diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/.DS_Store b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6
Binary files /dev/null and b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/.DS_Store differ
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/CITATION.cff b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/CITATION.cff
new file mode 100644
index 0000000000000000000000000000000000000000..1c0142072ee1e11a48dd0201ba5ceb7736b2e068
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/CITATION.cff
@@ -0,0 +1,19 @@
+cff-version: 0.0.1
+message: "If you use this software, please cite it as below."
+title: "QDarts"
+authors:
+ - family-names: Krzywda
+ given-names: Jan
+ orcid: https://orcid.org/0000-0001-6449-1396
+ - family-names: Liu
+ given-names: Weikun
+ - family-names: van Nieuwenburg
+ given-names: Evert
+ orcid: https://orcid.org/0000-0003-0323-0031
+ - family-names: Krause
+ given-names: Oswin
+ orcid: https://orcid.org/0000-0002-0990-559X
+version: 1.0.1
+date-released: 2024-04-02
+license: MIT License
+repository-code: "https://github.com/condensedAI/QDarts"
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/LICENCE.md b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/LICENCE.md
new file mode 100644
index 0000000000000000000000000000000000000000..335ea9d070ad1c319906aeff798584ded23c7387
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/LICENCE.md
@@ -0,0 +1,19 @@
+Copyright (c) 2018 The Python Packaging Authority
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/Makefile b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..50d836e97383353e3eb47c9559db6dd1f2e79fe1
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/Makefile
@@ -0,0 +1,17 @@
+# Minimal makefile for Sphinx documentation
+#
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS ?=
+SPHINXBUILD ?= sphinx-build
+SPHINXSOURCEDIR = sphinx
+BUILDDIR = build
+PYTHONBUILD ?= python3 -m build
+
+.PHONY: doc build
+
+doc:
+ @$(SPHINXBUILD) -M html "$(SPHINXSOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
+ cp -r $(BUILDDIR)/html/* docs/
+build:
+ @$(PYTHONBUILD)
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/README.md b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..da68dddb9cd362b81d1b5ec65f968dd00f007417
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/README.md
@@ -0,0 +1,58 @@
+# QDarts
+Efficient **Q**uantum **D**ot **ar**ray **t**ransition **s**imulator.
+
+## Description
+We provide an efficient simulation package, QDarts, generating realistic charge conductance signals from medium, more than 10 quantum dot arrays. By levering the polytope finding algorithm from [O. Krause, A. Chatterjee, F. Kuemmeth and E. van Nieuwenburg, Learning coulomb diamonds in large quantum dot arrays, SciPost Physics 13(4), 084 (2022)](https://scipost.org/SciPostPhys.13.4.084), the QDarts allows for:
+- Transition finding in high-dimensional voltage space,
+- Selection of arbitrary cuts in the voltage space,
+- Simulating effects of finite tunnel couplings,
+- Including non-constant charging energies,
+- Simulation of multiple sensor dot,
+- Tunable noise parameters,
+- User-friendly interface.
+
+## Installation
+The package supports Python 3.6 and later. To install the package, run the following command:
+
+ pip install qdarts
+
+## Manuscript
+The package is based on the manuscript by [Krzywda et al., QDarts: A Quantum Dot Array Transition Simulator for finding charge transitions in the presence of finite tunnel couplings, non-constant charging energies and sensor dots](). The manuscript has been submitted to the SciPost Physics Codebases.
+
+## Examples
+The package provides a simple example to demonstrate the usage of the package. The example is available in the examples qatpack/examples folder. The example demonstrates the simulation of a quantum dot array with sensor dots, tunnel couplings, and non-constant charging energy.
+
+As a proof of principle, in the example we reconstruct the figure from the paper [Neyens et al.](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.12.064049z), which shows the measured charge conductance signal from two sensor dots, which detect simultanous four-dot transition in the quantum dot array. The figure, visible below, has been computed in about a minute on a standard laptop.
+
+<p align="center">
+ <img src="https://github.com/condensedAI/QDarts/blob/main/examples/figures/neyens.png" />
+<p/>
+
+## Files in this repository
+ qdarts
+ |-- qdarts
+ |-- model.py
+ |-- noise_processes.py
+ |-- experiment.py
+ |-- plotting.py
+ |-- polytope.py
+ |-- simulator.py
+ |-- tunneling_simulator.py
+ |-- util_functions.py
+ |-- examples
+ |-- examples_scipost.ipynb # notebook to reproduce figures from paper
+ |-- README.md
+ |-- LICENCE.md
+ |-- CITATION.cff
+
+
+## Roadmap
+The package is under active development. The future plans include:
+- [ ] Adding barrier gates,
+- [ ] Including realistic noise processes, including 1/f noise,
+- [ ] Adding more examples,
+- [ ] Adding a method for generating capacitance matrices from:
+ - [ ] QD array layout,
+ - [ ] Experimental data,
+ - [ ] Finite element method simulations,
+- [ ] Scaling up to larger quantum dot arrays N>10,
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/__init__.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/.buildinfo b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/.buildinfo
new file mode 100644
index 0000000000000000000000000000000000000000..db07d507b6577ee57156ab6b380be4f5ee35b85a
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: ab1c3a24d5e5ee1e1634daa8e070fdb1
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/.nojekyll b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/.nojekyll
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.capacitance_model.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.capacitance_model.html
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,136 @@
+<!DOCTYPE html>
+
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+ <head>
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+ <section id="module-qdarts.capacitance_model">
+<span id="qdarts-capacitance-model"></span><h1>qdarts.capacitance_model<a class="headerlink" href="#module-qdarts.capacitance_model" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseCapacitanceModel</span></code>(num_dots, num_inputs, ...)</p></td>
+<td><p>Base Class for all capacitance models.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">CapacitanceModel</span></code>(C_g, C_D, bounds_limits[, ...])</p></td>
+<td><p>Implementation of a slight generalization of the constant interaction model.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
+ </div>
+ </div>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.noise_processes.html">qdarts.noise_processes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.plotting.html">qdarts.plotting</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
+ <li><a href="qdarts.html">qdarts</a><ul>
+ <li>Previous: <a href="qdarts.html" title="previous chapter">qdarts</a></li>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.experiment.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.experiment.html
new file mode 100644
index 0000000000000000000000000000000000000000..e58aef13797a7c3ac5471e399b895430864938eb
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@@ -0,0 +1,133 @@
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+
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+ <head>
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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+
+ <section id="module-qdarts.experiment">
+<span id="qdarts-experiment"></span><h1>qdarts.experiment<a class="headerlink" href="#module-qdarts.experiment" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Experiment</span></code>(capacitance_config[, ...])</p></td>
+<td><p>Experiment class that helps setup the different parts of the simulator into an easy to ue interface.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.tunneling_simulator.html">qdarts.tunneling_simulator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.html
new file mode 100644
index 0000000000000000000000000000000000000000..e5ea4f4ef3af249ad160e6b4f83457dc502a9b75
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.html
@@ -0,0 +1,153 @@
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+
+ <section id="module-qdarts">
+<span id="qdarts"></span><h1>qdarts<a class="headerlink" href="#module-qdarts" title="Link to this heading">¶</a></h1>
+<p class="rubric">Modules</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="qdarts.capacitance_model.html#module-qdarts.capacitance_model" title="qdarts.capacitance_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.capacitance_model</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="qdarts.experiment.html#module-qdarts.experiment" title="qdarts.experiment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.experiment</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="qdarts.noise_processes.html#module-qdarts.noise_processes" title="qdarts.noise_processes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.noise_processes</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="qdarts.plotting.html#module-qdarts.plotting" title="qdarts.plotting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.plotting</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="qdarts.polytope.html#module-qdarts.polytope" title="qdarts.polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.polytope</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="qdarts.simulator.html#module-qdarts.simulator" title="qdarts.simulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.simulator</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="qdarts.tunneling_simulator.html#module-qdarts.tunneling_simulator" title="qdarts.tunneling_simulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.tunneling_simulator</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="qdarts.util_functions.html#module-qdarts.util_functions" title="qdarts.util_functions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">qdarts.util_functions</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</section>
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+
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.experiment.html">qdarts.experiment</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.noise_processes.html">qdarts.noise_processes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.plotting.html">qdarts.plotting</a></li>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.noise_processes.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.noise_processes.html
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@@ -0,0 +1,140 @@
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+ <section id="module-qdarts.noise_processes">
+<span id="qdarts-noise-processes"></span><h1>qdarts.noise_processes<a class="headerlink" href="#module-qdarts.noise_processes" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">AbstractNoiseProcess</span></code>(num_elements)</p></td>
+<td><p>Models a noise process that produces possibly dependent samples <span class="math notranslate nohighlight">\(\epsilon(v)_t\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cosine_Mean_Function</span></code>(noise_model, a, W[, b])</p></td>
+<td><p>Decorator of an random process that models an additive mean term that depends on the gate voltages.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">OU_process</span></code>(sigma, tc, dt, num_elements)</p></td>
+<td><p>Implements the Ornstein-Uhlenbeck noise process</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
+ </div>
+ </div>
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+<li class="toctree-l1"><a class="reference internal" href="../simulator.html">Simulator</a></li>
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+<li class="toctree-l2 current"><a class="reference internal" href="qdarts.html">qdarts</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="qdarts.capacitance_model.html">qdarts.capacitance_model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.experiment.html">qdarts.experiment</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">qdarts.noise_processes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.plotting.html">qdarts.plotting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.polytope.html">qdarts.polytope</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.tunneling_simulator.html">qdarts.tunneling_simulator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
+</li>
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+</li>
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+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
+ <li><a href="qdarts.html">qdarts</a><ul>
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+ <li>Next: <a href="qdarts.plotting.html" title="next chapter">qdarts.plotting</a></li>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.plotting.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.plotting.html
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@@ -0,0 +1,151 @@
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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+ <section id="module-qdarts.plotting">
+<span id="qdarts-plotting"></span><h1>qdarts.plotting<a class="headerlink" href="#module-qdarts.plotting" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_feasible_point</span></code>(halfspaces)</p></td>
+<td><p>Computes a feasible point by a polytope defined in halfspace format.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_2D_polytope</span></code>(A, b, color, lower_bounds[, ...])</p></td>
+<td><p>computes the corners of a 2D polytope from the provided polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_CSD_data</span></code>(simulation, P, v_0, lower_left, ...)</p></td>
+<td><p>Function that computes a Charge Stability Diagram from a simulation of a device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_polytopes</span></code>(states, simulation_slice, ...)</p></td>
+<td><p>For each unique state in the provided state list, computes the corners of the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_2D_polytope</span></code>(ax, A, b, color, lower_bounds)</p></td>
+<td><p>Plots a single 2D polytope.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_polytopes</span></code>(ax, polytopes[, fontsize, ...])</p></td>
+<td><p>Plot the polytopes computes by get_polytopes</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">raster_CSD_states</span></code>(simulation, P, v_0, minV, ...)</p></td>
+<td><p>Creates a grid of points in 2D space and computes for each point the ground state.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.tunneling_simulator.html">qdarts.tunneling_simulator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
+</li>
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+</li>
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+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
+ <li><a href="qdarts.html">qdarts</a><ul>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.polytope.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.polytope.html
new file mode 100644
index 0000000000000000000000000000000000000000..daa1faebadfaa597ef4e023a7af8eba58e4c1511
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+ <section id="module-qdarts.polytope">
+<span id="qdarts-polytope"></span><h1>qdarts.polytope<a class="headerlink" href="#module-qdarts.polytope" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Polytope</span></code>(state)</p></td>
+<td><p>Represents the polytope P(n) defined by all gate voltages v in a device that have capacitive ground state n.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">qdarts.polytope</a></li>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.simulator.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.simulator.html
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+ <section id="module-qdarts.simulator">
+<span id="qdarts-simulator"></span><h1>qdarts.simulator<a class="headerlink" href="#module-qdarts.simulator" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="../interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator" title="qdarts.simulator.AbstractPolytopeSimulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AbstractPolytopeSimulator</span></code></a>(num_dots, num_inputs)</p></td>
+<td><p>Base class for all simulation objects that can compute and return polytopes.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseCapacitiveDeviceSimulator</span></code>(capacitance_model)</p></td>
+<td><p>Base class for all objects that create device simulations from a Capacitive Model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">CapacitiveDeviceSimulator</span></code>(capacitance_model)</p></td>
+<td><p>This class simulates a quantum dot device based on a capacitance model.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">CapacitiveDeviceSimulatorProxy</span></code>(simulator, P, m)</p></td>
+<td><p>This class is a slice proxy for the CapacitiveDeviceSimulator class.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
+ </div>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.experiment.html">qdarts.experiment</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.noise_processes.html">qdarts.noise_processes</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.polytope.html">qdarts.polytope</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">qdarts.simulator</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
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+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.tunneling_simulator.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.tunneling_simulator.html
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+ <section id="module-qdarts.tunneling_simulator">
+<span id="qdarts-tunneling-simulator"></span><h1>qdarts.tunneling_simulator<a class="headerlink" href="#module-qdarts.tunneling_simulator" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">softmax</span></code>(v[, axis])</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="../interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim" title="qdarts.tunneling_simulator.AbstractSensorSim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AbstractSensorSim</span></code></a>(num_sensors)</p></td>
+<td><p>Base class defining the interface for all sensor simulations</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ApproximateTunnelingSimulator</span></code>(polytope_sim, ...)</p></td>
+<td><p>Simulator for approximate charge tunneling in a quantum dot device.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">LocalSystem</span></code>(v, H, state, sim)</p></td>
+<td><p>Class describing a quantum system defined by the gate voltages of a simulated device.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">NoisySensorDot</span></code>(sensor_dot_ids)</p></td>
+<td><p>Simulates a sensor signal by computing the conductance of the sensor dots.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">TunnelBarrierModel</span></code>(gate_offsets[, gate_levers])</p></td>
+<td><p>Model of the tunnel barriers of a device</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.capacitance_model.html">qdarts.capacitance_model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.experiment.html">qdarts.experiment</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.noise_processes.html">qdarts.noise_processes</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="qdarts.polytope.html">qdarts.polytope</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.simulator.html">qdarts.simulator</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">qdarts.tunneling_simulator</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.util_functions.html">qdarts.util_functions</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+ <li><a href="../index.html">Documentation overview</a><ul>
+ <li><a href="../api.html">API</a><ul>
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+ <li>Next: <a href="qdarts.util_functions.html" title="next chapter">qdarts.util_functions</a></li>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.util_functions.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_autosummary/qdarts.util_functions.html
new file mode 100644
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+ <section id="module-qdarts.util_functions">
+<span id="qdarts-util-functions"></span><h1>qdarts.util_functions<a class="headerlink" href="#module-qdarts.util_functions" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">axis_align_transitions</span></code>(simulator, ...[, proxy])</p></td>
+<td><p>Transform the simulators coordinate system such that transitions are aligned with coordinate axes</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compensate_simulator_sensors</span></code>(simulator, ...)</p></td>
+<td><p>Transforms the simulation to compensate the sensors against all other gates.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_maximum_inscribed_circle</span></code>(A, b, ...)</p></td>
+<td><p>Computes the maximum inscribed circle in a polytope intersected with a set of linear inequalities.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_polytope_slacks</span></code>(A, b, maximum_slack)</p></td>
+<td><p>Computes the slacks of each candidate transition of a ground state polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_label</span></code>(labels, label)</p></td>
+<td><p>helper function that finds the position of a state in a matrix of states</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_point_on_transitions</span></code>(polytope, indizes)</p></td>
+<td><p>Finds a point on a facet (or intersection point of multiple facets) of a polytope</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fix_gates</span></code>(simulator, gate_ids, gate_values)</p></td>
+<td><p>Fixes a number of gate voltages in the simulator object</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_invertible_matrix</span></code>(A[, max_cond])</p></td>
+<td><p>Returns true if A is an invertible matrix.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">solve_linear_problem</span></code>(prob)</p></td>
+<td><p>Internal helper function to solve supplied linear cvxpy problems</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+ </div>
+
+ </div>
+ </div>
+ <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+ <div class="sphinxsidebarwrapper">
+<h1 class="logo"><a href="../index.html">QDarts</a></h1>
+
+
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+
+
+
+
+<h3>Navigation</h3>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../simulator.html">Simulator</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="qdarts.html">qdarts</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="qdarts.capacitance_model.html">qdarts.capacitance_model</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.experiment.html">qdarts.experiment</a></li>
+<li class="toctree-l3"><a class="reference internal" href="qdarts.noise_processes.html">qdarts.noise_processes</a></li>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">qdarts.util_functions</a></li>
+</ul>
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+<h3>Related Topics</h3>
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+ <a href="../_sources/_autosummary/qdarts.util_functions.rst.txt"
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.capacitance_model.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.capacitance_model.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b5326f6ce93728e35f81dc09f25ffe68dfcd06c4
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.capacitance_model.rst.txt
@@ -0,0 +1,30 @@
+qdarts.capacitance\_model
+=========================
+
+.. automodule:: qdarts.capacitance_model
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ BaseCapacitanceModel
+ CapacitanceModel
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.experiment.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.experiment.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..701a0ba2f6c80616b1bdf787105fbc18ade5b373
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.experiment.rst.txt
@@ -0,0 +1,29 @@
+qdarts.experiment
+=================
+
+.. automodule:: qdarts.experiment
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ Experiment
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.noise_processes.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.noise_processes.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f5e44f11572851e41435e2ff40d47e52aa36cb09
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.noise_processes.rst.txt
@@ -0,0 +1,31 @@
+qdarts.noise\_processes
+=======================
+
+.. automodule:: qdarts.noise_processes
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ AbstractNoiseProcess
+ Cosine_Mean_Function
+ OU_process
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.plotting.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.plotting.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0de01ee15761231194dc38c547a559a62028563e
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.plotting.rst.txt
@@ -0,0 +1,35 @@
+qdarts.plotting
+===============
+
+.. automodule:: qdarts.plotting
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+
+ find_feasible_point
+ get_2D_polytope
+ get_CSD_data
+ get_polytopes
+ plot_2D_polytope
+ plot_polytopes
+ raster_CSD_states
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.polytope.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.polytope.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..22ea6284c285787b50e019dc737570b3a3757288
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.polytope.rst.txt
@@ -0,0 +1,29 @@
+qdarts.polytope
+===============
+
+.. automodule:: qdarts.polytope
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ Polytope
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2b5a3a0037e02e30c72bd8fd82c70fb0ddeba519
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.rst.txt
@@ -0,0 +1,38 @@
+qdarts
+======
+
+.. automodule:: qdarts
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :recursive:
+
+ qdarts.capacitance_model
+ qdarts.experiment
+ qdarts.noise_processes
+ qdarts.plotting
+ qdarts.polytope
+ qdarts.simulator
+ qdarts.tunneling_simulator
+ qdarts.util_functions
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.simulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.simulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..35cce9a290c94982d268232b82915c90add680a8
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.simulator.rst.txt
@@ -0,0 +1,32 @@
+qdarts.simulator
+================
+
+.. automodule:: qdarts.simulator
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ AbstractPolytopeSimulator
+ BaseCapacitiveDeviceSimulator
+ CapacitiveDeviceSimulator
+ CapacitiveDeviceSimulatorProxy
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.tunneling_simulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.tunneling_simulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e67b010a846b8cbedb0ad2972d7451b81acb3736
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.tunneling_simulator.rst.txt
@@ -0,0 +1,39 @@
+qdarts.tunneling\_simulator
+===========================
+
+.. automodule:: qdarts.tunneling_simulator
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+
+ softmax
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+
+ AbstractSensorSim
+ ApproximateTunnelingSimulator
+ LocalSystem
+ NoisySensorDot
+ TunnelBarrierModel
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.util_functions.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.util_functions.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..542eafc7dbc71a831d9bb13eb14f714165c7087a
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/_autosummary/qdarts.util_functions.rst.txt
@@ -0,0 +1,37 @@
+qdarts.util\_functions
+======================
+
+.. automodule:: qdarts.util_functions
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+
+ axis_align_transitions
+ compensate_simulator_sensors
+ compute_maximum_inscribed_circle
+ compute_polytope_slacks
+ find_label
+ find_point_on_transitions
+ fix_gates
+ is_invertible_matrix
+ solve_linear_problem
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c6da2729323c05fde359a82343a6cb5de201f576
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api.rst.txt
@@ -0,0 +1,94 @@
+API
+===
+.. currentmodule:: qdarts
+
+In the following, we will describe the core components of our API
+
+Experiment Class
+----------------
+For a quick start for uing the simulator, there is the Experiment class that is also used
+in the example notebook. We refer to the notebook for a detailled usage example.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ experiment.Experiment
+
+
+Simulation Components
+---------------------
+The main components to build your own simulations are shown below.
+We first need to create a Capacitance model that generates a
+capacitive energy function :math:`E(v,n)`. With this, we can start
+to generate a purely capacitive simulation.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ capacitance_model.CapacitanceModel
+ simulator.CapacitiveDeviceSimulator
+
+This simulation is rather basic and can only compute areas of voltages that lead to the same ground
+state, so called coulomb diamonds. This can already be used to generate
+Charge Stability diagrams by computing 2D slices through the set of polytopes,
+but those will not look very realistic, nor will they include tunneling.
+
+To move beyond this simulation, we need to drastically improve the modeling.
+This next step is based on the full tunneling simulator that requires a simulation
+of the sensor dot and a simulation of tunnel barriers - the latter can be simplified by
+just providing a matrix of constant tunnel couplings.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ tunneling_simulator.ApproximateTunnelingSimulator
+ tunneling_simulator.TunnelBarrierModel
+ tunneling_simulator.NoisySensorDot
+
+
+The simulation of the sensor dot can optionally make use of a noise model in order
+to generate dependent noise. For this, we currently offer the following two classes
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ noise_processes.OU_process
+ noise_processes.Cosine_Mean_Function
+
+
+
+Data Classes
+-------------
+Finally, both levels of simulations have their own data classes. The polytope class is returned by the
+boundaries method by any simulator and the local system is returned by the tunneling simulator. Both describe
+the local state of the simulator in some region.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ tunneling_simulator.LocalSystem
+ polytope.Polytope
+
+Interfaces
+----------
+QDarts offers a set of interfaces and base classes that can serve as a primer to how to extend the simulator by
+replacing existing components. In most cases, only a few specialized functions need to be implemented as the base
+class implements most of the important logic. Please note that QDarts is under active development, all of these
+interfaces are subject to change.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ capacitance_model.AbstractCapacitanceModel
+ simulator.AbstractPolytopeSimulator
+ simulator.AbstractCapacitiveDeviceSimulator
+ noise_processes.AbstractNoiseProcess
+ tunneling_simulator.AbstractSensorSim
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api_tutorial.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api_tutorial.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dcef4fe5573d63ff497c7804c6f0aa0e1e12e9d9
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/api_tutorial.rst.txt
@@ -0,0 +1,202 @@
+API Usage Tutorial
+==================
+
+QDarts comes with two API-levels, the ``Experiment`` class that simplifies many of the most common applications at the cost
+of flexibility. It is build on top of the low-level API, that gives access to the full power of QDarts. It is possile to mix both
+APIs, for example, it is possible to construct a simulator using the ``Experiment`` class and then modify it using the low-level API.
+In the following, we will explain the low-level API (The usage of the high-level api is described in the ipython notebook located in ``examples/figures_scipost.ipynb``
+that also reproduces all figures of our publication).
+
+The code for this tutorial is provided in the ipython notebook ``examples/api_tutorial.ipynb``. To keep the tutorial concise,
+we only include the most important lines here that are needed to create the simulator,
+but we skip definition of capacitance parameters etc. For this, directly reading the code is best.
+
+The simulated device is a dcevice with 6 dots, of which three are sensor dots. It is controlled by 9 plunger gates, 1 for each dot and
+three that govern barrier gates between the three dots that are not sensor dots. The device is defined using a set of parameter matrices
+that govern the capacitances between the 9 gates and 6 dots, as well as the effect the 9 gates have on the barrier potential and therefore the tunnel coupling.
+
+
+Construction of the capacitive simulation
+-----------------------------------------
+
+As first step, we need to initialize the capacitive simulation.
+It depends on a capacitive model, which creates the capacitive energy function and which depends on a number of
+parameters, the capacitanc matrices between plunger gates and dots, ``C_DG``, the interdot capacitances ``C_DD``,
+a lower bound on the gate voltages ``bounds_limits`` and optionaly a parameter ``ks`` that governs the deviation from the constant interaction
+model and which can be set to ``None``. With these parameters, creating the capacitive simulation is straight forward::
+
+ from qdarts.capacitance_model import CapacitanceModel
+ from qdarts.simulator import CapacitiveDeviceSimulator
+
+ capacitance_model = CapacitanceModel(C_DG, C_DD, bounds_limits, ks=ks)
+ capacitive_sim = CapacitiveDeviceSimulator(capacitance_model)
+
+The capacitance simulation is purely classical and fits to the simulation of typical GaAs spin-qubit devices.
+It can only be used to query the ground state of the electron configuration given a set of gate voltages,
+as well as for each electron configuration the polytope of gate voltages that lead to the ground state configuration. Both features together are enough
+to plot a charge stability diagram and to exactly compute the boundaries of ground states within the diagram.
+
+
+Plotting the Charge Stability Diagram
+-------------------------------------
+For ploting, we have two helper functions. First, ``get_CSD_states`` computes for a 2D grid of gate voltages the ground state. This is done by computing a standard 2D grid
+that is then transformed into gate-voltage space. This transformation is done via a linear transformation :math:`v=m+P@v'`, where v is the full vector of gate plunger voltages,
+in our case 9 values. The variables :math:`m` and :math:`P` are projection parameters, where :math:`m` is a 9 dimensional vector and :math:`P` a 9x2 matrix.
+This defines a local 2D coordinate system transformation from the 2D grid :math:`v'` to :math:`v`. With this, it is possible to create a plot in any two coordinate axes.
+If we pick the grid of :math:`v'` values as symmetric around 0, then :math:`m` can be interpreted as the midpoint of the scan that we compute next.
+
+the question is, how we can obtain these values. We can use the capacitive simulation to query points of interest. For example, when we are interested in the scan around
+a point on the state where there is a single electron on each inner dot and 5 electrons on each sensor dot, we can query it simply via::
+
+ m = capacitive_sim.boundaries([1,1,1,5,5,5]).point_inside
+
+And if we want to perform a scan over the axes of the first and third plunger gate (which in our simulation are strongest coupled to the first and third inner dot) we can
+set::
+
+ P=np.zeros((N_gates,2))
+ P[0,0]=1
+ P[2,1]=1
+
+
+Now that we have the projection, we can define the minimum and maximum values of :math:`v'` in both directions and choose an image resolution.
+Then, we can use ``get_CSD_data``::
+
+ from qdarts.plotting import get_CSD_data
+
+ minV = [-0.01,-0.01]
+ maxV = [ 0.01, 0.01]
+ resolution = 100
+
+ sliced_csim, CSD_data, states = get_CSD_data(capacitive_sim, P,m, minV, maxV, resolution, target_state)
+
+The last parameter is needed to provide the simulation with an initial guess of which might be the ground state of the bottom left corner of the plot.
+This guess does not need to be exact. This function returns three things: first, a simulation ``sliced_csim`` that is defined on the transformed space :math:`v'` and serves as a cache for
+computed values. We will talk more about these slices later in the tutorial. The second result, ``CSD_data`` is a plottable 2D grid of values in which each ground state configuration is assigned
+a different value. Finally, ``states`` is an array of all ground states for each grid point. We can plot this directly with matplotlib::
+
+ xs = np.linspace(minV[0],maxV[0],resolution)
+ ys = np.linspace(minV[1],maxV[1],resolution)
+ plt.pcolormesh(xs,ys,CSD_data.T)
+
+The resulting grid is a rastered low resolution image.
+
+.. image:: /_static/api_csd_raster.png
+ :scale: 50 %
+ :align: center
+
+We can improve on this by plotting the exact boundaries between the two ground states using ``get_polytopes`` and ``plot_polytopes``::
+
+ polytopes = get_polytopes(states, sliced_csim, minV, maxV)
+ plt.xlim(minV[0],maxV[0])
+ plt.ylim(minV[1],maxV[1])
+ plot_polytopes(plt.gca(),polytopes, skip_dots=[3,4,5], fontsize=16)
+
+Note that this function computes the polytopes based on the low dimensional sliced simulation returned by ``get_CSD_data`` as well as its computed array of states. The function also prints the labels
+of each state, and the option ``skip_dots`` allows to skip the part of the electron configuration we are not interested in. In this case, we skipped the 3 sensor dots.
+Adding the last two plotting commands together, the resulting plot should look like this:
+
+.. image:: /_static/api_csd_grid.png
+ :scale: 50 %
+ :align: center
+
+
+Creation of a tunneling simulation
+----------------------------------
+Now that we are able to generate a CSD, we want to also simulate tunneling and simulate a more realistic sensor signal.
+For this, we need to create a few objects. First, we have to decide on a sensor simulator. The default choice is the ``NoisySensorDot``
+which simulates a sequence of sensor reflectometry peaks on one or more sensor dots::
+
+ sensor_model = NoisySensorDot(sensor_dots) #a model of the sensor dots that just needs which dot has which index
+ sensor_model.config_peak(g_max = 1.0, peak_width_multiplier = 20) #make the sensor peak broader
+
+The constructor takes the indexes of the dots we want to use as sensor dots and each entry in this list will produce one sensor signal, the quantum capacitance measured
+at the sensor dot. In the next line we add configuration options. ``g_max`` is just a scaling factor of the signal, which we leave at 1. The default sensor peak is simulated using only the
+temperature of the dot and does not take broadening due to RF power into account. To compensate for this, we introduce a broadening factor that can make the peak wider. in our experience, values between
+5 and 20 work well. There are more options to add here, for example the noise model of the signal, but for this tutorial, we do not add noise to keep it simple. You can check the documentation
+of ``NoisySensorDot`` for more details on that.
+
+Now, with our configured sensor dot, we create the simulation using tunnel coupling::
+
+ tunneling_sim = ApproximateTunnelingSimulator(capacitive_sim, #the underlying polytope simulation
+ tunnel_couplings, #symmetric tunnel coupling matrix T_ij which stores the tunnel coupling between dots i and j
+ temperature, #electron temperature, should be <=200mK
+ sensor_model) #our sensor model simulation
+
+The tunneling simulation is very expensive, and by default it tries to minimize the number of basis elements for the computed Hamiltonians by excluding states with energies that are large compared to
+a chosen slack. By default the slack is 0, and thus all states that are not direct neighbours of our polytope (i.e., they do not share a facet) will be excluded. By increasing the slack proportional to
+the inverse temperature, we add more states that might still be relevant for tunneling. Further, the sensor dot simulation relies on the existence of states that add or remove an electron from the sensor dot.
+These can easily be excluded from the simulation and therefore we enfore in the simulation that states with additional electrons added/subtracted on the sensor dot are always present.
+
+ capacitive_sim.set_maximum_polytope_slack(5/tunneling_sim.beta) #adding slack to keep more states that are likely to affect the hamiltonian
+ tunneling_sim.num_additional_neighbours[sensor_dots] = 2 #adding additional states for the sensor dots
+
+Finally, we can create a similar plot as before. We use the same values for ´´m´´ and ´´P´´ as for the previous simulation, so that we can compare the two plots.
+Note that since the device has three sensor dots, the output is a 3 dimensional tensor where the last dimension is the number of sensor dots and we plot below only
+the output of the third sensor::
+
+ sensor_values = tunneling_sim.sensor_scan_2D(P, m, minV, maxV, resolution, target_state)
+ plt.pcolormesh(xs,ys,sensor_values[:,:,2].T)
+
+
+This plots results in the sensor signal
+
+.. image:: /_static/api_tunnel_raster.png
+ :scale: 50 %
+ :align: center
+
+which again can be overlayed with our grid as before:
+
+.. image:: /_static/api_tunnel_grid.png
+ :scale: 50 %
+ :align: center
+
+
+Slicing and sensor compensation
+-------------------------------
+
+The last plot shows that most of the sensor signal is very low and there is only a small area with a peak. This is because of the missing sensor compensation: the sensor peak
+is capacitely affected by the plunger gates and thus changing the voltages changes the sensor signal. On the previous plot we already configured a wide width of the peak (``peak_width_multiplier = 20```) to make
+this effect small, but in reality many devices have very narrow and clear peaks.
+What we need is sensor compensation, i.e., a change of coordinate system where the sensor plunger voltage is corrected for the cross-talk from the other gates so that we stay on the sensor peak as well as possible.
+Moreover, the sensor peak is not centered. This is because we used as mid-point a point inside the polytope, but the sensor peaks are only on the boundaries between polytopes with different number of
+electrons on the sensor dots. Thus, we need to change our coordinate system and tune the sensor to a better point.
+
+This change of coordinate system is achieved in the simulator via slicing. In the previous examples, we defined a matrix ``P`` and vector ``m`` to generate the plot. We said that this changed the coordinate
+system via :math:`v=m+Pv'`, where ``v'`` was a 2D case. This operation is called slicing and a simulation can be sliced via::
+
+ sliced_sim = sim.slice(P,m)
+
+This works on all simulator objects and it returns a new simulation with gate voltages ``v'``. This works in any dimensions, not just 2 and it is used in most simulation transformation code.
+QDarts makes use of this in all its transformation code. For example, compensating the sensor dot::
+
+ compensated_tsim, _,m_comp = compensate_simulator_sensors(
+ tunneling_sim, #the simulator object to compensate
+ target_state=[1,1,1,5,5,5], #the electron configuration around which to compensate
+ compensation_gates= [6,7,8], #the ids of the sensor gate plungers.
+ sensor_ids = [3,4,5],#the ids of the sensor dots.
+ sensor_detunings = np.array([-0.0005,-0.0005,-0.0005]) #shift of the sensor dot plungers
+ )
+
+This function returns the sensor compensated simulator object, which has its gates transformed such, that changing the gate voltages of any gate plunger other the sensor dot plungers does not change the
+chemical potential of the sensor dot and thus leaves the sensor signal constant, except when a transition happens. This function relies on the user to supply the ids of the sensor dots and which elements of the voltage
+vector are the sensor plungers (for example, the sensor dot with id 3 has plunger 6 as its plunger gate). Since in our model the capacitance parameters of the model vary with the occupation number, we have to
+indicate the target state. Further, we have to supply a vector of sensor detunings that indicate on which position we are on the snesor detuning axis, i.e., the sensor peak. This value is relative to the peak maximum,
+a value of 0 will return the exact peak max, which is a value that is typically avoided in practice due to the low signal to noise ratio. Additional to the sensor compensation object, the function returns also as third
+return value the exact tuning point, i.e., the voltage vector that identifies the flank point for the given state. We can use this to plot a 2D charge stability diagram with the returned point as its center.
+We also show in the point below how to query the internal modified capacitance simulator using ``compensated_tsim.poly_sim`` in order to compute the state boundaries::
+
+ sensor_values = compensated_tsim.sensor_scan_2D(P, m_comp, minV, maxV, resolution, target_state)
+ sliced_tsim, CSD_data, states = get_CSD_data(compensated_tsim.poly_sim, P,m_comp, minV, maxV, resolution, target_state)
+ polytopes = get_polytopes(states, sliced_tsim, minV, maxV)
+
+
+Plotting the computed ```sensor_values`` and ``polytopes`` as before, leads to
+
+
+.. image:: /_static/api_tunnel_comp.png
+ :scale: 50 %
+ :align: center
+
+As you can see, the sensors are almost perfectly compensated, leading to almost perfectly flat polytopes with no visible shape of a sensor peak. The [1,1,1] state indicates a color in the middle of the peak
+and we can see how single electron transitions change the position on the peak due to the change of color on the polytopes. It is similarly possible to compensate any other gates and thus to compute virtual coordinates,
+for this we refer to the documentation of ``axis_align_transitions``.
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/index.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/index.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed00c6157f0399baf0e0b40c0eba2e28d08d46aa
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/index.rst.txt
@@ -0,0 +1,23 @@
+QDarts
+======
+
+Installation
+------------
+Installation can be done via ``python build``. In the QDarts directory, run::
+
+ make build
+ python3 -m pip install QDarts/dist/QDarts-<Version>-py3-none-any.whl
+
+where ``<Version>`` is the current QDarts version.
+
+
+Documentation
+-------------
+A usage example is provided in the ipython notebook in the ``examples/`` folder.
+Below you find the `detailed documentation of ``QDarts``
+
+.. toctree::
+ :maxdepth: 1
+
+ api_tutorial
+ api
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb5cf5ee733bf865d3a57ef28e4a6117313f08d9
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.rst.txt
@@ -0,0 +1,27 @@
+qdarts.capacitance\_model.AbstractCapacitanceModel
+==================================================
+
+.. currentmodule:: qdarts.capacitance_model
+
+.. autoclass:: AbstractCapacitanceModel
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~AbstractCapacitanceModel.__init__
+ ~AbstractCapacitanceModel.compute_polytope_for_state
+ ~AbstractCapacitanceModel.compute_transition_equations
+ ~AbstractCapacitanceModel.enumerate_neighbours
+ ~AbstractCapacitanceModel.slice
+ ~AbstractCapacitanceModel.verify_polytope
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.CapacitanceModel.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.CapacitanceModel.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d67e76555d2eac743e5c95c9638ecdfb6bed334
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.capacitance_model.CapacitanceModel.rst.txt
@@ -0,0 +1,27 @@
+qdarts.capacitance\_model.CapacitanceModel
+==========================================
+
+.. currentmodule:: qdarts.capacitance_model
+
+.. autoclass:: CapacitanceModel
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~CapacitanceModel.__init__
+ ~CapacitanceModel.compute_polytope_for_state
+ ~CapacitanceModel.compute_transition_equations
+ ~CapacitanceModel.enumerate_neighbours
+ ~CapacitanceModel.slice
+ ~CapacitanceModel.verify_polytope
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.experiment.Experiment.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.experiment.Experiment.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..17f3ac2d4120dc1a9252e5de4ccb98683cd1107d
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.experiment.Experiment.rst.txt
@@ -0,0 +1,30 @@
+qdarts.experiment.Experiment
+============================
+
+.. currentmodule:: qdarts.experiment
+
+.. autoclass:: Experiment
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Experiment.__init__
+ ~Experiment.center_transition
+ ~Experiment.deploy_capacitance_sim
+ ~Experiment.deploy_sensor_model
+ ~Experiment.deploy_tunneling_sim
+ ~Experiment.generate_CSD
+ ~Experiment.get_compensated_sim
+ ~Experiment.get_plot_args
+ ~Experiment.get_virtualised_sim
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.AbstractNoiseProcess.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.AbstractNoiseProcess.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..43e5d4451fd24e4b0f6692b21cf263d96756d34b
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.AbstractNoiseProcess.rst.txt
@@ -0,0 +1,24 @@
+qdarts.noise\_processes.AbstractNoiseProcess
+============================================
+
+.. currentmodule:: qdarts.noise_processes
+
+.. autoclass:: AbstractNoiseProcess
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~AbstractNoiseProcess.__init__
+ ~AbstractNoiseProcess.slice
+ ~AbstractNoiseProcess.start_sequence
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.Cosine_Mean_Function.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.Cosine_Mean_Function.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..21a45fa15dc868234952a6891808d5b69d990091
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.Cosine_Mean_Function.rst.txt
@@ -0,0 +1,24 @@
+qdarts.noise\_processes.Cosine\_Mean\_Function
+==============================================
+
+.. currentmodule:: qdarts.noise_processes
+
+.. autoclass:: Cosine_Mean_Function
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Cosine_Mean_Function.__init__
+ ~Cosine_Mean_Function.slice
+ ~Cosine_Mean_Function.start_sequence
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.OU_process.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.OU_process.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20a415d2298e9d9148edfb960f40602afa48c655
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.noise_processes.OU_process.rst.txt
@@ -0,0 +1,25 @@
+qdarts.noise\_processes.OU\_process
+===================================
+
+.. currentmodule:: qdarts.noise_processes
+
+.. autoclass:: OU_process
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~OU_process.__init__
+ ~OU_process.next_val
+ ~OU_process.slice
+ ~OU_process.start_sequence
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.polytope.Polytope.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.polytope.Polytope.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1746ede5d5cab7bfdbce659d52732726d4a5cdca
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.polytope.Polytope.rst.txt
@@ -0,0 +1,25 @@
+qdarts.polytope.Polytope
+========================
+
+.. currentmodule:: qdarts.polytope
+
+.. autoclass:: Polytope
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Polytope.__init__
+ ~Polytope.invertible_transform
+ ~Polytope.lazy_slice
+ ~Polytope.set_polytope
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c7fdf8b97becb582cf24d57a89c979d2c03f1719
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.rst.txt
@@ -0,0 +1,32 @@
+qdarts.simulator.AbstractCapacitiveDeviceSimulator
+==================================================
+
+.. currentmodule:: qdarts.simulator
+
+.. autoclass:: AbstractCapacitiveDeviceSimulator
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~AbstractCapacitiveDeviceSimulator.__init__
+ ~AbstractCapacitiveDeviceSimulator.boundaries
+ ~AbstractCapacitiveDeviceSimulator.cached_polytopes
+ ~AbstractCapacitiveDeviceSimulator.compute_polytope
+ ~AbstractCapacitiveDeviceSimulator.compute_transition_equations
+ ~AbstractCapacitiveDeviceSimulator.find_boundary_intersection
+ ~AbstractCapacitiveDeviceSimulator.find_state_of_voltage
+ ~AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack
+ ~AbstractCapacitiveDeviceSimulator.inside_state
+ ~AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack
+ ~AbstractCapacitiveDeviceSimulator.slice
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractPolytopeSimulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractPolytopeSimulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..22a1c453374f3fdfa80107947af9a24d18dafddc
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.AbstractPolytopeSimulator.rst.txt
@@ -0,0 +1,24 @@
+qdarts.simulator.AbstractPolytopeSimulator
+==========================================
+
+.. currentmodule:: qdarts.simulator
+
+.. autoclass:: AbstractPolytopeSimulator
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~AbstractPolytopeSimulator.__init__
+ ~AbstractPolytopeSimulator.boundaries
+ ~AbstractPolytopeSimulator.slice
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.CapacitiveDeviceSimulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.CapacitiveDeviceSimulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed202ba09b1ea7629fb7a65004fbf903093dfac3
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.simulator.CapacitiveDeviceSimulator.rst.txt
@@ -0,0 +1,32 @@
+qdarts.simulator.CapacitiveDeviceSimulator
+==========================================
+
+.. currentmodule:: qdarts.simulator
+
+.. autoclass:: CapacitiveDeviceSimulator
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~CapacitiveDeviceSimulator.__init__
+ ~CapacitiveDeviceSimulator.boundaries
+ ~CapacitiveDeviceSimulator.cached_polytopes
+ ~CapacitiveDeviceSimulator.compute_polytope
+ ~CapacitiveDeviceSimulator.compute_transition_equations
+ ~CapacitiveDeviceSimulator.find_boundary_intersection
+ ~CapacitiveDeviceSimulator.find_state_of_voltage
+ ~CapacitiveDeviceSimulator.get_maximum_polytope_slack
+ ~CapacitiveDeviceSimulator.inside_state
+ ~CapacitiveDeviceSimulator.set_maximum_polytope_slack
+ ~CapacitiveDeviceSimulator.slice
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.AbstractSensorSim.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.AbstractSensorSim.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0788ddef13533b2d5dddf1d37a69dde04dcabf14
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.AbstractSensorSim.rst.txt
@@ -0,0 +1,27 @@
+qdarts.tunneling\_simulator.AbstractSensorSim
+=============================================
+
+.. currentmodule:: qdarts.tunneling_simulator
+
+.. autoclass:: AbstractSensorSim
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~AbstractSensorSim.__init__
+ ~AbstractSensorSim.precompute_sensor_state
+ ~AbstractSensorSim.sample_sensor_configuration
+ ~AbstractSensorSim.sample_sensor_equilibrium
+ ~AbstractSensorSim.slice
+ ~AbstractSensorSim.start_measurement
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5849a30c4763d4c3e179118e634897afc48877a2
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.rst.txt
@@ -0,0 +1,27 @@
+qdarts.tunneling\_simulator.ApproximateTunnelingSimulator
+=========================================================
+
+.. currentmodule:: qdarts.tunneling_simulator
+
+.. autoclass:: ApproximateTunnelingSimulator
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~ApproximateTunnelingSimulator.__init__
+ ~ApproximateTunnelingSimulator.boundaries
+ ~ApproximateTunnelingSimulator.compute_local_system
+ ~ApproximateTunnelingSimulator.sensor_scan
+ ~ApproximateTunnelingSimulator.sensor_scan_2D
+ ~ApproximateTunnelingSimulator.slice
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.LocalSystem.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.LocalSystem.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ceddb10883ef52cfc1b946826a5490185beaa3d
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.LocalSystem.rst.txt
@@ -0,0 +1,33 @@
+qdarts.tunneling\_simulator.LocalSystem
+=======================================
+
+.. currentmodule:: qdarts.tunneling_simulator
+
+.. autoclass:: LocalSystem
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~LocalSystem.__init__
+ ~LocalSystem.compute_mixed_state_of_subset
+ ~LocalSystem.sample_sensor_configuration
+ ~LocalSystem.sample_sensor_equilibrium
+
+
+
+
+
+ .. rubric:: Attributes
+
+ .. autosummary::
+
+ ~LocalSystem.basis_labels
+ ~LocalSystem.core_basis_indices
+ ~LocalSystem.mixed_state
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.NoisySensorDot.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.NoisySensorDot.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..28e8975169b321c537d6fe278c0d37bd8467fe41
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.NoisySensorDot.rst.txt
@@ -0,0 +1,29 @@
+qdarts.tunneling\_simulator.NoisySensorDot
+==========================================
+
+.. currentmodule:: qdarts.tunneling_simulator
+
+.. autoclass:: NoisySensorDot
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~NoisySensorDot.__init__
+ ~NoisySensorDot.config_noise
+ ~NoisySensorDot.config_peak
+ ~NoisySensorDot.precompute_sensor_state
+ ~NoisySensorDot.sample_sensor_configuration
+ ~NoisySensorDot.sample_sensor_equilibrium
+ ~NoisySensorDot.slice
+ ~NoisySensorDot.start_measurement
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a43274b9d8765f46f4eb192c50bcaaa619f9e33
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.rst.txt
@@ -0,0 +1,24 @@
+qdarts.tunneling\_simulator.TunnelBarrierModel
+==============================================
+
+.. currentmodule:: qdarts.tunneling_simulator
+
+.. autoclass:: TunnelBarrierModel
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~TunnelBarrierModel.__init__
+ ~TunnelBarrierModel.get_tunnel_matrix
+ ~TunnelBarrierModel.slice
+
+
+
+
+
+
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/simulator.rst.txt b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/simulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..64423f0816039e68c7211093f151e97f70e6abce
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_sources/simulator.rst.txt
@@ -0,0 +1,3 @@
+Simulator
+---------
+Lorem Ipsum.
