StatusBar-GroupItem {
+ display: flex;
+ align-items: center;
+ flex-direction: row;
+}
+
+.jp-Statusbar-ProgressCircle svg {
+ display: block;
+ margin: 0 auto;
+ width: 16px;
+ height: 24px;
+ align-self: normal;
+}
+
+.jp-Statusbar-ProgressCircle path {
+ fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-Statusbar-ProgressBar-progress-bar {
+ height: 10px;
+ width: 100px;
+ border: solid 0.25px var(--jp-brand-color2);
+ border-radius: 3px;
+ overflow: hidden;
+ align-self: center;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar > div {
+ background-color: var(--jp-brand-color2);
+ background-image: linear-gradient(
+ -45deg,
+ rgba(255, 255, 255, 0.2) 25%,
+ transparent 25%,
+ transparent 50%,
+ rgba(255, 255, 255, 0.2) 50%,
+ rgba(255, 255, 255, 0.2) 75%,
+ transparent 75%,
+ transparent
+ );
+ background-size: 40px 40px;
+ float: left;
+ width: 0%;
+ height: 100%;
+ font-size: 12px;
+ line-height: 14px;
+ color: #fff;
+ text-align: center;
+ animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
+}
+
+.jp-Statusbar-ProgressBar-progress-bar p {
+ color: var(--jp-ui-font-color1);
+ font-family: var(--jp-ui-font-family);
+ font-size: var(--jp-ui-font-size1);
+ line-height: 10px;
+ width: 100px;
+}
+
+@keyframes jp-Statusbar-ExecutionTime-progress-bar {
+ 0% {
+ background-position: 0 0;
+ }
+
+ 100% {
+ background-position: 40px 40px;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+ padding-bottom: 0;
+ color: var(--jp-ui-font-color1);
+ background: var(--jp-layout-color1);
+
+ /* This is needed so that all font sizing of children done in ems is
+ * relative to this base size */
+ font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+ position: absolute;
+ z-index: 10000;
+ top: 38px;
+ left: 30%;
+ margin: 0;
+ padding: 4px;
+ width: 40%;
+ box-shadow: var(--jp-elevation-z4);
+ border-radius: 4px;
+ background: var(--jp-layout-color0);
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+ max-height: 40vh;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+ display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+ display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+ margin-left: 4px;
+ margin-right: 4px;
+}
+
+.jp-ModalCommandPalette
+ .lm-CommandPalette
+ .lm-CommandPalette-item.lm-mod-disabled {
+ display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+ padding: 4px;
+ background-color: var(--jp-layout-color1);
+ z-index: 2;
+}
+
+.lm-CommandPalette-wrapper {
+ overflow: overlay;
+ padding: 0 9px;
+ background-color: var(--jp-input-active-background);
+ height: 30px;
+ box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+ box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+ inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-SearchIconGroup {
+ color: white;
+ background-color: var(--jp-brand-color1);
+ position: absolute;
+ top: 4px;
+ right: 4px;
+ padding: 5px 5px 1px;
+}
+
+.jp-SearchIconGroup svg {
+ height: 20px;
+ width: 20px;
+}
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-input {
+ background: transparent;
+ width: calc(100% - 18px);
+ float: left;
+ border: none;
+ outline: none;
+ font-size: var(--jp-ui-font-size1);
+ color: var(--jp-ui-font-color0);
+ line-height: var(--jp-private-commandpalette-search-height);
+}
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+ color: var(--jp-ui-font-color2);
+ font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+ margin-top: 0;
+}
+
+.lm-CommandPalette-header {
+ border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+ color: var(--jp-ui-font-color1);
+ cursor: pointer;
+ display: flex;
+ font-size: var(--jp-ui-font-size0);
+ font-weight: 600;
+ letter-spacing: 1px;
+ margin-top: 8px;
+ padding: 8px 0 8px 12px;
+ text-transform: uppercase;
+}
+
+.lm-CommandPalette-header.lm-mod-active {
+ background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-header > mark {
+ background-color: transparent;
+ font-weight: bold;
+ color: var(--jp-ui-font-color1);
+}
+
+.lm-CommandPalette-item {
+ padding: 4px 12px 4px 4px;
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+ font-weight: 400;
+ display: flex;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled {
+ color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item.lm-mod-active {
+ color: var(--jp-ui-inverse-font-color1);
+ background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+ color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+ fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+ color: var(--jp-ui-inverse-font-color1);
+ background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+ background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-itemContent {
+ overflow: hidden;
+}
+
+.lm-CommandPalette-itemLabel > mark {
+ color: var(--jp-ui-font-color0);
+ background-color: transparent;
+ font-weight: bold;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+ color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+ margin: 0 4px 0 0;
+ position: relative;
+ width: 16px;
+ top: 2px;
+ flex: 0 0 auto;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+ opacity: 0.6;
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+ flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemCaption {
+ display: none;
+}
+
+.lm-CommandPalette-content {
+ background-color: var(--jp-layout-color1);
+}
+
+.lm-CommandPalette-content:empty::after {
+ content: 'No results';
+ margin: auto;
+ margin-top: 20px;
+ width: 100px;
+ display: block;
+ font-size: var(--jp-ui-font-size2);
+ font-family: var(--jp-ui-font-family);
+ font-weight: lighter;
+}
+
+.lm-CommandPalette-emptyMessage {
+ text-align: center;
+ margin-top: 24px;
+ line-height: 1.32;
+ padding: 0 8px;
+ color: var(--jp-content-font-color3);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+ position: absolute;
+ z-index: 10000;
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+ justify-content: center;
+ top: 0;
+ left: 0;
+ margin: 0;
+ padding: 0;
+ width: 100%;
+ height: 100%;
+ background: var(--jp-dialog-background);
+}
+
+.jp-Dialog-content {
+ display: flex;
+ flex-direction: column;
+ margin-left: auto;
+ margin-right: auto;
+ background: var(--jp-layout-color1);
+ padding: 24px 24px 12px;
+ min-width: 300px;
+ min-height: 150px;
+ max-width: 1000px;
+ max-height: 500px;
+ box-sizing: border-box;
+ box-shadow: var(--jp-elevation-z20);
+ word-wrap: break-word;
+ border-radius: var(--jp-border-radius);
+
+ /* This is needed so that all font sizing of children done in ems is
+ * relative to this base size */
+ font-size: var(--jp-ui-font-size1);
+ color: var(--jp-ui-font-color1);
+ resize: both;
+}
+
+.jp-Dialog-content.jp-Dialog-content-small {
+ max-width: 500px;
+}
+
+.jp-Dialog-button {
+ overflow: visible;
+}
+
+button.jp-Dialog-button:focus {
+ outline: 1px solid var(--jp-brand-color1);
+ outline-offset: 4px;
+ -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+ border: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+ outline-offset: 4px;
+ -moz-outline-radius: 0;
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
+ outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
+ outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
+}
+
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+ outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
+}
+
+button.jp-Dialog-close-button {
+ padding: 0;
+ height: 100%;
+ min-width: unset;
+ min-height: unset;
+}
+
+.jp-Dialog-header {
+ display: flex;
+ justify-content: space-between;
+ flex: 0 0 auto;
+ padding-bottom: 12px;
+ font-size: var(--jp-ui-font-size3);
+ font-weight: 400;
+ color: var(--jp-ui-font-color1);
+}
+
+.jp-Dialog-body {
+ display: flex;
+ flex-direction: column;
+ flex: 1 1 auto;
+ font-size: var(--jp-ui-font-size1);
+ background: var(--jp-layout-color1);
+ color: var(--jp-ui-font-color1);
+ overflow: auto;
+}
+
+.jp-Dialog-footer {
+ display: flex;
+ flex-direction: row;
+ justify-content: flex-end;
+ align-items: center;
+ flex: 0 0 auto;
+ margin-left: -12px;
+ margin-right: -12px;
+ padding: 12px;
+}
+
+.jp-Dialog-checkbox {
+ padding-right: 5px;
+}
+
+.jp-Dialog-checkbox > input:focus-visible {
+ outline: 1px solid var(--jp-input-active-border-color);
+ outline-offset: 1px;
+}
+
+.jp-Dialog-spacer {
+ flex: 1 1 auto;
+}
+
+.jp-Dialog-title {
+ overflow: hidden;
+ white-space: nowrap;
+ text-overflow: ellipsis;
+}
+
+.jp-Dialog-body > .jp-select-wrapper {
+ width: 100%;
+}
+
+.jp-Dialog-body > button {
+ padding: 0 16px;
+}
+
+.jp-Dialog-body > label {
+ line-height: 1.4;
+ color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+ margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Input-Boolean-Dialog {
+ flex-direction: row-reverse;
+ align-items: end;
+ width: 100%;
+}
+
+.jp-Input-Boolean-Dialog > label {
+ flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+ outline: none;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error {
+ padding: 6px;
+}
+
+.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
+ width: auto;
+ padding: 10px;
+ background: var(--jp-error-color3);
+ border: var(--jp-border-width) solid var(--jp-error-color1);
+ border-radius: var(--jp-border-radius);
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+ white-space: pre-wrap;
+ word-wrap: break-word;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+ --md-red-50: #ffebee;
+ --md-red-100: #ffcdd2;
+ --md-red-200: #ef9a9a;
+ --md-red-300: #e57373;
+ --md-red-400: #ef5350;
+ --md-red-500: #f44336;
+ --md-red-600: #e53935;
+ --md-red-700: #d32f2f;
+ --md-red-800: #c62828;
+ --md-red-900: #b71c1c;
+ --md-red-A100: #ff8a80;
+ --md-red-A200: #ff5252;
+ --md-red-A400: #ff1744;
+ --md-red-A700: #d50000;
+ --md-pink-50: #fce4ec;
+ --md-pink-100: #f8bbd0;
+ --md-pink-200: #f48fb1;
+ --md-pink-300: #f06292;
+ --md-pink-400: #ec407a;
+ --md-pink-500: #e91e63;
+ --md-pink-600: #d81b60;
+ --md-pink-700: #c2185b;
+ --md-pink-800: #ad1457;
+ --md-pink-900: #880e4f;
+ --md-pink-A100: #ff80ab;
+ --md-pink-A200: #ff4081;
+ --md-pink-A400: #f50057;
+ --md-pink-A700: #c51162;
+ --md-purple-50: #f3e5f5;
+ --md-purple-100: #e1bee7;
+ --md-purple-200: #ce93d8;
+ --md-purple-300: #ba68c8;
+ --md-purple-400: #ab47bc;
+ --md-purple-500: #9c27b0;
+ --md-purple-600: #8e24aa;
+ --md-purple-700: #7b1fa2;
+ --md-purple-800: #6a1b9a;
+ --md-purple-900: #4a148c;
+ --md-purple-A100: #ea80fc;
+ --md-purple-A200: #e040fb;
+ --md-purple-A400: #d500f9;
+ --md-purple-A700: #a0f;
+ --md-deep-purple-50: #ede7f6;
+ --md-deep-purple-100: #d1c4e9;
+ --md-deep-purple-200: #b39ddb;
+ --md-deep-purple-300: #9575cd;
+ --md-deep-purple-400: #7e57c2;
+ --md-deep-purple-500: #673ab7;
+ --md-deep-purple-600: #5e35b1;
+ --md-deep-purple-700: #512da8;
+ --md-deep-purple-800: #4527a0;
+ --md-deep-purple-900: #311b92;
+ --md-deep-purple-A100: #b388ff;
+ --md-deep-purple-A200: #7c4dff;
+ --md-deep-purple-A400: #651fff;
+ --md-deep-purple-A700: #6200ea;
+ --md-indigo-50: #e8eaf6;
+ --md-indigo-100: #c5cae9;
+ --md-indigo-200: #9fa8da;
+ --md-indigo-300: #7986cb;
+ --md-indigo-400: #5c6bc0;
+ --md-indigo-500: #3f51b5;
+ --md-indigo-600: #3949ab;
+ --md-indigo-700: #303f9f;
+ --md-indigo-800: #283593;
+ --md-indigo-900: #1a237e;
+ --md-indigo-A100: #8c9eff;
+ --md-indigo-A200: #536dfe;
+ --md-indigo-A400: #3d5afe;
+ --md-indigo-A700: #304ffe;
+ --md-blue-50: #e3f2fd;
+ --md-blue-100: #bbdefb;
+ --md-blue-200: #90caf9;
+ --md-blue-300: #64b5f6;
+ --md-blue-400: #42a5f5;
+ --md-blue-500: #2196f3;
+ --md-blue-600: #1e88e5;
+ --md-blue-700: #1976d2;
+ --md-blue-800: #1565c0;
+ --md-blue-900: #0d47a1;
+ --md-blue-A100: #82b1ff;
+ --md-blue-A200: #448aff;
+ --md-blue-A400: #2979ff;
+ --md-blue-A700: #2962ff;
+ --md-light-blue-50: #e1f5fe;
+ --md-light-blue-100: #b3e5fc;
+ --md-light-blue-200: #81d4fa;
+ --md-light-blue-300: #4fc3f7;
+ --md-light-blue-400: #29b6f6;
+ --md-light-blue-500: #03a9f4;
+ --md-light-blue-600: #039be5;
+ --md-light-blue-700: #0288d1;
+ --md-light-blue-800: #0277bd;
+ --md-light-blue-900: #01579b;
+ --md-light-blue-A100: #80d8ff;
+ --md-light-blue-A200: #40c4ff;
+ --md-light-blue-A400: #00b0ff;
+ --md-light-blue-A700: #0091ea;
+ --md-cyan-50: #e0f7fa;
+ --md-cyan-100: #b2ebf2;
+ --md-cyan-200: #80deea;
+ --md-cyan-300: #4dd0e1;
+ --md-cyan-400: #26c6da;
+ --md-cyan-500: #00bcd4;
+ --md-cyan-600: #00acc1;
+ --md-cyan-700: #0097a7;
+ --md-cyan-800: #00838f;
+ --md-cyan-900: #006064;
+ --md-cyan-A100: #84ffff;
+ --md-cyan-A200: #18ffff;
+ --md-cyan-A400: #00e5ff;
+ --md-cyan-A700: #00b8d4;
+ --md-teal-50: #e0f2f1;
+ --md-teal-100: #b2dfdb;
+ --md-teal-200: #80cbc4;
+ --md-teal-300: #4db6ac;
+ --md-teal-400: #26a69a;
+ --md-teal-500: #009688;
+ --md-teal-600: #00897b;
+ --md-teal-700: #00796b;
+ --md-teal-800: #00695c;
+ --md-teal-900: #004d40;
+ --md-teal-A100: #a7ffeb;
+ --md-teal-A200: #64ffda;
+ --md-teal-A400: #1de9b6;
+ --md-teal-A700: #00bfa5;
+ --md-green-50: #e8f5e9;
+ --md-green-100: #c8e6c9;
+ --md-green-200: #a5d6a7;
+ --md-green-300: #81c784;
+ --md-green-400: #66bb6a;
+ --md-green-500: #4caf50;
+ --md-green-600: #43a047;
+ --md-green-700: #388e3c;
+ --md-green-800: #2e7d32;
+ --md-green-900: #1b5e20;
+ --md-green-A100: #b9f6ca;
+ --md-green-A200: #69f0ae;
+ --md-green-A400: #00e676;
+ --md-green-A700: #00c853;
+ --md-light-green-50: #f1f8e9;
+ --md-light-green-100: #dcedc8;
+ --md-light-green-200: #c5e1a5;
+ --md-light-green-300: #aed581;
+ --md-light-green-400: #9ccc65;
+ --md-light-green-500: #8bc34a;
+ --md-light-green-600: #7cb342;
+ --md-light-green-700: #689f38;
+ --md-light-green-800: #558b2f;
+ --md-light-green-900: #33691e;
+ --md-light-green-A100: #ccff90;
+ --md-light-green-A200: #b2ff59;
+ --md-light-green-A400: #76ff03;
+ --md-light-green-A700: #64dd17;
+ --md-lime-50: #f9fbe7;
+ --md-lime-100: #f0f4c3;
+ --md-lime-200: #e6ee9c;
+ --md-lime-300: #dce775;
+ --md-lime-400: #d4e157;
+ --md-lime-500: #cddc39;
+ --md-lime-600: #c0ca33;
+ --md-lime-700: #afb42b;
+ --md-lime-800: #9e9d24;
+ --md-lime-900: #827717;
+ --md-lime-A100: #f4ff81;
+ --md-lime-A200: #eeff41;
+ --md-lime-A400: #c6ff00;
+ --md-lime-A700: #aeea00;
+ --md-yellow-50: #fffde7;
+ --md-yellow-100: #fff9c4;
+ --md-yellow-200: #fff59d;
+ --md-yellow-300: #fff176;
+ --md-yellow-400: #ffee58;
+ --md-yellow-500: #ffeb3b;
+ --md-yellow-600: #fdd835;
+ --md-yellow-700: #fbc02d;
+ --md-yellow-800: #f9a825;
+ --md-yellow-900: #f57f17;
+ --md-yellow-A100: #ffff8d;
+ --md-yellow-A200: #ff0;
+ --md-yellow-A400: #ffea00;
+ --md-yellow-A700: #ffd600;
+ --md-amber-50: #fff8e1;
+ --md-amber-100: #ffecb3;
+ --md-amber-200: #ffe082;
+ --md-amber-300: #ffd54f;
+ --md-amber-400: #ffca28;
+ --md-amber-500: #ffc107;
+ --md-amber-600: #ffb300;
+ --md-amber-700: #ffa000;
+ --md-amber-800: #ff8f00;
+ --md-amber-900: #ff6f00;
+ --md-amber-A100: #ffe57f;
+ --md-amber-A200: #ffd740;
+ --md-amber-A400: #ffc400;
+ --md-amber-A700: #ffab00;
+ --md-orange-50: #fff3e0;
+ --md-orange-100: #ffe0b2;
+ --md-orange-200: #ffcc80;
+ --md-orange-300: #ffb74d;
+ --md-orange-400: #ffa726;
+ --md-orange-500: #ff9800;
+ --md-orange-600: #fb8c00;
+ --md-orange-700: #f57c00;
+ --md-orange-800: #ef6c00;
+ --md-orange-900: #e65100;
+ --md-orange-A100: #ffd180;
+ --md-orange-A200: #ffab40;
+ --md-orange-A400: #ff9100;
+ --md-orange-A700: #ff6d00;
+ --md-deep-orange-50: #fbe9e7;
+ --md-deep-orange-100: #ffccbc;
+ --md-deep-orange-200: #ffab91;
+ --md-deep-orange-300: #ff8a65;
+ --md-deep-orange-400: #ff7043;
+ --md-deep-orange-500: #ff5722;
+ --md-deep-orange-600: #f4511e;
+ --md-deep-orange-700: #e64a19;
+ --md-deep-orange-800: #d84315;
+ --md-deep-orange-900: #bf360c;
+ --md-deep-orange-A100: #ff9e80;
+ --md-deep-orange-A200: #ff6e40;
+ --md-deep-orange-A400: #ff3d00;
+ --md-deep-orange-A700: #dd2c00;
+ --md-brown-50: #efebe9;
+ --md-brown-100: #d7ccc8;
+ --md-brown-200: #bcaaa4;
+ --md-brown-300: #a1887f;
+ --md-brown-400: #8d6e63;
+ --md-brown-500: #795548;
+ --md-brown-600: #6d4c41;
+ --md-brown-700: #5d4037;
+ --md-brown-800: #4e342e;
+ --md-brown-900: #3e2723;
+ --md-grey-50: #fafafa;
+ --md-grey-100: #f5f5f5;
+ --md-grey-200: #eee;
+ --md-grey-300: #e0e0e0;
+ --md-grey-400: #bdbdbd;
+ --md-grey-500: #9e9e9e;
+ --md-grey-600: #757575;
+ --md-grey-700: #616161;
+ --md-grey-800: #424242;
+ --md-grey-900: #212121;
+ --md-blue-grey-50: #eceff1;
+ --md-blue-grey-100: #cfd8dc;
+ --md-blue-grey-200: #b0bec5;
+ --md-blue-grey-300: #90a4ae;
+ --md-blue-grey-400: #78909c;
+ --md-blue-grey-500: #607d8b;
+ --md-blue-grey-600: #546e7a;
+ --md-blue-grey-700: #455a64;
+ --md-blue-grey-800: #37474f;
+ --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+:root {
+ /* This is the padding value to fill the gaps between lines containing spans with background color. */
+ --jp-private-code-span-padding: calc(
+ (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+ );
+}
+
+.jp-RenderedText {
+ text-align: left;
+ padding-left: var(--jp-code-padding);
+ line-height: var(--jp-code-line-height);
+ font-family: var(--jp-code-font-family);
+}
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+ color: var(--jp-content-font-color1);
+ font-size: var(--jp-code-font-size);
+ border: none;
+ margin: 0;
+ padding: 0;
+}
+
+.jp-RenderedText pre a:link {
+ text-decoration: none;
+ color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:hover {
+ text-decoration: underline;
+ color: var(--jp-content-link-color);
+}
+
+.jp-RenderedText pre a:visited {
+ text-decoration: none;
+ color: var(--jp-content-link-color);
+}
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+ color: #3e424d;
+}
+
+.jp-RenderedText pre .ansi-red-fg {
+ color: #e75c58;
+}
+
+.jp-RenderedText pre .ansi-green-fg {
+ color: #00a250;
+}
+
+.jp-RenderedText pre .ansi-yellow-fg {
+ color: #ddb62b;
+}
+
+.jp-RenderedText pre .ansi-blue-fg {
+ color: #208ffb;
+}
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+.jp-RenderedText pre .ansi-magenta-fg {
+ color: #d160c4;
+}
+
+.jp-RenderedText pre .ansi-cyan-fg {
+ color: #60c6c8;
+}
+
+.jp-RenderedText pre .ansi-white-fg {
+ color: #c5c1b4;
+}
+
+.jp-RenderedText pre .ansi-black-bg {
+ background-color: #3e424d;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-bg {
+ background-color: #e75c58;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-bg {
+ background-color: #00a250;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-bg {
+ background-color: #ddb62b;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-bg {
+ background-color: #208ffb;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-bg {
+ background-color: #d160c4;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-bg {
+ background-color: #60c6c8;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-bg {
+ background-color: #c5c1b4;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+ color: #282c36;
+}
+
+.jp-RenderedText pre .ansi-red-intense-fg {
+ color: #b22b31;
+}
+
+.jp-RenderedText pre .ansi-green-intense-fg {
+ color: #007427;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+ color: #b27d12;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-fg {
+ color: #0065ca;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+ color: #a03196;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+ color: #258f8f;
+}
+
+.jp-RenderedText pre .ansi-white-intense-fg {
+ color: #a1a6b2;
+}
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+ background-color: #282c36;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-red-intense-bg {
+ background-color: #b22b31;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-green-intense-bg {
+ background-color: #007427;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+ background-color: #b27d12;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-blue-intense-bg {
+ background-color: #0065ca;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+ background-color: #a03196;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+ background-color: #258f8f;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-white-intense-bg {
+ background-color: #a1a6b2;
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-default-inverse-fg {
+ color: var(--jp-ui-inverse-font-color0);
+}
+
+.jp-RenderedText pre .ansi-default-inverse-bg {
+ background-color: var(--jp-inverse-layout-color0);
+ padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-bold {
+ font-weight: bold;
+}
+
+.jp-RenderedText pre .ansi-underline {
+ text-decoration: underline;
+}
+
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+ background: var(--jp-rendermime-error-background);
+ padding-top: var(--jp-code-padding);
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedLatex {
+ color: var(--jp-content-font-color1);
+ font-size: var(--jp-content-font-size1);
+ line-height: var(--jp-content-line-height);
+}
+
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+ padding: var(--jp-code-padding);
+ text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedHTMLCommon {
+ color: var(--jp-content-font-color1);
+ font-family: var(--jp-content-font-family);
+ font-size: var(--jp-content-font-size1);
+ line-height: var(--jp-content-line-height);
+
+ /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+ padding-right: 20px;
+}
+
+.jp-RenderedHTMLCommon em {
+ font-style: italic;
+}
+
+.jp-RenderedHTMLCommon strong {
+ font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon u {
+ text-decoration: underline;
+}
+
+.jp-RenderedHTMLCommon a:link {
+ text-decoration: none;
+ color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:hover {
+ text-decoration: underline;
+ color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:visited {
+ text-decoration: none;
+ color: var(--jp-content-link-color);
+}
+
+/* Headings */
+
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+ line-height: var(--jp-content-heading-line-height);
+ font-weight: var(--jp-content-heading-font-weight);
+ font-style: normal;
+ margin: var(--jp-content-heading-margin-top) 0
+ var(--jp-content-heading-margin-bottom) 0;
+}
+
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+ margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
+}
+
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+ margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
+}
+
+.jp-RenderedHTMLCommon h1 {
+ font-size: var(--jp-content-font-size5);
+}
+
+.jp-RenderedHTMLCommon h2 {
+ font-size: var(--jp-content-font-size4);
+}
+
+.jp-RenderedHTMLCommon h3 {
+ font-size: var(--jp-content-font-size3);
+}
+
+.jp-RenderedHTMLCommon h4 {
+ font-size: var(--jp-content-font-size2);
+}
+
+.jp-RenderedHTMLCommon h5 {
+ font-size: var(--jp-content-font-size1);
+}
+
+.jp-RenderedHTMLCommon h6 {
+ font-size: var(--jp-content-font-size0);
+}
+
+/* Lists */
+
+/* stylelint-disable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+ padding-left: 2em;
+}
+
+.jp-RenderedHTMLCommon ul {
+ list-style: disc;
+}
+
+.jp-RenderedHTMLCommon ul ul {
+ list-style: square;
+}
+
+.jp-RenderedHTMLCommon ul ul ul {
+ list-style: circle;
+}
+
+.jp-RenderedHTMLCommon ol {
+ list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol ol {
+ list-style: upper-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol {
+ list-style: lower-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol {
+ list-style: lower-roman;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+ list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+ margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+ margin-bottom: 0;
+}
+
+/* stylelint-enable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon hr {
+ color: var(--jp-border-color2);
+ background-color: var(--jp-border-color1);
+ margin-top: 1em;
+ margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon > pre {
+ margin: 1.5em 2em;
+}
+
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+ border: 0;
+ background-color: var(--jp-layout-color0);
+ color: var(--jp-content-font-color1);
+ font-family: var(--jp-code-font-family);
+ font-size: inherit;
+ line-height: var(--jp-code-line-height);
+ padding: 0;
+ white-space: pre-wrap;
+}
+
+.jp-RenderedHTMLCommon :not(pre) > code {
+ background-color: var(--jp-layout-color2);
+ padding: 1px 5px;
+}
+
+/* Tables */
+
+.jp-RenderedHTMLCommon table {
+ border-collapse: collapse;
+ border-spacing: 0;
+ border: none;
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+ table-layout: fixed;
+ margin-left: auto;
+ margin-bottom: 1em;
+ margin-right: auto;
+}
+
+.jp-RenderedHTMLCommon thead {
+ border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+ vertical-align: bottom;
+}
+
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+ vertical-align: middle;
+ padding: 0.5em;
+ line-height: normal;
+ white-space: normal;
+ max-width: none;
+ border: none;
+}
+
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+ max-width: none;
+}
+
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+ text-align: right;
+}
+
+.jp-RenderedHTMLCommon th {
+ font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+ background: var(--jp-layout-color0);
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+ background: var(--jp-rendermime-table-row-background);
+}
+
+.jp-RenderedHTMLCommon tbody tr:hover {
+ background: var(--jp-rendermime-table-row-hover-background);
+}
+
+.jp-RenderedHTMLCommon p {
+ text-align: left;
+ margin: 0;
+ margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon img {
+ -moz-force-broken-image-icon: 1;
+}
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+ display: block;
+ margin-left: 0;
+ margin-right: 0;
+ margin-bottom: 1em;
+}
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+ background-color: var(--jp-inverse-layout-color1);
+}
+
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+ background-color: var(--jp-inverse-layout-color1);
+}
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+ max-width: 100%;
+ height: auto;
+}
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+ max-width: none;
+}
+
+.jp-RenderedHTMLCommon .alert {
+ padding: var(--jp-notebook-padding);
+ border: var(--jp-border-width) solid transparent;
+ border-radius: var(--jp-border-radius);
+ margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon .alert-info {
+ color: var(--jp-info-color0);
+ background-color: var(--jp-info-color3);
+ border-color: var(--jp-info-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-info hr {
+ border-color: var(--jp-info-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+ margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-warning {
+ color: var(--jp-warn-color0);
+ background-color: var(--jp-warn-color3);
+ border-color: var(--jp-warn-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-warning hr {
+ border-color: var(--jp-warn-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+ margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-success {
+ color: var(--jp-success-color0);
+ background-color: var(--jp-success-color3);
+ border-color: var(--jp-success-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-success hr {
+ border-color: var(--jp-success-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+ margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-danger {
+ color: var(--jp-error-color0);
+ background-color: var(--jp-error-color3);
+ border-color: var(--jp-error-color2);
+}
+
+.jp-RenderedHTMLCommon .alert-danger hr {
+ border-color: var(--jp-error-color3);
+}
+
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+ margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon blockquote {
+ margin: 1em 2em;
+ padding: 0 1em;
+ border-left: 5px solid var(--jp-border-color2);
+}
+
+a.jp-InternalAnchorLink {
+ visibility: hidden;
+ margin-left: 8px;
+ color: var(--md-blue-800);
+}
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+ visibility: visible;
+}
+
+.jp-RenderedHTMLCommon kbd {
+ background-color: var(--jp-rendermime-table-row-background);
+ border: 1px solid var(--jp-border-color0);
+ border-bottom-color: var(--jp-border-color2);
+ border-radius: 3px;
+ box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+ display: inline-block;
+ font-size: var(--jp-ui-font-size0);
+ line-height: 1em;
+ padding: 0.2em 0.5em;
+}
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+ margin-bottom: 0.5em;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+.lm-cursor-backdrop {
+ position: fixed;
+ width: 200px;
+ height: 200px;
+ margin-top: -100px;
+ margin-left: -100px;
+ will-change: transform;
+ z-index: 100;
+}
+
+.lm-mod-drag-image {
+ will-change: transform;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-lineFormSearch {
+ padding: 4px 12px;
+ background-color: var(--jp-layout-color2);
+ box-shadow: var(--jp-toolbar-box-shadow);
+ z-index: 2;
+ font-size: var(--jp-ui-font-size1);
+}
+
+.jp-lineFormCaption {
+ font-size: var(--jp-ui-font-size0);
+ line-height: var(--jp-ui-font-size1);
+ margin-top: 4px;
+ color: var(--jp-ui-font-color0);
+}
+
+.jp-baseLineForm {
+ border: none;
+ border-radius: 0;
+ position: absolute;
+ background-size: 16px;
+ background-repeat: no-repeat;
+ background-position: center;
+ outline: none;
+}
+
+.jp-lineFormButtonContainer {
+ top: 4px;
+ right: 8px;
+ height: 24px;
+ padding: 0 12px;
+ width: 12px;
+}
+
+.jp-lineFormButtonIcon {
+ top: 0;
+ right: 0;
+ background-color: var(--jp-brand-color1);
+ height: 100%;
+ width: 100%;
+ box-sizing: border-box;
+ padding: 4px 6px;
+}
+
+.jp-lineFormButton {
+ top: 0;
+ right: 0;
+ background-color: transparent;
+ height: 100%;
+ width: 100%;
+ box-sizing: border-box;
+}
+
+.jp-lineFormWrapper {
+ overflow: hidden;
+ padding: 0 8px;
+ border: 1px solid var(--jp-border-color0);
+ background-color: var(--jp-input-active-background);
+ height: 22px;
+}
+
+.jp-lineFormWrapperFocusWithin {
+ border: var(--jp-border-width) solid var(--md-blue-500);
+ box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-lineFormInput {
+ background: transparent;
+ width: 200px;
+ height: 100%;
+ border: none;
+ outline: none;
+ color: var(--jp-ui-font-color0);
+ line-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+ display: flex;
+ flex-direction: column;
+ width: 100%;
+}
+
+.jp-JSONEditor-host {
+ flex: 1 1 auto;
+ border: var(--jp-border-width) solid var(--jp-input-border-color);
+ border-radius: 0;
+ background: var(--jp-layout-color0);
+ min-height: 50px;
+ padding: 1px;
+}
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+ border-color: red;
+ outline-color: red;
+}
+
+.jp-JSONEditor-header {
+ display: flex;
+ flex: 1 0 auto;
+ padding: 0 0 0 12px;
+}
+
+.jp-JSONEditor-header label {
+ flex: 0 0 auto;
+}
+
+.jp-JSONEditor-commitButton {
+ height: 16px;
+ width: 16px;
+ background-size: 18px;
+ background-repeat: no-repeat;
+ background-position: center;
+}
+
+.jp-JSONEditor-host.jp-mod-focused {
+ background-color: var(--jp-input-active-background);
+ border: 1px solid var(--jp-input-active-border-color);
+ box-shadow: var(--jp-input-box-shadow);
+}
+
+.jp-Editor.jp-mod-dropTarget {
+ border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+ box-shadow: var(--jp-input-box-shadow);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+.jp-DocumentSearch-input {
+ border: none;
+ outline: none;
+ color: var(--jp-ui-font-color0);
+ font-size: var(--jp-ui-font-size1);
+ background-color: var(--jp-layout-color0);
+ font-family: var(--jp-ui-font-family);
+ padding: 2px 1px;
+ resize: none;
+}
+
+.jp-DocumentSearch-overlay {
+ position: absolute;
+ background-color: var(--jp-toolbar-background);
+ border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+ border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+ top: 0;
+ right: 0;
+ z-index: 7;
+ min-width: 405px;
+ padding: 2px;
+ font-size: var(--jp-ui-font-size1);
+
+ --jp-private-document-search-button-height: 20px;
+}
+
+.jp-DocumentSearch-overlay button {
+ background-color: var(--jp-toolbar-background);
+ outline: 0;
+}
+
+.jp-DocumentSearch-overlay button:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-overlay button:active {
+ background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-overlay-row {
+ display: flex;
+ align-items: center;
+ margin-bottom: 2px;
+}
+
+.jp-DocumentSearch-button-content {
+ display: inline-block;
+ cursor: pointer;
+ box-sizing: border-box;
+ width: 100%;
+ height: 100%;
+}
+
+.jp-DocumentSearch-button-content svg {
+ width: 100%;
+ height: 100%;
+}
+
+.jp-DocumentSearch-input-wrapper {
+ border: var(--jp-border-width) solid var(--jp-border-color0);
+ display: flex;
+ background-color: var(--jp-layout-color0);
+ margin: 2px;
+}
+
+.jp-DocumentSearch-input-wrapper:focus-within {
+ border-color: var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper {
+ all: initial;
+ overflow: hidden;
+ display: inline-block;
+ border: none;
+ box-sizing: border-box;
+}
+
+.jp-DocumentSearch-toggle-wrapper {
+ width: 14px;
+ height: 14px;
+}
+
+.jp-DocumentSearch-button-wrapper {
+ width: var(--jp-private-document-search-button-height);
+ height: var(--jp-private-document-search-button-height);
+}
+
+.jp-DocumentSearch-toggle-wrapper:focus,
+.jp-DocumentSearch-button-wrapper:focus {
+ outline: var(--jp-border-width) solid
+ var(--jp-cell-editor-active-border-color);
+ outline-offset: -1px;
+}
+
+.jp-DocumentSearch-toggle-wrapper,
+.jp-DocumentSearch-button-wrapper,
+.jp-DocumentSearch-button-content:focus {
+ outline: none;
+}
+
+.jp-DocumentSearch-toggle-placeholder {
+ width: 5px;
+}
+
+.jp-DocumentSearch-input-button::before {
+ display: block;
+ padding-top: 100%;
+}
+
+.jp-DocumentSearch-input-button-off {
+ opacity: var(--jp-search-toggle-off-opacity);
+}
+
+.jp-DocumentSearch-input-button-off:hover {
+ opacity: var(--jp-search-toggle-hover-opacity);
+}
+
+.jp-DocumentSearch-input-button-on {
+ opacity: var(--jp-search-toggle-on-opacity);
+}
+
+.jp-DocumentSearch-index-counter {
+ padding-left: 10px;
+ padding-right: 10px;
+ user-select: none;
+ min-width: 35px;
+ display: inline-block;
+}
+
+.jp-DocumentSearch-up-down-wrapper {
+ display: inline-block;
+ padding-right: 2px;
+ margin-left: auto;
+ white-space: nowrap;
+}
+
+.jp-DocumentSearch-spacer {
+ margin-left: auto;
+}
+
+.jp-DocumentSearch-up-down-wrapper button {
+ outline: 0;
+ border: none;
+ width: var(--jp-private-document-search-button-height);
+ height: var(--jp-private-document-search-button-height);
+ vertical-align: middle;
+ margin: 1px 5px 2px;
+}
+
+.jp-DocumentSearch-up-down-button:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-up-down-button:active {
+ background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-filter-button {
+ border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-filter-button:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-DocumentSearch-filter-button-enabled:hover {
+ background-color: var(--jp-layout-color3);
+}
+
+.jp-DocumentSearch-search-options {
+ padding: 0 8px;
+ margin-left: 3px;
+ width: 100%;
+ display: grid;
+ justify-content: start;
+ grid-template-columns: 1fr 1fr;
+ align-items: center;
+ justify-items: stretch;
+}
+
+.jp-DocumentSearch-search-filter-disabled {
+ color: var(--jp-ui-font-color2);
+}
+
+.jp-DocumentSearch-search-filter {
+ display: flex;
+ align-items: center;
+ user-select: none;
+}
+
+.jp-DocumentSearch-regex-error {
+ color: var(--jp-error-color0);
+}
+
+.jp-DocumentSearch-replace-button-wrapper {
+ overflow: hidden;
+ display: inline-block;
+ box-sizing: border-box;
+ border: var(--jp-border-width) solid var(--jp-border-color0);
+ margin: auto 2px;
+ padding: 1px 4px;
+ height: calc(var(--jp-private-document-search-button-height) + 2px);
+}
+
+.jp-DocumentSearch-replace-button-wrapper:focus {
+ border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+}
+
+.jp-DocumentSearch-replace-button {
+ display: inline-block;
+ text-align: center;
+ cursor: pointer;
+ box-sizing: border-box;
+ color: var(--jp-ui-font-color1);
+
+ /* height - 2 * (padding of wrapper) */
+ line-height: calc(var(--jp-private-document-search-button-height) - 2px);
+ width: 100%;
+ height: 100%;
+}
+
+.jp-DocumentSearch-replace-button:focus {
+ outline: none;
+}
+
+.jp-DocumentSearch-replace-wrapper-class {
+ margin-left: 14px;
+ display: flex;
+}
+
+.jp-DocumentSearch-replace-toggle {
+ border: none;
+ background-color: var(--jp-toolbar-background);
+ border-radius: var(--jp-border-radius);
+}
+
+.jp-DocumentSearch-replace-toggle:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.cm-editor {
+ line-height: var(--jp-code-line-height);
+ font-size: var(--jp-code-font-size);
+ font-family: var(--jp-code-font-family);
+ border: 0;
+ border-radius: 0;
+ height: auto;
+
+ /* Changed to auto to autogrow */
+}
+
+.cm-editor pre {
+ padding: 0 var(--jp-code-padding);
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
+ background-color: var(--jp-layout-color0);
+ color: var(--jp-content-font-color1);
+}
+
+.jp-CodeMirrorEditor {
+ cursor: text;
+}
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+ .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+ border-left: var(--jp-code-cursor-width1) solid
+ var(--jp-editor-cursor-color);
+ }
+}
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+ .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
+ border-left: var(--jp-code-cursor-width2) solid
+ var(--jp-editor-cursor-color);
+ }
+}
+
+.cm-editor.jp-mod-readOnly .cm-cursor {
+ display: none;
+}
+
+.jp-CollaboratorCursor {
+ border-left: 5px solid transparent;
+ border-right: 5px solid transparent;
+ border-top: none;
+ border-bottom: 3px solid;
+ background-clip: content-box;
+ margin-left: -5px;
+ margin-right: -5px;
+}
+
+.cm-searching,
+.cm-searching span {
+ /* `.cm-searching span`: we need to override syntax highlighting */
+ background-color: var(--jp-search-unselected-match-background-color);
+ color: var(--jp-search-unselected-match-color);
+}
+
+.cm-searching::selection,
+.cm-searching span::selection {
+ background-color: var(--jp-search-unselected-match-background-color);
+ color: var(--jp-search-unselected-match-color);
+}
+
+.jp-current-match > .cm-searching,
+.jp-current-match > .cm-searching span,
+.cm-searching > .jp-current-match,
+.cm-searching > .jp-current-match span {
+ background-color: var(--jp-search-selected-match-background-color);
+ color: var(--jp-search-selected-match-color);
+}
+
+.jp-current-match > .cm-searching::selection,
+.cm-searching > .jp-current-match::selection,
+.jp-current-match > .cm-searching span::selection {
+ background-color: var(--jp-search-selected-match-background-color);
+ color: var(--jp-search-selected-match-color);
+}
+
+.cm-trailingspace {
+ background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
+ background-position: center left;
+ background-repeat: repeat-x;
+}
+
+.jp-CollaboratorCursor-hover {
+ position: absolute;
+ z-index: 1;
+ transform: translateX(-50%);
+ color: white;
+ border-radius: 3px;
+ padding-left: 4px;
+ padding-right: 4px;
+ padding-top: 1px;
+ padding-bottom: 1px;
+ text-align: center;
+ font-size: var(--jp-ui-font-size1);
+ white-space: nowrap;
+}
+
+.jp-CodeMirror-ruler {
+ border-left: 1px dashed var(--jp-border-color2);
+}
+
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .cm-ySelectionCaret {
+ position: relative;
+ border-left: 1px solid black;
+ margin-left: -1px;
+ margin-right: -1px;
+ box-sizing: border-box;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
+ white-space: nowrap;
+ position: absolute;
+ top: -1.15em;
+ padding-bottom: 0.05em;
+ left: -1px;
+ font-size: 0.95em;
+ font-family: var(--jp-ui-font-family);
+ font-weight: bold;
+ line-height: normal;
+ user-select: none;
+ color: white;
+ padding-left: 2px;
+ padding-right: 2px;
+ z-index: 101;
+ transition: opacity 0.3s ease-in-out;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionInfo {
+ transition-delay: 0.7s;
+ opacity: 0;
+}
+
+.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
+ opacity: 1;
+ transition-delay: 0s;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+ outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-private-filebrowser-button-height: 28px;
+ --jp-private-filebrowser-button-width: 48px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser .jp-SidePanel-content {
+ display: flex;
+ flex-direction: column;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+ flex-wrap: wrap;
+ row-gap: 12px;
+ border-bottom: none;
+ height: auto;
+ margin: 8px 12px 0;
+ box-shadow: none;
+ padding: 0;
+ justify-content: flex-start;
+}
+
+.jp-FileBrowser-Panel {
+ flex: 1 1 auto;
+ display: flex;
+ flex-direction: column;
+}
+
+.jp-BreadCrumbs {
+ flex: 0 0 auto;
+ margin: 8px 12px;
+}
+
+.jp-BreadCrumbs-item {
+ margin: 0 2px;
+ padding: 0 2px;
+ border-radius: var(--jp-border-radius);
+ cursor: pointer;
+}
+
+.jp-BreadCrumbs-item:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-BreadCrumbs-item:first-child {
+ margin-left: 0;
+}
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+ background-color: var(--jp-brand-color2);
+ opacity: 0.7;
+}
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item {
+ flex: 0 0 auto;
+ padding-left: 0;
+ padding-right: 2px;
+ align-items: center;
+ height: unset;
+}
+
+.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
+ width: 40px;
+}
+
+/*-----------------------------------------------------------------------------
+| Other styles
+|----------------------------------------------------------------------------*/
+
+.jp-FileDialog.jp-mod-conflict input {
+ color: var(--jp-error-color1);
+}
+
+.jp-FileDialog .jp-new-name-title {
+ margin-top: 12px;
+}
+
+.jp-LastModified-hidden {
+ display: none;
+}
+
+.jp-FileSize-hidden {
+ display: none;
+}
+
+.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
+ display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+ flex: 1 1 auto;
+ display: flex;
+ flex-direction: column;
+ outline: 0;
+}
+
+.jp-DirListing-header {
+ flex: 0 0 auto;
+ display: flex;
+ flex-direction: row;
+ align-items: center;
+ overflow: hidden;
+ border-top: var(--jp-border-width) solid var(--jp-border-color2);
+ border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+ box-shadow: var(--jp-toolbar-box-shadow);
+ z-index: 2;
+}
+
+.jp-DirListing-headerItem {
+ padding: 4px 12px 2px;
+ font-weight: 500;
+}
+
+.jp-DirListing-headerItem:hover {
+ background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-headerItem.jp-id-name {
+ flex: 1 0 84px;
+}
+
+.jp-DirListing-headerItem.jp-id-modified {
+ flex: 0 0 112px;
+ border-left: var(--jp-border-width) solid var(--jp-border-color2);
+ text-align: right;
+}
+
+.jp-DirListing-headerItem.jp-id-filesize {
+ flex: 0 0 75px;
+ border-left: var(--jp-border-width) solid var(--jp-border-color2);
+ text-align: right;
+}
+
+.jp-id-narrow {
+ display: none;
+ flex: 0 0 5px;
+ padding: 4px;
+ border-left: var(--jp-border-width) solid var(--jp-border-color2);
+ text-align: right;
+ color: var(--jp-border-color2);
+}
+
+.jp-DirListing-narrow .jp-id-narrow {
+ display: block;
+}
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+ display: none;
+}
+
+.jp-DirListing-headerItem.jp-mod-selected {
+ font-weight: 600;
+}
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+ flex: 1 1 auto;
+ margin: 0;
+ padding: 0;
+ list-style-type: none;
+ overflow: auto;
+ background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-content mark {
+ color: var(--jp-ui-font-color0);
+ background-color: transparent;
+ font-weight: bold;
+}
+
+.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
+ color: var(--jp-ui-inverse-font-color0);
+}
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+ outline: 5px dashed rgba(128, 128, 128, 0.5);
+ outline-offset: -10px;
+ cursor: copy;
+}
+
+.jp-DirListing-item {
+ display: flex;
+ flex-direction: row;
+ align-items: center;
+ padding: 4px 12px;
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.jp-DirListing-checkboxWrapper {
+ /* Increases hit area of checkbox. */
+ padding: 4px;
+}
+
+.jp-DirListing-header
+ .jp-DirListing-checkboxWrapper
+ + .jp-DirListing-headerItem {
+ padding-left: 4px;
+}
+
+.jp-DirListing-content .jp-DirListing-checkboxWrapper {
+ position: relative;
+ left: -4px;
+ margin: -4px 0 -4px -8px;
+}
+
+.jp-DirListing-checkboxWrapper.jp-mod-visible {
+ visibility: visible;
+}
+
+/* For devices that support hovering, hide checkboxes until hovered, selected...