\ No newline at end of file
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new file mode 100644
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+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/alabaster.css
@@ -0,0 +1,708 @@
+@import url("basic.css");
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+}
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+ border: 1px solid #ddb;
+ padding: 7px;
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+ width: 40%;
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+/* -- content of sidebars/topics/admonitions -------------------------------- */
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\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..2a924f1d6a8bc930c5296bdb2d5c2d3e39b04a1c
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/custom.css
@@ -0,0 +1 @@
+/* This file intentionally left blank. */
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/doctools.js b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/doctools.js
new file mode 100644
index 0000000000000000000000000000000000000000..4d67807d17d27b16d4ac115dc383523157635f93
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+ "TEXTAREA",
+ "INPUT",
+ "SELECT",
+ "BUTTON",
+]);
+
+const _ready = (callback) => {
+ if (document.readyState !== "loading") {
+ callback();
+ } else {
+ document.addEventListener("DOMContentLoaded", callback);
+ }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+ init: () => {
+ Documentation.initDomainIndexTable();
+ Documentation.initOnKeyListeners();
+ },
+
+ /**
+ * i18n support
+ */
+ TRANSLATIONS: {},
+ PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+ LOCALE: "unknown",
+
+ // gettext and ngettext don't access this so that the functions
+ // can safely bound to a different name (_ = Documentation.gettext)
+ gettext: (string) => {
+ const translated = Documentation.TRANSLATIONS[string];
+ switch (typeof translated) {
+ case "undefined":
+ return string; // no translation
+ case "string":
+ return translated; // translation exists
+ default:
+ return translated[0]; // (singular, plural) translation tuple exists
+ }
+ },
+
+ ngettext: (singular, plural, n) => {
+ const translated = Documentation.TRANSLATIONS[singular];
+ if (typeof translated !== "undefined")
+ return translated[Documentation.PLURAL_EXPR(n)];
+ return n === 1 ? singular : plural;
+ },
+
+ addTranslations: (catalog) => {
+ Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+ Documentation.PLURAL_EXPR = new Function(
+ "n",
+ `return (${catalog.plural_expr})`
+ );
+ Documentation.LOCALE = catalog.locale;
+ },
+
+ /**
+ * helper function to focus on search bar
+ */
+ focusSearchBar: () => {
+ document.querySelectorAll("input[name=q]")[0]?.focus();
+ },
+
+ /**
+ * Initialise the domain index toggle buttons
+ */
+ initDomainIndexTable: () => {
+ const toggler = (el) => {
+ const idNumber = el.id.substr(7);
+ const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+ if (el.src.substr(-9) === "minus.png") {
+ el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+ toggledRows.forEach((el) => (el.style.display = "none"));
+ } else {
+ el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+ toggledRows.forEach((el) => (el.style.display = ""));
+ }
+ };
+
+ const togglerElements = document.querySelectorAll("img.toggler");
+ togglerElements.forEach((el) =>
+ el.addEventListener("click", (event) => toggler(event.currentTarget))
+ );
+ togglerElements.forEach((el) => (el.style.display = ""));
+ if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+ },
+
+ initOnKeyListeners: () => {
+ // only install a listener if it is really needed
+ if (
+ !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+ !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+ )
+ return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+ if (!event.shiftKey) {
+ switch (event.key) {
+ case "ArrowLeft":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const prevLink = document.querySelector('link[rel="prev"]');
+ if (prevLink && prevLink.href) {
+ window.location.href = prevLink.href;
+ event.preventDefault();
+ }
+ break;
+ case "ArrowRight":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const nextLink = document.querySelector('link[rel="next"]');
+ if (nextLink && nextLink.href) {
+ window.location.href = nextLink.href;
+ event.preventDefault();
+ }
+ break;
+ }
+ }
+
+ // some keyboard layouts may need Shift to get /
+ switch (event.key) {
+ case "/":
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+ Documentation.focusSearchBar();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/documentation_options.js b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/documentation_options.js
new file mode 100644
index 0000000000000000000000000000000000000000..7e4c114f212585b10b77123e4cb1b1cc703a2d35
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+ VERSION: '',
+ LANGUAGE: 'en',
+ COLLAPSE_INDEX: false,
+ BUILDER: 'html',
+ FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt',
+ NAVIGATION_WITH_KEYS: false,
+ SHOW_SEARCH_SUMMARY: true,
+ ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/file.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/file.png
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/language_data.js b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/language_data.js
new file mode 100644
index 0000000000000000000000000000000000000000..367b8ed81b4e411bee6f81208dba76fbe37e0e15
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+ var step2list = {
+ ational: 'ate',
+ tional: 'tion',
+ enci: 'ence',
+ anci: 'ance',
+ izer: 'ize',
+ bli: 'ble',
+ alli: 'al',
+ entli: 'ent',
+ eli: 'e',
+ ousli: 'ous',
+ ization: 'ize',
+ ation: 'ate',
+ ator: 'ate',
+ alism: 'al',
+ iveness: 'ive',
+ fulness: 'ful',
+ ousness: 'ous',
+ aliti: 'al',
+ iviti: 'ive',
+ biliti: 'ble',
+ logi: 'log'
+ };
+
+ var step3list = {
+ icate: 'ic',
+ ative: '',
+ alize: 'al',
+ iciti: 'ic',
+ ical: 'ic',
+ ful: '',
+ ness: ''
+ };
+
+ var c = "[^aeiou]"; // consonant
+ var v = "[aeiouy]"; // vowel
+ var C = c + "[^aeiouy]*"; // consonant sequence
+ var V = v + "[aeiou]*"; // vowel sequence
+
+ var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
+ var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
+ var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
+ var s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ this.stemWord = function (w) {
+ var stem;
+ var suffix;
+ var firstch;
+ var origword = w;
+
+ if (w.length < 3)
+ return w;
+
+ var re;
+ var re2;
+ var re3;
+ var re4;
+
+ firstch = w.substr(0,1);
+ if (firstch == "y")
+ w = firstch.toUpperCase() + w.substr(1);
+
+ // Step 1a
+ re = /^(.+?)(ss|i)es$/;
+ re2 = /^(.+?)([^s])s$/;
+
+ if (re.test(w))
+ w = w.replace(re,"$1$2");
+ else if (re2.test(w))
+ w = w.replace(re2,"$1$2");
+
+ // Step 1b
+ re = /^(.+?)eed$/;
+ re2 = /^(.+?)(ed|ing)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = new RegExp(mgr0);
+ if (re.test(fp[1])) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = new RegExp(s_v);
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = /(at|bl|iz)$/;
+ re3 = new RegExp("([^aeiouylsz])\\1$");
+ re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re2.test(w))
+ w = w + "e";
+ else if (re3.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ else if (re4.test(w))
+ w = w + "e";
+ }
+ }
+
+ // Step 1c
+ re = /^(.+?)y$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(s_v);
+ if (re.test(stem))
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step2list[suffix];
+ }
+
+ // Step 3
+ re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step3list[suffix];
+ }
+
+ // Step 4
+ re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ re2 = /^(.+?)(s|t)(ion)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ if (re.test(stem))
+ w = stem;
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = new RegExp(mgr1);
+ if (re2.test(stem))
+ w = stem;
+ }
+
+ // Step 5
+ re = /^(.+?)e$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ re2 = new RegExp(meq1);
+ re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+ w = stem;
+ }
+ re = /ll$/;
+ re2 = new RegExp(mgr1);
+ if (re.test(w) && re2.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+ if (firstch == "y")
+ w = firstch.toLowerCase() + w.substr(1);
+ return w;
+ }
+}
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/minus.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/minus.png
new file mode 100644
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Binary files /dev/null and b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/minus.png differ
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/plus.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
Binary files /dev/null and b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/plus.png differ
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/pygments.css b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/pygments.css
new file mode 100644
index 0000000000000000000000000000000000000000..07454c6b2b47c25c567fb9362eec8a5d6b1d9082
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/pygments.css
@@ -0,0 +1,83 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #8f5902; font-style: italic } /* Comment */
+.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
+.highlight .g { color: #000000 } /* Generic */
+.highlight .k { color: #004461; font-weight: bold } /* Keyword */
+.highlight .l { color: #000000 } /* Literal */
+.highlight .n { color: #000000 } /* Name */
+.highlight .o { color: #582800 } /* Operator */
+.highlight .x { color: #000000 } /* Other */
+.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
+.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #8f5902 } /* Comment.Preproc */
+.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #a40000 } /* Generic.Deleted */
+.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #ef2929 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #745334 } /* Generic.Prompt */
+.highlight .gs { color: #000000; font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #a40000; font-weight: bold } /* Generic.Traceback */
+.highlight .kc { color: #004461; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #004461; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #004461; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #004461; font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: #004461; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #004461; font-weight: bold } /* Keyword.Type */
+.highlight .ld { color: #000000 } /* Literal.Date */
+.highlight .m { color: #990000 } /* Literal.Number */
+.highlight .s { color: #4e9a06 } /* Literal.String */
+.highlight .na { color: #c4a000 } /* Name.Attribute */
+.highlight .nb { color: #004461 } /* Name.Builtin */
+.highlight .nc { color: #000000 } /* Name.Class */
+.highlight .no { color: #000000 } /* Name.Constant */
+.highlight .nd { color: #888888 } /* Name.Decorator */
+.highlight .ni { color: #ce5c00 } /* Name.Entity */
+.highlight .ne { color: #cc0000; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #000000 } /* Name.Function */
+.highlight .nl { color: #f57900 } /* Name.Label */
+.highlight .nn { color: #000000 } /* Name.Namespace */
+.highlight .nx { color: #000000 } /* Name.Other */
+.highlight .py { color: #000000 } /* Name.Property */
+.highlight .nt { color: #004461; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #000000 } /* Name.Variable */
+.highlight .ow { color: #004461; font-weight: bold } /* Operator.Word */
+.highlight .pm { color: #000000; font-weight: bold } /* Punctuation.Marker */
+.highlight .w { color: #f8f8f8 } /* Text.Whitespace */
+.highlight .mb { color: #990000 } /* Literal.Number.Bin */
+.highlight .mf { color: #990000 } /* Literal.Number.Float */
+.highlight .mh { color: #990000 } /* Literal.Number.Hex */
+.highlight .mi { color: #990000 } /* Literal.Number.Integer */
+.highlight .mo { color: #990000 } /* Literal.Number.Oct */
+.highlight .sa { color: #4e9a06 } /* Literal.String.Affix */
+.highlight .sb { color: #4e9a06 } /* Literal.String.Backtick */
+.highlight .sc { color: #4e9a06 } /* Literal.String.Char */
+.highlight .dl { color: #4e9a06 } /* Literal.String.Delimiter */
+.highlight .sd { color: #8f5902; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4e9a06 } /* Literal.String.Double */
+.highlight .se { color: #4e9a06 } /* Literal.String.Escape */
+.highlight .sh { color: #4e9a06 } /* Literal.String.Heredoc */
+.highlight .si { color: #4e9a06 } /* Literal.String.Interpol */
+.highlight .sx { color: #4e9a06 } /* Literal.String.Other */
+.highlight .sr { color: #4e9a06 } /* Literal.String.Regex */
+.highlight .s1 { color: #4e9a06 } /* Literal.String.Single */
+.highlight .ss { color: #4e9a06 } /* Literal.String.Symbol */
+.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #000000 } /* Name.Function.Magic */
+.highlight .vc { color: #000000 } /* Name.Variable.Class */
+.highlight .vg { color: #000000 } /* Name.Variable.Global */
+.highlight .vi { color: #000000 } /* Name.Variable.Instance */
+.highlight .vm { color: #000000 } /* Name.Variable.Magic */
+.highlight .il { color: #990000 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/searchtools.js b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/searchtools.js
new file mode 100644
index 0000000000000000000000000000000000000000..92da3f8b22cf5aab480b06cdb4e380a156152b5a
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/searchtools.js
@@ -0,0 +1,619 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [docname, title, anchor, descr, score, filename]
+ // and returns the new score.
+ /*
+ score: result => {
+ const [docname, title, anchor, descr, score, filename] = result
+ return score
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {
+ 0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5, // used to be unimportantResults
+ },
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2,
+ };
+}
+
+const _removeChildren = (element) => {
+ while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+ string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+ const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+ const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+ const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+ const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+ const contentRoot = document.documentElement.dataset.content_root;
+
+ const [docName, title, anchor, descr, score, _filename] = item;
+
+ let listItem = document.createElement("li");
+ let requestUrl;
+ let linkUrl;
+ if (docBuilder === "dirhtml") {
+ // dirhtml builder
+ let dirname = docName + "/";
+ if (dirname.match(/\/index\/$/))
+ dirname = dirname.substring(0, dirname.length - 6);
+ else if (dirname === "index/") dirname = "";
+ requestUrl = contentRoot + dirname;
+ linkUrl = requestUrl;
+ } else {
+ // normal html builders
+ requestUrl = contentRoot + docName + docFileSuffix;
+ linkUrl = docName + docLinkSuffix;
+ }
+ let linkEl = listItem.appendChild(document.createElement("a"));
+ linkEl.href = linkUrl + anchor;
+ linkEl.dataset.score = score;
+ linkEl.innerHTML = title;
+ if (descr) {
+ listItem.appendChild(document.createElement("span")).innerHTML =
+ " (" + descr + ")";
+ // highlight search terms in the description
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ }
+ else if (showSearchSummary)
+ fetch(requestUrl)
+ .then((responseData) => responseData.text())
+ .then((data) => {
+ if (data)
+ listItem.appendChild(
+ Search.makeSearchSummary(data, searchTerms, anchor)
+ );
+ // highlight search terms in the summary
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ });
+ Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+ Search.stopPulse();
+ Search.title.innerText = _("Search Results");
+ if (!resultCount)
+ Search.status.innerText = Documentation.gettext(
+ "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+ );
+ else
+ Search.status.innerText = _(
+ "Search finished, found ${resultCount} page(s) matching the search query."
+ ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+ results,
+ resultCount,
+ searchTerms,
+ highlightTerms,
+) => {
+ // results left, load the summary and display it
+ // this is intended to be dynamic (don't sub resultsCount)
+ if (results.length) {
+ _displayItem(results.pop(), searchTerms, highlightTerms);
+ setTimeout(
+ () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+ 5
+ );
+ }
+ // search finished, update title and status message
+ else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+ const leftScore = a[4];
+ const rightScore = b[4];
+ if (leftScore === rightScore) {
+ // same score: sort alphabetically
+ const leftTitle = a[1].toLowerCase();
+ const rightTitle = b[1].toLowerCase();
+ if (leftTitle === rightTitle) return 0;
+ return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+ }
+ return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+ var splitQuery = (query) => query
+ .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+ .filter(term => term) // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+ _index: null,
+ _queued_query: null,
+ _pulse_status: -1,
+
+ htmlToText: (htmlString, anchor) => {
+ const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+ for (const removalQuery of [".headerlinks", "script", "style"]) {
+ htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+ }
+ if (anchor) {
+ const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+ if (anchorContent) return anchorContent.textContent;
+
+ console.warn(
+ `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+ );
+ }
+
+ // if anchor not specified or not found, fall back to main content
+ const docContent = htmlElement.querySelector('[role="main"]');
+ if (docContent) return docContent.textContent;
+
+ console.warn(
+ "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+ );
+ return "";
+ },
+
+ init: () => {
+ const query = new URLSearchParams(window.location.search).get("q");
+ document
+ .querySelectorAll('input[name="q"]')
+ .forEach((el) => (el.value = query));
+ if (query) Search.performSearch(query);
+ },
+
+ loadIndex: (url) =>
+ (document.body.appendChild(document.createElement("script")).src = url),
+
+ setIndex: (index) => {
+ Search._index = index;
+ if (Search._queued_query !== null) {
+ const query = Search._queued_query;
+ Search._queued_query = null;
+ Search.query(query);
+ }
+ },
+
+ hasIndex: () => Search._index !== null,
+
+ deferQuery: (query) => (Search._queued_query = query),
+
+ stopPulse: () => (Search._pulse_status = -1),
+
+ startPulse: () => {
+ if (Search._pulse_status >= 0) return;
+
+ const pulse = () => {
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ Search.dots.innerText = ".".repeat(Search._pulse_status);
+ if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+ };
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch: (query) => {
+ // create the required interface elements
+ const searchText = document.createElement("h2");
+ searchText.textContent = _("Searching");
+ const searchSummary = document.createElement("p");
+ searchSummary.classList.add("search-summary");
+ searchSummary.innerText = "";
+ const searchList = document.createElement("ul");
+ searchList.classList.add("search");
+
+ const out = document.getElementById("search-results");
+ Search.title = out.appendChild(searchText);
+ Search.dots = Search.title.appendChild(document.createElement("span"));
+ Search.status = out.appendChild(searchSummary);
+ Search.output = out.appendChild(searchList);
+
+ const searchProgress = document.getElementById("search-progress");
+ // Some themes don't use the search progress node
+ if (searchProgress) {
+ searchProgress.innerText = _("Preparing search...");
+ }
+ Search.startPulse();
+
+ // index already loaded, the browser was quick!
+ if (Search.hasIndex()) Search.query(query);
+ else Search.deferQuery(query);
+ },
+
+ _parseQuery: (query) => {
+ // stem the search terms and add them to the correct list
+ const stemmer = new Stemmer();
+ const searchTerms = new Set();
+ const excludedTerms = new Set();
+ const highlightTerms = new Set();
+ const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+ splitQuery(query.trim()).forEach((queryTerm) => {
+ const queryTermLower = queryTerm.toLowerCase();
+
+ // maybe skip this "word"
+ // stopwords array is from language_data.js
+ if (
+ stopwords.indexOf(queryTermLower) !== -1 ||
+ queryTerm.match(/^\d+$/)
+ )
+ return;
+
+ // stem the word
+ let word = stemmer.stemWord(queryTermLower);
+ // select the correct list
+ if (word[0] === "-") excludedTerms.add(word.substr(1));
+ else {
+ searchTerms.add(word);
+ highlightTerms.add(queryTermLower);
+ }
+ });
+
+ if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
+ localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+ }
+
+ // console.debug("SEARCH: searching for:");
+ // console.info("required: ", [...searchTerms]);
+ // console.info("excluded: ", [...excludedTerms]);
+
+ return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+ },
+
+ /**
+ * execute search (requires search index to be loaded)
+ */
+ _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+ const allTitles = Search._index.alltitles;
+ const indexEntries = Search._index.indexentries;
+
+ // Collect multiple result groups to be sorted separately and then ordered.
+ // Each is an array of [docname, title, anchor, descr, score, filename].
+ const normalResults = [];
+ const nonMainIndexResults = [];
+
+ _removeChildren(document.getElementById("search-progress"));
+
+ const queryLower = query.toLowerCase().trim();
+ for (const [title, foundTitles] of Object.entries(allTitles)) {
+ if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+ for (const [file, id] of foundTitles) {
+ let score = Math.round(100 * queryLower.length / title.length)
+ normalResults.push([
+ docNames[file],
+ titles[file] !== title ? `${titles[file]} > ${title}` : title,
+ id !== null ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // search for explicit entries in index directives
+ for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+ if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+ for (const [file, id, isMain] of foundEntries) {
+ const score = Math.round(100 * queryLower.length / entry.length);
+ const result = [
+ docNames[file],
+ titles[file],
+ id ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ];
+ if (isMain) {
+ normalResults.push(result);
+ } else {
+ nonMainIndexResults.push(result);
+ }
+ }
+ }
+ }
+
+ // lookup as object
+ objectTerms.forEach((term) =>
+ normalResults.push(...Search.performObjectSearch(term, objectTerms))
+ );
+
+ // lookup as search terms in fulltext
+ normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+ // let the scorer override scores with a custom scoring function
+ if (Scorer.score) {
+ normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+ nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+ }
+
+ // Sort each group of results by score and then alphabetically by name.
+ normalResults.sort(_orderResultsByScoreThenName);
+ nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+ // Combine the result groups in (reverse) order.
+ // Non-main index entries are typically arbitrary cross-references,
+ // so display them after other results.
+ let results = [...nonMainIndexResults, ...normalResults];
+
+ // remove duplicate search results
+ // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+ let seen = new Set();
+ results = results.reverse().reduce((acc, result) => {
+ let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+ if (!seen.has(resultStr)) {
+ acc.push(result);
+ seen.add(resultStr);
+ }
+ return acc;
+ }, []);
+
+ return results.reverse();
+ },
+
+ query: (query) => {
+ const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+ const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+ // for debugging
+ //Search.lastresults = results.slice(); // a copy
+ // console.info("search results:", Search.lastresults);
+
+ // print the results
+ _displayNextItem(results, results.length, searchTerms, highlightTerms);
+ },
+
+ /**
+ * search for object names
+ */
+ performObjectSearch: (object, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const objects = Search._index.objects;
+ const objNames = Search._index.objnames;
+ const titles = Search._index.titles;
+
+ const results = [];
+
+ const objectSearchCallback = (prefix, match) => {
+ const name = match[4]
+ const fullname = (prefix ? prefix + "." : "") + name;
+ const fullnameLower = fullname.toLowerCase();
+ if (fullnameLower.indexOf(object) < 0) return;
+
+ let score = 0;
+ const parts = fullnameLower.split(".");
+
+ // check for different match types: exact matches of full name or
+ // "last name" (i.e. last dotted part)
+ if (fullnameLower === object || parts.slice(-1)[0] === object)
+ score += Scorer.objNameMatch;
+ else if (parts.slice(-1)[0].indexOf(object) > -1)
+ score += Scorer.objPartialMatch; // matches in last name
+
+ const objName = objNames[match[1]][2];
+ const title = titles[match[0]];
+
+ // If more than one term searched for, we require other words to be
+ // found in the name/title/description
+ const otherTerms = new Set(objectTerms);
+ otherTerms.delete(object);
+ if (otherTerms.size > 0) {
+ const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+ if (
+ [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+ )
+ return;
+ }
+
+ let anchor = match[3];
+ if (anchor === "") anchor = fullname;
+ else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+ const descr = objName + _(", in ") + title;
+
+ // add custom score for some objects according to scorer
+ if (Scorer.objPrio.hasOwnProperty(match[2]))
+ score += Scorer.objPrio[match[2]];
+ else score += Scorer.objPrioDefault;
+
+ results.push([
+ docNames[match[0]],
+ fullname,
+ "#" + anchor,
+ descr,
+ score,
+ filenames[match[0]],
+ ]);
+ };
+ Object.keys(objects).forEach((prefix) =>
+ objects[prefix].forEach((array) =>
+ objectSearchCallback(prefix, array)
+ )
+ );
+ return results;
+ },
+
+ /**
+ * search for full-text terms in the index
+ */
+ performTermsSearch: (searchTerms, excludedTerms) => {
+ // prepare search
+ const terms = Search._index.terms;
+ const titleTerms = Search._index.titleterms;
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+
+ const scoreMap = new Map();
+ const fileMap = new Map();
+
+ // perform the search on the required terms
+ searchTerms.forEach((word) => {
+ const files = [];
+ const arr = [
+ { files: terms[word], score: Scorer.term },
+ { files: titleTerms[word], score: Scorer.title },
+ ];
+ // add support for partial matches
+ if (word.length > 2) {
+ const escapedWord = _escapeRegExp(word);
+ if (!terms.hasOwnProperty(word)) {
+ Object.keys(terms).forEach((term) => {
+ if (term.match(escapedWord))
+ arr.push({ files: terms[term], score: Scorer.partialTerm });
+ });
+ }
+ if (!titleTerms.hasOwnProperty(word)) {
+ Object.keys(titleTerms).forEach((term) => {
+ if (term.match(escapedWord))
+ arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+ });
+ }
+ }
+
+ // no match but word was a required one
+ if (arr.every((record) => record.files === undefined)) return;
+
+ // found search word in contents
+ arr.forEach((record) => {
+ if (record.files === undefined) return;
+
+ let recordFiles = record.files;
+ if (recordFiles.length === undefined) recordFiles = [recordFiles];
+ files.push(...recordFiles);
+
+ // set score for the word in each file
+ recordFiles.forEach((file) => {
+ if (!scoreMap.has(file)) scoreMap.set(file, {});
+ scoreMap.get(file)[word] = record.score;
+ });
+ });
+
+ // create the mapping
+ files.forEach((file) => {
+ if (!fileMap.has(file)) fileMap.set(file, [word]);
+ else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+ });
+ });
+
+ // now check if the files don't contain excluded terms
+ const results = [];
+ for (const [file, wordList] of fileMap) {
+ // check if all requirements are matched
+
+ // as search terms with length < 3 are discarded
+ const filteredTermCount = [...searchTerms].filter(
+ (term) => term.length > 2
+ ).length;
+ if (
+ wordList.length !== searchTerms.size &&
+ wordList.length !== filteredTermCount
+ )
+ continue;
+
+ // ensure that none of the excluded terms is in the search result
+ if (
+ [...excludedTerms].some(
+ (term) =>
+ terms[term] === file ||
+ titleTerms[term] === file ||
+ (terms[term] || []).includes(file) ||
+ (titleTerms[term] || []).includes(file)
+ )
+ )
+ break;
+
+ // select one (max) score for the file.
+ const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+ // add result to the result list
+ results.push([
+ docNames[file],
+ titles[file],
+ "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ return results;
+ },
+
+ /**
+ * helper function to return a node containing the
+ * search summary for a given text. keywords is a list
+ * of stemmed words.
+ */
+ makeSearchSummary: (htmlText, keywords, anchor) => {
+ const text = Search.htmlToText(htmlText, anchor);
+ if (text === "") return null;
+
+ const textLower = text.toLowerCase();
+ const actualStartPosition = [...keywords]
+ .map((k) => textLower.indexOf(k.toLowerCase()))
+ .filter((i) => i > -1)
+ .slice(-1)[0];
+ const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+ const top = startWithContext === 0 ? "" : "...";
+ const tail = startWithContext + 240 < text.length ? "..." : "";
+
+ let summary = document.createElement("p");
+ summary.classList.add("context");
+ summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+ return summary;
+ },
+};
+
+_ready(Search.init);
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/sphinx_highlight.js b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/sphinx_highlight.js
new file mode 100644
index 0000000000000000000000000000000000000000..8a96c69a1942318413af68fd459122b56edd8d69
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+ if (node.nodeType === Node.TEXT_NODE) {
+ const val = node.nodeValue;
+ const parent = node.parentNode;
+ const pos = val.toLowerCase().indexOf(text);
+ if (
+ pos >= 0 &&
+ !parent.classList.contains(className) &&
+ !parent.classList.contains("nohighlight")
+ ) {
+ let span;
+
+ const closestNode = parent.closest("body, svg, foreignObject");
+ const isInSVG = closestNode && closestNode.matches("svg");
+ if (isInSVG) {
+ span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+ } else {
+ span = document.createElement("span");
+ span.classList.add(className);
+ }
+
+ span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+ const rest = document.createTextNode(val.substr(pos + text.length));
+ parent.insertBefore(
+ span,
+ parent.insertBefore(
+ rest,
+ node.nextSibling
+ )
+ );
+ node.nodeValue = val.substr(0, pos);
+ /* There may be more occurrences of search term in this node. So call this
+ * function recursively on the remaining fragment.
+ */
+ _highlight(rest, addItems, text, className);
+
+ if (isInSVG) {
+ const rect = document.createElementNS(
+ "http://www.w3.org/2000/svg",
+ "rect"
+ );
+ const bbox = parent.getBBox();
+ rect.x.baseVal.value = bbox.x;
+ rect.y.baseVal.value = bbox.y;
+ rect.width.baseVal.value = bbox.width;
+ rect.height.baseVal.value = bbox.height;
+ rect.setAttribute("class", className);
+ addItems.push({ parent: parent, target: rect });
+ }
+ }
+ } else if (node.matches && !node.matches("button, select, textarea")) {
+ node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+ }
+};
+const _highlightText = (thisNode, text, className) => {
+ let addItems = [];
+ _highlight(thisNode, addItems, text, className);
+ addItems.forEach((obj) =>
+ obj.parent.insertAdjacentElement("beforebegin", obj.target)
+ );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+ /**
+ * highlight the search words provided in localstorage in the text
+ */
+ highlightSearchWords: () => {
+ if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
+
+ // get and clear terms from localstorage
+ const url = new URL(window.location);
+ const highlight =
+ localStorage.getItem("sphinx_highlight_terms")
+ || url.searchParams.get("highlight")
+ || "";
+ localStorage.removeItem("sphinx_highlight_terms")
+ url.searchParams.delete("highlight");
+ window.history.replaceState({}, "", url);
+
+ // get individual terms from highlight string
+ const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+ if (terms.length === 0) return; // nothing to do
+
+ // There should never be more than one element matching "div.body"
+ const divBody = document.querySelectorAll("div.body");
+ const body = divBody.length ? divBody[0] : document.querySelector("body");
+ window.setTimeout(() => {
+ terms.forEach((term) => _highlightText(body, term, "highlighted"));
+ }, 10);
+
+ const searchBox = document.getElementById("searchbox");
+ if (searchBox === null) return;
+ searchBox.appendChild(
+ document
+ .createRange()
+ .createContextualFragment(
+ '<p class="highlight-link">' +
+ '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+ _("Hide Search Matches") +
+ "</a></p>"
+ )
+ );
+ },
+
+ /**
+ * helper function to hide the search marks again
+ */
+ hideSearchWords: () => {
+ document
+ .querySelectorAll("#searchbox .highlight-link")
+ .forEach((el) => el.remove());
+ document
+ .querySelectorAll("span.highlighted")
+ .forEach((el) => el.classList.remove("highlighted"));
+ localStorage.removeItem("sphinx_highlight_terms")
+ },
+
+ initEscapeListener: () => {
+ // only install a listener if it is really needed
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+ if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+ SphinxHighlight.hideSearchWords();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+_ready(() => {
+ /* Do not call highlightSearchWords() when we are on the search page.
+ * It will highlight words from the *previous* search query.
+ */
+ if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+ SphinxHighlight.initEscapeListener();
+});
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/api.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/api.html
new file mode 100644
index 0000000000000000000000000000000000000000..3a7c7b34cd4504e1a9bd5ef311e2395bb53583d4
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/api.html
@@ -0,0 +1,214 @@
+<!DOCTYPE html>
+
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+ <section id="api">
+<h1>API<a class="headerlink" href="#api" title="Link to this heading">¶</a></h1>
+<p>In the following, we will describe the core components of our API</p>
+<section id="experiment-class">
+<h2>Experiment Class<a class="headerlink" href="#experiment-class" title="Link to this heading">¶</a></h2>
+<p>For a quick start for uing the simulator, there is the Experiment class that is also used
+in the example notebook. We refer to the notebook for a detailled usage example.</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment" title="qdarts.experiment.Experiment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">experiment.Experiment</span></code></a>(capacitance_config[, ...])</p></td>
+<td><p>Experiment class that helps setup the different parts of the simulator into an easy to ue interface.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="simulation-components">
+<h2>Simulation Components<a class="headerlink" href="#simulation-components" title="Link to this heading">¶</a></h2>
+<p>The main components to build your own simulations are shown below.
+We first need to create a Capacitance model that generates a
+capacitive energy function <span class="math notranslate nohighlight">\(E(v,n)\)</span>. With this, we can start
+to generate a purely capacitive simulation.</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.capacitance_model.CapacitanceModel.html#qdarts.capacitance_model.CapacitanceModel" title="qdarts.capacitance_model.CapacitanceModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">capacitance_model.CapacitanceModel</span></code></a>(C_g, C_D, ...)</p></td>
+<td><p>Implementation of a slight generalization of the constant interaction model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator" title="qdarts.simulator.CapacitiveDeviceSimulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulator.CapacitiveDeviceSimulator</span></code></a>(...)</p></td>
+<td><p>This class simulates a quantum dot device based on a capacitance model.</p></td>
+</tr>
+</tbody>
+</table>
+<p>This simulation is rather basic and can only compute areas of voltages that lead to the same ground
+state, so called coulomb diamonds. This can already be used to generate
+Charge Stability diagrams by computing 2D slices through the set of polytopes,
+but those will not look very realistic, nor will they include tunneling.</p>
+<p>To move beyond this simulation, we need to drastically improve the modeling.
+This next step is based on the full tunneling simulator that requires a simulation
+of the sensor dot and a simulation of tunnel barriers - the latter can be simplified by
+just providing a matrix of constant tunnel couplings.</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tunneling_simulator.ApproximateTunnelingSimulator</span></code></a>(...)</p></td>
+<td><p>Simulator for approximate charge tunneling in a quantum dot device.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.html#qdarts.tunneling_simulator.TunnelBarrierModel" title="qdarts.tunneling_simulator.TunnelBarrierModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tunneling_simulator.TunnelBarrierModel</span></code></a>(...)</p></td>
+<td><p>Model of the tunnel barriers of a device</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot" title="qdarts.tunneling_simulator.NoisySensorDot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tunneling_simulator.NoisySensorDot</span></code></a>(...)</p></td>
+<td><p>Simulates a sensor signal by computing the conductance of the sensor dots.</p></td>
+</tr>
+</tbody>
+</table>
+<p>The simulation of the sensor dot can optionally make use of a noise model in order
+to generate dependent noise. For this, we currently offer the following two classes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process" title="qdarts.noise_processes.OU_process"><code class="xref py py-obj docutils literal notranslate"><span class="pre">noise_processes.OU_process</span></code></a>(sigma, tc, dt, ...)</p></td>
+<td><p>Implements the Ornstein-Uhlenbeck noise process</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.noise_processes.Cosine_Mean_Function.html#qdarts.noise_processes.Cosine_Mean_Function" title="qdarts.noise_processes.Cosine_Mean_Function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">noise_processes.Cosine_Mean_Function</span></code></a>(...[, b])</p></td>
+<td><p>Decorator of an random process that models an additive mean term that depends on the gate voltages.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="data-classes">
+<h2>Data Classes<a class="headerlink" href="#data-classes" title="Link to this heading">¶</a></h2>
+<p>Finally, both levels of simulations have their own data classes. The polytope class is returned by the
+boundaries method by any simulator and the local system is returned by the tunneling simulator. Both describe
+the local state of the simulator in some region.</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem" title="qdarts.tunneling_simulator.LocalSystem"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tunneling_simulator.LocalSystem</span></code></a>(v, H, state, sim)</p></td>
+<td><p>Class describing a quantum system defined by the gate voltages of a simulated device.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope" title="qdarts.polytope.Polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polytope.Polytope</span></code></a>(state)</p></td>
+<td><p>Represents the polytope P(n) defined by all gate voltages v in a device that have capacitive ground state n.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="interfaces">
+<h2>Interfaces<a class="headerlink" href="#interfaces" title="Link to this heading">¶</a></h2>
+<p>QDarts offers a set of interfaces and base classes that can serve as a primer to how to extend the simulator by
+replacing existing components. In most cases, only a few specialized functions need to be implemented as the base
+class implements most of the important logic. Please note that QDarts is under active development, all of these
+interfaces are subject to change.</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel" title="qdarts.capacitance_model.AbstractCapacitanceModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">capacitance_model.AbstractCapacitanceModel</span></code></a>(...)</p></td>
+<td><p>Base Class for all capacitance models.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator" title="qdarts.simulator.AbstractPolytopeSimulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulator.AbstractPolytopeSimulator</span></code></a>(...)</p></td>
+<td><p>Base class for all simulation objects that can compute and return polytopes.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulator.AbstractCapacitiveDeviceSimulator</span></code></a>(...)</p></td>
+<td><p>Base class for all objects that create device simulations from a Capacitive Model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess" title="qdarts.noise_processes.AbstractNoiseProcess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">noise_processes.AbstractNoiseProcess</span></code></a>(...)</p></td>
+<td><p>Models a noise process that produces possibly dependent samples <span class="math notranslate nohighlight">\(\epsilon(v)_t\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim" title="qdarts.tunneling_simulator.AbstractSensorSim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tunneling_simulator.AbstractSensorSim</span></code></a>(...)</p></td>
+<td><p>Base class defining the interface for all sensor simulations</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+
+
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+ <section id="api-usage-tutorial">
+<h1>API Usage Tutorial<a class="headerlink" href="#api-usage-tutorial" title="Link to this heading">¶</a></h1>
+<p>QDarts comes with two API-levels, the <code class="docutils literal notranslate"><span class="pre">Experiment</span></code> class that simplifies many of the most common applications at the cost
+of flexibility. It is build on top of the low-level API, that gives access to the full power of QDarts. It is possile to mix both
+APIs, for example, it is possible to construct a simulator using the <code class="docutils literal notranslate"><span class="pre">Experiment</span></code> class and then modify it using the low-level API.
+In the following, we will explain the low-level API (The usage of the high-level api is described in the ipython notebook located in <code class="docutils literal notranslate"><span class="pre">examples/figures_scipost.ipynb</span></code>
+that also reproduces all figures of our publication).</p>
+<p>The code for this tutorial is provided in the ipython notebook <code class="docutils literal notranslate"><span class="pre">examples/api_tutorial.ipynb</span></code>. To keep the tutorial concise,
+we only include the most important lines here that are needed to create the simulator,
+but we skip definition of capacitance parameters etc. For this, directly reading the code is best.</p>
+<p>The simulated device is a dcevice with 6 dots, of which three are sensor dots. It is controlled by 9 plunger gates, 1 for each dot and
+three that govern barrier gates between the three dots that are not sensor dots. The device is defined using a set of parameter matrices
+that govern the capacitances between the 9 gates and 6 dots, as well as the effect the 9 gates have on the barrier potential and therefore the tunnel coupling.</p>
+<section id="construction-of-the-capacitive-simulation">
+<h2>Construction of the capacitive simulation<a class="headerlink" href="#construction-of-the-capacitive-simulation" title="Link to this heading">¶</a></h2>
+<p>As first step, we need to initialize the capacitive simulation.
+It depends on a capacitive model, which creates the capacitive energy function and which depends on a number of
+parameters, the capacitanc matrices between plunger gates and dots, <code class="docutils literal notranslate"><span class="pre">C_DG</span></code>, the interdot capacitances <code class="docutils literal notranslate"><span class="pre">C_DD</span></code>,
+a lower bound on the gate voltages <code class="docutils literal notranslate"><span class="pre">bounds_limits</span></code> and optionaly a parameter <code class="docutils literal notranslate"><span class="pre">ks</span></code> that governs the deviation from the constant interaction
+model and which can be set to <code class="docutils literal notranslate"><span class="pre">None</span></code>. With these parameters, creating the capacitive simulation is straight forward:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qdarts.capacitance_model</span> <span class="kn">import</span> <span class="n">CapacitanceModel</span>
+<span class="kn">from</span> <span class="nn">qdarts.simulator</span> <span class="kn">import</span> <span class="n">CapacitiveDeviceSimulator</span>
+
+<span class="n">capacitance_model</span> <span class="o">=</span> <span class="n">CapacitanceModel</span><span class="p">(</span><span class="n">C_DG</span><span class="p">,</span> <span class="n">C_DD</span><span class="p">,</span> <span class="n">bounds_limits</span><span class="p">,</span> <span class="n">ks</span><span class="o">=</span><span class="n">ks</span><span class="p">)</span>
+<span class="n">capacitive_sim</span> <span class="o">=</span> <span class="n">CapacitiveDeviceSimulator</span><span class="p">(</span><span class="n">capacitance_model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The capacitance simulation is purely classical and fits to the simulation of typical GaAs spin-qubit devices.
+It can only be used to query the ground state of the electron configuration given a set of gate voltages,
+as well as for each electron configuration the polytope of gate voltages that lead to the ground state configuration. Both features together are enough
+to plot a charge stability diagram and to exactly compute the boundaries of ground states within the diagram.</p>
+</section>
+<section id="plotting-the-charge-stability-diagram">
+<h2>Plotting the Charge Stability Diagram<a class="headerlink" href="#plotting-the-charge-stability-diagram" title="Link to this heading">¶</a></h2>
+<p>For ploting, we have two helper functions. First, <code class="docutils literal notranslate"><span class="pre">get_CSD_states</span></code> computes for a 2D grid of gate voltages the ground state. This is done by computing a standard 2D grid
+that is then transformed into gate-voltage space. This transformation is done via a linear transformation <span class="math notranslate nohighlight">\(v=m+P@v'\)</span>, where v is the full vector of gate plunger voltages,
+in our case 9 values. The variables <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(P\)</span> are projection parameters, where <span class="math notranslate nohighlight">\(m\)</span> is a 9 dimensional vector and <span class="math notranslate nohighlight">\(P\)</span> a 9x2 matrix.
+This defines a local 2D coordinate system transformation from the 2D grid <span class="math notranslate nohighlight">\(v'\)</span> to <span class="math notranslate nohighlight">\(v\)</span>. With this, it is possible to create a plot in any two coordinate axes.
+If we pick the grid of <span class="math notranslate nohighlight">\(v'\)</span> values as symmetric around 0, then <span class="math notranslate nohighlight">\(m\)</span> can be interpreted as the midpoint of the scan that we compute next.</p>
+<p>the question is, how we can obtain these values. We can use the capacitive simulation to query points of interest. For example, when we are interested in the scan around
+a point on the state where there is a single electron on each inner dot and 5 electrons on each sensor dot, we can query it simply via:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">capacitive_sim</span><span class="o">.</span><span class="n">boundaries</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</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">5</span><span class="p">,</span><span class="mi">5</span><span class="p">])</span><span class="o">.</span><span class="n">point_inside</span>
+</pre></div>
+</div>
+<p>And if we want to perform a scan over the axes of the first and third plunger gate (which in our simulation are strongest coupled to the first and third inner dot) we can
+set:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N_gates</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+<span class="n">P</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="o">=</span><span class="mi">1</span>
+<span class="n">P</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">=</span><span class="mi">1</span>
+</pre></div>
+</div>
+<p>Now that we have the projection, we can define the minimum and maximum values of <span class="math notranslate nohighlight">\(v'\)</span> in both directions and choose an image resolution.
+Then, we can use <code class="docutils literal notranslate"><span class="pre">get_CSD_data</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qdarts.plotting</span> <span class="kn">import</span> <span class="n">get_CSD_data</span>
+
+<span class="n">minV</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.01</span><span class="p">,</span><span class="o">-</span><span class="mf">0.01</span><span class="p">]</span>
+<span class="n">maxV</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">]</span>
+<span class="n">resolution</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">sliced_csim</span><span class="p">,</span> <span class="n">CSD_data</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">get_CSD_data</span><span class="p">(</span><span class="n">capacitive_sim</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span><span class="n">m</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">target_state</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The last parameter is needed to provide the simulation with an initial guess of which might be the ground state of the bottom left corner of the plot.
+This guess does not need to be exact. This function returns three things: first, a simulation <code class="docutils literal notranslate"><span class="pre">sliced_csim</span></code> that is defined on the transformed space <span class="math notranslate nohighlight">\(v'\)</span> and serves as a cache for
+computed values. We will talk more about these slices later in the tutorial. The second result, <code class="docutils literal notranslate"><span class="pre">CSD_data</span></code> is a plottable 2D grid of values in which each ground state configuration is assigned
+a different value. Finally, <code class="docutils literal notranslate"><span class="pre">states</span></code> is an array of all ground states for each grid point. We can plot this directly with matplotlib:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">minV</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">maxV</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">resolution</span><span class="p">)</span>
+<span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">minV</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">maxV</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">resolution</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span><span class="n">ys</span><span class="p">,</span><span class="n">CSD_data</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The resulting grid is a rastered low resolution image.</p>
+<a class="reference internal image-reference" href="_images/api_csd_raster.png"><img alt="_images/api_csd_raster.png" class="align-center" src="_images/api_csd_raster.png" style="width: 297.0px; height: 208.0px;" /></a>
+<p>We can improve on this by plotting the exact boundaries between the two ground states using <code class="docutils literal notranslate"><span class="pre">get_polytopes</span></code> and <code class="docutils literal notranslate"><span class="pre">plot_polytopes</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">polytopes</span> <span class="o">=</span> <span class="n">get_polytopes</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">sliced_csim</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</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="n">minV</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">maxV</span><span class="p">[</span><span class="mi">0</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="n">minV</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">maxV</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">plot_polytopes</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">(),</span><span class="n">polytopes</span><span class="p">,</span> <span class="n">skip_dots</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Note that this function computes the polytopes based on the low dimensional sliced simulation returned by <code class="docutils literal notranslate"><span class="pre">get_CSD_data</span></code> as well as its computed array of states. The function also prints the labels
+of each state, and the option <code class="docutils literal notranslate"><span class="pre">skip_dots</span></code> allows to skip the part of the electron configuration we are not interested in. In this case, we skipped the 3 sensor dots.
+Adding the last two plotting commands together, the resulting plot should look like this:</p>
+<a class="reference internal image-reference" href="_images/api_csd_grid.png"><img alt="_images/api_csd_grid.png" class="align-center" src="_images/api_csd_grid.png" style="width: 298.0px; height: 209.0px;" /></a>
+</section>
+<section id="creation-of-a-tunneling-simulation">
+<h2>Creation of a tunneling simulation<a class="headerlink" href="#creation-of-a-tunneling-simulation" title="Link to this heading">¶</a></h2>
+<p>Now that we are able to generate a CSD, we want to also simulate tunneling and simulate a more realistic sensor signal.
+For this, we need to create a few objects. First, we have to decide on a sensor simulator. The default choice is the <code class="docutils literal notranslate"><span class="pre">NoisySensorDot</span></code>
+which simulates a sequence of sensor reflectometry peaks on one or more sensor dots:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sensor_model</span> <span class="o">=</span> <span class="n">NoisySensorDot</span><span class="p">(</span><span class="n">sensor_dots</span><span class="p">)</span> <span class="c1">#a model of the sensor dots that just needs which dot has which index</span>
+<span class="n">sensor_model</span><span class="o">.</span><span class="n">config_peak</span><span class="p">(</span><span class="n">g_max</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">peak_width_multiplier</span> <span class="o">=</span> <span class="mi">20</span><span class="p">)</span> <span class="c1">#make the sensor peak broader</span>
+</pre></div>
+</div>
+<p>The constructor takes the indexes of the dots we want to use as sensor dots and each entry in this list will produce one sensor signal, the quantum capacitance measured
+at the sensor dot. In the next line we add configuration options. <code class="docutils literal notranslate"><span class="pre">g_max</span></code> is just a scaling factor of the signal, which we leave at 1. The default sensor peak is simulated using only the
+temperature of the dot and does not take broadening due to RF power into account. To compensate for this, we introduce a broadening factor that can make the peak wider. in our experience, values between
+5 and 20 work well. There are more options to add here, for example the noise model of the signal, but for this tutorial, we do not add noise to keep it simple. You can check the documentation
+of <code class="docutils literal notranslate"><span class="pre">NoisySensorDot</span></code> for more details on that.</p>
+<p>Now, with our configured sensor dot, we create the simulation using tunnel coupling:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tunneling_sim</span> <span class="o">=</span> <span class="n">ApproximateTunnelingSimulator</span><span class="p">(</span><span class="n">capacitive_sim</span><span class="p">,</span> <span class="c1">#the underlying polytope simulation</span>
+ <span class="n">tunnel_couplings</span><span class="p">,</span> <span class="c1">#symmetric tunnel coupling matrix T_ij which stores the tunnel coupling between dots i and j</span>
+ <span class="n">temperature</span><span class="p">,</span> <span class="c1">#electron temperature, should be <=200mK</span>
+ <span class="n">sensor_model</span><span class="p">)</span> <span class="c1">#our sensor model simulation</span>
+</pre></div>
+</div>
+<p>The tunneling simulation is very expensive, and by default it tries to minimize the number of basis elements for the computed Hamiltonians by excluding states with energies that are large compared to
+a chosen slack. By default the slack is 0, and thus all states that are not direct neighbours of our polytope (i.e., they do not share a facet) will be excluded. By increasing the slack proportional to
+the inverse temperature, we add more states that might still be relevant for tunneling. Further, the sensor dot simulation relies on the existence of states that add or remove an electron from the sensor dot.
+These can easily be excluded from the simulation and therefore we enfore in the simulation that states with additional electrons added/subtracted on the sensor dot are always present.</p>
+<blockquote>
+<div><p>capacitive_sim.set_maximum_polytope_slack(5/tunneling_sim.beta) #adding slack to keep more states that are likely to affect the hamiltonian
+tunneling_sim.num_additional_neighbours[sensor_dots] = 2 #adding additional states for the sensor dots</p>
+</div></blockquote>
+<p>Finally, we can create a similar plot as before. We use the same values for ´´m´´ and ´´P´´ as for the previous simulation, so that we can compare the two plots.
+Note that since the device has three sensor dots, the output is a 3 dimensional tensor where the last dimension is the number of sensor dots and we plot below only
+the output of the third sensor:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sensor_values</span> <span class="o">=</span> <span class="n">tunneling_sim</span><span class="o">.</span><span class="n">sensor_scan_2D</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">target_state</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span><span class="n">ys</span><span class="p">,</span><span class="n">sensor_values</span><span class="p">[:,:,</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This plots results in the sensor signal</p>
+<a class="reference internal image-reference" href="_images/api_tunnel_raster.png"><img alt="_images/api_tunnel_raster.png" class="align-center" src="_images/api_tunnel_raster.png" style="width: 297.0px; height: 208.0px;" /></a>
+<p>which again can be overlayed with our grid as before:</p>
+<a class="reference internal image-reference" href="_images/api_tunnel_grid.png"><img alt="_images/api_tunnel_grid.png" class="align-center" src="_images/api_tunnel_grid.png" style="width: 298.0px; height: 209.0px;" /></a>
+</section>
+<section id="slicing-and-sensor-compensation">
+<h2>Slicing and sensor compensation<a class="headerlink" href="#slicing-and-sensor-compensation" title="Link to this heading">¶</a></h2>
+<p>The last plot shows that most of the sensor signal is very low and there is only a small area with a peak. This is because of the missing sensor compensation: the sensor peak
+is capacitely affected by the plunger gates and thus changing the voltages changes the sensor signal. On the previous plot we already configured a wide width of the peak (<code class="docutils literal notranslate"><span class="pre">peak_width_multiplier</span> <span class="pre">=</span> <span class="pre">20`</span></code>) to make
+this effect small, but in reality many devices have very narrow and clear peaks.
+What we need is sensor compensation, i.e., a change of coordinate system where the sensor plunger voltage is corrected for the cross-talk from the other gates so that we stay on the sensor peak as well as possible.
+Moreover, the sensor peak is not centered. This is because we used as mid-point a point inside the polytope, but the sensor peaks are only on the boundaries between polytopes with different number of
+electrons on the sensor dots. Thus, we need to change our coordinate system and tune the sensor to a better point.</p>
+<p>This change of coordinate system is achieved in the simulator via slicing. In the previous examples, we defined a matrix <code class="docutils literal notranslate"><span class="pre">P</span></code> and vector <code class="docutils literal notranslate"><span class="pre">m</span></code> to generate the plot. We said that this changed the coordinate
+system via <span class="math notranslate nohighlight">\(v=m+Pv'\)</span>, where <code class="docutils literal notranslate"><span class="pre">v'</span></code> was a 2D case. This operation is called slicing and a simulation can be sliced via:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sliced_sim</span> <span class="o">=</span> <span class="n">sim</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="n">P</span><span class="p">,</span><span class="n">m</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This works on all simulator objects and it returns a new simulation with gate voltages <code class="docutils literal notranslate"><span class="pre">v'</span></code>. This works in any dimensions, not just 2 and it is used in most simulation transformation code.
+QDarts makes use of this in all its transformation code. For example, compensating the sensor dot:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">compensated_tsim</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span><span class="n">m_comp</span> <span class="o">=</span> <span class="n">compensate_simulator_sensors</span><span class="p">(</span>
+ <span class="n">tunneling_sim</span><span class="p">,</span> <span class="c1">#the simulator object to compensate</span>
+ <span class="n">target_state</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</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">5</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span> <span class="c1">#the electron configuration around which to compensate</span>
+ <span class="n">compensation_gates</span><span class="o">=</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span> <span class="c1">#the ids of the sensor gate plungers.</span>
+ <span class="n">sensor_ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span><span class="c1">#the ids of the sensor dots.</span>
+ <span class="n">sensor_detunings</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="o">-</span><span class="mf">0.0005</span><span class="p">,</span><span class="o">-</span><span class="mf">0.0005</span><span class="p">,</span><span class="o">-</span><span class="mf">0.0005</span><span class="p">])</span> <span class="c1">#shift of the sensor dot plungers</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>This function returns the sensor compensated simulator object, which has its gates transformed such, that changing the gate voltages of any gate plunger other the sensor dot plungers does not change the
+chemical potential of the sensor dot and thus leaves the sensor signal constant, except when a transition happens. This function relies on the user to supply the ids of the sensor dots and which elements of the voltage
+vector are the sensor plungers (for example, the sensor dot with id 3 has plunger 6 as its plunger gate). Since in our model the capacitance parameters of the model vary with the occupation number, we have to
+indicate the target state. Further, we have to supply a vector of sensor detunings that indicate on which position we are on the snesor detuning axis, i.e., the sensor peak. This value is relative to the peak maximum,
+a value of 0 will return the exact peak max, which is a value that is typically avoided in practice due to the low signal to noise ratio. Additional to the sensor compensation object, the function returns also as third
+return value the exact tuning point, i.e., the voltage vector that identifies the flank point for the given state. We can use this to plot a 2D charge stability diagram with the returned point as its center.
+We also show in the point below how to query the internal modified capacitance simulator using <code class="docutils literal notranslate"><span class="pre">compensated_tsim.poly_sim</span></code> in order to compute the state boundaries:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sensor_values</span> <span class="o">=</span> <span class="n">compensated_tsim</span><span class="o">.</span><span class="n">sensor_scan_2D</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">m_comp</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">target_state</span><span class="p">)</span>
+<span class="n">sliced_tsim</span><span class="p">,</span> <span class="n">CSD_data</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">get_CSD_data</span><span class="p">(</span><span class="n">compensated_tsim</span><span class="o">.</span><span class="n">poly_sim</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span><span class="n">m_comp</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">target_state</span><span class="p">)</span>
+<span class="n">polytopes</span> <span class="o">=</span> <span class="n">get_polytopes</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">sliced_tsim</span><span class="p">,</span> <span class="n">minV</span><span class="p">,</span> <span class="n">maxV</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Plotting the computed <code class="docutils literal notranslate"><span class="pre">`sensor_values</span></code> and <code class="docutils literal notranslate"><span class="pre">polytopes</span></code> as before, leads to</p>
+<a class="reference internal image-reference" href="_images/api_tunnel_comp.png"><img alt="_images/api_tunnel_comp.png" class="align-center" src="_images/api_tunnel_comp.png" style="width: 298.0px; height: 209.0px;" /></a>
+<p>As you can see, the sensors are almost perfectly compensated, leading to almost perfectly flat polytopes with no visible shape of a sensor peak. The [1,1,1] state indicates a color in the middle of the peak
+and we can see how single electron transitions change the position on the peak due to the change of color on the polytopes. It is similarly possible to compensate any other gates and thus to compute virtual coordinates,
+for this we refer to the documentation of <code class="docutils literal notranslate"><span class="pre">axis_align_transitions</span></code>.</p>
+</section>
+</section>
+
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+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.__init__">__init__() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.capacitance_model.CapacitanceModel.html#qdarts.capacitance_model.CapacitanceModel.__init__">(qdarts.capacitance_model.CapacitanceModel method)</a>
+</li>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.__init__">(qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess.__init__">(qdarts.noise_processes.AbstractNoiseProcess method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.Cosine_Mean_Function.html#qdarts.noise_processes.Cosine_Mean_Function.__init__">(qdarts.noise_processes.Cosine_Mean_Function method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process.__init__">(qdarts.noise_processes.OU_process method)</a>
+</li>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.__init__">(qdarts.polytope.Polytope method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.__init__">(qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator.__init__">(qdarts.simulator.AbstractPolytopeSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator.__init__">(qdarts.simulator.CapacitiveDeviceSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.__init__">(qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.__init__">(qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.__init__">(qdarts.tunneling_simulator.LocalSystem method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.__init__">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.html#qdarts.tunneling_simulator.TunnelBarrierModel.__init__">(qdarts.tunneling_simulator.TunnelBarrierModel method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.A">A (qdarts.polytope.Polytope attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel">AbstractCapacitanceModel (class in qdarts.capacitance_model)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator">AbstractCapacitiveDeviceSimulator (class in qdarts.simulator)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess">AbstractNoiseProcess (class in qdarts.noise_processes)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator">AbstractPolytopeSimulator (class in qdarts.simulator)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim">AbstractSensorSim (class in qdarts.tunneling_simulator)</a>
+</li>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.additional_info">additional_info (qdarts.polytope.Polytope attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator">ApproximateTunnelingSimulator (class in qdarts.tunneling_simulator)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.b">b (qdarts.polytope.Polytope attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.barrier_sim">barrier_sim (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.basis_labels">basis_labels (qdarts.tunneling_simulator.LocalSystem property)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.beta">beta (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.beta">(qdarts.tunneling_simulator.LocalSystem attribute)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.boundaries">boundaries() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator.boundaries">(qdarts.simulator.AbstractPolytopeSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.boundaries">(qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.bounds_limits">bounds_limits (qdarts.capacitance_model.AbstractCapacitanceModel attribute)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.cached_polytopes">cached_polytopes() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.CapacitanceModel.html#qdarts.capacitance_model.CapacitanceModel">CapacitanceModel (class in qdarts.capacitance_model)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator">CapacitiveDeviceSimulator (class in qdarts.simulator)</a>
+</li>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.center_transition">center_transition() (qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.compute_local_system">compute_local_system() (qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.compute_mixed_state_of_subset">compute_mixed_state_of_subset() (qdarts.tunneling_simulator.LocalSystem method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_polytope">compute_polytope() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator.compute_polytope">(qdarts.simulator.CapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.compute_polytope_for_state">compute_polytope_for_state() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.compute_transition_equations">compute_transition_equations() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.capacitance_model.CapacitanceModel.html#qdarts.capacitance_model.CapacitanceModel.compute_transition_equations">(qdarts.capacitance_model.CapacitanceModel method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_transition_equations">(qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.config_noise">config_noise() (qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.config_peak">config_peak() (qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.core_basis_indices">core_basis_indices (qdarts.tunneling_simulator.LocalSystem property)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.Cosine_Mean_Function.html#qdarts.noise_processes.Cosine_Mean_Function">Cosine_Mean_Function (class in qdarts.noise_processes)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.deploy_capacitance_sim">deploy_capacitance_sim() (qdarts.experiment.Experiment method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.deploy_sensor_model">deploy_sensor_model() (qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.deploy_tunneling_sim">deploy_tunneling_sim() (qdarts.experiment.Experiment method)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.enumerate_neighbours">enumerate_neighbours() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment">Experiment (class in qdarts.experiment)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_boundary_intersection">find_boundary_intersection() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_state_of_voltage">find_state_of_voltage() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.generate_CSD">generate_CSD() (qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.get_compensated_sim">get_compensated_sim() (qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack">get_maximum_polytope_slack() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator.get_maximum_polytope_slack">(qdarts.simulator.CapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.get_plot_args">get_plot_args() (qdarts.experiment.Experiment method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.html#qdarts.tunneling_simulator.TunnelBarrierModel.get_tunnel_matrix">get_tunnel_matrix() (qdarts.tunneling_simulator.TunnelBarrierModel method)</a>
+</li>
+ <li><a href="interfaces/qdarts.experiment.Experiment.html#qdarts.experiment.Experiment.get_virtualised_sim">get_virtualised_sim() (qdarts.experiment.Experiment method)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.H">H (qdarts.tunneling_simulator.LocalSystem attribute)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.inside_state">inside_state() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.invertible_transform">invertible_transform() (qdarts.polytope.Polytope method)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.labels">labels (qdarts.polytope.Polytope attribute)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.lazy_slice">lazy_slice() (qdarts.polytope.Polytope method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem">LocalSystem (class in qdarts.tunneling_simulator)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.mixed_state">mixed_state (qdarts.tunneling_simulator.LocalSystem property)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.must_verify">must_verify (qdarts.polytope.Polytope attribute)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process.next_val">next_val() (qdarts.noise_processes.OU_process method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot">NoisySensorDot (class in qdarts.tunneling_simulator)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.num_additional_neighbours">num_additional_neighbours (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.num_dots">num_dots (qdarts.capacitance_model.AbstractCapacitanceModel attribute)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator.num_dots">(qdarts.simulator.AbstractPolytopeSimulator attribute)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess.num_elements">num_elements (qdarts.noise_processes.AbstractNoiseProcess attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.num_inputs">num_inputs (qdarts.capacitance_model.AbstractCapacitanceModel attribute)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator.num_inputs">(qdarts.simulator.AbstractPolytopeSimulator attribute)</a>
+</li>
+ </ul></li>
+ </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process">OU_process (class in qdarts.noise_processes)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.point_inside">point_inside (qdarts.polytope.Polytope attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.poly_sim">poly_sim (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope">Polytope (class in qdarts.polytope)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.precompute_sensor_state">precompute_sensor_state() (qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.precompute_sensor_state">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_configuration">sample_sensor_configuration() (qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.sample_sensor_configuration">(qdarts.tunneling_simulator.LocalSystem method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_configuration">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_equilibrium">sample_sensor_equilibrium() (qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.sample_sensor_equilibrium">(qdarts.tunneling_simulator.LocalSystem method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_equilibrium">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan">sensor_scan() (qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan_2D">sensor_scan_2D() (qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_sim">sensor_sim (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack">set_maximum_polytope_slack() (qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator.set_maximum_polytope_slack">(qdarts.simulator.CapacitiveDeviceSimulator method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.set_polytope">set_polytope() (qdarts.polytope.Polytope method)</a>
+</li>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.slacks">slacks (qdarts.polytope.Polytope attribute)</a>
+</li>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.slice">slice() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.capacitance_model.CapacitanceModel.html#qdarts.capacitance_model.CapacitanceModel.slice">(qdarts.capacitance_model.CapacitanceModel method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess.slice">(qdarts.noise_processes.AbstractNoiseProcess method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.Cosine_Mean_Function.html#qdarts.noise_processes.Cosine_Mean_Function.slice">(qdarts.noise_processes.Cosine_Mean_Function method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process.slice">(qdarts.noise_processes.OU_process method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractCapacitiveDeviceSimulator.html#qdarts.simulator.AbstractCapacitiveDeviceSimulator.slice">(qdarts.simulator.AbstractCapacitiveDeviceSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.AbstractPolytopeSimulator.html#qdarts.simulator.AbstractPolytopeSimulator.slice">(qdarts.simulator.AbstractPolytopeSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html#qdarts.simulator.CapacitiveDeviceSimulator.slice">(qdarts.simulator.CapacitiveDeviceSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.slice">(qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.slice">(qdarts.tunneling_simulator.ApproximateTunnelingSimulator method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.slice">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ <li><a href="interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.html#qdarts.tunneling_simulator.TunnelBarrierModel.slice">(qdarts.tunneling_simulator.TunnelBarrierModel method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html#qdarts.tunneling_simulator.AbstractSensorSim.start_measurement">start_measurement() (qdarts.tunneling_simulator.AbstractSensorSim method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.NoisySensorDot.html#qdarts.tunneling_simulator.NoisySensorDot.start_measurement">(qdarts.tunneling_simulator.NoisySensorDot method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess.start_sequence">start_sequence() (qdarts.noise_processes.AbstractNoiseProcess method)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.noise_processes.Cosine_Mean_Function.html#qdarts.noise_processes.Cosine_Mean_Function.start_sequence">(qdarts.noise_processes.Cosine_Mean_Function method)</a>
+</li>
+ <li><a href="interfaces/qdarts.noise_processes.OU_process.html#qdarts.noise_processes.OU_process.start_sequence">(qdarts.noise_processes.OU_process method)</a>
+</li>
+ </ul></li>
+ <li><a href="interfaces/qdarts.polytope.Polytope.html#qdarts.polytope.Polytope.state">state (qdarts.polytope.Polytope attribute)</a>
+
+ <ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.state">(qdarts.tunneling_simulator.LocalSystem attribute)</a>
+</li>
+ </ul></li>
+ </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.ApproximateTunnelingSimulator.html#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.T">T (qdarts.tunneling_simulator.ApproximateTunnelingSimulator attribute)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.TunnelBarrierModel.html#qdarts.tunneling_simulator.TunnelBarrierModel">TunnelBarrierModel (class in qdarts.tunneling_simulator)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.tunneling_simulator.LocalSystem.html#qdarts.tunneling_simulator.LocalSystem.v">v (qdarts.tunneling_simulator.LocalSystem attribute)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="interfaces/qdarts.capacitance_model.AbstractCapacitanceModel.html#qdarts.capacitance_model.AbstractCapacitanceModel.verify_polytope">verify_polytope() (qdarts.capacitance_model.AbstractCapacitanceModel method)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+
+
+ </div>
+
+ </div>
+ </div>
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+<h1>QDarts<a class="headerlink" href="#qdarts" title="Link to this heading">¶</a></h1>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
+<p>Installation can be done via <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">build</span></code>. In the QDarts directory, run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">build</span>
+<span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">QDarts</span><span class="o">/</span><span class="n">dist</span><span class="o">/</span><span class="n">QDarts</span><span class="o">-<</span><span class="n">Version</span><span class="o">>-</span><span class="n">py3</span><span class="o">-</span><span class="n">none</span><span class="o">-</span><span class="nb">any</span><span class="o">.</span><span class="n">whl</span>
+</pre></div>
+</div>
+<p>where <code class="docutils literal notranslate"><span class="pre"><Version></span></code> is the current QDarts version.</p>
+</section>
+<section id="documentation">
+<h2>Documentation<a class="headerlink" href="#documentation" title="Link to this heading">¶</a></h2>
+<p>A usage example is provided in the ipython notebook in the <code class="docutils literal notranslate"><span class="pre">examples/</span></code> folder.
+Below you find the <cite>detailed documentation of ``QDarts`</cite></p>
+<div class="toctree-wrapper compound">
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+<li class="toctree-l1"><a class="reference internal" href="api_tutorial.html">API Usage Tutorial</a></li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API</a></li>
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+ <section id="qdarts-capacitance-model-abstractcapacitancemodel">
+<h1>qdarts.capacitance_model.AbstractCapacitanceModel<a class="headerlink" href="#qdarts-capacitance-model-abstractcapacitancemodel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.capacitance_model.</span></span><span class="sig-name descname"><span class="pre">AbstractCapacitanceModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dots</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_limits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_normals</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel" title="Link to this definition">¶</a></dt>
+<dd><p>Base Class for all capacitance models.</p>
+<p>This class provides all required meta information to compute capacitive energies of a system with electron configuration
+n and gate voltages v, E(v,n). The only required to this model is that E(v,n)-E(v,n’) is a linear function in v and that energies
+are measured in eV.
+The class provides basic abilities: enumeration of possible transition states from a given state and computation/verification of a polytope P(n) for a state.
+For this, the user only needs to provide functions to generate transition equations and a function that allows to slice the voltage space.</p>
+<p>As a base class it only needs to know the number of gates/inputs and the number of dots of the array. Additionally
+the user must supply voltage bounds that ensure that all computed polytopes are bounded. In practical devices these would be voltage limits
+e.g., for device protection.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.num_dots">
+<span class="sig-name descname"><span class="pre">num_dots</span></span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.num_dots" title="Link to this definition">¶</a></dt>
+<dd><p>the number of discrete dot locations on the device. This is the number of elements in the electron state n.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.num_inputs">
+<span class="sig-name descname"><span class="pre">num_inputs</span></span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.num_inputs" title="Link to this definition">¶</a></dt>
+<dd><p>The number of gate voltages of the device.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.bounds_limits">
+<span class="sig-name descname"><span class="pre">bounds_limits</span></span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.bounds_limits" title="Link to this definition">¶</a></dt>
+<dd><p>right hand side of the bound inequalities.
+set of linear inequalities A that provide bounds for the voltage space.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>N np.array of float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dots</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_limits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_normals</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Initializes the model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_dots</strong> (<em>int</em>) – the number of discrete dot locations on the device. This is the number of elements in the electron state n.</p></li>
+<li><p><strong>num_inputs</strong> (<em>int</em>) – The number of gate voltages of the device.</p></li>
+<li><p><strong>bounds_limits</strong> (<em>N np.array</em><em> of </em><em>float</em>) – right hand side of the bound inequalities. If bounds_normals is none, this is interpreted as lower bounds and thus N=num_inputs</p></li>
+<li><p><strong>bounds_normals</strong> (<em>N x num_inputs np.array</em><em> of </em><em>float</em>) – set of linear inequalities A that provide bounds for the voltage space. A valid voltage v fulfils Av+b<0, where b is
+bounds_limits. Can be none, in which case A=-Id.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.__init__" title="qdarts.capacitance_model.AbstractCapacitanceModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(num_dots, num_inputs, ...)</p></td>
+<td><p>Initializes the model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.compute_polytope_for_state" title="qdarts.capacitance_model.AbstractCapacitanceModel.compute_polytope_for_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_polytope_for_state</span></code></a>(state, maximum_slack)</p></td>
+<td><p>For a given state, computes P(N)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.compute_transition_equations" title="qdarts.capacitance_model.AbstractCapacitanceModel.compute_transition_equations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_equations</span></code></a>(state_list, state)</p></td>
+<td><p>For a given state n and a list of other states (n_1,...n_N), computes the set of linear equations E(v,n)-E(v,n_i).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.enumerate_neighbours" title="qdarts.capacitance_model.AbstractCapacitanceModel.enumerate_neighbours"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enumerate_neighbours</span></code></a>(state)</p></td>
+<td><p>Generates a state_list object for compute_transition_equations,</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.slice" title="qdarts.capacitance_model.AbstractCapacitanceModel.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Restricts the model to the affine subspace v=m+Pv'</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.capacitance_model.AbstractCapacitanceModel.verify_polytope" title="qdarts.capacitance_model.AbstractCapacitanceModel.verify_polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">verify_polytope</span></code></a>(polytope, maximum_slack)</p></td>
+<td><p>Verifies a polytope.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.compute_polytope_for_state">
+<span class="sig-name descname"><span class="pre">compute_polytope_for_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_slack</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.compute_polytope_for_state" title="Link to this definition">¶</a></dt>
+<dd><p>For a given state, computes P(N)</p>
+<p>Calls enumerate_neighbours and compute_transition_equations to obtain a list of
+possible transitions and then removes from this list iteratively
+all transitions that are not sufficiently close to the polytope.</p>
+<p>This is computed by computing the slack. The slack is 0 if the ith transition is a facet of
+the polytope, otherwise it is a positive number computed as</p>
+<p><span class="math notranslate nohighlight">\(s_i = min_v A_i^Tv +b_i, v \in P(n)\)</span></p>
+<p>This function retains all facets that have slack smaller than maximum_slack.
+Since enregy differences are measure din eV, the slack represents the minimum
+energy difference between the ground state and the state represented by the transition
+for any point inside the polytope.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>D np.array</em><em> of </em><em>int</em>) – The electron configuration n for which to compute P(n)</p></li>
+<li><p><strong>maximum_slack</strong> (<em>float</em>) – The maximum distance in eV after which the transition is discarded</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A Polytope object representing P(n). See documentation of Polytope.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.compute_transition_equations">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_transition_equations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.compute_transition_equations" title="Link to this definition">¶</a></dt>
+<dd><p>For a given state n and a list of other states (n_1,…n_N), computes the set of
+linear equations E(v,n)-E(v,n_i). Must be implemented by derived classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>K np.array</em><em> of </em><em>int</em>) – State n</p></li>
+<li><p><strong>state_list</strong> (<em>NxK np.array</em><em> of </em><em>int</em>) – list of other states (n_1,…n_N)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>(A,b) set of linear equations represented by matrix A and offset b. The ith element computes</em></p></li>
+<li><p><em>E(v,n)-E(v,n_i) as a function of v.</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.enumerate_neighbours">
+<span class="sig-name descname"><span class="pre">enumerate_neighbours</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.enumerate_neighbours" title="Link to this definition">¶</a></dt>
+<dd><p>Generates a state_list object for compute_transition_equations,</p>
+<p>Enumerates the set of neighbours of a transition to return all possible
+state transitions. In this class it is implemented by returning all possible
+states that can be reached by any combintion of adding or removing an electron on any dot.
+As a result, this list has 3^D-1 elements, where D is the number of dots.</p>
+<p>Derived classes may overwrite this if they want to consider a more restricted set of transitions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>D np.array</em><em> of </em><em>int</em>) – The state for which to enumerate all neighbours</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>List of lists of neighbours. By default only a list including a list of all neighbours is returned.</em></p></li>
+<li><p><em>Derived classes may decide to instead return several lists each representing a batch of transitions.</em></p></li>
+<li><p><em>Aong those lists, states do not need to be unique but instead represent groups of elements that can</em></p></li>
+<li><p><em>filtered efficiently by the solver, e.g., by adding all states that can be used to filter a lot of other</em></p></li>
+<li><p><em>states in the list quickly. This is only ever relevant when larger arrays need to be computed.</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.slice">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the model to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. Must be implemented by derived classes. Note
+that derived classes also need to apply the affine transformation to the bounds variables</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>M np.array</em><em> of </em><em>floats</em>) – offset of the affine trnsformation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A model object describing the simulation on the affine subspace.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.AbstractCapacitanceModel.verify_polytope">
+<span class="sig-name descname"><span class="pre">verify_polytope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polytope</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_slack</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.AbstractCapacitanceModel.verify_polytope" title="Link to this definition">¶</a></dt>
+<dd><p>Verifies a polytope.</p>
+<p>After slicing, polytopes that have been computed earlier also need to be sliced. It is inefficient to recompute
+the polytopes from scratch, as slicing can only remove, but never add transitions. verify_polytope allows to take
+a polytope that has been modified via polytope.layz_slice and verify/filter all transitions. This recomputes
+all slack variables and removes all transitions that have slack larger than maximum slack</p>
+<p>Note that this does not touch any other internal information stored in the polytope.
+This function doe snothing if polytope.must_verify=False</p>
+<p>TOOD: this should be moved somewhere else.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>polytope</strong> (<a class="reference internal" href="qdarts.polytope.Polytope.html#qdarts.polytope.Polytope" title="qdarts.polytope.Polytope"><em>Polytope</em></a>) – The polytope P(n) to be verified</p></li>
+<li><p><strong>maximum_slack</strong> (<em>float</em>) – The maximum distance in eV after which a transition of the polytope is discarded</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The updated polytope after filtering out transitions.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+ <section id="qdarts-capacitance-model-capacitancemodel">
+<h1>qdarts.capacitance_model.CapacitanceModel<a class="headerlink" href="#qdarts-capacitance-model-capacitancemodel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.CapacitanceModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.capacitance_model.</span></span><span class="sig-name descname"><span class="pre">CapacitanceModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">C_g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C_D</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_limits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_normals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_C_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.CapacitanceModel" title="Link to this definition">¶</a></dt>
+<dd><p>Implementation of a slight generalization of the constant interaction model.</p>
+<p>The constant interaction model defines
+<span class="math notranslate nohighlight">\(E(v,n)=1/2 n^T C_{DD}^{-1}n - n^T C_{DD}^{-1}C_{DG}v\)</span></p>
+<p>where <span class="math notranslate nohighlight">\(C_{DD}\)</span> and <span class="math notranslate nohighlight">\(C_{DG}\)</span> are part of the maxwell capacitance matrix created by the system
+of plunger gates G and quantum dots D. Thus, <span class="math notranslate nohighlight">\(C_{DD}\)</span> are the interdot capacitances (mawell) and
+<span class="math notranslate nohighlight">\(C_{DG}\)</span> the dot to gate capacitances.</p>
+<p>This model is a generalization of the constant interaction model as it makes <span class="math notranslate nohighlight">\(C_{DD}\)</span> and <span class="math notranslate nohighlight">\(C_{DG}\)</span> a function
+of electron state n. The speed of this change from the constant interaction is governed by a parameter k for each dot. The larger
+k is, the smaller the deviation. if k=None, this is exactly the constant interaction model.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.CapacitanceModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">C_g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C_D</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_limits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounds_normals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_C_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.CapacitanceModel.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Initializes the model</p>
+<p>The parameters here are normal capacitances and not maxwell capacitances given in atto Farrad.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>C_g</strong> (<em>DxK np.array</em><em> of </em><em>float</em>) – Capacitances in atto Farrad between the K gates and D dots.</p></li>
+<li><p><strong>C_D</strong> (<em>DxD np.array</em><em> of </em><em>float</em>) – Capacitances in atto Farrad between the D dots. Self capacitances are possible via the diagonal matrix elements.</p></li>
+<li><p><strong>bounds_limits</strong> (<em>N np.array</em><em> of </em><em>float</em>) – right hand side of the bound inequalities. If bounds_normals is none, this is interpreted as lower bounds and thus N=num_inputs</p></li>
+<li><p><strong>bounds_normals</strong> (<em>N x num_inputs np.array</em><em> of </em><em>float</em>) – set of linear inequalities A that provide bounds for the voltage space. A valid voltage v fulfils Av+b<0, where b is
+bounds_limits. Can be none, in which case A=-Id.’</p></li>
+<li><p><strong>ks</strong> (<em>D np.array</em><em> of </em><em>float</em><em> or </em><em>None.</em>) – How quickly the capacitances change as deviation from the constant interaction model for each dot. Can be None in which this is just the constant interaction model.
+Larger integers give smaller changes. Realistic values are 3-5.</p></li>
+<li><p><strong>transform_C_g</strong> – Internal. Used to implement slicing. Should be None.</p></li>
+<li><p><strong>offset</strong> – Internal. Used to implement slicing. Should be None.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.CapacitanceModel.__init__" title="qdarts.capacitance_model.CapacitanceModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(C_g, C_D, bounds_limits[, ...])</p></td>
+<td><p>Initializes the model</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_polytope_for_state</span></code>(state, maximum_slack)</p></td>
+<td><p>For a given state, computes P(N)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.CapacitanceModel.compute_transition_equations" title="qdarts.capacitance_model.CapacitanceModel.compute_transition_equations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_equations</span></code></a>(state_list, ...)</p></td>
+<td><p>For a given state n and a list of other states (n_1,...n_N), computes the set of linear equations E(v,n)-E(v,n_i).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">enumerate_neighbours</span></code>(state)</p></td>
+<td><p>Generates a state_list object for compute_transition_equations,</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.capacitance_model.CapacitanceModel.slice" title="qdarts.capacitance_model.CapacitanceModel.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Restricts the model to the affine subspace v=m+Pv'</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">verify_polytope</span></code>(polytope, maximum_slack)</p></td>
+<td><p>Verifies a polytope.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.CapacitanceModel.compute_transition_equations">
+<span class="sig-name descname"><span class="pre">compute_transition_equations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_from</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.CapacitanceModel.compute_transition_equations" title="Link to this definition">¶</a></dt>
+<dd><p>For a given state n and a list of other states (n_1,…n_N), computes the set of
+linear equations E(v,n)-E(v,n_i). Must be implemented by derived classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>K np.array</em><em> of </em><em>int</em>) – State n</p></li>
+<li><p><strong>state_list</strong> (<em>NxK np.array</em><em> of </em><em>int</em>) – list of other states (n_1,…n_N)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>(A,b) set of linear equations represented by matrix A and offset b. The ith element computes</em></p></li>
+<li><p><em>E(v,n)-E(v,n_i) as a function of v.</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.capacitance_model.CapacitanceModel.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.capacitance_model.CapacitanceModel.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the model to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. Must be implemented by derived classes. Note
+that derived classes also need to apply the affine transformation to the bounds variables</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>M np.array</em><em> of </em><em>floats</em>) – offset of the affine trnsformation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A model object describing the simulation on the affine subspace.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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+ <section id="qdarts-experiment-experiment">
+<h1>qdarts.experiment.Experiment<a class="headerlink" href="#qdarts-experiment-experiment" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.experiment.</span></span><span class="sig-name descname"><span class="pre">Experiment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tunneling_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_logs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment" title="Link to this definition">¶</a></dt>
+<dd><p>Experiment class that helps setup the different parts of the simulator into an easy to ue interface.