+*/
+@media (hover: hover) {
+ .jp-DirListing-checkboxWrapper {
+ visibility: hidden;
+ }
+
+ .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
+ .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
+ visibility: visible;
+ }
+}
+
+.jp-DirListing-item[data-is-dot] {
+ opacity: 75%;
+}
+
+.jp-DirListing-item.jp-mod-selected {
+ color: var(--jp-ui-inverse-font-color1);
+ background: var(--jp-brand-color1);
+}
+
+.jp-DirListing-item.jp-mod-dropTarget {
+ background: var(--jp-brand-color3);
+}
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+ background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-itemIcon {
+ flex: 0 0 20px;
+ margin-right: 4px;
+}
+
+.jp-DirListing-itemText {
+ flex: 1 0 64px;
+ white-space: nowrap;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ user-select: none;
+}
+
+.jp-DirListing-itemText:focus {
+ outline-width: 2px;
+ outline-color: var(--jp-inverse-layout-color1);
+ outline-style: solid;
+ outline-offset: 1px;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
+ outline-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-itemModified {
+ flex: 0 0 125px;
+ text-align: right;
+}
+
+.jp-DirListing-itemFileSize {
+ flex: 0 0 90px;
+ text-align: right;
+}
+
+.jp-DirListing-editor {
+ flex: 1 0 64px;
+ outline: none;
+ border: none;
+ color: var(--jp-ui-font-color1);
+ background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
+ color: var(--jp-success-color1);
+ content: '\25CF';
+ font-size: 8px;
+ position: absolute;
+ left: -8px;
+}
+
+.jp-DirListing-item.jp-mod-running.jp-mod-selected
+ .jp-DirListing-itemIcon::before {
+ color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+ font-size: var(--jp-ui-font-size1);
+ padding-left: 4px;
+ margin-left: 4px;
+ width: 160px;
+ background-color: var(--jp-ui-inverse-font-color2);
+ box-shadow: var(--jp-elevation-z2);
+ border-radius: 0;
+ color: var(--jp-ui-font-color1);
+ transform: translateX(-40%) translateY(-58%);
+}
+
+.jp-Document {
+ min-width: 120px;
+ min-height: 120px;
+ outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+ overflow-y: auto;
+}
+
+.jp-OutputArea-child {
+ display: table;
+ table-layout: fixed;
+ width: 100%;
+ overflow: hidden;
+}
+
+.jp-OutputPrompt {
+ width: var(--jp-cell-prompt-width);
+ color: var(--jp-cell-outprompt-font-color);
+ font-family: var(--jp-cell-prompt-font-family);
+ padding: var(--jp-code-padding);
+ letter-spacing: var(--jp-cell-prompt-letter-spacing);
+ line-height: var(--jp-code-line-height);
+ font-size: var(--jp-code-font-size);
+ border: var(--jp-border-width) solid transparent;
+ opacity: var(--jp-cell-prompt-opacity);
+
+ /* Right align prompt text, don't wrap to handle large prompt numbers */
+ text-align: right;
+ white-space: nowrap;
+ overflow: hidden;
+ text-overflow: ellipsis;
+
+ /* Disable text selection */
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.jp-OutputArea-prompt {
+ display: table-cell;
+ vertical-align: top;
+}
+
+.jp-OutputArea-output {
+ display: table-cell;
+ width: 100%;
+ height: auto;
+ overflow: auto;
+ user-select: text;
+ -moz-user-select: text;
+ -webkit-user-select: text;
+ -ms-user-select: text;
+}
+
+.jp-OutputArea .jp-RenderedText {
+ padding-left: 1ch;
+}
+
+/**
+ * Prompt overlay.
+ */
+
+.jp-OutputArea-promptOverlay {
+ position: absolute;
+ top: 0;
+ width: var(--jp-cell-prompt-width);
+ height: 100%;
+ opacity: 0.5;
+}
+
+.jp-OutputArea-promptOverlay:hover {
+ background: var(--jp-layout-color2);
+ box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
+ cursor: zoom-out;
+}
+
+.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
+ cursor: zoom-in;
+}
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+ width: 100%;
+ display: block;
+}
+
+/*
+When drag events occur, `lm-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+ position: relative;
+}
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
+ content: '';
+ position: absolute;
+ top: 0;
+ left: 0;
+ right: 0;
+ bottom: 0;
+ background: transparent;
+}
+
+/* pre */
+
+.jp-OutputArea-output pre {
+ border: none;
+ margin: 0;
+ padding: 0;
+ overflow-x: auto;
+ overflow-y: auto;
+ word-break: break-all;
+ word-wrap: break-word;
+ white-space: pre-wrap;
+}
+
+/* tables */
+
+.jp-OutputArea-output.jp-RenderedHTMLCommon table {
+ margin-left: 0;
+ margin-right: 0;
+}
+
+/* description lists */
+
+.jp-OutputArea-output dl,
+.jp-OutputArea-output dt,
+.jp-OutputArea-output dd {
+ display: block;
+}
+
+.jp-OutputArea-output dl {
+ width: 100%;
+ overflow: hidden;
+ padding: 0;
+ margin: 0;
+}
+
+.jp-OutputArea-output dt {
+ font-weight: bold;
+ float: left;
+ width: 20%;
+ padding: 0;
+ margin: 0;
+}
+
+.jp-OutputArea-output dd {
+ float: left;
+ width: 80%;
+ padding: 0;
+ margin: 0;
+}
+
+.jp-TrimmedOutputs pre {
+ background: var(--jp-layout-color3);
+ font-size: calc(var(--jp-code-font-size) * 1.4);
+ text-align: center;
+ text-transform: uppercase;
+}
+
+/* Hide the gutter in case of
+ * - nested output areas (e.g. in the case of output widgets)
+ * - mirrored output areas
+ */
+.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
+ display: none;
+}
+
+/* Hide empty lines in the output area, for instance due to cleared widgets */
+.jp-OutputArea-prompt:empty {
+ padding: 0;
+ border: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| executeResult is added to any Output-result for the display of the object
+| returned by a cell
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+ margin-left: 0;
+ width: 100%;
+}
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+ padding-top: var(--jp-code-padding);
+ border-top: var(--jp-border-width) solid transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| The Stdin output
+|----------------------------------------------------------------------------*/
+
+.jp-Stdin-prompt {
+ color: var(--jp-content-font-color0);
+ padding-right: var(--jp-code-padding);
+ vertical-align: baseline;
+ flex: 0 0 auto;
+}
+
+.jp-Stdin-input {
+ font-family: var(--jp-code-font-family);
+ font-size: inherit;
+ color: inherit;
+ background-color: inherit;
+ width: 42%;
+ min-width: 200px;
+
+ /* make sure input baseline aligns with prompt */
+ vertical-align: baseline;
+
+ /* padding + margin = 0.5em between prompt and cursor */
+ padding: 0 0.25em;
+ margin: 0 0.25em;
+ flex: 0 0 70%;
+}
+
+.jp-Stdin-input::placeholder {
+ opacity: 0;
+}
+
+.jp-Stdin-input:focus {
+ box-shadow: none;
+}
+
+.jp-Stdin-input:focus::placeholder {
+ opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Output Area View
+|----------------------------------------------------------------------------*/
+
+.jp-LinkedOutputView .jp-OutputArea {
+ height: 100%;
+ display: block;
+}
+
+.jp-LinkedOutputView .jp-OutputArea-output:only-child {
+ height: 100%;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+@media print {
+ .jp-OutputArea-child {
+ break-inside: avoid-page;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+ .jp-OutputPrompt {
+ display: table-row;
+ text-align: left;
+ }
+
+ .jp-OutputArea-child .jp-OutputArea-output {
+ display: table-row;
+ margin-left: var(--jp-notebook-padding);
+ }
+}
+
+/* Trimmed outputs warning */
+.jp-TrimmedOutputs > a {
+ margin: 10px;
+ text-decoration: none;
+ cursor: pointer;
+}
+
+.jp-TrimmedOutputs > a:hover {
+ text-decoration: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Table of Contents
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-private-toc-active-width: 4px;
+}
+
+.jp-TableOfContents {
+ display: flex;
+ flex-direction: column;
+ background: var(--jp-layout-color1);
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+ height: 100%;
+}
+
+.jp-TableOfContents-placeholder {
+ text-align: center;
+}
+
+.jp-TableOfContents-placeholderContent {
+ color: var(--jp-content-font-color2);
+ padding: 8px;
+}
+
+.jp-TableOfContents-placeholderContent > h3 {
+ margin-bottom: var(--jp-content-heading-margin-bottom);
+}
+
+.jp-TableOfContents .jp-SidePanel-content {
+ overflow-y: auto;
+}
+
+.jp-TableOfContents-tree {
+ margin: 4px;
+}
+
+.jp-TableOfContents ol {
+ list-style-type: none;
+}
+
+/* stylelint-disable-next-line selector-max-type */
+.jp-TableOfContents li > ol {
+ /* Align left border with triangle icon center */
+ padding-left: 11px;
+}
+
+.jp-TableOfContents-content {
+ /* left margin for the active heading indicator */
+ margin: 0 0 0 var(--jp-private-toc-active-width);
+ padding: 0;
+ background-color: var(--jp-layout-color1);
+}
+
+.jp-tocItem {
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+.jp-tocItem-heading {
+ display: flex;
+ cursor: pointer;
+}
+
+.jp-tocItem-heading:hover {
+ background-color: var(--jp-layout-color2);
+}
+
+.jp-tocItem-content {
+ display: block;
+ padding: 4px 0;
+ white-space: nowrap;
+ text-overflow: ellipsis;
+ overflow-x: hidden;
+}
+
+.jp-tocItem-collapser {
+ height: 20px;
+ margin: 2px 2px 0;
+ padding: 0;
+ background: none;
+ border: none;
+ cursor: pointer;
+}
+
+.jp-tocItem-collapser:hover {
+ background-color: var(--jp-layout-color3);
+}
+
+/* Active heading indicator */
+
+.jp-tocItem-heading::before {
+ content: ' ';
+ background: transparent;
+ width: var(--jp-private-toc-active-width);
+ height: 24px;
+ position: absolute;
+ left: 0;
+ border-radius: var(--jp-border-radius);
+}
+
+.jp-tocItem-heading.jp-tocItem-active::before {
+ background-color: var(--jp-brand-color1);
+}
+
+.jp-tocItem-heading:hover.jp-tocItem-active::before {
+ background: var(--jp-brand-color0);
+ opacity: 1;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapser {
+ flex: 0 0 var(--jp-cell-collapser-width);
+ padding: 0;
+ margin: 0;
+ border: none;
+ outline: none;
+ background: transparent;
+ border-radius: var(--jp-border-radius);
+ opacity: 1;
+}
+
+.jp-Collapser-child {
+ display: block;
+ width: 100%;
+ box-sizing: border-box;
+
+ /* height: 100% doesn't work because the height of its parent is computed from content */
+ position: absolute;
+ top: 0;
+ bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Hiding collapsers in print mode.
+
+Note: input and output wrappers have "display: block" propery in print mode.
+*/
+
+@media print {
+ .jp-Collapser {
+ display: none;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Header/Footer
+|----------------------------------------------------------------------------*/
+
+/* Hidden by zero height by default */
+.jp-CellHeader,
+.jp-CellFooter {
+ height: 0;
+ width: 100%;
+ padding: 0;
+ margin: 0;
+ border: none;
+ outline: none;
+ background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Input
+|----------------------------------------------------------------------------*/
+
+/* All input areas */
+.jp-InputArea {
+ display: table;
+ table-layout: fixed;
+ width: 100%;
+ overflow: hidden;
+}
+
+.jp-InputArea-editor {
+ display: table-cell;
+ overflow: hidden;
+ vertical-align: top;
+
+ /* This is the non-active, default styling */
+ border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+ border-radius: 0;
+ background: var(--jp-cell-editor-background);
+}
+
+.jp-InputPrompt {
+ display: table-cell;
+ vertical-align: top;
+ width: var(--jp-cell-prompt-width);
+ color: var(--jp-cell-inprompt-font-color);
+ font-family: var(--jp-cell-prompt-font-family);
+ padding: var(--jp-code-padding);
+ letter-spacing: var(--jp-cell-prompt-letter-spacing);
+ opacity: var(--jp-cell-prompt-opacity);
+ line-height: var(--jp-code-line-height);
+ font-size: var(--jp-code-font-size);
+ border: var(--jp-border-width) solid transparent;
+
+ /* Right align prompt text, don't wrap to handle large prompt numbers */
+ text-align: right;
+ white-space: nowrap;
+ overflow: hidden;
+ text-overflow: ellipsis;
+
+ /* Disable text selection */
+ -webkit-user-select: none;
+ -moz-user-select: none;
+ -ms-user-select: none;
+ user-select: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+ .jp-InputArea-editor {
+ display: table-row;
+ margin-left: var(--jp-notebook-padding);
+ }
+
+ .jp-InputPrompt {
+ display: table-row;
+ text-align: left;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Placeholder {
+ display: table;
+ table-layout: fixed;
+ width: 100%;
+}
+
+.jp-Placeholder-prompt {
+ display: table-cell;
+ box-sizing: border-box;
+}
+
+.jp-Placeholder-content {
+ display: table-cell;
+ padding: 4px 6px;
+ border: 1px solid transparent;
+ border-radius: 0;
+ background: none;
+ box-sizing: border-box;
+ cursor: pointer;
+}
+
+.jp-Placeholder-contentContainer {
+ display: flex;
+}
+
+.jp-Placeholder-content:hover,
+.jp-InputPlaceholder > .jp-Placeholder-content:hover {
+ border-color: var(--jp-layout-color3);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon {
+ width: 32px;
+ height: 16px;
+ border: 1px solid transparent;
+ border-radius: var(--jp-border-radius);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon:hover {
+ border: 1px solid var(--jp-border-color1);
+ box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
+ background-color: var(--jp-layout-color0);
+}
+
+.jp-PlaceholderText {
+ white-space: nowrap;
+ overflow-x: hidden;
+ color: var(--jp-inverse-layout-color3);
+ font-family: var(--jp-code-font-family);
+}
+
+.jp-InputPlaceholder > .jp-Placeholder-content {
+ border-color: var(--jp-cell-editor-border-color);
+ background: var(--jp-cell-editor-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-private-cell-scrolling-output-offset: 5px;
+}
+
+/*-----------------------------------------------------------------------------
+| Cell
+|----------------------------------------------------------------------------*/
+
+.jp-Cell {
+ padding: var(--jp-cell-padding);
+ margin: 0;
+ border: none;
+ outline: none;
+ background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Common input/output
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-inputWrapper,
+.jp-Cell-outputWrapper {
+ display: flex;
+ flex-direction: row;
+ padding: 0;
+ margin: 0;
+
+ /* Added to reveal the box-shadow on the input and output collapsers. */
+ overflow: visible;
+}
+
+/* Only input/output areas inside cells */
+.jp-Cell-inputArea,
+.jp-Cell-outputArea {
+ flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Collapser
+|----------------------------------------------------------------------------*/
+
+/* Make the output collapser disappear when there is not output, but do so
+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+ border: none !important;
+ background: transparent !important;
+}
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+ min-height: var(--jp-cell-collapser-min-height);
+}
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+ margin-top: 5px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+ overflow-y: auto;
+ max-height: 24em;
+ margin-left: var(--jp-private-cell-scrolling-output-offset);
+ resize: vertical;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
+ max-height: unset;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
+ content: ' ';
+ box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
+ width: 100%;
+ height: 100%;
+ position: sticky;
+ bottom: 0;
+ top: 0;
+ margin-top: -50%;
+ float: left;
+ display: block;
+ pointer-events: none;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
+ padding-top: 6px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+ width: calc(
+ var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+ );
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
+ left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
+}
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+ display: table-cell;
+ width: 100%;
+ margin-top: 0;
+ margin-bottom: 0;
+ padding-left: var(--jp-code-padding);
+}
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+ overflow: auto;
+}
+
+/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
+.jp-collapseHeadingButton {
+ display: flex;
+ min-height: var(--jp-cell-collapser-min-height);
+ font-size: var(--jp-code-font-size);
+ position: absolute;
+ background-color: transparent;
+ background-size: 25px;
+ background-repeat: no-repeat;
+ background-position-x: center;
+ background-position-y: top;
+ background-image: var(--jp-icon-caret-down);
+ right: 0;
+ top: 0;
+ bottom: 0;
+}
+
+.jp-collapseHeadingButton.jp-mod-collapsed {
+ background-image: var(--jp-icon-caret-right);
+}
+
+/*
+ set the container font size to match that of content
+ so that the nested collapse buttons have the right size
+*/
+.jp-MarkdownCell .jp-InputPrompt {
+ font-size: var(--jp-content-font-size1);
+}
+
+/*
+ Align collapseHeadingButton with cell top header
+ The font sizes are identical to the ones in packages/rendermime/style/base.css
+*/
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
+ font-size: var(--jp-content-font-size5);
+ background-position-y: calc(0.3 * var(--jp-content-font-size5));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
+ font-size: var(--jp-content-font-size4);
+ background-position-y: calc(0.3 * var(--jp-content-font-size4));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
+ font-size: var(--jp-content-font-size3);
+ background-position-y: calc(0.3 * var(--jp-content-font-size3));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
+ font-size: var(--jp-content-font-size2);
+ background-position-y: calc(0.3 * var(--jp-content-font-size2));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
+ font-size: var(--jp-content-font-size1);
+ background-position-y: top;
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
+ font-size: var(--jp-content-font-size0);
+ background-position-y: top;
+}
+
+/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
+ display: none;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton
+ :is(.jp-MarkdownCell:hover, .jp-mod-active)
+ .jp-collapseHeadingButton {
+ display: flex;
+}
+
+/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
+is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
+ margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
+ margin-top: var(--jp-code-padding);
+ border: 1px solid var(--jp-border-color2);
+ background-color: var(--jp-border-color3) !important;
+ color: var(--jp-content-font-color0) !important;
+ display: flex;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
+ background-color: var(--jp-border-color2) !important;
+}
+
+.jp-showHiddenCellsButton {
+ display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Using block instead of flex to allow the use of the break-inside CSS property for
+cell outputs.
+*/
+
+@media print {
+ .jp-Cell-inputWrapper,
+ .jp-Cell-outputWrapper {
+ display: block;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
+}
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+ padding: var(--jp-notebook-toolbar-padding);
+
+ /* disable paint containment from lumino 2.0 default strict CSS containment */
+ contain: style size !important;
+}
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+ border: none;
+ box-shadow: none;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+ height: 24px;
+ font-size: var(--jp-ui-font-size1);
+ line-height: 14px;
+ border-radius: 0;
+ display: block;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+ top: 5px !important;
+}
+
+.jp-Toolbar-responsive-popup {
+ position: absolute;
+ height: fit-content;
+ display: flex;
+ flex-direction: row;
+ flex-wrap: wrap;
+ justify-content: flex-end;
+ border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+ box-shadow: var(--jp-toolbar-box-shadow);
+ background: var(--jp-toolbar-background);
+ min-height: var(--jp-toolbar-micro-height);
+ padding: var(--jp-notebook-toolbar-padding);
+ z-index: 1;
+ right: 0;
+ top: 0;
+}
+
+.jp-Toolbar > .jp-Toolbar-responsive-opener {
+ margin-left: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-ExecutionIndicator {
+ position: relative;
+ display: inline-block;
+ height: 100%;
+ z-index: 9997;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip {
+ visibility: hidden;
+ height: auto;
+ width: max-content;
+ width: -moz-max-content;
+ background-color: var(--jp-layout-color2);
+ color: var(--jp-ui-font-color1);
+ text-align: justify;
+ border-radius: 6px;
+ padding: 0 5px;
+ position: fixed;
+ display: table;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.up {
+ transform: translateX(-50%) translateY(-100%) translateY(-32px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.down {
+ transform: translateX(calc(-100% + 16px)) translateY(5px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.hidden {
+ display: none;
+}
+
+.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
+ visibility: visible;
+}
+
+.jp-Notebook-ExecutionIndicator span {
+ font-size: var(--jp-ui-font-size1);
+ font-family: var(--jp-ui-font-family);
+ color: var(--jp-ui-font-color1);
+ line-height: 24px;
+ display: block;
+}
+
+.jp-Notebook-ExecutionIndicator-progress-bar {
+ display: flex;
+ justify-content: center;
+ height: 100%;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*
+ * Execution indicator
+ */
+.jp-tocItem-content::after {
+ content: '';
+
+ /* Must be identical to form a circle */
+ width: 12px;
+ height: 12px;
+ background: none;
+ border: none;
+ position: absolute;
+ right: 0;
+}
+
+.jp-tocItem-content[data-running='0']::after {
+ border-radius: 50%;
+ border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+ background: none;
+}
+
+.jp-tocItem-content[data-running='1']::after {
+ border-radius: 50%;
+ border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+ background-color: var(--jp-inverse-layout-color3);
+}
+
+.jp-tocItem-content[data-running='0'],
+.jp-tocItem-content[data-running='1'] {
+ margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Notebook-footer {
+ height: 27px;
+ margin-left: calc(
+ var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+ var(--jp-cell-padding)
+ );
+ width: calc(
+ 100% -
+ (
+ var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+ var(--jp-cell-padding) + var(--jp-cell-padding)
+ )
+ );
+ border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+ color: var(--jp-ui-font-color3);
+ margin-top: 6px;
+ background: none;
+ cursor: pointer;
+}
+
+.jp-Notebook-footer:focus {
+ border-color: var(--jp-cell-editor-active-border-color);
+}
+
+/* For devices that support hovering, hide footer until hover */
+@media (hover: hover) {
+ .jp-Notebook-footer {
+ opacity: 0;
+ }
+
+ .jp-Notebook-footer:focus,
+ .jp-Notebook-footer:hover {
+ opacity: 1;
+ }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+ --jp-side-by-side-output-size: 1fr;
+ --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
+ --jp-private-notebook-dragImage-width: 304px;
+ --jp-private-notebook-dragImage-height: 36px;
+ --jp-private-notebook-selected-color: var(--md-blue-400);
+ --jp-private-notebook-active-color: var(--md-green-400);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+/* stylelint-disable selector-max-class */
+
+.jp-NotebookPanel {
+ display: block;
+ height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+ min-width: 240px;
+ min-height: 120px;
+}
+
+.jp-Notebook {
+ padding: var(--jp-notebook-padding);
+ outline: none;
+ overflow: auto;
+ background: var(--jp-layout-color0);
+}
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+ display: block;
+ content: '';
+ min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
+ contain: strict;
+}
+
+.jp-Notebook .jp-Cell {
+ overflow: visible;
+}
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+ cursor: move;
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+| - Command
+| - Edit
+| - Cell
+| - None
+| - Active (only one can be active)
+| - Selected (the cells actions are applied to)
+| - Multiselected (when multiple selected, the cursor)
+| - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+ opacity: var(--jp-cell-prompt-not-active-opacity);
+ color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+ opacity: var(--jp-cell-prompt-not-active-opacity);
+ color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+ background: var(--jp-brand-color1);
+}
+
+/* cell is dirty */
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
+ color: var(--jp-warn-color1);
+}
+
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
+ color: var(--jp-warn-color1);
+ content: '•';
+}
+
+.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
+ background: var(--jp-warn-color1);
+}
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+ box-shadow: var(--jp-elevation-z2);
+ background: var(--jp-brand-color1);
+ opacity: var(--jp-cell-collapser-not-active-hover-opacity);
+}
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+ background: var(--jp-brand-color0);
+ opacity: 1;
+}
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+ background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+ .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+ background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+ border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+ box-shadow: var(--jp-input-box-shadow);
+ background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+ opacity: 0.5;
+}
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+ .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+ border-top-color: var(--jp-private-notebook-selected-color);
+ border-top-style: solid;
+ border-top-width: 2px;
+}
+
+.jp-dragImage {
+ display: block;
+ flex-direction: row;
+ width: var(--jp-private-notebook-dragImage-width);
+ height: var(--jp-private-notebook-dragImage-height);
+ border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+ background: var(--jp-cell-editor-background);
+ overflow: visible;
+}
+
+.jp-dragImage-singlePrompt {
+ box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+.jp-dragImage .jp-dragImage-content {
+ flex: 1 1 auto;
+ z-index: 2;
+ font-size: var(--jp-code-font-size);
+ font-family: var(--jp-code-font-family);
+ line-height: var(--jp-code-line-height);
+ padding: var(--jp-code-padding);
+ border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+ background: var(--jp-cell-editor-background-color);
+ color: var(--jp-content-font-color3);
+ text-align: left;
+ margin: 4px 4px 4px 0;
+}
+
+.jp-dragImage .jp-dragImage-prompt {
+ flex: 0 0 auto;
+ min-width: 36px;
+ color: var(--jp-cell-inprompt-font-color);
+ padding: var(--jp-code-padding);
+ padding-left: 12px;
+ font-family: var(--jp-cell-prompt-font-family);
+ letter-spacing: var(--jp-cell-prompt-letter-spacing);
+ line-height: 1.9;
+ font-size: var(--jp-code-font-size);
+ border: var(--jp-border-width) solid transparent;
+}
+
+.jp-dragImage-multipleBack {
+ z-index: -1;
+ position: absolute;
+ height: 32px;
+ width: 300px;
+ top: 8px;
+ left: 8px;
+ background: var(--jp-layout-color2);
+ border: var(--jp-border-width) solid var(--jp-input-border-color);
+ box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
+}
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+ display: block;
+ min-width: var(--jp-sidebar-min-width);
+ color: var(--jp-ui-font-color1);
+ background: var(--jp-layout-color1);
+
+ /* This is needed so that all font sizing of children done in ems is
+ * relative to this base size */
+ font-size: var(--jp-ui-font-size1);
+ overflow: auto;
+}
+
+.jp-ActiveCellTool {
+ padding: 12px 0;
+ display: flex;
+}
+
+.jp-ActiveCellTool-Content {
+ flex: 1 1 auto;
+}
+
+.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
+ background: var(--jp-cell-editor-background);
+ border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+ border-radius: 0;
+ min-height: 29px;
+}
+
+.jp-ActiveCellTool .jp-InputPrompt {
+ min-width: calc(var(--jp-cell-prompt-width) * 0.75);
+}
+
+.jp-ActiveCellTool-CellContent > pre {
+ padding: 5px 4px;
+ margin: 0;
+ white-space: normal;
+}
+
+.jp-MetadataEditorTool {
+ flex-direction: column;
+ padding: 12px 0;
+}
+
+.jp-RankedPanel > :not(:first-child) {
+ margin-top: 12px;
+}
+
+.jp-KeySelector select.jp-mod-styled {
+ font-size: var(--jp-ui-font-size1);
+ color: var(--jp-ui-font-color0);
+ border: var(--jp-border-width) solid var(--jp-border-color1);
+}
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label,
+.jp-NumberSetter label {
+ line-height: 1.4;
+}
+
+.jp-NotebookTools .jp-select-wrapper {
+ margin-top: 4px;
+ margin-bottom: 0;
+}
+
+.jp-NumberSetter input {
+ width: 100%;
+ margin-top: 4px;
+}
+
+.jp-NotebookTools .jp-Collapse {
+ margin-top: 16px;
+}
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+ --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+ --jp-code-font-size: var(--jp-code-presentation-font-size);
+}
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+ flex: 0 0 110px;
+}
+
+/*-----------------------------------------------------------------------------
+| Side-by-side Mode (.jp-mod-sideBySide)
+|----------------------------------------------------------------------------*/
+.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
+ margin-top: 3em;
+ margin-bottom: 3em;
+ margin-left: 5%;
+ margin-right: 5%;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
+ display: grid;
+ grid-template-columns: minmax(0, 1fr) min-content minmax(
+ 0,
+ var(--jp-side-by-side-output-size)
+ );
+ grid-template-rows: auto minmax(0, 1fr) auto;
+ grid-template-areas:
+ 'header header header'
+ 'input handle output'
+ 'footer footer footer';
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
+ grid-template-columns: minmax(0, 1fr) min-content minmax(
+ 0,
+ var(--jp-side-by-side-resized-cell)
+ );
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
+ grid-area: header;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
+ grid-area: input;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
+ /* overwrite the default margin (no vertical separation needed in side by side move */
+ margin-top: 0;
+ grid-area: output;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
+ grid-area: footer;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
+ grid-area: handle;
+ user-select: none;
+ display: block;
+ height: 100%;
+ cursor: ew-resize;
+ padding: 0 var(--jp-cell-padding);
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
+ content: '';
+ display: block;
+ background: var(--jp-border-color2);
+ height: 100%;
+ width: 5px;
+}
+
+.jp-mod-sideBySide.jp-Notebook
+ .jp-CodeCell.jp-mod-resizedCell
+ .jp-CellResizeHandle::after {
+ background: var(--jp-border-color0);
+}
+
+.jp-CellResizeHandle {
+ display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-Placeholder {
+ padding-left: 55px;
+}
+
+.jp-Cell-Placeholder-wrapper {
+ background: #fff;
+ border: 1px solid;
+ border-color: #e5e6e9 #dfe0e4 #d0d1d5;
+ border-radius: 4px;
+ -webkit-border-radius: 4px;
+ margin: 10px 15px;
+}
+
+.jp-Cell-Placeholder-wrapper-inner {
+ padding: 15px;
+ position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body {
+ background-repeat: repeat;
+ background-size: 50% auto;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+ background: #f6f7f8;
+ background-image: -webkit-linear-gradient(
+ left,
+ #f6f7f8 0%,
+ #edeef1 20%,
+ #f6f7f8 40%,
+ #f6f7f8 100%
+ );
+ background-repeat: no-repeat;
+ background-size: 800px 104px;
+ height: 104px;
+ position: absolute;
+ right: 15px;
+ left: 15px;
+ top: 15px;
+}
+
+div.jp-Cell-Placeholder-h1 {
+ top: 20px;
+ height: 20px;
+ left: 15px;
+ width: 150px;
+}
+
+div.jp-Cell-Placeholder-h2 {
+ left: 15px;
+ top: 50px;
+ height: 10px;
+ width: 100px;
+}
+
+div.jp-Cell-Placeholder-content-1,
+div.jp-Cell-Placeholder-content-2,
+div.jp-Cell-Placeholder-content-3 {
+ left: 15px;
+ right: 15px;
+ height: 10px;
+}
+
+div.jp-Cell-Placeholder-content-1 {
+ top: 100px;
+}
+
+div.jp-Cell-Placeholder-content-2 {
+ top: 120px;
+}
+
+div.jp-Cell-Placeholder-content-3 {
+ top: 140px;
+}
+
+</style>
+<style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+ /* Elevation
+ *
+ * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+ *
+ * https://github.com/material-components/material-components-web
+ * https://material-components-web.appspot.com/elevation.html
+ */
+
+ --jp-shadow-base-lightness: 0;
+ --jp-shadow-umbra-color: rgba(
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ 0.2
+ );
+ --jp-shadow-penumbra-color: rgba(
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ 0.14
+ );
+ --jp-shadow-ambient-color: rgba(
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ var(--jp-shadow-base-lightness),
+ 0.12
+ );
+ --jp-elevation-z0: none;
+ --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
+ 0 1px 1px 0 var(--jp-shadow-penumbra-color),
+ 0 1px 3px 0 var(--jp-shadow-ambient-color);
+ --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
+ 0 2px 2px 0 var(--jp-shadow-penumbra-color),
+ 0 1px 5px 0 var(--jp-shadow-ambient-color);
+ --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
+ 0 4px 5px 0 var(--jp-shadow-penumbra-color),
+ 0 1px 10px 0 var(--jp-shadow-ambient-color);
+ --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
+ 0 6px 10px 0 var(--jp-shadow-penumbra-color),
+ 0 1px 18px 0 var(--jp-shadow-ambient-color);
+ --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
+ 0 8px 10px 1px var(--jp-shadow-penumbra-color),
+ 0 3px 14px 2px var(--jp-shadow-ambient-color);
+ --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
+ 0 12px 17px 2px var(--jp-shadow-penumbra-color),
+ 0 5px 22px 4px var(--jp-shadow-ambient-color);
+ --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
+ 0 16px 24px 2px var(--jp-shadow-penumbra-color),
+ 0 6px 30px 5px var(--jp-shadow-ambient-color);
+ --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
+ 0 20px 31px 3px var(--jp-shadow-penumbra-color),
+ 0 8px 38px 7px var(--jp-shadow-ambient-color);
+ --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
+ 0 24px 38px 3px var(--jp-shadow-penumbra-color),
+ 0 9px 46px 8px var(--jp-shadow-ambient-color);
+
+ /* Borders
+ *
+ * The following variables, specify the visual styling of borders in JupyterLab.
+ */
+
+ --jp-border-width: 1px;
+ --jp-border-color0: var(--md-grey-400);
+ --jp-border-color1: var(--md-grey-400);
+ --jp-border-color2: var(--md-grey-300);
+ --jp-border-color3: var(--md-grey-200);
+ --jp-inverse-border-color: var(--md-grey-600);
+ --jp-border-radius: 2px;
+
+ /* UI Fonts
+ *
+ * The UI font CSS variables are used for the typography all of the JupyterLab
+ * user interface elements that are not directly user generated content.
+ *
+ * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+ * is applied to a parent element. When children elements, such as headings, are sized
+ * in em all things will be computed relative to that body size.
+ */
+
+ --jp-ui-font-scale-factor: 1.2;
+ --jp-ui-font-size0: 0.83333em;
+ --jp-ui-font-size1: 13px; /* Base font size */
+ --jp-ui-font-size2: 1.2em;
+ --jp-ui-font-size3: 1.44em;
+ --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
+ helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+ 'Segoe UI Symbol';
+
+ /*
+ * Use these font colors against the corresponding main layout colors.
+ * In a light theme, these go from dark to light.
+ */
+
+ /* Defaults use Material Design specification */
+ --jp-ui-font-color0: rgba(0, 0, 0, 1);
+ --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+ --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+ --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+ /*
+ * Use these against the brand/accent/warn/error colors.
+ * These will typically go from light to darker, in both a dark and light theme.
+ */
+
+ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+ --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+ --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+ --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+ /* Content Fonts
+ *
+ * Content font variables are used for typography of user generated content.
+ *
+ * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+ * is applied to a parent element. When children elements, such as headings, are sized
+ * in em all things will be computed relative to that body size.
+ */
+
+ --jp-content-line-height: 1.6;
+ --jp-content-font-scale-factor: 1.2;
+ --jp-content-font-size0: 0.83333em;
+ --jp-content-font-size1: 14px; /* Base font size */
+ --jp-content-font-size2: 1.2em;
+ --jp-content-font-size3: 1.44em;
+ --jp-content-font-size4: 1.728em;
+ --jp-content-font-size5: 2.0736em;
+
+ /* This gives a magnification of about 125% in presentation mode over normal. */
+ --jp-content-presentation-font-size1: 17px;
+ --jp-content-heading-line-height: 1;
+ --jp-content-heading-margin-top: 1.2em;
+ --jp-content-heading-margin-bottom: 0.8em;
+ --jp-content-heading-font-weight: 500;
+
+ /* Defaults use Material Design specification */
+ --jp-content-font-color0: rgba(0, 0, 0, 1);
+ --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+ --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+ --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+ --jp-content-link-color: var(--md-blue-900);
+ --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
+ 'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
+ 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+ /*
+ * Code Fonts
+ *
+ * Code font variables are used for typography of code and other monospaces content.
+ */
+
+ --jp-code-font-size: 13px;
+ --jp-code-line-height: 1.3077; /* 17px for 13px base */
+ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+ --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
+ --jp-code-font-family: var(--jp-code-font-family-default);
+
+ /* This gives a magnification of about 125% in presentation mode over normal. */
+ --jp-code-presentation-font-size: 16px;
+
+ /* may need to tweak cursor width if you change font size */
+ --jp-code-cursor-width0: 1.4px;
+ --jp-code-cursor-width1: 2px;
+ --jp-code-cursor-width2: 4px;
+
+ /* Layout
+ *
+ * The following are the main layout colors use in JupyterLab. In a light
+ * theme these would go from light to dark.
+ */
+
+ --jp-layout-color0: white;
+ --jp-layout-color1: white;
+ --jp-layout-color2: var(--md-grey-200);
+ --jp-layout-color3: var(--md-grey-400);
+ --jp-layout-color4: var(--md-grey-600);
+
+ /* Inverse Layout
+ *
+ * The following are the inverse layout colors use in JupyterLab. In a light
+ * theme these would go from dark to light.
+ */
+
+ --jp-inverse-layout-color0: #111;
+ --jp-inverse-layout-color1: var(--md-grey-900);
+ --jp-inverse-layout-color2: var(--md-grey-800);
+ --jp-inverse-layout-color3: var(--md-grey-700);
+ --jp-inverse-layout-color4: var(--md-grey-600);
+
+ /* Brand/accent */
+
+ --jp-brand-color0: var(--md-blue-900);
+ --jp-brand-color1: var(--md-blue-700);
+ --jp-brand-color2: var(--md-blue-300);
+ --jp-brand-color3: var(--md-blue-100);
+ --jp-brand-color4: var(--md-blue-50);
+ --jp-accent-color0: var(--md-green-900);
+ --jp-accent-color1: var(--md-green-700);
+ --jp-accent-color2: var(--md-green-300);
+ --jp-accent-color3: var(--md-green-100);
+
+ /* State colors (warn, error, success, info) */
+
+ --jp-warn-color0: var(--md-orange-900);
+ --jp-warn-color1: var(--md-orange-700);
+ --jp-warn-color2: var(--md-orange-300);
+ --jp-warn-color3: var(--md-orange-100);
+ --jp-error-color0: var(--md-red-900);
+ --jp-error-color1: var(--md-red-700);
+ --jp-error-color2: var(--md-red-300);
+ --jp-error-color3: var(--md-red-100);
+ --jp-success-color0: var(--md-green-900);
+ --jp-success-color1: var(--md-green-700);
+ --jp-success-color2: var(--md-green-300);
+ --jp-success-color3: var(--md-green-100);
+ --jp-info-color0: var(--md-cyan-900);
+ --jp-info-color1: var(--md-cyan-700);
+ --jp-info-color2: var(--md-cyan-300);
+ --jp-info-color3: var(--md-cyan-100);
+
+ /* Cell specific styles */
+
+ --jp-cell-padding: 5px;
+ --jp-cell-collapser-width: 8px;
+ --jp-cell-collapser-min-height: 20px;
+ --jp-cell-collapser-not-active-hover-opacity: 0.6;
+ --jp-cell-editor-background: var(--md-grey-100);
+ --jp-cell-editor-border-color: var(--md-grey-300);
+ --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+ --jp-cell-editor-active-background: var(--jp-layout-color0);
+ --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+ --jp-cell-prompt-width: 64px;
+ --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+ --jp-cell-prompt-letter-spacing: 0;
+ --jp-cell-prompt-opacity: 1;
+ --jp-cell-prompt-not-active-opacity: 0.5;
+ --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+
+ /* A custom blend of MD grey and blue 600
+ * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+ --jp-cell-inprompt-font-color: #307fc1;
+
+ /* A custom blend of MD grey and orange 600
+ * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+ --jp-cell-outprompt-font-color: #bf5b3d;
+
+ /* Notebook specific styles */
+
+ --jp-notebook-padding: 10px;
+ --jp-notebook-select-background: var(--jp-layout-color1);
+ --jp-notebook-multiselected-color: var(--md-blue-50);
+
+ /* The scroll padding is calculated to fill enough space at the bottom of the
+ notebook to show one single-line cell (with appropriate padding) at the top
+ when the notebook is scrolled all the way to the bottom. We also subtract one
+ pixel so that no scrollbar appears if we have just one single-line cell in the
+ notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+ */
+ --jp-notebook-scroll-padding: calc(
+ 100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+ var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+ );
+
+ /* Rendermime styles */
+
+ --jp-rendermime-error-background: #fdd;
+ --jp-rendermime-table-row-background: var(--md-grey-100);
+ --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+ /* Dialog specific styles */
+
+ --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+ /* Console specific styles */
+
+ --jp-console-padding: 10px;
+
+ /* Toolbar specific styles */
+
+ --jp-toolbar-border-color: var(--jp-border-color1);
+ --jp-toolbar-micro-height: 8px;
+ --jp-toolbar-background: var(--jp-layout-color1);
+ --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
+ --jp-toolbar-header-margin: 4px 4px 0 4px;
+ --jp-toolbar-active-background: var(--md-grey-300);
+
+ /* Statusbar specific styles */
+
+ --jp-statusbar-height: 24px;
+
+ /* Input field styles */
+
+ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+ --jp-input-active-background: var(--jp-layout-color1);
+ --jp-input-hover-background: var(--jp-layout-color1);
+ --jp-input-background: var(--md-grey-100);
+ --jp-input-border-color: var(--jp-inverse-border-color);
+ --jp-input-active-border-color: var(--jp-brand-color1);
+ --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+ /* General editor styles */
+
+ --jp-editor-selected-background: #d9d9d9;
+ --jp-editor-selected-focused-background: #d7d4f0;
+ --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+ /* Code mirror specific styles */
+
+ --jp-mirror-editor-keyword-color: #008000;
+ --jp-mirror-editor-atom-color: #88f;
+ --jp-mirror-editor-number-color: #080;
+ --jp-mirror-editor-def-color: #00f;
+ --jp-mirror-editor-variable-color: var(--md-grey-900);
+ --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
+ --jp-mirror-editor-variable-3-color: #085;
+ --jp-mirror-editor-punctuation-color: #05a;
+ --jp-mirror-editor-property-color: #05a;
+ --jp-mirror-editor-operator-color: #a2f;
+ --jp-mirror-editor-comment-color: #408080;
+ --jp-mirror-editor-string-color: #ba2121;
+ --jp-mirror-editor-string-2-color: #708;
+ --jp-mirror-editor-meta-color: #a2f;
+ --jp-mirror-editor-qualifier-color: #555;
+ --jp-mirror-editor-builtin-color: #008000;
+ --jp-mirror-editor-bracket-color: #997;
+ --jp-mirror-editor-tag-color: #170;
+ --jp-mirror-editor-attribute-color: #00c;
+ --jp-mirror-editor-header-color: blue;
+ --jp-mirror-editor-quote-color: #090;
+ --jp-mirror-editor-link-color: #00c;
+ --jp-mirror-editor-error-color: #f00;
+ --jp-mirror-editor-hr-color: #999;
+
+ /*
+ RTC user specific colors.
+ These colors are used for the cursor, username in the editor,
+ and the icon of the user.