+The usage of this class is best described by the tutorials in the example folder.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tunneling_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_logs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>capacitance_config: dictionary containing the capacitance model parameters
+tunneling_config: dictionary containing the tunneling model parameters
+sensor_config: dictionary containing the sensor model parameters
+print_logs: bool, whether to print logs</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.__init__" title="qdarts.experiment.Experiment.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(capacitance_config[, ...])</p></td>
+<td><p>capacitance_config: dictionary containing the capacitance model parameters tunneling_config: dictionary containing the tunneling model parameters sensor_config: dictionary containing the sensor model parameters print_logs: bool, whether to print logs</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.center_transition" title="qdarts.experiment.Experiment.center_transition"><code class="xref py py-obj docutils literal notranslate"><span class="pre">center_transition</span></code></a>(simulator, target_state, ...)</p></td>
+<td><p>Function that center the CSD at a given facet (transition) of the polytope (occupation state).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.deploy_capacitance_sim" title="qdarts.experiment.Experiment.deploy_capacitance_sim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deploy_capacitance_sim</span></code></a>(config)</p></td>
+<td><p>Function that deploys a capacitance simulator.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.deploy_sensor_model" title="qdarts.experiment.Experiment.deploy_sensor_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deploy_sensor_model</span></code></a>(sensor_config)</p></td>
+<td><p>Function that deploys a sensor model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.deploy_tunneling_sim" title="qdarts.experiment.Experiment.deploy_tunneling_sim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deploy_tunneling_sim</span></code></a>(capacitance_sim, ...)</p></td>
+<td><p>Function that deploys a tunneling simulator.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.generate_CSD" title="qdarts.experiment.Experiment.generate_CSD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_CSD</span></code></a>(x_voltages, y_voltages, plane_axes)</p></td>
+<td><p>Function that renders the capacitance CSD for a given set of voltages and axes.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.get_compensated_sim" title="qdarts.experiment.Experiment.get_compensated_sim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_compensated_sim</span></code></a>(simulator, target_state)</p></td>
+<td><p>Function that takes a capacitance simulator and compensates the sensors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.get_plot_args" title="qdarts.experiment.Experiment.get_plot_args"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_plot_args</span></code></a>(x_voltages, y_voltages, plane_axes)</p></td>
+<td><p>Function that returns the arguments for plotting the CSD.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.experiment.Experiment.get_virtualised_sim" title="qdarts.experiment.Experiment.get_virtualised_sim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_virtualised_sim</span></code></a>(simulator, target_state)</p></td>
+<td><p>Function that takes a simulator and virtualises the gates specified by inner_dots.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.center_transition">
+<span class="sig-name descname"><span class="pre">center_transition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">simulator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plane_axes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_virtual_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compensate_sensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.center_transition" title="Link to this definition">¶</a></dt>
+<dd><p>Function that center the CSD at a given facet (transition) of the polytope (occupation state).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>simulator</strong> (<em>any simulator object</em>)</p></li>
+<li><p><strong>target_state</strong> (<em>int</em><em>, </em><em>the state at which the transition happens</em><em>, </em><em>e.g.</em><em> [</em><em>2</em><em>,</em><em>2</em><em>]</em>)</p></li>
+<li><p><strong>target_transition</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>the transition point e.g.</em><em> [</em><em>1</em><em>,</em><em>-1</em><em>] </em><em>would be the transition from</em><em> [</em><em>2</em><em>,</em><em>2</em><em>] </em><em>to</em><em> [</em><em>1</em><em>,</em><em>1</em><em>]</em>)</p></li>
+<li><p><strong>plane_axes</strong> (<em>2xN array</em><em>, </em><em>the axes</em><em> of </em><em>the transition which span the plane</em>)</p></li>
+<li><p><strong>use_virtual_gates</strong> (<em>bool</em><em>, </em><em>whether to use virtual gates</em>)</p></li>
+<li><p><strong>compensate_sensors</strong> (<em>bool</em><em>, </em><em>whether to compensate the sensors</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>plane_axes</strong> (<em>2xN array, the axes spanning the cut through volage plane</em>)</p></li>
+<li><p><strong>transition_sim</strong> (<em>CapacitanceSimulator object, the transition simulator</em>)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.deploy_capacitance_sim">
+<span class="sig-name descname"><span class="pre">deploy_capacitance_sim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.deploy_capacitance_sim" title="Link to this definition">¶</a></dt>
+<dd><p>Function that deploys a capacitance simulator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>config</strong> (<em>dictionary containing the capacitance model parameters</em>)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>sim</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>CapacitiveDeviceSimulator object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.deploy_sensor_model">
+<span class="sig-name descname"><span class="pre">deploy_sensor_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sensor_config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.deploy_sensor_model" title="Link to this definition">¶</a></dt>
+<dd><p>Function that deploys a sensor model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>config</strong> (<em>dictionary containing the sensor model parameters</em>)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>sensor_sim</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>NoisySensorDot object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.deploy_tunneling_sim">
+<span class="sig-name descname"><span class="pre">deploy_tunneling_sim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_sim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tunneling_config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.deploy_tunneling_sim" title="Link to this definition">¶</a></dt>
+<dd><p>Function that deploys a tunneling simulator.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>capacitance_sim</strong> (<em>CapacitiveDeviceSimulator object</em>)</p></li>
+<li><p><strong>tunneling_config</strong> (<em>dictionary containing the tunneling model parameters</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>tunneling_sim</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ApproximateTunnelingSimulator object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.generate_CSD">
+<span class="sig-name descname"><span class="pre">generate_CSD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_voltages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_voltages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plane_axes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transition</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_virtual_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compensate_sensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_polytopes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_sensor_signal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.generate_CSD" title="Link to this definition">¶</a></dt>
+<dd><p>Function that renders the capacitance CSD for a given set of voltages and axes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x_voltages</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>the x-axis voltages</em>)</p></li>
+<li><p><strong>y_voltages</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>the y-axis voltages</em>)</p></li>
+<li><p><strong>plane_axes</strong> (<em>2xN array</em><em>, </em><em>the axes</em><em> of </em><em>the plane in which the CSD is to be rendered</em>)</p></li>
+<li><p><strong>target_state</strong> (<em>int</em><em>, </em><em>the guess state</em><em> or </em><em>the state at which the transition happens</em>)</p></li>
+<li><p><strong>target_transition</strong> (<em>list</em><em> of </em><em>integers</em><em>, </em><em>the transition point e.g.</em><em> [</em><em>1</em><em>,</em><em>-1</em><em>] </em><em>would be the transition from</em><em> [</em><em>2</em><em>,</em><em>2</em><em>] </em><em>to</em><em> [</em><em>1</em><em>,</em><em>1</em><em>]</em>)</p></li>
+<li><p><strong>use_virtual_gates</strong> (<em>bool</em><em>, </em><em>whether to use virtual gates</em>)</p></li>
+<li><p><strong>compensate_sensors</strong> (<em>bool</em><em>, </em><em>whether to compensate the sensors</em>)</p></li>
+<li><p><strong>compute_polytopes</strong> (<em>bool</em><em>, </em><em>whether to compute the polytopes</em>)</p></li>
+<li><p><strong>use_sensor_signal</strong> (<em>bool</em><em>, </em><em>whether to use the sensor signal</em>)</p></li>
+<li><p><strong>v_offset</strong> (<em>Nx1 array</em><em>, </em><em>the offset voltage</em><em> of </em><em>all</em><em> of </em><em>the gates</em><em>, </em><em>which defines the origin</em><em> of </em><em>the plot</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>xout, yout</strong> (<em>list of floats, the x and y voltages</em>)</p></li>
+<li><p><strong>CSD_data</strong> (<em>2D array, the CSD data</em>)</p></li>
+<li><p><strong>polytopes</strong> (<em>dictionary, the polytopes of the CSD. None if compute_polytopes is False</em>)</p></li>
+<li><p><strong>sensor_values</strong> (<em>3D array, the sensor signal [size(xout),size(yout),num_sensors]. None if use_sensor_signal is False</em>)</p></li>
+<li><p><strong>v_offset</strong> (<em>Nx1 array, the offset voltage of all of the gates</em>)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.get_compensated_sim">
+<span class="sig-name descname"><span class="pre">get_compensated_sim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">simulator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.get_compensated_sim" title="Link to this definition">¶</a></dt>
+<dd><p>Function that takes a capacitance simulator and compensates the sensors.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>simulator</strong> (<em>AbstractPolytopeSimulator object</em>)</p></li>
+<li><p><strong>target_stater</strong> (<em>int</em><em>, </em><em>the state at which sensor compensation happens</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>simulator</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>AbstractPolytopeSimulator object, the compensated simulator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.get_plot_args">
+<span class="sig-name descname"><span class="pre">get_plot_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_voltages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_voltages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plane_axes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.get_plot_args" title="Link to this definition">¶</a></dt>
+<dd><p>Function that returns the arguments for plotting the CSD.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x_voltages</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>the x-axis voltages</em>)</p></li>
+<li><p><strong>y_voltages</strong> (<em>list</em><em> of </em><em>floats</em><em>, </em><em>the y-axis voltages</em>)</p></li>
+<li><p><strong>plane_axes</strong> (<em>2xN array</em><em>, </em><em>the axes</em><em> of </em><em>the plane in which the CSD is to be rendered</em>)</p></li>
+<li><p><strong>v_offset</strong> (<em>Nx1 array</em><em>, </em><em>the offset voltage</em><em> of </em><em>all</em><em> of </em><em>the gates</em><em>, </em><em>which defines the origin</em><em> of </em><em>the plot</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>v_offset</strong> (<em>Nx1 array, the offset voltage of all of the gates</em>)</p></li>
+<li><p><strong>minV</strong> (<em>2x1 array, the minimum voltage of selected axes</em>)</p></li>
+<li><p><strong>maxV</strong> (<em>2x1 array, the maximum voltage of selected axes</em>)</p></li>
+<li><p><strong>resolution</strong> (<em>list of integers, the resolution of the plot</em>)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.experiment.Experiment.get_virtualised_sim">
+<span class="sig-name descname"><span class="pre">get_virtualised_sim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">simulator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.experiment.Experiment.get_virtualised_sim" title="Link to this definition">¶</a></dt>
+<dd><p>Function that takes a simulator and virtualises the gates specified by inner_dots.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>simulator</strong> (<em>AbstractPolytopeSimulator object</em>)</p></li>
+<li><p><strong>target_state</strong> (<em>int</em><em>, </em><em>the initial corner state guess</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>simulator</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>AbstractPolytopeSimulator object, the virtualised simulator</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.AbstractNoiseProcess.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.AbstractNoiseProcess.html
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+ <section id="qdarts-noise-processes-abstractnoiseprocess">
+<h1>qdarts.noise_processes.AbstractNoiseProcess<a class="headerlink" href="#qdarts-noise-processes-abstractnoiseprocess" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.noise_processes.AbstractNoiseProcess">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.noise_processes.</span></span><span class="sig-name descname"><span class="pre">AbstractNoiseProcess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_elements</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.AbstractNoiseProcess" title="Link to this definition">¶</a></dt>
+<dd><p>Models a noise process that produces possibly dependent samples <span class="math notranslate nohighlight">\(\epsilon(v)_t\)</span>. The noise process
+can depend on the device voltages v and the noise can depend on all previous samples in the sequence. We assume
+that <span class="math notranslate nohighlight">\(\epsilon(v)_t\)</span> is vector valued and the number of elements is stored in the <code class="docutils literal notranslate"><span class="pre">num_elements</span></code> attribute</p>
+<p>A sequence is started by calling <code class="docutils literal notranslate"><span class="pre">start_sequence</span></code>, at which point the newly sampled points are independent from
+all previous samples.</p>
+<p>Note that currently, the elements in the noise process are assumed to be drawn with equal time difference between samples.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.noise_processes.AbstractNoiseProcess.num_elements">
+<span class="sig-name descname"><span class="pre">num_elements</span></span><a class="headerlink" href="#qdarts.noise_processes.AbstractNoiseProcess.num_elements" title="Link to this definition">¶</a></dt>
+<dd><p>the dimensionality of the noise variable</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.AbstractNoiseProcess.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_elements</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.AbstractNoiseProcess.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.AbstractNoiseProcess.__init__" title="qdarts.noise_processes.AbstractNoiseProcess.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(num_elements)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.noise_processes.AbstractNoiseProcess.slice" title="qdarts.noise_processes.AbstractNoiseProcess.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.AbstractNoiseProcess.start_sequence" title="qdarts.noise_processes.AbstractNoiseProcess.start_sequence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_sequence</span></code></a>()</p></td>
+<td><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.AbstractNoiseProcess.slice">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.AbstractNoiseProcess.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix.</p></li>
+<li><p><strong>m</strong> (<em>M np.array</em><em> of </em><em>floats</em>) – offset of the affine transformation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A noise process object describing the noise on the affine subspace. The current noise object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.AbstractNoiseProcess.start_sequence">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">start_sequence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.AbstractNoiseProcess.start_sequence" title="Link to this definition">¶</a></dt>
+<dd><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
+ </div>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.Cosine_Mean_Function.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.Cosine_Mean_Function.html
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+
+ <section id="qdarts-noise-processes-cosine-mean-function">
+<h1>qdarts.noise_processes.Cosine_Mean_Function<a class="headerlink" href="#qdarts-noise-processes-cosine-mean-function" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.noise_processes.Cosine_Mean_Function">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.noise_processes.</span></span><span class="sig-name descname"><span class="pre">Cosine_Mean_Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.Cosine_Mean_Function" title="Link to this definition">¶</a></dt>
+<dd><p>Decorator of an random process that models an additive mean term that depends on the gate voltages.
+This term is added to noise values sampled from the decorated noise model</p>
+<p>The mean term of the ith noise element is given as a set of cosine functions:</p>
+<p><span class="math notranslate nohighlight">\(\mu_i(v)= \sum_j a_{ij} \cos(2\pi (w_{ij}^T v+b_{ij}))\)</span></p>
+<p>and the resulting noise is</p>
+<p><span class="math notranslate nohighlight">\(\epsilon(v)_{t}=\mu(v) + \epsilon(v)^D_{t}\)</span></p>
+<p>where <span class="math notranslate nohighlight">\(\epsilon(v)^D_{t}\)</span> is the decorated noise process.</p>
+<p>The user supplies the weight tensor W with elements <span class="math notranslate nohighlight">\(W_{ijk}\)</span> so that W[i,j] is the vector <span class="math notranslate nohighlight">\(w_{ij}\)</span>
+and a matrix a with the amplitude values <span class="math notranslate nohighlight">\(a_{ij}\)</span>. Finally, b is the matrix of offsets <span class="math notranslate nohighlight">\(b_{ij}\)</span>, which can be left as None,
+in which case it is sampled uniformly between 0 and 1.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.Cosine_Mean_Function.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">W</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.Cosine_Mean_Function.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Initialized the cosine mean function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>noise_model</strong> (<a class="reference internal" href="qdarts.noise_processes.AbstractNoiseProcess.html#qdarts.noise_processes.AbstractNoiseProcess" title="qdarts.noise_processes.AbstractNoiseProcess"><em>AbstractNoiseProcess</em></a>) – The noise process with N dimensions to decorrate.</p></li>
+<li><p><strong>a</strong> (<em>NxM np.array</em><em> of </em><em>float</em>) – Amplitudes of the M overlapping cosine functions</p></li>
+<li><p><strong>W</strong> (<em>NxMxK np.array</em><em> of </em><em>float</em>) – K dimensional weights of the M consine functions for the N outputs.</p></li>
+<li><p><strong>b</strong> (<em>NxM np.array</em><em> of </em><em>float</em><em> or </em><em>None</em>) – Phases of the M overlapping cosine functions. If none, it is drawn uniformly between 0 and 1.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.Cosine_Mean_Function.__init__" title="qdarts.noise_processes.Cosine_Mean_Function.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(noise_model, a, W[, b])</p></td>
+<td><p>Initialized the cosine mean function.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.noise_processes.Cosine_Mean_Function.slice" title="qdarts.noise_processes.Cosine_Mean_Function.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.Cosine_Mean_Function.start_sequence" title="qdarts.noise_processes.Cosine_Mean_Function.start_sequence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_sequence</span></code></a>()</p></td>
+<td><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.Cosine_Mean_Function.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.Cosine_Mean_Function.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix.</p></li>
+<li><p><strong>m</strong> (<em>M np.array</em><em> of </em><em>floats</em>) – offset of the affine transformation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A noise process object describing the noise on the affine subspace. The current noise object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.Cosine_Mean_Function.start_sequence">
+<span class="sig-name descname"><span class="pre">start_sequence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.Cosine_Mean_Function.start_sequence" title="Link to this definition">¶</a></dt>
+<dd><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
+ </div>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.OU_process.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.noise_processes.OU_process.html
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+ <section id="qdarts-noise-processes-ou-process">
+<h1>qdarts.noise_processes.OU_process<a class="headerlink" href="#qdarts-noise-processes-ou-process" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.noise_processes.OU_process">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.noise_processes.</span></span><span class="sig-name descname"><span class="pre">OU_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_elements</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.OU_process" title="Link to this definition">¶</a></dt>
+<dd><p>Implements the Ornstein-Uhlenbeck noise process</p>
+<p>This noise is independent of v. We have that</p>
+<div class="math notranslate nohighlight">
+\[\epsilon(v)_{t+1}=a \epsilon(v)_t + \sqrt{1-a^2} \sigma \epsilon_t\]</div>
+<p>Where <span class="math notranslate nohighlight">\(\epsilon_t\)</span> is independent gaussian noise and</p>
+<div class="math notranslate nohighlight">
+\[a=e^{-\frac{\Delta t}{t_c}}\]</div>
+<p>It is possible to generate multiple independent samples of the same process simultaneously.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.OU_process.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_elements</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.OU_process.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Instantiates the OU process with its process parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> (<em>float</em>) – Standard deviation of the OU process</p></li>
+<li><p><strong>tc</strong> (<em>float</em>) – time correlation parameters, the higher the more samples are correlated</p></li>
+<li><p><strong>dt</strong> (<em>float</em>) – time step between samples. The higher, the less samples are correlated</p></li>
+<li><p><strong>num_elements</strong> (<em>int</em>) – How many independnet samples are computed simultaneously</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.OU_process.__init__" title="qdarts.noise_processes.OU_process.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(sigma, tc, dt, num_elements)</p></td>
+<td><p>Instantiates the OU process with its process parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.noise_processes.OU_process.next_val" title="qdarts.noise_processes.OU_process.next_val"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next_val</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.noise_processes.OU_process.slice" title="qdarts.noise_processes.OU_process.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.noise_processes.OU_process.start_sequence" title="qdarts.noise_processes.OU_process.start_sequence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_sequence</span></code></a>()</p></td>
+<td><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.OU_process.next_val">
+<span class="sig-name descname"><span class="pre">next_val</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.OU_process.next_val" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.OU_process.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.OU_process.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the noise to the affine subspace <span class="math notranslate nohighlight">\(v=m+Pv\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix.</p></li>
+<li><p><strong>m</strong> (<em>M np.array</em><em> of </em><em>floats</em>) – offset of the affine transformation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A noise process object describing the noise on the affine subspace. The current noise object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.noise_processes.OU_process.start_sequence">
+<span class="sig-name descname"><span class="pre">start_sequence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.noise_processes.OU_process.start_sequence" title="Link to this definition">¶</a></dt>
+<dd><p>Restart the sequence so that the next sample drawn is independent from all previous samples.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
+ </div>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.polytope.Polytope.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.polytope.Polytope.html
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+ <div class="body" role="main">
+
+ <section id="qdarts-polytope-polytope">
+<h1>qdarts.polytope.Polytope<a class="headerlink" href="#qdarts-polytope-polytope" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.polytope.</span></span><span class="sig-name descname"><span class="pre">Polytope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.polytope.Polytope" title="Link to this definition">¶</a></dt>
+<dd><p>Represents the polytope P(n) defined by all gate voltages v in a device that have
+capacitive ground state n. They are stored as a set of linear inequalities (A,b), and
+A point v in P(n) fulfills</p>
+<p><span class="math notranslate nohighlight">\(Av+b <0\)</span></p>
+<p>Each inequality represents a facet of the polytope and each facet marks a transition from P(n) to
+some other ground state polytope P(n’). The state difference t=n’-n is stored as label for each
+inequality.</p>
+<p>Not each inequality stored must touch the polytope. There might be others that could be removed without changing P(n).
+The distance from the polytope is given by the slack variable s that for each inequality either is 0 if the side is touching
+(or some number numerically close to 0, e..g, 1.e-8) and otherwise we have</p>
+<p><span class="math notranslate nohighlight">\(s_i = min_v A_i^Tv +b_i, v \in P(n)\)</span></p>
+<p>If the inequalities measure difference in capacitive energy from the ground state (which is default in the simulator) the slack
+therefore indicates the minimum energy gap between the transition state indicated by the inequality and the ground state.</p>
+<p>Finally, for optimization reason, not every polytope might be fully computed and must be verified. This should never happen to a user
+and is mostly an internal detail of the simulator. This holds as well for additional information that can be stored inside a dict in the
+polytope. The simulators can store additional info in the polytope via this way.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.state">
+<span class="sig-name descname"><span class="pre">state</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.state" title="Link to this definition">¶</a></dt>
+<dd><p>The D dimensional array that stores the electron configuration n of the current ground state polytope. All points inside
+the polytope have this state as ground state.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>D np.array of int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.labels">
+<span class="sig-name descname"><span class="pre">labels</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.labels" title="Link to this definition">¶</a></dt>
+<dd><p>Matrix of transitions. Each of the N inequalities is labeled by the state difference t=n’-n.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>NxD np.array of int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.A">
+<span class="sig-name descname"><span class="pre">A</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.A" title="Link to this definition">¶</a></dt>
+<dd><p>Linear factors of the N inequalities for a K-dimensional gate space.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>NxK np.array of float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.b">
+<span class="sig-name descname"><span class="pre">b</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.b" title="Link to this definition">¶</a></dt>
+<dd><p>constant offsets of the N linear inequalities</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>N np.array of float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.slacks">
+<span class="sig-name descname"><span class="pre">slacks</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.slacks" title="Link to this definition">¶</a></dt>
+<dd><p>Measures the distance of the inequality from the boundary of the polytope. ~0 if touching.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>N np.array of float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.point_inside">
+<span class="sig-name descname"><span class="pre">point_inside</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.point_inside" title="Link to this definition">¶</a></dt>
+<dd><p>A point inside the polytope.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>K np.array of float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.must_verify">
+<span class="sig-name descname"><span class="pre">must_verify</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.must_verify" title="Link to this definition">¶</a></dt>
+<dd><p>Internal variable for bookkeeping whether the polytope needs recomputing (mostly after slice). Should ALWAYS be False for polytopes
+queried from the simulator. TODO: can we remove this?</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.additional_info">
+<span class="sig-name descname"><span class="pre">additional_info</span></span><a class="headerlink" href="#qdarts.polytope.Polytope.additional_info" title="Link to this definition">¶</a></dt>
+<dd><p>Internal additional information that later steps of the simulation can store inside a polytope for bookkeeping.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.polytope.Polytope.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.polytope.Polytope.__init__" title="qdarts.polytope.Polytope.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(state)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.polytope.Polytope.invertible_transform" title="qdarts.polytope.Polytope.invertible_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">invertible_transform</span></code></a>(P, m)</p></td>
+<td><p>Apply an invertible affine transformation to the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.polytope.Polytope.lazy_slice" title="qdarts.polytope.Polytope.lazy_slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lazy_slice</span></code></a>(P, m)</p></td>
+<td><p>Slices a polytope lazyily, i.e., without recomputing the slacks and boundaries.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.polytope.Polytope.set_polytope" title="qdarts.polytope.Polytope.set_polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_polytope</span></code></a>(labels, A, b, slacks, point_inside)</p></td>
+<td><p>Sets the internal variables of the polytope.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.invertible_transform">
+<span class="sig-name descname"><span class="pre">invertible_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.polytope.Polytope.invertible_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Apply an invertible affine transformation to the polytope. This can be done without changing slacks and thus no verification is needed.</p>
+<p>Changes the space of the polytope via the transformation <span class="math notranslate nohighlight">\(v=Av'+b\)</span>. Returns the polytope in the coordinate system of v’</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>KxK np.array</em><em> of </em><em>float</em>) – Invertible linear transformation matrix</p></li>
+<li><p><strong>m</strong> (<em>K np.array</em><em> of </em><em>float</em>) – Affine offset of the transformation.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.lazy_slice">
+<span class="sig-name descname"><span class="pre">lazy_slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.polytope.Polytope.lazy_slice" title="Link to this definition">¶</a></dt>
+<dd><p>Slices a polytope lazyily, i.e., without recomputing the slacks and boundaries.</p>
+<p>As a result, after this must_verify is True. P is not required to be invertible.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>KxK np.array</em><em> of </em><em>float</em>) – Invertible linear transformation matrix</p></li>
+<li><p><strong>m</strong> (<em>K np.array</em><em> of </em><em>float</em>) – Affine offset of the transformation.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.polytope.Polytope.set_polytope">
+<span class="sig-name descname"><span class="pre">set_polytope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slacks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">point_inside</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">must_verify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.polytope.Polytope.set_polytope" title="Link to this definition">¶</a></dt>
+<dd><p>Sets the internal variables of the polytope.</p>
+<p>Helper function to ensure thateverything is set as it should be.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
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+ <section id="qdarts-simulator-abstractcapacitivedevicesimulator">
+<h1>qdarts.simulator.AbstractCapacitiveDeviceSimulator<a class="headerlink" href="#qdarts-simulator-abstractcapacitivedevicesimulator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.simulator.</span></span><span class="sig-name descname"><span class="pre">AbstractCapacitiveDeviceSimulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator" title="Link to this definition">¶</a></dt>
+<dd><p>Base class for all objects that create device simulations from a Capacitive Model.</p>
+<p>This class includes all tools to compute and cache polytopes from the provided capacitive model.
+Polytopes are queried using a call to boundaries() which queries the internal cache and then
+computes the polytope on demand.</p>
+<p>The computed polytope P(n) is the set of voltages v for which n is the ground state of the capacitance
+energy function E(v,n), i.e., n=min_n’ E(v,n’). A facet of the polytope is given by the equality
+E(v,n’)-E(v,n) = 0 for a suitable choice of n’. As a result, when shooting a ray through
+a facet of the polytope created by state n’, there is a transition from state n->n’ in the ground state.</p>
+<p>The computation of the polytope discards by default all states and inequalitis that do not form a facet of P(n).
+However, this can be relaed by allowing a maximum slack, which also keeps facets for which
+min_v E(v,n’)-E(v,n) < max_slack, where v is restricted to elements in the polytope P(n). i.e., max slack keeps facet
+in which the energy difference is small.</p>
+<p>This class supports slicing of the voltage space into affine subspaces.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.__init__" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(capacitance_model)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.boundaries" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.boundaries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">boundaries</span></code></a>(state)</p></td>
+<td><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.cached_polytopes" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.cached_polytopes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cached_polytopes</span></code></a>()</p></td>
+<td><p>Returns a sequence including all computed and cached polytopes for inspection and modification.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_polytope" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_polytope</span></code></a>(state)</p></td>
+<td><p>Computes the polytope for a given state.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_transition_equations" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_transition_equations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_equations</span></code></a>(state_list, ...)</p></td>
+<td><p>Computes the energy difference equations from target states to all states in the list.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_boundary_intersection" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_boundary_intersection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_boundary_intersection</span></code></a>(old_v, new_v, state)</p></td>
+<td><p>Computes an intersection of a ray with the boundary of a polytope and computes the new state</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_state_of_voltage" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_state_of_voltage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_state_of_voltage</span></code></a>(v, state_hint[, ...])</p></td>
+<td><p>Searches the ground state for a given voltage, given an initial guess.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_maximum_polytope_slack</span></code></a>()</p></td>
+<td><p>Returns the maximum slack value for inclusing of a facet into the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.inside_state" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.inside_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inside_state</span></code></a>(v, state)</p></td>
+<td><p>Returns true if a point v is fully within the polytope of a given state.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_maximum_polytope_slack</span></code></a>(maximum_slack)</p></td>
+<td><p>Sets the maximum slack value for inclusing of a facet into the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.slice" title="qdarts.simulator.AbstractCapacitiveDeviceSimulator.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m[, proxy])</p></td>
+<td><p>Restricts the simulator to the affine subspace v=m+Pv'</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.boundaries">
+<span class="sig-name descname"><span class="pre">boundaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.boundaries" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p>
+<p>If the polytope is not cached, it needs to be computed. This can take some time for large devices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – The state n for which to compute the polytope P(n)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The polytope P(n)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.cached_polytopes">
+<span class="sig-name descname"><span class="pre">cached_polytopes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.cached_polytopes" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a sequence including all computed and cached polytopes for inspection and modification.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_polytope">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_polytope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_polytope" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the polytope for a given state.</p>
+<p>Is implemented by the derived class and called when the polytope for a state is not found in cache.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – the state identifying the polytope</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A Polytope object containing the full computed polytope.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_transition_equations">
+<span class="sig-name descname"><span class="pre">compute_transition_equations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_from</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.compute_transition_equations" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the energy difference equations from target states to all states in the list.</p>
+<p>For a given state and list of neighbour states, computes the linear equations Av+b that compute the energy differences
+Between the target state_from and the other states. That is, if state_list contains a list of states n’, this
+function constains linear equations E(v,n’)-E(v,state_from)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state_list</strong> (<em>numpy array</em><em> of </em><em>ints</em><em> of </em><em>size NxK</em>) – A list containing N states for which to compute the energy differences</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>A</strong> (<em>NxK np.array, containing the linear factors for each state in state_list</em>)</p></li>
+<li><p><strong>b</strong> (<em>np.array, containing the N offsets, one for each equation.</em>)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_boundary_intersection">
+<span class="sig-name descname"><span class="pre">find_boundary_intersection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old_v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deep_search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_boundary_intersection" title="Link to this definition">¶</a></dt>
+<dd><p>Computes an intersection of a ray with the boundary of a polytope and computes the new state</p>
+<p>For a given state and a voltage old_v within the polytope of this state and a point new_v outside the polytope,
+computes the intersection of the ray old_v+t*(new_v-old_v) with the boundary of the polytope.
+the intersection point and new target state is computed.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>old_v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – A point within the current polytope</p></li>
+<li><p><strong>new_v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – Another point on the ray</p></li>
+<li><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – The ground state n of old_v. It is assumed that v is element of P(n)</p></li>
+<li><p><strong>epsilon</strong> (<em>float</em>) – slack value added to the ray t to ensure that the point is numerically clearly outside the polytope.</p></li>
+<li><p><strong>deep_search</strong> (<em>bool</em>) – whether an iterative search is performed for the new point in case none of the direct neighbours of the polytope match.
+If false, will throw an exception in that case. An exception is also raised when the deep search failed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The first intersection point of the ray with the polytope, together with the new state</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_state_of_voltage">
+<span class="sig-name descname"><span class="pre">find_state_of_voltage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deep_search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.find_state_of_voltage" title="Link to this definition">¶</a></dt>
+<dd><p>Searches the ground state for a given voltage, given an initial guess.</p>
+<p>For a given state voltage, computes the state for which is within the polytope of the state.
+Note that the choice of the hint is not arbitrary, since the search starts from a point in state_hint
+in order to find iteratively intersections with the boundary that are closer to v. A specific requirement
+is that the polytope must not be empty, i.e., in case of a sliced simulator, the polytope must intersect
+with the affine space. This can sometimes be tricky and we recommend perform this type of computations
+only on spaces where all plungers are available and then perform the slicing through v.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – Voltage vector for which to find the ground state</p></li>
+<li><p><strong>state_hint</strong> (<em>list</em><em> of </em><em>ints</em>) – a likely candidate for the state.</p></li>
+<li><p><strong>deep_search</strong> (<em>bool</em>) – whether an iterative search is performed in case none of the direct neighbours of a polytope match.
+If false, will throw an exception in that case. An exception is also raised when the deep search failed.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_maximum_polytope_slack</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.get_maximum_polytope_slack" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the maximum slack value for inclusing of a facet into the polytope.</p>
+<p>Returns the maximum energy distance the closest point of a transition can have to the polytope
+before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+are kept.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.inside_state">
+<span class="sig-name descname"><span class="pre">inside_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.inside_state" title="Link to this definition">¶</a></dt>
+<dd><p>Returns true if a point v is fully within the polytope of a given state.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – The state n identifying the polytope P(n)</p></li>
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – The point v</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The truth value of whether v is element of P(n)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_maximum_polytope_slack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maximum_slack</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.set_maximum_polytope_slack" title="Link to this definition">¶</a></dt>
+<dd><p>Sets the maximum slack value for inclusing of a facet into the polytope.</p>
+<p>Sets the maximum distance the closest point of a transition can have to the polytope
+before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+are kept.</p>
+<p>Note that changing this value clears the cache.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractCapacitiveDeviceSimulator.slice">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractCapacitiveDeviceSimulator.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the simulator to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. Must be implemented by derived classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>offset</em><em> of </em><em>the affine trnsformation.</em>)</p></li>
+<li><p><strong>proxy</strong> (<em>bool</em>) – Whether a proxy is returned. A proxy shares the cache, if possible. This is the case when P is invertible,
+especially this entails M=K. If cache sharing is possible, the simulation computes the original polytope and then
+applies the affine transformation. This can reduce run time a lot if several slices need to be computed for the
+same simulation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
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+ <section id="qdarts-simulator-abstractpolytopesimulator">
+<h1>qdarts.simulator.AbstractPolytopeSimulator<a class="headerlink" href="#qdarts-simulator-abstractpolytopesimulator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.simulator.</span></span><span class="sig-name descname"><span class="pre">AbstractPolytopeSimulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dots</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_inputs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator" title="Link to this definition">¶</a></dt>
+<dd><p>Base class for all simulation objects that can compute and return polytopes.</p>
+<p>The class only has a single method boundaries which returns the boundary description of the polytope
+with meta information as well as two attributes:</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator.num_dots">
+<span class="sig-name descname"><span class="pre">num_dots</span></span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator.num_dots" title="Link to this definition">¶</a></dt>
+<dd><p>number of dots in the device, i.e., number of entries in the state vector of the polytope</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator.num_inputs">
+<span class="sig-name descname"><span class="pre">num_inputs</span></span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator.num_inputs" title="Link to this definition">¶</a></dt>
+<dd><p>number of gate voltages in the device. The polytope lives in a space that is num_inputs dimensonal.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dots</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_inputs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractPolytopeSimulator.__init__" title="qdarts.simulator.AbstractPolytopeSimulator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(num_dots, num_inputs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractPolytopeSimulator.boundaries" title="qdarts.simulator.AbstractPolytopeSimulator.boundaries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">boundaries</span></code></a>(state)</p></td>
+<td><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.AbstractPolytopeSimulator.slice" title="qdarts.simulator.AbstractPolytopeSimulator.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m[, proxy])</p></td>
+<td><p>Restricts the simulator to the affine subspace v=m+Pv'</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator.boundaries">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">boundaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator.boundaries" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – The state n for which to compute the polytope P(n)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The polytope P(n)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.AbstractPolytopeSimulator.slice">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.AbstractPolytopeSimulator.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the simulator to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. Must be implemented by derived classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>offset</em><em> of </em><em>the affine trnsformation.</em>)</p></li>
+<li><p><strong>proxy</strong> (<em>bool</em>) – Whether a proxy is returned. A proxy can share computation between instances, if supported by the derived class</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.simulator.CapacitiveDeviceSimulator.html
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+ <section id="qdarts-simulator-capacitivedevicesimulator">
+<h1>qdarts.simulator.CapacitiveDeviceSimulator<a class="headerlink" href="#qdarts-simulator-capacitivedevicesimulator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.simulator.</span></span><span class="sig-name descname"><span class="pre">CapacitiveDeviceSimulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator" title="Link to this definition">¶</a></dt>
+<dd><p>This class simulates a quantum dot device based on a capacitance model.</p>
+<p>The simulator interally keeps track of the Coulomb diamonds (polytopes) and their transitions (facets),
+and takes care of keeping track of which transitions are feasible, with what precision, etc.
+This allows one to ask questions such as: “which transition does this facet correspond to?†and
+“what is the orthogonal axis in voltage space (i.e. virtual gate) that tunes across it?â€.
+The simulator will return, for each transition, a point on the transition line and the virtual gate.</p>
+<p>It also has the ability to take 2D slices through high dimensional voltage spaces to construct 2D
+projections of charge stability diagrams. See documentation of AbstractCapacitiveDeviceSimulator for more details.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">capacitance_model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.CapacitiveDeviceSimulator.__init__" title="qdarts.simulator.CapacitiveDeviceSimulator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(capacitance_model)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">boundaries</span></code>(state)</p></td>
+<td><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cached_polytopes</span></code>()</p></td>
+<td><p>Returns a sequence including all computed and cached polytopes for inspection and modification.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.CapacitiveDeviceSimulator.compute_polytope" title="qdarts.simulator.CapacitiveDeviceSimulator.compute_polytope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_polytope</span></code></a>(state)</p></td>
+<td><p>Computes the polytope for a given state.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_transition_equations</span></code>(state_list, ...)</p></td>
+<td><p>Computes the energy difference equations from target states to all states in the list.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_boundary_intersection</span></code>(old_v, new_v, state)</p></td>
+<td><p>Computes an intersection of a ray with the boundary of a polytope and computes the new state</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_state_of_voltage</span></code>(v, state_hint[, ...])</p></td>
+<td><p>Searches the ground state for a given voltage, given an initial guess.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.CapacitiveDeviceSimulator.get_maximum_polytope_slack" title="qdarts.simulator.CapacitiveDeviceSimulator.get_maximum_polytope_slack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_maximum_polytope_slack</span></code></a>()</p></td>
+<td><p>Returns the maximum slack value for inclusing of a facet into the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">inside_state</span></code>(v, state)</p></td>
+<td><p>Returns true if a point v is fully within the polytope of a given state.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.simulator.CapacitiveDeviceSimulator.set_maximum_polytope_slack" title="qdarts.simulator.CapacitiveDeviceSimulator.set_maximum_polytope_slack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_maximum_polytope_slack</span></code></a>(maximum_slack)</p></td>
+<td><p>Sets the maximum slack value for inclusing of a facet into the polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.simulator.CapacitiveDeviceSimulator.slice" title="qdarts.simulator.CapacitiveDeviceSimulator.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m[, proxy])</p></td>
+<td><p>Restricts the simulator to the affine subspace v=m+Pv'</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator.compute_polytope">
+<span class="sig-name descname"><span class="pre">compute_polytope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator.compute_polytope" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the polytope for a given state.</p>
+<p>Is implemented by the derived class and called when the polytope for a state is not found in cache.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – the state identifying the polytope</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A Polytope object containing the full computed polytope.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator.get_maximum_polytope_slack">
+<span class="sig-name descname"><span class="pre">get_maximum_polytope_slack</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator.get_maximum_polytope_slack" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the maximum slack value for inclusing of a facet into the polytope.</p>
+<p>Returns the maximum energy distance the closest point of a transition can have to the polytope
+before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+are kept.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator.set_maximum_polytope_slack">
+<span class="sig-name descname"><span class="pre">set_maximum_polytope_slack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maximum_slack</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator.set_maximum_polytope_slack" title="Link to this definition">¶</a></dt>
+<dd><p>Sets the maximum slack value for inclusing of a facet into the polytope.</p>
+<p>Sets the maximum distance the closest point of a transition can have to the polytope
+before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+are kept.</p>
+<p>Note that changing this value clears the cache.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.simulator.CapacitiveDeviceSimulator.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.simulator.CapacitiveDeviceSimulator.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the simulator to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. Must be implemented by derived classes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>offset</em><em> of </em><em>the affine trnsformation.</em>)</p></li>
+<li><p><strong>proxy</strong> (<em>bool</em>) – Whether a proxy is returned. A proxy shares the cache, if possible. This is the case when P is invertible,
+especially this entails M=K. If cache sharing is possible, the simulation computes the original polytope and then
+applies the affine transformation. This can reduce run time a lot if several slices need to be computed for the
+same simulation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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+
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/interfaces/qdarts.tunneling_simulator.AbstractSensorSim.html
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+ <section id="qdarts-tunneling-simulator-abstractsensorsim">
+<h1>qdarts.tunneling_simulator.AbstractSensorSim<a class="headerlink" href="#qdarts-tunneling-simulator-abstractsensorsim" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.tunneling_simulator.</span></span><span class="sig-name descname"><span class="pre">AbstractSensorSim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_sensors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim" title="Link to this definition">¶</a></dt>
+<dd><p>Base class defining the interface for all sensor simulations</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_sensors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Initialized a sensor configuration with num_sensors sensor dots</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.__init__" title="qdarts.tunneling_simulator.AbstractSensorSim.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(num_sensors)</p></td>
+<td><p>Initialized a sensor configuration with num_sensors sensor dots</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.precompute_sensor_state" title="qdarts.tunneling_simulator.AbstractSensorSim.precompute_sensor_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precompute_sensor_state</span></code></a>(state, A, b, ...)</p></td>
+<td><p>Allows the sensor to precompute internal information that is valid for a whole ground state polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_configuration" title="qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_configuration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_configuration</span></code></a>(...)</p></td>
+<td><p>samples a sensor response for a given sampled elecron configuration</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_equilibrium" title="qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_equilibrium"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_equilibrium</span></code></a>(v, H, mixed_state, ...)</p></td>
+<td><p>Computes a noisy average of the sensor response for a given mixed state.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.slice" title="qdarts.tunneling_simulator.AbstractSensorSim.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Takes an affine subspace of the simulated model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.AbstractSensorSim.start_measurement" title="qdarts.tunneling_simulator.AbstractSensorSim.start_measurement"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_measurement</span></code></a>()</p></td>
+<td><p>Reinitializes the sensor as to generate independent noise samples</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.precompute_sensor_state">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">precompute_sensor_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_labels</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.precompute_sensor_state" title="Link to this definition">¶</a></dt>
+<dd><p>Allows the sensor to precompute internal information that is valid for a whole ground state polytope.</p>
+<p>This allows the sensor to precompute and cache information that is valid for all voltages v that are inside a
+ground state polytope P. The returned values are cached in the polytope objects of the simulator and
+supplied as sensor_state argument during a call of sample_sensor_equilibrium and sample_sensor_configuration.
+The supplied information provides all information of the basis labels considered by the simulation for P(n),
+and the linear functions defining the facets of P(n) Av+b. Note that as everywhere else, these linear functions define
+energy differences for each considered basis label to the ground state.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>np.array</em><em> of </em><em>ints</em>) – the state of N dots, identifying the ground state polytope for which to generate the sensor state information</p></li>
+<li><p><strong>A</strong> (<em>LxK np.array</em><em> of </em><em>floats</em>) – LxK linear parameters of the energy difference function for the K sensor gates</p></li>
+<li><p><strong>b</strong> (<em>np.array</em><em> of </em><em>floats</em>) – The affine offsets of the energy difference function for the L basis states</p></li>
+<li><p><strong>basis_labels</strong> (<em>LxN np.array</em><em> of </em><em>ints</em>) – The labels of the L basis states</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_configuration">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sample_sensor_configuration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampled_configuration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_configuration" title="Link to this definition">¶</a></dt>
+<dd><p>samples a sensor response for a given sampled elecron configuration</p>
+<p>This is intended to simulate a short (<<1mus) time integration of the sensor signal,
+where we can not assume that electrons transitions during the measurement. In this case,
+the user supplied the relevant configuration and the sensor returns a sampled signal for this configuration.
+Care should be taken that the configuration sampled has all information needed in the base to compute the sensor
+signal, e.g., there should be a state with one more or less electrons on each sensor dot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sampled_configuration</strong> (<em>np.array</em><em> of </em><em>ints</em>) – vector of N elements describing the sampled electron configuration.</p></li>
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – vector of K gate voltages defining the current system</p></li>
+<li><p><strong>H</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state</p></li>
+<li><p><strong>mixed_state</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Mixed state matrix computed via the Hamiltonian using expm(-beta*H)</p></li>
+<li><p><strong>sensor_state</strong> – Cached information returned by precompute_sensor_state. All information therein are internal to the
+sensor simulator</p></li>
+<li><p><strong>beta</strong> (<em>float</em>) – scaled inverse temperature parameter</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_equilibrium">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sample_sensor_equilibrium</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.sample_sensor_equilibrium" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a noisy average of the sensor response for a given mixed state.</p>
+<p>This is intended to simulate a long (>1mus) time integration of the sensor signal, thus
+we can assume that states are thermalized but the signal is still affected by noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – vector of K gate voltages defining the current system</p></li>
+<li><p><strong>H</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state</p></li>
+<li><p><strong>mixed_state</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Mixed state matrix computed via the Hamiltonian using expm(-beta*H)</p></li>
+<li><p><strong>sensor_state</strong> – Cached information returned by precompute_sensor_state. All information therein are internal to the
+sensor simulator</p></li>
+<li><p><strong>beta</strong> (<em>float</em>) – scaled inverse temperature parameter</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.slice">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Takes an affine subspace of the simulated model.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.AbstractSensorSim.start_measurement">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">start_measurement</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.AbstractSensorSim.start_measurement" title="Link to this definition">¶</a></dt>
+<dd><p>Reinitializes the sensor as to generate independent noise samples</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
+ </div>
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+ <section id="qdarts-tunneling-simulator-approximatetunnelingsimulator">
+<h1>qdarts.tunneling_simulator.ApproximateTunnelingSimulator<a class="headerlink" href="#qdarts-tunneling-simulator-approximatetunnelingsimulator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.tunneling_simulator.</span></span><span class="sig-name descname"><span class="pre">ApproximateTunnelingSimulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polytope_sim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">barrier_sim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_sim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator" title="Link to this definition">¶</a></dt>
+<dd><p>Simulator for approximate charge tunneling in a quantum dot device.</p>
+<p>The simulator extends the supplied capacitive simulation by creating a Hamiltionian H,
+where on the diagonals are the capacitive energies of the simualation, while the off-diagonals
+have added tunnel coupling parameters. Locally the hamiltonian is approximated via L basis states,
+where each state is an electron configurtion on the dots. This mixed state is then used to create a sensor simulation.</p>
+<p>It is possible to query the state of single hamiltonian, their mixed state and their sensor simulation via the class
+LocalSystem, returned by compute_local_system, but the primary use of tis class lies in its ability to compute
+1D or 2D sensor scans via sensor_scan and sensor_scan_2D.</p>
+<p>For computing the tunnel coupling parameters, this class can make use of an additional Tunnel barrier simulation, but it
+is also possible to just supply a NxN constant matrix of tunnel couplings between all D dots in the array.</p>
+<p>Finally, the class follows the interface of AbstractPolytopeSimulator, which means it is possible to directly query the information
+of the underlying polytopes of the simulation. This is there to unify slicing between simulators.</p>
+<p>Implementation details:</p>
+<p>The basis used for a gate voltage v is queried by finding its ground states n and then the facets of the
+ground state polytope P(n) create the basis. Thus, this basis becomes extended as the slack variable in the underlying
+capacitance simulation is increased. This is called the core state set.
+Additionally, the simulation allows to add additional states. For example, for most sensor simulations to work, we also need
+other higher energy states to compute correct conductance. These additional states can be added by modifying the vector
+num_additional_neighbours. if the ith element in this vector is R>0, and s is a state in the core basis, then
+the extended basis will also include the states <span class="math notranslate nohighlight">\(s+ke_i\)</span> where <span class="math notranslate nohighlight">\(|k|<=R\)</span> and <span class="math notranslate nohighlight">\(e_i\)</span> is the ith basis vector.</p>
+<p>The tunnel couplings T are included into the Hamiltonian the following way: let <span class="math notranslate nohighlight">\(s_i\)</span> and <span class="math notranslate nohighlight">\(s_j\)</span> be two states in the basis of the Hamiltonian
+that differ only in the value of the electron configuration at dots i and j.
+More exactly, we have that <span class="math notranslate nohighlight">\(s_i\)</span> and <span class="math notranslate nohighlight">\(s_j\)</span> are related by moving an electron from state <span class="math notranslate nohighlight">\(s_i\)</span> to <span class="math notranslate nohighlight">\(s_j\)</span> or vice versa.
+Let <span class="math notranslate nohighlight">\(H_{kl}\)</span> be the off-diagonal matrix element of those states. Then we have <span class="math notranslate nohighlight">\(H_{kl} = T_{ij}\)</span>.
+In all other cases, tunnel coupling is 0.</p>
+<p>The mixed state is then again computed approximately, for more info on that, see documentation of LocalSystem.</p>
+<p>The sensor signal of the computed mixed state is computed via the sensor_sim.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.beta">
+<span class="sig-name descname"><span class="pre">beta</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.beta" title="Link to this definition">¶</a></dt>
+<dd><p>Scaled inverse temperature 1/k_BT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.T">
+<span class="sig-name descname"><span class="pre">T</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.T" title="Link to this definition">¶</a></dt>
+<dd><p>Temperature</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.poly_sim">
+<span class="sig-name descname"><span class="pre">poly_sim</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.poly_sim" title="Link to this definition">¶</a></dt>
+<dd><p>the capacitive simulation object</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.barrier_sim">
+<span class="sig-name descname"><span class="pre">barrier_sim</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.barrier_sim" title="Link to this definition">¶</a></dt>
+<dd><p>the barrier simulation object. Note that even if the supplied object to init was a matrix, this will be a TunnelBarrierModel.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_sim">
+<span class="sig-name descname"><span class="pre">sensor_sim</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_sim" title="Link to this definition">¶</a></dt>
+<dd><p>the sensor simulation object</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.num_additional_neighbours">
+<span class="sig-name descname"><span class="pre">num_additional_neighbours</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.num_additional_neighbours" title="Link to this definition">¶</a></dt>
+<dd><p>for each dot defines how many additional states should be added for each state in the core basis. This is done
+by adding or subtracting electrons on the ith element where the maximum is given by the ith element of num_additional_neighbours.