+ */
+
+ --jp-collaborator-color1: #ffad8e;
+ --jp-collaborator-color2: #dac83d;
+ --jp-collaborator-color3: #72dd76;
+ --jp-collaborator-color4: #00e4d0;
+ --jp-collaborator-color5: #45d4ff;
+ --jp-collaborator-color6: #e2b1ff;
+ --jp-collaborator-color7: #ff9de6;
+
+ /* Vega extension styles */
+
+ --jp-vega-background: white;
+
+ /* Sidebar-related styles */
+
+ --jp-sidebar-min-width: 250px;
+
+ /* Search-related styles */
+
+ --jp-search-toggle-off-opacity: 0.5;
+ --jp-search-toggle-hover-opacity: 0.8;
+ --jp-search-toggle-on-opacity: 1;
+ --jp-search-selected-match-background-color: rgb(245, 200, 0);
+ --jp-search-selected-match-color: black;
+ --jp-search-unselected-match-background-color: var(
+ --jp-inverse-layout-color0
+ );
+ --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+ /* Icon colors that work well with light or dark backgrounds */
+ --jp-icon-contrast-color0: var(--md-purple-600);
+ --jp-icon-contrast-color1: var(--md-green-600);
+ --jp-icon-contrast-color2: var(--md-pink-600);
+ --jp-icon-contrast-color3: var(--md-blue-600);
+
+ /* Button colors */
+ --jp-accept-color-normal: var(--md-blue-700);
+ --jp-accept-color-hover: var(--md-blue-800);
+ --jp-accept-color-active: var(--md-blue-900);
+ --jp-warn-color-normal: var(--md-red-700);
+ --jp-warn-color-hover: var(--md-red-800);
+ --jp-warn-color-active: var(--md-red-900);
+ --jp-reject-color-normal: var(--md-grey-600);
+ --jp-reject-color-hover: var(--md-grey-700);
+ --jp-reject-color-active: var(--md-grey-800);
+
+ /* File or activity icons and switch semantic variables */
+ --jp-jupyter-icon-color: #f37626;
+ --jp-notebook-icon-color: #f37626;
+ --jp-json-icon-color: var(--md-orange-700);
+ --jp-console-icon-background-color: var(--md-blue-700);
+ --jp-console-icon-color: white;
+ --jp-terminal-icon-background-color: var(--md-grey-800);
+ --jp-terminal-icon-color: var(--md-grey-200);
+ --jp-text-editor-icon-color: var(--md-grey-700);
+ --jp-inspector-icon-color: var(--md-grey-700);
+ --jp-switch-color: var(--md-grey-400);
+ --jp-switch-true-position-color: var(--md-orange-900);
+}
+</style>
+<style type="text/css">
+/* Force rendering true colors when outputing to pdf */
+* {
+ -webkit-print-color-adjust: exact;
+}
+
+/* Misc */
+a.anchor-link {
+ display: none;
+}
+
+/* Input area styling */
+.jp-InputArea {
+ overflow: hidden;
+}
+
+.jp-InputArea-editor {
+ overflow: hidden;
+}
+
+.cm-editor.cm-s-jupyter .highlight pre {
+/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
+ padding: var(--jp-code-padding) 4px;
+ margin: 0;
+
+ font-family: inherit;
+ font-size: inherit;
+ line-height: inherit;
+ color: inherit;
+
+}
+
+.jp-OutputArea-output pre {
+ line-height: inherit;
+ font-family: inherit;
+}
+
+.jp-RenderedText pre {
+ color: var(--jp-content-font-color1);
+ font-size: var(--jp-code-font-size);
+}
+
+/* Hiding the collapser by default */
+.jp-Collapser {
+ display: none;
+}
+
+@page {
+ margin: 0.5in; /* Margin for each printed piece of paper */
+}
+
+@media print {
+ .jp-Cell-inputWrapper,
+ .jp-Cell-outputWrapper {
+ display: block;
+ }
+}
+</style>
+<!-- Load mathjax -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+<!-- MathJax configuration -->
+<script type="text/x-mathjax-config">
+ init_mathjax = function() {
+ if (window.MathJax) {
+ // MathJax loaded
+ MathJax.Hub.Config({
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+ }
+ },
+ tex2jax: {
+ inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+ displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+ processEscapes: true,
+ processEnvironments: true
+ },
+ displayAlign: 'center',
+ CommonHTML: {
+ linebreaks: {
+ automatic: true
+ }
+ }
+ });
+
+ MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+ }
+ }
+ init_mathjax();
+ </script>
+<!-- End of mathjax configuration --><script type="module">
+ document.addEventListener("DOMContentLoaded", async () => {
+ const diagrams = document.querySelectorAll(".jp-Mermaid > pre.mermaid");
+ // do not load mermaidjs if not needed
+ if (!diagrams.length) {
+ return;
+ }
+ const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.6.0/mermaid.esm.min.mjs")).default;
+ const parser = new DOMParser();
+
+ mermaid.initialize({
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+ startOnLoad: false,
+ fontFamily: window
+ .getComputedStyle(document.body)
+ .getPropertyValue("--jp-ui-font-family"),
+ theme: document.querySelector("body[data-jp-theme-light='true']")
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+ });
+
+ let _nextMermaidId = 0;
+
+ function makeMermaidImage(svg) {
+ const img = document.createElement("img");
+ const doc = parser.parseFromString(svg, "image/svg+xml");
+ const svgEl = doc.querySelector("svg");
+ const { maxWidth } = svgEl?.style || {};
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+
+ img.setAttribute("src", `data:image/svg+xml,${encodeURIComponent(svg)}`);
+ if (maxWidth) {
+ img.width = parseInt(maxWidth);
+ }
+ if (firstTitle) {
+ img.setAttribute("alt", firstTitle.textContent);
+ }
+ if (firstDesc) {
+ const caption = document.createElement("figcaption");
+ caption.className = "sr-only";
+ caption.textContent = firstDesc.textContent;
+ return [img, caption];
+ }
+ return [img];
+ }
+
+ async function makeMermaidError(text) {
+ let errorMessage = "";
+ try {
+ await mermaid.parse(text);
+ } catch (err) {
+ errorMessage = `${err}`;
+ }
+
+ const result = document.createElement("details");
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+ const warning = document.createElement("pre");
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+ return [result];
+ }
+
+ async function renderOneMarmaid(src) {
+ const id = `jp-mermaid-${_nextMermaidId++}`;
+ const parent = src.parentNode;
+ let raw = src.textContent.trim();
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+ results = makeMermaidImage(svg);
+ output = document.createElement("figure");
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+ parent.classList.add("jp-mod-warning");
+ results = await makeMermaidError(raw);
+ output = results[0];
+ } finally {
+ el.remove();
+ }
+ parent.classList.add("jp-RenderedMermaid");
+ parent.appendChild(output);
+ }
+
+ void Promise.all([...diagrams].map(renderOneMarmaid));
+ });
+</script>
+<style>
+ .jp-Mermaid:not(.jp-RenderedMermaid) {
+ display: none;
+ }
+
+ .jp-RenderedMermaid {
+ overflow: auto;
+ display: flex;
+ }
+
+ .jp-RenderedMermaid.jp-mod-warning {
+ width: auto;
+ padding: 0.5em;
+ margin-top: 0.5em;
+ border: var(--jp-border-width) solid var(--jp-warn-color2);
+ border-radius: var(--jp-border-radius);
+ color: var(--jp-ui-font-color1);
+ font-size: var(--jp-ui-font-size1);
+ white-space: pre-wrap;
+ word-wrap: break-word;
+ }
+
+ .jp-RenderedMermaid figure {
+ margin: 0;
+ overflow: auto;
+ max-width: 100%;
+ }
+
+ .jp-RenderedMermaid img {
+ max-width: 100%;
+ }
+
+ .jp-RenderedMermaid-Details > pre {
+ margin-top: 1em;
+ }
+
+ .jp-RenderedMermaid-Summary {
+ color: var(--jp-warn-color2);
+ }
+
+ .jp-RenderedMermaid:not(.jp-mod-warning) pre {
+ display: none;
+ }
+
+ .jp-RenderedMermaid-Summary > pre {
+ display: inline-block;
+ white-space: normal;
+ }
+</style>
+<!-- End of mermaid configuration --></head>
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+<main>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="GA-2.3:-Beam-Beats">GA 2.3: Beam Beats<a class="anchor-link" href="#GA-2.3:-Beam-Beats">¶</a></h1><h1 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; top: 60px;right: 30px; margin: 0; border: 0">
+<style>
+ .markdown {width:100%; position: relative}
+ article { position: relative }
+ </style>
+<img \="" alt="No description has been provided for this image" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="width:100px"/>
+<img \="" alt="No description has been provided for this image" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="width:100px"/>
+</h1>
+<h2 style="height: 10px">
+</h2>
+<p><em><a href="http://mude.citg.tudelft.nl/">CEGM1000 MUDE</a>: week 2.3. Due: Friday, November 29, 2024.</em></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Analyzing-cantilever-beam-accelerations-and-global-Mean-Sea-Level-measurements">Analyzing cantilever-beam accelerations and global Mean Sea-Level measurements<a class="anchor-link" href="#Analyzing-cantilever-beam-accelerations-and-global-Mean-Sea-Level-measurements">¶</a></h2><p>This project covers the week on Signal Processing (week 2.3).</p>
+<p>As a warming up you will create and analyze some elementary signals yourself, and next, you will carry out frequency domain analyses on two given data-sets, namely acceleration measurements of a Cantilever-Beam experiment, and (in optional Task 10) Global Mean Sea-Level measurements.</p>
+<p>Most of the Tasks in this notebook consist of both coding, producing a plot, and answering (open) questions. Typically, as you work your way through the Tasks, you can often re-use code, or part of it, from earlier Tasks and assignments. That will save you a lot of work!!</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>In many of the code blocks below, template code to create figures is provided. Note that there is a lot of code missing, and one line of <code>YOUR_CODE_HERE</code> does not imply that only one line of code is missing!</p></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Data-Acquisition-System">Data Acquisition System<a class="anchor-link" href="#Data-Acquisition-System">¶</a></h3><p>A data acquisition (DAQ) system usually consists of four components:</p>
+<img alt="No description has been provided for this image" src="https://gitlab.com/JelleKnibbe/public-files/-/raw/main/SP_Practical/DAQ.png" style="margin:auto">
+<ul>
+<li>A sensor transforms a physical signal into a small voltage. The transformation is often frequency dependent or nonlinear, and can show drift or bias.</li>
+<li>Signal conditioning electronics (often included in the sensor housing) filter unwanted frequencies, such as the 50 Hz line frequency caused by power plant generators, using low-pass or notch filters. In addition the sensor voltage may be amplified.</li>
+<li>An analog-to-digital (A/D) converter samples the analog voltage with a certain rate, such as 100 Hz. The A/D converter can measure analog voltages within a certain range, the measurement range. The measured voltage is quantized with a certain resolution, such as 14-bit, which means that the full measurement range is divided into $2^{14}=16,384$ discrete intervals. Together, the resolution and measurement range determine the precision. Usually the sample rate, the measurement range, and the sample resolution are configurable. Sampling and quantizing together turns an analog signal into a digital signal.</li>
+<li>A digital computer reads the sampled data from the A/D converter at specific times, and allows for further processing, analysis and storage of the (digital) signal.</li>
+</ul>
+<p>The result is that an analog, physical signal, is turned into a series of numbers (samples of the signal, in the time domain), ready for processing and analysis in a digital computer.</p>
+<p><img alt="verticalcantileverbeam.png" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAcsAAAKNCAMAAAB4NbCZAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAbUExURQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///5TvxDIAAAAHdFJOUwA8QHaAuL+mjBuqAAAACXBIWXMAADLAAAAywAEoZFrbAAAWlElEQVR4Xu3bi3baCLZF0fgRu///i1taWgJssAMSAja157ijQ52DhNEKj1Tq/qmqqqqqqqqqqqqqqqqqqqqqqqqqqqqq/5jPz09vVbq2fB5t+TzaMsjb5+eLN09pyyBDyzdvntKWQdryebTl3b0MDT4///7ls264MUzGfyYLu2mDt79/xzu/zZPx7uN9/k4nmQzL4Z/G/x3vvMs73J5P6ATcaX9Gf4Jx+OVuPPDBT1InjOHApRuvFxd38LLbeVV3d919yRnvzj+/Hbfc3Xt3388/+1Pr9CmP7jYPDkZ1ZLyYby8vb2/TC3G4kuOAFwsvhenmdAWHW3//vk3Zpsl09+F/3v5wx3E7nme4y/B/832569hyuMd0vvndlpSnTvn1bkxeXmjsqI6NV92bo/EC8k7my22cDVdwemG+ze9xw2Q6iDs5PDwVbTiI3yvMGHHf3Wi8NZ16zDTdOnG34Ry+M+weuE4YrvrcYrS7uLwWps2Ygxs7w2TfZ3f4t5bOx5vcGH711MNourULOD3IXPDE3eYHmU9WJ+wu2GR/rQ4WRxdw90KZA2A44qDl7vUz3+fgLPPNw8PnQ07ebfcjHmSt78bXw0HM/ZU8yPH1Ak6fW7uW/IpFLfl1NBzCOY/vNp5seFC05W/GmP4JZLC/kidbTt9IRue3HA7h9sF9vbl7eY/Gn2P89fhuw8kO7U5cR/wTwHSJhhv8erKl98T9WrKqn9Do2zvciZbDr/4rhG1afvsJ5puHJ6t/G3/vj7/ur+Rxy2Hipb+0Jb8TjiPNL0XMZz++2/5x6yy+evZX8iDHvqWTi1rO9zmONP5K5tGvL9/dqM7g1d9ftt9aDrf+2XKuNBx8dN/55m53EOzE3YZfd8nrZ/Pl93rtr+TJltNyvO4nWh68fsa7ToePw+k8B/edb+6XJ0853zz4nfFn/y/h67vxGk7/PpRLub+Sxy2578vL9OeSH1r+HbbDfWk5nID7+tIbbk03Dm5yquHhz7zbeD/vVse4TKPvcU60HGNhuKYnWs4Fp5a7vzmZL/5w01tfH0W/3m33U7blb6a/kpzfuobb041TLf3bx+FqvnlJ93cHr8xxw8HTveeTHN53mHtrutP8t6ej3+/G38LULdGynkJbPo+2fB5t+Tza8nm0ZT2p8c9TW/D0dUNe+qvz9Hvvr96orXjpr87T77x/fjbmxrz0V+fpZx+fn+/erK146a/O009eh0FflZs7vvCrHZ1yeH/98GZt6OjCr/f9lEPKvr/ewvcLfwXfTtmPylv5duGv4csph4/Kj35U3saXC38dh6fsR+UNHV74Kzk4ZT8qb+ngwl/L7pSv/ai8qd2Fv575lP2ovLH5wl+Rp+xH5a154a9pOmU/Km9uuvBXxSmHj8q+v94YF97bV8Ip+1F5e1x3b18Jp+xH5e1x4b19JRucss6xwYXf4JR1jg0u/AanrHNscOE3OGWdY4MLv8Ep6xwbXPgNTlnn2ODCb3DKOscGF36DU9Y5NrjwG5yyzrHBhd/glHWODS78Bqesc2xw4Tc4ZZ1jgwu/wSnrHBtc+A1OWefY4MJvcMo6xwYXfoNT1jm48Bvw9HVDXvqr8/R1Q176q/P0dUNe+qvz9HVDXvqr8/T1UEjzvwMMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWFNKZEQxcVhTSmREMXFYU0pkRDFxWjNfX13fSmREM3t9fX71TPbQh4cfHB9EmZoSj0cdHmz6wsaKlxlYfP74uD1sPST28HsM+45BweHudpgzMCAbTbnj9vu+SDi/RaVqbOesKv+4zfnvTZGpGMHA5Gd+SmfYFurEPf/3R6+tU4igjWJkRDFwe2L1Eh9e0o7qy999b+oIcOjr4jj5mBAOX380v0ObcxOcvLecX5G9XnnuYEQxcnvI+fV3617tta1/s/ceWU8h/viMSxoxg4PIH/qn015x96V7qdajlzS+mz8hfX5CiihnBwOXPfLf9uebHPz/H66vhBXLimk0hzym5uOWInD+9OD9+6VwnjO9131v63nruW9x454Uth8fnzfZktOGn8FadZbySX1ue/d46G0+xuOVgqnmcc/g5+sK8ANfxsOXFJde3/OnFOf4k3qwzjNfwoCUlL30xcA4zgoHLc52oueRn+Q/jCu5aLip5pZYnao4/zedF7xD/ZcOfR0ZTy7HkZW+u4hxmBAOXl/hWk5ZfP8vrR9PLkuu1uOQVW04/0O5bEC37wjyPKYeWK0peteWXmpymL8zzTBdruFxcQIeX4xxmBAOXl9v9MJymL8yzzC/L0fKSV2+5q8lp+sI8i9dqtOr3PmcwIxi4XGao+eEXs74wz3D4svz9Lyz+gROYEQxcLnT4w/WF+S9fUo4W1+RoM4KBy8Wmb7HoC/MfjloONZddNI41Ixi4XIh/bTHrC/N3J1IOFr3VcqQZwcDlEl9CjtZ8M/sP8Codu/y6cZgZwcDl5Y5KDlzVKadflqPL/zafw8wIBi4vdCrkoC/MX3iN9sb/AH33Xy9fhuPNCAYuL7H7j2iP9evPj/YvyxUNZ5zHjGDg8hLvB/+N+zf9+vOTIeUVGs642GYEA5eXO/z/WtjrC/MmuNZmBAOXSw1FvyTtC/MmuNZmBAOXK+1fpH1h3gKX2oxg4PIqxhdpv8reAunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYuKwrpzAgGLisK6cwIBi4rCunMCAYu68871yOIGeEoz7tX/7riWj6Ftnwebfk82vJ5tOXzaMvnsU3LRebfAIt+pFUH3+/odQ/8sOan9eo/X2TVwfc7et0DP6yP6Vl9+I+XWXXw/Y5e98CP6tWntei9ZtXB9zt63QM/rNfpWS1Muebg+x297oEf1vyxsehprTr4fkeve+CHNT+tfuuJ58fGx6Knterg+x297oEf1fwNYNGXuVUH3+/odQ/8sPqt52nMHxuLntaqg+939LoHfljz01r0sbHq4Psdve6BH9b8sbHoaa06uN96rqvfep5Gv/U8jfljY9HTWnXw/Y5e98APa35aiz42Vh18v6PXPfDDmj82Fj2tVQf3W8+VzU/Lf7zMqoPvd/S6B35U/dbzNOaPjUVPa9XB9zt63QM/rKZ8GvPTWvQNYNXB9zt63QM/rH7reRb91vM0mvJpzB8bi57WqoPvd/S6B35Y89Pqt5548zeARU9r1cH3O3rdAz+q/mXl0+i3nqcxf2wselqrDr7f0ese+GHNT2vRx8aqg+939LoHfljzx8aip7Xq4H7rua5+63ka/dbzNOaPjUVPa9XB9zt63QM/rPlpLfrYWHXw/Y5e98APa/7YWPS0Vh3cbz3X1W89T6Pfep7G/LGx6GmtOvh+R6974Ic1P61FHxurDr7f0ese+GH5tJZ9A1h18P2OXvfAD2vVN4B1Xx/udnS/9Rzpt56H4u/QZU9r1cH3O3rdAz+u6ffowqe16uD7Hb3ugR/Y+MQWfwNYdfD9jl73wA/sfc03gFUH3+/odQ9ctaU/f/4PDvMXtjrnOloAAAAASUVORK5CYII="/></p>
+<p>One of the data sets that you will analyze during this MUDE Q2 project was acquired by an accelerometer in a smartphone, on top of a vertical cantilever-beam. The accelerometer measures the side-ward accelerations, expressed in m/s<sup>2</sup>, and sampled at 50 Hz. Detailed information about the experiment and the sensor can be found in "Experimental evaluation of smartphone accelerometer and low-cost dual frequency GNSS sensors for deformation monitoring", by Alexandru Lapadat, Christian Tiberius and Peter Teunissen, Sensors 2021, 21, 7946, <a href="https://doi.org/10.3390/s21237946">https://doi.org/10.3390/s21237946</a>.</p>
+<p>A quick impression of the test setup can be gained by watching the short video <a href="https://youtu.be/o4moRwvlBLU?si=aKelBMWm3HB2Of26">Cantilever Beam Experiment</a> (1 minute).</p>
+<p>A theoretical description of the motion of the smartphone accelerometer fixed to the cantilever beam is presented in Appendix A. Pulling the beam at the tip and releasing it, results in a (nearly) horizontal side-ward motion of the smartphone, and the (horizontal) position can be described by a <em>damped harmonic</em> as a function of time. Consequently, also the first and second derivative with respect to time, the velocity and acceleration as a function of time as the smartphone will measure it, are harmonics.</p>
+<h3 id="Basic-sinusoid-signal">Basic sinusoid signal<a class="anchor-link" href="#Basic-sinusoid-signal">¶</a></h3><p>You will start with first creating (and analyzing) a few simple signals yourself. In the first few Tasks of this project, we will take the damping ratio zero and use a fairly short measurement time (duration), and hence the acceleration measured by the smartphone is a plain (undamped) sinusoid as shown in Appendix A (a stationary signal). In the following Tasks we also add a phase offset $\varphi$ to the plain sinusoid:</p>
+$$
+x(t) = A \sin(2 \pi f_c t + \varphi)
+$$<p>The result is taken from the last equation in Appendix A. We consider here acceleration, though for convenience, we omit the dots on top of the $x(t)$.</p>
+</img></div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="o">%</span><span class="k">matplotlib</span> inline
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 1:</b>
+<ul>
+<li>Create a time-array starting at $t=0$ s, ending at $t=5.0$ s, with a sampling rate of 100 Hz. Hint: what is the number of samples $N$? And what should then be the last value in the time-array? Note that, for example, with $N=10$ samples at a sampling rate of $f_s = 100$ Hz, we have signal samples at times $t=0.00, 0.01, 0.02, ... , 0.08, 0.09$ seconds, hence $T_{meas}= N \Delta t =0.1$ seconds (the sample-and-hold convention).</li>
+<li>Create a sinusoidal signal $x(t) = A \sin(2 \pi f_c t + \varphi)$, with amplitude $A=1.0$ Volt, carrier frequency $f_c=1.0$ Hz, and initial phase $\varphi = 5$ degrees, to be converted into radians.</li>
+<li>Make a plot of the signal against time. Note that this is strictly a <em>sampled signal</em> $x_n$ rather than $x(t)$, but since we use a rather high sampling rate, the signal shown is close to continuous in time. Connecting the sample points of $x_n$ in the graph by lines, as done in the graph below, corroborates the suggestion of a continuous-time signal. Be aware!</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#facb8e; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"> <p>We will give you the answers in this code cell for free!</p></div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">T_meas</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">f_s</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1">#sampling rate [Hz]</span>
+
+<span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">f_s</span><span class="p">)</span> <span class="c1"># ends at 4.99, length 500 according to the sample-and-hold convention</span>
+
+<span class="n">A</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">f_c</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">phi</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t_vec</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$t \: [s]$'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$x(t) \: [V]$'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">fr</span><span class="s1">'Sinusoidal signal with $A$=</span><span class="si">{</span><span class="n">A</span><span class="si">}</span><span class="s1"> V, $f_c$=</span><span class="si">{</span><span class="n">f_c</span><span class="si">}</span><span class="s1"> Hz and initial phase $\phi$=</span><span class="si">{</span><span class="n">phi</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> °'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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UVFRRg1ahQOHz6MHTt2AGBdnX379kV4eLjDx7fE2XpWX1+PBQsWYPLkySgvLzdNMcDv8+eff5qcs7CwMKSmprpUTmeZOnUqXn/9daxevRrPPPMMLl68iB07duChhx5yKYn7wIEDSE9Pt3t7KTUAWPfLsGHDWnweFhZms7szNzcX9957L6ZMmYK5c+e2e3xH74H26p2jtsURhLStthDaHlhypR7dunWDVqttkSdmzzXaW8727GxxcTHKy8vxwQcf4IMPPrBZblt5nwDQ1NSEjRs34vbbb4efnx/27t0Lo9FocjRramqsyu/sfeTn54eGhoYWn/P5eu2lN9i7/9q1azF79mycOHECHTt2BMC6xY1GI5566ilMmTIFERERLY5TXV2N7777Dunp6Ta/dxce6ZzxeHt7Y/jw4Rg+fDh69uyJmTNn4uuvv8aCBQva3K+1Bwd/A7WWoGurFXznnXeaWk6bN2/Gyy+/jBdffBH/+9//MGTIEAevyHkefPBBrFq1Co888ghSUlIQEhICjUaDyZMnw2g0Ony8tq5rwoQJgpW7vd9C6OviByts374dnTp1QnR0NHr27IlRo0bh3XffRUNDA3bs2CHIJI3tXVtrvPnmm7h48SI+/PBDfPjhhy2+P3ToEMaPHw8AaGxstHvus6ioKEFHviUlJaF3795Ys2YNnnnmGaxZswYcxzk9ShNgydYXLlzAgAED7N5HSg0c5fLly7jtttvQs2dPfPTRR3bt4+g94Gy9EwJ3n1toe9AWrT0H7LlGe8vZnp3lt/3nP/+J6dOn2zyvZVTPklOnTqGmpsZ0Lx08eBAAizQDrJGQmJiIkJAQAM7fR3FxcTbnDcvPzweAdnte7N3/3XffxZAhQ0yOGc9NN92ETz/9FPv377fpXH777bd2j9IUEo92zizhW678D+oKYWFhNidEPHfunM3t4+LicP/99+P+++9HUVERhg4diueffx6//vor/P39cfz48Rb7HDt2DFqtttVEyaioKPj5+eHkyZMtvrN1vG+++QbTp0/Hq6++avqsvr7epYkdW7suW85ZdHQ0fH19cerUqRbf2frMXoS+Lm9vb1x11VXYsWMHOnXqhFGjRgFgEbWGhgZ89dVXKCwstGu6CmdXaWiLy5cv44UXXsDs2bMxbty4Ft9Pnz7dasTmrl272u0W4zlz5oxdybCOMHXqVDz33HM4ePAgVq9ejR49erg0JQX/ALnygcNxHD788EO8+OKLKCgoQLdu3fDTTz8hISFBcg3sxWg0YurUqSgvL8fPP/9sd/K60PeAo7bF3ThyH7nDzvGcPHkSXbp0Mf1/6tQpGI1Gp+qLI+Vsy85GRUUhKCgIzc3NDke1Ll++DACmRPkDBw4gMjIS8fHxKCkpwa+//mrVRevsfTR48GBs3bq1Rdf17t27Td+3hb37FxYWmqJ8lvADb5qammwe/6uvvkJgYKBTo8ddweOcs61bt2LMmDEtbmg+FG+rC9FRunXrhoqKChw8eND0kMjPz8f69euttmtubkZ1dbWp5QEwJyU+Ph4NDQ3Q6XRIS0vDd999Z5WXU1hYiNWrV2PkyJGthvp1Oh3S09Px7bff4vz586ZQ+tGjR7Fp0yab21/ZOn377bdbzXlpi/auq7Xypqam4ttvv7XKnzh16hQ2bNjgcBksjyvUdfGMGjUKr732Gk6fPm2axyYyMhJ9+vQxjbjinba24I2ekDObL1u2DM3NzXjxxRdtThezaNEiqxGbUudb8c7Z/PnzkZubi4ULF7p0vAMHDgBo6ZwtWbIEP/30EzIzM9G9e3fs3LnT1EUhtQb2smjRImzatAkbNmywcgLaQ+h7wFHb4m4cuY/cYQ94VqxYYRqdxx8XgFM9BfaU0x47q9PpcNttt2H16tU2ZygoLi5udWQrn/qQnZ2Ne++9FwcPHjRFzR599FFotVqrkZ7O3ke33347XnnlFXzwwQemecoaGhqwatUqJCcnWwUgamtrcf78eURGRppG5tq7f8+ePbF582acOHHCKsdzzZo10Gq1NiOIxcXF+PnnnzFlyhSXRvI6g8c5Zw8++CBqa2txyy23oHfv3mhsbMSuXbuwbt06dO7cGTNnznT5HJMnT8ZTTz2FW265BQ899BBqa2vx3nvvoWfPnlbzz1RVVaFjx464/fbbMWjQIAQGBuLnn3/G3r17TS2mpUuXIisrCyNHjsT9998PLy8vvP/++2hoaMBLL73UZjkWLVqEjRs3YtSoUbj//vvR1NSEt99+G/369TNFGHhuvPFGfPHFFwgJCUHfvn2RnZ2Nn3/+2ak+dnuuyxYLFy7E5s2bcc011+C+++5Dc3Mz3nnnHfTv3x+5ubkOl0Po6+IZNWoUnn/+eVy4cMHKCRs9ejTef/99dO7cuUXo3BZJSUkAgH//+9+YPHky9Ho9Jk6c6HS5Lly4gHfeeQdz5syx6ZgBrOGwefNmGI1GaLVal/Kt3nnnHZSXlyMvLw8A8MMPP+DixYsA2H1m+dBojS5dumDEiBH47rvvAKDVrgONRoNrr70W27Zta/N4Bw8eRIcOHazy/QoKCvDqq68iJyfHlL947bXXmr6XWgN7+PPPP7FkyRKMHj0aRUVF+PLLL62+/+c//9nqvu64BxyxLe6mtfvI1tQI7tCC58yZM7jpppswfvx4ZGdn48svv8Q//vEPk0PjCPaU0147+8ILL2Dr1q1ITk7GrFmz0LdvX5SVlWHfvn34+eefW+2K7NSpE8aMGYMvvvgCMTExOHDgAPr374+JEydiw4YN+Oyzz6waCc7eR8nJybjjjjswb948FBUVoXv37vjss89w9uxZfPzxx1bb7tmzB9dddx0WLFhgasjZu/8TTzyBDRs2YNSoUZgzZw4iIiLw448/YsOGDfjXv/5ls/t03bp1aGpqEr1LE4DnTaWxYcMG7u677+Z69+7NBQYGmpZyevDBB7nCwkLTdm1NpXHl8hu2tt28eTPXv39/ztvbm+vVqxf35ZdftphKo6GhgXviiSe4QYMGcUFBQVxAQAA3aNCgFsN19+3bx6Wnp3OBgYGcv78/d91113G7du1qcW22yvHrr79ySUlJnLe3N9e1a1du5cqVNqf0uHz5Mjdz5kwuMjKSCwwM5NLT07ljx45xiYmJ3PTp09s8x5XYe122jrVlyxZuyJAhnLe3N9etWzfuo48+4h577DHO19fXal97fwshr4unsrKS0+l0XFBQENfU1GT6/Msvv+QAcP/3f//XYp/Wjr9kyRKuQ4cOnFarNX3vSD2zZMaMGZy3t7fVVCRXMnfuXA4Ad+LEiXavsz0SExMdnnLCFitWrOAAWE1RYklVVRUHgJs8eXK7x7rqqqu4CRMmWH32+eefu20pJ1c0cGT5pvamKWgLe+8BR+udvbalrWu/cioNe85t6zNb95Gtbe3Voq3rvhK+7EeOHOFuv/12LigoiAsLC+PmzJnD1dXVtdjOnmu0p5z22lmO47jCwkLugQce4BISEji9Xs/FxsZyY8eO5T744IM2ry0/P5+78cYbOV9fXw4A5+3tzV1zzTXcli1b2tzPUerq6rjHH3+ci42N5Xx8fLjhw4dzGzdubLEdfx8sWLDAqf13797NTZgwgYuNjeX0ej3Xs2dP7vnnn291qourr76ai46OtrLzYqHhOBGyPAnCBSZNmoTDhw/bzHEh1E9mZiZuvPFGHDhwwKFEf54333zTFB0nCKFZuHAhFi1ahOLiYlNXm9r48ccfMXHiROzfv7/dHDBCGDxu+SZC3lw5p83JkyeRmZmJMWPGSFMgQnK2bt2KyZMnO+WYASz/bOvWrTh58iSMRiP27dsnyMAfgvAUjh07Bo1GI0hONmEfHpdzRsibrl27YsaMGejatSvOnTuH9957D97e3g4tGEuoi5dfftml/a+77jo88MADGD16NKqrq9GnTx+X5uIiCE/j2LFj6NSpk1NLqhHOQc4ZISvGjx+PNWvWoKCgAD4+PkhJScGyZcscXjSWICxZsGBBu/MXEgRhm2PHjqF3795SF8OjoJwzgiAIgiAIGUE5ZwRBEARBEDKCnDOCIAiCIAgZQTlnAmI0GpGXl4egoCC3LM1DEARBEITwcByHqqoqxMfHQ6uVPm5FzpmA5OXltbrWJUEQBEEQ8ubChQt2rfDibsg5E5CgoCAA7Mdtbc1LZzAYDNi8eTPS0tKg1+sFOy7REtJaHEhn8SCtxYF0Fgd36VxZWYmEhATTc1xqyDkTEL4rMzg4WHDnzN/fH8HBwXTTuxnSWhxIZ/EgrcWBdBYHd+ssl5Qk6TtWCYIgCIIgCBPknBEEQRAEQcgIcs4IgiAIgiBkBOWcEQRBEIQMaG5uhsFgkLoYssZgMMDLywv19fVobm62ez+9Xg+dTufGkgkLOWcEQRAEISEcx6GgoADl5eVSF0X2cByH2NhYXLhwweHk/dDQUMTGxsom6b8tyDkjCIIgCAnhHbPo6Gj4+/srwnmQCqPRiOrqagQGBto9WSzHcaitrUVRUREAIC4uzp1FFARyzgiCIAhCIpqbm02OWUREhNTFkT1GoxGNjY3w9fV1aCZ/Pz8/AEBRURGio6Nl38VJAwIIgiAIQiL4HDN/f3+JS6J+eI2VkNdHzhlBEARBSAx1ZbofJWlMzhlBEARBEISMUKRztn37dkycOBHx8fHQaDT49ttv291n27ZtGDp0KHx8fNC9e3d8+umnLbZZsWIFOnfuDF9fXyQnJ2PPnj3CF54gCIIgVMyMGTMwadIk0c+7cOFCDB48WPTzugNFOmc1NTUYNGgQVqxYYdf2Z86cwQ033IDrrrsOubm5eOSRR/Cvf/0LmzZtMm2zbt06zJ07FwsWLMC+ffswaNAgpKenm0Z3EARBEATRPm+++abNAAhhP4ocrTlhwgRMmDDB7u1XrlyJLl264NVXXwUA9OnTBzt37sTrr7+O9PR0AMBrr72GWbNmYebMmaZ9fvrpJ3zyySd4+umnhb8IiSktBTZvBsLDgbQ0QEFd8YqiuRn4+WegpATIyADCwqQukXo5ehTYswcYOhQYMEDq0qiXykpg40bA1xe44QZA5oPeFAvHAVVVgMEABAcDSlpLPSQkROoiKB5FOmeOkp2djdTUVKvP0tPT8cgjjwAAGhsbkZOTg3nz5pm+12q1SE1NRXZ2dqvHbWhoQENDg+n/yspKAGwkiJCjQfhjCXXMffuAm2/2QmEh88jGjzfi66+b4eMjyOEVjZBa19UBkyfrsGEDC1DHxnL47rsmDBni8qEVj9B1+uOPNZgzR4fmZg00Gg6LFxvx1FNGQY6tdITU+sQJYOJEL5w5w2xHSooRP/zQjOBglw+teJzV2WAwgOM4GI1GGI1GcBxQWwucO6fB5ctMZ52OQ5cuQGAgJ3i5LfH3d6yh/s0332DJkiU4deoU/P39MWTIEKxfvx5z5sxBeXk51q9fDwCoqqrCfffdh++++w7BwcF44okn8P3332PQoEF4/fXXAQBdu3bFrFmzcOrUKXzzzTcICwvDM888g9mzZ5vO9/TTT+Pbb7/FxYsXERsbi3/84x947rnnoP/bc+U4po/RaPveZ/pyMBgMLabSkNsITo9wzgoKChATE2P1WUxMDCorK1FXV4fLly+jubnZ5jbHjh1r9bjLly/HokWLWny+efNmtwyLzsrKcvkYdXU6PPzwdSgq0iMoqAF1dV7YuFGHqVP/wt13HxaglOpACK0/+aQfNmzoDi8vI/z9DSgo8MFNNzXijTe2wc+vSYBSKh8hdD55MhRPPTUaRqMG4eF1KCvzw3PP6WAw7MHQoZSWwOOq1k1NGjzxxGicORMKf38Dmpo0yM72wm23XcKjj+4TqJTKx1Gdvby8EBsbi+rqajQ2NqKmBujYMfSKrTRXvLqHixfLERBg37YFBQWYOnUqFi1ahBtvvBFVVVXIzs5GRUUFDAYDmpqaTAGLhx9+GDt37sTq1asRFRWF5cuXY9++fejTp49pG6PRiFdffRXPPPMMHnzwQXz33Xd44IEHkJSUhB49egAAvL298fbbbyMuLg6HDx/GI488Ar1ej4cffhgAC5g0NzebjnkljY2NqKurw/bt29HUZG2Da2trnZHMbXiEc+Yu5s2bh7lz55r+r6ysREJCAtLS0hAsYFPSYDAgKysL48aNM7UQnOW557QoKtKhUycO+/ZpsX07h1tvBX74oRuWLUtEz54CFVqhCKX14cPAjz+y2+ubb4wYMUKL4cM5nDsXgCNHxuO55zw7qiOUzhwHLFmig9GowR13GPHll1545JFmvPeeDqtXX41585o8vttNKK3ffluLM2d0CA/nkJsL/PUXcP31HH79NQFLlsTh6qvdG9WRO87qXF9fjwsXLiAwMBC+vr5oJegjCsHBwXY7Z6dOnUJTUxOmTJmCxMREAEBKSgoAto6ll5cXgoODUVVVhTVr1uDLL7/ExIkTAQCff/45OnbsCG9vb9OzUqvVIiMjw/RMHTRoEFauXIm9e/ciKSkJALB48WJwHIeqqir069cPFy9exLp16/Dcc88BAHx8fKDT6Vp9/tbX18PPzw+jR4+Gr6+v1XetOXRS4RHOWWxsLAoLC60+KywsRHBwMPz8/KDT6aDT6WxuExsb2+pxfXx84GOjL1Cv17vsRNnC1eNWVwPvv8/ev/66BhERetxyC3DjjcCPP2rw2mt6fPyxQIVVOK5q/fbbgNEITJrEupAB4KWXgLvuAlas0OHJJ3UIDBSosArGVZ23bQNycgA/P+Cdd7Tw9tbipZeAtWuBU6c0+P57Pe68U7jyKhlXtG5qAt56i71fulSDhAQ9EhKAGTOATz4BXnrJCz/8IFxZlYyjOjc3N0Oj0UCr1UKr1aKmBti+HQgIAHr2ZN2MVVXAyZPsff/+7ss/8/fX2t2tOWTIEIwdO9Y0eC4tLQ233347wsLCoNFoTNd09uxZGAwGXH311aYZ/cPCwtCrVy/TNjyDBg2y+j82NhYlJSWmz9atW4e33noLp06dQk1NDZqamhAcHGz6np/HrLWVA7RaLTQajc3fyB3PbFdQ5GhNR0lJScGWLVusPsvKyjJ5+d7e3khKSrLaxmg0YsuWLaZt1MBXXwHl5UD37sxp4OFT7b76CqiokKJk6qK0lGkJAE88Yf78ttuY9mVlwLp10pRNbbz9NnudPh2IjmbvAwOBBx9k7999V5pyqY0ffwTOnQMiI5lDxvPUU8xh+PFH4Px5yYqnGoxGZj/8/PB3jhlz0mJimPa+viyXNSDAPX+O5JvpdDpkZWVhw4YN6Nu3L95++2306tULZ86ccfr6r3SQNBqNKX8sOzsbU6dOxYQJE7B27Vrk5OTg3//+NxobG50+n5xRpHNWXV2N3Nxc5ObmAmBTZeTm5uL839Zh3rx5mDZtmmn7e++9F3/99ReefPJJHDt2DO+++y7+85//4NFHHzVtM3fuXHz44Yf47LPPcPToUdx3332oqakxjd5UA6tXs9f/9/8Ay4ZFSgrQpw/Q0AD8nb9JuMD69UzLgQOZtjw6HXD33ew9/1sQzlNRwZwCALjvPuvveJ23bwcuXhS3XGqEr68zZjDHgadnT2D0aPZ+zRrRi6U6KitZlFKvB0JDzZ9rNMw5A1jjTi5oNBpcc801WLRoEfbv3w9vb2/TIACerl27Qq/XY+/evabPKioqcOLECYfOtWvXLiQmJuKZZ57BkCFD0KNHD5w7d06Q65AjinTO/vjjDwwZMgRD/h72NnfuXAwZMgTz588HAOTn55scNQDo0qULfvrpJ2RlZWHQoEF49dVX8dFHH5mm0QCAu+66C6+88grmz5+PwYMHIzc3Fxs3bmwxSECp5OcDO3aw91d282g0wNSp7D05Da7zn/+w17vuatkSnTyZvW7dyn4Twnm+/x5obAR69245dUZiIjBqFMtJW7tWmvKphZoasxN8110tvyfbIRy84xUW1tJ28J/V1bHRnFKze/duLFu2DH/88QfOnz+P//3vfyguLkafPn2stgsKCsL06dPxxBNPYOvWrTh8+DDuueceUxejvfTo0QPnz5/H2rVrcebMGbz99tstHEE1oUjnbMyYMeA4rsUfP+ndp59+im3btrXYZ//+/WhoaMDp06cxwzI2/zdz5szBuXPn0NDQgN27dyM5Odn9FyMS333HHlTJyUCnTi2/v+MO9rptG8tNI5yjvBz45Rf2ntfUki5dgOHD2W+xYYOoRVMdvF2+4w7b3TG8/j/9JF6Z1MimTcwh6NIF+Dsv24pbb2WR+IMHgQsXxC+fWjAazWkltuZD9PKCacoSOaSfBAcHY/v27cjIyEDPnj3x7LPP4tVXX7U5B+lrr72GlJQU3HjjjUhNTcU111yDPn36tEjKb4ubbroJjz76KB566CGMHj0au3btMg0EUCMeMSCAYBPOAsDfg2Va0KMH0LUrG4G1dWvr2xFts3Urm3i2Vy+mqS0mTAD27mUTefLdb4RjNDUBfIpoa3WVf0bs3MkSqoOCxCmb2uBtx4032naCIyKAq64Cfv+dOXL/+pe45VMLdXXMdnh5odXBQiEhzDGrqADi4sQt35X06dMHGzdutPndlasDBAUF4Ss+ERdslZ9FixZZzWF29uzZFsfhU5d4XnrpJbzwwguorKw0DQTg5ysF2PJNCxcudPRSZIkiI2eEYzQ1maM5aWm2t9FozA8ziug4D/8ga01nwKzz5s3styEcZ88elp8THs5WBLBF9+7sz9KRIxyHn7bLnjpNtsN5amrYa1BQ64n5/MT71dXKsh379+/HmjVrcPr0aezbtw9T/+4Lv/nmmyUumXwh58wD+OMP1tIKC2v9QQaYje/WreKUS438/DN7HTeu9W2GD2fJvhUVwBUNQ8JOeJ3Hjm17+SA+rZTqtHP89Rf78/ICrr229e14nbdtg6TzdCkZPp2krSkyfXxgWslFaeknr7zyCgYNGoTU1FTU1NRgx44diORHORAtIOfMA+AHAlx7bdsPsmuuYa/HjgHFxe4vl9ooKABOnWKtXn4Emy10OrPW/G9DOAav23XXtb3dqFHW2xOOwes2fHjb3cJDh7Klf8rKmP0gHIPjWLcm0H73O/+9kpyzIUOGICcnB9XV1SgrK0NWVhYG0AK4bULOmQfw22/slXcIWiMiAujXz3ofwn54zQYMMHc/tAbvNOzc6d4yqZGmJpbfBLRfp0eOZK8HDrBuUMIxdu1ir+3prNcDV1/N3pMj7DgNDcxB0+vR7hrHfD6akpwzwnHIOVM5HGe/gQXMDzMysI7jrM6cZ6964zCHDrEHU3CwuTHRGh06sFGGRiOQnS1O+dSEM3WaGhyO09AAAJxdE8HyzllNDXUhOwqnIGNLzpnKOX2adVH6+LSdb8YzYgR73bPHveVSI448yIYNY63k4mI28zphP7zOKSltd9PzUJ12jooKtkYsYNawLUhn59Dr9X8PBqi1a0k3Hx+WA2jZFUrYB7+4udyWarIFTaWhcvbtY6+DBrUfLgdYbgm/X3OzfQ8/gnW18cn9vIZt4ePDVhDIyWEDNjp3dmfp1AVfp+3RGWCO8FdfMZ0J+8nNZQ5AYqJ5aay24OdAO3GCzfdnOcM90To6nQ4//RSKjIwixMUBdXX+7U7O6uvLosfl5Z5no41GIxobG1FfX9/qGppXwnEcamtrUVRUhNDQUOgUIBo5ZyqHdxgGD7Zv+549Wdi8upol9rbXbUQwTp4E6uvZ+nTdu9u3z7BhZufs9tvdWz414Wid5p04cs4cw1GdIyNZF/KZM8yBvv56d5VMXZSWAm+8EYuKCmDu3CK7ciPLy1lks77e83IpOY5DXV0d/Pz8HFphAABCQ0MRGxvrppIJCzlnKufAAfZqr4HV6Vj35/btbKJUcs7sg3+QDRxovW5pWwwbBrz/PtOZsI+mJpZzBthfpwcPZr9JXh77i493V+nUhaPOGcDq9JkzrE6Tc2YfBw4AHKfBtm1x+OCDaBgMhnb3+eUX4P77WWP6++9FKKSMMBgM2L59O0aPHu1Q96Rer1dExIyHnDOVwxvYQYPs32fYMOac7dvHFjom2sdRJxhgOgNMZ45rPxGYAI4fZ8nTQUEsSmMPAQFAnz4sf2rfPnLO7MWZOp2UBHz9tbnrmWgfSydYp9PZ5UAMHsxyVfnlshxYBUnx6HQ6NDU1wdfXVxG5Y85CAwJUTHExixRoNC0Xhm4Lfls+GZhoH2ec4N69WaSyvJwWQbcXZyKUANVpR2lsNGvlSJ0mnR2Hd4Id0Tkujq2OYTTSvHJqhZwzFcPf9N26ObauIN+VyXcfEe3jTBeQr685P420tg9ndAaA/v3ZK+lsH8eOMQctONixwSq87Th+nO1PtI8zdVqjITutdsg5UzHOPsj69mWvRUW0UoA9FBQAhYUskuPopNe800CRBvtwpqsNMD/ISGf7sNTZke72Tp3YgKKmJjZIhmibhgbgyBH23tkGB9VpdULOmYpx9kEWEGDO56Ebv314nXv0YEvYOAK1fu2H45zrPgbMD7KjR9kUMUTbOKszRXQc4+hR5siGhgIJCY7tSzqrG3LOVIyzBhagVpkjOBuhBEhnR8jPZ5Fcrdasm7106QL4+bGpB/76yz3lUxNUp8XBUmdHBwSRzuqGnDOV0thoThR1xTmjVln7HDzIXl11ghW0sogk8Dr36sUcLUfQ6diITYDqtD0IUadJ5/ZxRWc+cnbmDK2zqUbIOVMpf/3FwuWBgUDHjo7vTzk69nP8OHvlc/UcoXt3toxTdTVw/ryw5VIbrugMUKTBXkpLgZIS9r53b8f3J9thP67U6chIICaGvT96VLgyEfKAnDOVwt/0PXs6N3+WZeuXIjqtw3HWWjuKXm9+AFKkoW1c0RmgiI69nDjBXjt2ZPmnjsLrfOoUrf3YHlSnidYg50yl8Dd9r17O7d+rF8vtuXyZ5uBqi4ICFvXSatmUJc5Aib32wTsNztZp0tk+XLUdsbFAWBjNwdUeDQ2sSxKgOk20hJwzlcI/yJxtkfn6stGHAN34bcE/yLp0Aby9nTsGtX7tQ6goA83B1Tau2g6Nhuq0Pfz1F3NgAwOZQ+sMpLN6IedMpbja+gXMCdS8sSZaQjqLQ00NcPEie++s1gkJbKqTpibg7FnBiqY6qE6Lg6XOzi7dRjqrF3LOVIoQBpafvf7UKdfLo1ZcjTIApLM98BOaRkSwZWucQaMhre3B1QglQDrbg5A2+vx51k1KqAdyzlTI5cvmmf35rklnIAPbPkIYWD5XrayM/REtEUJngOp0ezQ3m7Whhp17EaJhFxPDBm0YjRQNVhvknKkQ/qaPj3dsTc0rIQPbPkIY2IAAtpAxAJw+7XqZ1IgQOgNUp9uDj8B4ewOJic4fh9f55Eka7d0aQjQ4KBqsXsg5UyGujmrj4W/6v/6iJW9s0dhonm2eIjruhSJn4sDbju7d2cS9zsJHgysqKBrcGkLbaarT6oKcMxUiRM4IwBKofXwAgwG4cMH1cqmNM2eY0xoQwKKUrsB3P5OBtQ09yMRBKCfY3x/o0IG9J61bIlTqieX+pLO6IOdMhQhlYLVaoGtX9p5u/Ja4OtGvJeQ0tI6rE/1awut85gwbtUlYI5TOANXptuB17tCBTaXhCqSzOiHnTIXwI9uENLD8MQkz7tCZDGxLiouBykrmADs70S9Phw4sGtzURMtl2YLqtDiQjSbag5wzlcFx5jwoVx9kABnYtiCdxYHXuWNHNjmyK1iu5EBat4TqtDi4Q+ezZ1kKCqEOyDlTGWVlQFUVe+/KaCseMrCtwy+90qWL68fijXRREYsSEWaE1BmgOt0azc3maKIQWpPOrSNknY6LA/z82O937pzrxyPkgWKdsxUrVqBz587w9fVFcnIy9uzZ0+q2Y8aMgUajafF3ww03mLaZMWNGi+/Hjx8vxqUICn/Tx8ayG9ZVyMC2Dq91586uHys4GIiOZu9pOg1rhNQZoDrdGpcusciLl5c5md8VSOfWEbJOUzRYnSjSOVu3bh3mzp2LBQsWYN++fRg0aBDS09NRVFRkc/v//e9/yM/PN/0dOnQIOp0Od9xxh9V248ePt9puzZo1YlyOoLgrynD6NJvokGBwnHnSR4rouBeKnIkDr3OnTq5No8HDOwwlJUB5uevHUxNUp4n2UKRz9tprr2HWrFmYOXMm+vbti5UrV8Lf3x+ffPKJze3Dw8MRGxtr+svKyoK/v38L58zHx8dqu7CwMDEuR1CEdhg6dWIt6YYG89qGBFBQANTXs1Zrp07CHJMSe23jrgcZ6WyN0LYjKIjNYA+Q02BJYyOLUgJUp4nW8ZK6AI7S2NiInJwczJs3z/SZVqtFamoqsrOz7TrGxx9/jMmTJyMgIMDq823btiE6OhphYWG4/vrrsXTpUkRERLR6nIaGBjRYLGhW+XeykMFggEHAzEz+WPYc8/RpLQAdOnVqhsEgTKirUycv/PWXBqdONSEuTt3Tfdur9cmTGgBe6NiRA9AkSCJup07st/vrLyMMBnXP+utInT5zxguABgkJTTAYXK9/HTsCgB7nznFobGxyeRoUuWOv1qdOsfqXmChc/evcWYfCQi1OnWrCoEFkOwA2GMBo1MPPj0N4ONkOR3HEdjhzXLmgOOespKQEzc3NiOGbZH8TExODY8eOtbv/nj17cOjQIXz88cdWn48fPx633norunTpgtOnT+OZZ57BhAkTkJ2dDV0rMf7ly5dj0aJFLT7fvHkz/P39Hbgq+8jKymp3m717rwYQg6qqg8jMFGaugICAEQCi8P33B1BV5Rnhs/a0/vXXjgCSEBRUiszM3wQ5Z0VFAoChyMkpQWamfQ0NpdOezizJeSIADf76awuqqupdPqfBoIVGcyPq6jRYs+ZnhIY2unxMJdCe1r/9NgRAJzQ0HENmpjAhGG/vJAAdsWnTMfj5eUYyZXs6HzgQBWAEIiKqsWHDL4Kcs6goGkAKDh2qQmbmNkGOKXfseR46Qm1traDHcxXFOWeu8vHHH2PAgAG46qqrrD6fPHmy6f2AAQMwcOBAdOvWDdu2bcPYsWNtHmvevHmYO3eu6f/KykokJCQgLS0NwcHBgpXZYDAgKysL48aNg16vb3PbJ59kP+lNNw3Addf1F+T869fr8OefQFjYYGRkDBTkmHLFXq1zc1lGQFJSODIyMgQ5t7+/Bm+/DdTWRgl2TLlir87nzgHNzVro9Rz++c/rBcmFAtgIt7w8oGfPcRg2TP0RHXu0fuUVJm56ek9kZLg4bf3f7NypxY4dQEBAH2RkuDgrtsyxV+f8fBaqHTAgQLD7PDERWLoUKC8PJtvhJJUyGyavOOcsMjISOp0OhYWFVp8XFhYiNja2zX1ramqwdu1aLF68uN3zdO3aFZGRkTh16lSrzpmPjw98fHxafK7X6wWtNPYe12g0D6Xu0cMLQhWBz4u4eFEHvV6gp6PMaU9rfsqBbt200OuFSd3kE6jPn9fAy0uv+u42oH2d+TzHxEQNfH2Fu6cSE5lzdumSF1JSBDusrGlPaz7nTEjbwa8wcuEC2Q4e3nZ07Sqc7eBzzsrLNair00PA2IBsEfo5645ntisobkCAt7c3kpKSsGXLFtNnRqMRW7ZsQUo7Vvbrr79GQ0MD/vnPf7Z7nosXL6K0tBRxcXEul1ksCgpY4r5Ox9bFFAp+vjSaQ8eM0EnqAMuF0mjYQINWBh57HO7QGaA6fSUNDcInqQOksy3cUacDA4HwcPaetFYHinPOAGDu3Ln48MMP8dlnn+Ho0aO47777UFNTg5kzZwIApk2bZjVggOfjjz/GpEmTWiT5V1dX44knnsDvv/+Os2fPYsuWLbj55pvRvXt3pKeni3JNQsDf9AkJbISlUPAGlm9ZE+4xsN7e5gXUycAy3OWc8fNLUZ1mnD/Ppofx8zPPtycEZDta4u4GB2mtDhTXrQkAd911F4qLizF//nwUFBRg8ODB2Lhxo2mQwPnz56HVWvudx48fx86dO7F58+YWx9PpdDh48CA+++wzlJeXIz4+HmlpaViyZInNbku5IvRknTz88c6fZ12nWkW69MLR1ARcuMDeu0PrS5eYc3ZFWqRHwj9ohNaZIjrWWNoOIbvTeZ0rKthfSIhwx1Yq7rTT+/dTnVYLinTOAGDOnDmYM2eOze+2bdvW4rNevXqB42wn/vr5+WHTpk1CFk8ShJ6niKdjR+aQNTSw7rZ2UvtUz8WLbBShZaRLKBITgd9+o9YvD3VrioO7bEdgIBARAZSWMq0Hqns8UbvU1ppTFqhOE23h4TEQdeEuA6vXU3ebJbzOiYnCRxHJwFrjrjrNRy1IZ4a7dAaoTlvCaxASAgg9xznprC7IOVMR/CggoWast4RufDOkszg0NbERlYDwWvPHq6igpYUAqtNiQToT9kLOmYrg86CEHKnJQ8mmZsTQmQwsc8yMRha5vWLOaZcJCAAiI9l70ppsh1iQzoS9kHOmEjjO3Cpzx41P3UBmxND57Fn2m3oyvM4dOrhnEAo5wmbIdoiDGDoXFQF1dcIfnxAXcs5UwuXLLNkU4NcOFBZ6kJlxZ+uX7+6oqqLuNl5nd3QBAeQ08BiN5jnOqLvNvbjTdoSFsQEYgNkJJJQLOWcqgb/pIyPZXEVCQwbWjDsNrL8/EBXF3nu61u7UGaA6zVNYCBgMLDop9OhjgHS2xJ11WqMhrdUEOWcqQawHGeUzkNMgFlSnxYHXOS5O2MmreXidqbuN6jRhP+ScqQR33/R8V2l1NSCz9WFFpaqKjfAD3Kc1f1x+XUlPxd11mj8u36Xnqbhb57AwFhEGPFtrjqM6TdgPOWcqwd03fWAgEBrK3nuy08DrHBwMty0uzDvCnqwzIF6Dg3Rmr+7SWaMhrQGgrMwcOXRHXrDlcT1ZZ7VAzplKcLeBBejGB0hnMRHLOcvPZ3OqeSpUp8WB1zkqCvD1dc85SGf1QM6ZSiADKw6kszjU1wPFxey9u7SOjmY5Vs3NLCneU6E6LQ6kM+EI5JypBHdPOwDQjQ+QzmLBX7ufHxAe7p5z6HTm0YmerDU5DeJAtoNwBHLOVIDRaL4ZycC6F7EfZJ46Ea2lzhqN+85DdZqcM7EQQ+cOHdhrZaVnD9xSA+ScqYDiYqCxkT3E3DFPEQ8ZWHEMLP8b1tezJGJPRAydAarTTU0s5w4g58zdiFGng4LYouoAjdhUOuScqQDLeYr0evedhzewnnzTi2FgfX3NE9F6qtZidAEBVKfduX6pJZ6uMyB+g8OTtVYD5JypALFuej5k7qmtXzHmKeLxdK1JZ3Hgde7Y0T3rl/LwDkNhIYvyeyIUDSYcgZwzFWBpYN0Jf/yyMvM6np5Eebl71y+1xNMNLH/dpLN7EUvnyEjA25s1cPhuVE+C48yRLKrThD2Qc6YC8vLYq7tv+pAQICCAvffEkDmvc3i4e9YvtcTTDaxYdZp0Zq/u1tnTJ6ItLTVHDN2ZFwx4ts5qgpwzFcAbWHff9J5uYHmd4+Lcfy5P1hkQT2vL/Byj0b3nkiNUp8WB1zkqyr15wYBn66wmyDlTAXwUi8+fcSeefOOL5QQDnq2zwcAWyQbcr3VcHGt0NDYCJSXuPZccoTotDqQz4SjknKkAuvHFgXQWh8JClqPj5cVyldyJXg/ExrL3nqg11WlxIJ0JRyHnTAVQ5EwcyMCKg2VXmztHEPKQ1lSn3Y0UOpeWmhdaJ5QHOWcKp6qK/QGUN+JuxDSwvKNdVeV5M32LqTNAdRog58zdiKmzpw/cUgvknCkcflh6UBD7cze80+CJN72YBjYw0HNn+hbbOfPUOl1VBVRXs/diNOw8VWdA3Dqt0Xi21mqBnDOFI2aXJmA24p44VxF/zWI5DZ6qNX+9YjgMlufxVJ2DglhjwN3wOhcUeN6asWI3ODy1TqsJcs4UjlQ3fWEh0NwszjnlAMeRgRUL0lkcxNaZXx7KYGD5UJ4E1WnCUcg5UzhiR85iYljYvLnZs6YeKC1lDxXAPLrP3XiqgaUHmTiIrbO3t3n0rSdp3dzMooUA1WnCfsg5UzhiG1gvL/Oi3J504/M688vQiIGnGlhyzsRBbJ0Bz9S6pIQ5aBoNEB0tzjk9UWe1Qc6ZwiEDKw6ks3hI5ZwVFZmjo54A1Wlx4HWOiWGNWzHwRJ3VBjlnCkfsbk3AfOPzRscTkPJB5kk6NzSYu8vF0joy0vzQLCwU55xygOq0OJDOhDOQc6ZwpLjx+XN5UquMdBYHPjfH25stMC8GWq05j9CTtKbImTiQzoQzKNY5W7FiBTp37gxfX18kJydjz549rW776aefQqPRWP35+vpabcNxHObPn4+4uDj4+fkhNTUVJ0+edPdluITlCEIpImeedOOLPY0G4Nk682teioWnay0WnqizlM5ZeTmtEqBUFOmcrVu3DnPnzsWCBQuwb98+DBo0COnp6SjiV0u2QXBwMPLz801/586ds/r+pZdewltvvYWVK1di9+7dCAgIQHp6Ourr6919OU5TWsoWbAbEG0EIkIEVC17nqiqgpka880qJFDoDnlenpZgaxvJcnqIzII3OoaEAH3/go9GEslCkc/baa69h1qxZmDlzJvr27YuVK1fC398fn3zySav7aDQaxMbGmv5i+El3wKJmb7zxBp599lncfPPNGDhwID7//HPk5eXh22+/FeGKnIO/6aOjxRtBCHjegwyQxsAGBQH+/uy9p2hNzpk4VFYCtbXsPUXO3IsUdVqj8Uyt1YRIY0eEo7GxETk5OZg3b57pM61Wi9TUVGRnZ7e6X3V1NRITE2E0GjF06FAsW7YM/fr1AwCcOXMGBQUFSE1NNW0fEhKC5ORkZGdnY/LkyTaP2dDQgIaGBtP/lX8vgmgwGGAQcNgXf6wrj3n+vAaAF2JjORgMTYKdrz2ioth58/PFPa8YtKZ1Xp4XAA2io5tgMIg3vXlcnBdOn9bgwoUmJCaqZ1r11nS+cEELQIfY2GYYDEbRyhMdzc578aIRBoO6Zle2pTXrONAjNJSDXt8k2ihVNs+ZHvn5HBobm0TtunY3rdXpS5eksR2xsTqcOaPFhQvintfdtKazUMeVC4pzzkpKStDc3GwV+QKAmJgYHDt2zOY+vXr1wieffIKBAweioqICr7zyCkaMGIHDhw+jY8eOKPg77mvrmAVtxISXL1+ORYsWtfh88+bN8OdDHgKSlZVl9f+WLQkAhsLLqwiZmb8Lfr7WKCz0A5CGS5eM+OmnTFUZWB5LrY1GIC9vIgANjh7dgqIi8bq6vb1HAohAZuZ+VFaqb+jVlXV6794hADqhsvI4MjPFy/ksKUkEMBgHDhQhM3O3aOcVE0utDxyIBHANgoKqkJm5VbQyNDRoAUxEXZ0G33yzGQEB6mrcAS3r9Nmz6QB88ddfO5GZWSFiSYYDiMeWLUfg63tGxPOKw5U6u0otH0qWCYpzzpwhJSUFKSkppv9HjBiBPn364P3338eSJUucPu68efMwd+5c0/+VlZVISEhAWloagoODXSqzJQaDAVlZWRg3bhz0er3p80OHWK90//5RyMjIEOx87VFfD/y//wc0NemQkpIh2qg6MbCldWEhYDRqodFwmDLletHmKgKAL77Q4ehRIC5uKDIyBot3YjfTWp1+5x0dAGDMmJ7IyOghWnmMRg3eew8wGqNFvZfEwJbWZWWsRdWzZ6Do1xsSwqGiQoMBA9LQu7eop3YrtnRuagLKy5nBuP32a0TNDd68WYvsbCA8vB8yMvqId2I305rtcBW+50suKM45i4yMhE6nQ+EVExIVFhYi1s6ar9frMWTIEJw6dQoATPsVFhYiziIBo7CwEIMHD271OD4+PvDx8bF5fCErTWvHLS5mr/HxWuj14qUP6vVsmoOyMqC4WI8rAo6qwFJrXufoaA38/IT/XduiY0f2Wliog16vE/XcYnBlnebzYzp18oIbbqFW6dSJvRYUiHsviYml1vzYqQ4dxL/euDigooLZjgEDRD21KFxpOzgO0OmA+Hg9dCLewvwIfk+xHUIcT04ozgp5e3sjKSkJW7ZsMX1mNBqxZcsWq+hYWzQ3N+PPP/80OWJdunRBbGys1TErKyuxe/duu48pBXyPq5itMR5PSjaVKkkd8CydAbPWYiapW56vsJAttaN2pNLZ8pyeUKd5nWNjIapjBniWzmpEcZEzAJg7dy6mT5+OYcOG4aqrrsIbb7yBmpoazJw5EwAwbdo0dOjQAcuXLwcALF68GFdffTW6d++O8vJyvPzyyzh37hz+9a9/AWAjOR955BEsXboUPXr0QJcuXfDcc88hPj4ekyZNkuoy24W/6aRyzg4f9owbX4o5zng8ycDW1wOXL7P3YmsdHc1GuDU3sxUK1BgNtoTqtDhQw45wFkU6Z3fddReKi4sxf/58FBQUYPDgwdi4caMpof/8+fPQas1BwcuXL2PWrFkoKChAWFgYkpKSsGvXLvTt29e0zZNPPomamhrMnj0b5eXlGDlyJDZu3Nhislo5QZEzcSADKw78Nfr6snmaxMTLizlohYXs91a7c0Z1WhxIZ8JZFOmcAcCcOXMwZ84cm99t27bN6v/XX38dr7/+epvH02g0WLx4MRYvXixUEd0OOWfiQAZWHCx1lmIEcFwcc87y84EhQ8Q/v5hQnRYHOehcXAwYDBA1h5NwHcXlnBGM2lo2kSRAeSPuRg4GtqyMLQquZqTUGfCcOi3V6gA8nrRKgJQ6R0bCNLL8ivFzhAIg50yh8Debnx+bSV5sPOVBBkizBiFPeLh59Qe1L8Mipc6W51V7nS4vNzv6FHV3L1LWaa3W/Pt6gtZqg5wzhWI5GECKLiBPav3yjrAUeUietAyLlDoDnlOneZ1DQszrL4qJp9RnQPo67Ulaqw1yzhSKlPlmgPmmz8tj3SRqhePMc0JFR0tTBkut1YxcdFb7g0wuOldWAjU10pRBLOSitdpthxoh50yhyMU5q60FqqqkKYMYVFaau4CkNrBqdxrkEmVQ+4NMap2DggB+dTs112mOk15rT7EdaoScM4UitXMWEGDOdVPzjc+3fAMDzQ8UsfEUAyuXKAPp7F48pau+shJobGTvqU4TjkLOmUKR2jkDPOPGl7rlC3iGzoD0WvM6FxSou6teap0Bz6jTvM5BQWzglhR4gs5qhZwzhSL1yDbLc6v5xpc6ygB4hs6AWWupnAa+odPYyKYuUStS6wx4Rp0m20G4AjlnCoUiZ+JAUQZxqK5m+YuAdA8zHx82dQmgbq35Ok1Og3sh20G4AjlnCkUOzhl/bjXPvyWH1q8n6ezvz/L7pMKTtJbSafAkneVgOwoLAaNRunIQjkPOmQIxGs2tMjk4Z2qefVoOrV9e5+Ji9RpYOURzAM+q03JxGtSKHGwH/xs3N6u7q16NkHOmQC5fZmulAdIaWN7oqNnAyqH1GxXFXpubgdJS6crhTuQQzbE8vyfUaSm19iSdpbQd3t7mrno1a61GyDlTIHz+QHg4y5ORCk8wsHJo/er1QESEdXnUhhyiOYD663RdnXleQmrYuRc52A7L86tZazVCzpkCkUO+GeAZN70cWr+A+rWWQzTH8vxq19nHBwgOlq4cvM5FRertqifbQbgCOWcKhHfOpJxGA/AMA0utX3GgyJk4WOosxZq8PPzv3NSk3lwosh2EK5BzpkDkEjmzTDZVYy5UQwNQUcHey8VpUOvoNrlFzkhn9+LtDYSFsfdqdRrkFjlTa51WK+ScKRC5OGd6vbqTTXnj6uVlfpBIhdpbvxQ5Ewe56AyoW+v6enPDTmpHWM06qxlyzhQIPyBAaucMUPeNL5cuIEDdOgPy6wJSa1e9XHS2LIMa63RxMXvV64HQUEmLomqd1Qw5ZwpELpEzyzKo8caXSxcQoG6dAfl0Aal9Xii56Ayou07LqWGnZp3VDDlnCkROzpmaW2UUZRAHg8HsCEmttdrnhaI6LQ5yatipWWc1Q86ZAiHnTBzkFGVQs858F5BOZ3aMpETNWlOdFgc55vYVFQEcJ21ZCPsh50xhNDbKJ8pgWQY1G1g56azGXChe56goQCsDi0R1WhzUPIpQTpEz3kE0GNjqMoQykIEpJByBjzJoteZZ46VEzQ8yOUUZLOeFUpuBlZPOANVpsVCzznKKnPn4mAclqFFrtULOmcLgjStFGdyPnKIMap4XSk46A+qt001NQEkJey8HrdWqMyCvyBmgbq3Vigwe74QjyPWmV3PXhBxav4B6tSadxaG0lOUcaTTyirqrMRdKTpEzQL11Ws2Qc6Yw5HrTqzkXSm6OsNpav6SzOPDXExnJJlaWGjXnQsm1Ea22Oq1myDlTGHK76dWaC2U0mvP75OIIq3W+IrlFztSqc3Exm3BLLjr7+qo3F0pujWi11mk1Q86ZwpDbTe/jo85cqNJScyQwKkrasvCotfVLkTNxkJvOgDq72ywbdnLRWq11Ws2Qc6Yw5BY5A9R54/M6h4ezJVjkgBp1BuQXOVNrLpTcImeAOuu0ZcMuMlLasvCoUWe1Q86ZwpBb5AxQ541fVMQeZOQEux+5RXTUmgslN50BddZpvrEREUENO8J5yDlTGBQ5Ewe5RXMAderMcfLTWq3zQlHkTBz4hh3pTLiCYp2zFStWoHPnzvD19UVycjL27NnT6rYffvghRo0ahbCwMISFhSE1NbXF9jNmzIBGo7H6Gz9+vLsvw2EociYOFDkTh8uX2WASgOq0u6GGnTjIXWc1ddWrGUU6Z+vWrcPcuXOxYMEC7Nu3D4MGDUJ6ejqK+LviCrZt24YpU6Zg69atyM7ORkJCAtLS0nDp0iWr7caPH4/8/HzT35o1a8S4HLuxjDLI8cZXU1Kv3KI5gDoNLK9zSAiLWMkFqtPioE7nTL6Rs8ZGoLxc0qIQdqJI5+y1117DrFmzMHPmTPTt2xcrV66Ev78/PvnkE5vbf/XVV7j//vsxePBg9O7dGx999BGMRiO2bNlitZ2Pjw9iY2NNf2H8MESZYBllkMsIQkDdBlZOTrAac6HkqDNAdVos1Kkze5WTzr6+QHAwe68mrdWMDKYidIzGxkbk5ORg3rx5ps+0Wi1SU1ORnZ1t1zFqa2thMBgQHh5u9fm2bdsQHR2NsLAwXH/99Vi6dCki2phKu6GhAQ0NDab/KysrAQAGgwEGg8GRy2oT/lh5eU0A9AgJ4aDTNUHAU7hEZKQGgBcKCowwGJqlLo5L8FoXFhoBaBER0QSDQR5hKp0OCA31Qnm5BhcvGhAUJHWJnIfXOT+/GYAXoqLkVXeio7UAdMjLa4bBoOzZlQ0Gg1XUPSzMIEPbwcFgaJK6OC7B1+mCAmYvIiLkVXdiYrxQWanBpUtN6NZNHjbNGXidhXzGuuN4rqI456ykpATNzc2IuaJZEhMTg2PHjtl1jKeeegrx8fFITU01fTZ+/Hjceuut6NKlC06fPo1nnnkGEyZMQHZ2NnQ6nc3jLF++HIsWLWrx+ebNm+Hv7+/AVdnHhg05AEYiIKAGmZlb2t1eLE6dCgVwLc6da0Bm5mapiyMIJ09WAQjDhQs5yMyUT99WQMD1KC8Pwvff78aZM6VSF8dltm8/DmAgjMYCZGbulbo4JsrLewLog5yci8jMzJW6OC5TW+uFhgYWOdu/fxOOHJGHI1xU5AcgDQUFRvz0UyY0GqlL5DpHjhQDiENR0Z/IzDwndXFM6PXXAIjExo37UV2dJ3VxXCYrK0vQ49XW1gp6PFdRnHPmKi+88ALWrl2Lbdu2wdfX1/T55MmTTe8HDBiAgQMHolu3bti2bRvGjh1r81jz5s3D3LlzTf9XVlaa8tmC+RiyABgMBmRlZaFTp+EAgC5d/JGRkSHY8V3l/HngiSeAqipfTJiQoWgDy2vd2BgCAJgwIQlXXy2fVmbXrjpcugR06XI1MjLkUy5H4XWOiOgDABg4MEZWdbqgQIOvvgJ8fBKQkREvdXFcwmAw4LPPdgEAAgM53HJLusQlMlNfD8yeDTQ16TBiRAZklkniEHyd5jiWf3D99f2RkdFP4lKZ+fxzHY4cATp0GIqMjMFSF8dpeJ3HjRsHvYBzlfA9X3JBcc5ZZGQkdDodCq/oOC8sLEQsv0ZFK7zyyit44YUX8PPPP2PgwIFtbtu1a1dERkbi1KlTrTpnPj4+8LGRxazX6wWtNDxlZeznionRQq+XT7pghw7stbFRg5oavaINLMAPvGAeZocOXrKZqwgwL8NSWiqvcjlLSQmrx3FxOuj1tiPUUhD/tz9WVCSve81ZysuZnYqJ0bjFNjmLXs9yoSorgbIyvayS6J2Fr9Nysx1xcey1pERe95qzCP2cldN9AShwQIC3tzeSkpKskvn55P6UlJRW93vppZewZMkSbNy4EcOGDWv3PBcvXkRpaSni+BotA+Q4iSTAkk1DWKBJFcmm9fU61NXJb8QVoL4EajmObAPUp3NFBXPO5KYzoC6tOU6e0x0B6tLZE1CccwYAc+fOxYcffojPPvsMR48exX333YeamhrMnDkTADBt2jSrAQMvvvginnvuOXzyySfo3LkzCgoKUFBQgOrqagBAdXU1nnjiCfz+++84e/YstmzZgptvvhndu3dHerp8ugDkthC3JWq68fkog78/EBgocWGuQE06A/Ic2Qaob9oSc+RM4oLYQE112rJhJzet1aSzJ6C4bk0AuOuuu1BcXIz58+ejoKAAgwcPxsaNG02DBM6fPw+t1ux3vvfee2hsbMTtt99udZwFCxZg4cKF0Ol0OHjwID777DOUl5cjPj4eaWlpWLJkic1uS6koLJTnTQ+wMp04YX7YKhklRBnUoDMgz1nrAXN5GhtZlxsfGVYqVKfFwbJhFxAgcWGuQE06ewKKdM4AYM6cOZgzZ47N77Zt22b1/9mzZ9s8lp+fHzZt2iRQydyHnCNnfJnU0CqTc5RBTToD8u2q9/MDgoKAqipWRqU7Z1SnxYF3gklnwlUU2a3pqchxEkkeNbXKKMogDg0NOlRXyzNyBqhLa6rT4sA7waQz4SrknCkIOS6/wqOmVplSogxKz4UqL/cGwJZtEnDmGcGgOi0OatJZCZGz2lrg73RrQsaQc6YQGhq0qKqiyJkYKCHKUFcH1NRIWxZXsXyQyXFuPDXWabId7kXOkbPAQJYLB6hDa7VDzplC4I2rtzdFGdyNnKMMAQFmA6t0reX8IAPUWaflqLWadJazEwyoS2u1Q86ZQrA0rhRlcC9yjpwB6tFa7g8ytejc0ADU1rIJNuWotVp0BuTtBAPq0lrtkHOmEOT+IFNTi4y0Fge5P8jUojP/INbrOYSGSloUm/A6V1ezfCglQ7aDEApyzhSCUqI5ajKwctda6a1fuT/I1KKz5RQ8coy6BwezQSGA8rUm20EIBTlnCkHOeVAAmxNKDQbWYACqqtgoQrlqrZbWr9wfZGrRmZ+8OipK4oK0gkajHq3l3uBQi86eADlnCkHuDzKNRh2tMj7KoNNxCA+XtiytoQadAfk3ONSiM1+nY2LkO/eKGrS2bNjJ1U6rQWdPgZwzhSD3BxmgjlYZX/aoKEAr07tDDToD8m9w8OWqqADq66UtiyvIPXIGqKNOK6FhpwadPQWZPn6IK5H7gwxQR6uMX+tRzg8yNegMyL8LKDQU0LNBjqYHrxIx55xR5MydKKFhpwadPQWZViHiSihyJg7mtR7l+yBTg85NTUBlpby7gNSSC8VHzuSqM6AOnfmGHelMCAE5ZwqhokLeDzJAHa0yipyJQ2kpwHEaaDQcIiOlLk3rqEFripyJA+/wKEHnsjKWI0fIFy97N/z+++8dPvi4cePg5+fn8H6ENc3NQFUVRc7EgH84KCFyxhtYvutNSfB1JDIS0OmkLUtbqKFOU+RMHJQQOQsPZ/dbczNz2uPjpS4R0Rp2O2eTJk1y6MAajQYnT55E165dHS0TcQWlpYDRyEcZZDhR0d+oofVbVCT/yJkaDKwSHmSAOuo0Rc7EgS+7nHXWapltKyhg5VWi7fAUHOrWzM/Ph9FotOvPn18AkHAZvjUZEQF42e1Oi48aWr9KiJzxBhZQrtZK6AIClF+njUbrSWjlitJ1BswNOznrDKhDa0/Abuds+vTpMDjQSf3Pf/4TwXJcoVuBKCEPClBL65e0FgOKnIlDWRnQ3Cz/Os3rXFrKBosoESVEzgDl12lPwW7nbNWqVRg8eDBWrlwJjmu/8r333nuIlHOmr4JQQjQHMD9oS0qUb2CVorVSW78UORMHvtxBQY2yzk2MiGCjYzmO2Q8lQpEzQkgc6tZ87LHH8OSTT2LIkCHYsWOHu8pEXIFSojmRkco2sBxnds7krrXSW78UORMHvtwhIQ3SFqQdvLxgGrWrdK3l3uDg6zQ5Z/LGIeds3rx5OH78OIYMGYLrrrsOU6ZMwaVLl9xVNuJvlBLN0emUbWAvXwaampThNCi99auUB5nSdebLLXfnDFC21pYNO6XYDiXaaE/C4XnO4uLisGrVKuzevRsXL15Er169sHTpUjQ0yP/mVyrl5exV7tEcQNmtMr7M/v4G0yLuckXpER1zt6a05WgPXufiYpZcrzR4nUND5W+flVynLRt2crfTSrbRnoTTk9AmJSVhx44d+Pjjj/Hxxx+jT58+WL9+vZBlI/7mnXeMWLv2Rzz4oPyfDkpulfFlVsKDTMlRBkA53Zp8JNhoZMnqSkMp3ZqAsus0r3NAQKPsG3ZKttGehMsrBNx11104duwY7rnnHkyfPh3jxo0TolzEFfj6NiMwUOpStI+SW2VK6gJScpSB45QzIECvZ8nqgDK1psiZOJhtR6O0BbEDJdtoT8LpWbMaGxtx7NgxHDp0yPTn5+eHX375RcjyEQpDya0yipyJQ2Ul0NiojMgZwMpYWsq07tdP6tI4BkXOxMFsO+oByDt0ZmmjOY4N4iLkh0PO2aJFi0yO2OnTp9HU1ISQkBD0798fAwcOREZGBgYOHOiushIKQMmtMqVGzpRmYHmd/fwMUMLqbjExwNGjymxwUORMHJQUOeOds6YmlisXHi5teQjbOOScff311xgwYACmTZuGAQMGYODAgejUqZO7ykYoEIqciQOfdKxEA2sdzZF3lAFQR0RHCQ0ONeisBNvh4wOEhAAVFazcSrIdnoRDztmhQ4fcVQ5CJVDkTBx8fIDQUDaSV2kG1jqaI3/nTKkRHcvcPiU4DUrVGVCW7QCY1hUVrNy9e0tdGsIWLg8IIAhLKHImHkqNNJijOfLvAgKUq3NNDVBXx94rwWmw1NmORWhkhVJthxLttKdgt3N28OBBGB2Y6Ofw4cNoUuoaPoTTWEbOlGZgldj6BZRnYM3RnHppC2InStfZ35+Dn1+ztIWxA95haGxkg0aUhFJth9IaHJ6E3c7ZkCFDUOrARD8pKSk4f/68U4UilIuSDaxSW79KM7AUORMHpcxYz+PvD9N0QUrVWmm2Q2kNjv/8B1i0SIvjx8OkLorbsTvnjOM4PPfcc/D397dr+8ZGZRheQlj8/ICgIKCqihnYkBCpS2QftbVAdTV7r7TWr9IMrJLyoADl6yz3ueQsiYlh92FREdCzp9SlsR+KnInD//4HrFunwz33qN85sztyNnr0aBw/fhz79++36y8lJQV+bhwnv2LFCnTu3Bm+vr5ITk7Gnj172tz+66+/Ru/eveHr64sBAwYgMzPT6nuO4zB//nzExcXBz88PqampOHnypNvKr2aU2Crjy+rjw8HfXxnd8UqP6CjlQabUXCheZ7kvJ2SJEuu0ZcNOKQ0OJdpoQHm2wxXsjpxt27bNjcVwjHXr1mHu3LlYuXIlkpOT8cYbbyA9PR3Hjx9HtI0Y/q5duzBlyhQsX74cN954I1avXo1JkyZh37596N+/PwDgpZdewltvvYXPPvsMXbp0wXPPPYf09HQcOXIEvr6+Yl+ioomJAU6fVpaBtVzrUSlzhik9oqOUBxmvc10dS7JXwkodgFlnvvxKQIl12rJh5+enjIadUiNnnuScKXK05muvvYZZs2Zh5syZ6Nu3L1auXAl/f3988sknNrd/8803MX78eDzxxBPo06cPlixZgqFDh+Kdd94BwKJmb7zxBp599lncfPPNGDhwID7//HPk5eXh22+/FfHK1IESW2Xm/BzlhEaUGGUAlGdgAwJYPhSgLK3NkTOq0+6E1zkmRjkNOyXaaEB5tsMVnF6+SSoaGxuRk5ODefPmmT7TarVITU1Fdna2zX2ys7Mxd+5cq8/S09NNjteZM2dQUFCA1NRU0/chISFITk5GdnY2Jk+ebPO4DQ0NaGgwV5LKvzPgDQYDDAaDU9dnC/5YQh7TnURFaQHokJfXDINB/ou1A0BengaAFyIj2YNMCVpHRLAyFxVxMBiU0WKvrwcqKvQAWORMCToDQEyMF86c0SAvrwmdOinD2cnP1wHQIjKSjdRUgtaRkcx2FBQox3ZcusTbDlZeJejM5kXUo7BQObajuRkoKfECoEFoaKPgOsvtd1Occ1ZSUoLm5mbEXBGrj4mJwbFjx2zuU1BQYHP7goIC0/f8Z61tY4vly5dj0aJFLT7fvHmz3QMnHCErK0vwY7qDyspeAHrjjz/OIzPzoNTFsYsdO3oA6IumposAlKF1Xl4AgFRcutTcIodSrhQX+wJIh5eXEQEBBkXoDAB6/SgA4cjMzEFZWes2QU4cO3YNgEgUFBxEjx7KqNMlJV0ADMSBA4XIzNwrdXHsYsuWTgCGQKstBqAMnWtrvQDcgOpqDdav3wQfH/lPtVJe7g2OmwCNhkNQUKPgOtfW1gp6PFdx2jkzGAwoKChAbW0toqKiEK6kKcoFYt68eVYRucrKSiQkJCAtLQ3BwcGCncdgYA+xcePGQa/XC3Zcd3HunBbr1gG+vonIyOgodXHs4uefWQ//oEHxAHIVoXVlJXD//UB9vRfGjMmAG9oDgpOTY17wXKOBInQGgI8+0uHECaBTpyRkZCgjcvbUU8y8jx3bH42NlxShdW2tBh98AOh0scjIyJC6OHZx8CCzHX36RAJQRp3mOJYj19CgwZAh6ejcWeoStQ+/QFFEBKDTcYLrXCmzuZ8ccs6qqqrw5ZdfYu3atdizZw8aGxvBcRw0Gg06duyItLQ0zJ49G8OHD3dXeREZGQmdTofCK5ISCgsLERsba3Of2NjYNrfnXwsLCxEXF2e1zeDBg1sti4+PD3x8Wi4/o9fr3XJzuuu4QhMfz15LSrTQ65WR1lhSwl7j4lh5laB1eDhbxqmhAbh8Wa+IaUvKythrdDRz0pSgMwDwpqWkxAsKKC4AoJgFchAXp8O5c8rQukMH9lpUpDzbERurrDodEwOcP89sR48eUpemfS5fZq/86GOhdZbbb2Z37X/ttdfQuXNnrFq1Cqmpqfj222+Rm5uLEydOIDs7GwsWLEBTUxPS0tIwfvx4t01D4e3tjaSkJGzZssX0mdFoxJYtW5CSkmJzn5SUFKvtARZ65rfv0qULYmNjrbaprKzE7t27Wz0m0TpKTOrly6qk5GmNRnmj28wjCJWjM6A8nQ0GsyOspNGaSkxUV9pkvzxKs9NKHLTlCnZHzvbu3Yvt27ejX79+Nr+/6qqrcPfdd2PlypVYtWoVduzYgR5ucsfnzp2L6dOnY9iwYbjqqqvwxhtvoKamBjNnzgQATJs2DR06dMDy5csBAA8//DCuvfZavPrqq7jhhhuwdu1a/PHHH/jggw8AABqNBo888giWLl2KHj16mKbSiI+Px6RJk9xyDWpGaQ8ywHrEVYOCBgJFR7PWr/IMrLTlcBSlPcj4qJlOxyd/KwPedlRUsMEjSpjFSImT/QLKs9NKnLfPFex2ztasWWPXdj4+Prj33nudLpA93HXXXSguLsb8+fNRUFCAwYMHY+PGjaaE/vPnz0OrNQcFR4wYgdWrV+PZZ5/FM888gx49euDbb781zXEGAE8++SRqamowe/ZslJeXY+TIkdi4cSPNceYE/INMiQY2KorDxYvSlsURlGZglRihBJSsM6BVRu8gACA0FNDrWeSvuBhISJC6RO1j2eBQWsMOUE6DgyJndjBixAhs3LhR0KR3R5kzZw7mzJlj8ztbE+becccduOOOO1o9nkajweLFi7F48WKhiuixKM3ANjUB/LKxMTFQlHOmVAOrpK42gHQWC42GaX3pEtNa7rYDsI6cXbggbVkcQckNDk/AqTbV77//jvr6+hafV1ZW4qmnnnK5UISy4Q0soIyHWUkJG72k0bCRQEpCuQZWWa1fpeqstO5jQFl5Z5YNO6VprSQbDVg2OJRlO5zFIefs9ttvxwsvvACNRoMiG3dOTU0NXnnlFcEKRygXJT3M+DJGRgJeCpv5T7kGVtpyOAqvc1kZiwjLHaXqDChraSHLhl1kpNSlcQwl2WiAcs7apFOnTvjxxx/BcRwGDRqEiIgIDBo0CIMGDcLgwYNx/Phxq6koCM9FSU6DkqMMSjOwlpGzvDxpy+II4eEsub65mXXV89PFyBUl12klRc4sG3Y6nbRlcRQl2WjAOrePn1ZDzTjknL322msA2HQWv/32G/Ly8rB//37k5uZi/fr1MBqNeOmll9xSUEJZKMlpUHKUQUkG1mg0jyKMiYGinDOtlrXYCwqY1nJ3zpRcp5UUOVPi4vI8SrLRgPVaseSctUJNTY1pwrabb75Z0AIR6kBJToOSowxKMrClpcxBA5TXBQQwrQsKlKG1kuu0EiNnSta5pITlzsk5paO2FqiuZu+jo4ETJ6QtjxjYnXN2/vx503t7ZtK9dOmScyUiVIGSnAYlRxmuNLByhtc5IgKKmWXfEiU1OJRcpylyJg6RkSxXjuPMgxrkCh9x9/EBgoKkLYtY2O2cDR8+HP/v//0/7N3b+mK0FRUV+PDDD9G/f3/897//FaSAhDJR0oNMyVEGJRlYJesMKKvBoWStKXImDjqdOYItdzttqbNGI21ZxMLuQOaRI0fw/PPPY9y4cfD19UVSUhLi4+Ph6+uLy5cv48iRIzh8+DCGDh2Kl156STGL1hLuQUkPMiVHGXgDW1zMDKycr0HJOgPKaXBwnLK1psiZeMTEMNshdzutZCfYWeyOnEVEROC1115Dfn4+3nnnHfTo0QMlJSWmNTSnTp2KnJwcZGdnk2NGKOZBBig7ygAoxxEmncXh8mVzF7cSpx3g60dxsTlHUa4o3WlQip1Wus7O4HAKoJ+fH9LT03H77be7ozyESuAfZLyBlfMSMkqOMgDKM7Cks3vhdQ4NZTk6SpiXzRLeoTQaWVe9nB1MNUTOAPk3ODzROXPqkTlq1CgUFBQIXRZCRfC5DLyBlSscRxEdsSCdxUHpOuv15sXa5a610p0GpTU4lKqzMzjlnA0ZMgTJyck4duyY1ee5ubnUpUkAYAaWXwpJzga2shJobGTvlXrjK83AKjXKQDqLhxLyziwbdkrVWikNDnLO7GTVqlWYMWMGRo4ciZ07d+LEiRO48847kZSUBJ3Spkkm3IYSHmZ82QIDAX9/acviLEoxsEqP6FjqzMl4eT+l6wwoY8QmNezEwxOdM6ennVu0aBF8fHwwbtw4NDc3Y+zYscjOzsZVV10lZPkIBRMTAxw9Ku8bXw1RBqUZWKVqzec+NTWxpHu+601uKF1nQBmRM75sQUGAn5/ycvsAZegMqKPB4ShORc4KCwvx8MMPY+nSpejbty/0ej1mzJhBjhlhhRJav2q46ZUQOVNDbp+PD0uyB+SttdJ1BpRhO9QQzVGCzoA6GhyO4pRz1qVLF2zfvh1ff/01cnJy8N///hezZ8/Gyy+/LHT5CAWjhFaZGm56JUTOamqAujr2nrR2L2qo00qwHUrPNwOsdZZrV73lmrxKdoQdxaluzU8++QSTJ082/T9+/Hhs3boVN954I86ePYsVK1YIVkBCuSihVaaGKMOVBlaOM2jzOvv7AwEByuwCApjWJ04ow2lQcp1WkhOsBp0bG1kOXUiItOWxRXm5suftcxanImeWjhnP0KFDsWvXLvzyyy8uF4pQB0po/aohynClgZUjatAZUEaDQw1aK6GrXg2RMz8/81qVcrXTlvP2eXtLWhRREXRq0M6dO2PXrl1CHpJQMEp4kKkhyqAEA6sGnQFlNDjUoDVFzsRD7nZaLTo7iuDztoeFhQl9SEKhKOFBpoYoA6AcA0s6u5faWqC6mr1XstZKmLZEDZEzQP52mpwzghAYyweZ3A2s0m98uRtY0lkc+AeZj485mqpE+HpSV2d2NuWGWpwGuTc41KKzo5BzRrgN/kGmBAOr9NavUgws6exeLHWW48AQe7GcFFquWlPkTBzIOSMIgQkIkLeBra8HKirYe6Xf+HI3sBQ5Ewe16AzIP+9MLU6DUhocStfZUcg5I9yKnB9m/E3v5QUoPVVS7gZWLVEG0lk85Dxi07Jhp3St5WyjAXLOCMItyPlhZnnTK7kLCCADKxa8ztXVLPlebqhFZ0DekTN+UlS93rxqhFKRs40G1FWnHYGcM8KtyNlpUFOUQe4GVi1aBwWxZHtAnlqrRWdA3pEzy+5jati5F3LOCMINyNlpUNNNL2cD29jIFgoHlK+1RiNvrdVUp+UcOVOjznK00YC68igdgZwzwq3I+UGmpiiDnA0s3wWk0wHh4dKWRQjkrLWa6rQSImdq0rmiguXSyYnGRrZ8E0DOGUEIipwfZGpq/crZwPI6R0UBWhVYHDk3ONRUpylyJg6hoSx3DjA3pOQCXx41DNpyFBWYSkLOyPlBpqbWr5wNrJp0BuTd4FCT1hQ5EweNRr6OsNoado7gYZdLiI2cH2Rqav0qwcCqQWdAvg2OpiagtJS9V4PWcq3PgPrqtFzttKVz5mkozjkrKyvD1KlTERwcjNDQUNxzzz2obmP6+bKyMjz44IPo1asX/Pz80KlTJzz00EOo4Cep+RuNRtPib+3ate6+HNUj1wcZoK7WLyBfA0s6i0NJCVsmTasFIiOlLo3r8PXl8mWWeyQn1Fan5Wqn1aazI3hJXQBHmTp1KvLz85GVlQWDwYCZM2di9uzZWL16tc3t8/LykJeXh1deeQV9+/bFuXPncO+99yIvLw/ffPON1barVq3C+PHjTf+HKn0CGxnAP8h4A+vtLW15LCkoYK+xsdKWQyjkamB5ndViYOWuc2QkG3yhdMLD2XU0N7Ou+g4dpC6RGbXVabk2ONSmsyMoyjk7evQoNm7ciL1792LYsGEAgLfffhsZGRl45ZVXEB8f32Kf/v3747///a/p/27duuH555/HP//5TzQ1NcHLyyxBaGgoYtXypJYJcjWwTU0s0gCo58aXq4HlnRi13Fqkszhotaw7q6CAaS0X2wGoT2u5NjjUprMjKMo5y87ORmhoqMkxA4DU1FRotVrs3r0bt9xyi13HqaioQHBwsJVjBgAPPPAA/vWvf6Fr16649957MXPmTGjamGGwoaEBDQ0Npv8rKysBAAaDAQaDwZFLaxP+WEIeU0yiorxQUKDBpUsG2eRoFBQAHKeHRsMhJKQJvLRK1joyUgtAh/z8ZhgMRqmLY6KgQAdAi4iIJhgMHABl68ymA9GjsJCDwdAkdXFMXLqkAeCF6GgjDIZm0+dK1tpsO5rQvz8ndXEA8A07LwAahIcbVGE7IiKY7SgosK47UpOfz2xHZKTZprlLZ7n9bopyzgoKChB9xdPdy8sL4eHhKODjn+1QUlKCJUuWYPbs2VafL168GNdffz38/f2xefNm3H///aiursZDDz3U6rGWL1+ORYsWtfh88+bN8OdX/BaQrKwswY8pBr6+YwCE4Kef/kB+vjzCDWfOBAO4DsHBjdi8eWOL75WodVlZNwD9kZubh8zMfVIXx8SpU2MAhODcuT3IzLQeSqpEncvLfQCMR2kp8MMPG6DTycNp2L69O4B+MBgu2fz9lai1VpsCIBo//3wQzc0XpC4OAODyZR9w3HhotRz27Mls0YWsRJ3z8zsCSMKRIyXIzMyWujgmjhxhv39+fi4yMy9afSe0zrUyW49NFs7Z008/jRdffLHNbY4ePeryeSorK3HDDTegb9++WLhwodV3zz33nOn9kCFDUFNTg5dffrlN52zevHmYO3eu1fETEhKQlpaG4OBgl8vLYzAYkJWVhXHjxkHPz5egIN55R4ezZ4HExOHIyJDHgywri0VEO3b0RkZGhulzJWtdVqbBp58CXl4dkJEhn36A2bOZmbnxxuEYNIh9pmSdm5uBmTM5cJwGw4dPkE2Xy6+/svFdQ4fGW/3+StZ63TodDhwAYmMHISNjgNTFAQAcOMBeIyOBiRPVYTu8vDR4803AaIyysodS8+yzzHakpQ3CuHEDAbhPZ77nSy7Iwjl77LHHMGPGjDa36dq1K2JjY1F0RaJHU1MTysrK2s0Vq6qqwvjx4xEUFIT169e3+6MmJydjyZIlaGhogA+/mN4V+Pj42PxOr9e75eZ013HdDf/TlJZ6QS7F56cciI3V2NRUiVrzKZfFxVro9fIYiN3cbM7t69hR3+L3V6LOej17MBcXA5cv65GQIHWJGPz8dnFxOuj1LUcEKFHruDj2Wlpq+5qkoKyMvcbEqMd28Pl8RUW2r0kq+Md9hw4tnx1C6yyn6wZk4pxFRUUhyo6JTFJSUlBeXo6cnBwkJSUBAH755RcYjUYkJye3ul9lZSXS09Ph4+OD77//Hr6+vu2eKzc3F2FhYa06ZoT9yHEySTUO0ZZjUm9JCWA0snnY1DC9A09MDHOG5KQ11WlxULPOxcXsfpXDhK+WDTs1aW0vsnDO7KVPnz4YP348Zs2ahZUrV8JgMGDOnDmYPHmyaaTmpUuXMHbsWHz++ee46qqrUFlZibS0NNTW1uLLL79EZWWlKXwZFRUFnU6HH374AYWFhbj66qvh6+uLrKwsLFu2DI8//riUl6sa5DiZpNqm0QDMOsvJwFpO7+ClKGvTNnIcsanGaQdIZ3HgG05GI+tVkMOkr7wdU1vDzl4UZy6/+uorzJkzB2PHjoVWq8Vtt92Gt956y/S9wWDA8ePHTcl9+/btw+7duwEA3bt3tzrWmTNn0LlzZ+j1eqxYsQKPPvooOI5D9+7d8dprr2HWrFniXZiKociZOPAGVU4GVo06AxTREQvSWRz0eiAigtmNoiJ52Q61NezsRXGXHB4e3uqEswDQuXNncJw56XzMmDFW/9ti/PjxVpPPEsIix8iZWg1seDjLiZGbgVVThBKQX0THsgtITVrLTWdA3XW6tJRdX79+UpdGvTrbiww6Pgi1Q5Ez8ZBbpIF0Fgc15/YBzHYYZTJ1n9rrtFzstFp1thdyzgi3Y9n6JQPrXuQWaSCdxUGtXUB89LepCSgvl7QoJtRep+XS4FCrzvZCzhnhdvibXi4Gll9KClDfjS+3iI5aDSzpLA4+PgC/xDFp7V4ociYvyDkj3I63t7wMbGmpuQtIDnlZQiLXiI7aDCzpLB5y0lrNDTuKnMkLcs4IUZBTq4y/6SMiIJtJcYWCIjriYKlzO+ONREGtOgPyqtNqbtjJyUYD6q7T9kDOGSEKcmqVqXGeIh45RRkA9WrN69zYCMhh1Re16gzIq07zOkdEqCu3D5CXjQbUXaftgZwzQhTk1CpTc4tMTlEGNXcB+fkBQUHsvRy0pjotDp6gsxxsNKBure2BnDNCFOTUKlPzTS+nKAPfBQSorwsIkJfWVKfFwRN0lkNXvZobdvZCzhkhCtT6FQc55UJZTu+gttw+QJ51Wo0TdpLO4sDrXFcHVFdLWxY15/bZCzlnhChQ61cceJ3r6oCaGmnLomadAarTYkE6i0NAAODvz95LrbWaB23ZCzlnhChQ61ccAgPNBlZqrdX8IAPkU6eNRnV3AclFZ8syqFFnQD5aq11neyDnjBAFav2Kh1y0Jp3FobSU5egA6uwCkovOANVpsVC7zvZAzhkhCnJpkVmWQa03vly0Jp3FQe1dQLzO1dVAba20ZaE6LQ5q19keyDkjRIFvkdXUSJsLZTSaW4VqvfHl0vpV+zxFpLM4BAWxZZwA0trdUJ2WD+ScEaIQFAT4+rL3Ut74ll1AvCFSG9T6FQfSWRw0GnlorfbcPkAeOlueX6062wM5Z4QoaDTyaJXxN314uDq7gAB56Ayo38CSzuIhB609oWEnB50Bz6jT7UHOGSEacmiVqXmkJo8cdLY8v1q15nWuqADq66Urh9p1BuRRp9We2wfIQ2fL86u5TrcHOWeEaMihVeYJLTI56OwJuX2hoeaHNN/dJQVUp8WBdBYPT9C6Pcg5I0RDDq0yT0g0lYPOap/eAbDuqqc67V7kUKdJZ3HwhIadPZBzRoiGHFpl+fnsNS5OujK4GznpHBkJeHtLVw53IyetqU67F0/S+fJloLFRmjIUF7OGneVAEE+EnDNCNOTQKvMEA8vrXFYGGAzSlMETdAaoTosF6SwO4eGATsfeS9VVz+scFQV4eUlTBjlAzhkhGtT6FQc5GVg16wxIX6cNBvNvrGatpdYZ8Iw6rdWa0xCk0toTdLYHcs4I0aDWrzhYGliptPYEnQHp6zR/Xi8v1oWsVqTWGaA6LRaeonN7kHNGiAa1fsVDaq1JZ3HgdY6JYU65WuF1Li0FmpqkKQPVaXHgdY6Pl+b8ckHFtzMhN/gWmVQGtq4OKC9n79VuYKn1Kw6kszhERrIEcY4DSkqkKYOnaE11Wh6Qc0aIRkQEa91znDS5UPxQeF9fNkeVmpF6igdPaf2SzuKg05m7baXQuqYGqKxk79WutVzqNDlnBCESOp35xucdJTGxvOk1GvHPLya8YZNCZ8BzDKzUOuflWZdDzUipNV+f/f3ZOsFqhuq0PCDnjBAVvtXJ34Bi4ikOAyCtzhznOVrzOhcXSzMvlKfoDMjHdqi9YSelzoBn1em2IOeMEBX+hpPixvekFpmUBraiguX3AerX2nKdRamjwWpHSttBOouDJzXs2oOcM0JU5NL6VTtyeJCFhAB+fuKfX0w0Gnlo7Ql1mmyHOEips+XKBJ686DlAzhkhMvyNzxs7MfFUA8tx4p7bk3QGyGkQC9JZHHidq6qA6mpxz83rHB7OBm55MopzzsrKyjB16lQEBwcjNDQU99xzD6rbqUFjxoyBRqOx+rv33nuttjl//jxuuOEG+Pv7Izo6Gk888QSapJpQR8WQgRUH/hpra5mRFRNP0hmQrsHR3GweUecJWlPDThyCgoDAQPZebK09Sef2UNzKVVOnTkV+fj6ysrJgMBgwc+ZMzJ49G6tXr25zv1mzZmHx4sWm//39/U3vm5ubccMNNyA2Nha7du1Cfn4+pk2bBr1ej2XLlrntWjwRcs7EISCAdStWVDCtg4PFO7cn6QxIV6dLSjxrgWiyHeIRHw+cOMG07tFDvPN6ms5toajI2dGjR7Fx40Z89NFHSE5OxsiRI/H2229j7dq1yGvnjvX390dsbKzpL9jiabV582YcOXIEX375JQYPHowJEyZgyZIlWLFiBRqlGIKlYuRgYNU+TxGPVFqTzuLAny862jMWiLaMnBmN4p6b15rqtHvxpEFb7aGoWzo7OxuhoaEYNmyY6bPU1FRotVrs3r0bt9xyS6v7fvXVV/jyyy8RGxuLiRMn4rnnnjNFz7KzszFgwADEWDQ/09PTcd999+Hw4cMYMmSIzWM2NDSgoaHB9H/l37MUGgwGGAwGl67VEv5YQh5TKtiaj3oUFnKoq2sS7aHCFohmw+oiIw1oTUo1aR0Xp8PRo1qcP98Eg0G8xLNLl3QAtIiObobBYPspqiado6M1ALxw6ZIRBkOzaOe9cIGdNzaWg8HQegqGWrQODwc0Gi80NWmQn28wzZkoBvn5XgA0HmM7YmPZPXzhQuv3sDu4dEkLQCeJ7ZDb76Yo56ygoADRV9yRXl5eCA8PR0Eb49j/8Y9/IDExEfHx8Th48CCeeuopHD9+HP/73/9Mx425ol+A/7+t4y5fvhyLFi1q8fnmzZutuk2FIisrS/Bjik1zM6DVToTRqMXatb8gPLxelPOWlPgCSIdOZ8Tu3ZntrkOoBq2NxqEAErB163GEhZ0S7byHD48AEIWCgv3IzLzU5rZq0PnixSgAI3D8eDUyM7eKdt6srE4AhkCnK0Jm5u92bK98rUNC0lFe7ov//GcnunatFOWcBoMWZWUTAQCHDmXh/Pm2H+Jq0Lm+vi+AHvjttzPo1euwaOfdt28YgA64fPkIMjP/anNboXWura0V9HiuIgvn7Omnn8aLL77Y5jZHjx51+vizZ882vR8wYADi4uIwduxYnD59Gt26dXP6uPPmzcPcuXNN/1dWViIhIQFpaWlW3aauYjAYkJWVhXHjxkHPT6qkYGJjNcjLA/r2vR5Dh4pzzj/+YDNHxsVpcOONGa1upyatd+7UYts2ICSkNzIyeop23iefZGZlwoTBuPbaQTa3UZPOiYnAwoVAdXUQMjJar1tCk5vLWhgDB0a1eV51ae2F8nKgW7dRmDBBnGjwuXPs1dubw113jWt1Elo16XzihBbffgv4+nZFRkaiaOd95RUdAGDs2D7IyOhtcxt36cz3fMkFWThnjz32GGbMmNHmNl27dkVsbCyKioqsPm9qakJZWRliHZgUJTk5GQBw6tQpdOvWDbGxsdizZ4/VNoV/D4Nq67g+Pj7w8fFp8bler3fLzemu44pNfDzLLSgq0kOsy+GrTVycxi4N1aB1QgJ7LSjQQa/XiXZePucsIcGr3d9XDTon/v3sKivToLlZL9oUAHyd7tBBC72+/fRhNWjdoQNw4ABQVNR+3RIKfqH12FgNvL09zXbYV7eEgu+oksJ2yO03k4VzFhUVhSiWjNQmKSkpKC8vR05ODpKSkgAAv/zyC4xGo8nhsofc3FwAQNzfWYcpKSl4/vnnUVRUZOo2zcrKQnBwMPr27evg1RDtIUWyqSeOApJC55oa89QdnqJ1aCibk6m+ntWzLl3EOS/VaXEgncXDE7VuDUWN1uzTpw/Gjx+PWbNmYc+ePfjtt98wZ84cTJ48GfF/16ZLly6hd+/epkjY6dOnsWTJEuTk5ODs2bP4/vvvMW3aNIwePRoDBw4EAKSlpaFv3774v//7Pxw4cACbNm3Cs88+iwceeMBmZIxwDTKw4sBfq5hzFXnSAtE8lqsESKG1J9VpKeY683SdxZrEuqqKNe4Az9K6NRTlnAFs1GXv3r0xduxYZGRkYOTIkfjggw9M3xsMBhw/ftyU3Oft7Y2ff/4ZaWlp6N27Nx577DHcdttt+OGHH0z76HQ6/Pjjj9DpdEhJScE///lPTJs2zWpeNEI4yDkTBylWCfCkBaItkaJOe+K0A6SzOPDXahkJdze8zkFBbJ5GT0cW3ZqOEB4e3uaEs507dwZn8SRKSEjAr7/+2u5xExMTkZmZKUgZibaR0jnzlHmKALOBra8HysuBsDD3n9MTdQbEr9McZ87P8SStyXaIgxSTWHtiA7otFBc5I5QPRc7EwdeXzQ0FiKe1J+oMiF+ny8o8c4Fosh3iIbbWnugEtwU5Z4ToUH6OeEhlYEln92K5QLQnpcXy9aqggM2ZKAaeWqf56yXbIQ3knBGiwz/IiorQ6mzbQuJpC0RbQs6ZOJDO4hAdDWi1bPmmK2ZVchueqjXVaWkh54wQnchIthYgx5mdJndiuUC0mEu+yAEysOJAOouDl5d5kXcxtG5qspwj0f3nkxNUp6WFnDNCdLRacUPml/5eQSgmxjMWiLZEbAPLa+1peSOks3iIqXVBAWtE6nT8usCeA9VpaSHnjJAEMW/8ixfZa8eO7j+X3BDbwHqq1rzOFRWAGEv08Q8yT9MZELdOWzoMOvEW2ZAFUjlnnlinbUHOGSEJUhhYT7zpxZy0s6oK4Jen69DB/eeTE5ZzM4mhtac6wYC4dZp0FkdnjvNsrW1BzhkhCRQ5Ewcpuo9DQjxndQAey1UCqE67F7Id4iDmJNaXLwN1ddbn9XTIOSMkQczpNMjAimNgPVlngJwGsSAnWByunMTanfA6R0Wx+RkJcs4IiaAHmTjwE5QaDEBpqXvP5ck6A+LV6cZG8yhnT9SabIc4+PqaVxVxt9aerHNrkHNGSAJvYPmuMHfiyTe+t7d5lJm7tfZknQHx6jT/oPT2ZtPSeBpkO8RDLK09XWdbkHNGSAJ/E/I3pbugRFPxtCad2auYOnvS4vI8vM5FRUBDg3vPRXWavZLtEB9yzghJSEhgr5cvAzU17jtPebl5agNPG0HIw2t94YJ7z+PpBpZ0FofISHNekjsjOkajZ4/0BqhOSwk5Z4QkBAezP8C9Nz5/01sadE+DDKw4kM7ioNGYr92dWhcXs1xNy5G4ngbVaekg54yQDDFufLrpycCKBa9zXp57F+X29GgOIE6d5nWOjQX0evedR86IZTuoTreEnDNCMvgb//x5952Ddxg8tUsTEMfA1tWZR4N6qtb88mDNze6dIobqtLgNO9JZvIadJ2t9JeScEZJBkTNxEDPK4O8PhIa67zxyRqczP1yoTrsXsh3iYKmzu+ZJrKz03JVF2oKcM0IyyMCKA6/zxYvuM7CePoKQh+q0OJDO4sDrXF3N1o11B3zDLjQUCAx0zzmUCDlnhGSQgRWHDh2Yw9TQwJKc3QHpzHB3nW5qMneZerLWZDvEwd8fCA9n792lNelsG3LOCMkgAysOer15pQAysO7F3XW6sJDltOl0LMfNUyHbIR7u1pp0tg05Z4RkiJHPQImmDLEMLOnMXt2tc1wcc9A8FV7nsjLzPIZCQ3WaQbZDGsg5IySDbynV1LhnYd3ycnOiaadOwh9fSbjbwJ47x14TE91zfKVAOotDSIg5P8kdWhuN5lHknq411WlpIOeMkAx/fyAigr13x43P3/SRkUBAgPDHVxLunraEDCyDdBYHjca9WvNLQ2m11N1GdVoayDkjJMWdrbKzZ9kr3fTu1ZnjSGseXufCQves+0g6mxHDdnTo4LkT0PK4O3JGddo25JwRkuLOG59vkXXuLPyxlYY7dS4vB6qq2HtPN7DuXveR6rQZMWyHp9dnwL06W3YfU522hpwzQlLcGTInA2tGjAdZVBTrqvZk3L3uI9VpM+SciYM750ksLAQaG60ncCYY5JwRksIn6pNz5l54nfPy2FxZQkI6W+OuOs1xpLUlZDvEgZ9YuqGB5eIJiWX3sZeXsMdWOuScEZLCGz/eGAoJGVgzsbGAtzebI0vo7jbS2Rp31WnL7mNPH30MiGM7qKuN2Y24OPZeaK3JdrQOOWeEpHTpwl7PnBH+2JRoakarNesgtNakszXuqtO8ztR9zOB1PnuW5S4JCdVpa9xVp8k5ax1yzghJ4W/6vDxhR7fV1AAlJew9tX4Z7jawpDODdBaHhASWq9TYaF7SSgio+7glVKfFh5wzQlL4KIClQRQCPg8lOJgtqEtQ61csSGdx8PIyJ6sLqfXly2yhb4C6j3ncHQ2mOt0SxTlnZWVlmDp1KoKDgxEaGop77rkH1fydZIOzZ89Co9HY/Pv6669N29n6fu3atWJckkej0bjnxqcHWUvIaRAHXucLF4QdfEE6t8SdtiMmBvDzE+64SoZsh/gozjmbOnUqDh8+jKysLPz444/Yvn07Zs+e3er2CQkJyM/Pt/pbtGgRAgMDMWHCBKttV61aZbXdpEmT3Hw1BEDOmVi4Q2fL7mPSmhEbC/j4sMEXQk7zQHW6JWQ7xMEdOlP3cdsoavDq0aNHsXHjRuzduxfDhg0DALz99tvIyMjAK6+8gvj4+Bb76HQ6xMbGWn22fv163HnnnQjkF2f7m9DQ0BbbEu7HHTc+hctb4s4HGXUfm9FqWQ7N8eNMa153V6E63RKyHeLA63zuHGt06HSuH7OsjDXuAOo+toWinLPs7GyEhoaaHDMASE1NhVarxe7du3HLLbe0e4ycnBzk5uZixYoVLb574IEH8K9//Qtdu3bFvffei5kzZ0Kj0bR6rIaGBjRYZLFX/r3KtsFggMFgcOTS2oQ/lpDHlBMJCVoAOpw+bYTB0CzIMc+c0QHQIiGhGQaD/UO51Kw1m+RRj7w8oKrKYJrJ3hVOn9YA8EJiIgeDwf4+PDXrDACJiTocP67FqVNNGDVKmJk7z53zAqBBhw4GOCKbmrVOSGD176+/hLQdzB6R7TATEwN4eXnBYNDg3DmDKdfPFU6dAgA9YmM56HRNdtdpd+kst99NUc5ZQUEBoqOjrT7z8vJCeHg4CgoK7DrGxx9/jD59+mDEiBFWny9evBjXX389/P39sXnzZtx///2orq7GQw891Oqxli9fjkWLFrX4fPPmzfB3w1j3rKwswY8pB8rK4gBchQMHKpCZuV2QY+bmjgIQjtLSfcjMzHN4fzVqzXGAr+8NqK/3whdfbEeHDq3natrLhg2dAQyCr28BMjP3OLy/GnUGAK12IIAu+Pnn04iJOeby8erqdCgtvREAcPz4Zly86Hgymxq1LigIAzAaR4/WIzNTmOvbvfsqAHGorj6MzEzHQ3Jq1BkAIiJSUVgYgLVrd6Nfv1KXj7drF7P7wcGXkZm5w+H9hda5trZW0OO5iiycs6effhovvvhim9scPXrU5fPU1dVh9erVeO6551p8Z/nZkCFDUFNTg5dffrlN52zevHmYO3eu6f/KykokJCQgLS0NwcHBLpeXx2AwICsrC+PGjYNehavwxscDL7wAlJeHIiMjQ5BjzprFqvbttw/GkCGD7d5P7Vp366bD4cNAYuK1SEtzPaKzbRtLWx0xItqh307tOh85osXGjYCXVw9kZHR1+Xi5uew1MpLDHXekObSvmrUeMgR4+mmgtNQP48ZlCLJI+b//zWzHTTf1RVpaH7v3U7POANCvnw6FhUBs7NXIyHDddhw5wmxHUpJjdt9dOvM9X3JBFs7ZY489hhkzZrS5TdeuXREbG4uiK9aPaGpqQllZmV25Yt988w1qa2sxbdq0drdNTk7GkiVL0NDQAB8fH5vb+Pj42PxOr9e75eZ013GlpmdP9lpSokFDgx5XpAI6TEUFUFzM3vfurXfKYKtV665dgcOHgQsXvAR5kP31F3vt2VMHvd7xRBS16ty9O3s9d04Lvd71cVd8bl/37hqn9VKj1gkJbKH5+noNCgr06OqiH2w0mut0797O3SNq1BlgtuOXX9xhO5y7R4TWWW6/mSycs6ioKERFRbW7XUpKCsrLy5GTk4OkpCQAwC+//AKj0Yjk5OR29//4449x00032XWu3NxchIWFteqYEcIREgKEhbH5hc6cAQYMcO14p0+z1+holqhOmBE6gZrljZidEYJBOouDRsMGXxw7xrR21TnLzwfq6tgcajQgwBqq0+KiqKk0+vTpg/Hjx2PWrFnYs2cPfvvtN8yZMweTJ082jdS8dOkSevfujT17rPNfTp06he3bt+Nf//pXi+P+8MMP+Oijj3Do0CGcOnUK7733HpYtW4YHH3xQlOsihL3x6aZvHSF1NhrNjjBpbQ2vM/+wdxWq063jDtvRuTMtxH0l5JyJi6KcMwD46quv0Lt3b4wdOxYZGRkYOXIkPvjgA9P3BoMBx48fb5Hc98knn6Bjx45IS2uZr6HX67FixQqkpKRg8ODBeP/99/Haa69hwYIFbr8egsHf+Hyo2xXopm8dIXW+dIktueXlRUPhryQ8HAgKYu/5qRlcgep065DtEAchda6rAy5eZO9Ja9sorm0QHh6O1atXt/p9586dwXEtkxWXLVuGZcuW2dxn/PjxGD9+vGBlJByHv0FPnnT9WGRgW8dSZ45j3ULOwuvcpQtFGa5Eo2Fa79/PtO5jf165TahOtw7ZDnHgNbl4EaitZcvuOQvv4IWEABERrpdNjSguckaok1692Ovx464fiwxs63TvziZJrawECgtdOxavc48erpdLjQhVp2trWZQSIK1tQbZDHCIjWUQYcN0RttTZlQaimiHnjJAFvIE9ccL1Y5GBbR0fH5ZPA7iuNencNkLVaT7KEBZmfjgSZnidT55keZCuQHW6bfiR9WQ73A85Z4Qs4G/6CxfMS3o4Q3U1S8IG6MZvDV5rVyMNfOuZdLYN6SwOiYmAXg/U17u2linHkdbtIVSUkpyz9iHnjJAFliFz/sZ1Bn70YHg4izQQLSEDKw6kszh4eZm1cUXrwkLWMOTXRiVaIlQ0mOp0+5BzRsgGIR5mdNO3jxAGluNI6/bgI2dFRUB5ufPHIZ3bR4g6zevcqRPr/idaIlQ0mOp0+5BzRsgGIfIZ6KZvHyEMLD9/l05Hk3W2RlAQEBfH3lOddi9C1GnSuX0snWAbkyLYRUMDcP48e09atw45Z4RsECJyxu9Lo9pah9f5r78Ag8G5Y/A6d+4MeHsLUixVQnVaHEhncejWjY2uLC83L5HnKKdPs4EbgYFATIygxVMV5JwRskGI1u/hw+y1Xz/Xy6NW4uPZHEVNTc7P9k0624er0eDycvM0Gn37ClIkVSJE1J3qdPv4+Zkj5c7a6UOH2Gu/fjSNRluQc0bIBldD5kaj2cD27y9cudSGVuu6I8wbWNK5bVyN6PD1OSGBTdhJ2IbX+fx555fLojptH646wmSj7YOcM0I28BMSVlSwJGpHOXeOjbby9qZchvZw1WmwbP0SrUM6i0NkJBudbTkdhiNUV5ujyKR121CdFgdyzgjZ4OtrDpkfO+b4/nyLrFcvNu8R0Tp869cZnTmOWr/2YhllaG52fH/S2T40Gtfq9NGj7DUmhjl6ROu4ojNAEUp7IeeMkBUDBrDXgwcd35duevtxRee8PJYLpdOZW9GEbbp2ZXk69fXOzd9HUQb7EcJ2kM7t44rOlvcBad025JwRsmLwYPZ64IDj+1KUwX4GDWKvf/7JBgY4Aq9zjx40H1R76HTmhxnVaffC12nS2b3wOp875/j8fceOsdzgsDDzNDOEbcg5I2QFf+Pn5jq+L7V+7adbNyAggLVkHc3RoQilY/ANDkfrdHExy73UaIA+fYQulfpwVmeA6rQjhIaa008cdYQtnWAaqdk25JwRsoI3sIcOORbRaW42542QgW0fy4iOow8zmnLAMZxtcPA6d+nCHGmibQYOZK8XLwKlpY7tS3XaMZx1hKkBbT/knBGyoksXNrN6Q4Njo4FOn2b7+PmxYxDt42wXMkUZHIN0FofgYJbjBzimdXk5c+gAchrsheq0+yHnjJAVWq25BexIq4y/6fv2Zccg2seZ1q/lXHL0ILOPAQNYF05enmNTxFCUwXGccRr4+tyxI80lZy/ORoOpTtsPPcYI2eGM07BvH3vljQbRPs4Y2JMn2Vxyfn60zI29BAWxHD/AMaeB6rTjOFOnSWfH4W304cP2LwFXVgacPcve8w1wonXIOSNkhzOt3z/+YK/DhwteHNXCR3QKC4GCAvv24XUeMgTw8nJf2dSGo3W6sdG8LdVp+yHbIQ6dO7Nu5MZG++c7y8lhr926AeHhbiuaaiDnjJAdlq1fe5Zx4jizgR02zG3FUh0BAebol70PM9LZORyN6Bw6xB58YWGUQ+kIvM5HjjD97IHqtONoNI7XadLZMcg5I2RH//5sNGFxMXDhQvvbnzvHRmfp9eYRiIR9DBnCXnnD2R5797JXMrCO4YrONOWA/XTqxBxag8G+SVKrqsyjvKlOO4ajdZqcM8cg54yQHX5+5u6JXbva3373bvY6aBBNiuooV1/NXu3RubHRnJ9DXUCOkZzMXo8ft2+aB75Ok86OodE4Vqf/+INF3hMS2NJNhP04ojPHUZ12FHLOCFkyYgR7tefG37GDvV5zjfvKo1Z4zbKz2UjMtti3D6irAyIiaNkmR4mMNGv2++/tb8/X6ZEj3VcmtcLXaUdsB+nsOLzO+/ezQUJtcfYscOkS690g58w+yDkjZAl/4//2W/vb8gZ21Cj3lUetDB7MIpWXL7ef2Gv5IKOuNsfhGxzt1emCArb+oEYDpKS4v1xqw16dAXLOXCEhAejQgU0AznfDtwavc1IS4O/v/rKpAXLOCFnCO2e5uW2v31ZeztaHBMjAOoNeb+5y+/XXtrelB5lr8LrZq/PAgWypHMIxrrqKjSS+eBH466/Wt2tqYhFjgBp2zqDROF6nSWf7IeeMkCUdOwI9e7Kutq1bW99u61aWz9CzJ+WMOMvYsew1K6v1bQwGswEePdr9ZVIjvM67dwMVFa1vt2ULeyWdnSMgwBxxbKtO797NuuPCw2lSVGexx3ZwHPDzz+w91Wn7IeeMkC1paey1rRt/wwb2On68+8ujVnidf/ml9fVMf/8dqKxk+WZJSeKVTU0kJrJGRHMzsG2b7W04Dti4kb2nOu0848ax17ZsB69zWhqtKuIsvM68fbDFiRMs58zbG7juOtGKpnioShKyhb/xN2ywPd8Zx5mdswkTxCuX2khKYtMPVFS0nqzO65yezqY5IZzDsk7b4tgxNjWMjw8wZoxoxVIdvM4//9z6fGdkO1ync2ege3fW4OCjY1fC6zx6NItqEvZBzhkhW8aOZcnqZ8+ap3CwJCeH5ZX4+QHXXit68VSDTgfccAN7/803Lb/nOGD9evY+I0O8cqmRm25ir+vX245S8jqPGUOJ064wfDhLc6ioMHcTW3LuHLMfGo05ckw4B1+nbdkOwFynyQl2DHLOCNkSEADceCN7/5//tPx+9Wr2etNNzEEjnOfOO9nrN9+0nFIjN5dFdHx9gYkTRS+aqrjuOpbjVFQEbN9u/R3HAV99xd7fdZf4ZVMTOh1w++3svS3bsXYtex0zBoiNFa1YqoS3Hd9/z6baseTCBXM9v+MOcculdMg5I2QN/5D6/HPr7onGRmDNGvZ+6lTxy6U20tKAkBA2F9GmTdbfffYZe504ka2nRziPXg/cdht7/9FH1t/t28eWHfLxAW69VfyyqQ3ednz9tfWIb45j9gQA/vEP0YulOq66inVv1tQA69ZZf/fFF+x19Gg29QZhP4pzzp5//nmMGDEC/v7+CLVznDnHcZg/fz7i4uLg5+eH1NRUnDx50mqbsrIyTJ06FcHBwQgNDcU999yD6upqN1wB4QgTJwLx8WzuJ8sb/4sv2GdxcSwPinANHx/gnnvY+9dfN39eWmp2Iu6+W/xyqZH77mOvX3/NuuV5XnqJvd52G3OUCdcYOZKNwqypsXaEf/qJOcGBgeboGuE8Gg1w773s/euvm/OD6+qAt95i78l2OI7inLPGxkbccccduI+3cHbw0ksv4a233sLKlSuxe/duBAQEID09HfX19aZtpk6disOHDyMrKws//vgjtm/fjtmzZ7vjEggH8PYGHniAvX/uOaC6mo0KWrqUffbYY2wbwnUefJCNWsvKMo9kW7KEPdwGDyYnWCiGDGE5kk1NwFNPsc/27DHn7PCfEa6h0QCPPsreL1/OupIbGoBnn2Wf3X8/zSMnFLNmsRzJgwfNUcnXXwcKC9l6pxShdAJOoaxatYoLCQlpdzuj0cjFxsZyL7/8sumz8vJyzsfHh1uzZg3HcRx35MgRDgC3d+9e0zYbNmzgNBoNd+nSJbvLVFFRwQHgKioq7L8QO2hsbOS+/fZbrrGxUdDjKoWqKo5LTOQ4gONGjOC4lBT2PjGRfScknq71o48ybSMjOW7aNPYe4LjMTGHP4+k6793LcVot0/auuziuY0f2/h//EP5cnqx1YyPHDR7MtB08mONSU9n7iAiOKygQ+lyeqzPHcdzy5UzbwECOmzmT47y82P+ffirsedyls7ue387iJbFv6HbOnDmDgoICpKammj4LCQlBcnIysrOzMXnyZGRnZyM0NBTDhg0zbZOamgqtVovdu3fjlltusXnshoYGNDQ0mP6v/HuiF4PBAIPBINg18McS8phKwscH+PhjDW66SYdduzR/f8bh88+b4ePDQUhZPF3r+fOBbdu8sH+/xtQCfvjhZqSmGklnARk0CFi4UIv583Wm7vru3Tm88UaToDoDpPWHHwJpaV7IzWW2Q6vl8NFHzQgPJ9shJA89BGzerMPWrVqsWsU+mzLFiClTmhWhs9x+N9U7ZwUFBQCAmCumj4+JiTF9V1BQgOjoaKvvvby8EB4ebtrGFsuXL8eiRYtafL5582b4u2EcfFZbMyp6AMuWBSMzswuMRg1uvvk0Ll+uQmame87lyVo/9ZQO69d3R35+AJKSijB69EXS2Q0MHAg89VQcfv89DpGRdbjllpPYtauVWYAFwJO1XrbMHz/80A319V7IyDgDjaac6rQbuP9+DRISuuHMmRD071+CcePOtTqnn6sIrXNtba2gx3MVWThnTz/9NF588cU2tzl69Ch69+4tUonsY968eZg7d67p/8rKSiQkJCAtLQ3BAg5rMxgMyMrKwrhx46DX6wU7rhK5/37+Xbxbjk9aM8yjBWMBDBT8+KQzw3reuC5uOQdpzeAHvABxbjk+6cy4+Wb+XQwA4dfFcpfOla0tcSARsnDOHnvsMcyYMaPNbbp27erUsWP/nsSmsLAQcXHmm7KwsBCDBw82bVNUVGS1X1NTE8rKykz728LHxwc+Pj4tPtfr9W65Od11XKIlpLU4kM7iQVqLA+ksDkLrLLffTBbOWVRUFKKiotxy7C5duiA2NhZbtmwxOWOVlZXYvXu3acRnSkoKysvLkZOTg6S/Fw785ZdfYDQakZyc7JZyEQRBEARB2EJxU2mcP38eubm5OH/+PJqbm5Gbm4vc3FyrOcl69+6N9X+vGaHRaPDII49g6dKl+P777/Hnn39i2rRpiI+Px6RJkwAAffr0wfjx4zFr1izs2bMHv/32G+bMmYPJkycjPt493WcEQRAEQRC2kEXkzBHmz5+Pz/gpywEMGTIEALB161aM+Xul4OPHj6OiosK0zZNPPomamhrMnj0b5eXlGDlyJDZu3AhfX1/TNl999RXmzJmDsWPHQqvV4rbbbsNb/Ax6BEEQBEEQIqE45+zTTz/Fp59+2uY2HD9F8d9oNBosXrwYixcvbnWf8PBwrOYXayQIgiAIgpAIxXVrEgRBEARBqBlyzgiCIAiCIGQEOWcEQRAEQRAygpwzgiAIgiAIGUHOGUEQBEEQhIwg54wgCIIgCEJGkHNGEARBEAQhI8g5IwiCIAiCkBGKm4RWzvCT3wq9ur3BYEBtbS0qKytltzir2iCtxYF0Fg/SWhxIZ3Fwl878c/vKSeylgpwzAamqqgIAJCQkSFwSgiAIgiAcpaqqCiEhIVIXAxpOLm6iCjAajcjLy0NQUBA0Go1gx62srERCQgIuXLiA4OBgwY5LtIS0FgfSWTxIa3EgncXBXTpzHIeqqirEx8dDq5U+44siZwKi1WrRsWNHtx0/ODiYbnqRIK3FgXQWD9JaHEhncXCHznKImPFI7x4SBEEQBEEQJsg5IwiCIAiCkBHknCkAHx8fLFiwAD4+PlIXRfWQ1uJAOosHaS0OpLM4eIrONCCAIAiCIAhCRlDkjCAIgiAIQkaQc0YQBEEQBCEjyDkjCIIgCIKQEeScEQRBEARByAhyzgiCIAiCIGQEOWcKYMWKFejcuTN8fX2RnJyMPXv2SF0k1bF9+3ZMnDgR8fHx0Gg0+Pbbb6UukipZvnw5hg8fjqCgIERHR2PSpEk4fvy41MVSHe+99x4GDhxomkU9JSUFGzZskLpYqueFF16ARqPBI488InVRVMfChQuh0Wis/nr37i11sdwGOWcyZ926dZg7dy4WLFiAffv2YdCgQUhPT0dRUZHURVMVNTU1GDRoEFasWCF1UVTNr7/+igceeAC///47srKyYDAYkJaWhpqaGqmLpio6duyIF154ATk5Ofjjjz9w/fXX4+abb8bhw4elLppq2bt3L95//30MHDhQ6qKoln79+iE/P9/0t3PnTqmL5DZonjOZk5ycjOHDh+Odd94BwBZXT0hIwIMPPoinn35a4tKpE41Gg/Xr12PSpElSF0X1FBcXIzo6Gr/++itGjx4tdXFUTXh4OF5++WXcc889UhdFdVRXV2Po0KF49913sXTpUgwePBhvvPGG1MVSFQsXLsS3336L3NxcqYsiChQ5kzGNjY3IyclBamqq6TOtVovU1FRkZ2dLWDKCEIaKigoAzHEg3ENzczPWrl2LmpoapKSkSF0cVfLAAw/ghhtusLLVhPCcPHkS8fHx6Nq1K6ZOnYrz589LXSS34SV1AYjWKSkpQXNzM2JiYqw+j4mJwbFjxyQqFUEIg9FoxCOPPIJrrrkG/fv3l7o4quPPP/9ESkoK6uvrERgYiPXr16Nv375SF0t1rF27Fvv27cPevXulLoqqSU5OxqeffopevXohPz8fixYtwqhRo3Do0CEEBQVJXTzBIeeMIAhJeOCBB3Do0CFV541ISa9evZCbm4uKigp88803mD59On799Vdy0ATkwoULePjhh5GVlQVfX1+pi6NqJkyYYHo/cOBAJCcnIzExEf/5z39U2VVPzpmMiYyMhE6nQ2FhodXnhYWFiI2NlahUBOE6c+bMwY8//ojt27ejY8eOUhdHlXh7e6N79+4AgKSkJOzduxdvvvkm3n//fYlLph5ycnJQVFSEoUOHmj5rbm7G9u3b8c4776ChoQE6nU7CEqqX0NBQ9OzZE6dOnZK6KG6Bcs5kjLe3N5KSkrBlyxbTZ0ajEVu2bKHcEUKRcByHOXPmYP369fjll1/QpUsXqYvkMRiNRjQ0NEhdDFUxduxY/Pnnn8jNzTX9DRs2DFOnTkVubi45Zm6kuroap0+fRlxcnNRFcQsUOZM5c+fOxfTp0zFs2DBcddVVeOONN1BTU4OZM2dKXTRVUV1dbdUCO3PmDHJzcxEeHo5OnTpJWDJ18cADD2D16tX47rvvEBQUhIKCAgBASEgI/Pz8JC6depg3bx4mTJiATp06oaqqCqtXr8a2bduwadMmqYumKoKCglrkSwYEBCAiIoLyKAXm8ccfx8SJE5GYmIi8vDwsWLAAOp0OU6ZMkbpoboGcM5lz1113obi4GPPnz0dBQQEGDx6MjRs3thgkQLjGH3/8geuuu870/9y5cwEA06dPx6effipRqdTHe++9BwAYM2aM1eerVq3CjBkzxC+QSikqKsK0adOQn5+PkJAQDBw4EJs2bcK4ceOkLhpBOMXFixcxZcoUlJaWIioqCiNHjsTvv/+OqKgoqYvmFmieM4IgCIIgCBlBOWcEQRAEQRAygpwzgiAIgiAIGUHOGUEQBEEQhIwg54wgCIIgCEJGkHNGEARBEAQhI8g5IwiCIAiCkBHknBEEQRAEQcgIcs4IgiAIgiBkBDlnBEEQrTBmzBhoNBpoNBrk5ubatc+MGTNM+3z77bduLR9BEOqEnDOCIDyaRx99FLfeemur38+aNQv5+fl2r5X45ptvIj8/X6jiEQThgZBzRhCER7Nnzx4MGzas1e/9/f0RGxsLLy/7liIOCQlBbGysUMUjCMIDIeeMIAiPpLGxEXq9Hrt27cK///1vaDQaXH311e3uZzQasWzZMvTo0QO+vr6IiYmhRdsJghAU+5qCBEEQKsPLywu//fYbkpOTkZubi5iYGPj6+ra73/Lly7Fu3Tp88MEH6Nq1Ky5duoRjx46JUGKCIDwFcs4IgvBItFot8vLyEBERgUGDBtm936ZNmzBx4kRcd911AIDExESMGDHCXcUkCMIDoW5NgiA8lv379zvkmAHATTfdhBdeeAHp6en46KOPcPnyZTeVjiAIT4WcM4IgPJbc3FyHnbPHH38cR48exdixY/H666+je/fuOHPmjJtKSBCEJ0LOGUEQHsuff/6JwYMHO7xfz5498eSTTyInJwdVVVU4cuSI8IUjCMJjoZwzgiA8FqPRiOPHjyMvLw8BAQEICQlpc/uXXnoJsbGxGD58OLRaLd5//31ERERQzhlBEIJCkTOCIDyWpUuX4tNPP0WHDh2wdOnSdrevr6/H888/j6FDh2LkyJH466+/8MsvvyAsLEyE0hIE4SloOI7jpC4EQRCEHBkzZgwGDx6MN954w+F9NRoN1q9fj0mTJgleLoIg1A1FzgiCINrg3XffRWBgIP7880+7tr/33nsRGBjo5lIRBKFmKHJGEATRCpcuXUJdXR0AoFOnTvD29m53n6KiIlRWVgIA4uLiEBAQ4NYyEgShPsg5IwiCIAiCkBHUrUkQBEEQBCEjyDkjCIIgCIKQEeScEQRBEARByAhyzgiCIAiCIGQEOWcEQRAEQRAygpwzgiAIgiAIGUHOGUEQBEEQhIwg54wgCIIgCEJGkHNGEARBEAQhI8g5IwiCIAiCkBH/H21ozNmgsmTDAAAAAElFTkSuQmCC"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>The number of samples is $N=\frac{T_{meas}}{\Delta t}=T_{meas}\cdot f_s=5\cdot 100=500$.
+The last value should be $4.99$ because we use a $\Delta t=\frac{1}{f_s}=0.01$ s.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="The-Discrete-Fourier-Transform-(chapter-5)">The Discrete Fourier Transform (chapter 5)<a class="anchor-link" href="#The-Discrete-Fourier-Transform-(chapter-5)">¶</a></h3><p>To discover which frequencies are contained in the signal $x(t)$, we can transform it from the time domain to the frequency domain using the Fourier transform:</p>
+$$
+X(f) = \int_{-\infty}^{\infty} x(t) e^{-j 2\pi f t} dt
+$$<p>Which can then be expressed in terms of magnitude and phase:</p>
+$$
+X(f) = |X(f)|e^{j\theta(f)}
+$$<p>Where $|X(f)|$ plotted against frequency $f$ is called the <em>magnitude spectrum</em> (and practically often referred to as amplitude spectrum).</p>
+<p>The set and number of sinusoids required to approximate or re-create a given signal $x(t)$ depends on the shape of that signal. Of course, when the signal is a pure sinusoid, we only need one term. For an example, consider a 2 Hz sinusoidal signal and its magnitude spectrum below (the magnitude spectrum is expressed in [Vs], which equals [V/Hz], hence a magnitude or amplitude density).</p>
+<img alt="No description has been provided for this image" src="https://gitlab.com/JelleKnibbe/public-files/-/raw/main/SP_Practical/SignalSpectrumExample.png" style="margin:auto" width="800/"/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>You can see a sharp spike in the magnitude plot (which has two logarithmic axes, and we only plot the spectrum for positive frequencies $f>0$). A continuous time sinusoid with amplitude $A$ will produce a magnitude of $\frac{A T_{meas}}{2}$ in the amplitude spectrum (with $T_{meas}$ the <em>measurement time</em> or duration), rather than $\frac{A}{2}$ which you would expect (in a double-sided spectrum). This is because of the limited time duration of the input signal, which actually implies multiplication of $x(t)$ by a <em>rectangular time window</em> $w(t)=\Pi(\frac{t}{T_{meas}})$. Since the sinusoid has an amplitude of $A=1$ V, and was measured for $T_{meas}=5$ seconds, the magnitude becomes 2.5. The magnitude at all other frequencies is very small, approximately $10^{-15}$, or approximately zero.</p>
+<p>The Fourier transform is a continuous-time operation, mapping the continuous time-domain to the continuous frequency domain. Usually, we don't have continuous-time signals, but discrete time signals $x_n$ sampled at a certain sampling rate. The discrete Fourier Transform (DFT) is the discrete-time equivalent of the continuous time-Fourier transform:</p>
+$$
+X_k = \sum_{n=0}^{N-1}x_ne^{-j2\pi kn/N}, k=0,1,...,N-1
+$$<p>Where $X_k$ is the sequence of frequency domain samples. NumPy contains a function to perform the DFT using a fast numerical algorithm, the Fast Fourier Transform:</p>
+<p><code>X_discr = np.fft.fft(x)</code></p>
+<p>For more information on the function see <a href="https://numpy.org/doc/stable/reference/generated/numpy.fft.fft.html">here</a>.</p>
+<p>The discrete-time signal $x_n$ and the frequency domain samples $X_k$ returned by Python are of equal length ($N$ samples in, $N$ samples out). When you compare the equations of the DFT with the continuous Fourier Transform, you will see that in the continuous equation we integrate over time $t$, while in the DFT <em>no information</em> about the time or sample rate is required (there is no $\Delta t$ in the above equation for $X_k$, as noted at the end of Chapter 5). This results in a <em>scale factor</em> in the spectrum when the DFT of signals with different sample rates are compared. To solve this, we have to multiply the DFT as calculated by NumPy by the sample time interval $\Delta t = 1/f_s$ to restore the time dimension and obtain a <em>discrete approximation of the continuous Fourier Transform</em>. To account for the measurement duration as described above, we have to divide by $T_{meas} = N \Delta t$. So, in total, multiply by $\Delta t$ and divide by $T_{meas}$, hence, we have to divide the NumPy result by the number of samples $N$:</p>
+<p><code>X_cont = np.fft.fft(x) / N</code></p>
+<p>The DFT only contains information (the complex value of $X_k$) at discrete frequencies, known as the <em>analysis frequencies</em>, which are integer multiples of the frequency resolution $f_0$. If we have a sampled signal $x_n$ consisting of $N$ samples, sampled at $f_s$ Hz with $f_s=1/ \Delta t$, the frequency resolution is:</p>
+$$
+f_0 = \frac{1}{T_{meas}} = \frac{1}{N \Delta t} = \frac{f_s}{N}
+$$<p>The first value of the complex vector $X_n$ returned by NumPy is the mean of the time-domain signal corresponding to $f=0$ Hz. So, the frequency vector corresponding to the discrete Fourier transform starts at zero and has $N$ elements, the analysis frequencies:</p>
+<p><code>f_vec = np.arange(0, f_s, f_0)</code></p>
+<p>With task 2 you visualize the entire output as you get it from the <code>np.fft.fft</code>, hence for frequencies $[0,f_s)$. From Chapter 4 you know that the spectrum of a sampled signal repeats every integer multiple of $f_s$ (copies of the spectrum).</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 2:</b>
+<ul>
+<li>Calculate the Fast Fourier Transform of the signal you generated in Task 1. Remember: the signal has 500 samples and thus does <em>not</em> end at exactly 5 second.</li>
+<li>Create the frequency vector $f$ with the analysis frequencies.</li>
+<li>Plot the modulus of the Fourier Transform against the frequency $f$ using a log scale on both axes. Use plot markers to see at which frequencies the Fourier transform was calculated.</li>
+</ul>
+<p>On top of that, answer to the following questions:</p>
+<ol>
+<li>Describe the amplitude spectrum.</li>
+<li>What is the magnitude of $X(f)$ at $f$ = 1 Hz?</li>
+<li>Do you notice anything peculiar about the amplitude spectrum?</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+
+<span class="c1"># Write your own code or use the template below to create your figure(s)</span>
+
+<span class="c1"># f, axes = plt.subplots(1,1,figsize=(10,5))</span>
+
+<span class="c1"># axes[0].plot(YOUR_CODE_HERE, YOUR_CODE_HERE, 'x', color='b', label='Fourier transform')</span>
+<span class="c1"># axes[0].loglog()</span>
+<span class="c1"># axes[0].set_xlabel('$f \: \: [Hz]$')</span>
+<span class="c1"># axes[0].set_ylabel('$|X(f)| \: [V]$')</span>
+<span class="c1"># axes[0].grid()</span>
+<span class="c1"># axes[0].set_title('Log/Log')</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">## SOLUTION</span>
+
+<span class="n">T_meas</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">f_s</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">f_s</span><span class="p">)</span> <span class="c1"># ends at 4.99, length 500 according to the sample-and-hold convention</span>
+
+<span class="n">A</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">f_c</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">phi</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
+
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">X_cont</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
+
+<span class="n">f_0</span> <span class="o">=</span> <span class="n">f_s</span> <span class="o">/</span> <span class="n">N</span>
+<span class="n">f_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">f_s</span><span class="p">,</span> <span class="n">f_0</span><span class="p">)</span>
+
+<span class="n">f</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transform'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">loglog</span><span class="p">()</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$f \: \: [Hz]$'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$|X(f)| \: [V]$'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Log/Log'</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transform'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$f \: \: [Hz]$'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$|X(f)| \: [V]$'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Linear'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">f_vec</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">)</span><span class="o">></span><span class="mf">0.1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>[ 1. 99.]