+We advise to set this to 2 for sensor dots. Note that computation time can quickly explode when increasing this parameter.
+Outside of sensor dots, we advise therefore to increase the slack in the capacitive simulation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.array of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polytope_sim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">barrier_sim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_sim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Creates a tunneling simulation</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>polytope_sim</strong> – capacitance simulator object that computes ground state polytopes and capacitive energy differences</p></li>
+<li><p><strong>barrier_sim</strong> (<em>Object</em><em> or </em><em>Matrix</em>) – Either a DxD basis that describes a constant tunnel coupling between all D dots. Note that the diagonal of this matrix is zero.
+Alternatively an object with a method barrier_sim.get_tunnel_matrix(v) returning a DxD matrix, and which supports the slice operation.</p></li>
+<li><p><strong>T</strong> (<em>float</em>) – Temperature in Kelvin. Good values are < 0.1</p></li>
+<li><p><strong>sensor_sim</strong> (<em>Derived from AbstractSensorSim</em>) – A sensor simulation that follows the interface of AbstractSensorSim and which computes the sensor signal.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.__init__" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polytope_sim, barrier_sim, T, ...)</p></td>
+<td><p>Creates a tunneling simulation</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.boundaries" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.boundaries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">boundaries</span></code></a>(state)</p></td>
+<td><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.compute_local_system" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.compute_local_system"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_local_system</span></code></a>(v, state[, ...])</p></td>
+<td><p>Computes a full description of the local quantum system and returns the LocalSystem object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sensor_scan</span></code></a>(v_start, v_end, resolution, ...)</p></td>
+<td><p>Computes a 1D sensor ramp scan.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan_2D" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan_2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sensor_scan_2D</span></code></a>(P, m, minV, maxV, resolution, ...)</p></td>
+<td><p>Computes the sensor signal on a 2D grid of points.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.slice" title="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m[, proxy])</p></td>
+<td><p>Restricts the simulator to the affine subspace v=m+Pv'</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.boundaries">
+<span class="sig-name descname"><span class="pre">boundaries</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.boundaries" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.</p>
+<p>If the polytope is not cached, it needs to be computed. This can take some time for large devices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>list</em><em> of </em><em>ints</em>) – The state n for which to compute the polytope P(n)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>The polytope P(n)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.compute_local_system">
+<span class="sig-name descname"><span class="pre">compute_local_system</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">search_ground_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.compute_local_system" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a full description of the local quantum system and returns the LocalSystem object.</p>
+<p>This is a full locla simulation of the device and can be used to query sensor values but also the mixed state matrix.
+See LocalSystem for more info.</p>
+<p>Note that unlike in most other places, v does not need to belong to the ground state polytope of state.
+This might be useful for the computation of signals in which the device is far out of equilibrium.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – The vector of gate voltages of the device</p></li>
+<li><p><strong>state</strong> (<em>np.array</em><em> of </em><em>ints</em>) – The ground state polytope relative to which the local system is computed. This is in most cases the ground state.</p></li>
+<li><p><strong>search_ground_state</strong> (<em>bool</em>) – If True, verifies that state is the ground state of v and searches it otherwise. If you know that this is the case,
+you can safely set it to False for a speed-up. In the general case, setting this to false will compute the
+LocalSystem relative to a different basis state.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan">
+<span class="sig-name descname"><span class="pre">sensor_scan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v_end</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v_start_state_hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_new_measurement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a 1D sensor ramp scan.</p>
+<p>Computes a linear set of points between v_start and v_end and for each point computes the sensor signal.
+To be more exact, for each point, the ground state polytope is computed which is then used to define the local_system.
+Returns the sensor signal for each sensor and dot</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>v_start</strong> (<em>np.array</em><em> of </em><em>floats</em>) – Vector of gate voltages of the device describing the first measurement point</p></li>
+<li><p><strong>v_end</strong> (<em>np.array</em><em> of </em><em>floats</em>) – Vector of gate voltages of the device describing the last measurement point</p></li>
+<li><p><strong>resolution</strong> (<em>int</em>) – number of measured points on the linear scan between v_start and v_end, including both end points.</p></li>
+<li><p><strong>v_start_state_hint</strong> (<em>np.array</em><em> of </em><em>int</em>) – Guess for the state n for which holds that v_start is element of P(n). The simulator will use this
+guess as a starting point for the search of the correct state if this guess is wrong. Note that P(n)
+must intersect with the affine slice, if slicing was used.</p></li>
+<li><p><strong>cache</strong> (<em>bool</em>) – Whether the simulation should try to cache the computed polytopes. This might lead to a slower computation time
+for a scan compared to not using caching, but consecutive scans with similar ranges tend to be quicker.</p></li>
+<li><p><strong>start_new_measurement</strong> (<em>bool</em>) – Whether the seimulated sensor measurement should be independent of any previous measurements.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan_2D">
+<span class="sig-name descname"><span class="pre">sensor_scan_2D</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minV</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxV</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_hint_lower_left</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.sensor_scan_2D" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the sensor signal on a 2D grid of points.</p>
+<p>For the exact computation of points, see sensor_scan.</p>
+<p>The grid is defined the following way: Let w_ij be a 2D vector that is part of a regular
+rectangular grid spanned by the lower left corner given by minV and the upper right corner given
+by maxV and let (m,n) be the number of points in both grid directions. We have that w_00=minV and w_m-1,n-1=maxV.</p>
+<p>This grid is then affinely transformed into the K-dimensional space of gate vectors via
+v_ij = m+ P w_ij</p>
+<p>and thus P must be a Kx2 matrix and m a K-vector.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>Kx2 np.array</em><em> of </em><em>floats</em>) – linear transformation of grid-points into the K-dimensional voltage space</p></li>
+<li><p><strong>m</strong> (<em>np.array</em><em> of </em><em>floats:</em>) – affine offset of the grid</p></li>
+<li><p><strong>minV</strong> (<em>np.array</em><em> of </em><em>floats</em>) – 2D vector describing the minimum value of the grid points</p></li>
+<li><p><strong>maxV</strong> (<em>np.array</em><em> of </em><em>floats</em>) – 2D vector describing the maximum value of the grid points</p></li>
+<li><p><strong>resolution</strong> (<em>int</em><em> or </em><em>list</em><em> of </em><em>ints</em>) – if integer, describes the same number of points in both grid directions. If a list of 2 elements,
+describes the number of points along each axes of the grid.</p></li>
+<li><p><strong>state_hint_lower_left</strong> (<em>np.array</em><em> of </em><em>int</em>) – Guess for the state n for point described by the grid position minV. The simulator will use this
+guess as a starting point for the search of the correct state if this guess is wrong. Note that P(n)
+must intersect with the affine slice, if slicing was used.</p></li>
+<li><p><strong>cache</strong> (<em>bool</em>) – Whether the simulation should try to cache the computed polytopes. This might lead to a slower computation time
+for a scan compared to not using caching, but consecutive scans with similar ranges tend to be quicker.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.ApproximateTunnelingSimulator.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.ApproximateTunnelingSimulator.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Restricts the simulator to the affine subspace v=m+Pv’</p>
+<p>Computes the slice through the simulated device by setting v=m+Pv’, where v is the plunger gate voltages of the
+original device and v’ is the new coordinate system. This is implemented here by slicing all the different parts
+of the simulation, capacitance model, barrier model and sensor model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>MxK np.array</em><em> of </em><em>floats</em>) – The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+and K the dimensionality of the subspace.</p></li>
+<li><p><strong>m</strong> (<em>offset</em><em> of </em><em>the affine trnsformation.</em>)</p></li>
+<li><p><strong>proxy</strong> (<em>bool</em>) – Whether a proxy is returned. A proxy shares the cache, if possible. This is the case when P is invertible,
+especially this entails M=K. If cache sharing is possible, the simulation computes the original polytope and then
+applies the affine transformation. This can reduce run time a lot if several slices need to be computed for the
+same simulation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+ <section id="qdarts-tunneling-simulator-localsystem">
+<h1>qdarts.tunneling_simulator.LocalSystem<a class="headerlink" href="#qdarts-tunneling-simulator-localsystem" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.tunneling_simulator.</span></span><span class="sig-name descname"><span class="pre">LocalSystem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem" title="Link to this definition">¶</a></dt>
+<dd><p>Class describing a quantum system defined by the gate voltages of a simulated device.</p>
+<p>For a given set of gate voltages, the simulator first computes a set of core states that are most
+likely relevant for the computation of the hamiltonian and then extends it by adding additional
+states. These are then used to define a basis of the vector space for the Hamiltonian, which
+is then used to compute the mixed state. Finally, the mixed state is then used to simulate a sensor signal.</p>
+<p>This class stores all this information and includes some minimal tools to query information on
+different sub-bases. This class is tightly coupled to tunneling_simulator.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.v">
+<span class="sig-name descname"><span class="pre">v</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.v" title="Link to this definition">¶</a></dt>
+<dd><p>gate voltages that define the parameters of this system</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.array of floats</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.state">
+<span class="sig-name descname"><span class="pre">state</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.state" title="Link to this definition">¶</a></dt>
+<dd><p>the ground state configuration of v</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.array of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.beta">
+<span class="sig-name descname"><span class="pre">beta</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.beta" title="Link to this definition">¶</a></dt>
+<dd><p>the scaled inverse temperature 1/k_bT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.H">
+<span class="sig-name descname"><span class="pre">H</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.H" title="Link to this definition">¶</a></dt>
+<dd><p>Hamiltonian over the subspace spanned by the L basis state of the extended basis.
+See methods basis_labels and core_basis_indices</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>LxL np.array of floats</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Creates the LocalSystem.</p>
+<p>This is an internal function used by the tunneling simulator.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.__init__" title="qdarts.tunneling_simulator.LocalSystem.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(v, H, state, sim)</p></td>
+<td><p>Creates the LocalSystem.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.compute_mixed_state_of_subset" title="qdarts.tunneling_simulator.LocalSystem.compute_mixed_state_of_subset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_mixed_state_of_subset</span></code></a>(subset_indices)</p></td>
+<td><p>Computes the mixed state for a subset, ignoring the existance of any other state entirely.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.sample_sensor_configuration" title="qdarts.tunneling_simulator.LocalSystem.sample_sensor_configuration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_configuration</span></code></a>(...)</p></td>
+<td><p>Samples the sensor signal for a given sampled electron configuration.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.sample_sensor_equilibrium" title="qdarts.tunneling_simulator.LocalSystem.sample_sensor_equilibrium"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_equilibrium</span></code></a>()</p></td>
+<td><p>Samples a boisy averaged sensor response from the current system over all basis elements.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.basis_labels" title="qdarts.tunneling_simulator.LocalSystem.basis_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">basis_labels</span></code></a></p></td>
+<td><p>The labels of the basis elements, indentified by their ground state electron configuration</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.core_basis_indices" title="qdarts.tunneling_simulator.LocalSystem.core_basis_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">core_basis_indices</span></code></a></p></td>
+<td><p>Indices into basis_labels that define the subset of core basis elements.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.LocalSystem.mixed_state" title="qdarts.tunneling_simulator.LocalSystem.mixed_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mixed_state</span></code></a></p></td>
+<td><p>Computes an approximate mixed state matrix over the full basis.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.basis_labels">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">basis_labels</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.basis_labels" title="Link to this definition">¶</a></dt>
+<dd><p>The labels of the basis elements, indentified by their ground state electron configuration</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.compute_mixed_state_of_subset">
+<span class="sig-name descname"><span class="pre">compute_mixed_state_of_subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subset_indices</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.compute_mixed_state_of_subset" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the mixed state for a subset, ignoring the existance of any other state entirely.</p>
+<p>The result is a KxK matrix where K is the length of subset_indices.
+This function is not equivalent to selecting a subset of mixed_state, since this assumes that
+the states not referenced by subset_indices are ruled out for some other reason, i.e., they are
+assigned probability 0 and probabilities are renormalized to sum to 1 over the elements in the subset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subset_indices</strong> (<em>np.array</em><em> of </em><em>ints</em>) – The L’ indices into the basis element matrix as returned by basis_labels</p></li>
+<li><p><strong>m</strong> (<em>np.array</em><em> of </em><em>floats</em><em> or </em><em>None</em>)</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.core_basis_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">core_basis_indices</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.core_basis_indices" title="Link to this definition">¶</a></dt>
+<dd><p>Indices into basis_labels that define the subset of core basis elements.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.mixed_state">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mixed_state</span></span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.mixed_state" title="Link to this definition">¶</a></dt>
+<dd><p>Computes an approximate mixed state matrix over the full basis.</p>
+<p>the mixed state matrix, defined as expm(-beta*H)</p>
+<p>Note that this function approximated the true mixed state matrix by inly taking basis eleemnts into account
+that have a small energy difference to the ground state. This is a multiple of the polytope slack used by the
+capacitive simulation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.sample_sensor_configuration">
+<span class="sig-name descname"><span class="pre">sample_sensor_configuration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampled_configuration</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.sample_sensor_configuration" title="Link to this definition">¶</a></dt>
+<dd><p>Samples the sensor signal for a given sampled electron configuration.</p>
+<p>For a short time simulation it is more prudent to externally sample a state from the basis and then generate a sensor signal
+from it. This function allows this. Note that only selecting states from the set of core_basis_indices is safe as otherwise
+the sensor might miss information required to correctly compute the response.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sampled_configuration</strong> (<em>list</em><em> of </em><em>ints</em>) – the sampled state for which to generate the sensor response.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.LocalSystem.sample_sensor_equilibrium">
+<span class="sig-name descname"><span class="pre">sample_sensor_equilibrium</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.LocalSystem.sample_sensor_equilibrium" title="Link to this definition">¶</a></dt>
+<dd><p>Samples a boisy averaged sensor response from the current system over all basis elements.</p>
+<p>This returns the average signal with added sensor noise. This is an approximation to long
+average measurements at a single point.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+ <section id="qdarts-tunneling-simulator-noisysensordot">
+<h1>qdarts.tunneling_simulator.NoisySensorDot<a class="headerlink" href="#qdarts-tunneling-simulator-noisysensordot" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.tunneling_simulator.</span></span><span class="sig-name descname"><span class="pre">NoisySensorDot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sensor_dot_ids</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot" title="Link to this definition">¶</a></dt>
+<dd><p>Simulates a sensor signal by computing the conductance of the sensor dots.</p>
+<p>This class implements the interface of AbstractSensorSim and for most points, the
+documentation there should be referred to. This simulation combines a simple
+estimation of the conductance g with two noise sources. A fast noise source
+that simulates gaussian white noise that is drawn for each query of the sensor response
+and a slow noise source that models time dependent noise between different invocations
+of the sensor.</p>
+<p>The shape of the simulated sensor peak can be configured ´via config_peak, in which
+height and with of the peak can be adapted. Currently, all sensor dots share these
+parameters.</p>
+<p>The noise can be configured via config_noise. The noise is modeled as an additive
+noise on the sensor peak position in voltage space. Thus, at peaks or valleys, the noise
+is small while on the sides of the peak, where the derivatives are largest, the noise will
+affect measurements the most. Additional signal noise is then modeled by adding white gaussian noise.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sensor_dot_ids</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Initialized a sensor configuration with num_sensors sensor dots</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.__init__" title="qdarts.tunneling_simulator.NoisySensorDot.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(sensor_dot_ids)</p></td>
+<td><p>Initialized a sensor configuration with num_sensors sensor dots</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.config_noise" title="qdarts.tunneling_simulator.NoisySensorDot.config_noise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_noise</span></code></a>(sigma, signal_noise_scale[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.config_peak" title="qdarts.tunneling_simulator.NoisySensorDot.config_peak"><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_peak</span></code></a>(g_max, peak_width_multiplier)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.precompute_sensor_state" title="qdarts.tunneling_simulator.NoisySensorDot.precompute_sensor_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precompute_sensor_state</span></code></a>(state, A, b, labels)</p></td>
+<td><p>Allows the sensor to precompute internal information that is valid for a whole ground state polytope.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_configuration" title="qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_configuration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_configuration</span></code></a>(...)</p></td>
+<td><p>samples a sensor response for a given sampled elecron configuration</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_equilibrium" title="qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_equilibrium"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sample_sensor_equilibrium</span></code></a>(v, H, mixed_state, ...)</p></td>
+<td><p>Computes a noisy average of the sensor response for a given mixed state.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.slice" title="qdarts.tunneling_simulator.NoisySensorDot.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Takes an affine subspace of the simulated model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.NoisySensorDot.start_measurement" title="qdarts.tunneling_simulator.NoisySensorDot.start_measurement"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_measurement</span></code></a>()</p></td>
+<td><p>Reinitializes the sensor as to generate independent noise samples</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.config_noise">
+<span class="sig-name descname"><span class="pre">config_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal_noise_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">slow_noise_gen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.config_noise" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.config_peak">
+<span class="sig-name descname"><span class="pre">config_peak</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">g_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peak_width_multiplier</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.config_peak" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.precompute_sensor_state">
+<span class="sig-name descname"><span class="pre">precompute_sensor_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.precompute_sensor_state" title="Link to this definition">¶</a></dt>
+<dd><p>Allows the sensor to precompute internal information that is valid for a whole ground state polytope.</p>
+<p>This allows the sensor to precompute and cache information that is valid for all voltages v that are inside a
+ground state polytope P. The returned values are cached in the polytope objects of the simulator and
+supplied as sensor_state argument during a call of sample_sensor_equilibrium and sample_sensor_configuration.
+The supplied information provides all information of the basis labels considered by the simulation for P(n),
+and the linear functions defining the facets of P(n) Av+b. Note that as everywhere else, these linear functions define
+energy differences for each considered basis label to the ground state.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state</strong> (<em>np.array</em><em> of </em><em>ints</em>) – the state of N dots, identifying the ground state polytope for which to generate the sensor state information</p></li>
+<li><p><strong>A</strong> (<em>LxK np.array</em><em> of </em><em>floats</em>) – LxK linear parameters of the energy difference function for the K sensor gates</p></li>
+<li><p><strong>b</strong> (<em>np.array</em><em> of </em><em>floats</em>) – The affine offsets of the energy difference function for the L basis states</p></li>
+<li><p><strong>basis_labels</strong> (<em>LxN np.array</em><em> of </em><em>ints</em>) – The labels of the L basis states</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_configuration">
+<span class="sig-name descname"><span class="pre">sample_sensor_configuration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampled_configuration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_configuration" title="Link to this definition">¶</a></dt>
+<dd><p>samples a sensor response for a given sampled elecron configuration</p>
+<p>This is intended to simulate a short (<<1mus) time integration of the sensor signal,
+where we can not assume that electrons transitions during the measurement. In this case,
+the user supplied the relevant configuration and the sensor returns a sampled signal for this configuration.
+Care should be taken that the configuration sampled has all information needed in the base to compute the sensor