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ol>
+<li>A peak of 0.5 should be found (from theory) at a frequency of 1 Hz and one around 100 Hz (exactly at 99 Hz), the rest all close to zero.</li>
+<li>0.5 (as we divide the fft-result by N to get the equivalent of the continuous-time Fourier transform).</li>
+<li>The peak at 100 Hz (or 99 Hz to be precise) should not be there. We did not input a signal with such a frequency.</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Symmetry">Symmetry<a class="anchor-link" href="#Symmetry">¶</a></h3><p>Due to symmetry properties of the DFT, the following holds for a real signal $x(t)$:</p>
+$$
+|X_k| = |X_{-k}|
+$$<p>Which means that the modulus of the Fourier coefficients is symmetric about $f=0$. The NumPy <code>fft</code> command appends the Fourier coefficients for $k<0$ to the right side of the Fourier coefficients for $k \ge 0$. This is called the <em>two-sided</em> spectrum.</p>
+<p>Since we are working with real signals, the symmetry property is valid, and we can just ignore the coefficients for $k<0$. This means that we consider the following range in the frequency domain:</p>
+<p>$[0, \frac{f_s}{2}]$ for even values of $N$</p>
+<p>$[0, \frac{f_s}{2})$ for odd values of $N$</p>
+<p>In other words, we are only considering frequencies up to half of $f_s$. This is easily implemented in Python using the floor division operator <code>//</code>:</p>
+<p><code>X_cont = X_cont[:N//2]</code>
+<br/>
+<code>f_vec = f_vec[:N//2]</code></p>
+<p>If you're unfamiliar with this, consider that <code>A//B</code> returns the number of times <code>B</code> 'fits into' <code>A</code>. In principle, this will always return an integer, so it can be easily used for indexing an array. In this specifc example, <code>N//2</code> will return $\frac{N}{2}$ for even $N$ and $\frac{N-1}{2}$ for odd N, which is exactly what we want. See the example below.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">N</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s1"> floor divided by 2: </span><span class="si">{</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">N</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s1"> floor divided by 2: </span><span class="si">{</span><span class="p">(</span><span class="n">N</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">N</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s1"> floor divided by 2: </span><span class="si">{</span><span class="p">(</span><span class="n">N</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>100 floor divided by 2: 50
+101 floor divided by 2: 50
+99 floor divided by 2: 49
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 3:</b>
+<ul>
+<li><p>Repeat Task 2 with different measurement times $T_{meas}$ for the signal. Use measurement times such that the $f_c$ = 1 Hz oscillation fits <em>exactly</em> 1 time, 5 times and 20 times.</p>
+</li>
+<li><p>Plot the amplitude spectrum for all three measurement times, <strong>only for positive frequencies</strong>, in separate graphs (log-log scale) with the same domains and answer to the following questions:</p>
+</li>
+<li><p>What is the effect of changing $T_{meas}$ on the frequency range in the amplitude spectrum? Does the highest analysis frequency change?</p>
+</li>
+<li><p>Does the frequency resolution change?</p>
+</li>
+<li><p>Does the magnitude of at the peaks change?</p>
+</li>
+</ul>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+
+<span class="c1"># Write your own code or use the template below to create your figure(s)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">T_meas</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">YOUR_CODE_HERE</span><span class="p">):</span>
+ <span class="n">YOUR_CODE_HERE</span>
+
+ <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+
+ <span class="n">YOUR_CODE_HERE</span>
+
+ <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+<span class="n">T_lst</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">20</span><span class="p">]</span>
+<span class="n">f_s</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">T_meas</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">T_lst</span><span class="p">):</span>
+ <span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">f_s</span><span class="p">)</span>
+ <span class="n">A</span> <span class="o">=</span> <span class="mi">1</span>
+ <span class="n">f_c</span> <span class="o">=</span> <span class="mi">1</span>
+ <span class="n">phi</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
+
+ <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+ <span class="n">X_cont</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
+
+ <span class="n">f_0</span> <span class="o">=</span> <span class="n">f_s</span> <span class="o">/</span> <span class="n">N</span>
+ <span class="n">f_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">f_s</span><span class="p">,</span> <span class="n">f_0</span><span class="p">)</span>
+
+ <span class="n">X_cont</span> <span class="o">=</span> <span class="n">X_cont</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+ <span class="n">f_vec</span> <span class="o">=</span> <span class="n">f_vec</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+
+ <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transform'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.04</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">19</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$f \: \: [Hz]$'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$|X(f)| \: [V]$'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ol>
+<li>No, the highest analysis frequency stays the same (as it is related to the sampling frequency, which we did not change).</li>
+<li>Yes, the frequency resolution becomes better/finer (gets smaller).</li>
+<li>No, because we already divide by (we already account for the measurement duration).</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Aliasing-(chapter-4)">Aliasing (chapter 4)<a class="anchor-link" href="#Aliasing-(chapter-4)">¶</a></h3><p>So far we have been analyzing a signal with a fairly low frequency, just $1$ or $2$ Hz. Now suppose, due to some unexpected source, our signal contains a second sinusoid with a much higher frequency. This is often caused by the line frequency ($50$ Hz) due to power plant generators providing the $220$ V current in regular buildings, or by electrical interference.</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 4: with this task we'll make you run into an aliasing problem . . .</b>
+<ul>
+<li><p>Create a time-array starting $t=0$, with a sampling rate of 100 Hz, and ending such that a sinusoid with a frequency of 1 Hz fits exactly 5 times in the measurement time.</p>
+</li>
+<li><p>Re-create the signal $x(t) = A \sin(2 \pi f_c t + \varphi)$ from the first Task, with amplitude $A=1.0$ V, carrier frequency $f_c=1.0$ Hz, and initial phase $\varphi = 5$ degrees, to be converted into radians.</p>
+</li>
+<li><p>Add to this signal a second sinusoid with a frequency of $80$ Hz and amplitude of $0.1$ V (and zero initial phase).</p>
+</li>
+<li><p>Create a plot of the signal against time, and of the amplitude spectrum as in the previous Tasks.</p>
+</li>
+</ul>
+<p>Then answer to the following questions:</p>
+<ol>
+<li>What do you see in the frequency plot? Are there peaks? How many? Where?</li>
+<li>Does this match what you see in the time plot?</li>
+<li>Does changing the measurement time (duration) help?</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+
+<span class="c1"># Write your own code or use the template below to create your figure(s)</span>
+
+<span class="c1"># plt.figure(figsize=(12,6))</span>
+<span class="c1"># plt.subplot(211)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.title('Time signal')</span>
+<span class="c1"># plt.legend()</span>
+
+<span class="c1"># plt.subplot(212)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.loglog()</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.tight_layout()</span>
+<span class="c1"># plt.title('Amplitude spectrum')</span>
+<span class="c1"># plt.legend()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+<span class="n">T_meas</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">f_s</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">f_s</span><span class="p">)</span> <span class="c1"># ends at 4.99, length 500</span>
+
+<span class="n">A</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">f_c</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">phi</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
+
+<span class="n">A_i</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">f_i</span> <span class="o">=</span> <span class="mi">80</span>
+<span class="n">x</span> <span class="o">+=</span> <span class="n">A_i</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_i</span> <span class="o">*</span> <span class="n">t_vec</span><span class="p">)</span>
+
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">X_cont</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
+
+<span class="n">f_0</span> <span class="o">=</span> <span class="n">f_s</span> <span class="o">/</span> <span class="n">N</span>
+<span class="n">f_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">f_s</span><span class="p">,</span> <span class="n">f_0</span><span class="p">)</span>
+
+<span class="n">X_cont</span> <span class="o">=</span> <span class="n">X_cont</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+<span class="n">f_vec</span> <span class="o">=</span> <span class="n">f_vec</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t_vec</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$t \: [s]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$x(t) \: [V]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Time signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transform'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.04</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">19</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$f \: \: [Hz]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$|X(f)| \: [V]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Amplitude spectrum'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ol>
+<li>There are two peaks, one of $0.5$ at $1$ Hz and one of $0.05$ at $20$ Hz, which should be at $80$ Hz (again exact values are hard to read but should be derived from theory).</li>
+<li>Yes, there is a large amplitude sinusoid with a frequency of $1$ Hz, and a small amplitude sinusoid with a frequency of $20$ Hz on top if it (count the number of wiggles - there are $20$ wavies within 1 second). However, it does not match the input signal we created (with $1$ Hz and $80$ Hz).</li>
+<li>No, the frequency resolution would change, but not the largest frequency of the amplitude spectrum, as that one is determined by the sampling frequency $f_s$.</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>The effect seen in Task 4 is called <em>aliasing</em>, and the cause is that if your sample rate is too low for the signals you're measuring, you will not capture their oscillation period sufficiently - with at least two samples per cycle. Instead, the signal will appear at a much lower frequency, as you can see in the figure below. Here $f_c = 5$ Hz, and the signal was sampled with $f_c = 7$ Hz, and based on the discrete time samples we incorrectly conclude that there is a frequency component at $2$ Hz (at the end of Chapter 4).</p>
+<img alt="No description has been provided for this image" src="https://gitlab.com/JelleKnibbe/public-files/-/raw/main/SP_Practical/NewSlide38.png" style="margin:auto" width="800/"/>
+<img alt="No description has been provided for this image" src="https://gitlab.com/JelleKnibbe/public-files/-/raw/main/SP_Practical/NewSlide51.png" style="margin:auto" width="800/"/>
+<p>So, how fast do we need to sample to capture an $80$ Hz signal?</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 5:</b>
+<p>Repeat Task 4 with different sample rates: $110, 150, 160,$ and $200$ Hz. Plot the signal and amplitude spectrum for each one (you might want to use a loop).</p>
+<p>Then answer the following questions:</p>
+<ol>
+<li>At what frequency does the (aliased) 80 Hz signal appear in the spectrum, for the above values of $f_s$ (provide numerical answers)?</li>
+<li>Can you figure out the relationship (a simple equation) between the sample rate and the frequency of the original signal, and the frequency at which the alias appears?</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+
+<span class="c1"># Write your own code or use the template below to create your figure(s)</span>
+
+<span class="c1"># for YOUR_CODE_HERE:</span>
+
+<span class="c1"># YOUR_CODE_HERE</span>
+
+<span class="c1"># plt.figure(figsize=(12,6))</span>
+<span class="c1"># plt.suptitle(f'$f_s = {YOUR_CODE_HERE}$ Hz')</span>
+<span class="c1"># plt.subplot(211)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.legend()</span>
+
+<span class="c1"># plt.subplot(212)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.loglog()</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.tight_layout()</span>
+<span class="c1"># plt.legend()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+
+<span class="n">f_s_lst</span> <span class="o">=</span> <span class="p">[</span><span class="mi">110</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">200</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">f_s</span> <span class="ow">in</span> <span class="n">f_s_lst</span><span class="p">:</span>
+ <span class="n">T_meas</span> <span class="o">=</span> <span class="mi">5</span>
+ <span class="c1"># f_s = 100</span>
+
+ <span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">f_s</span><span class="p">)</span>
+
+ <span class="n">A</span> <span class="o">=</span> <span class="mi">1</span>
+ <span class="n">f_c</span> <span class="o">=</span> <span class="mi">1</span>
+ <span class="n">phi</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_c</span> <span class="o">*</span> <span class="n">t_vec</span> <span class="o">+</span> <span class="n">phi</span><span class="p">)</span>
+
+ <span class="n">A_i</span> <span class="o">=</span> <span class="mf">0.1</span>
+ <span class="n">f_i</span> <span class="o">=</span> <span class="mi">80</span>
+ <span class="n">x</span> <span class="o">+=</span> <span class="n">A_i</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_i</span> <span class="o">*</span> <span class="n">t_vec</span><span class="p">)</span>
+
+ <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+ <span class="n">X_cont</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
+
+ <span class="n">f_0</span> <span class="o">=</span> <span class="n">f_s</span> <span class="o">/</span> <span class="n">N</span>
+ <span class="n">f_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">f_s</span><span class="p">,</span> <span class="n">f_0</span><span class="p">)</span>
+
+ <span class="n">X_cont</span> <span class="o">=</span> <span class="n">X_cont</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+ <span class="n">f_vec</span> <span class="o">=</span> <span class="n">f_vec</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+
+ <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s1">'$f_s = </span><span class="si">{</span><span class="n">f_s</span><span class="si">}</span><span class="s1">$ Hz'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t_vec</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'signal'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$t \: [s]$'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$x(t) \: [V] $'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transform'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.04</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span> <span class="mi">19</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$f \: \: [Hz]$'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'$|X(f)| \: [V]$'</span><span class="p">)</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+ <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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V9+Kd+vHEmVkQHs3SvfJp3eV6WKeFyxwlokmsibSEcAbt8OvPyyfL9aX6F2s89S7PmDD8TCM9WqAcePuzRUIs2dPStqc+7bZ9126JDtaKjt263Twi1u3xaPmZnAAw+I57duAUWKuC9e8k0ff/wxzDz5zuaTianOnTujc+fOTh8/Z84cVKhQAdP/G2ZTo0YNbNu2DTNnzmRiSmfSYedqSShph2Khlpj67Tdxd/zECfF63jwmpsg7SetJHTwoT0oBwD//2L5n82bbbRcviumAam2IyJup9RXKdiK96WFhMgFTp8oLo2/ZwsQUeT9LgXMpZ/uKffvEDRBLMspyHkXkze7csb1hoaxhC6i3iVu3gOeeA44ds267cYOJKcq9QoUK6R2CR/HJqXy5tWPHDrRr1062rWPHjtixY4dOEZFFSIj1uWU1JSm1ed737tlumztXfjLF5DR5K2liats22/3OtomffxajpaQX7MppHUTe6MiR+3tfZiYwc6ZI+FpIp/cReRPpKA+1qUbO9hWffcYRUuR7jh2zXV3P2TZx6JC4gSEddSs9NyNSWrZsGerUqYP8+fOjYsWK6NChA9LS0mym8qWkpKBPnz7Inz8/oqKiMHPmTLRu3RrDhw/PPiYmJgbvv/8+Bg4ciIIFC6JcuXL4UjFMfPTo0ahatSrCw8NRsWJFjB8/XvMV9u6HT46Yyq2rV6+iVKlSsm2lSpVCcnIy7t69i3z58tm8Jz09HemSK7rk//4iGY1Gr/iPV2OJW6/4U1KA554LxBNPmNClixm9ewdi2zYDALEywO7d5uznubVjh/y9JpMZRiOvwilnereLTz8NwPbtBixYkIUPPwzA998HwPK7rPy9zo21a23fe+OGEcWL5y1e8n16t4mjR4FRowLx1lsmhIYCo0YFQHqf7X6LmB8+bMa1a/I2kZWVBaOR2SlyTO82YTYDQ4cGoFgxYMIEE/r2DcSmTdbzJ1f3Fd56nkva0rtdrFljwBdfBGDu3Cxs3WrA119b+wq1UbPOWrdO7fwpE0Yj73q7i9FohNlshslkgum/O0Zms341g8PDxRRnZ8THx6NXr1748MMP0a1bN1y9ehX79+9HVlYWzGZz9s8FAK+99hr+/vtvrFixAqVKlcI777yDffv2ITY2NvsYAJg+fTreffddjBkzBj///DNeeuklPPjgg6hWrRoAoECBAvj2229RunRpHDp0CC+88AIKFCiA1/9batUyfdBk5+6byWSC2WyG0WhEYGBg9nZ3t2Umpu7TlClTMHHiRJvtf/zxB8LDw3WIyHXWr1+vy+f+/HMVrFlTE2vWBGDYsP3YtKm+bP+9e/e/dGVCgvy9JpMZa9asue/vR/5Hr3YxcqSYR1S2bBw+/zwWmZnW3+W8tAnlBTgArFixGaVLc2kyco5ebeLVV1vj338L4c8/A9Cp0zn8/XcFl3xftTZx9uy/WLNGZbgukQq92kR8fH58/bUY+V+s2N9YurSVbL+r+wqeP1Fu6NUuuncX50/PPHMNN2+G4fRp18y1U2sTGzfuR0bGFZd8f7IVFBSEyMhIpKamIuO/oW5paUDZsoV1iefSpUTkz+/csadPn0ZmZibatWuHYsWKoVixYqhVqxZMJhOMRiMyMzORnJyMlJQULFiwAF999RUaN24MAJg1axZq1qyJjIyM7EEwJpMJ7dq1Q58+fQAAL774ImbOnIm1a9ciKioKAPCypKjgQw89hKFDh2LJkiV44YUXAIgBNllZWdnfUykjIwN3797Fli1bkCmZTnHHzZlAJqYAREZG4pqlcup/rl27hoiICNXRUgAwduxYjBgxIvt1cnIyoqOj0aFDB0RERLg1XncxGo1Yv3492rdvj2BpcSeNbN1qveMdElLXzZ9mQOfOXZCYCGzZYsD06QGYNy8LlSq5+WPJ6+jdLizS02ORmRmY84F5EBvbGtWqmWEyAT16BOKJJ8x46SWOFiE5vdvEc89ZT13u3Cnv1s8qWTIGHTtGIzUVmDs3AJs3G/DLL1kIDXXrx5KX0btNSKevms0PuP3zOnbsguRkMerkf/8LxIQJJrRty9EiJKd3u7BISYl0+2qSMTEN8PDD9WAyiTZRuLAZs2bx/MlV7t27h4sXL6JAgQIICwsDAAS695TYoYiICKcTUy1atEDbtm3RsmVLdOjQAQ8++CD69OmDokWLIjg4GEFBQYiIiMC5c+dgNBrx0EMPZecSIiIiUK1aNYSEhGRvCwgIQMOGDWX5hqioKKSkpGRv+/HHH/HZZ5/hzJkzSE1NRWZmJiIiIrL3h4aGIjAw0G7O4t69e8iXLx9atWqV/e8NwG4iy1WYmALQvHlzm7s/69evR/Pmze2+JzQ0FKEqZ6bBwcG6/vF1Bb1+BmkO8Phx9/61MZkM+OCDYEyYYK039fLLAdDppg55AT3ahbTm06FD7u+BDxwIQtu21poKmzcDr7yiY89PHk2vvkJae/D4cfeWykxKCkDPngH480/g7l2x7eefA9Cvn1s/lryUXm1CWgfn77/d/zd7yZJgDBhgfd25cwBrd5Jdel8bnTtnyP777S43bgSiTp1A/PuvdduMGYG8ieEiWVlZMBgMCAgIQECA6PcLFABSU/WJJzw8wOmpfAEBAVi/fj22b9+O33//HV9++SXee+897Ny5EwaDwebnkj63sBxjERISInttMBhgNpsREBCAHTt24LnnnsPEiRPRsWNHFCpUCEuWLMH06dOz32P4L3jl50hjNhgMNm3X3e3YJxNTqampOH36dPbrc+fOIS4uDkWLFkW5cuUwduxYXL58GQsWLAAghsB99tlneOONNzBw4EBs3LgRP/30E3777Te9fgS/JP1dV1stxlnh4TnPOTabgaVL5UXQr3AELnkYafFaaUHmnJQrB1y4IN9WqhSgGBhq4/vv1Qt9EnkS6Yn+9evOved+28SFC2Klpqws6zYWuSVPk9OiGPaotYuSJXNuV4o6u0QeLTdJqfvtK/74A7KkFADcvg1ERjr/2ZQ7BgOcHrWkN4PBgAceeADNmzfHq6++itjYWCxfvlx2TMWKFREcHIzdu3ejXLlyAICkpCScPHkSrVq1Uvu2qrZv347y5ctj3Lhx2dv+Vf5yeiifXJVvz549qF+/PurXFzWKRowYgfr16+Ptt98GIIqQXZD81alQoQJ+++03rF+/HrGxsZg+fTq+/vprdOzYUZf4/VFGhvyC+NYt595XvjygTPbWr69+rJJymVgiT2I2AwkJ1tfSJFVO2ra13da+fc7vy0tCmEgLGRnyEVPOatLEdptiMV5Ve/fKk1JEniYrS1wAWzh7/gQATZvKX+fPD9SunfP72FeQp1OuuOcsV54/Sdsl+a+dO3fi/fffx549e3DhwgX8+uuvuHHjBmrUqCE7rmDBgujXrx9ef/11/PXXXzhy5AgGDRqUPXrJWVWqVMGFCxewZMkSnDlzBp988olNEsxT+WRiqnXr1tlV7qVf8+fPBwDMnz8fmxRr57Zu3Rr79+9Heno6zpw5g/79+2set7+6fFncoZsyJffvbdAACFKM+1PrVJzJqHMYOnmSZ58FqlbN+bjyihI7BgPw8MPybaVLA2ozk5XtQq0NuHv4O5Gz1q8XQ/fPn3d8nFppyAYNbLc99ZTtNmfaRCYXdCUPYTQCdeoAzzyT87HKviImBjarsLZpIx+9buFMuyDyFFOnAgUL5nycsk0AtjcsgoOBbt1sj3OmTTAxRYCoE7VlyxZ06dIF1atXx3vvvYdp06ahc+fONsfOmDEDzZs3x6OPPop27drhgQceQI0aNWR1nnLy2GOP4bXXXsOwYcNQr149bN++HePHj3flj+Q2PpmYIs939y7QqhXw7ruijk1SUs7vCQoSQ2wtxo8H3n9fXvxuzx5AMnIxm/JCncgTTZ8ONGsGJCYCq1Y5956HHrI+HzQImD8fqF7duu2vv4Ddu4ESJWzfq5bEVUpMdC4OInc4cUKMgl22DFi7VlyI56RWLfnrt94C/luIBoBoH3v3ql9sONMmOJWP9Na/P/D008Dp08CxY869Rzpq8M03gW+/lZ8/HToELFpkW1C4XDmgTJk8h0zkVqtXA/XqibIHq1Y5N2JKev7UurW4pujRw7rtvfdEu6hXz/a9zvQVTEwRANSoUQPr1q3D9evXcefOHezevRtDhw4FIAbLrFixIvvYggULYtGiRUhLS0N8fDyGDBmCEydOoHLlytnHnD9/HsOHD5d9RlxcHCZMmJD9+qOPPkJCQgJSUlKwZMkSDB8+HImSE/oJEyYgLi7ODT9t3jAxRbpYsgTYuhV45x3g5Enn3lOtmqgfZfHuu+ICQ3oS1bCh+jQPaUdjD+8Akt5GjQJ27gRGj3a+oKP0xOqll4C+fcWoEovYWDFiSnp3LyAAaNlStKmc8MSK9PTii0BcnPgb7mxfIZ2KVKQIMGkSULSodVtwsBhBpRwZHxQEp4qas02QnlJTge++A376SdS1cUZAAFBXstjxpElidJR0xHnt2kBEhPycKigIeOIJ25IJRJ6ma1fgwIHc9RXSZG2LFsDYsfIRg4ULi/Mkaf8BiJpTjz6a8/dnX0G5tX//fvzwww84c+YM9u3bhz59+gAAuqndSfNB7GpIF9I7GceP53x8/vxi2oXaEPM33hCPTz6p/t6kJOC553L+DJNJFPxctUoUOfz9d7GNSGvbtzt3XKVK8mHnlsSt9O62ZSVY6cVGYiKwaZPtyZaa27fFncjLl8VF0I0bzsVG5ArSE/sTJ3I+PixMPhJKOsKqTRvxOGSI7fu6dhW1eVq3di6mEydEGzp1SiSTibQirTe4Z49z73n0UfnUJkuiqXt38ViypHWftK+4eROYMcM2iasmMRH45RfRR6xZw2ngpI+TJ51bFKNgQaBDB+trtdG4Dz4oHgsXtm5bsQI4d04+g8Oe27fF+dzhw6IGlWRdLiK7pk2bhtjYWLRr1w5paWnYunUriivnXfson1yVjzyfdFWlXbtyPj4xUdy5y8gQw2ql7XPsWNF5SO98vPYaMHMm8L//WS/MlQwG+Sip5GQx3UMyohILFjiX1CJyJXuF+cuUEQkiQIw6VNbIsSSaChYEjh4VbcZykVGnjvU4ywWKWg0GZbtYvBj44gvr65gYcVJGpAVpX+HM711Kivi9r1tXTOl4/HHrvlWrgH37gAcesG6rX1+suvfcc6I9qBU6V7aJW7dEMdxLl6zbz59Xr1dC5GrSqaR//61+jLSvOHkSqFxZlE1QeughYMcOcZPDom9fcR5Ut671/MmZEVPjxsn7ikGDgK+/zvl9RFqIigLi48XzgQOBuXPlIwaldQivXBHtx3LeJP39b9hQ1DFUu7Gn7CsOHQJGjJDXJeTsDHKkfv362Lt3r95h6IYjpkgX6enW5/YuNqQnSpbOY/x44NNP5cmsoCBxl1s6ze+DD4AtW0Ryyh5lMehbt2yHxS9bZv/9RK7kzMpflrt3AFCokEg6BQaK39vly8XwcosaNYAqVayvS5cWJ0nSZZCVFxtBQUDjxvJtS5bIX+dUeJrIlaSJKbU2UqSI/LWlr1i3Dvj4Y9FfWBQoIGobSkeEbNok7mhbkrzK+jqAmOIhtX07cPGi/ALj6NEcfxQil5AmpuydP0nPb4oWFRfMrVuLGoTKEX7NmslrEHbvLn7Ht2yxblOOmKpTx3aRAWVf8c03Dn4IIo1Jp3hnZFj7igMHRJKqZ0/r/qgooFEj+fsvXhQ3O8qWFa+VfQ9ge13x88+2i2VwJgaRfUxMkS6kQ9HVhIaqr0iWLx8wbBhQoYLj94eEiIt4e8uKBwaKejxSGRnAnTuOvy+RuzhTU0qamJJeQLdvb52S4Ujt2kB0tPW1NDFVpIj4HpaTLovcLD1O5Go5LUSjWG05W1QU8MorIoHrSESEuJiwN1WpQAExgkRKrU3wLjhpxZni+9JizdJR4/36yUeXqzEYRJuQth1pXxEcDPTubdtm2FeQJ5P2FdJpe3XriundOY0KLFtWPvJcOmIqNFTUolKu6KfWJnK6/iHHzOxsNaHXvzMTU6S5tWvFXGtHatYUU4ZcSVpfJDUV6NXLtd+f6H6dPy+K2eZEekFhL+maG9Kh6/Hxopiu2l1AIq1lZooRGDnVNKtVy3rhLa0D4gpLlwIJCeorMinxpgZpYc8eUfPPkZAQ+WgPtdqcuWVZQKZiRZEYGzPGubpTRO52+zbwww85HycdMeWKcj3SxO2//wJHjshHrdvDguj3J/i/P2R32NlqwvLvHOyKDiQXWGOKNHXiBNCli+Njhg0TyyDXqCEKBQ4c6JrPXrZMFOR8/PGc78ITaWnAADGlyJ6WLUXx2oYNxWqUR4/KR0/dr8aNRd2dChWsU6ZyGmECiKHoXKWJ3GnpUjEqw5HRo8WNhhdeAF5/HfjwQ9d89unTwN69YkENg8G5ZC0vNsjdMjPFiIykJPvHdO8OPPOMGEXbq5dYudgV3nhD9BMPP2w9f2JiijzBpEmOy3ZERwN9+oj2EBEhppi+807ePzcwUNRnu3vXmpBypq9ITMz7Z/ujwMBAFC5cGNf/q2wfHh4Ogxf+ETKZTMjIyMC9e/cQ4IEn0mazGXfu3MH169dRuHBhBKrVN3AjJqZIEzduiJX1Dh7M+dgZM6x3+JxdCtkZRYsCzz7ruu9HlBeZmWL1x9Klgbg4x8f26iUK+QOizporde0qf+1MH5Sc7PrRKUSAKCgeFZVzmwBELUGLjRtdF0OlSvIah86uXknkDmlp4uI3KclxUgoQi8FYRtYuXuy6GEJCxMW9lBdeE5KPMJtFzafoaFEjypEaNYApU8Tzp58WX67SrJn8NW9iuFdkZCQAZCenvJHZbMbdu3eRL18+j06sFS5cOPvfW0tMTJHb3bwpliKOigJefjnn4zUeNegQlzsmd+nZUxQsX7FC/Q6adAWZ/Pm1i8vZZcGZmCJX27RJTLl+4gm9I5Fz5nedd8HJXSpVEjcx7E1XkvYV9lYhdgcPvqYiH/fhhyIJO306cOqU7f7AQOtiGdLFltyNNzHcy2AwICoqCiVLloRRWijMixiNRmzZsgWtWrXSfJqcs4KDgzUfKWXBxBS5naWeVHy8mMqnpmpVsaSx3qpVk8fIDoTcZfly8fjii+r7mzcHfvlFPFeu6uJOyouN8HBxF9Cy9Dgg2oWra8ARTZ8uHn/5RV5kVm9q52fsK0gLJpNISgH2R0A98IB1BWE9E1PKNgGIkS1MYJGrjR0rHkeOVN9fsaI1YZWRoU1MgHopBGW74E2MvAsMDNQtcZJXgYGByMzMRFhYmMcmpvTkeZMbyedIl/vetUv9GL0uQqQnTCEhtiO6bt8GFiwAOncWQ+hv3tQ2PvJ9V6+qb69Vy/pcyzt+0tUwIyLEimTKgp5HjgAdOwIrV4ppulwkhVxBuvz86dO2+0uVkvcneggKEnEop4Xfvi0uloYMEYXQnVllkygnaWnW54cOqR9Tv771uZaJqYcesj4PCQGGD7c95s8/gdatRV3EnBYyIHKVatWsz7U8f5KOri1fHmjaVCSOpa5eFeUZpk8X/YaXDvwhcgsmpsjt7t2zPj92zHZ/eDgwYYJ43ratJiFlO3BA3HG5eVNcSHTuLN9/65YoqrtunehwihcXJ1hE7latGhAbK54r60C5U79+wLhx4oIiIQGYPdt2KtN774n6b927i2m6ahckRLklXZRCbRp1tWri9xEQv6NaWrNGFMxNTxd1sKpUke8/cEDUvPrqKzH1tkQJbS+IyDclJ1ufnz+vfswjj4jHggW1nfb91Vfi/OnYMXH+pDb699VXgc2bxY2WkiVdW/eKyJ6qVYHBg8XziRO1+9xSpYAvvgDmzRM3V3bssJ3et3ChWHF21CixT5rgJfJ3nMpHbpeS4nh/27ZiGderV52bn+1KdeoA06ZZXysvwNWmZ8ydC3z8sVvDIj8XGirusj31lLgwKVFCu88ODAQmT5ZvU7aL48flrz/5hG2C8i6n1VLbthUrWHboIBYN0FLnztYbFwEBObeJe/dEIkF6554ot3I6f4qJAWrWFOcqQUHaTpsrWVJ+/qRGeTPyhRdyXm2TKK/atgU6dRI3vbXuK156Sf5a2Vco28SOHW4Nh8irMDFFbie946e0cyfQsKF4rpwupAe1+eFKrJdAeWUy2d83fLgYmWE5mdEyKWVPEHsK0oCjKaE3b1pXPCpTRpt4HHFm9SWOmKK8cnT+9O23wDPPiAVjvGUxCp4/kSsEBanX3mzQQIzmLlZMvNY6KaXGmb6CtdiIBE7lI7cyGh0vb1yunHPL02vFmVjYeVBe3bplf1+RIt5zkUHkKhkZjkeHFC3qWX97uSw4uZvZ7LiuZalS8rps3sCT2jB5p4wM+7/39+5Zk1Kewpm+4s4d98dB5A2YmCK3uXNH1OEYPdr+MVoW6iTyBOPGOR4FxUU6yN8cOyaSsT/+qHckznPmYoOrL1FedO4MdOlifz/Pn8jf/PQTUKCA/ZsY0pq2nsKZmRjsK4gEJqbIbQ4fBv7913a7tI6It93tA7RdepZ8z+rVttukw829dfoPV+aj+7Vxo3qxc0/Giw1yp/R0MSVJqWJF63NvTExxxBTlxdq1jlex88TzJ2dKIbCvIBKYmCK3OXNGfXvt2tbn3nCSIj0RBBxPTSRyxGxWbxfSFb488Y5f+fLy12XK2I7s8rbEAnkOe32FJ1Mb2ajsK3ixQffr3Dn1ZH+LFtbnWq7A5yxlX6FsE95wzkeeS62vkCZoPTExpXYTIyZG/pp9BZHAxBS5lMkEdOwI9Olj/2KjVy9tY8qttWuBnj2BqVOBZs2AESPk+1k3hHJr/nyxOte2bUBamu3+ypWtz5s00Swsp731liiy+9VXQKNGwLvvOreCJZE916+LmxQffgicPWu7PyAAeP118Vx5cespPv0UePFF4LXXgMceA554Qr6fbYJya9Qo0QccPqy+v1kz63NnppNqbc0a4MkngdmzgdhYYMoU+X4mpii3duwQN+9Wr1a/rqhc2bqIUo8e2sbmjCZNgJdfFitYPvIIMHKk7Wqt7CuIBK61RC51+rR1+HnPnurH9O8PREfLR4l4kk6dxBcgThI3bZLv550Nyq0BA8Rju3bq+ytXFnV2du8GHn9cu7icFREB/PCDeP788+Lxgw+AGzesxyQmesZqaeQdPv4YOHIEGDNGPorWonx5YNIkcQJv+XvsaYYNk7+eNk3+mn0F5db06eJRmdCxqF4d2LxZjFAtWlS7uJxVsyawbJl4/uKLtqsKeuKIYPJsTz0FXLkCdO2qvr9yZXGTYNUqcQPN0xgMwCefiOcjR4pHZZzsK4gEjpgil5Iu3/rPP7b7o6LEyVSPHkC9epqFlSfKu5KJicDy5WIkVVaWLiGRl7JXn6x2bXHB8dxz3nNHWTli6tQpYPBgYP9+XcIhLyOtE6I2Yqp2bSA0FBg0yHsSnmp9xYwZ1osSImft22e7zWAAatQAWrUSI9O9QYEC8td37wI7d4p27WjFQSILR6u1AqKvKFlS3DRT/r55KuX50/Xr4ppi1SpdwiHyGBwxRS4l7UAuXJDvGzRIfHkbtQ5kwABRa6pSJdEheuLwYfJ8desCL70kVl/yNsqCnqNHAydPAgkJ4s7mo4+KtkGkRroIhnKp7ClTvPNvqrKv2LED+O47kVCIjhbLmLdqpUto5AUcLazSpYs4f5IulOENAlRuf/ftK/qK4sXFDZnevUUSmkhNWJh6cio0VIxSffZZ7WPKK+VNjLlzRZtYtw5ITRVTE5XT/Yj8ARNT5FKO7mwMGeKZ9XNyouxAEhKszy1TOTZv5gUH5V7jxmK6gzdSFuY9eVI8rlghvpo0EXfGidSEhNjf9/rrQGCgdrG4irKvsLQJs9laf8podG6VJvI/js6f1GqYeStLu/joI/F47pyoW0ikxt7q3RERttOpvYXyJoalTRw7Jmr0AlzpmPwTp/KRSzk6sfLGpY0B54YGc/oS3Y/wcL0jcJ9du/SOgDyZcpSUlDcmpQDbiw01XNWV7PHF8ydnLF+udwTkyewlpry5XpnaSn1ExMQUuZiy0KVUwYLaxeFKakPR7+cYIiVHUzc8He/mUV446iu8lTOrpLHILdnji+dPzuD5EzkinfYtdfeutnG4En/nidSxaZDLXL4sCiDb48t3/NjJkJqMDGDvXvv7vfmOHxNTdL9OnADi4/WOwvU4YoruV1KS7chr6UgRJqbI35hMwMGD9hNQ0sWWfJHJpHcERNpjd0Auce+eKOT83nvy7VWrWp/nz69tTFpKS9M7AvJEY8YAjRrJt0lHVXjzHb/GjfWOgLzR0aOi4PEvv+gdies5c/OFI6ZIzaOPAv37y7c1bGh97s03AnIq2M7EFKlZtAiIjbXWX/Il5crlfExqqvvjIPI07A7IJc6cAW7dst3+yCPW59588rFpk0gytGwpXrdrJ9/Piw1So1b8u1Il63PL75M3eu89YNQo4I03xGtlmyBSs3u3+vbnnhOP3lwYPDAQWLAAmDFDJKBDQoAHH5Qfw76ClEwm9XZRu7b1ubetxie1aRMwYoRYfQ+w7SsMBs1DIi+gVqcyOBho00Y879pV23hcqWNHUfB/6lTxulIloEIF+THsK8gfefEpIHmSc+fUtz/6KNCiBRAZqW08rvbQQ+Lr3DkxDeXiReDPP6372YGQmrNnbbdVqgQsXgxs3AgMHKh9TK5SqJA4qcrKEnf227UTy39L7+ybzbzoIDm1NgEAs2YBDzwAPPywpuG4nCXB1qqVmGry7bfA1q3W/ewrSCk+HkhPt91eqZL43bl6VT763NtUqQJMny5uXnbvDpQvLz9/IlKj1lfExABLlwJLlgC9emkekssYDMD48eJ53bpAxYrA00/Lr6USE50bWUXkS5iYojw5elScfNu72KhUyfsvNKQqVBBfS5fKt/NigyxSU4G//waaNxcXFEqVKokT9SpVtI/NHQIDgZ49xfNCheRtIS3NuVUtyfdt2yYuKtRuYhQvDhQtCrzwguZhuY1lGpZyyiL7CrK4cgU4fdr+aPJKlbx7VK1S0aJAjx5ihL0UpyyRRWamuGnXtKl6X1GpElCsGDB0qPaxuUuHDuJRWaOQfQX5Iyam6L5lZgK1aonnaiM/IiKAsmW1jUkr7EDInt69gV9/BZ58Un2/pc34ovz55W0hMZGJKRILAFimtD3wgO1+6ZQlX8O+guypVk0kZQYMUN/vq30F2wTZM2sW8PrrIiGrlphiX0Hk27y46g/pTbq08ZYt8n3PPgusWSNGU/gitQ4kMVHcASX/9uuv4vHnn+XbK1YEvvlG3DH2F7dvi8Klvr56Djm2fbv1uXJ07VtvAV9+qW08WlLrK+LjRdsg/2YZKTRvnnx7mzai//Dm6XuOFCokf52YKFawVY6kIv/z7bficds221WLv/pK1Hr1VWp9xfnz3r1IDlFu+Wxi6vPPP0dMTAzCwsLQtGlT7FKrovef+fPnw2AwyL7CwsI0jNY7paRYn58+Ld/39NPqd8Z9hbIDuX1bTFmsWhW4cUOXkMjD1aolRhYGB+sdifsoV4764AMxKuCLL/SJhzyDdNn7+Hj5vqFDfWdaqxplX3HihLjrryyKTmTRujXwxBN6R+E+ygUO0tPFqLHKleVJbPI/4eH29z3/vJjG56uUfcXq1aJNvPSSLuEQ6cInE1M//vgjRowYgXfeeQf79u1DbGwsOnbsiOvXr9t9T0REBOLj47O//v33Xw0j9k7SxJRSwYLaxaGHIkXkr0+cAPbvFzV1vvpKrMxEJOUPuW5lYuqHH8TjH38A06YBDv4Ekw9zNHLW3/qK9etFEegjR8TogA0b9ImLPJevtwk1lr5i0SJg5kwxgor8j/Qmhr9RJuWWLROLy+zcKaY4Hj2qS1hEmvLJGlMzZszA4MGDMeC/iftz5szBb7/9hm+//RZj7IwDNRgMiPT2peM05s+JKeVQdOkF+bhx4jE2VnwRAbbD0n2RySR/bWkXv/0mvlavFkuHk39xVNzY0R1yX6C8Cy7tKwYNst1G/kFtFT4LXz9/UmNpA5bRtZmZotYQ+Rdf7w8cUfYDltfHjwOvvaZ+DJGv8bkRUxkZGdi7dy/atWuXvS0gIADt2rXDjh077L4vNTUV5cuXR3R0NLp164YjR45oEa5Xc5SYiojQLg49ODMd68IF98dB3sMf6gTkdNK0ebM2cZBncdRXGAzaxaEHZWJKDWuw+R9HbYILRoiVbcn/hIbqHYF+mHQi8sERUwkJCcjKykKpUqVk20uVKoXjx4+rvqdatWr49ttvUbduXSQlJWHatGlo0aIFjhw5grJ2lpVLT09HuuSWV/J/lcCNRiOMRqOLfhptWeJ2Nv7btw2Q/gqFh5tx5464yggLM8JL/xlywXF2ymTKhNHInsbb5bZd2Pu9uHPHBKMxy0VReabAwCAAjjMN3vr3kaxy31cEAFCfz+frvw/iJobjvuLmTSOKFtUkHHKT3LaJmzcB6e+F9PwpPd0fzh0ctwmDwff7S3+Q23aRlhYIe2MmfL2vMJns95MWvv5v4A9yf03hWdwdt88lpu5H8+bN0bx58+zXLVq0QI0aNTB37lxMmjRJ9T1TpkzBxIkTbbb/8ccfCPfysajr16/P8Zjk5GD88UcMgJrZ28qUScSpU6KgxvbtvyM01LdPKtq2rYc9eyKRlKR+i2fbtv0AuEyfr8ipXWRlAfHxBQC0zd5mMJhhNouLjZs3E7FmzVZ3hqi7//2vGCZPbobAQDPu3FG/8FizZo3GUZG7ONNX3LiRD3v3VgdQLntbQIDpv5Nw3/99yMoCKlR4CAkJ4UhJCVE9ZsWKTYiMvKNxZOQOzrSJ9PQA7N9fEkDT7G0lSybj/HlRI2Dv3kMoXNi3h1w//3wFLFxYE1lZBhiNthfj16/HY82aPTpERu6QU7swm4ErVwrg/PmGAAqrHuPrfUXVqqEoWPBhmM1Aaqp6X+Hr/wb+xJm+whPduePecxWD2exbgwczMjIQHh6OZcuWoXv37tnb+/Xrh8TERKxcudKp79OjRw8EBQXhB0tFRgW1EVPR0dFISEhAhJfOYzMajVi/fj3at2+PYAdz1cxmoGrVIPz7r3xkxLhxWfj11wAULGjGxo1ZPj9FAxBTMFq2DMT+/QY0b27G9u3WOz2ff56FwYNNDt5N3sDZdjFlSgDeeUd+gl22rBnDh5swcWIAfv89C40a+dSfW1WZmcC8eQEYOjQQDz5owtat8rufGRneeZeIrJxtE5cuAdWqBcFolHcGc+Zk4s03A/HssyZMm+b7fyNNJlFnq3z5IAQGAjExwMGD1n+TnTuNqF9fv/go75xtEwDw5JOB+PVX+d/Fxx4zoWBBYONGAw4cyHRqCqi3y8wEhg0LxLffBqBlSxO2bbP+mzz1lAmLF/v2zU1/4Gy7WLvWgG7dbMdKLFiQiZdeCsTHH2ehb1//OH+KizOgRYsgNGxowpEjBty7Z+0r0tONfnFt5cty01d4ouTkZBQvXhxJSUluyXf43IipkJAQNGzYEBs2bMhOTJlMJmzYsAHDhg1z6ntkZWXh0KFD6NKli91jQkNDEaoyGTo4ONgrf9GkcvoZrl4F1BYtrF49EO++KwrJGww+V75MVXAwsHQpcOWKOKGULnWcmhqI4GDHw3LJe+TULvao3NytWNGAkSMDMXy4ZZqb7wsOFss6lykD1K0bgIoVlfu9++8jWeXUJg4fhuqU7tatg3D9OhAYGIicpi74itBQYMsW8Th2LHDwoHVfamqwU3ULyfM5cw64e7fttsqVAzB9uhhhFxjoH78MwcHAhx8CTz0FGI0B2LbNus9gCEBwsH+cR/qDnNrF3r222woUAJ59Ngi9e/vX+VPz5mL11po1A9CwobjmskhPD/bLxRF8kbfmC9wds0+29BEjRqBfv35o1KgRmjRpglmzZiEtLS17lb6+ffuiTJkymDJlCgDg3XffRbNmzVC5cmUkJiZi6tSp+Pfff/H888/r+WN4rHPn1LdXrAgE+OF5RMWK4mvfPvn2xERdwiGdqLULS1Im0D+uvbOFhADduvlHwXeyT61NGAxA+fL+1yYAoEED8ahc1ZV9hf+4e1d+oWnhr31F8eJA587AVsUsd0fF4cn32Dt/Mhj8r00AgGX9rsKF5X8vEhP9c9VO8h8+mZh6+umncePGDbz99tu4evUq6tWrh3Xr1mUXRL9w4QICJBmU27dvY/Dgwbh69SqKFCmChg0bYvv27ahZs6a9j/Br9hJTFSpoG4enUQ6958WG/zCb1duFv7eJsDDbbWaz76/ERoJamyhdWv33wp8o+4qkJF3CIB2cP6++3d/7Cp4/+TeeP6lTaxfR0XpEQqQNn0xMAcCwYcPsTt3btGmT7PXMmTMxc+ZMDaLybjNmiM4jMtJ236uvAlFR2sfkSXgX3P8cOABMnAgMHw6kpcn3degA9OmjS1geQy0BdecOkD+/9rGQNkwm4KWXgDp1bC82ihQBxo/XJy5Pwotw/7NyJbB4MfDEE7b7uncH2rTRPCSPwvMn/5OQAPzvf8DAgbZ9xQMPAC+/rE9cnoTtgvyNzyamyPVGjhSPtWrJt8+ZA7zwgvbxeBreBfc/nTqJYdbLl8u3N2gA/P67PjF5usREJqZ82datwJdfiud168r3rV8PNGyofUyehokp/2NZi+fXX+XbhwwB5s7VPByPw/Mn//P226JG69Kltjextm7lyGrANjHFdkG+zg8rAtH9yMiwPj9yRL6P850FtYuN2bOBSZP0iIa0oFYrBADCw7WNw5v88w/Qv7/6Agrk/aR1xU6elO9jXyEo+4qEBFEQfdEiXcIhDSnr7rFNCAUKyF8nJgLr1onRl5IFsMmHXL5sfa5cH55JKUG5xtbZsyKZ/fff+sRD5G4cMUVOcVSIkidWgvJi4/hxMUwZEBfinBfuP6SJXJJ78UVxIV6uHPDuu3pHQ64mvaC4d0++j32FoLwL/tNPok2UKsXpv/6GbUJQLpxz9y7w3HOiXXTuDDz2mD5xkfvwBl7OlAm7SZNEm7hyBVi9Wp+YiNyJI6bIKUxM5UyZmLp1y/p8yhSxLDL5hzt39I7AcyUkiMetW8Xd8EuX9I2HXIt9Rc6UiSlLm0hIAMaMAZYt0z4m0gfbhH2WdrF4sajjyBs+voVT+nNmMslfW9rEyZPixvfu3drHROROHDFFTnF0sRERoV0cnszRCebs2eLx6aeBmBhNwiENhIerJ6GYmMrZpk3i69gx8Ui+wVFfwQsRISREfXtWlvUGhvJOOXkvR/+XygtPsvXjj+KxcmXAzppG5IWCg/WOwPPZ+/tw6pT4mj2bfQX5Fo6YIqcoLzaky33zjp+gHIquJjXV/XGQduwNRVfWEfFnlSo53r9zpzZxkDYcJaZYN0RQ1tMh36ac0irFmxjOU67cRt6N58M5K1dO7wiItMXEFDlFebHRoIH1ubI4nz/7/HNg0CD7+9kR+xZ7v/ulSmkbhyf79VexRLq91dgCA7WNh9xL2VeUKKFPHJ6sQQOxFHrPnvaPMRq1i4fcS9kmihWzPs+XT9tYPNmaNUCPHkCZMur72Vf4Fkc3MUh4800x06J9e70jIdIGE1OUoytXbEc11K4tTqz79mVRb6n//Q/4+mtrYqJiRfl+LgvuG4xGYPt2+dK9gYFiFaEHHgC+/16/2DxNjRrAzz8DXbqI18oRVM6MNCTvcOwYcOKEfNvbbwOdOgEffaRPTJ7IYAA++QSYOlW8Dg+3TWZzWXDfkJwMbNwo31ahgriJ9fDDYjEIEjp3FgsB1KkjXiv7Co649A1mM7Brl7i2kPr5Z3H+tHKlPnF5okKFgCVLRD1OwPaagsjXsMYU5ejhh20vNipUEEVaSd2PPwLXrokT0rlzrdt5seEbPv0UGDlSvq1cOaBjR/FFtl55RSwQUL+++JtiwbvgviEhQfzfKpd2r16ddWHsKVdOXHRERQEjRog+wyIpCSheXL/YyDVefBH44Qf5tgoVxE0sy6q9JDdjhnUVPum/EWvp+IbNm4E2bWy3P/ywGF1Ntrp2BT7+WJxfVq+udzRE7sPEFDmUlmablALEiRXZ99BD4nHvXvl2jpjyDfv22W5jm3CseHFx8X3mjHw7R0z5hqNHbZNSANtFTp5+WjwqV+pjX+Eb2FfkXo0a4stS9NwiOVmfeMi11NpE4cK2K1uTVVCQuLkHiLq+0mmQJhPPo8h38FeZHPr3X/XtPLFyjrKj5cWGbzh/3nYb24RzlG2CJ1S+Qa1NBASweKuz2Ff4HrNZ/RyKfYVz2CZ8E8+f8kbZLli7lnwJLwnIIbUOJCKCQ0mdpexAOJXPN6i1i6ZNNQ/DKylHhnC5dN+gtmJWgwZcEtxZ7Ct8z9Wr6ivysa9wDkcR+ia1voJtwnnsK8iXMTFFqo4cAX77zfYC/NVXxdS+iAhdwvI6PLHyHWlpwOLFog6MtGhnqVKiTTz/vH6xeZMgxQTypCTWDvFmf/0F7Nlj21fMnm1b9JnsY1/hO+LjRe2wU6fk2zt0AM6eFbXYKGccMeU7zGZg2TLRTyj7iq1bxWIQ5Bz2FeTLWGOKVNWuLR5bt5ZvL1sWiIzUPByvxTsbvmPMmADMnSumJkkTKQULAlWr6heXt8vKAu7cAfLn1zsSyq3r162F7Fu1ku+rXl20DXIO+wrf0aoVcPo0EBsr3168OKcs5YbyApxtwnv98osBvXqJ5wUKyPfVr8+RtbmhXDCG7YJ8CUdMkUObNslf80Ijd9Tu+O3cKb7Iu3z/vfhzeeGCfHtGhg7B+Jh//xWj0fhv6V1u3LA+j4uT72NfkTtqfcXatbajbsjznT4tHg8ckG9nm8gdtTZx5owYzU/eZds2Q/ZzZU2k8HCNg/FyyvIHCQlioYCEBH3iIXIll42YWrVqVa7f0759e+TLl89VIZCLOJpWwxOr3FHe8Tt+XNxNDQkRow346+89wsPFyB4ltW2UO089BRw7JlZ1GzBA72jIWdJEonLFLPYVuaOcHr90KfDuu0CzZsCOHfrERK7FNpE7YWHy17dvA+3bixpFBw8CderoExflnqNzXYPB/j6ylZUlf/3mm+L86eWXOSWSvJ/LElPdu3fP1fEGgwGnTp1CxYoVXRUCuYijC22eWOWO8o7f2bPiMSNDnFyVLct6Xd4if371O1JMTOXdsWPi8exZ4PJloEwZfeMh56Sk2L+iYF+RO8rpGZY2cf68KKJdrBinu3g71tLLHWXCIj3dWjj75ElR37FkSe3jotzjqCjXUY6YkvYVly6J8ycm+8hbuXQqX3x8PEwmk1Nf4fwr5bFSUuzv48VG7igTU1K1agElSnBVMm9h744fE1OuM3mySNb+8ovekZAz2Fe4jr1+4OpVICoKaNdO23jI9dLS9I7Adzz1lEhM/fuv3pGQM5SjfOj+2esrfv0ViI4Gxo7VNh4iV3JZYqpfv34wGo1OH//ss88igkNFPJLyYkM6nDqAVclyJaf8a0YGCxd6i/Bw3u52FUvBbHveekubOChvHCWmWMw+dywLjtizZYs2cZD78CaG661cqXcE5AxlXSm6f926Od7/4YfaxEHkDi5LM8ybNw/16tXDnDlzYHZivPLs2bNRvHhxV308uZCyA6le3fpcWTOJHDMYRH2QRYvsH8PElHdr0kTvCLzP0qXAvHmiXogaJsC9g7KvqFvX+pxTCXKnYUNg+XJeVHg75WgG6RRN6bkUOefUKXH+VLas+n72Fd5BOe27aFGdAvEBo0YB330HvPii3pEQuZ5L/6SPHDkSb7zxBurXr4+tW7e68luThpQdSIUKwLJlwNSptssfU86aNQN69LCOnlKeYDEx5R2U7WLvXnGCsHSpTgF5saJFgf79gapVxevoaPl+Zb0d8kzKNvH006KfWLZMp4C8XPfuQKdO4nmRIrYjblmjyPMpk7XR0eLm1JgxwPDhuoTk1SpXBnr3ttYdVPYVTEx5B+Xo2m+/BUaPBnbt0icebxYSAvTtCzRuLF4r2wSRN3NZ8XMAGDt2LPr3748333wTbdq0QY8ePTBt2jSUYSVbr/H77+Xx/ffyq8KYGODJJ/WJx1cEB4uLtbt3gYULRYFCi8RE3cIiJ5w6BUya1BSnTlkvwvPnB+rXBxo00DEwHzBmjPj7UqGCqBliwcSU5/v669pYvdq2r+jdW594fEXdusA33wA1awI9e8qnf925wymSniwurgQmTJCfVsfEiJtTzZrpE5Ov+OILYPdu4No14J13rNvT0/WLiXKWlgZ88EFj/POPPINYs2bOU9LIsd69gZs3gTZtrEkqIm/n8nsNUVFRmDdvHnbu3IlLly6hWrVqmDx5MtLZe3iF2bPrITXVdsQU5V3nzsATT9gWROeIKc+2aFEA9u6NlG2rUIFTlVyhbFkx6kx574KJKc926RKwenUlm+3sK1xj4ECRyGBf4V1WraqEuDh5xxATo08svqZBA+CFF8RIQim2Cc+2fr0B//xTWrbNYADKldMpIB8SFga8/nrO9QmJvInbBsE2bNgQW7duxTfffINvvvkGNWrUwPLly931ceQCd++qb+eJlWsp63RxxJRn+/df2wwU24RrKdsEp2d4NrU2AbBduBr7Cu9y/brtaidsE67FNuFd1PqK0qWB0FAdgvFRYWH89yTf4fbT/6effhrHjx/HoEGD0K9fP7S3V+2WdGdv2d0aNbSNw9fxLrh3UWsXtWppH4cvU7YJrl7l2c6ft91WtKhYvp1ch32F9zCb1RNT7Ctci23Cu/D8SRvKdpGRoUsYRHnm0hpTUhkZGTh+/DgOHz6c/ZUvXz5s3LjRXR9JeaS8s/Hgg8DYsaL4JLkO7/h5F2W7WLgQeOQRnYLxUWwT3kXZJnr3BkaM4Eg3V2O78B7XrwMZGdY5yNHRwEcfiYL25DrKC3C2Cc92/ry8rxg3Dhg8WKdgfJiy9mBSElCihD6xEOWFSxNTEydOzE5CnTlzBpmZmShUqBBq166NunXrokuXLqgrXU+aPIryYqNZM1EXiVyLd/w8X3y8WDFm8GDg8mXr9vz5gT599IvLV+XLJ3/NNuGZPvgAyMqy7Su6dQMaNtQpKB/GvsLz7d4NfPIJ0LWrvE1UrQo884xOQfkwZbKWbcLzZGYCw4aJotwXLsjbxcCBQPnyOgXmw0wm+WsmpjzP0qXAn38CM2fqHYlnc2liaunSpahTpw769u2LOnXqoG7duijHCndeQzk9o2BBXcLwebwL7vleeUWsovj99wBgPbGyV4eN8kZZSD4lRSRAWATdc6SliRG0AFCzpvw/jH2Fe7Cv8HxNmojHhQvlp9NsE+7BEVOe78cfgblzxZfybxjbhXsoE1NsF56nZ0/xWLduAMqW1TcWT+bSxNThw4dd+e1IY8q74OxA3EN5YnX7NjBmDBAVBbz6qi4hkcLJk+rblZ0/uc+KFSI5OHu2bZsh7aWkWJ8fPy7fx77CPZQjCS9cAAYNEtPDunbVJSRyknJqDbmG2ijCuXOBo0eBWbO4Wq4nuHXL+jwpidcVWlCem+7dC0ybJqZO1qmjT0yk7uxZMDHlgNtqTJH3URYpZAfiHso7SH/8Afzyixgd8vLLrNPiCXhRob/+/YHUVKBdO3ExTvqSJqZMJl5saEF5sfHpp6JNHD7MxJSnMxr1jsA3Kf/WXLkCDB0qRtgOHAjExuoTF1mF264BkI2rx7lHVpb89ahRoq8oVgz4/HN9YiJ1aWl6R+DZXHYJfPDgQZhyMZzgyJEjyMzMdNXHkwsoR0wVKKBTID5OeccvNVU8ZmUBK1cCcXFaR0RKjk6sSBuWdnH4sJibrzzxIm1JE1NKTEy5h/KUytImEhLEaMJLl7SPiZzDlUXdQ3nj7t49a9+webOo4UL6ctRXc0Sbe9jrKy5cAJYscdx/k/tJ/3/u3GEjcMRlian69evj5s2bTh/fvHlzXLhwwVUfTy6wfXsmJk7cnv2aeUP3cJTwe+IJoH597WIhdRwx5TlmzRJz87/+Wu9I/BsTU9qzN4X17FmgRw9O0fBkvCuuvVdfBdq3B06d0jsS/8YkiPaio9W3r14N9OrFUed6syQKAfYNOXHZVD6z2Yzx48cj3MmhBhkZGa76aHKR6GggNvZG9uv0dB2D8WElSwIvvAAcOwZs2aJ+jNEIBAdrGxdZmc16R+B/1qwRq74dPSpGhCj9+qtoN6QP5cVGgQIZSE0NAcDElLsMHgysWyfaw969tvtZ4NZz8cae+8yaBfz+O7B2rfr+kyeBKlU0DYkkmJjS3vffi/Oj+Hj1xOzSpdrHRFbSNpGcrF8c3sBlI6ZatWqFEydOYP/+/U59NW/eHPmUlT1d6PPPP0dMTAzCwsLQtGlT7Nq1y+HxS5cuRfXq1REWFoY6depgzZo1bovN0/XubUJkpBi9Q+4xZw6wcKH9/fzDpS/liVWvXscQHm7GvHn6xOMPOncWUzEaNlTfzxX69KVsE40aXUPNmma0bMm6Ie6SP79ITL35pt6RkDMMBjMmTcpCgQLAjBl6R+O7Xn1V3MgoVkx9P+t06kvZVzz55EkULmzGa6/pE48/qF5dnD899ZTekZAaaZu4do1T+Rxx2YipTZs2uepb5dmPP/6IESNGYM6cOWjatClmzZqFjh074sSJEyhZsqTN8du3b0evXr0wZcoUPProo1i8eDG6d++Offv2oXbt2jr8BPqaPz8LAQEBvBB0s8hIcVcvXz5xIiWtLZWYaP+ki9zLbLY9sWrf/l98+21lhIVxGJu7PfQQsHGjOMH64Qfrdv490o9am4iKSsW6dZkICQlm3RA3a9xY9BNPPgksXszVQT2F8v+haNF7GD06CGPHBvLvlQYeegjYuhVo1Eg+eopTZfSj1lfUqHETCxdW4PmTBlq1EiPP+/YFvvtO72jIQp6Y0i8Ob+CT9xVmzJiBwYMHY8CAAahZsybmzJmD8PBwfPvtt6rHf/zxx+jUqRNef/111KhRA5MmTUKDBg3w2WefaRy55+BJlfsFB4tl13fvBkqUkO9LStInJn+3f7/4v5BOmwkNNaNw4XS2CY2MHStGDD7+uHw7//31YTYDHTrYTqMsWfIuAgJYzFYL0dHiZsX8+UBEhHwfpx3rY+FCICxMvq1kSVHxnH+rtLFsmSjurKy1xvMnfSQmAhUrAl99Jd9eqtRdtgmNdOokFl4YP17vSMji9deBJk2srxMSuJiPIz6XmMrIyMDevXvRrl277G0BAQFo164dduzYofqeHTt2yI4HgI4dO9o9nshVAgKAkBDbIrc8sdLHunWAcg2HcuU4NUBrYWG2bYIJEH3cuqW+0pXlIpy0ERIiEh72VnUlbS1fLmpBSrFNaMtgUO8rWHtNHzt3AufP224vUYLtQktqbYL089NP8tcmkwHJyax/YI/LpvJ5ioSEBGRlZaFUqVKy7aVKlcLx48dV33P16lXV469evWr3c9LT05EuqQ6e/F9RIKPRCKPybMVLWOL21vi9WcGCgZDmiRMSMmE08la41s6eDQAgv7VXrpyYr8F2oa38+Q2QdlEpKSYYjbzNpLXTpwHAdgpGyZJ32CZ0EBERBMCapU1IMNqM3CH3O3dO/v8AsE3opUABeb9961YWjEbOd9XamTO250/Fi5sRFpbFdqExUcJZ3m/z/0B7RiNw6ZJtX5GYGOq1/x/ujtttiSmj0YirV6/izp07KFGiBIoWLequj9LFlClTMHHiRJvtf/zxh9MrE3qq9evX6x2C37l1qyYA6zIyW7ceREjIRf0C8lN79jQHIK9DFx19DADbhdYuX84PwDqS9dy5RKxZs1W/gPzU9u1RAJrItkVHJ6No0btsEzrIynoAQPHs17/+uhXly3MZLK2dPt0ZQEj268BAE+rUScD69eo3QMl9zp0rA6BR9usDB/7FmjWH9AvIT23cWANAVdm2WrUuAeD5kx4CArrCZLLe8PbnRb30cu1aOEym9jbbExNDvbZN3Lnj3hGQLk1MpaSkYOHChViyZAl27dqFjIwMmM1mGAwGlC1bFh06dMCQIUPQuHFjV36sTPHixREYGIhriupi165dQ2RkpOp7IiMjc3U8AIwdOxYjRozIfp2cnIzo6Gh06NABEcoiEF7CaDRi/fr1aN++PYKDWaRQS3FxAVixwvq6fPlYdOlSx+7x5B6vvy7/k3j1qhEFC1bE+vVn2C40du0aMHSo9XVAQBF06dJFv4D81IkT8nmsH3yQhZdeCsJff4FtQgdffx2II0esr2NjW6FFC46u1VJyMpCSYv29r17djG3b7mHbtgS2CV0YMHOm9VWRIjHo0iVav3D81OLF8tFSa9dm4sEHi+HPP9lX6MFslo/SefjhLhxdq7FNm+T/Bw8+aMKCBfewb98Nr20TyW5eNt5liakZM2bgvffeQ6VKldC1a1e8+eabKF26NPLly4dbt27h8OHD2Lp1Kzp06ICmTZvi008/RZUqVXL+xrkUEhKChg0bYsOGDejevTsAwGQyYcOGDRg2bJjqe5o3b44NGzZg+PDh2dvWr1+P5s2b2/2c0NBQhKqskR0cHOyVv2hSvvAzeBvlCnypqYEIDma1SC2cOQN07AgMHy4KqUqVLBmMzEzxnO1CW8WLy18nJRn476+hvn2Bc+cA5cK0xYsHIl8+8f/ANqE95eDz1NQg8L9AG2vWiGS55J4kACAiwoCICLYJvSj7iuTkAAQHszCkFu7dA1q2FMWdledPUVFBCAkRSXO2C+0pF8a4cycYBQvqE4u/mTpVLALQt698e926AShTJhgHDnhvm3B3zC5LTO3evRtbtmxBrVq1VPc3adIEAwcOxJw5czBv3jxs3brVLYkpABgxYgT69euHRo0aoUmTJpg1axbS0tIwYMAAAEDfvn1RpkwZTJkyBQDw6quv4qGHHsL06dPxyCOPYMmSJdizZw++/PJLt8RHpFSokPw1i3dqZ9w4kZx6+WXbfSy4rR/lnT0uCKAdsxn4/nvx/OBB+T6e2OorJET+mu1CO488Ih5feUW+vUAB7WMhK+X5E9uEdtavF6sY790LKCeZsK/wLElJgKKcMrnJG2+IR+XqiGwTOXPZLYUffvjBblJKKjQ0FC+++CIGDhzoqo+28fTTT2PatGl4++23Ua9ePcTFxWHdunXZBc4vXLiA+Pj47ONbtGiBxYsX48svv0RsbCyWLVuGFStWoLbyVjGRmyhX0LhwQdyB+t//dAnHr3hp/UG/k5YGTJgAVK4MOFiXglxAWkJAOWqbJ1b6Ui4z/fffok3MmaNPPMQ+RG/K86fbt4GnngLatEH2iGdyj0DJwH5lv8y+wrPMnw9ERwNbWapTN2wTOXNL8fMWLVpg3bp1utZaGjZsmN2pe5s2bbLZ1qNHD/To0cPNURGpU97xW7ECMJmAY8eAL77QJSS/kT+/3hGQsyZNEu1i0ybgmWf0jsZ3pTiopc0TK30pE1Nz5og28dNPwIsv6hOTv3NzLVjKgfL86fBh60jP8+dF4pbcw+Rg8UP2FZ7lww/F/9evvwIPPqh3NP6JbSJnbpmE/c8//+DevXs225OTkzF69Gh3fCSRV1NO2bV09qmpwLBhwIIF2sfkL7x8EU2/YmkXCxcCgwcDd+/qG4+vYmLKcykvBC2vz5wB+vfn3XA9pKXpHYF/U06llLaRiRPFSFtyD0d9hUoZXtKRpV1s2SJqH/37r77x+CNl3S+y5dLE1FNPPYUPPvgABoMB169ft9mflpaGadOmufIjiXxCiRL2933+OdCvn3ax+BvWkfI+v/0GfP018Nlnekfim5QXG9I2wsSUvho2VN9+4QLw3XdAq1baxuNP7K1oxRFT+nLUhy9cKJJTvAh3D0eJKZ5b6evhh9W379wpakj26aNtPP7C0ShC9hU5c+lUvnLlymH16tUwm82IjY1FsWLFEBsbi9jYWNSrVw8nTpxAVFSUKz+SyCdUrixWcDh9Wgy3Je04OrEifR08CKxcCcybB5w9a7v/4kXtY/IHyjZRsyZw5Ih4ni+f9vGQ1f/+J05uT5zgSFqt5c8vViFTKltW+1hIbt064NAh4PXX1fenpmobj79Q9hWhoUB6uj6xkNyiRcDcucDmzcBff9nuj4vTPCS/oBxBK20TXCgjZy5NTM2YMQMAEBISgr///htXrlzB/v37ERcXh+XLl8NkMuGjjz5y5UcS+YznnweOHrUmpoKD5UVV09M5NNodlCdW8+YBS5bYLglO2qtTR3ytWycSU8o2EcAVwd1C2Sbq1AEefVSccEVFsdiznkJCgDffFEmpBQts2wS5j3LK/erVwKefAp98ok88ZNWxI9Chg1hlNyPDtl3wBpR7KP9d33lHrNBnb7QOaScyUvx/vPyySEzx/EkbyjZRvrxYpe/XXwE3rvvmM9xS/DwtLQ3B//Xg3bp1c8dHEPmkatWAbt2AcuXEMrzHj1v3JSUBJUvqF5uvMZuBf/4BLl2Sb2/VStRqIc/x/PNieHTTpsCsWdbt0hWByDUuXQJ27JBvi4kBpkzRJRyyo1MnoHFjMR1j+HC9o/FtGRli+svt29ZtRYsCjzwivgAmBz2BwQC8+qo4bwoOBn75xbovKUm/uHzV4cNigR6pqlWBsWP1iYfUPfOMWL31ySeBt96ybuf5k+slJoprN6mYGGDQIPEFsK/IicvypRcuXMh+Hqy8raTi8uXLrvpoIp8RGChW5PvkE9slkBMTdQjIh23aBLRoAezbZ90WEMBpGZ6of39g+3agbl35dhaSdL02bYD335dvK19en1jIvpIlgV27gFdeAYLccouRLD7+WNywkE5RYpvwTB99BKxaJUaLSPH8ybWuXxf17n76Sb6d7cLzPPCAOM+1JNEtOGLK9YYMsb2xzTaROy77tWzcuDFeeOEF7N692+4xSUlJ+Oqrr1C7dm38/PPPrvpoIp+kXAKZd/xca/9+221lyoipMuSZlG2C0zNcKyVF1LlTionRPBRyksFg2y6ysvSJxVep9RVsE56N50/udfSoGEmoxHbhuZRtgiOmXI99Rd657D7b0aNH8d5776F9+/YICwtDw4YNUbp0aYSFheH27ds4evQojhw5ggYNGuCjjz5Cly5dXPXRRD6JI6bc6/x52228s+HZ2Cbcy97KVWwXnq1wYeDmTevr5GSgSBHdwvE57Cu8D/sK9zp3znZb/vxAsWLax0LOUbYJjphyLZNJ/RyKfUXuuOzXslixYpgxYwbi4+Px2WefoUqVKkhISMCpU6cAAH369MHevXuxY8cOJqWInKC8u8ETK9dSXmwUKQK8+KIuoZCT2CbcS+0C/PHHRe078lxsF+6lbBcNGwK9e+sSCjmJbcK91PqK114TIzjJM0VEyF9nZuoTh6+Kj7etH9WsmagHSc5zeWWCfPnyoWPHjnjqqadc/a2J/Iry7gaHouedyQQsXCg6C+Udv6NHbetSkGdhm3CPfftEe7hyRb793XeB8eP1iYmcx2lLrpecLOrndOokLjgsypYF9uzRLy5yDtuEe6xZI/5tlYmpNWuAzp11CYmcpJy6l5Qk6nQymZg3Fy4Af/4JVKgg3969O7B8uS4heTW3lMx88MEHsW7dOkTyKo/ovnEouuv9+CPQr594XqCAfF/BgtrHQ7nDNuEeDRuKx5Yt5dvZJryDcr0Ztou8e+UV4LvvgBIl5NuV/QZ5JvYVrnflirWANvsK75eZCdy5I6Zg0v1r2hS4ehWoXl2+nW3i/rhlhmn9+vXRtGlTHJeudQ8gLi6O0/iInKR2x++338RSyHR//vnH+jw1Vb4vPFzbWCj3lEPRk5JEse6VK/WJx9ds2yZ/zRMr76Asdn7zJrB4sVg5i+7PDz+Ixxs35Nu51Ld3UDt/2rFDrO5K9ychwfr8wAH5PvYV3unff4Hvv1cvZE/OuXpVPCqvzdgm7o9bElPz5s1D//790bJlS2zbtg0nT55Ez5490bBhQwRyGQAipyhPrJYtAx59FOjbV594fIGj5BOHM3s+5ciQ27eBDh3EkOm4OD0i8n4mk/19PLHyDsr/w7feAvr0ASZM0CUcn2Cvr7hzR9s46P4oR0wdOwY89JDoL+7d0yUkr5eebn2uXBGXfYV3evJJcU2xaJHekfgeton745apfAAwceJEhIaGon379sjKykLbtm2xY8cONGnSxF0fSeRTlCtmWLLx//4L/PGHGDZarpz2cXkzjoryLUajtVbYpk1ipEjbtrqG5HWUIweleGLlHZQjpix9xcmTYjRh69a2NzrIsfBw9elfaWmah0L3Qfn7fvaseDQaxflTuXJAvXqah+XVlMkoKfYV3snSV+zfL1ZU7NzZ9gYgkZbcMmLq2rVrePXVVzF58mTUrFkTwcHB6N+/P5NSRLlgbyTD9etAx45ATIym4fgErkLiu157DWjXTj5dk3LGiw3vZ6+v2LBBjCbkWjS5xxFT3k05YkqqWzegfn1Oy8wt9hW+69NPRbuYMUPvSHwH+4r745bEVIUKFbBlyxYsXboUe/fuxc8//4whQ4Zg6tSp7vg4Ip+UUx7XbNYmDl/i6MSKvINy5ROlHTu0icNXKNuE9IJcOWqTPNPAgY73//mnNnH4EnuJKd7c8A758uV8THKy++PwJY7On0JDtYuD7t9zzzne//332sThDzi69v645bTz22+/xf79+/HIf8s3dOrUCX/99RdmzpyJoUOHuuMjiXxOlSpiGfd58/SOxHcoT6w4FdL77NkjCtiWL6++n8mU3FG2CenKMsWLaxsL3Z9+/YCtW4ExY/SOxHcoaw5aLrw7d9Y+Fso9g0FM39u82f4xXKkvd5R9RaNG1ues0ekdvvxSlD3o2lV9P/8fc0d5oyJIUiCpQQNtY/EVbjmFf+aZZ2y2NWjQANu3b8fGjRvd8ZFEPql+fWvnX7iw7bKuytoiZF9Wlu2J1eefA9OnA0eO6BMT5V7RokDz5kDp0uJ12bLy/UxM5Y7y4qxCBWD9emDBAqBqVV1ColwKCBDLt1erJl4r2wTljlpfERcHfPQR8N13uoRE96FCBdFXWEZPKdsFE1POM5nEyoZSTz0lbpzyss57hIWJRQAsN2V5/pQ3yjZRrhxw6BAwcyYwZIg+MXk7txU/VxMTE4PtXKuVKFdq1wa++gqoUUOstCQdHpqS4riWAgmXLwN16wK3bsm3V60qVjok7/Ppp8DOneL/dPx46/a7d/WLydv06ycSUFIxMaJWF3mfZ54Brl0T/3/S0QzkvD//FLVWpPVBIiJE0k86mpC8Q3AwsHSp6BcWLwYuXbLuU15UkjqjEWjYUFxwS8XEAE8/rUtIlEejR4skSqVK8hqEgYH6xeRtpk8HRo2Sb4uJEddstWvrEpJP0DQxBQBFihTR+iOJvN7zz4vHwoXFqnwWiYlMTDljyxbbpBTAqXzerGFD8fXFF/LtvAvuHLMZ+OUX2+1cVMF7hYWJCw4Wdb5/a9bYFq2NieEUF2/2X1URrFkj386+wjlnztgmpQD2Fd4sOhp44w3bxWI4Ysp5K1fabmObyDv+ChJ5EWUSiidWzjl3znZb6dLiQo68m7JN8C64c27eBFJTbbfnVFyePF9wsO20by6W4Ry1voIXG76BfcX9UWsTAPsKX6BsE0zAO0+tXbBN5B0TU0RepFAh+Wsmppxz/rzttpo1NQ+D3IBt4v7Yu9ioUUPbOMg9lO2CS1c7h32F72JfcX/U2kSxYkCJEpqHQi6mbBNqN6vIVkaGKBGixL4i7zSfykdE9493/JxnMgHvvy+KnyovwseNAwYN0icuci2OIsydv/4Ctm0Tq35KtWolhvZXrKhPXORayhFTiYm220i4dQuYNk0spa7sKxYssL+CFXkX9hW5M3++GGmpbBN9+gDDh3N0jS9gm8id48fFAhiPPy4fhVy+vLje6NZNv9h8BRNTRF6Ed/yct3KltSh2pUryfc8/z+kZvkLZJpisdezhh8VjrVry7Q88YK3FQt7PZJK/TkoCypTRJxZPN3KkuAifMkW+vWBBkawi38C+wnmpqcCAAeK5cjGMJ57g4gq+QlnOgm3CsebNxXXXl1/Kt1evDvTurUtIPodT+Yi8iPLuxq1bwDffAEeO6BKOR7t40fr8wgX5voIFtY2F3Eftjt/OncAPP+gRjfdQ/s1gm/AtysTU5cvAJ5+IVftITlkA2IIrfPoWtb5i5UoxipTkkpOtz/fske9jX+E7lKPe7t4FTp0Sqx5zEQ1blsEAysWU2CZchyOmiLxIvnzy17Nni06kTRtg40Z9YvJU4eHW58oOlp2I71BebFy7BnTqJE4gGjWynbJG6tgmfIsyMTV0qOgrLl0CPvpIn5g8lbSvkMrM1DYOci9lX/H332JaTqFCwO3bnJomlZJifa4cmc++wrc98ojoK0qVAnr21Dsa78Bp8q7DEVNEXkR5sXHqlHg8cgTo3h34+WfNQ/JYjlahCgnRLg5yL+UJQUKC9UR61CgxHUHZbvyVozbBiw3fkpUlf23pK9atAx59FNi/X/uYPJW9xBT5FuVUvtOnxWNSEvDss8AHH2gfk6eSJqaU2Ff4Nktf8cUXomZSQoK+8XiD9HS9I/AdHDFF5EXsXWBfvy6GpK9cyWXBLRydWJHvcHSXe9Uq8fjss0DbttrE48nS0uzvK1BAuzjI/ez1FYcOia+//xajRMh2JDL5JmViSmrxYvE4ejRHTgFMTBGwebN4fOstYM4cfWPxdFz11nU4YorIi5QqpXcE3kNaI4H8G5dAFpRtIkByBsBpS76lRg3H+7lwhlVwsN4RkBYiInI+5t4998fhDZiYIovLl/WOwPMxMeU6TEwReZH+/cXojy5d9I7E83HElP/47juxehYvMB1TtonoaOtzjrT0Ld98Azz5JNC0qd6ReD4mrv1D8eLAuHHA00/bP4arkgnKmxjSlY2ZmPItW7YATz0FVKigvj+AmYIccdSt6/DXjciLhIQA338PvPKKeB0RwU5DzbffAn/+Kd/26adAbCwL//qivn2BBQuAyEjxumhR+X4mKYG4OGDqVPm2ChXE1JVmzUQtCfId5csDy5aJelIAUKSIvvF4ojt3gFmzxNRGqYULxfLfv/2mS1jkRpMni9UpLZTtgiMJgbVrRX8q1b070Lmz6GuDWATGpzz4ILB0KVC3rnitbBO8xhCjxqZNk28rVAiYPx+oVQuYPl2XsHwS/7wQeaE2bYB+/YCOHcVqS9JaIffuAWFh+sWmt7g4YNAg2+2NGgHDhmkeDmlo0iRgxw5x92rWLOt2XmwAgwfbLvtdoQIL/vq6554TfxN79nQ8UsQfLVoEvPaafJvBIEYP9OmjT0zkfiVLiro5oaHAX3/JVzT2977i3j3giSdspzRWrGh7YU6+5bXXxLVD8+bA8OHW7UxMAe++C3z5pXxbTIy4DuvXT5eQfBYTU0ReKCREZOoBMTRdmphKSvLvxNSxY+rb7Q1TJt9hOUlQJlv8/WLDbFZvF2wTvs8ycurmTb0j8TxqbaJMGZGwIN82aZJ4VK5O6e99xfnz6nW22Ff4voceEl+//y7frlzh1R/x/Ek7PpcHvXXrFvr06YOIiAgULlwYgwYNQmoOBQRat24Ng8Eg+3rxxRc1ipgobwoXlr/29xOrc+dst+XLJ+6Skn9gm5BLSFBfkS8mRvNQSCeOViTzV2p9BduEf2FfIafWJgBehPsTZZvgQkLq7YJtwj18bsRUnz59EB8fj/Xr18NoNGLAgAEYMmQIFlvWgrVj8ODBePfdd7Nfh4eHuztUIpfgiZUYETJunJjrffas7f6aNbkEtD9RXoT7Y5sARJ21X36xP4WrVi1t4yH9BAUBgYHyu9+Zmf5XLyYxERg7VkzVU7vYYJvwLzx/Er76Crh61bY+IyBu7JUvr31MpA+2CeHYMWDGDGDUKPWVCWvW1D4mf+BTpyTHjh3DunXrsHv3bjRq1AgA8Omnn6JLly6YNm0aSpcubfe94eHhiLRUziXyIuxEgK1bgSlTxPOHH5bve/VVgAMg/QvbhNC+vXj86y/59saNgTfeABo00D4m0o9y5cXkZPULUV82YQIwZ474Uq4uNneuKPJM/oN9hfi7MGSIeN6xo3zfk0+KcyiuOuY/2CaEjh2BixeBH36Q950lS4pFlFiz0T18airfjh07ULhw4eykFAC0a9cOAQEB2Llzp8P3Llq0CMWLF0ft2rUxduxY3Llzx93hErmEcnTIrVtiVTq1kUO+SrrE8/Hj8n0DB4oVlsh/qJ1Ybd/uv6tsKdtEkyaiwDP5F5NJ/vrCBWD2bP+aqnHypPW5crXOIUM45dvfqI2uXbEC2L1bj2j0cfeu9fmOHfJ9XbuKVdvIf6i1idOngXnzbG9u+LKLF8WjsgxC5cqilqk/1/J1J58aMXX16lWUVJxVBAUFoWjRorh69ard9/Xu3Rvly5dH6dKlcfDgQYwePRonTpzAL7/8Yvc96enpSE9Pz36d/N+ZndFohNFozONPog9L3N4av78KDg4AEJj9+qOPzIiLM6B7dxOmTs1C2bJiCocvM5sNsPw5u3JFvi8szIi8/EqzXXgfMRM7OPv1hQtmtG8PpKcDhw5lonBhoHhxvaLTUrDq1vDwLBiNJtV9zmCb8Fby34cBA0RfcetWFnr0MCEmxvenPIeGBsLePdm8/D6zTXinAgWs5w4AsG6dGR9+aECFCmZs2iT6Cl8fLXTrFmD526BMUufLlwmj8f6zEWwX3kdcL1j7iqQkMx5/HDh82IDixTNRu7YZZcvqFp6G1M+fwsJMMBrvvyK8t7cJd8ftFYmpMWPG4MMPP3R4zDF7S3E5YYhlDCuAOnXqICoqCm3btsWZM2dQqVIl1fdMmTIFEydOtNn+xx9/eH19qvXr1+sdAuXC+fOxAGKyX8fFiSuLFSsCsGJFAFq1uogRI/bpE5xGtm0rDaCx6r6dO//EsWMZef4MtgvvcetWGADrnIRTp6xX2zVripONH35YjXz5fH25mW6qW+PjT2DNmlN5/u5sE95G/vtg6SveeisQb70ViCFDDqBLl/M6xKWd27cbAIhW3bdmzZo8f3+2Ce9y5kwkgKbZrw8cEG3i3DkDypcPRlRUKmbP3qBTdNq4ciU/gHaq+44d24XQ0Bt5/gy2C29j7StMJgMOHxbPH3tMpA2mT9+ESpWS1N7oQ+ydP93GmjXb8vzdvbVNuHtGmcFs9vyBeTdu3MDNHNY6rlixIhYuXIiRI0fi9u3b2dszMzMRFhaGpUuX4vHHH3fq89LS0lCgQAGsW7cOHZUTrv+jNmIqOjoaCQkJiIiIcOpzPI3RaMT69evRvn17BAerZ4rJ87zwQiDmzXM8Kzcjwzsz886aN8+AF15Qz7OnpBjztPw324X3uXMHKFzY8f/Vzp1G1K+vUUA6MJuB0FD1f4OZM7MwdGjeRkyxTXifkBDH/1cBAWbcu5epUTT6cNRf5qWfZJvwTlu2GNCuneN79L5+/rR/P9C0qfrv7LZtmWjSJG8jptguvE9OfcX//peFWbPu/xzCG9j7N2jQwIR//snbiClvbhPJyckoXrw4kpKS3JLv8IoRUyVKlECJEiVyPK558+ZITEzE3r170bBhQwDAxo0bYTKZ0LRp0xzebRUXFwcAiIqKsntMaGgoQlWudoODg73yF03KF34Gf+LM1Atf//90lMAvUMA1Pzvbhfdwpq80GILhy/+dyroIUiZTIIKD8z6/l23CtxgMBp///5TcT7Thip+dbcK7OFMnJjAwGAE+VZFXTlpjSqlIkSCX9JNsF74lMNA15xCeLDgYqmVA7t0L+K+ESl6/v3e2CXfH7FN/amvUqIFOnTph8ODB2LVrF/7++28MGzYMzzzzTPaKfJcvX0b16tWxa9cuAMCZM2cwadIk7N27F+fPn8eqVavQt29ftGrVCnXr1tXzxyFySi5yrj4pK8u/ivdSzpxJ1ib5+Ch05Uo60vsoXNvDP3Xt6ni/L198A6KvUBY8J//m4P5zNl/+nTGbAckkEwDyfxPlypXkH3LqC/yhr7BXW47nT+7lc79aixYtQvXq1dG2bVt06dIFLVu2xJdffpm932g04sSJE9lzJENCQvDnn3+iQ4cOqF69OkaOHIknn3wSv/76q14/AlGuDBoEfPIJMGKEeO3rHYZUWhpQpQrwzjvy7S1a6BMPeY4tW8QqMkH/jQtWtgtfXgJ5/HjYFCeNibE+L19e03DIQ8ybB0ybBjzxhHitbBO+XPj86FGxWqfy1K5cOfGYl+ne5L0qVgQWLwamTrVu86e+4pFHAGWVk2bNrM8LFNA2HvIMR48CM2cClvEZ/tRX/PSTSMgqb3hb/g38fTCAu3nFVL7cKFq0KBYvXmx3f0xMDKRltaKjo7F582YtQiNyi8BA4OWXgUOHREfSty+wYIF8aXCTyTcTVgcOAOfO2W4fNQo4fBho0kT7mMgzPPig+PrqK7FEfIsWwKpV1v2+fLGxfLnttooVgdmzgU2bgN69NQ+JPECxYsDIkWJFyl9+AQYMAL75xrrfF/sIiz//BFJTbbevXg0sXAg895z2MZFn6NVLXIROmWJN2u/fb92fmOibyfy7d4G1a223164NPPYYkJEBFCmifVykv2rVxNfp08CxY6KNLFhg3e9oSrS3W73adnprqVLi3GnePOD113UJy2/48GkIkX+pUwe4dg348ktxZ1hK7YTcF5w9q769YkUxasTO2gXkRzZsEImpGjXk2301MWU2q7eLChWANm2AiRMty0GTv+rbF4iPB958U77dl++Cnzljuy0kBKhVC/jwQ3ExTv4rIkLc5NqyBShaVL7PV/sKe+dPFSoA/fsDkgXLyU/NmgVcuiRG1kn5cikEtb6iYkWgenXRVxQvrn1M/oSJKSIfUqKEKNinTEz50olVZqYYSvvkk+odCCBOrIgAUdy2SBHfbhMA8NlnQJky4q6eWjHbihU1D4k8lMEAREbatglfGzF16ZIY6TJpknpfERPjez8z3b+ICDF1zdf7isGDRSL20CH1/ewryCIoCChZ0vfbxMaNorbaihX2E1OkDZ+bykdE6p2IpZaGtzt8GNi1S3wFqfwFi4x0blU28i++fmL18svisVMn9f1Vq2oXC3kH5d/JjAwx4s5XRk5NnQpcuAC8/bbtiEmAbYLU+Xpf8fXX4nHGDPX9lStrFwt5B19vE926iZklynprFuwrtMN7RUQ+qFAh+euzZ4Fhw4C4OF3CcZudO+Wv27cXc8CJlJQnVrdvA198AXz+uS7huE1Ghvx1VJSYvte5sz7xkOdSJvaNRmDvXuCll4CEBH1iciVJOVGbaUv/+x/w/vvaxkPeQdlX3LgBjBsnas94O2mbOHhQvq9ePVEKokwZTUMiL6B2/vTbb8CYMfJ6tt7q3j37+8aNA158UbtY/B1HTBH5IOUFx9ixwPHjwB9/iKlwH3wA9OypT2x5JV26+d9/5fvefhto2VLbeMg7KE+s9u0TqzEBwA8/iLoBy5f7zmgRi/btRbsgckbfvqLY7c6doo7I8uXWlZm8TViY9bmyWO+MGVyJj9Qp+4pvvhHnTyVLin6iRw9gwgQ9Iss76VL3yjYxcKCY5kekpGwTCQmir7h1S9RlS00F/v5brGbnjcLC1GvxVq0KTJ6sfTz+jCOmiHyQ8g7G8ePi8dQpUeDz6ae1j8lVpIkpJU7hI3uUJ1aWNgGIE6qVK31veDrgvSeKpI9jx8Tj/v1ilNHAgfrGkxchIfb3MSlF9tjrK65fB44eFSNQvZWj8yf2FWSPchbGjRsiKQUAO3aIemXffad9XK6SL5/6dkcjqcg9mJgi8kHS4dq+JjnZ/j6eWJE9yosNNY5+t7xV/vx6R0DezJtXX+JFBd0PX77B5aiP8+Wfm/LGmUS+cgSeN5GOrpViH6I9JqaIfJAvJ6Z4x4/uhzOJKW8dMeWoxoOy5hRRbnjz1FZHfQWRPc6s1Oit51g8fyJ38ea+wt6IKbUVjsm9mJgi8kG+UIxQyWQCunYFhgyxfwxPrMgeZxJTt2+7PQyX+/57sey9PTyxorzwxouNhARrIWei3HIm6ZSW5v44XG3MGKBRI/k26QU5z58oL7zxumPnTrFi68mT6vt5/qQ9JqaIfNCAAXpH4HpnzqivilOhgvU564aQPfaGakt544ipb78FLl6UbytWzPqcJ1bkSL9+jvd7Y2Jq/XrgwAG9oyBv1bRpzsd4W19hNquvQNu4sfaxkHcqV87xfm8shfDTT/J6o0qZmdrFQgITU0Q+6LnngM2brSciDzxge4y3DEUfO1bc6Tt9Wn1/nz7axkPe6+xZUdS5QAFxwd2smXy/t4yY2rIF6NYNOH9evV1UqiRWkwGAp57SNDTyMnPmABs3AkOHitfKvsJbElPp6UCvXsC8efb7itdfF49NmmgXF3mfqlWBvXvFKsaAWI2vUiX5Md7SV8ybJ9rFxYvqq47Vrm19XrGidnGR99m3D9i2zbrytbKv8JY2cf68OH/askW9rwgJsd7ct/QZpJ2gnA8hIm8TEAC0agU0by6G13bvLk46jEbrMWlp4gLdkyUlAR98IJ4HB6sf8/DD4uK7aFHt4iLvZBldt3KlGEm0di3wzz/W/d5yF/yhh8TjlSvApUu2+ytWBL74Qqyw1ry5trGRdwkLA9q0EdMZqlUTSRtpwtZbpmd8/z2wZIn46ttX/Zi33gIeewyoU0fb2Mj7NGggHhcvBqpUAYYNE6O2Lbylr7CsqmmvblalSuJCPSVFJOCI7ClWTCSjvvhCJG5NJrGisYW3tInnnwc2bABWrQJq1rTdX6ECMHu2GE3cooX28fk7JqaIfFhwsDihAoAiRcRyxxa3b4vRIw0bipEkNWoAgYH6xGmPtFDnrl3qx1SqlPMQYyKphx8Wj9KkFCBOrE6cEFNCAwOB8HD5tDhPs2eP+vZKlUR750kVOSsyEnj5ZdtpocnJYqTFgQOiPo2lr/A00ouiU6ds95coIVYds9ztJ3JGr17isUgR+fbERHFxXr68qGlWvrz9AsqeYNMm9e2VKonYiZxVp474Wr5cvj0xUdwsu3ULKFNG9B2e+Lt17pz1uTTZbFGpkjgHtNwAJG1xKh+Rn1AuG//uu2JUVbFiopMZM0afuByRJqaUSQRA/ExlymgXD/kWZUH0EyfExXft2iLZWby4LmHlWfXqekdA3krZJm7fBl56SSR0wsLEHeZVq3QJzSFpDbkjR2z3s01QXijbxcqVoq+oXFkkatu31yUsp125or69WjVt4yDfoWwTt24BbduKm91Fi4pFWez93ulJ2lekp9vuZ5vQFxNTRH5CWVNq0SLxeO+eeJw2Tdt4nCEtpqgsrPjoo8CPP3reKC/yHsoTq1WrxOgQ6YpL3lT8skwZ4P33WVeK7p9yend6OvDzz/JtakWUPYmyrxg6FPjkE31iId+g7CsWLxaPSUniUTqlyVOoXXRbNGkCzJ3LhC3dP2Wb2L9fFBLPyLBu27lT05Cc4miRpMmTgVGjtIuFbHEqH5GfUCamvGG1LkerfLzyiuffpSTPpjyxUmsTiYneM3KqeXOxWADR/VIrdu4NfYV0dK3SpEm2U7GIcsOZvsLTOGoTTzwBDBmiXSzke7yxTQCOE1PjxmkXB6ljYorIT3jLKnwWhw/br4sAAAULahYK+ShnLlZv3/acxFRKilhJxp4g9uikAU/rS7ZsAQ4dsr+ffQXlVXi43hHkzuXLYnEPeyIitIuFfJMz50/S0VN6y8wEfv/dexJo/oqnsUR+wtMuJtRcuSI6jXLlRP0rR8vP8sSK8kp5x0+N3ksgG41AXJxYJWrCBGDGDPvHWqblEvm648dFfcSEhJyL1DJhS3nlzPlTVpa+pQVSUkQx53r1gN69Hd/EYLKW8sqZc3C9z5/MZmDfPlEH7pdfgOee0zceyhlrTBH5CW9ITJUpI4qJ7tql3qFJ71ryxIryyhsSU6+9JuqBTJhgfxU+CyamyB+cPy8uNKKixEUHkbs5c/4kXRlSDw88ANSvD/z5p3pfIT1/Ui6GQ5RbAU5kEPQ+f1qyRCxS8OijYhVN8nxMTBH5iago+WtHd/bu3gU2btR2GK7RaH2uXIbWIjbW+pwjpiiv1JKbynYhvdjYswe4dMmtIdmwFJqePBk4edJ2v/RnYGKK3EHZJqQLAiQkANu2aRuPZYXWrCyxkqYajpIiV1I737DXV5hMogyBoxqZ7mCZzjplCnDnju3+Ro2szz1pihX5DkfnT2fOiBIdWpozRzz+9Zf6+RN5HiamiPzEd9+JE5POncXrRx6xPeboUWDgQLHka9u2wPjx2sUnLdRpr15I06bW58rVo4hyq3hxoG9fUQg2LEwUxWzbVn7MrVvAm2+KouKNGwPR0frECgBXr9puq1zZ+rx8ee1iId+1apXoK5o1E68ffVS+PzERWL8eeOkloEoV4MEHgXXrtItPWrzW3ogpSz9H5AqDB4vf8yefFK9r1QJiYuTH/PuvOK5fP6BNG6BTJ83DBGD/4l+6Ah/Pn8gVPvxQlN2oVEm8VvYVt28DX34JvP22OFepU0ecU2lF2leoJaaCgoCqVcVzzsLwDLynROQnatQAdu8WoyoWLBAnWMqizq+/DqxZY3390Uei49GC9O7i7t3qxzz6KBASAhQtqm8tB/INBoNI2AJihGBwsHUZcItff3VcRFZvVaoA06aJkz+t2ir5tq5dxdfly8Aff4iLiZUrrftv3xaroh4/bt32yy/aXYhLR2xt365+zNy5YpTLE09oExP5tgIFrDWbliwRU+aefVZ+zLRp8r5ixw7t4pO6fl19e+XKwFdfiVIJeiXNyLe88Yb4OnAAOHtWjKCV9hXnzwPffitGt1pcuCDO4bUgTUydO2e7v0IFYPVqsRofVzT2DExMEfmZsDD7ywSvX69tLFLSxJS9eemVK9uOaCFyhYcfFo/SxCwg6nUopac7XnJYS5Uri9gt8RO5SpkywIABtif0ly7Jk0OAtjUMc+orihcXU9cXLtQuJvIfzzwjHpU1CtX6Cq040/6qVAG6dweef97t4ZCfiY0VX0uXyrdv2iRPSgHalhyQnqcp4wDE+VOVKsBPP2kXEznGqXxElE1a58ndFiwARo0SJ1RLl4qhvo7UqQOULatNbOS/lEsgq7UJdxX0PHtWJAKOHhXTQkaOdHx8cDDQvr17YiGyULYJZVIKcG9i6u23gdmzxWd89JG4A++I2jR1Ildzpq9wly1bgEGDxLTaHTvECEZHChcGmjfXIjLyZ3qeP6WliZvuv/8uaqy98YbjxTGCg4EuXdwTC90/jpgiIqckJgJ//w106CD+oOdVv37i8eGHgf791Yt1WixaBDz9NKfvkfspT6zU3L4NREaK5xs3ijturqg91bOnWDnm11+BPn2ATz6xf2yLFuKzPWXkFvkuZxaakCamLlwQSdbWrfP+2UePApMmiecPPgiMHu34+GPHgGrV8v65RDkJC3P+2IwMMSL9wQdds3DLQw+Jx5AQ4OBB+1NaAdFmJk5kX0Hul9uVjuPixOp+devm/bNnzBBTVb/6Cpg/H5g61fHxKSlsE56II6aIyCndu4saTx99lPfvJb2I2b7dcVIKYE0p0o6ziSlA3LVu29Z1RcctyxnfvJnz6jWeNJ2QfJszy4JLR1GVLy+KP7uixk5amvW5M6v/FS8uascRuZszv2eWESPvvy/OnyzF013l4MGc+4pChdhXkDZyc/6UmipqtcXGumaVyPPnrc+dWf2PbcIzMTFFRA4tXSruQG/eLF5/+WXuv0dWlvwuiXSO+cGD6u+RnvS54g4jkTOcueN3/jzQsKH1rvX9TmNKSJC/VzoSUVpYWo2WdRqIcpKYCEyYANSrZ922aVPuv09amvxGhfT33JnEFFdWIk+yebNY9WviRPH6fupQmc2ir1Bz4YK85pqF9Pwpf/7cfybR/XAmMXXrlkjQPvCAfFtu3b4trxsVEmJ9ntP5E3kuJqaIKJvaHcDPP5cvs2oyWZ+fOycSVTnd7ejcWYx6shTRlZ5I7d+v/p46dazPmZgiragto61sFwsX2q9dkJUFfPMNcOKE489ZsQIoUQJ46y3rNumJ1ZUrtu+RjlxJT3f8/YncSdkmEhKAmTPF6kwW0r5i1y7bwrhKmZmijmCZMtYRWNK+YutW9fcVKmR9zrvgpCdlu5g5Ezh1Sv3YxERgzhz7SSeLceNEXyFd7czi0iX199SsaX2uVhOOyB2kf4stlG3in3/EKq7Sm9LSG9crVuR8E+LsWXFN0bmzdZv0/EnaD5F3YWKKyI8VLy5//eCDtscop2NILzbq1QNeeAGYPt26beVKsYxyaqp1m2W1vwULgA8+kH+OvRMr6d0U3gUnrUhPbiykv4sAsGGD7TF374rH+fPFqkfVq1v33bgB9O4tf5+lWO3774sTtJYt5dOW1MTEWJ9zxBTpqWVL+es9e2xHbkhHAzZtKmqoxcVZt02fLgrUWty4IS7WExPFkvd9+wIDB1r3X7igHov0JgaRVpQX4TExQOnS8m2ORkgNHgy89JKon2kRFydenz5t3TZlingcNkxc0FtG6jpSq5b1OfsK0opayQ3ldYVam7Akps6dAx5/XP4ekwl48UXg66+t277/XjyuXy/e27atuCFocfGieny5qQtH+mBiisiPrV4N1K4NTJ4MNGsmpmIoKUdDmUziDtzFi9YLkbVrrfu7dxfFytUKD2ZkAO+9Z/8OolSLFtbnaqNYiNyhXj2xUsvzzwNPPAG8/roobi6lNkLw9m3g8mX1pNWYMcAPPwDt2lm3BUmWHpk/XywskJOqVa1toVmznI8ncpX580UtkEmTRKL21Vfl+9XaREaGSNheu2bdZpliYTKJVVmnThUFzgH5zYx//hEXH9ev5xxbt265+lGIXOKtt0SNnLfeAlq1EqtHKqcyqbULs1lMB1+2TLzeuNG678EHxdL1jz9u+77MTGDWLFHbMCdVq1qfu6oGIpEzXn9dLHwxapT4PVb+fVZrE7duidqalr4AsN7sW7sWmDtXJHItpOdPv/0m2lBON/ZKlhTXJwCQL5+zPw1pjavyEfmxpk2BQ4fE83HjnHuP5YLi44+t26TzvC3OnRPHJiVZt509K7/4cKR+fbEseEYGUKyYc+8hyquAAHGiIzVyZM7v27QJeO45+YhCizNn5K9v35afWDlTqBMQFxszZoh2IR1pQuRu/fpZV1J96y3nangkJgLt28uTrpa+IiVFfty9e0B8vHWbpaZhTvLlA157TfQrzZs79x4iVyhZ0nZK91df5fy+77+3tiUly/nR4cMigSWd4mQ05q6vWL9eJLGeeca59xC5gnKBpIULc37PhQtide6bN63bbt0S07ql9adMJtF3SMsa7N7tXFxVq4rSJGXLis8iz8TEFBHlislke+GelSVWFJNuz8wU05d+/NG6TTqNw5F27UTBdelwdCK9OFPQc/Fi26TUvXti6qol+QuIYezt28uPO3Ik5+9fubKYClWjRs7LIBO5mzNt4swZ25GA6eliuvc//1i3paSI5cKlI2mdGRUCiCkegYHqo32JtObMSn1qtdYuXRKjQqQ++kiMtrWQXrQ7UqsW0KEDUKqUfJQukR6c6SvWrrX9/b51S0xd3bnTuu3YMaBxY+toKsC5vqJKFWDAAFGXiudPno2JKSLKlaQk22KdJhPw8svyelRGI/D77/LjcioIDYi7K3365D1OIldx5sTqr79st335pXU1JgvptFcLtULnUhERzk1/JdKKM21CbVW+a9dEXyFdeW/nTtvfb2eK1x4/Lm5gEHkT6dQ9iylTgC++kG9bsyb33/u118SoWiJP4UxfodYm/vrLdsr4zz/Lk1KAc32FdAEn8mw+V2PqvffeQ4sWLRAeHo7Czqz7DcBsNuPtt99GVFQU8uXLh3bt2uEUrwKIVBmNttvS022HtJ85I5+uAciL4UpJOy6uwEeexpnfSemFtsW6dbbb9uzJ/efntOolkdbUFglQUmsTW7fabldL6rKvIG/kzIgptXaxa5ftNmdXFpO2CXvthkgvziSmnD1/yk1fQd7J5xJTGRkZ6NGjB1566SWn3/PRRx/hk08+wZw5c7Bz507kz58fHTt2xD0uZUEkW13MnuPHbZevd7YWAiBf4YkXG+Rp1FaacaZdqNXJkU5hckRa8F/Ztog80f22ie3bnf+M+vWtz9lXkKdRJqZKlLBdvU+NdLq3hbQ+pyOxsdbn7CvI06glprToK7iat3fyucTUxIkT8dprr6GOk+sHm81mzJo1C2+99Ra6deuGunXrYsGCBbhy5QpWrFjh3mCJvEDPnjkfozaiQ21klT3SpWFDQ51/H5EWlBcbVaqI1ftyonYX0NnRT9ITN94RJG/gTF+RlzYByC/Cw8Odfx+RHtq3ly90YU9eEko1alifc3QteRplYiosDOjaNef35aWvCAlxLvlFnsfnElO5de7cOVy9ehXtJBUCCxUqhKZNm2KHtGAOkZ+YNEk8TpkCLF8OjB3r/s9s2tT6PCzM/Z9HlBsPPyweK1UStRDWrnVueHpe1KhhvdPOOjrkibp0EY/Ll4s2obbEvSsZDECrVvLXRJ5k/Hjx2KsXsGoV8Mkn7v89lV6Aly7t3s8iyq3QUMBSWWfbNlGsvGpV935mlSqAk+NTyMP4ffHzq1evAgBKlSol216qVKnsfWrS09ORLrnFkZycDAAwGo0w5maoiAexxO2t8ZNrvPGGKD5etqx0a7DLvr/BYIbZbD1Ta9XKhAYNsjBtWgDOnwdq1jTlarSVu7FdUNGiwOXLYmi4JXEaEREAQGWOnwvUqGFG795ZePVVM95/PxDvvJPFNkEeZ9ky4MYNICpKvL5wAXBlXxEQYIbJJPqKfPnMePZZEzp3NuGFFwJQuzZgNJpy+A7aYZsgAGjTBjh3TrQJy5L2AQFBAFyTnZK2CQCoVcuMLl0yUbasAT/9FIDhw9lXkOc5d06MCixaVLw+e9YAd6UgqlY1Y/BgE55+2oSUlED072+C0eg5w869vU24O26D2ez5kwTGjBmDDz/80OExx44dQ3XJbYP58+dj+PDhSExMdPi+7du344EHHsCVK1cQZTm7AtCzZ08YDAb8KF3rXmLChAmYqFxuCcDixYsRzvHl5GO6d+8me12kyD3cvn1/Q5sqVkzE2bOFAQDDhu1Hu3YX8hoekeZ++KEafvxRPlY8L+1CasWKlXn+HkRau3s3CL16PSLb5qq+YtasjYiJSXH8BiIP1L9/RyQmWttAvnxG3L17fwnc6OhkXLwoiqs1bhyPceNUqqYTebhDh4pj/PgHZNvy0ldERKQjOVnUAeH5k3vduXMHvXv3RlJSEiLcUOjRK0ZMjRw5Ev3793d4TMWKFe/re0dGRgIArl27JktMXbt2DfUcFBEZO3YsRowYkf06OTkZ0dHR6NChg1v+o7RgNBqxfv16tG/fHsHBrrvrSb6nW7cQzJ9/f+999NGC+OQT8bxZszro0qW2y+JyB7YLUrNvn3wmfJEiZrRsGYJff83994qJMeP8eetd8C6WOVIeim2C1Kjd5sxLX9G1a0F8/LF4/sgjD6J8+fsOze3YJsiefPmCIL1H3qJFIDZsuL/v1aRJAVy8KJ4XKVKKfQV5pdKlrdNeLR57LATffXd/369x4+DsNsU24V6WGWLu4hWJqRIlSqBEiRJu+d4VKlRAZGQkNmzYkJ2ISk5Oxs6dOx2u7BcaGopQlSrNwcHBXvmLJuULPwO5T8WKwMsvB9hcbBQvDiQkyLdFRyP7JMqidevA7MSUwRAEb/lVY7sgKWlB21q1gCefNODkSdvpGmFhQE4LvNaubcD589bX3vJ7xjZBjsTGAr17339f8cADgdmJqWLFgr2ir2CbICVpjamKFYEhQwJsElMREYDyeq9UKeDaNfm2unUD8PPP4nlmZgCCg72jVDDbBUmVLGl9XquWqDnVqFGATWJKra8oWxa4dMn6ukwZoFQpazvwlt8zb20T7o7ZO/6i5cKFCxcQFxeHCxcuICsrC3FxcYiLi0Nqamr2MdWrV8fy5csBAAaDAcOHD8fkyZOxatUqHDp0CH379kXp0qXRvXt3nX4KIs9iGUzYvz9w5gzQoIHtMe3b225r08b6vHBhoHVr+XGB7inRQ+R20sKahw8DEyfa/j4XLGhbuLxoUfny4Y0bAy++aC0OSuQLfv4ZiIsDKlSw3ZdTX1G6NDBokLiIt+DS3+StOncWj5GR4vxJbfVKtXMq6QqUgOgrnn7a+jp/ftfFSKQlS60pAPjnH+CXX4BixWyPU+srmje3Pq9XDxg50v2L0ZB2vGLEVG68/fbb+E6Scq1fvz4A4K+//kLr1q0BACdOnEBSUlL2MW+88QbS0tIwZMgQJCYmomXLlli3bh3CuDwYEQDg77+BJUuA//3P/jFduwI//CDf9vDDwIIF4vmYMcDo0eL5iBHA1q3AY4+5J14id+vWDfj4Y6BRI+u2AMWtnhYtgFu35NtiY4FDh6yvd/1XIuS334B+/YBZs9wSLpEmtm8Hdu60rtAnvQABRBt55BHHfcWvv4oL9awsoG1bkagK8rmzVfIX06eLFV2lSSWlli2BTZvk2+rVA/74QzyvUsXaV3z9NTB1qvi+RN6oYEFg6VIx/btAAbFN2VeUKgU0aSLvKwoXlt+w2LdPjEi8ehX46y9g4EC3h05u5nNd/fz58zE/h4IGynrvBoMB7777Lt599103RkbkvSpUAMaOVd9XuTLw4YdA9+5A795iW6lSYinxnj3FKCsAspVieEJF3s5gAF55Rb5NOmJqxQqRtHrqKeu2554TI0F69bL9fi1aAKdOuSVUIs00by6/oy0dHfjmm0C7dtYRuIAYTdKwIdC0qe17AgOBP/90b7xE7laokP3zJwD46SfRDiZPtm7r3VuMpP3oI/Faev40aJD4IvJm0nMjAJCuG7ZypUhA/fOPdVufPkCHDsDp09ZtlmmykZHAkSPui5W043OJKSLSVp06wBNPyLd16QJ8+618m8lzVvYmcgtpKcRu/y1kKR3pYRkRolKekMgnSZO1DRuKKXvXr1u3vf020KyZvJYOFzYmX1eypLUd9Ohhu3/RIm3jIdKb9O/+o4+K0bV79li3ffed6E8mTdI+NtKOz9WYIiJt/PijuKCYOdPxcS+8IIboDhmiTVxEehkzBnjgAWDOHOs2tRnh338P5MuH7EUAiHzZO+8AHTuK6d6AfMqGZQB7iRIicVWvnhhxS+TL1qwRNaP++svxcV98IfqK+13Zkshb1K8PPPkkMHy4tSxCSIh1v+UmxwsviHpUnLbnmzhiiojuS8+etkU8x44VJ1Ljxlm3zZkDfPYZa4SQ7ytaFNi2Tb5t5kwxTW/MGOu2li3FCkxsE+QPJkyQvw4KElMy/v1XJKMAcSGya5dIVClrtRH5moYNrTWjLH74AXj+eXHTz+Kll4DBg9lXkO8LCACWLZNve+wxoFw5+VTvkiVFTSm2Cd/E/1Yicpn33xfDbJWrk7EDIX9VuzZw+zbbBJHUunViere0XTAhRf7smWfEtD72FURCgQLA2bO2fQPbhO/ify0RuZTypIrI37FNEMkZDGwXREpsE0RybBP+hfeniIiIiIiIiIhIF0xMERERERERERGRLpiYIiIiIiIiIiIiXTAxRUREREREREREumBiioiIiIiIiIiIdMHEFBERERERERER6SJI7wB8hdlsBgAkJyfrHMn9MxqNuHPnDpKTkxEcHKx3OEQege2CSI5tgkiObYLIFtsFkZy3twlLnsOS93A1JqZcJCUlBQAQHR2tcyRERERERERERK6VkpKCQoUKufz7GszuSnn5GZPJhCtXrqBgwYIwGAx6h3NfkpOTER0djYsXLyIiIkLvcIg8AtsFkRzbBJEc2wSRLbYLIjlvbxNmsxkpKSkoXbo0AgJcXxGKI6ZcJCAgAGXLltU7DJeIiIjwysZC5E5sF0RybBNEcmwTRLbYLojkvLlNuGOklAWLnxMRERERERERkS6YmCIiIiIiIiIiIl0wMUXZQkND8c477yA0NFTvUIg8BtsFkRzbBJEc2wSRLbYLIjm2CcdY/JyIiIiIiIiIiHTBEVNERERERERERKQLJqaIiIiIiIiIiEgXTEwREREREREREZEumJiibJ9//jliYmIQFhaGpk2bYteuXXqHRKSbLVu2oGvXrihdujQMBgNWrFihd0hEupoyZQoaN26MggULomTJkujevTtOnDihd1hEupk9ezbq1q2LiIgIREREoHnz5li7dq3eYRF5jA8++AAGgwHDhw/XOxQi3UyYMAEGg0H2Vb16db3D8jhMTBEA4Mcff8SIESPwzjvvYN++fYiNjUXHjh1x/fp1vUMj0kVaWhpiY2Px+eef6x0KkUfYvHkzhg4din/++Qfr16+H0WhEhw4dkJaWpndoRLooW7YsPvjgA+zduxd79uzBww8/jG7duuHIkSN6h0aku927d2Pu3LmoW7eu3qEQ6a5WrVqIj4/P/tq2bZveIXkcrspHAICmTZuicePG+OyzzwAAJpMJ0dHRePnllzFmzBidoyPSl8FgwPLly9G9e3e9QyHyGDdu3EDJkiWxefNmtGrVSu9wiDxC0aJFMXXqVAwaNEjvUIh0k5qaigYNGuCLL77A5MmTUa9ePcyaNUvvsIh0MWHCBKxYsQJxcXF6h+LROGKKkJGRgb1796Jdu3bZ2wICAtCuXTvs2LFDx8iIiMhTJSUlARAX4kT+LisrC0uWLEFaWhqaN2+udzhEuho6dCgeeeQR2bUFkT87deoUSpcujYoVK6JPnz64cOGC3iF5nCC9AyD9JSQkICsrC6VKlZJtL1WqFI4fP65TVERE5KlMJhOGDx+OBx54ALVr19Y7HCLdHDp0CM2bN8e9e/dQoEABLF++HDVr1tQ7LCLdLFmyBPv27cPu3bv1DoXIIzRt2hTz589HtWrVEB8fj4kTJ+LBBx/E4cOHUbBgQb3D8xhMTBEREVGuDB06FIcPH2aNBPJ71apVQ1xcHJKSkrBs2TL069cPmzdvZnKK/NLFixfx6quvYv369QgLC9M7HCKP0Llz5+zndevWRdOmTVG+fHn89NNPnPYtwcQUoXjx4ggMDMS1a9dk269du4bIyEidoiIiIk80bNgwrF69Glu2bEHZsmX1DodIVyEhIahcuTIAoGHDhti9ezc+/vhjzJ07V+fIiLS3d+9eXL9+HQ0aNMjelpWVhS1btuCzzz5Deno6AgMDdYyQSH+FCxdG1apVcfr0ab1D8SisMUUICQlBw4YNsWHDhuxtJpMJGzZsYJ0EIiICAJjNZgwbNgzLly/Hxo0bUaFCBb1DIvI4JpMJ6enpeodBpIu2bdvi0KFDiIuLy/5q1KgR+vTpg7i4OCaliCAWBzhz5gyioqL0DsWjcMQUAQBGjBiBfv36oVGjRmjSpAlmzZqFtLQ0DBgwQO/QiHSRmpoqu5Nx7tw5xMXFoWjRoihXrpyOkRHpY+jQoVi8eDFWrlyJggUL4urVqwCAQoUKIV++fDpHR6S9sWPHonPnzihXrhxSUlKwePFibNq0Cb///rveoRHpomDBgjZ1B/Pnz49ixYqxHiH5rVGjRqFr164oX748rly5gnfeeQeBgYHo1auX3qF5FCamCADw9NNP48aNG3j77bdx9epV1KtXD+vWrbMpiE7kL/bs2YM2bdpkvx4xYgQAoF+/fpg/f75OURHpZ/bs2QCA1q1by7bPmzcP/fv31z4gIp1dv34dffv2RXx8PAoVKoS6devi999/R/v27fUOjYiIPMSlS5fQq1cv3Lx5EyVKlEDLli3xzz//oESJEnqH5lEMZrPZrHcQRERERERERETkf1hjioiIiIiIiIiIdMHEFBERERERERER6YKJKSIiIiIiIiIi0gUTU0REREREREREpAsmpoiIiIiIiIiISBdMTBERERERERERkS6YmCIiIiIiIiIiIl0wMUVERERERERERLpgYoqIiIiIiIiIiHTBxBQRERGRF2jdujUMBgMMBgPi4uKcek///v2z37NixQq3xkdERER0P5iYIiIiIvIQr732Gp544gm7+wcPHoz4+HjUrl3bqe/38ccfIz4+3lXhEREREbkcE1NEREREHmLXrl1o1KiR3f3h4eGIjIxEUFCQU9+vUKFCiIyMdFV4RERERC7HxBQRERGRzjIyMhAcHIzt27dj3LhxMBgMaNasWY7vM5lMeP/991GlShWEhYWhVKlS6N+/v/sDJiIiInIR5263EREREZHbBAUF4e+//0bTpk0RFxeHUqVKISwsLMf3TZkyBT/++CO+/PJLVKxYEZcvX8bx48c1iJiIiIjINZiYIiIiItJZQEAArly5gmLFiiE2Ntbp9/3+++/o2rUr2rRpAwAoX748WrRo4a4wiYiIiFyOU/mIiIiIPMD+/ftzlZQCgMceewwffPABOnbsiK+//hq3b992U3RERERE7sHEFBEREZEHiIuLy3ViatSoUTh27Bjatm2LmTNnonLlyjh37pybIiQiIiJyPSamiIiIiDzAoUOHUK9evVy/r2rVqnjjjTewd+9epKSk4OjRo64PjoiIiMhNWGOKiIiIyAOYTCacOHECV65cQf78+VGoUCGHx3/00UeIjIxE48aNERAQgLlz56JYsWKsMUVERERehSOmiIiIiDzA5MmTMX/+fJQpUwaTJ0/O8fh79+7hvffeQ4MGDdCyZUucPXsWGzduRJEiRTSIloiIiMg1DGaz2ax3EERERETkWOvWrVGvXj3MmjUr1+81GAxYvnw5unfv7vK4iIiIiPKCI6aIiIiIvMQXX3yBAgUK4NChQ04d/+KLL6JAgQJujoqIiIjo/nHEFBEREZEXuHz5Mu7evQsAKFeuHEJCQnJ8z/Xr15GcnAwAiIqKQv78+d0aIxEREVFuMTFFRERERERERES64FQ+IiIiIiIiIiLSBRNTRERERERERESkCyamiIiIiIiIiIhIF0xMERERERERERGRLpiYIiIiIiIiIiIiXTAxRUREREREREREumBiioiIiIiIiIiIdMHEFBERERERERER6YKJKSIiIiIiIiIi0gUTU0REREREREREpAsmpoiIiIiIiIiISBdMTBERERERERERkS6YmCIiIiIiIiIiIl0wMUVERERERERERLoI0jsAX2EymXDlyhUULFgQBoNB73CIiIiIiIiIiPLMbDYjJSUFpUuXRkCA68c3MTHlIleuXEF0dLTeYRARERERERERudzFixdRtmxZl39fJqZcpGDBggDEf1RERITO0ZCWjEYj/vjjD3To0AHBwcF6h0NEpAn+7SMif8S/fUTkj27duoUKFSpk5z1cjYkpF7FM34uIiGBiys8YjUaEh4cjIiKCJyhE5Df4t4+I/BH/9hGRPzIajQDgtrJFLH5ORERERERERES6YGKKiIiIiIiIiIh0wcQUERERERERERHpgjWmNJaVlZU9P5N8g9FoRFBQEO7du4esrCy9w7lvwcHBCAwM1DsMIiIiIiIi8iNMTGnEbDbj6tWrSExM1DsUcjGz2YzIyEhcvHjRbcXgtFK4cGFERkZ6/c9BRERERERE3oGJKY1YklIlS5ZEeHg4L/x9iMlkQmpqKgoUKICAAO+cHWs2m3Hnzh1cv34dABAVFaVzREREREREROQPmJjSQFZWVnZSqlixYnqHQy5mMpmQkZGBsLAwr01MAUC+fPkAANevX0fJkiU5rY+IiIiIiIjcznuvor2IpaZUeHi4zpEQOWb5HWUdNCJSM2ECMGmS+r5Jk8R+IiIiIqLcYGJKQ5y+R56Ov6NE5EhgIPD227bJqUmTxHYOtCQiIiKi3OJUPiIiInLK+PHi8e23gaysANSvD7z3XgAmTgTefde6n4iIiIjIWUxMkcfZtGkT2rRpg9u3b6Nw4cJ6h+NWV69exXPPPYft27cjODiYqzYSkcezJqcCERT0KDIzA5mUIiIiIqL7xql8ZFf//v1hMBhsvk6fPu3Wz23RogXi4+NRqFAhl3/v1q1bY/jw4S7/vvdr5syZiI+PR1xcHE6ePKl3OEREThk/HggJMSMzMxAhIWYmpYiIiIjovjExJfH444+jSJEieOqpp/QORUbPYrOdOnVCfHy87KtChQpu+zyj0YiQkBBERkbmqd5RRkbGfb/XbDYjMzPzvt+fG2fOnEHDhg1RpUoVlCxZ8r6+R15+ViKi+zFpEpCRYUBQUBYyMgx2+ygiIiIiopwwMSXx6quvYsGCBXqHYUPPYrOhoaGIjIyUfQX+94GbN29GkyZNEBoaiqioKIwZM0aW0ImJicGsWbNk369evXqYIMmkGQwGzJ49G4899hjy58+P9957D5s2bYLBYJBNa9u2bRsefPBB5MuXD9HR0XjllVeQlpYm+6xJkyahb9++iIiIwJAhQ2x+lv79+2Pz5s34+OOPs0d/nT9/Pvvz1q5di4YNGyI0NBTbtm3DmTNn0K1bN5QqVQoFChRA48aN8eeff8q+Z0xMDKZMmYJhw4ahUKFCKFeuHL788svs/RkZGRg2bBiioqIQFhaG8uXLY8qUKdnv/fnnn7FgwQIYDAb0798fAHDhwgV069YNBQoUQEREBHr27Ilr165lf88JEyagXr16+Prrr1GhQgWEhYVl/1vOnTsXjz76KMLDw1GjRg3s2LEDp0+fRuvWrZE/f360aNECZ86cceJ/nohInaXveeedLCxbthrvvJOl2kcRERERETmDiSmJ1q1bo2DBgnqHYWP8eFFUVnrib7kw0Kuux+XLl9GlSxc0btwYBw4cwOzZs/HNN99g8uTJuf5eEyZMwOOPP45Dhw5h4MCBNvvPnDmDTp064cknn8TBgwfx448/Ytu2bRg2bJjsuGnTpiE2Nhb79+/HeJV/lI8//hjNmzfH4MGDs0d/RUdHZ+8fM2YMPvjgAxw7dgx169ZFamoqunTpgg0bNmD//v3o1KkTunbtigsXLsi+74wZM1CvXj3s3bsX//vf//DSSy/hxIkTAIBPPvkEq1atwk8//YQTJ05g0aJFiImJAQDs3r0bnTp1Qs+ePREfH4+PP/4YJpMJ3bp1w61bt7B582asX78eZ8+exdNPPy37zNOnT+Pnn3/GL7/8gri4uOztluRcXFwcqlevjt69e+OFF17A2LFjsWfPHpjNZpt/NyIiZ0n7nnHjTADEo7KPIiIiIiJyltcUP9+yZQumTp2KvXv3Ij4+HsuXL0f37t1lx3z++eeYOnUqrl69itjYWHz66ado0qSJPgG7mHQlpMmTgYwMbZJSq1evRoECBbJfd+7cGUuXLsUXX3yB6OhofPbZZzAYDKhevTquXLmC0aNH4+2330ZAgPM5z969e2PAgAHZr8+ePSvbP2XKFPTp0ye7NlSVKlXwySef4KGHHsLs2bOzRww9/PDDGDlypN3PKVSoEEJCQhAeHo7IyEib/e+++y7at2+f/bpo0aKIjY3Nfj1p0iQsX74cq1atkiV3OnfujOeffx4REREYPXo0Zs6cib/++gvVqlXDhQsXUKVKFbRs2RIGgwHly5fPfl+JEiUQGhqKfPnyZcezfv16HDp0COfOnctOmi1YsAC1atXC7t270bhxYwBiJNaCBQtQokQJ2c8wYMAA9OzZEwAwevRoNG/eHOPHj0fHjh0BiFGB0n9rIqLcyMqy9j1Go3W7pS/KytInLiIiIiLyXl6TmEpLS0NsbCwGDhyIJ554wmb/jz/+iBEjRmDOnDlo2rQpZs2ahY4dO+LEiRPZtXvq1aunWjvojz/+QOnSpd3+M+TV+PHWpFRIiDYjpf7f3r3HR1Hf+x9/7y5JIFwSKRrkGqyXnwokFA2lp5VwF0612FKpPecYtcW2p2htWluoknCppUcspVUqj9M+EO1FKZ6KnoI+wGjEc8AiYBSKiLZBPSjhYklIgNx2fn+ss5mdnd3sbvaW7Ov5eMwj7Ozsdz7f78x+d+fDd747efJkPfzww/7Hffv2lSS9+eabmjhxYsA8UP/0T/+kxsZG/d///Z9GjBgR8T6uuuqqsM+//vrreuONN/T73//ev84wDHm9XtXW1uryyy+PqJxo42hsbNSSJUu0efNmffjhh2pra9PZs2eDRkyNHTvW/2+Xy6XBgwfr2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+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ul>
+<li> Different frequencies:
+ <ul>
+<li>$f_s=110$ Hz: peak at $30$ Hz.
+ <li>$f_s=150$ Hz: peak at $70$ Hz.
+ <li>$f_s=160$ Hz: no peak (the $80$ Hz signal is sampled exactly twice per cycle, exactly at the 'zero-passes'; we now don't see it at all).
+ <li>$f_s=200$ Hz: peak at $80$ Hz.
+ </li></li></li></li></ul>
+</li>
+<li>The sample rate needs to be more than twice the (highest) frequency of the signal, which is the *Nyquist* rate. As long as we do not meet this requirement the alias appears mirrored about the Nyquist frequency (which is half the sampling frequency), e.g. with $f_s=110$ Hz, the alias of $f_i=80$ Hz appears at $110-80=30$ Hz.</li>
+
+</ul></p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>As you probably figured out, the sample rate needs to be faster than twice the largest frequency in the signal. In other words, you can measure signals with a frequency up to half the sample rate without aliasing becoming a problem. The frequency above which aliasing occurs (half the sampling rate) is called the <em>Nyquist frequency</em> (Chapter 4).</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Damped-vibrations">Damped vibrations<a class="anchor-link" href="#Damped-vibrations">¶</a></h3><p>We now know how to create an amplitude/magnitude spectrum. We've seen that we get aliasing if our signal has components at frequencies higher than the <em>Nyquist frequency</em> (and leakage if the measurement time is too short). Now, let's consider the damped vibration aceleration signal that an accelerometer would measure (see <em>Appendix A</em>). In this case the damping ratio is not equal to zero, unlike in Tasks 1 to 5.</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 6:</b>
+<ul>
+<li>Create a time-array starting at $t=0$ s, ending at $t = 50$ s, with a sampling rate of $100$ Hz.</li>
+<li>Create a damped harmonic acceleration signal $x(t) = \frac{x(0)}{\sqrt{1-\zeta^2}} e^{-\zeta \omega_0 t} \sin(\omega_d t)$ with $\zeta = 0.05$, $\omega_0 = 10 \pi$ rad/s (corresponding to $5$ Hz), $\omega_d = \omega_0 \sqrt{1-\zeta^2} = 9.987 \pi$ rad/s, and initial displacement $x(0)=1$ (for convenience the initial phase of the signal is kept to zero).</li>
+<li>Create a plot of the signal against time, and of the amplitude spectrum as in the previous Tasks.</li>
+</ul>
+<p>Then answer the following questions:</p>
+<ol>
+<li>Do you see any changes in the time plot, compared to the earlier plot? Describe them!</li>
+<li>What is the dominant frequency of the signal now?</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+
+<span class="c1"># # Write your own code or use the template below to create your figure(s)</span>
+
+<span class="c1"># plt.figure(figsize=(12,6))</span>
+<span class="c1"># plt.subplot(211)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.legend()</span>
+
+<span class="c1"># plt.subplot(212)</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.loglog()</span>
+<span class="c1"># YOUR_CODE_HERE</span>
+<span class="c1"># plt.grid()</span>
+<span class="c1"># plt.tight_layout()</span>
+<span class="c1"># plt.legend()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+<span class="n">T_meas</span> <span class="o">=</span> <span class="mi">50</span>
+<span class="n">f_s</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">t_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T_meas</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">f_s</span><span class="p">)</span>
+
+<span class="n">x_0</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">zeta</span> <span class="o">=</span> <span class="mf">0.05</span>
+<span class="n">omega_0</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
+<span class="n">omega_d</span> <span class="o">=</span> <span class="n">omega_0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zeta</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">x_0</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zeta</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">zeta</span> <span class="o">*</span> <span class="n">omega_0</span> <span class="o">*</span> <span class="n">t_vec</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">omega_d</span> <span class="o">*</span> <span class="n">t_vec</span><span class="p">)</span>
+
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">X_cont</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">N</span>
+
+<span class="n">f_0</span> <span class="o">=</span> <span class="n">f_s</span> <span class="o">/</span> <span class="n">N</span>
+<span class="n">f_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">f_s</span><span class="p">,</span> <span class="n">f_0</span><span class="p">)</span>
+
+<span class="n">X_cont</span> <span class="o">=</span> <span class="n">X_cont</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+<span class="n">f_vec</span> <span class="o">=</span> <span class="n">f_vec</span><span class="p">[:</span><span class="n">N</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t_vec</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$t \: [s]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$x(t) \: [V] $"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Time signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f_vec</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">),</span> <span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Fourier transfrom'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.04</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">7</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$f \: \: [Hz]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"$|X(f)| \: [V]$"</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Amplitude spectrum'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">f_vec</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">X_cont</span><span class="p">))])</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>4.98
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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+</div>
+</div>
+</div>
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ol>
+<li>The signal is now clearly a sinusoid with an exponentially decaying amplitude, a.k.a. a damped vibration!</li>
+<li>Around $5$ Hz (max value of $|X_{cont}|$ at $4.98$ Hz).</li>
+</ol>
+Instead of a 'crisp' spectrum, with a very clear, distinct and ultimately narrow peak, you now get a kind of smoothed or faded peak. This is simply the result of dealing with a damped harmonic, rather than a perfect harmonic. The signal somehow still looks pretty periodic, but strictly spoken, the signal is not periodic anymore, as the amplitude slightly changes (decreases) with time.
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Cantilever-beam-acceleration-measurements">Cantilever-beam acceleration measurements<a class="anchor-link" href="#Cantilever-beam-acceleration-measurements">¶</a></h3><p>Now you're ready to start analyzing the cantilever-beam acceleration measurements.</p>
+<p>Read in the data-file: <em>cantileverbeam_acc50Hz.csv</em>.</p>
+<p>This dataset contains 5 minutes of measurements with a sampling rate of $50$ Hz (a total of $N=15001$ samples). The first column in the file contains the UTC time of day (in seconds), the second column contains the measured acceleration (in m/s<sup>2</sup>).</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Detrending">Detrending<a class="anchor-link" href="#Detrending">¶</a></h3><p>In this project the signal of interest is the damped, harmonic motion of the cantilever-beam. In addition the measurements may contain (unwanted) effects of the sensor (think of an offset, due to imperfect manufacturing and/or prior calibration, or a drift over time, for instance due to temperature changes in the electronics during the experiment). By detrending we mean to remove such effects, prior to our spectral analysis, so that we can start with a nice, zero mean signal. A-priori detrending is good practice and generally yields a cleaner spectrum. Next week (week 2.4 on Time Series Analysis) we cover the subject of removing unwanted effects from the signal in more detail, as to obtain a so-called <strong>stationary</strong> signal, meaning that the properties or characteristics of the signal (such as the mean) do not change over time, and, we provide practical means to “stationarize†a given or measured signal.</p>
+<p>There are built-in functions in Python to remove such an offset and/or trend (and you're free to use them). But, earlier in the MUDE, in week 1.3, you learned about least-squares parameter estimation, and that's what you can apply to do the detrending (and actually built-in functions just apply the very same principle). Next week, on Time Series Analysis, you will actually be covering (again) the estimation of a trend in a time series of measurements.
+As a re-cap for week 1.3: when a series of observations $y_1, …, y_m$ (in our case with $m=N$) is supposed or expected to exhibit a functional linear trend (a straight line in terms of a graph), this can be modelled as</p>
+$$
+\mathbb{E} = \begin{pmatrix} \begin{bmatrix} Y_{1} \\ Y_{2} \\ \vdots \\ Y_{m} \end{bmatrix} \end{pmatrix}
+ =
+ \begin{bmatrix} 1 & t_1 - t_1 \\ 1 & t_2 - t_1 \\ \vdots & \vdots \\ 1 & t_m - t_1 \end{bmatrix}
+ \begin{pmatrix} x_{1} \\ x_{2} \end{pmatrix}
+$$<p>with $x_1$ the offset at time $t_1$ (rather than $t=0$), and $x_2$ the slope of the line. The two unknown parameters in this vector $x$ can be estimated through (unweighted) least-squares, $\hat{x}=(A^T A)^{-1} A^T y$, and next the residuals are obtained as $\hat{\epsilon}=y-\hat{y}=y-A\hat{x}$. The residuals are the 'left-over part' of the observations, once the (estimated) trend has been taken out; these residuals are of interest for further spectral analysis!</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 7:</b>
+<ul>
+<li>For the cantilever-beam acceleration measurements, perform a least-squares estimation according to the above model, and report the estimated offset and slope of the trend.</li>
+<li>The detrended acceleration measurements, hence the elements of vector $\hat{\epsilon}$ are the input to your spectral analysis, so from here on, we denote them by $x(t)$ (in continuous time), and by $x_n$ (in discrete time) with $n=0,…,N-1$.</li>
+<li>Make a plot of the input signal as a function of time, hence of the detrended accelerations.</li>
+</ul>
+<p>Report the estimated offset and slope of the trend (i.e. numerical values).</p>
+
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'cantileverbeam_acc50Hz.csv'</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'time'</span><span class="p">])</span> <span class="c1">#</span>
+<span class="n">dat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'acceleration'</span><span class="p">])</span> <span class="c1">#</span>
+
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dat</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'acceleration signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [s]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'acceleration [m/s2]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Vertical cantilever beam acceleration'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="c1"># observation record length (as N*dt, according to sample-and-hold convention)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">N</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">N</span><span class="o">/</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">T</span><span class="o">/</span><span class="n">N</span>
+
+<span class="n">YOUR_CODE_HERE</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># LOAD DATA</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'cantileverbeam_acc50Hz.csv'</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'time'</span><span class="p">])</span> <span class="c1">#</span>
+<span class="n">dat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'acceleration'</span><span class="p">])</span> <span class="c1">#</span>
+
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+
+<span class="c1"># observation record length (as N * dt, according to sample-and-hold convention)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">N</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">N</span> <span class="o">/</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">T</span> <span class="o">/</span> <span class="n">N</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dat</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'acceleration signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [s]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'acceleration [m/s2]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Vertical cantilever beam acceleration'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="c1">### SOLUTION</span>
+
+<span class="c1">#detrend data</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">N</span><span class="p">),</span> <span class="n">t</span> <span class="o">-</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+<span class="c1">#xhat = np.linalg.lstsq(A, dat, rcond=None)[0]</span>
+<span class="n">xhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">((</span><span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="nd">@A</span><span class="p">)</span><span class="nd">@A</span><span class="o">.</span><span class="n">T</span><span class="nd">@dat</span>
+<span class="n">yhat</span> <span class="o">=</span> <span class="n">A</span><span class="nd">@xhat</span>
+<span class="n">ehat</span> <span class="o">=</span> <span class="n">dat</span> <span class="o">-</span> <span class="n">yhat</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ehat</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'detrended signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [s]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'detrended accelerations [m/s2]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Detrended vertical cantilever beam acceleration'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>In addition to the code output above, write your answer in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>The mean is $-0.43$ m/s<sup>2</sup>, so that's considerable, and the slope is $-1.3e^{-4}$ m/s<sup>2</sup>, which is pretty much negligible over the duration of this experiment; mean/offset when one forgets to subtract $t[0]$ from the time column in the A-matrix, hence reporting the offset for 00:00h UTC, this is $8.08$ m/s<sup>2</sup>.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 8:</b>
+<p>Previously, in Task 3, you computed and plotted the magnitude spectrum $|X_k|$ of a signal for positive frequencies $f>0$ (putting a lot of attention to correctly labelling the horizontal frequency axis of the plot!), and now, with the material of Chapter 6 on spectral estimation, you will estimate the power spectral density $S$ of the signal through the periodogram, which is just: $S(k\Delta f)=\frac{|X_k|^2}{T}$ (in [W/Hz] when $x_n$ is a voltage signal; and $|X_k|$ being the result straight from the <code>np.fft</code>, multiplied by sampling interval $\Delta t$), for frequency $k\Delta f$, with frequency resolution $\Delta f=\frac{1}{T}$, and $k=0,…,N-1$ (hence, pretty much the same procedure as with the magnitude spectrum, though just taking the square of the modulus, and dividing by $T$).</p>
+<p>Compute and plot the periodogram for the detrended accelerometer measurements of Task 7 (if you prefer, feel free to use a linear scaling of the axes here, rather a log-log, and, use $T$ as defined already in the code of Task 7). Please, pay attention to correctly labelling the axes, and stating dimensions of the quantities along the axes!</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+
+<span class="c1">#estimate and plot power spectral density (PSD)</span>
+<span class="c1"># in this solution code we use np.fft (rather than scipy.fft), and we actually compute and plot a (symmetric) </span>
+<span class="c1">#two-sided PSD-estimate (using also the fftshift), as we like to (optionally) compare it with the Python </span>
+<span class="c1">#built-in periodogram</span>
+
+<span class="n">Fs</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">dt</span>
+<span class="n">f0</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">T</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">Fs</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">f0</span><span class="p">)</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">Fs</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">f0</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">f0</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Fs</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">f0</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">ehat</span><span class="p">)</span> <span class="o">*</span> <span class="n">dt</span>
+<span class="n">psd</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">T</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fftshift</span><span class="p">(</span><span class="n">psd</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'psd'</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'frequency [Hz]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'PSD [m2/s4/Hz]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Power Spectral Density (PSD)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>50.0
+0.0033331111259249386
+[-24.99833344 -24.99500033 -24.99166722 ... 24.99166722 24.99500033
+ 24.99833344]
+[-6.43751719e-14+3.12638804e-15j 3.67542391e-01-1.20851972e+00j
+ -2.53291804e+00+1.78096365e-01j ... -2.52083697e-01-2.44469498e+00j
+ -2.53291804e+00-1.78096365e-01j 3.67542391e-01+1.20851972e+00j]
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 9:</b>
+<p>Answer the following questions:</p>
+<ol>
+<li>Report the damped natural frequency (in Hertz) of this one-degree-of-freedom (1DOF) mechanical system. Does it match the motion of the beam shown in the cantilever-beam video?</li>
+<li>The acceleration was measured at quite a high sample rate of $50$ Hz. What is the minimum sampling frequency to correctly identify the damped natural frequency in the periodogram?</li>
+</ol>
+<p>
+</p></p></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>Write your answer(s) in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<ol>
+<li>Around $1.36$ Hz, and yes, this matches the motion in the video.</li>
+<li>Nyquist rate is twice the (highest) frequency in the signal, hence $2 \times 1.36 = 2.72$ Hz, so, any value larger than $2.72$ Hz.</li>
+</ol>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Task-10:-Global-Mean-Sea-Level-(GMSL)-data-(optional)">Task 10: Global Mean Sea-Level (GMSL) data (optional)<a class="anchor-link" href="#Task-10:-Global-Mean-Sea-Level-(GMSL)-data-(optional)">¶</a></h2><p>Repeat the steps with Tasks 7-9, but now with the Global Mean Sea Level data set. Data is from the Australia's National Science Agency (CSIRO): <a href="https://www.cmar.csiro.au/sealevel/sl_hist_last_decades.html">GMSL data</a>.</p>
+<p>These data result from nearly 3 decades of satellite altimetry (with satellite missions such as TOPEX/Poseidon and the Jason-series). The first column contains the time tag or epoch (in decimal years), the second column is the global mean sea level (in mm). There is one measurement per month (monthly average, so that for instance tide-effects are averaged out, and the measurement typically refers to the middle of the month, hence 1993.042 is mid January in 1993). The single monthly measurement is the global mean sea level, so, the average of the entire world.</p>
+<p>The sampling frequency $f_s = 12$ per year ($\Delta t = 1/12 \sim 0.083$ year), and there are $N=331$ measurements in total.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'CSIRO_Alt_seas_inc.txt'</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">'month'</span><span class="p">,</span> <span class="s1">'sl'</span><span class="p">])</span>
+<span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+<div>
+<style scoped="">
+ .dataframe tbody tr th:only-of-type {
+ vertical-align: middle;
+ }
+
+ .dataframe tbody tr th {
+ vertical-align: top;
+ }
+
+ .dataframe thead th {
+ text-align: right;
+ }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>month</th>
+<th>sl</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>1993.042</td>
+<td>-48.7</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1993.125</td>
+<td>-52.4</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1993.208</td>
+<td>-44.4</td>
+</tr>
+<tr>
+<th>3</th>
+<td>1993.292</td>
+<td>-50.4</td>
+</tr>
+<tr>
+<th>4</th>
+<td>1993.375</td>
+<td>-46.3</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># create time-array, time relative to t0 [yr] (epoch-time of the first observation;</span>
+<span class="c1"># t0=1993.042 refers to mid January 1993)</span>
+<span class="n">t</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+
+<span class="c1"># number of observations</span>
+<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+
+<span class="c1"># observation record length (as N * dt, according to sample-and-hold convention)</span>
+<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">N</span> <span class="o">/</span> <span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Delta t [yr]; dt = T/N = (N*dt)/N</span>
+<span class="n">dt</span> <span class="o">=</span> <span class="n">T</span> <span class="o">/</span> <span class="n">N</span>
+
+<span class="c1"># observed sea-level height</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># plot observed time-series, as it is, versus epoch-time in [year]</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'sea level'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [yr]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'sea-level height [mm]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Global Mean Sea-Level (GMSL) rise'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>Task 10 (Optional!):</b>
+<p>Detrend the data.</p>
+<p>Estimate and plot power spectral density (PSD), hence the periodogram, for the (detrended) global mean sea-level data.</p>
+<p>Identify the largest peak in the spectrum, what is the frequency, and can you come up with a physical explanation of this behaviour?</p>
+
+</p>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">YOUR_CODE_HERE</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+
+<span class="c1"># prepare for detrending the data, we'll estimate an offset and a slope (two unknown parameters)</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="n">A</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">t</span>
+
+<span class="c1"># (unweighted) least-squares estimation of two parameters</span>
+<span class="n">xhat</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">A</span><span class="p">)</span> <span class="o">@</span> <span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">@</span> <span class="n">y</span>
+
+<span class="c1"># estimated observations (fitted by a straight line with offset and slope)</span>
+<span class="n">yhat</span> <span class="o">=</span> <span class="n">A</span> <span class="o">@</span> <span class="n">xhat</span>
+
+<span class="c1"># difference of observed value and estimated observation (least-squares residuals)</span>
+<span class="n">ehat</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">yhat</span>
+
+<span class="c1"># hence observed time-series but detrended, this will act as our signal x(t), or x_0,...,x_{N-1}</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">ehat</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'detrended signal'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'time [yr]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'detrended sea-level height [mm]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Detrened Global Mean Sea-Level'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre><matplotlib.legend.Legend at 0x181d3d592b0></pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### SOLUTION</span>
+
+<span class="c1"># sampling frequency [Hz]</span>
+<span class="n">Fs</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">dt</span>
+
+<span class="c1"># observed signal to length NFFT ( record length is NFFT*dt)</span>
+<span class="n">NFFT</span> <span class="o">=</span> <span class="n">N</span>
+
+<span class="c1"># Discrete Fourier Transform (DFT) by fft, to NFFT samples</span>
+<span class="c1"># and multiply by Delta t (as to maintain analogy with continuous-time Fourier transform)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">ehat</span><span class="p">,</span> <span class="n">NFFT</span><span class="p">)</span>
+
+<span class="c1"># frequency resolution</span>
+<span class="n">f0</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">NFFT</span> <span class="o">*</span> <span class="n">dt</span><span class="p">)</span>
+
+<span class="c1"># frequency array (centered at f=0, and conform fftshift) for NFFT even, covers interval [-Fs/2,Fs/2)</span>
+<span class="c1">#f = np.concatenate((np.arange(- Fs / 2, 0, f0), np.arange(0, Fs / 2 , f0))); #+ f0/4</span>
+<span class="n">f</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">Fs</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">f0</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">f0</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Fs</span> <span class="o">/</span> <span class="mi">2</span> <span class="p">,</span> <span class="n">f0</span><span class="p">)))</span> <span class="c1">#- f0 / 4 </span>
+
+
+<span class="c1"># for NFFT odd, use instead: f=np.concatenate((np.arange(-Fs/2 + f0/2, 0, f0), np.arange(0, Fs/2, f0))); covers interval (-Fs/2,Fs/2)</span>
+<span class="c1">#|Xk|^2 / T periodogram (centered, with f=0 in the center), division by T, which is actual data record length</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fftshift</span><span class="p">((</span><span class="nb">abs</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="n">T</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'psd'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'frequency [$yr^{-1}$]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">'PSD [$mm^2$ yr]'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Power Spectral Density of GMSL data'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>Write your answer(s) in this Markdown cell.</strong></p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Solution:</b>
+<p>The largest peak is $f = 0.996$/ year, hence the annual cycle, related to summer and winter, there is also a peak at $f = 1.992$/year, hence the double frequency (related to a half year cycle), and this one typically shows up if the once per year periodic cycle is not a perfect harmonic (sine or cosine), but instead a bit distorted/skewed, finally, there is also a large peak at $f = \frac{1}{T} = 0.0362$/year, a long term effect, which implies a cycle with the duration of the entire data set, which here seems just coincidence, that a full cycle occurs in $T=27.58$ years.</p>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%;">
+<p>
+<b>End of task.</b>
+</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#C8FFFF; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%">
+<p>
+<b>Appendix A: Cantilever-beam dynamics:</b>
+<p>The dynamics of the smartphone suspended on a cantilever beam can be considered as the mass-spring-damper system shown below:</p>
+<img alt="No description has been provided for this image" src="https://gitlab.com/JelleKnibbe/public-files/-/raw/main/SP_Practical/mass_spring_damper.png" style="margin:auto" width="200/"/>
+<p>The equation of motion of the deflection $x$ of this mass-spring-damper system (a damped harmonic oscillator) can be described by the following second order differential equation:</p>
+$$
+\ddot{x}(t) + \frac{c}{m} \dot{x}(t) + \frac{k}{m} x(t) =0
+$$<p>Where $\ddot{x}(t)$, $\dot{x}(t)$ and $x(t)$ are the acceleration, velocity and displacement as a function of time of the oscillating mass $m$ [kg] respectively. (with a unit transfer function, $x(t)$ describes the motion of the proof mass inside the smartphone accelerometer). For simplicity, we consider here the homogeneous equation, corresponding to free motion. The other parameters are the damping coefficient $c$ [kg/s] and spring constant $k$ [N/m]. The damping ratio can be obtained from the system parameters: $\zeta = \frac{c}{2 \sqrt{mk}}$, which is dimensionless. The undamped natural frequency is $\omega_0 = \sqrt{\frac{k}{m}}$ [rad/s]. The differential equation becomes:</p>
+$$
+\ddot{x}(t) + 2 \zeta \omega_0 \dot{x}(t) + \omega_0^2 x(t) =0
+$$<p>For the under-damped case ($0 \le \zeta \le 1)$ of our smartphone and assuming an initial zero tip velocity $\dot{x}(t=0)=0$ (release from stand-still), the solution for the position as a function of time is given by:</p>
+$$
+x(t)=e^{-\zeta \omega_0 t} \frac{x(0)}{\sqrt{1-\zeta^2}}\sin(\omega_d t + \varphi)
+$$<p>Where $x(0)$ is the initial position $x(t=0)$, $\omega_d$ is the damped natural frequency $\omega_d = \omega_0 \sqrt{1-\zeta^2}$, and the phase shift $\varphi = \arctan \left( \frac{\sqrt{1-\zeta^2}}{\zeta} \right)$. The sinusoid term represents the harmonic motion, and the exponential term represents the damping of that motion over time. Next, the velocity of the smartphone's oscillation can be derived as:</p>
+<p>$\dot{x}(t) = e^{-\zeta \omega_0 t} \frac{x(0)}{\sqrt{1-\zeta^2}}\sin(\omega_d t)$.</p>
+<p>The acceleration of the smartphone (which is what is being measured) is found as:</p>
+<p>$\ddot{x}(t) = e^{-\zeta \omega_0 t} \frac{x(0)}{\sqrt{1-\zeta^2}} \sin (\omega_d t - \phi)$.</p>
+<p>Note that $\omega_0$ and $\omega_d$ are the angular frequencies expressed in radians per second. $\ddot{x}(t)$ is a damped harmonic signal where the rate of damping is determined by the damping ratio $\zeta$.</p>
+<p>In order to get a pure harmonic signal (as used in Tasks 1 to 5), set the damping ratio $\zeta=0$, and optionally set the phase-shift $\phi$ to zero as well, then the tip acceleration is given by the following simple sinusoidal expression:</p>
+$$
+\ddot{x}(t)=x(0) \sin(\omega_0 t).
+$$</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>End of notebook.</strong></p>
+<h2 style="height: 60px">
+</h2>
+<h3 style="position: absolute; display: flex; flex-grow: 0; flex-shrink: 0; flex-direction: row-reverse; bottom: 60px; right: 50px; margin: 0; border: 0">
+<style>
+ .markdown {width:100%; position: relative}
+ article { position: relative }
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+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px">
+</img></a>
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+</img></a>
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+<img alt="MUDE" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/mude-logo/MUDE_Logo-small.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px">
+</img></a>
+</h3>
+<span style="font-size: 75%">
+© Copyright 2024 <a href="http://mude.citg.tudelft.nl/" rel="MUDE">MUDE</a> TU Delft. This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" rel="license">CC BY 4.0 License</a>.
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+</main>
+</body>
+<script type="application/vnd.jupyter.widget-state+json">
+{"state": {}, "version_major": 2, "version_minor": 0}
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diff --git a/src/teachers/GA_2_3/CSIRO_Alt_seas_inc.txt b/src/teachers/GA_2_3/CSIRO_Alt_seas_inc.txt
new file mode 100644
index 00000000..9163035c
--- /dev/null
+++ b/src/teachers/GA_2_3/CSIRO_Alt_seas_inc.txt
@@ -0,0 +1,331 @@
+1993.042, -48.70
+1993.125, -52.40
+1993.208, -44.40
+1993.292, -50.40
+1993.375, -46.30
+1993.458, -51.50
+1993.542, -45.70
+1993.625, -44.90
+1993.708, -37.40
+1993.792, -38.50
+1993.875, -39.30
+1993.958, -40.40
+1994.042, -44.30
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-<h1 id="readme-for-group-assignment-23">README for Group Assignment 2.3</h1>
+</style></head><body><h1 id="readme-for-group-assignment-2-3">README for Group Assignment 2.3</h1>
<p><em><a href="http://mude.citg.tudelft.nl/">CEGM1000 MUDE</a>: Week 2.3, Friday, Nov 29, 2024.</em></p>
<p>The focus of this assignment is on signal processing, and specifically on spectral analysis (i.e. analysis of signals in the frequency domain).</p>
-<p>Your primary objective is to complete all tasks in the notebook <code>Analysis.ipynb</code>. Unlike other weeks, for this GA it is not required to put your answers in a <code>Report.md</code> file (yay!).** Make sure you use the space provided in the notebooks to complete the tasks, and that the output is included in the notebook when you commit and push it to GitHub.</p>
+<p>Your primary objective is to complete all tasks in the notebook <code>Analysis.ipynb</code>. Unlike other weeks, for this GA it is not required to put your answers in a <code>Report.md</code> file (yay!).** Make sure you use the space provided in the notebooks to complete the tasks, and that the output is included in the notebook when you commit and push it to GitHub. </p>
<h3 id="overview-of-material">Overview of material</h3>
<ul>
<li>this <code>README.md</code> with instructions</li>
@@ -383,7 +19,6 @@ code {
<p>As mentioned above: there is no <code>Report.md</code>, only a notebook!</p>
<p>That said, we strongly <strong>dis</strong>courage you from working on this project in smaller sub-teams. The Tasks build on each other sequentially, so skipping ahead to a later task will only result in confusion!</p>
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