+signal, e.g., there should be a state with one more or less electrons on each sensor dot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sampled_configuration</strong> (<em>np.array</em><em> of </em><em>ints</em>) – vector of N elements describing the sampled electron configuration.</p></li>
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – vector of K gate voltages defining the current system</p></li>
+<li><p><strong>H</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state</p></li>
+<li><p><strong>mixed_state</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Mixed state matrix computed via the Hamiltonian using expm(-beta*H)</p></li>
+<li><p><strong>sensor_state</strong> – Cached information returned by precompute_sensor_state. All information therein are internal to the
+sensor simulator</p></li>
+<li><p><strong>beta</strong> (<em>float</em>) – scaled inverse temperature parameter</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_equilibrium">
+<span class="sig-name descname"><span class="pre">sample_sensor_equilibrium</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensor_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.sample_sensor_equilibrium" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a noisy average of the sensor response for a given mixed state.</p>
+<p>This is intended to simulate a long (>1mus) time integration of the sensor signal, thus
+we can assume that states are thermalized but the signal is still affected by noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em>) – vector of K gate voltages defining the current system</p></li>
+<li><p><strong>H</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state</p></li>
+<li><p><strong>mixed_state</strong> (<em>LxL np.array</em><em> of </em><em>floats</em>) – Mixed state matrix computed via the Hamiltonian using expm(-beta*H)</p></li>
+<li><p><strong>sensor_state</strong> – Cached information returned by precompute_sensor_state. All information therein are internal to the
+sensor simulator</p></li>
+<li><p><strong>beta</strong> (<em>float</em>) – scaled inverse temperature parameter</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Takes an affine subspace of the simulated model.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.NoisySensorDot.start_measurement">
+<span class="sig-name descname"><span class="pre">start_measurement</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.NoisySensorDot.start_measurement" title="Link to this definition">¶</a></dt>
+<dd><p>Reinitializes the sensor as to generate independent noise samples</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+ <section id="qdarts-tunneling-simulator-tunnelbarriermodel">
+<h1>qdarts.tunneling_simulator.TunnelBarrierModel<a class="headerlink" href="#qdarts-tunneling-simulator-tunnelbarriermodel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.TunnelBarrierModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">qdarts.tunneling_simulator.</span></span><span class="sig-name descname"><span class="pre">TunnelBarrierModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gate_offsets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate_levers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.TunnelBarrierModel" title="Link to this definition">¶</a></dt>
+<dd><p>Model of the tunnel barriers of a device</p>
+<p>This class defines a mapping between gate voltages of the device and
+the tunnel coupling between the dots. To be more exact, the tunnel
+coupling between dots i, and j and the gate voltages v is given by</p>
+<p>T_ij = exp(W_ij^Tv+b_ij)</p>
+<p>where W_ij is a vector of couplings and b_ij is an offset.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.TunnelBarrierModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gate_offsets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate_levers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.TunnelBarrierModel.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Creates a tunnel barrier model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>gate_offsets</strong> (<em>NxN np.array</em><em> of </em><em>floats</em>) – the offsets b_ij provided as matrix.</p></li>
+<li><p><strong>gate_levers</strong> (<em>NxNxK np.array</em><em> of </em><em>floats</em><em> or </em><em>None</em>) – Here, K is the number of plunger gates. The first two indices describe the index of the tunnel coupling matrix ij.
+If None, it is assumed to be 0.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.TunnelBarrierModel.__init__" title="qdarts.tunneling_simulator.TunnelBarrierModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(gate_offsets[, gate_levers])</p></td>
+<td><p>Creates a tunnel barrier model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.TunnelBarrierModel.get_tunnel_matrix" title="qdarts.tunneling_simulator.TunnelBarrierModel.get_tunnel_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_tunnel_matrix</span></code></a>(v)</p></td>
+<td><p>Returns the tunnel matrix for a given gate voltage</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#qdarts.tunneling_simulator.TunnelBarrierModel.slice" title="qdarts.tunneling_simulator.TunnelBarrierModel.slice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>(P, m)</p></td>
+<td><p>Takes an affine subspace of the simulated model.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.TunnelBarrierModel.get_tunnel_matrix">
+<span class="sig-name descname"><span class="pre">get_tunnel_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.TunnelBarrierModel.get_tunnel_matrix" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the tunnel matrix for a given gate voltage</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>v</strong> (<em>np.array</em><em> of </em><em>floats</em><em> or </em><em>None</em>) – Array of size K storing the gate voltages</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="qdarts.tunneling_simulator.TunnelBarrierModel.slice">
+<span class="sig-name descname"><span class="pre">slice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#qdarts.tunneling_simulator.TunnelBarrierModel.slice" title="Link to this definition">¶</a></dt>
+<dd><p>Takes an affine subspace of the simulated model.</p>
+<p>Let v=Pv’+m. Computes a new parameterization such, that
+T_ij = exp(W’_ij^Tv’+b’_ij)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>P</strong> (<em>KxM np.array</em><em> of </em><em>floats</em>) – The linear transformation matrix</p></li>
+<li><p><strong>m</strong> (<em>np.array</em><em> of </em><em>floats</em><em> or </em><em>None</em>) – Array of size K storing the affine offset.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+ </div>
+
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12, 13, 16], "transition_sim": 5, "tri": 1, "tricki": 10, "trnsformat": [3, 4, 10, 11, 12, 14], "true": [5, 9, 10, 14, 15], "truth": 10, "try": 14, "tune": [1, 12], "tunnel": [0, 5, 14, 15, 17], "tunnel_coupl": 1, "tunnelbarriermodel": 14, "tunneling_config": 5, "tunneling_sim": [1, 5], "tunneling_simul": [], "tutori": [2, 5], "tv": [3, 9, 17], "two": [0, 1, 11, 14, 16, 17], "type": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15], "typic": 1, "u": 0, "ue": 5, "uhlenbeck": 8, "unchang": [6, 7, 8, 10, 11, 12, 14], "under": 0, "underli": [1, 14], "unifi": 14, "uniformli": 7, "uniqu": 3, "unlik": 14, "up": 14, "updat": 3, "upper": 14, "us": [0, 1, 3, 4, 5, 10, 13, 14, 15, 16], "usag": [0, 2, 5], "use_sensor_sign": 5, "use_virtual_g": 5, "user": [1, 3, 7, 9, 13, 16], "v": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "v_end": 14, "v_ij": 14, "v_offset": 5, "v_start": 14, "v_start_state_hint": 14, "valid": [3, 4, 13, 16], "vallei": 16, "valu": [1, 4, 6, 7, 10, 12, 13, 14, 16], "vari": 1, "variabl": [1, 3, 4, 6, 9, 14], "vector": [1, 6, 7, 10, 11, 13, 14, 15, 16, 17], "veri": [0, 1], "verif": [3, 9], "verifi": [3, 9, 14], "verify_polytop": 3, "versa": 14, "version": 2, "via": [1, 2, 3, 4, 9, 13, 14, 16], "vice": 14, "virtual": [1, 5, 12], "virtualis": 5, "visibl": 1, "volag": 5, "voltag": [0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17], "w": [7, 17], "w_": 7, "w_00": 14, "w_ij": [14, 17], "w_m": 14, "wa": [1, 14], "wai": [9, 14], "want": [1, 3], "we": [0, 1, 6, 8, 9, 10, 13, 14, 16], "weight": 7, "well": [1, 9, 11], "what": [1, 12], "when": [1, 3, 10, 12, 14], "where": [1, 2, 3, 4, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17], "whether": [5, 9, 10, 11, 12, 14], "which": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16], "while": [14, 16], "white": 16, "whl": 2, "whole": [13, 16], "wide": 1, "wider": 1, "width": 1, "within": [1, 10], "without": 9, "work": [1, 14], "would": [3, 5], "wrong": 14, "x": [1, 3, 4, 5], "x_voltag": 5, "xlim": 1, "xout": 5, "y": [1, 5], "y_voltag": 5, "ylim": 1, "you": [1, 2, 14], "your": 0, "yout": 5, "zero": [1, 14]}, "titles": ["API", "API Usage Tutorial", "QDarts", "qdarts.capacitance_model.AbstractCapacitanceModel", "qdarts.capacitance_model.CapacitanceModel", "qdarts.experiment.Experiment", "qdarts.noise_processes.AbstractNoiseProcess", "qdarts.noise_processes.Cosine_Mean_Function", "qdarts.noise_processes.OU_process", "qdarts.polytope.Polytope", "qdarts.simulator.AbstractCapacitiveDeviceSimulator", "qdarts.simulator.AbstractPolytopeSimulator", "qdarts.simulator.CapacitiveDeviceSimulator", "qdarts.tunneling_simulator.AbstractSensorSim", "qdarts.tunneling_simulator.ApproximateTunnelingSimulator", "qdarts.tunneling_simulator.LocalSystem", "qdarts.tunneling_simulator.NoisySensorDot", "qdarts.tunneling_simulator.TunnelBarrierModel"], "titleterms": {"abstractcapacitancemodel": 3, "abstractcapacitivedevicesimul": 10, "abstractnoiseprocess": 6, "abstractpolytopesimul": 11, "abstractsensorsim": 13, "api": [0, 1], "approximatetunnelingsimul": 14, "capacit": 1, "capacitance_model": [3, 4], "capacitancemodel": 4, "capacitivedevicesimul": 12, "charg": 1, "class": 0, "compens": 1, "compon": 0, "construct": 1, "cosine_mean_funct": 7, "creation": 1, "data": 0, "diagram": 1, "document": 2, "experi": [0, 5], "instal": 2, "interfac": 0, "localsystem": 15, "noise_process": [6, 7, 8], "noisysensordot": 16, "ou_process": 8, "plot": 1, "polytop": 9, "qdart": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "sensor": 1, "simul": [0, 1, 10, 11, 12], "slice": 1, "stabil": 1, "tunnel": 1, "tunnelbarriermodel": 17, "tunneling_simul": [13, 14, 15, 16, 17], "tutori": 1, "usag": 1}})
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/simulator.html b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/docs/simulator.html
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+<h1>Simulator<a class="headerlink" href="#simulator" title="Link to this heading">¶</a></h1>
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/__init__ .py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/__init__ .py
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/api_tutorial.ipynb b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/api_tutorial.ipynb
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@@ -0,0 +1,465 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a384e681-3052-4135-b026-0d7b17cc258e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "# add Code folder, if the package is not installed.\n",
+ "import sys\n",
+ "sys.path.append('../src')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "000937df-d546-41a4-b067-e2994fcdb2b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import main class and plotting function\n",
+ "from qdarts.plotting import plot_polytopes\n",
+ "\n",
+ "# import standard libraries\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.gridspec as gridspec"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "088bd49e-1cd2-45e0-82b3-d3d683f744c7",
+ "metadata": {},
+ "source": [
+ "### Construction\n",
+ "\n",
+ "First we need to define the layout and capacitance parameters of the device.\n",
+ "\n",
+ "We start by defining the dot and plunger layout of the device. \n",
+ "Not all of this is relevant for the simulator, but it will help us greatly\n",
+ "in keeping track of what the indices mean.\n",
+ "our device is a device with 6 dots, with three inner dots and three sensor dots.\n",
+ "We assume that the device is in a triangle formation, where the three inner dots form\n",
+ "an equilateral triangle and behind each dot is another sensor dot. Additionally,\n",
+ "we will have three barrier gates, between the three inner dots that modify the tunnel-coupling\n",
+ "between the inner dots. \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "47dd6aad-b503-4750-81b2-ec0f5a5d779c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the system\n",
+ "\n",
+ "N_dots = 6 #number of dots\n",
+ "N_gates = 9 # 6 dot plungers + 3 barrier gates\n",
+ "inner_dots = [0,1,2]\n",
+ "sensor_dots = [3,4,5]\n",
+ "\n",
+ "dot_plungers = [0,1,2]\n",
+ "barrier_plungers = [3,4,5]\n",
+ "sensor_plungers = [6,7,8]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6923460d-67fb-4c70-ae7d-eb2eb9224b67",
+ "metadata": {},
+ "source": [
+ "Next, we setup the capacitance matrices. These depend on the target device layout. For a device such as this, the number of parameters grows already very large. Feel free to skip the definition of the parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a640116d-ccbb-4b95-84ad-1ad39e132c14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "#All capacitances are given in atto Farrad\n",
+ "\n",
+ "#let us first define the inter-dot dot capacitance matrix. We will first set up\n",
+ "#only one pair and then we will symmetrize the matrix in the end.\n",
+ "C_DD=20* np.eye((N_dots))/2 #The self-capacitance of each dot, NOTE: factor of 2 due to symmetrization\n",
+ "\n",
+ "#capacitances inner dots\n",
+ "C_DD[inner_dots[0],inner_dots[1]] = 10\n",
+ "C_DD[inner_dots[1],inner_dots[2]] = 10\n",
+ "C_DD[inner_dots[2],inner_dots[0]] = 10\n",
+ "\n",
+ "#setup the sensor-dot <->inner dot capacitances.\n",
+ "for i in range(3):\n",
+ " for j in range(3):\n",
+ " if i == j:\n",
+ " C_DD[inner_dots[i], sensor_dots[j]] = 4 #sensor dot closest to the inner dot.\n",
+ " else:\n",
+ " C_DD[inner_dots[i], sensor_dots[j]] = 1.0 #sensor dot further away\n",
+ "\n",
+ "#symmetrize\n",
+ "C_DD = C_DD + C_DD.T\n",
+ "\n",
+ "#dot-gate capacitances.\n",
+ "C_DG = np.zeros((N_dots, N_gates))\n",
+ "#dot to plunger-gate capacitances\n",
+ "for i in range(3):\n",
+ " C_DG[inner_dots[i],dot_plungers[i]] = 11 #ith dot plunger to the ith dot\n",
+ " C_DG[sensor_dots[i],sensor_plungers[i]] = 11 #ith sensor plunger to the ith sensor\n",
+ " C_DG[sensor_dots[i],dot_plungers[i]] = 1.5 #ith dot plunger to the ith sensor (cross-talk)\n",
+ " \n",
+ "#further cross-talk inner dot plungers -> sensor for the plungers that are further away\n",
+ "C_DG[sensor_dots[0],dot_plungers[1]] = 0.5\n",
+ "C_DG[sensor_dots[0],dot_plungers[2]] = 0.5\n",
+ "C_DG[sensor_dots[1],dot_plungers[0]] = 0.5\n",
+ "C_DG[sensor_dots[1],dot_plungers[2]] = 0.5\n",
+ "C_DG[sensor_dots[2],dot_plungers[0]] = 0.5\n",
+ "C_DG[sensor_dots[2],dot_plungers[1]] = 0.5\n",
+ "\n",
+ "#cross-talk inner dots <-> dot plungers for the plungers that are further away\n",
+ "C_DG[inner_dots[0],dot_plungers[1]] = 1.5\n",
+ "C_DG[inner_dots[0],dot_plungers[2]] = 1.5\n",
+ "C_DG[inner_dots[1],dot_plungers[0]] = 1.5\n",
+ "C_DG[inner_dots[1],dot_plungers[2]] = 1.5\n",
+ "C_DG[inner_dots[2],dot_plungers[0]] = 1.5\n",
+ "C_DG[inner_dots[2],dot_plungers[1]] = 1.5\n",
+ "\n",
+ "#cross-talk barrier gates <-> inner dots\n",
+ "C_DG[inner_dots[0],barrier_plungers[0]] = 1.2\n",
+ "C_DG[inner_dots[0],barrier_plungers[1]] = 0.8\n",
+ "C_DG[inner_dots[0],barrier_plungers[2]] = 1.2\n",
+ "\n",
+ "C_DG[inner_dots[1],barrier_plungers[0]] = 1.2\n",
+ "C_DG[inner_dots[1],barrier_plungers[1]] = 1.2\n",
+ "C_DG[inner_dots[1],barrier_plungers[2]] = 0.8\n",
+ "\n",
+ "C_DG[inner_dots[2],barrier_plungers[0]] = 0.8\n",
+ "C_DG[inner_dots[2],barrier_plungers[1]] = 1.2\n",
+ "C_DG[inner_dots[2],barrier_plungers[2]] = 1.2\n",
+ "\n",
+ "#cross talk barrier gates -> sensor dots\n",
+ "C_DG[inner_dots[0],barrier_plungers[0]] = 0.5\n",
+ "C_DG[inner_dots[0],barrier_plungers[1]] = 0.1\n",
+ "C_DG[inner_dots[0],barrier_plungers[2]] = 0.5\n",
+ "\n",
+ "C_DG[inner_dots[1],barrier_plungers[0]] = 0.5\n",
+ "C_DG[inner_dots[1],barrier_plungers[1]] = 0.5\n",
+ "C_DG[inner_dots[1],barrier_plungers[2]] = 0.1\n",
+ "\n",
+ "C_DG[inner_dots[2],barrier_plungers[0]] = 0.1\n",
+ "C_DG[inner_dots[2],barrier_plungers[1]] = 0.5\n",
+ "C_DG[inner_dots[2],barrier_plungers[2]] = 0.5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60cfc726-e28a-41fc-ad93-98ea6d023c6e",
+ "metadata": {},
+ "source": [
+ "To define the model, we need to introduce lower bound values for each capacitances in order to prevent unbounded polytopes.\n",
+ "We will pick as capcitance model a slight generalization of the constant interaction model that is defined using the capacitances above \n",
+ "and which additionally allows to include deviations from the constant interaction model using a parameter k for each dot. These deviations model a slight change of capacitance values as the number of electrons increases. Larger values lead to smaller effects. Set k to None to disable this feature.\n",
+ "\n",
+ "Using the capacitance model, it is straight forward to define the capacitive simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d1d28ae4-4ffa-4c29-8d81-c52e292fe531",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from qdarts.capacitance_model import CapacitanceModel\n",
+ "from qdarts.simulator import CapacitiveDeviceSimulator\n",
+ "\n",
+ "#minimum voltages for each plunger gate. \n",
+ "bounds_limits = -1.0*np.ones(N_gates)\n",
+ "\n",
+ "#deviation from the constant interaction model. Set to None to have no deviation\n",
+ "ks = 4*np.ones(N_dots)\n",
+ "\n",
+ "capacitance_model = CapacitanceModel(C_DG, C_DD, bounds_limits, ks=ks)\n",
+ "capacitive_sim = CapacitiveDeviceSimulator(capacitance_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "25f066e1-c481-4443-b327-6a45206d1296",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/oswin/miniconda3/envs/devel/lib/python3.11/site-packages/cvxpy/problems/problem.py:1407: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.QuadMesh at 0x7fd1c8705350>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "target_state = [1,1,1,5,5,5]\n",
+ "m = capacitive_sim.boundaries(target_state).point_inside\n",
+ "\n",
+ "P = np.zeros((N_gates,2))\n",
+ "P[0,0] = 1\n",
+ "P[2,1] = 1\n",
+ "\n",
+ "from qdarts.plotting import get_CSD_data\n",
+ "\n",
+ "minV = np.array([-0.02,-0.02])\n",
+ "maxV = np.array([ 0.01, 0.01])\n",
+ "resolution = 100\n",
+ "\n",
+ "sliced_csim, CSD_data, states = get_CSD_data(capacitive_sim, P,m, minV, maxV, resolution, target_state)\n",
+ "\n",
+ "xs = np.linspace(minV[0],maxV[0],resolution)\n",
+ "ys = np.linspace(minV[1],maxV[1],resolution)\n",
+ "plt.pcolormesh(xs,ys,CSD_data.T)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "93c06d8a-7986-4420-a064-a43145ba91df",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from qdarts.plotting import get_polytopes, plot_polytopes\n",
+ "\n",
+ "plt.pcolormesh(xs,ys,CSD_data.T)\n",
+ "polytopes = get_polytopes(states, sliced_csim, minV, maxV)\n",
+ "plt.xlim(minV[0],maxV[0])\n",
+ "plt.ylim(minV[1],maxV[1])\n",
+ "plot_polytopes(plt.gca(),polytopes, skip_dots=[3,4,5], fontsize=16)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e6ff9f72-a7b6-4e0a-94e2-94e4a2929b8b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "c0db7d1e-2a71-4f71-8b67-a7aad289f3fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Definition of the tunnel couplings in eV \n",
+ "# NOTE: we use the convention that tc is the energy gap at avoided crossing H = tc/2 sx\n",
+ "tunnel_couplings = np.zeros((N_dots,N_dots))\n",
+ "tunnel_couplings[0,1] = 30*1e-6\n",
+ "tunnel_couplings[0,2] = 30*1e-6\n",
+ "tunnel_couplings[1,2] = 30*1e-6\n",
+ "tunnel_couplings = tunnel_couplings+ tunnel_couplings.T\n",
+ "\n",
+ "temperature = 0.1 # 100mK\n",
+ "\n",
+ "from qdarts.tunneling_simulator import NoisySensorDot, ApproximateTunnelingSimulator\n",
+ "\n",
+ "\n",
+ "\n",
+ "sensor_model = NoisySensorDot(sensor_dots) #a model of the sensor dots that just needs which dot has which index\n",
+ "sensor_model.config_peak(g_max = 1.0, peak_width_multiplier = 20) #make the sensor peak broader\n",
+ "tunneling_sim = ApproximateTunnelingSimulator(capacitive_sim, #the underlying polytope simulation\n",
+ " tunnel_couplings, #constant tunnel couplings\n",
+ " temperature, #electron temperature, should be <=200mK\n",
+ " sensor_model) #our sensor model simulation\n",
+ "\n",
+ "capacitive_sim.set_maximum_polytope_slack(5/tunneling_sim.beta) #adding slack to keep more states that are likely to affect the hamiltonian\n",
+ "tunneling_sim.num_additional_neighbours[sensor_dots] = 2 #adding additional states for the sensor dots\n",
+ "\n",
+ "sensor_values = tunneling_sim.sensor_scan_2D(P, m, minV, maxV, resolution, target_state)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "9405f712-f029-4155-8186-83599d58ac61",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.QuadMesh at 0x7fd1c87b1b10>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.pcolormesh(xs,ys,sensor_values[:,:,2].T)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "ed0c88f3-7f0e-40ee-8127-9d6a6895dc8e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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5eRlKlVpftNfboxtWLb8PgX7OeGbBCLz/6U6DccfjL+N4/GUAQFiIOzp1krWYjc1N1pV8pKdkI2SAN7r+bYmissqmB3E4nBbl7/VxeOSJEZj5YCR2fbLV1OZw2hDcoWoCM7EaGiIgS6VSkBNaGjOin5LQVWmIW0A1EnG90hpanc6K0B352HfDWG9fqDQavHJwF6o1Kn2JlP2Z6fgn5RzuDwrGk5H9sXjbdoOxpNaKIW3C3wnJkNTb8NGgxi7RbaRdYEyHoFJp8ehrv9U1iOs0T5czC/HlzwexbPFEjBwWgI++jIG6nr6N0gBBJBJqZVg1QGQbowaI1GQRC1Mt1//386V/4uONi/HmnFF44YvNBt2YtW7Eu5yUupB3cbOViqHTK1DpJNi0UWRqC7K8CGMbYxkcshwNs56LVePFpsMjtVYUpNaKbe21xIsuIj6f6Nv+GduYtVFsqQXIW/cpzQy1BqT2iJqW0FCZ69ZFDSDh5BX0j+gJRw975FwrbHBAI7RlrOkVyGucShNBrAGll2IsR8OcXoFZG8VW3uZ2+/HSM5w7JtpLl1X7VM51XC8vFTy/6WIKAGBkT29IqS/1ds6lK3kAAAtzM9jadDKxNc1LauI13LiSj6hQb1hatJ/dNQ7nbuarr/YCABasmGFiSzhtibvv27UDEmTvAAA4m09Ll5Nyde1WMhk8u9i1llmthqtTFwCAQqlCWVm1ia1pfr5c/i/EIhFeeYDnv+Fw2gIZ1wqRl1eK/qN6m9oUThuCO1R3AW42NgCAG8TuFACUKxUolevCSK62d19xzwem9QcAxJ1Ih1LFGidpPyQcTEXezXKMHdALMil/y3I4bYF//z4BM3MpJj86zNSmcNoIXEPVBFMcFuK04iJiC85DUy8BipWZ8I4xVr2UlMhXpVJT5Wi0EEvqtEliiRZqQkNlJdOFgirVijqNlcow5lypVMDG3BzWFjIDPRNr+Re65E29/4vq/mXPY8WYX+oW+qtHZw5CsL8LKqsU+Prng4J5DOaotVEiEmh0mDVAlP6A0qY0swbo22/2441Xp+CFcVH4vCbcUOohDAGy5udif43Yzk1LaRyMKGVDlgJizYVE6dJYc/RQOcWo/E1tKjcVYYsxuamMKkfDqO2hpAcaYhFIrRBbbioRMa+WzJlEaY+IEj8NTP57QzweWzAc9z49Blt+jat7QsF4NzGptWqFNaD0RdS6MOa6ut2yME2OpXLfMZay0a8B11C1TcY4B+Hd0EewZ+T7+DdqGVaGPIoJTgNgIeZ6FlMzblgg5s2IhFqtwf++3Ims7GJTm9Ri7N2fguKSSkwaH2pqUzgcTg0Jh1Lh4NoVLl68ogGHO1RNMmHvJ/jq0hacLU6HVCzFwG4BeDlwJv4e/CH+iHwP74U8i+muI2EntTaZjRU1v4gspWaN9rE00zmA5ay/nto4IyL9sPTpcQCA97/ejf1xF01sUcvzy2+xMDc3w8MPDjK1KRwOB8C3b28EADz25jST2sFpG/CQXxMUK6vwZ8ZB/JlxEAAgE0sxyqEPhjsEwdfaHUG2Pgi288U8r6mo1iiQWZmHU0UXsSs7HplVwgScLUFWeSmCuzvC2dqGfN7aTAYbc13Sy6xSWmfVnhjW3wcrnp0IsUiED77ZjW37kk1tUqvw76ZTePSRIbhv+gD89Gusqc3hcDo816/kIy/rJsKH+ZvaFE4bgDtUTaBSi5FV2NmgLT3/EtYmpAPQbfGNdPHFRE9/9O3mCm8rF/h1dsNsj1FQqFXIrChGfP41bMo4i4TCLACAhblSMI+E0G6oxRqI6gWKRWINqUtJLsrBeC8/BHd3rKv1V09r1dupBwDdTtaV0iKD52h9EpUTS9jPIK5dO0TcWI25Jsbr56bG1tkzNLwn3lk4CWKxGB9+tweb9yXrNQZNaYD0Gb3q5bGq62ciDRBjfhqLdF2um43fH8KcZ8fgvsG9sO5yuvBwxDuaVSfHmhdMS2or2HRaItb8TYTGhsoHRR7PVLmpWGHMTQWilBIzpFCGLb8UmZuKOe9WK+SmonQ8jPo1uo6dsInMy0Rp3+xt8e/6Y3jqlQmY9tw4/PdbHETX84jjGZGHqrnXgKypx5ifi3opSU1WM9f8u02dFq/l107RANhz/RL2XL+kF+b26eaMe7yDEOnoAffOduhp0w2ze4ZDrdEgp6oUScUZ2HXjHPbnXDAQut8pu65dwkvhQ9HPwQXOVp1xo6LM4PmpvgEAgH1X0qGi3tjthKg+3vjfwkmQ1DhTm/acNbVJrc6vq2MwY8FwzFk4Buue/9rU5rQ5erh0wU/7XjFo++njnfhj9S6Dtv7DA+AX4gbfYDf4BLvB3lF39+tDfV5DQQto8XzCPBA2LBA+fTzhG+YJ554OEIvFeH/eV9j3R/PuNopEIgRG+qHf2FCEjQiCu78LLG06oaKkCpfPXMXuXw5hfyNzPrf6EUxcMMqgLdr64Wa1725j0+/H8NjzY3HPg5H477e4pgdw7lq4Q9UCJBbcQGLBDQCAzFIJD6suuMczFFEO3ujZuRuinYMxziUEGq0WN+UVOF9yAzE3UrEtMwlVGuHuVVNcKi7ErmuXEO3hiw+ixmP+nn+hqHHUhrt7YUav3lBrNPjq5HHB2OcGReK5wZE4npGJOX9uMO7EGejn44rvn7sPAND3yU+Yxw0K9cLKZ3XO1Pvr9mBLB3Smatn5Vzymzh2MsQP9sftYqqnNaZNUVchxZGcSAOBK6g3B8y+vfgjWrZgEds6r0zBocnirzOXk3QOfHnobAFBaWIaLp9JRfrMCTt4O6DsqGH1HBWP4fZF4Z9anUKkMf/mfP54Gs5ryTGMfHNIq9t4NnDh6CZHD/eHm1R1ZNTtUPdzs8VPC/xn0++n9LfijXnksW3tr9BsegPARAfALdUd3py7QaLXIv34TCYcv4N9vDyAvp3llGj7+Tgjr7wWfQGf4BjjB2a2rzrlf9i/27Uhq1rlEIiDQ3wUD+nmhTx8PeLjaw8pShvJKOdIu52H7gXOIOZRCjn1pwWhMG2t4E87AOaua1b7mhjtUTSARa+vCaPXQULeTq4R7pIrCTrhUWI0PMo7jA+gcGntxJ9zjH4RRXt4I6NYdUT18McTBD2+FTUapXI7UggIcuJqOv9OToVbWvEQaEVQ3LYBOKsEcIrEWr8XthK+dPYa4eOLwfQtwIvs67DtZIsLJDWKRCCuO7kVKUUFdaK52bE1IR6nV6MNALCGiQMceWDGuLtGkm73uF/59A0IwrJe3vn3RT1tQUFah31YX186nVjcSGhRu43a1tMB7z02GzEyK3MIyBPs6I6Snk3AdNMAXPx1ESVlVPbvpUAVLyI859YExISuybA0xVlZ3w8G3n+zGxNkDsejeKMT/eNKgX2Fo54ZDG0mlIJyWOY0FuVbE8Yi1osaylu4RsYRba/4svVmBj1/9q167Yb/YnWdxPT0PaclZuJyUiT/O/K+uX8PXk7KZSg9wi7BYyonLuJZ6HWmnryEt4QqWfL0AoUMD6roxzkGmV2gQc9FqNEjcl4wNq7YgYc9ZaGrDJSIxgof4491NL2PgxL6Y+eJkrH/PsJzRnt+OYM/vRwHUc6i02uZPpcB4PDrVA3G9qIl+1PuXtSwMZTMRFlPZ6LSpX63dh8jh/njs5XFYceScQZ+qimoc2ZoIALiSnFV3nlotHl92D0ZO7w+1WoNrF7JxLCYZFpYy+IW6Y+q8oRg7MwLvPPkjEo80uOmGfAOzremcx4dh0IgAoq9hfzKdBJXSgEyloOvn7GyHLz55EABQUlqFC5dyUFZeDWdHO/Tr44l+fTwxanAvvPn+JqhUmjo7ACRfuAGZme6DZcKI3jXHrT+H0Lza0CBZNqcV4A6VCSiqrsb3p0/h+9OnAAAWUikm9fRHdE8fhDg6oL+LCyJcXfHy4CGoVukcKFtzC/jY2SNNTmdDL6yuxJStP+Hp4IEY59ELYzx8UKlS4lDmFXxz9gRib2RARIgFevfQ6as2JN+esNvaXIZQd6FT42TXGU52dV/qMqnhN22Aq26+jccMP3RuhYW5GcxlukvVwb4zJg0JarTvD3/GGjhUdyMqhQoHd57FyElh6DewJ04eu2xqk9odn7zyJ9CKSWD/+nhb3R/Ul38zkp2ei1ei3yWfSzqcij8/3IxH3pqJ0XOGCBwqzp2RnV2M7OxihPf3hlgshqaeM1JaWIGPF/1CjisrrsAvH27Drj/iUJhTov9xYWEpw6IPZmH41HC8uvohzB/+P5SXNs/nWurZLFy7nIe0lGxcTs3GkremIaSfV7McW4AWSEi8ij82xOPE2Wt1zj2A0N5ueO+t6Rg8wAdzpkfgpz8Nw6U7Dp7DjoO674lah6qtwx2qNkC1SoV/Us7hn5Q6J2OEpxcmBvghwskNLmZm6Cwzx57750GhViOjvBjxuRnYmH4O8XlZ+jHlSgU+SDiEDxIOAYqmM2KYicUY4OqKpJwcbL1w4bZsjr+WhcDX6ZCd+BaZGSL9PVAlV+Krnexag+yCUgx4+GODNolS+AtEIjfNrxJT8PnbmzFiQiiefnEcHp3xhanN4bQj0k5fBQB0d7M3rSF3GRv+OIZFS8bjnqdH4Z/PY5jGrF32D9leXanApy/+jgEjg2DTxQr9RwZi/8ZTzWLnXz8ebpbjsHAjuxgvvPInAEAjNdzdOpOcifX/HMdjDw5B9IgggUPVHuEOVRtl/9Ur2JtzGa7WNjgy53FUqZS4XlaK7lZWcLe2g4+tPR7w66MTuleWISH/BrZfTcWuaxeZZe7hTi6wksnwwZEjLXoutZibSdDHywU/HziFwrLKVkmCNrCfN0YM7gUAd1Xh5KpKBa6k5cHbzwG/bl6EAzHJ2LcjGYWoNLVpnDaOi48jAKDoLk6Eawq2bEzAUwvHYOqCkcwO1a2QVyuRlZ5Xo62yM97ANsjF9Bq9WTehVKE9wh2qJpBINNCQeg62W1kp/RWlyRAR0QdpsQSSmnwDnaRm8Omi+0X56f6j2JqcintCgzCkpyd6duuKSZ7+mOwVAK1Wi6KKSiTn5iHmYhq2JKeisiZsqLYydLWOZ2XC+9OanZ/6ZWSoshVC6Rbdr5F2jRqoUqvR/9U1+vlY0yaw6n2o1Azent0wvkEBUy2R2oFOD8A2L7MGiPIgKT2SjaWwzczw5MZMCoWXr64odrcenTHzocGY+dBgqFRq5BWWIeVCNg4du4TDxy6hmsj3ypxegTpf6nUnb0dn1KVR6QsY27QN177+3/U1TWSqAkp3I9E9DCah0jUQOjfiOmjyVnaxSPdgLT3DeN+6lrrNXKyBeScZpj0TDQA48t9x5vQKWkJPY1QqBdY0AmTqA7axZCoA6jVn1EuR5YsavEbHYi9hyDB/eAa5orKiplC7CI2kibj1eUikYji4dgUAFOWVGB6DdQ2INsEa6PPJNNDKsaZDIMvHsH3+udUUti8sqmBKeVD/e/JW5Wh42gQOyfXSUvi8Xxdak9QLp60+GIfVB+MgUgN2nSwwNTQAI3p5w9+hO4Z6e2JYTy+8M240yuRyXMwvxP7MdPx9Phn5lR1nF+O3f+Px27/xBm1iIlzYnnhg/lDMfWI4qioVePKR75CdXQwfP0eMHBOE0AFecHPugpFD/DFqqM7BLi2vxuXMAhw/fRU7D59HflG5qU+BYyKe/Xw+nLwdUHC9CL+/t9HU5tx1rP1yL4YM88f85fdizcvrjTpW9KxI2Npbo7pKgZMH6Dvh2jPm5lJMn9wXAHAo9u6odMEdqruE4qpq/HQsET8d091NIukkxsTAXhjTywchTo7o6+qMfm4ueDEyCtUqFdJvFuFoZgb+PX8eFwsLTWw9h5VFSydi/D3huFlUgQUPfY3SUt2v4LSLOUi7mAP577pb3q2tzTF2WCAi+3nDp6cDwvxd0TfQDU/OjoJCqcb1vGKcvpCFPXEXkJCSdaspOXcJc167F2PnDoO8SoF3H/gUZUXlEBHF2zl3Tl5uKW5cyUdYVC+IyS1pNjz9nTD/jakAgN/X7EZxwd33I+j5hWPh7GiH/MIy/LrhmKnNaRa4Q3WXotBo8F9yCv5LrvtlExXogcl+vRDu5IJe3bojqIcDHg/vD4VajaySEpy4cR2bUlMQn37dhJZzGuOdT2ZjQJQfMq8W4Mk5X0Fxiw/s8nI5/t2WiH+3JULVSdcvItQDIwf2QkgvZ7g62MHbtRvuHRUGjUaDwpJKXEjPxZFT6dhzNBWV1XdHzUeOjumLJ+Lht2ZCUa3AWzNW4fxdsiPQFtmwZhcWf/IQJjwcdUfjuznZYcWPj8PS2gJxu5Lw15d7m9lC0/PQA4Mwbkww5HIlVry/BaVl1aY2qVngDlUTmInV0EiEAVkNoefQUKVFKA0V1UaW/SDaGPVXMiIX3MlD13Dy0DX930GuDpgSHoD+Pq5w794FXkFdcH/vYKg1GuSVlONsRjZ2nbmEfecuo7oTodNo5PucbCf1OYz9mMukUG2EhoCSUbSKBkh4chprmbCtgV5KLAY+/+Ex+Po54uzZTDy/6FdAJoVWKpyDCmdaVOiSxZ7Zm4Yze9N0/VQaODnZYdTY3ujX3wsent0xuK83osJ74pUFo1FZqUBmRiFOnbmGPXvP4eq1ul1MjRlbfinWMj1aVu0HpVFqWLKptotIZFjqh8zR08gF3HBu1vI2zJoiUb18cDV5r0jnWHiRk+WQmshNNXXhODy5ai4UciXenvkJTu4+U68bpQ9r0KbRkjmdSKh1Zu3HWo6G1EHdeV4mSu9D6qWoMj3E8XZtP4enVioxeubAmgnEjZyHsK1LN2us/OMZOLjZ4+T+81j55A+tsAZ1uZsM1sKINaC0g7X5yO67tz/mzx0ChUKFN/5vI5LPGe6Q31JDVW9+Og+VqMljtCTcoerAnMvKxfmMurxWzl1sMC0iEIP8PdHTwR5jQ/wQHdqrRuhehfPZediTehlbklJRqeA7GK1BJ0sZvv31CfRwsMH+fefx7jubmu3Y2dnF+PWnI/j1J91dnjIzCYYO98egKD/0CnCGr58j/AOcMWdWJFQqNXLzSnE+5QYOxV1E7InLdYn4OG2WKU9HY+Hq+VDIlXhn5ieI35FoapM6BHF7z2PYhJDbGmPbzRrv/fUsXHs6IOFQKt6e/y2UCpXwJol2zD1T+uLpJ0ZCoVBh+Tv/IT7hiqlNala4Q8XRc+NmKb7ceQyf79HFs20tLTC5bwCGB3qjl0t3RPl4YIivJ1ZMGolyuQKX8gpw8NJV/Hf6HPLKKkxs/d2HfTdrfPPLE+hsY4G//jyOb77e36LzKRQq7NmdjD2765K8+oa6YdSIAISFusPVpQtGjwzEmFFB0Gq1KCmtQvrVfBw/dQUxe86h6Ca/BtoSk54Yg2c/f0y3MzXjI8TvPNP0IE6z8P0H227LobK1t8Z7fy+Gu58TEg9fwFvzvoFSTtxa3Y6ZNrkPnntmjM6ZencjjsWnA8Que3uGO1ScRimprMavRxLx65FEqKwAmUSM6EA/jA30RYiLI8LcnNHX3QWLRw5CtUqFqwU3EXclExtPnENaLhe6G4OHV3d8/v2jMDeX4stPd+PfzQkmsSP1QjZSL2Tr/7a17YTRY3sjop8XfLx6oE+IO/qGeuDJecMgV6hw40YxziRlYt/BFJw7m2kSm1uSJZ8+hDH3D0TMH3H4eNHPLT7fmIeG4qXvn0TO1XzM9VvEPG78Y6Pw7Bd1ztTxbQlcgN6KFOSUIjuzCM7u9oJyXw2x6WqFlX8vgqe/MxIPX8CKR76Govr2a7q2ZSZN7oNFC8fWOVPH787qDtyhagKJWAOpRChSUKmF8WoxoY1SM2qoWPP9GKcnErZRsWYzIquCeTEAaLAvIxX7duoK8opVWkT29sDYCH+E+rnAt4c9Apx64NFB4VCq1MguLEXixSzsOJ6KuCzhnWR0LT/CRrImXGvUp2teDZDa2lw4Vio0OiTCCx/8byZEYhHeencTDh6+AI2lcGE0MuIaVBBaN1JHRuiviLFmxXKDvyuL5diy9iC24KBujFiM/oN9MGRkIAJC3eDm2gXeXt1xz5S+UKs1KCoow6WUbMQduoBDMcmopCQ2zFo1aixrHirD481+ehQGDOslONzyHxZApdS9odKSs/DFG38bHFNUo8tSqdSMNf90/QeMDcbsFyfpm917OQMAHnp9GqY8OVqva1k84u26ubQiiGuuD7VKDZFYROulGuiveoZ6YPHaxyEWi5GTfgNDp0di6PRI8ov9owXfEDY3zFOkoXM1MeqlmOv7UXmZqBxvxuivyGvozuv7UfordVcrAMDm/07hyUVjYWXTqdE1sLazxMoNi+AV4IKEgyl4i3KmGlmDJR/NxpgZAxDzdzw+fvH3O1+D+l0M8lAZdhwzOQwvrrgHOdnFmHvP6rrzoPJ91VvTCRPDsGhRNBQKFVa8/R+OH7+svxQp/emtNVT1/k+8F2pzU3ENFaddEpd8DXHJdUJ3f48eGB8VgP7+7nDv0QVTonpj6hCd0D2/pAJJ17Kx+8wl7DuTBiWZVLBjM2p0EF55fQrUag2ef2E9ks+17TsuNRoNjh++iPgDdaWLXDzsMXJiKPpG9oS7Z3dEDuuFQcP9seTNKaiokCPjagESTl7Bnl1JuJ550yR2O7l3hX8fD0G7T7Cb/v8KIuTiW/P87vWxtzWfrX1nBPTvKWh39naAs7dDo+N8++hqrO1cd4B5Lis7K4hrvoTdA1zhHuDaaF/SoeI0C0cPpOLJRWPRyVJ440ktiz9+EN5BrtBoNCgrrsDC92YJO4lFiNuVhLhdSQbNtT/aan8AsDJgaC/MfnK4/m/3nrr6qg8+OQKTZ0XU2TbvO3I+9W3UwOzp0wPPvzAeYrEI2TnFGDrUH0OH+tc7aN1/3/t4x22cRduEO1ScZiX1Wh5SsvL0fzvZ22DSoEBEhnrB29Eeo0N9MSbMD1qtFjfLq5B6PQ8HzqVj68kUlHfwW/Xvf2AgHnt8BKqrlVjw9Dpcv24aZ8NYrl8rxC9f7sPP3+p2sSwspBg6pjcih/aCb6Az/AOcEdjbFQ8+MgRKpRq5OSU4n5SFQwdScOJYGrk50Nx8/OoGfPziH8InVI3rVuwdbeHRywlHt59BasLV25ov5vdYxPxe44SphV9IWqINAMJHByM/qwgbP9/JPNfZg+cxRnyf8AniTkIeBmx5xGIR/Pp44mLiVcFzne2savqIMWxqv0aPkZtZJHCo9M79X8dvyx7brpYICHUXtDu72+tClI3gG6DbVd25+TTzXNbWFhDX7Nx6uHeDh3u3Rvtyh6oDYCah0yZIGt6uDUBNtFG3OWuoUBIRBiRDXYzhQjJ0Ru0cNx6laLKtMaT1UorkXy/FjxuO4affdIUvrS3NMX5oIAaHe8PPqwcie3lgkL8nlt47AhVVClzJLMCxxCvYtj8Z+YW6ZHZKK6rMB2XjnYf3yPAqsf7U60mFelREOgRqXrWF7kQWPzka0yb1QXFJJeYuWofi0irAqs4olYVwrERBlTqibqu/8xAnc0oIKqRRqXOQ5ZUKxPwZj5g/4yGq+XUb2NcDwyaGIri/F5zd7TFmfDDGTgiBVqNFyc0KpKfcwIlDF7BvcwJKi3QxaK0FUUOHSksAwKaLJZb8bwYA4EjMOcTvOSfsxPha1p5v32EBUKvUWPf+lkbKfBDXKVV2hTG9goNHD7j4OOKTp76DQq7WvYGp45F1axhp8LqNeWgoQkf0Fvajbpcn4ipGlaMhy+qwhrHYUh+ICG9dS4YfCfPI9xEx1Exs8C8APLZiBl6Z9nGDjlphG5V6Ryr8mrbvbgMPP0cc3XEGqaeu6o8ngFiDPf8lYM9/hppMSnrQ8O7C8IE9kZ9bio2/HzNYC2oNatMbnE24htFD/6/ROeqvkT4MeIvLuf4lQqdNqOnAmr6jmeEOFadVKa+UY8PORGzYmQixSguZVIyhkX4YEemHAF8nBPk6I7iXCx67fzDkChUys28iPiUD246mIC0z39Tmtwj/9+Y9iBroi6wbNzHvmR9RzVzeuv1yPuEazifUhIrVanTp3hkjp/RFv+H+8OrlhD6DfdE3yg+PL50EeZUSN64V4MypK9i/PQkXk5vO7N7J0hxjpunKWtzIKKQdqtsk5q/jiLnN3QBjyM0oQHSnh1ptPgAIGOiLsQ8NbdU573a0WqB3pC9e/OIRHN50Csd3JzU9qAn6Du2lc+4/2NYMFjaNg5MdXNzt8cm7m8lQeEsQPSoIfUKEO2ltGZGWysrFQWlpKWxtbRG56VnIzYSVsKvkwl/LCoXQP1UrCDEx0SaSUwJjqk1oq1hOJXgU9pPIhW3U8STEWLIfsUPS+HhhX7GK/sU3IMQDo4f4I9TfFY7dbSCV6tZLqVIju6AUpy9ex67jqYg/n9HgeGw2UskvJUQbJdIWK4k2ol/9X161NNwVEouBNV88jAA/JySnXMczL/6mG0uIzVl3qKhzo8+DGiv8aUidr4joR7WByFMlovQXRLhLKpNiwIgADB4bDP8+7ujhZAdpTcJTtVqDwrxSXEzOQuyBVBzZcw6K6novvhHzkiE/5rGMoTzWkB9RAJfqR46l9IlUyI9KFEyFAantbWI3hRxLtNH92HZnSJE21U/KNkfDwuMAoKXsI3dYiLE1qQAcHG3x218LDZ77aeUm/HGr0FZrrAHRRu0eNccaNDUH9TmpqRm75JmxmDohzOC5Ifd8JOhXH3VNYEClrMapDW+gpKQENjY2gn4tBXeoGoE7VAz9WsihaohXgAMmRgWiX4A73BztYG4mhUgkgkajQX5xBZLTs7H3xEUcPJYGVYMt/bbqUMlkUvzw/WNwdrbDwaMXsex/G+vGcoeK/MJwD3LGiIlh6DOwJ9w8u6GTlTlEIhG0Wi0qyqpx7XIeEo9dxp6NCcjJMtSfcYcK3KFqZI6Wcqjqs3XHi6iulGN24MvC4zZhX0d1qAzaiM9E7lC1I2odqqjNz0ApEzpUlQqhQ1VNtKnkhEOlJC5UwqGinSzqdnfqy1Y4BemMtYRDRc1DOTF36BA4dLfB+FFB6N+/Jzw9u8HSUqb/Yi0prUJaWi5i49KwOyYJpYS+hNKwiQl9BOkEUs4J4TioLAnn2lw3bxdbS/y8eh5sbTphw85EfLrOMGEnq4aNXmfK4aMcSMpRYnMgqX6kk0U4JyIquzrlsDA4Np2sZRg+pS8iRveGT7AbuvSw0d/dppQrkZNZhPMnLuPQ1tNIiE0THo/6wmB0lEjlPGEzs6OkJN501Eczo0NFlZRhdrJYHSBWZ4zReSLHMjsTrPNSX/5sjgPpeFH9iOO9vHgMRk/vjxemr0ZKzQ0N2nJhIty7eQ1I54nRodIS5a5oh6r2zsdqnPyHO1RtBu5QMfQzkUPVsJ+lpQxjxgRh0CA/+Po4wNa2k97BqqxS4EpGAeITrmDHvmTk5peZ1KFyd+mC71bNhYW5Gb786QB+2yFM2MkdKrDvFDVwbEIG+WLolL7oPaAnnDy6wcy8djdTi+LCMqSfu474AynY/98plFcQC8gdKu5QAc3uTHSrrsb6E2/j3Il0vDTzc10/7lDddQ4VF6Vz2j2VlQps2pSITVt0dcqkUjGGRPXC0KG9EBDggkA/J/T2d8G82YOhUKiQlV2MU8kZ2LE/GZeutJ7QPSTAFZ++MxMSsQhvrdqKvUdSAfPbuH2S0yRnYy/hbOwl/d/dnOww8t7+CB8VCE8/J4QP80e/4QF4avk9qK5U4PrVfJyJS8O+zYlIP3/DhJZz7maKC8px9UI2AsO9ILOQGmr+OHcNfIeqEfgOFUO/NrJDVdcobFLVZBjvF+qOkUMCEBrkCscetjCr+QWlUqmRk1+KpPNZiDmUgpOn65KUNucO1Yih/njzlUnQaLRYvPwvnKmpsK4mHCq+Q4U73qFqFJnuvSmVijFwbDAGjwuGf5gnujnZ6m96UKvUKMgpQerpa4jblYyju5OgUqj4DlUjbXyHin13RppVgMHjQ/DGV/Pw11d78eP7W/kOVWNt7XiHijtUjVDrUI3c9iTkpEMlzDNkjFBdSwjQ2YXqhENFCdApR4nox+5QCdt087A5T63iEBAOkEilgbePA0ZPDEGf/l5wdbeHzLxO6F5UUI7U5Os4eOQCDh9KharecTXEh4fKWvh6quuJKGdNCsfCucNQrVDhkWW/4eqNIv1zVDkgKocYtVatIbinHCCxgnKejBGvs7YRThar6Jscq7PZO9gNI2ZEIHSIP9x8HWFRT+xeXlyBq6nZSNh/Hnv/Oob82mSr1BcQ6QARzhPjeTAL2qkcUa0hVCfF63c+lnYmGOc1kUib1cEQK3Sv+X8Hl0KpUGHmmA8huircEaUd16ZzRAGgz03M2K8V1kBLOk+Uk0U5VIyidLM6h+rERh7y43BahfS0XHzzWYz+7249OiN6UhjCI3vCq2cPDB7hj6iRAdBqtSgtrcLlS7mIi72EXXvPo7y8+hZHNuS5h4dh5sRwlJZXY/bSn1BYQhRK5JiU9KRMpCdl6v+2trPE8OkDEBEdip4hbgiK6IngSF/MXToFSrkKOdcKkByfhkObE3HmyIVbHJnDMeRgzDmMm9oHQX3ccZ5wqDjtG75D1Qh8h6rhHES/drxD1RSW1jKMmBSGQVG+8PV1hK2dZZ3QvVKBa9cKEH8iHbt2JeF6hXDrXi0T490lkzF8oC9y8kvx4JJ1KCdSAPMdKph8h6opRFIp+g4LwJBp4Qgc0BOOHt0gM9e91zUaDW7mlyHtbCbiY87iwMaTqCyt53DzHSq+Q4W6HarONp2wYe/LSDmbiSWj3hX04ztUfIeKw7nrqCxXYMumBGzZpLsLTyoVY/AQPwwdEYiAAGf4+zshMNAFD8+NgkKpxvXsm0g4m4Gd+87hYnou1r47C8G9XJB6OQcLXvtNJ7khcklx2gcJB1OQcDBF/7eDV3eMnDEAfYcFwKOXEwaMDkLEmN5Y+P5sVFXIkZWWizNHLmDfhmO4ysXunBrKSquQfikX/sGukFmYQVFN/HrltFtaZYfqiy++wIcffoicnByEhoZizZo1GDBgQKP9N2zYgDfffBNXr16Fr68v3n//fUyYMEH/vFarxfLly/Htt9+iuLgYgwcPxldffQVfX199H09PT1y7ds3guCtXrsSrr77KZHPtDlX0jsdRTST2rFQSO1SEKF1O7FAp5cI2rZL4FUII2kWUKJ1I7CkhxevCKVh3nshEoY3tUDHuRjX7DgvzrgvbzgkttK47XsgAL4yYFIbAQT3hVE/ortVqIRKJcPD0ZbywZpO+Pym2pPSmjGtFr8ud96PE9eQOHyleZ9yNInZORMSOV3tLuimVSTFkWj8MmtoPvfp5o5tzV0hqb3xQqpCfVYSU+DQc3XwSx3eehYphl9SYZJ/UblRjBZgbwprYkzU5p4h1h4UaS+0yMe9QMQq8GXdnaDE3o3C73s5dZJQv3v7gfvzz1R589/Z/hh2p65lK9mmE+N9ka0DYYoxQXSNrXKiuUlYjfvObd98O1Z9//oklS5Zg7dq1iIiIwKefforo6GhcuHABPXr0EPSPjY3F7NmzsXLlSkyaNAnr16/HtGnTkJCQgN69dUU7P/jgA6xevRo//fQTvLy88OabbyI6Ohrnz5+HhYWF/lhvv/02FixYoP+7c2ehY8Th3Cln46/gbPwVlAZ2BQB4u9tj/PDemDWpHwAtln/b/qunc9hQKVTY/2cs9v8Zq2/zC/fGiFmDETI0AC4+DhgxMxIj7x+k0+UVlePquSyc3JuMfX/GoTC72HTGc1qVuCOXUFEhx5hZA4UOFadd0+I7VBEREejfvz8+/1yXzEyj0cDNzQ3PPvssuVt0//33o6KiAlu3btW3DRw4EGFhYVi7di20Wi2cnZ3xwgsv4MUXXwQAlJSUwMHBAevWrcOsWbMA6HaoFi9ejMWLFzPZKZfLIZfXCYpKS0vh5ubGd6hq+/EdqpqxwuPVOlS19At2x6fL7sPOYyl487ud+na+Q4W7dodK15Foq/eL3qabNUbcF4mICX3g3dsddt06QyTW6fIU1UpkX8lDcuxF7P/nOM7FXuI7VLg7d6gAYNFL4zHpnnC8et9qwxsb+A5Vu96halGHSqFQwNLSEn///TemTZumb3/44YdRXFyMTZs2Cca4u7tjyZIlBo7Q8uXLsXHjRpw5cwbp6eno2bMnEhMTERYWpu8zbNgwhIWF4bPPPgOgc6iqq6uhVCrh7u6OBx54AM8//zyk1MUEYMWKFXjrrbcE7ZN3z0cV4VCVK8wFbVVKIjcVIVRXNrtQvXlzU7EK2ht3qIi+rHmojHEIGHNE0eJ1xpxJlJNA8NORV9G1syUGLf5cX19QZSHsx3wDALl+1FgjHFKitqKpnFTyi4XK1UQ5NozOmFE5ohgdqlvliBKLxQgfG4JhMyMRGOkHR4/uMKsVu6s1KMopxsWEKzi29RQObjiGqvJq2omh5m1uoTolIifE68xic0K8To41Y8zBxCrSNiKnE6v4mjy3Bm3WnS3w956XceFcFhbP+bruiRt5wqHGiPBZHSpjclMxi9fZnCdjxOv1HarjW++ykF9BQQHUajUcHBwM2h0cHJCamkqOycnJIfvn5OTon69ta6wPADz33HPo27cvunbtitjYWCxduhTZ2dn4+OOPyXmXLl2KJUuW6P+u3aHicO6ENZuO4t1HxmHRPUOw6p+DpjaH0wbRaDQ4sfM0Tuw8rXdsXHydMGrOEPQdHQKPQFdETuqLQZPD8fzaBagsrULmhRtI3H8Oe9YfRdbFbNOeAOeOKS+rRtqFbPgFuaKTpQxVlY38OuW0K+7au/zqO0chISGQyWR44oknsHLlSpibC3eXzM3NyXYO507YFp+CF2cMw/Qhwdyh4jBz/VI2fl7xF35e8RdEUjNYWMoQdW8EBk/pB99wb/iGe8N/gA9mvzIVSoUKeZmFSDl+EYf/iUf8jtPQsGaO55ic2H3n4RvgjA1HXseZ+HScOHwR+37Yi9LCclObxrlDWtSh6tatGyQSCXJzcw3ac3Nz4ejoSI5xdHS8Zf/af3Nzc+Hk5GTQp34IsCERERFQqVS4evUqevXqdSenw+HcFut2n8Tie4fgkTH9sC7mpKnN4bRDqisV2PPrYez59TAAXfgnYKAPRsyMRHCUP5x7OmDU7CiMfmAItFotSgrKkJ6UgRM7T2Pv+iMozis18RlwKBa+MgGTpveDVquFRq1B30gfhA/yxRMvT4C8Vk937BIObTyJ5OPppjaXw0iLOlQymQzh4eHYu3evXkOl0Wiwd+9eLFy4kBwTGRmJvXv3GmioYmJiEBkZCQDw8vKCo6Mj9u7dq3egSktLcfz4cTz11FON2nL69GmIxWLyzsJbIRVpIBMLtQdmEmGbSi2M8yqJWLJaIvwVqSH0CFpSo0BI3ohQN1XXjjoeWTeOkmlQ5cyoeQGIGMcz20O2ERoRCXE8DZFokFhWLaVJEAnnEBFzULogh6NF2Hl0N54cH4FHR4Zjx4rduD6mq6Bfc68/89pTa6Um1pR6jag1UBOLSq0p9bpR68yosSHLvRCvOZlwktAKaUHow4jzIKWnlLyOmIPuKKQxzdP5Iyk4f6QuJ1YXpy4YOXsw+keHwTvYHX1GBKHvyN54/P05kFcpcONyLpKOpGLf70eRGp9mcDiNkrFIL6nTIrRWxGupFRFrSr2WFFRNQmIO+jpgq4VIfyBQx6O0g8JuJDVjO9ta4pPfnoCrRzdkZxZiyey1KC4sh1QqxoDh/hg0Kgj+YW5w9XGEV6ArJj86Qpc8tqAc6eevI/5AKg5sTkB5QRmTfeQakGtKnBvxPqIkd6CuA+J9Ts0rIl8joh9xPCJPsv7tRh63FWjxkN+SJUvw8MMPo1+/fhgwYAA+/fRTVFRUYN68eQCAuXPnwsXFBStXrgQALFq0CMOGDcOqVaswceJE/PHHHzh58iS++eYbALoP2sWLF+Pdd9+Fr6+vPm2Cs7Oz3mmLi4vD8ePHMWLECHTu3BlxcXF4/vnn8eCDD6JLly4tfcocjp5tG05g+tzBGD05DD9VZ5jaHM5dSHFeKf79bAf+/UyXpkMsFmPAhD4Ycm8EAiJ84NbLGd7B7pj61FioVRoUZd/ExYR0xG4+hQN/HIWimut3WoOBw/3x2kezYSaTYOc/J/DZG//qn1OpNIjdcx6xO5P0bU4e9hg5pQ/CBvnCw8cB/Yb5o//wADy9fJrOUb5agKTjl3FwcwJSTl01wRlxGtIqiT0///xzfWLPsLAwrF69GhEREQCA4cOHw9PTE+vWrdP337BhA9544w19Ys8PPviATOz5zTffoLi4GFFRUfjyyy/h5+cHAEhISMDTTz+N1NRUyOVyeHl54aGHHsKSJUuYdVK1iT3viZkHucxa8HyZkrjLj0ibQJWoocrRqIgUCRrizj8RY+kZKpUCfTegoIn5bkDqzr/GxjOXnjHmLjWqH3lHH3X3GWPpFOq2f6KfVqZ7jaVSMTYeW4bionKM+PAHwj5BUyN3VFLnxtaPNT0F85o2czka6s5J6q5Bo1IpMJaeuWvKwjQY6+bvjFGzBqHPiCC4+TvDsnMnfSmlipJKXDufhYQ9SYj59RCyL+cSh6N2/YxIpWBMOZq2lEaA2oUlWLx8KqLvCYdSocb/vbAexw9eYL+7teYrWiqTYtDoIESOCUKvEDd0d7aDtOYuSI1ag6L8UlxOykL83nM4sOkUKsuqjbwbkDFFArl+bHcDknf5MfYjUynUKz1zbPuyuyttQnuGO1RN9+MOVeP9ah0qAHjp/6Zj5MRQPPHNv4i7ZLhLxR0qcIdK11HY1owOVcM5OllbYOiMgRg4Pgx+/Xqiq6MdxDVfqkq5EjnX8nE+7iIO/hWHEztPc4cKd+ZQ2XW1wic/Pw4nt67IupqP5x/8BmU1BdJv16EyQKF787v5OGDEtL4IHewHdx8HWNnUOcrVlQpcT89D0vHL2L/xFC6drSkAzh2qFoM7VI1Q61DN3PsQFObCPFSlRB6qCjI3lfBiqyYcLyrZJ+VQwZjcVJSTxZhfirWIsm5u6phtyCEgvsDJ3EqseZTIBJZ1x7O0tsCG+GW4VlCMqe//JOzb0BbmZKstX4Sazk3Fun7N66QyO0qs/QjNiZbSFBmR7LNNJd1swhkLGuSLETMGovcgPzh59YDMwgwikQgajRYlBaW4fDYDJ3afxb6/4lBWVEGvFYUxxZZZnbFWyMtEnQepv6q5DgZH98YrH82C1EyCbeuP4Yvl/wr7snAbOaJkFlIMHhmIyBEB8AtyQTcHG0hr+qrVGhTll+HSues4tj8Fh3Ymo7pK9+Egol5L4tyMyk1F5vEiNhKoBMisualkdQ5V3A7uULUZuEPVcCzRjztUjbY1/HJ9+9t56DfED/d//BtSr+cL+9e3hTtU3KFqhJZ0qOoadf3sneww8v5I9BvVG1693dC5i1W93Q85rl/KwdnDKdj/ZxwunrrSuNEd0KF68YOZGDmlDxRyFd5e+DMSDtB5F5kwIukmRCJ4+jhgxIQQhPb3gptXN1hametfx6oKObKuFuDsscvYvyUR6SnZBmPvdF7uUHEM4A5Vw7FEP+5QNdrW8Mu1m6MNfj6wFMkZuZjz2e/C/vVt4Q4Vd6gaoTUdKgO0WkilYgyc0AeDpoQjYEBPdHfpqtfwqFVqFNy4iQsn0xG76QQObzwJVW31gQ7kUNn36IxVvz0JB5cuuJaWixdnf4Xy0mo6XM2KkQ5VQyykEkRF98bA4f7wC3KBfY/O+kLeapUaBbmluJSUheP7U3Bo51koquveE9yhujXcoWqEWofqof2zUSWzFTxfrhJqo8oIh4rSUFUTGipaV0XU/KMcKlJXRbSRuipBE7uGqpGbg5jLqZB6KaKfnFUbRR2PtRadEQ4B8WVN6SM+3PQsgnydMP2pb5Bbc9tzqYfwA4V0NMk1NWb92LRq1BpIqPp+lK7KCCcVpP7KGL0Uo+aJUWtllK6KUWvV7LoqRufpdvRNnoHOGDkjAqFRveDq44hO1nW7H+UllbiWegMJB1Kw7+945GYW6sdqFY38Grtj+xgdJVbH6zbDewAwbFIoXlw1GxKpBJt+OISv37qDwseMei5mfRNZAkbY1jPQGSPHBiG0rwdc3ezRyVJWd9NChRyZ1wpxOuEq9u1IwrUrhjvsIqJsFwVpC+l43XnpmdqxKmU1Yndxh6rNwB2qpvtxh6qmjdGh6jHBH+s+mov4M1ex5N1/AHCHqrE27lChXThUDbHsYonh0/phwJhg+IS4o0v3zhDXODEKuRI5GQU4f/wyDv0bj4T954Xz3LF9pnWoXl3zIIZOCIW8WokV877FmdhLwr4smMihaji2k6UMw8cEYeAgH/j2ckJXe2tIatZYpVIjP68UF87fQNyhCzgSkwwVVeCcxZa7zKG6a0vPcDhtjbRr+biaVYj+IZ6wtjJHeQURR+Vw2jGVpdXY/vMRbP/5iL4tdLAfhk3rh6CInnB07wY3H0eMezAKGo0GxfmluHw2E8d3ncGBf+JRXlxpQutvn+7Odli1YSG6O9khPeUGXpz5OapK2tc5UFRVKrBjy2ns2HJa3+Yf5IwRIwIR0tcdLq72GDYqEMNHB0H71j2oKKtGxpV8JManY+/2s7ieUdj4we9i+A5VI/Adqqb78R2qmjbGHarSIHv07e2O1cvvQ8yRVLz12Ta+Q9VIG9+hQrvcoSJ3ihp8xXR36YKR9/ZD+Mje8AxwhrVdPbF7hRyZl3Jw5nAq9m84hvRz1+983hbeoRp9T18s+t99kEjE+OfbA/j+vW26J6hriJU2skMFgNa+1XsvWHe2wPDRQYiI9EHPXo7o2s1avxupVKqRn1OC1OQsxO5PQdzBVCi1lMj97tqh4g5VI9Q6VPMOzITcQviClCosBG3lSqHzRAnVqxmF6qy5qUD0E5E5p4RtEkahOukQNbLBwix0bwWHgBaqU84Y5STcuSidcjBqPzx+3vUiunbvjGkD38HVEUJnXUx8HrM6ueQaUGtFrCntULVtJ5V0qJTERUQ5RYyCdmMcJVahOnNuKgKjhOptIEeUVCbF4HHBiBwXAv8wD3RztNWLpFUqNQqyi5F6+hqO7kzCsd1JUFURFz71WrKK16lQHuVQ1Xst3/z2UURGh6C6UoE353yBc8cv33oOCtK5o5wnI/JkMYfZiO8aKkcU8frW7xfc2xUjhvkjJNgNLs5dYG4u1TvLZeXVuHqtACcTryFm3zlk55RAUkW8z6nfFsTrcavcVCplNWL3LOcOVVuBO1QNxwrbuEN1i7ZbOFTDJwTj1ffux3+/HsWbZ+KFtnCHijtUQIdxqKixPr1dMXxqX4QO9IGLV3dY1BNJlxVX4mrqDZw6kIp9/55AQXZxqzlUju72+OjfRbB3sMWlpEy8PO1jVFc2eCN2YIeqrp9u/WxtOmHUiABE9POGj3cPdLGzgrhmbZVKFXJzSnH+/HUcOXwBcbEXodFwh+quhDtUDccK27hDdYu2WzhUAPDXoddgbmGG8Dc/F9rCHSruUAEd2qFqSGc7Swyf2hcDhgfAO9AFdt06QyzWOVhKuQrZ1/KRfCwNhzadwtlaQXgzO1TR9w3AsytnQiwW44/Pd+PnD7fT1xB3qMgC6rX0DXXHsCG9EBzkCmdHO4NdrNLSKlxNz8fJE+nYu+cc8nJLdcfjDlX7hjtUDccK27hDdYu2Jhyq6Q8PxoIXxuPT7Yfx3f6ThrZwh4o7VAB3qBqjRkvXd5g/hk7qg8AB3nB07Qozc91nqEatwc38UqQlZSJudxIObU5AVXnNh9UdOFRisRjLvn0UA0YGoqpCjtcf+AqpiVd1T3KH6rYdKgPzqlTo2tUKI0cFoX+EN3r2dICtrWWds6xUIyenGOfOXcfhgxdw4sRlffo47lC1I2odqicOTYfcgkjsqewkaKMcqnIyAaiwn5xRqK4mnCctVfOPSvapZEv22XqidKqteR0COlklo0NFOgmMomqG+nT/Jb4NBTSIeuMrg3bqDUmuaTPX/GtTTiprHUBSlG6EUJ3ox+ootSmhOuUAUWEsZkepbSfd1EolcHTtgtFT+qBPpA88fHrAqrOFQUbwjPR8nIlLw/7Np3EtrV4B6Arirrya18PFuzs+2vQC7Lp1RurJK3jlnlUGiS5JqHWm1oBaK1aHitEZYxWg0+J1RufJCBE5WY9PKkJEqAdGDOyF4F7OcO5hC5mZRP9aFpdWIS0zHydOXUHMoVQUFJXrx9Z+5qiU1YiN4Q5Vm4E7VA36cYeqxpbmc6gee2UCpj86FG/+vgubTtTl5OEOFXeoAO5QNd6PLeO2zFKKqNG9MWhkAPx6u8K+h01dLiWlGnnZxUg9nYGjW07h2K5kaOpnzlerMWneUDz5zn0QiUT47aNtWP/hVqEtFNyhMtqhakj37tYYOzgAESGe6OneDXad64pAKxQq3MgrQVLKDRw8lIqTiVe5Q9XW4A5Vg37coaqxpfkcKqlUjH/PvIObFVUY/da3+nbuUHGHCuAOVeP92BwqEIfr1csJI6f0QXB/Lzh7doN5TQForVaL0psVuJJyA6f2n8fQqX3h09sNlWXVWDpzNS6dyaCvKwruUDW7Q6VtYJ5YDEQFemHYQF/09neBY3cbyGS6ddJotMjIzIGXpzNP7MnhdBRUKg12JF7A5H4BGNzLA0cvXDO1SRzOXc3FpCxcTMrS/23TxRIjx/dG/xGB8A50QWikL8IG+0Gr1SLlZDpembG6riYhp82g0QCxJ9MRezJd3+bY3QbRUf7o18cT9l2EGxmtAXeomsBMrIFGJPxVKCOUw1KRcDnNxMJfNVSbUiz8hSCRCOfVkKJP4pcs0Ub9uG3o+QP0HRVUP+oXIAAQ+dugpfL9EW0aSl+rZrNHS+l6xcQ6UOdHJbEj14E4OQ0xlmij+n35UQwm/u6PpZOG48Ht3wMAivyIhK7Ua0eeL2Mb8/pRbcS6UJXpybWiElMS1y9x7zR1POraoHcI2F4P6pe/iNgp0lL3dlPiZgpiR4m8VxyMOyIUpKCdeNHJcyN2B6lzI9vYXsuGyT4bs4VsI64XEXE8LbHOGkvDL9piuRr//ZOA//5J0B1aLMazy6Zg/L39cC7uEpRl9fRVlM1UYlRqnY1ZA2qXs5EC1sLjEdcB63uQsIV6L0BN9KNeI+o0qPc0ZQrxlpHbGDZek5dj7aGTwKGTUCuqhQNaAcbbETgcTktQWa3A8VNX4ObcBX7eDqY2h8Pp0Gg0Gny2YiMqy6oQ/WCUqc3htDO4Q8XhmJiPvtwNAHjh6TEmtoTD4QDA/r/jYdPVGmHDAkxtCqcdwUN+TWAuUpKJgeRi4dLJJMI9TYVG2CYlQnlSYqxaTQjLiVCemjHkBxVj6IxqYwwXGjteS4X3qLAdFb2gdtqJLWXSFjIMSByPOfRBHU94QMv0YlSmFyPldAYCwtzhJQeKWEOm5BpQYTHWsCfjvOTrQa1zK4RRqRA4FeZgfd0osa+YumCoTNrCJjoMSIVIqBedWCtqDuKC1lIxEiKWIqLew1ToiLFNS9giIjUArHOQb9Y7Ph4ZGpQJP8t/WLkJ4x8eikdXTMei6Pd1/SqrhHNQYm7WNaCuU+r6Y10rMmQqbBIR85IRYsJmIhpM3jxBhQap9y/dTzgHOZaKwGoaP0ZrwHeoOJw2wGdvbwQAPLdsmknt4HA4QGVpNS6duQbfYHdY21qa2hxOO4E7VBxOG+BaWh6uXc5Dn4E9YWMhTKvB4XBalx//bxNEYhEefXOaqU3htBO4Q8XhtBG+/N8WiEQivHHPKFObwuF0eM4cuYCSonIMn9bP1KZw2glcQ9UEZmI11IypD2REm5TQX5gReikpEYtXEmPFVCoFqnAkpUshdFV0SgJCZ0DoryjNEkDfFW5MKgV2rRXrWEYNEDkHq+6G0slQiS7r2pLiLiM/uxhjQ/ywbP0uKOo9R6e3ELZRP5GYUyQw96PW6s51Wsy6NGKdqfQFItYkiqzaFOrWeOIipzRypGaHmoPUXzGmV2Cs+UfrkdhSWzR7KgVWnRu5VmxjKfuo86DWQOPUHQCw478EzJo/FEMeGYFDX24XjmXW67WhdBLkHMImuo2ag02nSmraSF2VcCidcoFo4xoqDodTyw+rdkIqEWPJ5KGmNoXD6fD88s0BqNUaPPjEcFObwmkHcIeKw2lDHNx+FjfLq3DvwN6mNoXD6fCoFGoknboKN89u6ObSxdTmcNo43KHicNoY3+45DguZGRaMGWBqUzicDs83H+8CADz+v1kmtoTT1uEaqiaQiZXQknmoiNIzjLoqBanJEgZ9lYReSkWl+ae0UazlaFhzClF6qUYqYzR3bivmkijNrAGiypWQ5U8Yy8yQ9RMIHcX2r+PxbPQgPDI0HP+sOQoAKAg2E9piTOkesiSPcCytg2LUSzFr3xh1aaReStiNucyMETnFyDmMOR55IkTRaGqdGXNTUaVnyBxCjWgjhYONyU3FmG+J0gqROigjclNR+r96xX0vp+WiML8MAyf0Eb52bT0/F6U3I8u9MOrcyPxrjKVniPcg+bl7C21U022iRp9rDfgOFYfTBtm4NRGdrS0wfmywqU3hcDo8/66Pg8zcDFMe53fgchqHO1QcThvk+58PQ6lUY/5cXk+MwzE1//waB6VChRnPjTO1KZw2DHeoOJw2iEqlwZ7952Hf1RoR/bxMbQ6H0+E5sfssurt2hYe/s6lN4bRRuIaqCcxEGmiIJBhmRJuM0FXJiJp/VG4qqpYflZtKQoxVM+ZBovJLkboqMjeVcCiZB+k2+rK3UTmwKC0E21h2DRCbFkfLmAOHrO9HzGGRVQIA+ObNfzF2VBCenT8cW9f+StgsnIK1zTitFZXjTNhEaStMpUtj1i1R4gvW+mpkPb47z01F5vdhzVdF1vdjreXHqJ2hoHQ3VL1FVs0Oq37NiNp21Gcldbxv/7cZkRP74PEP5uCNB77UdSsrJ+ZgPA9q7VsjPxelGWPMz0W/pxlr+ZFaK+HhjMpNVXs8wszWgO9QcThtlKpKBU4cvggXj24IcnEwtTkcTocm51ohblzNR9hgP0il/KuTI4RfFRxOG2ZNTdHkZbwcDYdjcv5YvRsSqQSzFkWb2hROG4SH/JrATKSGSKwUtMuJUJ45EfKrZkylICf2NOnQIBHyI9qoMApdjkbYRJaeIfvRYQDjypoYMdaI8B65DkQIjAzlGREuJLe365WcKcopxfnEawjq4wEPSxtcLyzVP6cmaiizrx9jySFj0liwhvcoW1hLiZBlZhhv06fCK0akXCDL0TCHH1lDklQso5H8JQJj2MI6WiKMKiLmJcvRUCV+yNQHJirFQrzmWrL8DmGKvS1i9qbimSoFJj48FL+si4W2pFTQj1wDCtZ1aUPpJMjwKGELHcJmC2sbFxrUGvzb2vAdKg6njbNmxSYAwLL7R5vYEg6HczgmGbZdrRDU18PUpnDaGNyh4nDaONfScnE5pxADfN1ha2lhanM4nA7Nt6t2AgDmL+ZhP44h3KHicNoB/9uwHyIR8Pp9I01tCofToSm9WYkrF3LgH+KKTtb8Bw6nDq6hagJzkRIixrQJZkQMm9JLyYgUCWZUm4YoW0PE58WUVoAsycGYSoHUIgm7UbfeA/St2K2RSqFVNEDMeilCh0LpD6jXhNBRXN10GXn3l2JUiA8cUpVQqTTI7W8uHMt8bqxtbNcC3caYnoJoI0uskOkVhPOyl4+h0o0QFwzjbfUgNI/krefNnEqB1EExy6qaOZVCc5diIXVubGkimMsNMZZO0ZrVLcy6tfvw9qdzMG/FDHz5YoOUJiZbA2M0aMImWi/Fdh2QqRlY0yuwpk0gUzPUlJ5hvP6bG75DxeG0E77+ej+kUgmeeorf8cfhmJLjhy+ivKwKo2cPNrUpnDYEd6g4nHbCgYOpKC6uxITxIaY2hcPp8MRsOQ0rW0tETupralM4bQTuUHE47Yhff4uFubkZHnxwkKlN4XA6NOu+2AuNWoOH37jX1KZw2ghcQ9UEZiI1tERAlso5ZS4h2jREbiqRmaCNzE1FaDLMiH5KQldF5abSkPmX2MrRkGUSGis9w6q7YdQ3kWVSmMdS+hxiLKsGiDielor5k1IXxnxGxByyvDIAwNa1+zH/kSGYOaM/Vv3vlPB4rOvcCmtPrxXRz0S6NNacYq2Sm8oYWPNVUYtPQfZjyy9F5qZizrtlRF4msuwKW0kuUr9GloAxXIPqSiVSTqYjcEBPdHXvgeJ83XtUU1UlHNoaa2Ci/Fy0XoroR6ZQY9OvkfmqSF1V48+1BnyHisNpZ2z+4xisO3fCjP69TW0Kh9Oh+f6tfwEAj62YbmJLOG0B7lBxOO2Mn1bvgVKhwsLRkaY2hcPp0KScSEdxfhmiJnMdFYc7VBxOu0Oj0WDftjPobmONob28TG0Oh9Oh2frjQZh3kiF6Dr/jr6PDNVRNYCZSkbX8qrXCpZMSeaOkZC0/oa5KRuigzNRseikpkcNKJSH0JkSblqrXROVzInUuwqGNtTO3kfmz2PoxtxmjASL1Aqw5kxi1OFRbg3nX/m8rRk/tg6UThiHu1BV9u9JSOJS5xBdrLURqXZjzUBH9TKRLY157Zv0Lo/6KypFjTH0/4oBkHi9qXka9FHu+Kka9jzF5mZjnaObadlS/rnb46+c4zH5hAmYumYBdO84BWZV3PEerrIEx+bmIDxNKa0VpskDV7KTeH8xaK6JfbTdqLVoBvkPF4bRDqioVOHzuCty626G3u4OpzeFwOiwqlQanY9Pg5NYVTu72pjaHY0K4Q8XhtFPe/WMPAOC1+3miTw7HlHzz3lYAwONLJ5rYEo4p4SG/JjCDBmKRMERHlZ6xIEKDcrFwiRVEeoVqNZFKgQjlyYnbg6VEigQJkXKBTqVAhAGpW9HJsi7Edi2aPxxHRFLJcAOZIoEK0RkVsmILfWpZt7fJ8ifU9jax1X60EMnJWejd2xW9C8yRnV2MnMhOxLyEfUakUiDLEBGfJCJqDcg5qFvFKfuaN4yqJdaeCmnQaQmIfkQ4ni5R0/LlaMjwHpkvhLiuqPAea1kdMlxoRCoFKqxIpjQwIixGpVxgfF9qO8kAABk3SpCXXYx+Q3pBLBZD08BGo9aAyjdgzBqwplJgvP6ocBy5VUOWmaHKxzCG925RosZUaRO4Q9VBcbG0xcFJzxq0fXT6ED5PihX0tTKT4Zk+AzHO2w8u1p1RqVTidF4Ovj95CnFZmc1ql2e3Lhjk54Eglx4IdHGAd/eukErEWLP9KL7ZE9+scwGAjbUFhvTxRoC7A3p5O8DXszsszM1w4uw1LFn2V6PjHrgvAk/MG2bQNuuhr5CbW9rsNt6Kjz/ajh/WPY4lL47HSy/83qpzczicOjasO4qFr03CjMXj8dfH20xtDscEtErI74svvoCnpycsLCwQERGB+PhbfzFu2LAB/v7+sLCwQHBwMLZv327wvFarxbJly+Dk5IROnTph9OjRuHTpkkGfoqIizJkzBzY2NrCzs8P8+fNRXl7e7OfW3qlQKvD35ST8fTkJKTfzBM/bW1hiy5S5eKbvQFibybDnajou3SzEcHcv/HLvDMwNDWtWe2YNDMHrU0ZgWngQ/By7QUoI6ZuTsF4uePOJcZgxvg+CeznDwly4U0iRfjUfO2KSsCMmCVVViha18VZkXCvElfR8hPXxhI0Nr3zP4ZiKrX/FQyFXYupTY01tCsdEtLhD9eeff2LJkiVYvnw5EhISEBoaiujoaOTlCb+8ASA2NhazZ8/G/PnzkZiYiGnTpmHatGlITk7W9/nggw+wevVqrF27FsePH4eVlRWio6NRXV2t7zNnzhycO3cOMTEx2Lp1Kw4dOoTHH3+8pU+33XFTXoUXY7fhxdht2JuVJnh+ZVQ0etp1xZGsaxi2/jssjNmC+zf9iUe3/wuNVos3hw6Hf7duzWbPpdxC/HDwJF7+fTsmrVqHTQnnm+3YFEUllfh37xm8//VuzH/lV3zwdQzTuGMn0vHeJzvw3ic7UFIizIzcmqz+dCdEImDR8+NNageH09GJ3Z8Ceyc7+IR5mtoUjglo8ZDfxx9/jAULFmDevHkAgLVr12Lbtm344Ycf8Oqrrwr6f/bZZxg3bhxeeuklAMA777yDmJgYfP7551i7di20Wi0+/fRTvPHGG5g6dSoA4Oeff4aDgwM2btyIWbNmISUlBTt37sSJEyfQr18/AMCaNWswYcIEfPTRR3B2dma230ykgpjSS4kIvRRRUobSWkmJNhmhq5IRdT+o0jMKEZHCgdBLqerpoMT6/2shbtC3VovkY2uPaA9fqDQavHJoJ6q1Sr2m4cD1dPyTcg73BwXjyf4DsGhX3RY3rc9h09P8nZBs8Lem9j5YEQzcf+b0Ck3ogpLSs5GUng2xUjePp2c3/Xyk5onSdNR2E9ezixpL3lZPHI9sEzaaFVYAAFIPXEDujZsYMsQP5vv3oFpleC0Zp2ljTR3BOJYqV8JceqbldWmgdkRJLQ5jP2PK0RiTSoEqtUPp8Jo7lQJ1Ho1oLYUHZNT7GFOKhUhFQ75GVCkWclkM1+XbVbswLDoYT7w/By9P/rBurFL4fWFcKgXG648sycOm4TMqnQSl8aK0ftTxSK0V22dJnYbqLkyboFAocOrUKYwePbpuQrEYo0ePRlxcHDkmLi7OoD8AREdH6/tfuXIFOTk5Bn1sbW0RERGh7xMXFwc7Ozu9MwUAo0ePhlgsxvHjx8l55XI5SktLDR4dnWgPPwDAqbwsXC8XrsemiykAgFGe3pBSX2KcVuO7VbsgkUrw0oShpjaFw+mwFOaXIeNCNoIifSGzYJMPcO4eWvRbsKCgAGq1Gg4OhnlyHBwckJOTQ47Jycm5Zf/af5vq06NHD4PnpVIpunbt2ui8K1euhK2trf7h5ubGeJZ3L0FddWt8toBes6TcXACAlUwGT1u71jKLQ3AkJhnFReW4NzzI1KZwOB2aX97bBIlEjIdenWpqUzitDN9WqGHp0qUoKSnRPzIzm/futfaIm7UtAOBGBb1bV65UoFQu1/W1tW01uzg0v3+zH+ZmUjw9IsLUpnA4HZYjm06hsqwK0Q9FmdoUTivTohqqbt26QSKRILdmJ6OW3NxcODo6kmMcHR1v2b/239zcXDg5ORn0CQsL0/dpKHpXqVQoKipqdF5zc3OYm5sL2s1EalJDRWmjzIk8VOZi4bavOVF6Rk60SYmxVG4qBVV6RiP0levnphLX/l8EiBrmmKrRm1jJdDlWKtUKWrMk0aJSqYCNuTmszWXQ1hyHPc8TW1tte31JGZVnhJCc0bmQblUSpfZf0e3Zpx9Uo0+gNWOULojIP0TlAaN0Iw3Y9NsxzF0cjYejwvH9f3WhbQXh61LaGXr9iLHGlK1h1KVRebyocirNrUsj15lZt8SYz4jI6cZaJqVN5aYiS88w5qtiLA3CXIqF0uxQa8+am4rS4FAaKkqbZ2eD/ZsTMXHOIPSZGI7TRy9Bm1dADDYCVt2SMfm5GHNEkXkLGfNVUZ911PGo657SVZk6D1WL7lDJZDKEh4dj7969+jaNRoO9e/ciMjKSHBMZGWnQHwBiYmL0/b28vODo6GjQp7S0FMePH9f3iYyMRHFxMU6dOqXvs2/fPmg0GkRE8F/vnLuXPw6eRudO5pge1dvUpnA4HZYf3tsKjUaLea9MMrUpnFakxUN+S5YswbfffouffvoJKSkpeOqpp1BRUaG/62/u3LlYunSpvv+iRYuwc+dOrFq1CqmpqVixYgVOnjyJhQsXAtBll128eDHeffddbN68GUlJSZg7dy6cnZ0xbdo0AEBAQADGjRuHBQsWID4+HkePHsXChQsxa9as27rDr6NTodTlV7KUyhrtY2mme65MabpcTJw6vtxyFAqVCk9OHGRqUzicDktleTUuJWXCN8gV1jbC6gWcu5MWT5tw//33Iz8/H8uWLUNOTg7CwsKwc+dOvag8IyMD4npbs4MGDcL69evxxhtv4LXXXoOvry82btyI3r3rfnG//PLLqKiowOOPP47i4mJERUVh586dsLCoS2z422+/YeHChRg1ahTEYjGmT5+O1atX37b9urQJbKVnmNuI25fNiD1KGREGlIlZUykI+9VPpSCtt60qEaRN0L0eWRXFCLZ3hEvnzsKwIABrc3PY1IRJs8pL9O65lixTI2hiK+NSe0yx4XNa4dIwh+huFRqsjSZoQYfoyLuBa/+ViqCV1hyH8RZ/KozFmjKA2vLumliBPTuSMGFyH0zu7Iy4I5dwfbgVYYtwCjLtBLl+rCV+iOMZE0JshTAqWd6GCD+SaTGoNqoMThtKpQBQcUBGGNMrUKEjLfF5Z1QpFmNSKTRzKRZtZ0sAwA9r9uD97x7Fo8um4dO5a4T9mrscTXOvARnCZgy3Mr4HyUwjZLoLtnBh7dct9XnUGrRK6ZmFCxfqd5gacuDAAUHbfffdh/vuu6/R44lEIrz99tt4++23G+3TtWtXrF+//rZt5dRxrigX4939EdzViXw+uLvOKa5QKnCl5GZrmsa5BV99ugvRE0LxxLNjEHfkUtMDOBxOs3MmPh0lNyswfFwIPjW1MZxWgd/lx2mU3VkXAQDh3V3hbNVZ8PxU3wAAwN6rl6GifqVwTEJ1tQrHjl6Es0sX+AfxEDeHYyp2/HMSnazMMey+gaY2hdMKcIeK0yiXSgqwO/MipGIx3h88HuaSug3N4S5emNGrN9QaDb5MFCZLXTQgEleefQG/3zOzVWzt19MVZ1Y/jzOrn2+V+do6n36gq3/53Au8HA2HYyp+XbsfarUGDy6dZmpTOK1Aq4T82jNmIjUkpDaKTVdlQaRSkGuEy24uFrbJiTYppb8i0iZQpWfEamHaBBEAcYNYtLieBuqNk9vha2uPIS6eODRjAU7kZcHewhIRDu4Qi0RYEbsXqSX5BtIJrVqk1wYptRqB/knfj2gL6NEDy8eP1P/t3kV3z//MASEY7u+tb1+0bgsKyir0f4tqtC9KtdpQa8VwS/66pbP0t0R3qRGQBno74pt3Z+v7/PjfMcSevkLfel573HqlZ+g0AmwaIFKzw6itkJTq6gqWlVYh+dRV9A73hFsXW2QWlTSYV3g4DVWVo7l1UMzpMyjdjWl0aWQ5GtZb1Mm0DlRpHMZ0A0ZInsi0E6zpFchUCnde8oY5lQJzKRYj0gMwpqwgy7hQ11W911elVCPp1FWE9vdCN9euKLheTxrBbJ8Ra8CaToJ685N6KbZ0EmTpHsI+qh8lcqXLzFBrVU8EawL4DhXnlhTKKzFt94/4MikOFSolxrj5wr9LDxy6cQUPbPsT684lkONq9VV/nU+6rfmszWUIc3XSP7pa6QSeTradEermpH/IpIZvuiBXXWb8/46fu91TRLC3E4J9dA/XHnY6OyzN0dvXWf+ws7G87eOams/e2ggAeOue0bfuyOFwWoxvVu0EADyx8gETW8JpafgOFadJylUKfJB4CB8kHjJ8Qk7742ZiMSJcXHE2Lwdb0i7c1lzx17LQ651PDNokcmE/cYMsDYN8PVAlV+KrnXSNyFsRvuATSBTCnzQSpYl+5jQTWVcLcOViDgb4uaGLpQVuVlab2iQOp8ORnpqNwhs3MXBiH1Obwmlh+A5VB8fO3BIrw6dgZfgUjHD0a5Zj9nNygZWZDO/HHW6W4zWFuVSCPl4u+OVgAgrLKltlzsi+3nj9mXF4/ZlxsG3DeWa++L/NEAFYNnWUqU3hcDos/365GzJzM0x5gu8W383wHaomMIMGGkIvRWqjtMJSMXQeKkLzRLRR/WSMbbSuqi5MVpt7ykoqwz0eYQCAzMoiHMpPhZrQX2mIeDqVjwgA4rIz4PnVR7o/al12Rg2VVkXNI+xXX9tTrVUjfNkaiOXQicLqH49Vs8NY/qR2bE+P7pgwwjAbuVYk0mt6SA0QlUeJ1COx6m4oXYHha3cuMQM3CkoxKtAHdiVSyJW6a7mqm/BwrHmoyH6sWivmftRasZWyoWxmzu9DrDOVD0pElpShTo5RJ8Oam4qYlzU/Eqm/Ys1XReacImhLuamMKcVC6aWo14i6Jglb/lt3FPOWz8CM5ydi6x/xOvOKiomxbTw3lRH5uahcUuSWDqWXorR+pHZL26hNrQF3qDoo2VXFCN/xOqoVQieQ0zS/bIrHL5t0H4xtPTT4yd8H8dFTU7Bk5jCs/G1v0wM4HE6zc2LfeURGB8PDzwnXLmab2hxOC8BDfhzOXc7+xMsoKq3E5EGB5EYXh8Npeb59+18AwGPLp5nWEE6LwT9eOZwOwDdbj8HcTIoFE3mCQQ7HFORcK8SNq/noE9ULUin/6r0b4SG/JpCJ1FAT4gMZoY0yFwl1VdUiYUjNnNBfmYuF/eRkLT9hm5TIV0Xpr6gagkqiTUxoqERELh6qvh8AgKrlR2ijWHNTsaaJYR5rTG6lZtYAUbleyNxUVH4ksk1otH1SOfYlHcVzUwdjzog++Pt/e5A1ylo4lNKqsaWJIXNnaYjrgHjbNKKDYtRLMWvf2PRSDs62+Hm7YXLYn9bswe/fHjBo6x/lD79gV/gGucAnyAX2PWwAAA8N+T8U5NTL+cWsf7l1bcCewW4Ii+oFn2A3+IR6wNm7O8RiMT544jvs+yOWcQ7qS1z4gojEInR3tceA8WHw7esN375e8Ahyg8zcDDu+34uPF6zVdSTeXM998TgmPT7GoG2sTJgugMpv1vx5mag3sBF17FiPR9lnp7s+fv/uMF78v+m4/+Up+OXFdXd8vPaYn4vUNZHFUan8a4y1/Go+E6mXrzXgDhWH00H47694zJk3BBPv6YuvSy+a2pw2TVWlHIf3nAcApBN6l5c/vB/WrXh35wMvTMCg8WGtNl/UvQPw1KqHb3tcyrFLkFnIAABj5w5rbrPaPXs2JmDhG5MxcVYE7VBx2jXcoeJwOgi/fHcA982JxEPzh+HrT7hDdStKiiuxavlGAIBILdzFiY05h+vXCpB2/gYun7+BP2LfaFF7Uk9dQcaFbFxKvIrLZzOwZM0jCInq1WLz5VzJw8bPd+BS4lWkJV7B0OkRmPP69CbHxfxyCDG/6PLVcYeK5vCuZIye2gdBkb44F8eLl99NcIeqCaSNlJ5RggjHMZaeqWZNr0C0UekVqLQJVIkaaROpFGpRqoRbqVQYUNOIwllLhQKpMCARsqJCOMRSs4cLjSiT0johK7ZQHlV2hfV2anFFXWbUvVsSMX56f4z074l9KZcbdBQejowYMJePodrufK3I1Bvkrd1GhFH1/UR1/yf6fbzsP+HY2nH1XwMyLQFjOZB6IZINn8fomlQq/TS6cSJyLOut7OTt6GogbsspxG05pW8bPLV/7Yi6cyLL0bCGjqiUK9Sbn4AKE1HlgVhDTE2EW299PLZSLPVDYN9+shOjp/bBY/+bhedHvUt0ZoD1PKglNVk6iTsP79EpYqjPZ22j87cGXBnH4XQgvn5/B9RqDV4eP9TUpnA4HZLSm5W4ciEH/gN8YGEpM7U5nGaEO1QcTgeiulqBYwdS4NbVFmHuTqY2h8PpkPz0+R6IxWLMe+s+U5vCaUa4Q8XhdDDWvLMZWgBvTh5palM4nA7J8UMXUF5cgdFzhpjaFE4zwjVUTSCBFiKi9IwZIdgxo/oxaqPIUjZEOgRzIm2CQkLoudTCsQpmrZWwTa0W+t6UrgpopCQNqW+iyrMQ/agUBKx3NbOmPjAmHYIxGiDiFn8ReYv/neuvGkoXiosqkHghC317ucJfbYvM3GIAQIULY1oC5lQU1PEYX3NG7Zsx60Lp0gxKF4lqywgRGiXifQNAp5Wpr+eh9CqkPoc4OWJsXTmaelovY1IzsJaeYYWxRA2ltRIR85KlWKgSP2Tqg1YoxULZR11rxDW0589jmPbEKETeOxDHd57VTVFeIRzLugYUrOtionQSpNaJ+DwlSxWRmkDTlp7hO1QcTgfk/9bpRM6vPzKmiZ4cDqcl+Ol/m6BRazD31SmmNoXTTHCHisPpgGTmFuNSZj769HKFnbWFqc3hcDoc1ZUKpJxMh1egC+y6dza1OZxmgDtUHE4H5b1f9kIEYCnfpeJwTML3b+nq+81f3nSOL07bh2uomsBMpIGIiBtTOaeY9VJEiRo5UaKGPh6heaLso3JOETlEzIh+CiI+L6bygDRWeobSFFF9yZxEwm50qRNhG6XFYR5Laq3Yysc0d/4rSttD64IobQ+bJqHLuXJknbuEnEeKMSysJxwuVeOymzDzN2kza24qxnWhNHeUtIeag9TdEGtK5uy6lf5KVPd/soxFY1WmRQ00TVQ/MaMGiBzbwBiRiFmTZUxuKv36ieo0flri+mPNTUWXnmHMU9TcpVhInRv1QURcvNTa1zvfHi5d8NO+Vwye/ml1DH7/+gDQxRYpaYUoKarA0Hv6QWJpDr8AJ3R37QqNRov8zEKc2puMf9fsRG5GQcOTE857G2ugL2UU6g6fEHc4e9WUMlq4Dvv/OVFzbneujWp4PXdztEX/EQHwCXKBb6AzPHwdIJNJsfPfk/j0rU0NxtbZ/NzSSZg4o7/B02MHvq07D+qzuPazjno/tQLcoeJwOjDffBaD5R/MxFMvROPFxEOmNofDuSupqpTjyO5zAID0CzkGzxXll8LO3hrDJobhanImjm1LhLmlOXqFe2Pa02MRPXco3p79GRL2nWs2e1q9lFF0bzzx2uTbHnc+KRNm5lJALMLYiWHNb1gzwx0qDqcDc/RAKm4WlWPMxFCIzxwif6xzmo8l792HMff2Q8zfJ/Dxy3+2+HxjZg/CC18+ityMAjzc+6UWn49DU3qzEqte/4d87tzJq/DwdUR+djGeinhd325hZY7FX87HiJmRWLruGcwLeRHlxZXNYk9tKaO0s5m4nJyJ5z99CCGD/Jrl2BQ5WTex6ddYpKXcQNr5Gxg6LhiznxjR5Lg9W89gz9Yz0JpJuEPF4XDaPr98exDPvTIRzwyPxJp9caY2p13wwOPD0X+I8Ato+RcPQaXUhYbSzt/AF8sNS9TUhstUKiJ8dAv6jwrCA4ujgZpIhrufIwDgwZcmYvKjdTXznh/3Hj2f8vbm6+poh2V/Pq//u5tLFwBA5KRweB55R9eoBVYv/B5piVdu69gcQ758exOc3O0RPsQPTl7dkX0lHwBQXSHHJ09+hwHjwmBjb40B48Kw74/YZplzw5rdzXIcVo7tPY+4gxf0fw8eHdSq87cW3KFqAgm0kEL4YVRNFGySkXmo2HJTmRN5qMzFQl2VOZFzylxD5KYidBoyIq+VkshNZUa0KQldFZWbCqBr4FG1/Og2YROpxSH1L4QxjFocY7RWxmiAyBxWZN4tStdCHI+1vp+87nrbvv4Y5j8zCnMH9sHnMYYOFXPOLkr+wqwjI9qITybmvGBkqhw2XZq23gtXmzuI0qU5uXVFQIiboN0n0EX/f4VcJVh73yBXAMDuv08Y6m9ILU5dm629Nfz7egm6OHv1gLNXD/3ftXmqtDXaMt8wTwDArl+PCI/fGCIxzMxlCIjwETxl18MWdj1s9X9b2gh1d0yQ+arYtGVkXibmvFutkJuq/vVS7zgNNWxa87qL/OtVO/DNED88/sEcvHXfp/p2eZUCWZey0SvcG92du+rna/Y10BtVz+bmWgP9tI3M27CdKhdYTy9V+39SU6k1/Le14Q4Vh8PBxvVxmPP4CMwaEII/4s+a2pw2z6pl/2FVwwLJxC5Q/a8z+x428PB1wNFdSUg9nXFb8+3ZEI89G+IBFVEpnCpYW0PfEYHIv3ETG7/ee1vz5WYUILrTQ/q/tWrhuVFtnDsjMz0fednF6D82FGKxGJqa2LtEKoGDezcAQFFOsQkt5LDA0yZwOBz89tUByFUqPDNqoKlNaRPY2lripdcn46XXJyOCCO3dCX2jfKFWqbFu1Y5mOV5TOLjZw6WnA357fzMU1cId8JZgzNxheOmHZ/DSD8+0ynx3Ext+PAwzmRQzFk/Qt417ZBjsutugulKOE7vPmNA6Dgt8h6oJpCItxMT+IZU2QUmEBun0CowlaogyM1Li1l0pVVKGMZVCNfHrlio9IyXGqiS0Py4ibrcl0yZQZUioW96NuCWfNWRFvHTs4T3q9l1jQlbk7cBsISuq7AVVdqXhtrpGo8H2Q+dwz8hQTOnWEwdOpQEASrzYUluwtjGvH3G+ZFiWDDXeeRi1dkupk6UMYyeGAgBuZBbh2JFLhv3uYO1jNiYgZmMCoFILQzGs4Rqq5Ah5K7sIudeLMK7b47q/xbdRogZEuIZMzSBsC4z0w9hHhjfoxxbeY0+vwBiiMyaVAvMcTZRiqT9GMN5w3q1/HMfjL4zHtIVjseHzXfAMdMFj/5sNAFi/chOK80rvwD62NdC/5FrtrUN+rGtAvRfql6Kqfa01ABq8X6k0Rdr6r1FteRnqDpqaOciSNq0Ad6g4HA4A4JNfD2LKsGA8O2eo3qHqaOTmlGDM4HcN2kRKfusjK5899S0+e+pbU5vRbonbnoih9/RHv9G98ezHc2HZ2QJxW07hz4+2mNo0DgM85MfhcAAACoUKhxIuw7m7LYJ9nExtDofT4fjmtT8AAG/+8gwcPLrh5J4k/N+Dn5vYKg4r3KHicDh63v8hBloAL88bZWpTOJwOh0qphkqhgrmlOU4fTMFbD6yBUkHciMBpk/CQXxOIAciI+ziVlOaJEMlQeilKV0WlXLAg2uREegU5kQ5BQaRXUGiE/ShdlZxRVyVpWAajBjXRV6MyQldF6V8Y9VfM2h7qFtzmLinDmjaBMSWEVk3NQZVYoUqYCOe1vVAGLcqQlHANIX09EFhphlhCx2eMfo1Vq2ZMKRtWXRoo6YwxujSqZBCx9mR5IEooSAnEGHVQIuJ4pC2M5Wio1422mU1jQ+qlqM8TSt9E6q+aOY0Apc8h85Q0oTOqr0dq8Lna0D7bLlZ47++FMDPXpcu5fDEXKitroLRCOEdzr0F9e/X6JiPWoMlUCnrRljCdBHn9aYX/p7aD9LabRkPFd6g4HI4Bn/2fTq+x+PXbLxXB4XBuHxs7S6z8cT48ezkh8chFVJZXY0yDGnactg93qDgcjgHXM4tw+WIOevfxgL2VpanN4XDuaqxtO2HlD/Ph5eeIhCMXsOKx73FgcyJsulghbLDvLce+8PUC7Kr4GS98vaBVbB193wDsyFqNdXHLW2W+9gYP+XE4HAFr3tuGT3+YjxWTR+HZP/gdRhxOS7H47Xvh7e8EjUaDsuJKLHx3OqRmEmi1Wry6Zi6OR+vKtMRuPom4LacMxoprQvzq2ywtNGBMMGa/NEn/t0cv3U0oD748CVPmD9c1ikR4ftJHDebT7cHcbumkLt07Y9nX8/R/d3PUZdsfOCIAnr876Ns/f3sz0i7lCMa3F7hD1QQykYisCmFGlp65c10VmYeKuR+Vh4otX5WMsfSMiihloyS0UgCgIvRNYkpXRWiA6NxURuiMjGmjPjOM0PYQEjaIyDUgjkdqEhhzUzGeh6ie+PVCwjVkZxVhZC9vWImkqKovjDUiDxWtjWLNxUUdj9It3XkeL0obZYwujdQFUcejcrqRJ0zpryj7WPVDrG1UMIPIDE+tM3X9GZObitI8Ua8lax4lCtax1Puyfgb5+v9vOL7m7862uvI9YrEYwyb1Mehi28UKYx8aCgDIvZZf51BpDEsL7Vx34LbOw9beGgH9vAVPNSxl1HC8T7CudNKu34/VPUdl6m9wDZmZieEf5i7oZmdvDTt7a/3fllYy0ub6Oiv9/ymNV43mrtEyNy0Md6g4HA7JNx/txIrP5uDVCcOxfOMeU5vD4dyVvPJwvbxd9RyRsMieWPn9fGz/YR8+e/p7wTh75y7wCHTFkU0nkBp/mXbKGyHm91jE/F6v0DIxViQVugd9h/mjIPsmNn1/gHkuAMi7fhPjfV8WPiElfj1K268SiTtUHA6H5NiBVOSXVWBq3wC8tXkP+YOQw+E0jU0XKyx5fyYA4OiuJBzfl9LkmNNxl1F6swIjZg4iHarwUb11pYyWbWh2eyl6uHWFi1cPfPri+lYrZTR6Sh+EDvC+9Z2JbYj26wpyOJwW58u9xyCTSrFw5CBTm8LhtFs6WZljzL39MObefvDyZ0+au/Pvk+hkbYFh9wlrbO7+5TAmdH4EmRezm9PURsnLLMJ4l2ex6/e4VpkPAAJC3TFmal+MmdKn6c5tAJGWTPrAKS0tha2tLS6mOMDMWugdVxL6hgqtmbCfRiY8tsZC0Fam6SRsU1P9hG0lKuGdWKUqYT+yTSmct1wptLlcYS5oq1IKzxcAquTCdoVCuBmqlhOaCSWx9SwXtokVRJtc+DpJFEL7xIxtEuJHGNmPbCN0ZEphG2kfUepETBxPQvYjctFQ/QgRq4hoK/Wzxe51CyESiTDm4TUAgKIA4dozrzOxpuzrx9qPWiuiH7FW1NqLVNQ6E+tH9KPWlGoDJfIl24gkj2qiH9GmJcdS9dCIsaxzUG2UzojSUBE6LRFRu5CsK0j1o+oekm1s85K6OSIsRobeyH7E51+DEJhUJsWmk8txPS0HC8KX1j1BvZamWgOqHxnKu7M10I0l8qpRbTXHU6mrsT9hJUpKSmBjYyM8XgvBd6g4HM4t+XNbAqw6yTA9OszUpnA4HQqVQoWkoxfg5ueEbi5dTG0Opwm4Q8XhcG7Jj//EQq5Q4dEZkaY2hcPpcHxbU9/viZUPmNgSTlNwUXoTSEQi8o5cMwi3sWXE/cFKsvQMVWZGGPeoFglDZ6xpE8yJkiFmRCkLGdFPRqRIoFIpKIl+AF2mhixHQ22NU7dYk2kOqBI1VD8j0iuwlkkhxmqINipFAust/uQaMJaeIW9lZ0wZ0Dm9DACwe3MiJs/oj/GervhNfIOYl7CP+rnGWgqITP/A+JobUx6InJexpAx1Oz9VjoZxDjL1AdXGWAaHLEfDaDNzKgXqeOSbmhHG9ApU+gIt8XlnVDkasqwOYyqFJkux1E4hbEu/ehOFuSUYOLEPxHa6/E2agkKmOdr8GlD2UalLqMuAqBdWW9bJVGkT+A4Vh8Npkm8+3gW1SoPHFo01tSkcTodj43cHIDM3w+R5Q0xtCucWcIeKw+E0iUKhwtEDKXB0tkNfN2dTm8PhdCj+++YAlAoVpj8xytSmcG4Bd6g4HA4Ta97bBq0WWDZxpKlN4XA6FBqNBif3p6CHSxd4+Dma2hxOI3ANVROIIaIi9pAQGipKo0SVqGHVQVkQ94lXE6kZqH5yos4JpauSi4X9pES5DDOJ0D4Jdcs1AAmhlxITegExoYNSE22U1oC59Ayj5om5nAqzZodRL9XcGiAjdEGk7qZeKoCygnKcPXkFof294NO5C67k39Q/pyEyaJDaMuLcRKxrwKwjI/qRbYzXVTPr0iiNCF16xgh9E1kap/FSHYZtlLaRTQdFpjQg5mAtR6MldDKUjocs8UOeG5sGSEvYIjLieKQIiLoOqPdlTYHybz7cjoFje+Oxt2bgtVHJQvuozzVm+4xYAyrjL3X9UW9+UhNI6a+ETSJiXm2tfSZKBsV3qDgcDjOfvbMZALDintEmtoTD6VjkZN5EdkYh+kT2hLQdl2e5m+GvCofDYSY7swgpN/LQ19MF9tbChLIcDqfl+P2rfZBIJZj16jRTm8Ih4A4Vh8O5Ld7euBciAMuncYEsh9Oa7PkvAdVVCkx+YoypTeEQtKhDVVRUhDlz5sDGxgZ2dnaYP38+ysvLbzmmuroazzzzDOzt7WFtbY3p06cjNzfXoE9GRgYmTpwIS0tL9OjRAy+99BJU9VLxHzhwACKRSPDIycm57XMQQwQzEfWA4CGBlnhoBA8zqAUPmUgleJgRDwuRUvAwE6mFD7HwYS5RCR5UPxnjw0xCPyRijfAhET7ExEMkFj60Eq3gAcYHNVYrhuChkQgfWupBjCUfjY0XHE9kxIOaVyR8MB9PLHyIRILH9T03cD2nGMP9veF8VYSuqWrmdQHxYF4/Y9aeda1EED6otSLGQkQ8xGKmB7XO5PEkEuIhFj4aG9/gIRKJBQ8KkVgseEAsEj7owWwPVrQa4YNCoxU8tBqN4AHyIRxLP4ixWi3bgzoe1a/Ba3t4VzLsetigd5Q/RBKJ/kGuS2usgTHny3g8EfEg51DXe5iAFnWo5syZg3PnziEmJgZbt27FoUOH8Pjjj99yzPPPP48tW7Zgw4YNOHjwIG7cuIF7771X/7xarcbEiROhUCgQGxuLn376CevWrcOyZcsEx7pw4QKys7P1jx49ejT7OXI4HZE1P+yHRCLGc4/yO/44nNbkuw+3AwAe+79ZJraE05AWc6hSUlKwc+dOfPfdd4iIiEBUVBTWrFmDP/74AzduCDMtA0BJSQm+//57fPzxxxg5ciTCw8Px448/IjY2FseOHQMA7N69G+fPn8evv/6KsLAwjB8/Hu+88w6++OILKBSG2cZ79OgBR0dH/UNM3XlQg1wuR2lpqcGDw+HQxJ5MR+HNckQPDyRv6OFwOC1D6c1KpCdlwH+ADywshYXsOaajxT4K4+LiYGdnh379+unbRo8eDbFYjOPHj5NjTp06BaVSidGj6+4g8vf3h7u7O+Li4vTHDQ4OhoODg75PdHQ0SktLce7cOYPjhYWFwcnJCWPGjMHRo0dvae/KlStha2urf7i5uQEAVNT9mgAkEAkeZiIt8dAIHjKRWvCgwnZ0PzoU2PBhLlYKHlKRWvCQiVVMD6lYI3hQ4ULdQyN4UGFAMuQn0TI9IBY+jAv/tEKIqblDjWR4T9gPIuGDDFkxPiwzy2GZWY5fvtgHmZkUT02KYF4X40KrwtAbeTzGscz9jAkNEqE8MrxHrTUVUmMM5TE/mn0OtlCeSCwSPEgYw3tajVbwoDveRoiqwUOr1QgezOPVGuGDOjfG4/3y/laIJWI8unIOxNZWEFtbtf01IB93vgZNhQZNQYs5VDk5OYIQm1QqRdeuXRvVMuXk5EAmk8HOzs6g3cHBQT8mJyfHwJmqfb72OQBwcnLC2rVr8c8//+Cff/6Bm5sbhg8fjoSEhEbtXbp0KUpKSvSPzMzM2zpfDqejsWNjAirKqjH1/gGmNoXD6VAc352E8pJKjLp/oKlN4dTjth2qV199lRR813+kpqa2hK3M9OrVC0888QTCw8MxaNAg/PDDDxg0aBA++eSTRseYm5vDxsbG4MHhcG7NP+vj0MnSHA8NCDO1KRxOh2LPH3GwtrVExLgQU5vCqeG2HaoXXngBKSkpt3x4e3vD0dEReXl5BmNVKhWKiorg6Einznd0dIRCoUBxcbFBe25urn6Mo6Oj4K6/2r8bOy4ADBgwAGlpabd7urB3PIcuPQ7Dyvb/IJEG3fZ4Dudu5rdvD0IuV+KpIRGmNoXD6VD89L9N0Kg1mPvqFFObwqnhtkvPdO/eHd27d2+yX2RkJIqLi3Hq1CmEh4cDAPbt2weNRoOICPrDNzw8HGZmZti7dy+mT58OQHenXkZGBiIjI/XH/b//+z/k5eXpQ4oxMTGwsbFBYGBgo/acPn0aTk5Ot3WuAFCquAzrziGwtHoEllaPQK1VoExxDZXVB3C97E9Uq+pCgy6SMsF4ukQNUcaFiPmaiYSlYmRGlKih5qXK0VSJhf1kVBtRjgYA5ES7VCKsi6AiSteoibI1GkL1rGUtUUOUAyFLzwiXgS51wlpOhfipoiFLwBA2E2OZS9kYURKFLLFClTVRGb5GuzYmYMr9EZjo7YtdSZf07epOwqFkiR/G6ifMZXqo15xxrag6U6QWhbXED1VehLFNS1z3IqK8FXmxUf2ouwcorQlZPoawhSxXImyjypWQpWeo8yDL0RBzsJaeIQuJEVDlVKj3ArV+xowlj0e02dmgGkDK6WsI7OsJu57OKEwgbqQiNWfNvAbU4YxZA6qkGVUWi2jT1l731PytQItpqAICAjBu3DgsWLAA8fHxOHr0KBYuXIhZs2bB2VlXrf769evw9/dHfHw8AMDW1hbz58/HkiVLsH//fpw6dQrz5s1DZGQkBg7UxYrHjh2LwMBAPPTQQzhz5gx27dqFN954A8888wzMzc0BAJ9++ik2bdqEtLQ0JCcnY/Hixdi3bx+eeeaZ2z6P2BuP4N8rg7AzYwYuFq9HuSILNjJPeNguQKRLDIa5n0a40wZ42D4JsbhbM60eh9N++PrjXVCpNXhhwhBTm8LhdCi+f38bAGD+KxNNbAkHaOHiyL/99hsWLlyIUaNGQSwWY/r06Vi9erX+eaVSiQsXLqCyslLf9sknn+j7yuVyREdH48svv9Q/L5FIsHXrVjz11FOIjIyElZUVHn74Ybz99tv6PgqFAi+88AKuX78OS0tLhISEYM+ePRgxYsQdn0uZ8grOFK7S/+3dyReOnaejq8VA2MiCYGseAq3dYmi0ZZArzqGsahtKy/+CGhV3PCeH0x5QKdTYm3wJY0P8EO7pjFNX6bQoHA6neUlJzEBxYTmixgXjA1Mbw4FIS+3RclBaWgpbW1v8cToQYmsLwfP2EsOM710thsDbZjI6yQZAKnGASCSGVquFWnMTVYrTKK3chJLKjQBUqCZiF2Ua4RwVWmGOkTIijkKNLVYL66yVqIRt5Wpz4VilcI5ypXCOchWdA6VMITxmpULYt1oh9OcVRJtKLmzTKogQhFzYJlYSbXLhVrFELmgCEUmFWMHWJqH6EceTKIRvP7qNOp5wW1tMjSX6iaix5PGI2IxK2K98miMOL3sSaTkFuOfTXwHQIT9ynVnXinw9qPOl5hX2EyupsUQ4iVgXiYJYU2JdqDUVKYVrSrVR6yxSErFpFTWW6Kcm+hFtWnIsEUKhxrLOQbVRoS0qZEWEBqm0CyJCZgCijexHhraIsWbEngQVWqXmkFJtxPGosfVCWg8sHI2HFo3FqgVfY9dPBw3tI8PabXwNxKxrJWzT1hSNVqnl2Hv+I5SUlLTqDWYtukN1N2AmUoEK919VdG/wdyoOFl+t+UsMv87D4GczAo6dAmFtMQKdO42Ec9dPUaEqQpH8DK6Ubcf1isP68S7Sm8K5iZkpXRWljbIQCb9V5MS3lFwjvAQoXZVCImyTqunLx4zQW0kJjYOE0LWICW2UmNJVUbogUmND6aqIfpTuhpJlUEFyRr0PdSHRWqFm1gCpiS8baiylSSB1QcKh0vgyJJ65hr6hHggvscG1zELkRAh1fKy6NHLtyfVjWyviMgfxtmnk9TVCl0Z9KVFfNoTNpC5IQonzKL0UozCNGEuVn9GKCGeHOjeyjdJ4Ub/jaU2m0Bg2XRCptSLm1RIXNOlgUFBOIHVurNoo8njUG67Ovr++O4jZz4zCrFemChyqdrkG1PmSH5S3mMNE+0TcoWoRNLhYth8Xy/ZDrjGDFDIE2o2Cv80QOHbyhZvVCLhbj4RWq0GFKhd5VQkorfgdJfJ4UxvO4dwxH67ehd+/fxwvPjsWz778u6nN4XA6BCqVBqfj0hAe5Qcnr+7IvpJvapM6LNyhagVUUOBs8Q6cLd4BAPC2UMHbZgrcrIbBztwHXp0nQGQzERqtCtXKTNysjkV22Z9QKC41cWQOp+2QnVuCi5dzERzoiq5drJADIpbH4XCanW/e24avt/rhiY/mYsX0VU0P4LQI3KEyAQpNKVKLf0VqsU5r0knSA33txqGr5QhYmnnDxWYOXGzmQKNVoEJ5BQWVh5BVtsEgRQOH0xZZtWYXvv50Ll58NhqP7tpianM4nA5BZno+cjMK0D86FGKxGBoTpQ3o6HCHqgnMRGpICLEFq5apEkIx9tlKN0FbzM04ALp6hQ7mzhjSfSQCOgeju3lPeNn1gqftY1BoFCiUX0Fa2XGcubkdVRpd3hEHsxImW1jzUJG5qYi4dmN5qGREYiYF0VdBxOjFKiLmz5xzijVfkLCbhtJkkjmOGMcSn2dkP0ovRei+jNEAseamonVBlABYaJ9Fru5O3Wu5V3AjswgD+3nD8oAMlQ0KlpNr0Bq5qRivA2qsiFWXRuqlqDZhE93GqD0iNVnUhco6ljGnUzPnpqLm0BLXH3NuKkoCRIrz7jxHlJY4HnlupM6N0rQRFyB1DRHn+/fqnVi46iHMeHkK/v5sp65bVbVwbFtfA2Pyc2ka/NvK8DrxbZBc+Q38nfUr3jr/KhYmzsMHqW/haOFBlKpK4NjJD0MdHsbCXn9gUa+/McvjffjbzoBUJLyDj8MxBWtX7YREIsYbY4eZ2hQOp8Ow9bv9UFQrMe3JMaY2pcPCd6jaAekVaUiv0JXN8e5UAC+rfuhtNwqulr3hZtkb7lYhGND9ecjVJSiQp+BK2R6klhyAhkr/zeG0MPFHL6GwoAyTgwPwxtYYU/1Y5HA6HHHbEzH0nv7wCfVA2plrpjanw8HzUDVCbR6qHWe9oLEW7v4Ykw+qXC3sV6ISHo8lH5QYYvSx648RDv3Qq7MHbM2sIRKJoNVqUaQoRUrpFezJPYHjRcnw65wnOJ6NVLglXKURhilLiTxUZSphvikAKFUQObWUwmNWELmpqHxVSqVw715J5qYi9vip3FQKIlxDtEmIfFVkfiRjciuROayonEnUvGx5lMREziQqtEr2o/IoEbmpRA3yCkVP7Ysly6bi+/3x+GT7UX27ylowlD23F2NeMOb1I9eKdU1Z15k1BxhjbioifxP1epA5p6j8Usw5rIgwG+McZD8iXEPmpqLGNnduKirmTKSnMCqvFZXugso5RYXAqH5UXiaFAvYONvjl2AqcPZaGV2d/CfX1bDb77pY1qGlTqeXYc/FjnoeKc3tooMHBgkQcLEgEAMjEUox2GICobmHwtXbD4G6hiOoeBo1Wg2JFIS6Vn8fxwoO4XJFiYss5dzO7NiXgsZfGYfagMAOHisPhtByFuaXITMtF7wE9IZVJWTN7cZoJ7lDdZSg0KmzPjsX27FgAQGepJcY6DsQg+xD0tHbCgK5DEWE/DGqtGoXyXKSXn8bJmzHIrb5qWsM5dx0/HTyFZ8cNwoNRffDrkURTm8PhdAh++2wXln7+MB56fhy+e+5bU5vToeCi9LucMlUl/snahxfOfIqXz87DiuSF2JX9H7KrMtFF1g0Du03AQt9PsDzoLzzj8wnGODyErrIepjabcxfw9d7jqFaq8MToCFObwuF0GA5tPY3K8mqMmzXQ1KZ0OPgOVRPIRGqom7sEjEhYkoM1pQFrCZhyQrMUl+sFANhx/QSAEwAAF3MnTPfoh0HdfeBm5YahnTwxpPs9qFYrkVaWi4N5qfg74yTsrYQ5sCwoUQvodArVhAZDSpSUkRJjVWpCB0WkSFBTaROINuaSLay35BtTToVMw0BoP6gSOsS7l7aPLTUDXcZFeDwq1QNVdsUprhI7N5/GtOn9MKuTBw7uS0FGtFBTSK4Vmf6BrY15/RjPl26j0nZQt/2zlf1hLWUjItqYy9Gwloqh9C/kreyspWeINlCldhhTKVBxLDKVAlvaCTq9AuOt+8akEWCeg7EUi6zuM//A1jOYMCsCfUb1RuLe5AaHu0vWgHov1KYzoWpPtgLcoergXC7Pxwfnduj/Du3iinvdw9Hf3guBdi4I7uKGZ/xGo0pdjfSKTBwrTMT+vOOo1hD5TTicBnz9RQwmTe2DBU+PwsF9XLfH4bQG33+wHeNmDsCj787Gs3tfN7U5HQbuUHEMOHMzC0kldbtRg7v7YrJrGPrbuyPIxhe9bf0w32smylQVSC+/gtjCeMQVxkPDb47nEKgUGhw5eAHDRgYgOMwdGSgwtUkczl1PZXk10s5lwbevN6ztLFFeXGlqkzoEXEPFuSVH8y/h1cQNePzUa5gZ9yw+vvg9Tt1MhhZahNr1xtM+8/HzgLX4vM+HeM7nCfS1CzW1yZw2xppVO6DVAs8uiTa1KRxOh+HHVTshFosw/38PmNqUDgPfoWoCkUgLGWMZF/ZyL0LtkblYqKsypgQMc/kXtTB4Xi0X2nI60wUAkJCZgy+wWWefxhzTvHpjooc/gu0dMaBrOCLs++F5Pw1uVJbieF4G/rp8GicLstCja6ngmBKixAWlq6L6qYk2SoNB6WkoXZWIKCVClbKhdDfNXk6FuY3RFlb9EGOJFXKsOZEXrMbmkvJqnE64ij7hnghW2yM9u9CgX6UTMa8R+jXqXnGj9HCtoUtj1R4ROXq0lC6ILFtD5Rpi1LAQtoiI42mJeVnL0ZD3+FM2U+V3KI0NBWvZFUJUqSV0acyvG2Uzq86NXCtibIO8TKdj01BaWIYRs6KwevHPdYdTEMnb2uMakKWUtI33bwX4DhXnjqlUqbD+0mnM2fMHQv78FH3++gzvJezH6cIb6CKzxL2ewfhz9FxcmPkK/hz8Al7wnwIfa+Lbk3PXs+qDbQCANx4cZWJLOJyOw451B2HZ2QJDpw8wtSkdAr5DxWk2ihXVWHvuOL69qCvy7Gxpg1neYRjh4oueNl0xw30QZrgPgkKtwrXKfBwvSMWm68eQXX3TxJZzWprcnBJkZ99EaE9nrHpyCn7afQJn04VZnDkcTvPx87v/YsbiCXjwtXtw6J94U5tz18MdKk6LcaOyFB8nH8LHyYfQo2spvKwcMM11AAbY+8LLqgd8OzthjudwVGuUSC/PxpG88/grIwHFCi6gvNt4dMFwODl1AQAMD/PGiD49oVKrkZlXgsPXruLPU2dxOZ871hxOc6JSqJB0JBWhQwNg72SHwuxiU5t0V8Nr+TVCbS2/I8nOEFkLa9ZRdfsqNMJ+xWorprElKmF+nlKiXiBrTb3mrqenUAh9bxVVOw/sNfXC7Z0xMzAYg1zd4NzZBhKxGFqtFmUKOc4X5mFH+kVsuJgElW05OY/AHrlwXg1RB5CyRcRY849qo+rxtUZ9OnJeou4cXduOrRadhGij0BK6B7WlBJYWMnz23iz49XRAQVE5nnx9PSAWYeroEAzq6w035y6QmemuLblShavZRThyJh1/nk5GdnGZ4RyEQIGQGRpZM5F1nYl6fGQdQNZafmz9qPp+IqJ+noioIchct4+1H6F/0SqpOoCMNfpY6wVSOaeofgSsdexYa9GRWjWqth1Vi47KH8ZYK4+5Ll5lFXyCXbF6x8s4tCUR7z31I9R5+cJ+rbAG5FhyXYxfA5VGjj3pq3ktP07HISEnGwk5dWGfYR5emB4UiP6OLhjg5IaBzu5YMXgUihVVOFN0Hdsyz2Fb5jmoeIqGdkFokCveXzEDFuZSHDhyAcvf3wyVuc7xWvvbYaz97TAAwCPYAfcMCcaAQA/0dOmGXu498OikCFQplLiYXYD9yZfxz/FkFFfz3Gcczu2SlpSFwpwSREYHm9qUux7uUHHaDAevXcGBnMsAAKlYjAlefpjiE4i+jo4Y5uiD4U6++KD/VORXlyOhMBP/XTuL/TmXTGw1h+LJJ0bgvvsGQK3W4N1V27DnYONJPS9m5OP93/bp/+7r54qJI3ujv48rQtydEObpjMUTo1BWLUfK9TzsOnMJm0+eRzW1a8LhcARs/P4AHntjGiY9PASbPvzX1ObctfCQXyPUhvyOn3NsJOQnbKNCfmUaYdiuWC0M75WrqTAgEfIj24RjqdAgVY6mXCG0uVIhTJtQTbQp5bQ/3hJhNkupFFN7B2J8gC+CHHqgs7k5RCIRNBoNbpSW4fi1LPyVkoSTN24YjFXZUaEK4hZwcl42+yRy4RRU2ElM9GMP+VFzsIX3jAlZkRB3Nas66dbKylKGr96ZjZ4e3ZFbWIoFb/2BvKImQrbER1B9m4f074nooYEI9nOGfRcriEQiaLValJRWITktGzFHU7E3LlUfgaq2o261FzaxhveYQ4OMYVQyvEeE6OgwIBHyY2yjQ3mM4T0yREeE8sixxI4ycxiQrR9VToVMkcAY2iLL4FD9jAhPkWOp8BlruJDqVxOKF4vF2Jj8LopyS/GQ19NEPyqU14bWgOonpdrqhfyurOEhPw6HolKlwu+nz+L302cBAF2kFrivT2+M7tUTvXp0w70hgZgeGgSVWo2MkhIcycjA72fO4Lw618SWdxz6Bbvj/VfvgblMit2HU7Ds6x1ND2Lg8InLOHziMsQqLWRSMUZGBWDUEH8E+DhicF9vRIX3xPJnJ6DgZjnOpt7AppPncfTc1WaZm8O5G9BoNDh58AIGjgqEu78LMlKvm9qkuxK+Q9UIfIfKEFPvUAnGNvgh7GTTGfdHBGO4lxd6du0K85pfKnK1CuklRdiXeRnrL5xBVnkp36FC8+9QLXxmNGaM7wOVWoN3Vm/D3tiLUFswprlrYodKb59K2M/cxhwThwdhWIQvenn2gGUnGUQiEdQaDXKKynDiQgY2HjmHs1eEKRr4DlVjbXyHqk3tzjTDDhUAOLp1xQ97X8HJXWfw+uT3GvTjO1TNAd+h4twVZJeWYdXRo1h19CgAwNfeHrOCgxHl7QEfO3sEdO2Bp0MGokqlwoWb+Yi5lobfL5xFUXWViS1v31hbW+DLj+bAw80e2XkleOK131DYinXDKqsV2LAzERt2JgIAuthYYsKkEAwP7QlvJ3tMHdQb0wYHQ6lSIyu/BHEpV/HPobO4ksNTNHA6FjmZRcjOKETf0cGQSsVQUXeCcoyC71A1Qu0O1clzDhBZE+kGNES6Aa2wrZTYoSoj0iGUELtWJVQ/YoeqjNihKiN2qEqVxK4aYyqFKqIcDZVKAQBUxM6VVsm6Q0W0yandI+G8t0pf0MfDGff2D8IAbzc42XXWp2gor1Yg9UYetl+4hP9On0NVvV/Yys5UiRpqXjb72HeeGPsx7kax7rCQ3EIvFRniifcWT4HMTIKtsefx1o+7mj4eMW1z7wrVTzfg5GiLKRP7ICLcC66uXSGT6a5NhUKFq5mFOHYyHZt3nEZ+oU7nJbehSqcI7aNTJFA7VEQbtRtF7GTR6RUYd6OIXRyRgq0fcyoFcmzz7jyxplJgTa/AvBNjTBoBxh0b1uORO093uJM1dkY/PL9yJn555x/8+n914nRq/VhTTLTKGrDugtXfobr2eavvUHGHqhG4Q2VIe3eoDOZVA0P9vTA5PAB9vVzQ3cYK4hqh883KaiTdyMG25AvYmJEKVYNcO9yh0jlUrzw6CtNGhECpUuONz7dh37nLbMdrZYdKP7amzp6PjwMmjA9Bv75ecHKyg0Sic6yrq5W4fDUfBxLTsPlgMkrL61I0cIcK3KHC3eFQAcB/Z95BVXk1ZrnXidO5Q9U88JAfp0NyKPUKDqVeAaBL0TBqgC8mB/sjxMURQ308MczXC+9ro5FXUYGT16/jv5Tz2HfliomtNj12Np3w1coH4ObYBZk5N7Hgrd9RXFYNCP33NklaWi5Wr4kBAKgtJAgNcsXEsSEI7e2KwF7O6B3ggmdmD0V5pRypV3KxL/4ith86D4WCp2jg3B0c+S8eox6IQtAgP5yLvWhqc+4q+A5VI/AdKkPuth2qhig71/2/k1SKe0IDMSbYF70dHNBZVid0vlFWhuMZWdhwLgkns27UzNsxdqii+vfE2y9MhplUgk0HkvDe93v0zxGXII2Jd6jqo7YQ/sKNGOGL8VE1KRps61I0FJdV4VxaNnYdScG++AtANd+h4jtUaJc7VNZVpfgr8yukHLuE50e8BYDvUDUX3KFqhFqH6vT5HhBbC52JSo2wrYJwqKi7/EqpO/oIh4q5RA2zQ8V251+FEXf+Aa1TAkZCOVmsd8wx3pVX+0Vva2mBaVG9MSLUBz4u9ugkM4NIJIJKrUFWfjGOpl3D38eTcCmnUD9WJaw4RJdJMcqhIvox3vlHoSXDe7rG5fOiMWlQIBQqNV78bisOn2t6t44OlQnbWG1u7TCbVCbGyNG9MXxUIHoFOqOzTaea3GdaFBaWIzkpE7t3JyE+Ph0AoLQWfqGJCEeOuluR/W7AO7/Lj70cjRF3/hH9mB0lJXHhU19PrHf+MZajafN3/jE7VIzzlpThiwNvwDPAGdO9FqO6UgF1cbGwH6tTSayLqe9+VGnk2JP5FXeo2grcoTKkIztUBmgBxy6dMX1oMKJ6e8HTsSvM69Wiu5JfhMOpV7H+zFlklxjWomuPDpWtgxV+WDobLt1tcTW7CI++9weK1MQCErR3h0rfVqOj62Qpw9hJYYga7g+fXk6wtKzZuVRrkJtbioRzGdi66yzOX6xL0cAdKu5QNdbPlA5VxLgQrPjlaWz6Zh/Wvv4Xd6iaCe5QNQJ3qAzhDlUNxLvF3cceMyJ6Y1AvT7jb20IqkUCr1aJKqcKlvALsTU3DhoRklJQKa9G1ZYdq5AA/vPXMBEglYvy17ww+/F1XHkZlQXyxENxtDlV91JZmsLOzxKRJYYgc5AtPz+4wN5fqdi5VamRlF+NE4hVs2X4G1zILDcZyhwrcoQJM6lABwIa0jwEA9/ks4Q5VM8FF6RyOkaTlFuK9zQcBHAQA9PF0xrRBQYjwdENvZweEujrh+VFRKJcrcP5GHnafu4SNiedQ1YaFziueGo/owQGQK1VYvPo/HD+fYWqT2hzFxZX49ddY/PprLACgh283TB4bgoHh3nBz6QLPKf1w35R+dSkaTqRj845EFOaUNXFkDqfl2fvnMUx9fCQixoUg9o9DpjbnroDvUDVC7Q7V2fM9ILMWeswVWqEvSgnVy7TE7hEhNqd2o6iM6qw7VFRbuRFC9UpGoTpAi9XVxK5VcwvVWXdx6F0Sxn53IKAeFOaFiUOCENLLBd3s6tWiK6tCUkYudp5Ixe5TF/UpGogNTHr3jdzJMk4v1d3WCj+/MAtOXW1w6UY+5n7xF8qrqa26hgckbDHCZrKf3Ijs5CbYFfL2d8KE6eEIG+gDJ5cukEh1O5fVVUpcuZyLowdTsWtzIkpLq6HuJHwviYjs30YJ1cldq2YWqjNmSid3j5h3t9gypTe3UN1kIm3WnSfWXZwaLCxl+Ofsu7h6IQdP9H5eaEtbWoPbFOurNHLsub6W71BxOHcbsaevIPZ0vRQNA/0wZpA/gno6ISrIE0N6e+GdR8ahoKQCpy9fx+bEFBw63/opGsaF98I7c6MhFYvxy95TWPXfIaiF/jaHkfTUbHz+7hb93yH9vTB2WjhCBnjBP8gVgcFueOyZ0Sgvr8ali7k4eDAFu3cl8xQNnFahulKB1MQMBPT1gF0PGxTnlZrapHYP36FqBL5DZQjfoWp8DmPSEoi6yzB5YCDG9PGDv1sPWNevRXezDPFpmdh4/BzOXLpBzMFmC0XDHaqVT07E6H5+qFao8OzajTh5KQsA2B0qvkNF7woRH68aK92iDhraC6PHhyAw2BVd7K3rdi6LK3H+/A3s33MOBw+cR30JF9+hou3jO1S47R0qAAgM98RHfz2DPb8ewgePfGloS1tag3ayQ8UdqkaodahSUxwgtha+mJUayskihOpkwWTCySLzVbGJ1+kiysQclOOluHOhelUjonQ54VBRhZS1CuKNQzhPZOFiyqEykVC9OR0CG2sLTBkdgqgIH3h7doOFuZle6Hw9pxgnT1/Fll1nkX6tANVdCPE/8RmjJkTktZeCk11n/PLcLDjYWiPlRh4e+m4DKhVNhPiYxeaM/VjXjxKvE2tKObNkG+l4NXM+KEYnRioRYfikMAybGAq/EDd0trPUp2goyitB8smr2PvfSZw4eU0wVisjXnTqPKhcQ8xFlI1wqAhRv1bJJnI3JjcV5TyxCtVZHYdWEWmT4mtWh6rpedcfW4ZOljJMsX1Y2FdgjBH5uViLHjfDGqg0cuy58TV3qNoK3KEyhDtUNf1a2KECDHdTHHvYYFJ0KAb284ZHw1p02UWIPXsF/+w/i9xCndD5dhyqqf0CsWLmGEjEIny/7wQ+3n9UOJiCO1QtvivUyUqGsfcNwOCxwfAOcIaltbk+RUNedjHOxKdj57+nkJqUxR2qxvpxh4rJoZrz7Bg8uGgsVi1Yi13rDgj7GxjDHapbwR2qRuAOlSHcoarp18oOVS21ITpvj26YHB2CfmGecHGy06doqJarkJaVjwNnLmPToSQU16tFRzlUq56cjBG9e6JSocRT3/yHxKs3QESiabhD1Tphtnp06d4Z/9/eecdHVaxv/Dlna3pCSIHQe+g9VGkBBL0ggigCVxEFVMpVroWfXgEboCgiiogFbHQVUHpXqhBCD6F3kkB63Xbm98embc4bmLCbbALz9XM+ktlpZ/bsZjLzzPM+PLQ9Ino3Rc16wTDkrVxabLh+LQmH95/Hut+jcPXibXsBMaESEyqAa0Kl1cpYfeJDxF26hVHh/1Hnd+iMmFDdCSFKFwgqEBcu38bchXY/qJwADVrUr4p/PdQUbcOro0mdUDSrVxXjH++CjGwzYq/EY8vBM1h7OAY5uULnsEBfLP7vU6js64UTV+Mwav7K/NcE5ZfkW+lY+uU2LFlsX0WsUr0S+g9ui7ZdGqBazUDUHNYBg4Z1gMlkwdVLt3FgVyzW/xaF2/FCaCy4M1arguhtJ9CmT3NUqR2MmxcT3N2lCotYoSqGvBWqMzEh0Hmr/+LIYupZNLVClUUI1dNcvEJF5aNWqFIs6rIZVvXKUxpl7MkZ8w8AcgixuoUSqhMrVIxaoXKx2Se5GuVqg01OE0ruFRZSQK3O17FdbfTp2xxNmlVDYCGhc0pWDkxmC4L9fAAJ+PLv/Zj31351x4tAmnM6IeDnL0uNH1WWMgXlGys6jTIApVZ2qJUsIl9ZrAopCuo1q4a+wzqhVdeGCK0RWGDRkGXCxZgb2LvxGDatjkZGUXNZagXDGbNPYjWKFJtzitf5V604zT4J8brLherk6ta9l5V0nMaevIJ2YrWnWiU9Fh54H/vXH8H0p+cBAGwZmVx9dsrwlBgXV4yBVTFj602xQiUQCJzgwL7zOLDvPAD7Un637uHoNLgZmtesghB/ewTop+ctRVRavDu7KXAx545fw7njK/J/bt6pPvo81QHNIuqhUetaaNy2Dka/NRCZaTk4e/I6/t5wDNvWRMFMbC8LHjyuxt5EwtVEtO3dDLIsQ6EmxoK7IiZUAsF9itWqYNvWk/glzR6896Ph/dG/ZUPUqBwgJlT3Ocf2nsWxvWfzf+7Urzl6PtEB4a1romXHumjVqR4mvDsIqUmZOH3sKnauO4K/N54Uv0gfYH6dtwkvfTwcj0/og1VzN7q7OxUSseVXDGLLzxGx5Zdbtpxv+VGi6rj29mdLr5Vx4P3xuJ2WiW5ffqfuNIHY8qsYW35ceNk//1q9Ft0fbWG3aGhWHT5+HoUsGtJw4tBFbPn9EA7/XTAhE1t+9/eWH7tljze5Nv5rpCVlYET4ZLHldw+IFaq7oJEkaIj3XEccddIz9QfYIqm/OPREmo5MU9dnIH4j5RCBmnWy+kvCIKvbMMvqh1cvqx8LE1GfTqZPzFiID4RNoy6vEB9ERn44KddIdRKj0qzq+ogDmpDIskQa8Rkm06j6bJx9sRGn/KixIttQpwUdK5ix7Np1Gr16NEaHBtWx58rd4/NpiIkr1Qb3uBBp4B4/znzEB5b6ncmIcQbx+FH1SQqRkTONEb8IJInvlzqofBTUZCIpGQBgAbDl263Y8u1WwKbAw9uIviO6oPO/WqNus+ro9kgLdH+0pd2i4Uoiju4+jQ3L/8GZo0WeF8pskYppRE0miHtj1Oyd+oVLplFjRYTuIb7HqOeKfGCILx1qgiZR90H9EuGFmgRSbVBrI1RZapJfyR8AsH/bSXTt3wL1H2qKM1ui1E2Qk1TqeaZOKhN/hFCTLIqSjoGb1onEClUx5K1QnT8dCp23+vUc4ouD10qBsk1II1eo+BzVSfd0omwa4ZSeTq5QEfkIR/XibBMoV3XKPd1KuKcrxKqVRDmlc1opkGm8K0/ESha3FQC1GkWubnGusFDWAqT7t/oLTzEUjKmnpx5//DoJF5KT0XfRD+oOqfpCjB/3Kh2Rj3fsecePzHfvq37U2EvEqh/pqM5rpUBaMzjjRM65YsO5KhQQ6oeHR3ZFx34tUKNhVRhy3futFituXLqNqJ0x2LBkH65evMVVH+9KmzMrT+XeSqEsnMOdsGbIo3IVP/y45x0c238Or/eYpnqdHKtyOAZWxYytcQvFCpVAICg9srLMOBh1Ce3b1kajoCCcvkX8UhQ80CTHp2Hp7HVYOnsdAKBK7SA88kIvtOkejmp1gjHohR4Y9EIPmHMsuHIuHv9sP4UNS/fi9s1UN/dc4Cy3b6bi6rkENG1fF1q9FlZhqVIixApVMeStUF08XQV6coVKPWw5nLqqdNLsk1hRIrRRVL5UYjUqg4zvp86XYqHK3ruuCgCyiJWrHCKNMvukVqhIs09OXRX3ChWnrsoZs8ryssISHOKLX1aMx9GrcXj662X56TmVKY3XvZuluitmIj3OfKt+GnLliXeFinPVilixoVbBQK5acWqPSnFVqF7LWnj4mW5o3aMxQmsFFVg0ZJpw8cRV7P4jCpt+2IWMlCwAgOylXkGn4F15Kle6KldripxZ2bmHWH4UDz3aClO+ehYrvtiCRTPWOrxmSyD+AOPUm5XlGFgVM7YmfCtWqAQCQemSEJ+GY9fi0KJ6KEJ8vRGfluHuLgkqEOeOXMIXRy7lT3aaPxSOvs90Q/MuDdGofV007lAfL7z/JDJTs3Am+hJ2rzuCbSv3w5wjVjsqAn/9GY1JHz+Fh4d3Uk2oBHem+M1UJ0lKSsLw4cPh6+sLf39/jB49GhkZd/7izsnJwcsvv4zAwEB4e3tj8ODBiI93PN49ceJEtGnTBgaDAS1btiTrOXbsGLp27Qqj0Yjq1avjo48+ctVtCQT3BdNWbwUATH8s0s09EVR0jv0Vg49HL8DIhq/gEf/RmP7059iz9hDMJitadW+MibOHY/Wlz7H0xCxM/XEcHhrQBjK1miIoN6TeTodvgBeWH5+B8TOfRJ0mYe7uUoWg1Faohg8fjps3b2LLli2wWCwYNWoUxowZgyVLlhRb5pVXXsG6deuwcuVK+Pn5Yfz48Xj88cexZ49j0NbnnnsOBw4cwLFjx1R1pKWloU+fPoiMjMSCBQtw/PhxPPfcc/D398eYMWNcfp8CQUUkNu42LtxKRuf6NeFt1CMjh9gnEwhKiKIo2Ls2CnvX2k+IafVaRP67G7oObIMGLWsiok9zdOjbwm7REJeCE/vPYcuK/Ti845Sbey4AgNbdGuHNL56Bj78nFIXBw9uIR0Z2wSMju8BisuBq7A3sX3cYf369BbevJ7m7u+WOUtFQxcTEoHHjxjh48CDatm0LANi4cSP69++Pa9euoWrVqqoyqampCAoKwpIlSzBkyBAAwOnTpxEeHo59+/ahQ4cODvmnTZuG1atX48iRIw7pX331Fd566y3ExcVBr7drfN58802sXr0ap0+f5r6HPA3V5dNV4eGjfj2H2GPPIo5IZzL1nDWT8KbKYpRfFaWXonyo+HRVVBBlKmByOnEaMI045ZdO6KoAINui1ktlE95U9Mk/KogyFTCZ0lARaZQGiPdEmqvzceqlXK0BorRWFm8t2raqhdnvPYEtO07hg0/W4Wof9djT2ijXjqmrT/5xa624/b74xpQKekxrqPi0Vk6FgKE0RZQfVBnrljx9PdH3mW7oMqg96raoBQ8fIyRJgs1qQ8KV2ziy6xQ2fL8dsQftZrSSgfiOIX5lMcr/ivPkH+9pQKe8qShbh7I49caJt58n3vlhLJpG1IPNquCXT9Zh2Wd2c89q9ULQf2QXtOnRGFVrB0OrK9DMXTh5DbtXH8Kmn/9CVqGwRsxMfKgpSmkMrIoZW299d39oqPbt2wd/f//8yRQAREZGQpZlHDhwAIMGDVKViYqKgsViQWRkwRZEo0aNUKNGDXJCdae2H3roofzJFAD07dsXs2bNQnJyMgICAshyJpMJJlOBMjktTQQVFdzfHIq+hFuJ6ejxUCPMmrvB3d0RPABkpWXh93kb8Nvc9QCASqH+6P9CL3R4tA1qNq6Gh5/tjn6jesBqtuL6+ThEbT+F9Yt24trZODf3/P5l6MS+GPnaI9BoNTh54BymP/M1MlKz8l+/di4eC6f+Ckz9FbApaNKhHvoO74zmnRsgvF0dNImohzEfPon05EzERl3AjlUHsGvJblipgxb3OaUyoYqLi0NwcLBjQ1otKlWqhLg4+oORt6Lk7+/vkB4SElJsmeLqqV27tqqOvNeKm1DNmDED06dP525HILgf+Gbx33jrv49gzLPdMOXK3+7ujuABIykuBT+/9yt+fu9XyDotqtQNwSOje6Jd3xYIqxeKmuHV8PjLfWDKNuNK7E0c2HgUGxbtQOLNFHd3vcJTp2k1TP1hHILDKiE9JRMfvbQIh7bffev15P5zOLn/HABAlmV07NMEvZ7qhPD2ddE2shna9W6O1756HolxyTi+Oxabf/wLh7edKO3bKReUaEL15ptvYtasWXfMExMT41SH3MWUKVPw6quv5v+clpaG6tWru7FHAkHps3nHSUwY2xMD+7XElK/FhErgXm6ej8e3/7cU3/7fUgBA/fYN0O/Zh9CyW2PUaVoN9VvWxPA3/mXfbjp+FXv/jMKmn/5GepI4qcqLVivjjQWj0fmRlmCM4c9Fu/DllOX3VJeiKNi9Ngq7czVzRk89Iod1RpeBbVC/ZS10f6IDegztCJvVhrhLtxC17QQ2fLcdF45fdeUtlRtKNKGaPHkynn322TvmqVOnDkJDQ5GQkOCQbrVakZSUhNDQULJcaGgozGYzUlJSHFap4uPjiy1TXD1FTwbm/XynegwGAwzEfr0MiTwKqSFiVOgIK3wd1MueeiKkDOVrReUzSEToGUmtT6JC1BhkdT4TEY5GS4SU0RNpxYWesRLhbCxE6BkrEVJGpkLUUGEbyLAmhLaCimzAG8bFmTAzvKFTyPA7fKFnyPA7RHgbqqwuveD5WP7LPrwwrifGtG6LhVGHHMvy3pszaVRYGN57I54h/rGn0qg2+NJAheAg3ksqZAYZ3oa4X+rZJdOoMDjOhIBxBko/BMLR36wWxMXuOYXYPQWrJi17NEHfUT3Q/KFwhEfURZOO9fH8+08iIyULZ6Mv4u/f/sHWX/bAXMwhC9IfiYh6wRuOhgq7Qoa8ob6HKPkyZxq37qsIkU92wPiPh8Pgocfl0zcw9al5iL+aWMwzRN0HoWXyKNDcmhiwbsk+rFtxEADgX9kb/Ud0QUTvpqjZsAoGjI3EgLGRMJssuHouHgc2HMX6H/5CYlyBKSwjngNyDIj7lfLeSzfZa5ZoQhUUFISgoKC75uvYsSNSUlIQFRWFNm3aAAC2b98ORVEQERFBlmnTpg10Oh22bduGwYMHAwBiY2Nx5coVdOzYkbuPHTt2xFtvvQWLxQKdzj6B2LJlCxo2bFjsdp9A8CCzfMk+jHy2K8a1a6eaUAkE5YkjO07i6F/2w0WyLKPTwDboNawLwjvUR6seTdC6Z1NMnDcKKbfSEPPPOexYtg+7fz8IhTeA9H1KUFglvLt0PGqFh8GcY8GciT9g8y977l7QSVJuZ2DJZxux5LONgCSher0QPDKyM1o/1Ag1G1ZB3SbVMGxyf2Tnrjju/jMamxbvRHZGzt0rL4eUmlN6v379EB8fjwULFuTbJrRt2zbfNuH69evo1asXfvzxR7Rv3x4A8OKLL2L9+vVYvHgxfH19MWHCBADA3r178+s9d+4cMjIysGDBAuzYsQPLl9uXKhs3bgy9Xo/U1FQ0bNgQffr0wRtvvIETJ07gueeew5w5c0pkm5B3yu/q6TDylJ+JmB1Tq0yZxJ/LWUQw4zQiRl8WIxzLCfd08uQfb8w/4pRfKuWeTpzyo9zTASCbcFDndU/nje8HIp9Enj5Tp3G7f1Ox/Jw5pcZ5os/lp9SoE2lFTkCNfbkXnniyAyZv3IDfTxds21OxeN12yo87ZiI1LrzjR8VM5BxT0lGdM74fFbCWiMvIe3qP/zQgZ2w77hOCfE7kTp2sK+bEl1avRc+nOqHbkAg0bFMH3gFekCQJiqIg8UYKTuyNxeYfdqr0PNwn/yhK0D8VvCcEyVODnCuVAF6c8RT+9Vx3SLKEveuPYMboheqQMk6sUJGnCymHdqLPzVrXQN/hndGsUwNUrhoAWZbAGENaUgZOH7yAHav2469fDxL7PHS7UuFYfre/L/NTfqU2oUpKSsL48ePxxx9/QJZlDB48GJ9//jm8ve1xXC5duoTatWtjx44d6N69OwC7sefkyZOxdOlSmEwm9O3bF/Pnz3fYquvevTt27dqlau/ixYuoVasWALux58svv4yDBw+icuXKmDBhAt54440S9V9MqBwRE6rcsvfphEqrlfHHltdxOysLnb/7piCfmFCJCVUxZcvbhKoonr4eePiZbug0sC3qNqtRyKJBQcKVW4jeeRIbF+3A6f1n+fpHUY4nVK27h2PKN2PgE+CFW9eTMX3klzh37Ao5UXLXhAo5BV+ysiyjy79aofvj7RDevi78Ar0LJsQ3U3B87xls+nl3/grlAzWhqujkTahuxFaDBxnLj08HZSL25ylvKmoClEl4U1GeU1TZFGKSxTuhotIyOL2pACCDmGg5401lIyZPjJhk0RMqPm8q7l/+3HHniLLkxKEMJgTUJIuYJEyaMwiPtmqEF779DfvPXgEAZIUR9bnY28upmH+8sfy4x48aq3v3q+L2puL0q+KO70dNqKhtL96JEtEur4cV7+SJdxLD6/NUNF9AiB/6P98DHfq3Rs3wMBg87N+vdouGeERtPY513+/AtTM3SR0P2T9nvKk4J0pSCV3lPX2NmLZsEpp1bmj3lJq1Bks/+uOObZCaI96YetTkiTvWIJEvtz6jpx69B7dFl4eboV7jMHh4GfI9y25eTcLhnaew/sc9uHzmZkHZ3OdZTKjKGWJC5YiYUBWfdr9MqDK6+WHvtBdx6XYKBsz+AYCYUAFiQlVcuxVtQgXAYSUmrF4I+j/XA20jmyOsXgh0Bvv3kSnbjCunr2P/n4ex/tutSLyRXHz/ytmEauir/fHv/xsEjU6Dk/vOYNpTn+cHqb5TG+VxQlWUwBBf9HsyAhE9G6NG3WDojfb3y5xjwZWzcTiw5TjWLdqF5IQ0MaEqb4gJlSNiQlV82v0yoYqL8MBXzz2GLg1rYfBnP+PMzdtiQgUxoSqu3Yo+oSpKg9a10PeZbmjVvQlCawVBo7U7gmdn5ODCsSvYu/oANny3HRkpmXdu1w0TqjpNq2Pa8kkIrh6IjJRMzBz9NQ5tOU5nrqATqqLUrOaPfiM7o023cFSpGejwfh3ZH4POfdqICVV5IW9CFRdbAx7exESJOPZLhaPJISZUWYr6Icpk6slFpkKEeyEmT+lEiBpqQpVB6a+IcDTO6KoAOiRNFqGryiY0VCZiQmUhw9FQuiriw24hfvlTEypOXZWG0FU5owui9VJUu86ETqHaUOezGTUIquyNFT++iJjYm3jplZ9xub96nGkNlYsnqbwTKt7x4524kmN175NUmdBB8U+eqHxu0lVxhre5l3A0+WmUtsfVIWB49U2ShBbdwtF3RBc0zRdMy2CMISM1C2ejL+GvNVHY9vPfKosG3rArJZ3wOZA7VlqtjDcWv4Suj7cHYwzrvt2OLyb9UOL6ihsDFdT46YgJEDV5otrgnVBxTMZaRNRBn8Ft0axtbeiMQECA//0RekYgEFRMbt3OwKnTN9C4UVWEBPngMrLuXkgguA85uisGR3fZT7zKsoxOA9qgxxMRaNy+Llp1b4zWPZpg0pyRuRYN57FjxT7sXn2I+FO7dOj1dGdM/HwUDJ56XI65jnce/wTxl2+XUevlj6MHLuBI1CUAgI1xxhJ0MWJCJRAIHPh03mZ8++WzeHViXzy173d3d0cgcDuKomD3H4ex+4/DAGC3aHgiAt0ea4MGrWqj4yOt0OnR1rkWDck4/vdpbP7pLxzeWsy2mxNUDgvAe79ORu2m1e2eUi99h02L1SffH2TIYNllgJhQCQQCB85fvIXLVxPRrnUteEfpkUE5FwsEDzBWsxWbf9mDTYt2AMi1aPh3V3QaYLdo6PFUJ/Qc1hk2qw3xl2/jyM6TWP/tdpyJuuBUuy9+PBwDxva2e0qtjcKH//5S7SklcBtCQ1UMeRqqhNia8PRRDxElSrdwelNlETEvKA1VlqLWHVF+VZSGKp3Tr4rWUPHpqjKstIaK8qfiFarnEEJ1CyVU59RQgRKlO+FN5WoNFa8uyCkNEK+uqpCOp2Xrmvj406exJjYGr27a6JiRsrHhFfrz+n1xaqi49VKcujRKW8avq1J/J5BCdTKN0EtR3lSUhxWvNoo3H/HXPaPE8NwaqnIuVL+Dn5FjPk79lUZGQLAv+o3ogoi+zVCzQZV8iwaL2YobF2/h0I5T2LBoB66ddQyTxrIJh3CmoFWPJvi/nyfAN9AHt64lYvoTc+55clZWY8BTllf4TuqqCP0V09rLWm0mbIv5RGioBAKB+zly+DJu3UrHI/Ub4vUtm2F9wEN3CAQlITkhDUs+XY8ln64HAITVDUa/EV3QpkdjhNUJxuCGvTB4XC+7RUPsTRzYfAwbfvgLty/EOdTj6euBqSv+gxYPhcNmVfDju6vwy4e/0wJ+gdsREyqBQEDy3dc7MOV/A/FG56744G+h0RAI7pXrF27h23d/x7fv2jWJ9VvUwMNPdUTLhxqhTtNqqN+yJoa/9iiyM3Jw8fhV7F5zCCE1A/Ho873snlJ7z2DaE58iPSnDzXciuBNiQiUQCEi2bT2Jcf/tg6ebNRcTKoHAhZw9egVn/jmX/3PLbuHoM6wTmnasj/AO9dCkUwMAQEZKJmY9Ox//bDzipp4KSoKYUHEgQ72nqyOEJIpEaDeIfFRZPaXJktS6BT2RpiPT1PUZCMGJQVZrlgwadX0GRZ2WI6nLAoBeVret06jTrDb1uFqIPXWbRr3dpBD+KoqGMsWjDOvUSUTIRTAr1YY6HyHVAGE/BkI6x59G3BtdloibRowVeb+EXOX7LQcxecBDmFC3HRZtPwQASK9NaF3IMSXapaQznH2h8lFn1HnHVKHCjdl4x4ovHho1BpJCxU3jS2OEH49EBVykHkoyMCM1WJyx3ohBlYiylExXIj4g1HtOf7iI54+MO0fUR/lakR9WyhOLujdCD0fVRxl2ehVoWo8euoyjhy5DSUqGLMt47r2hGDKpH6K2HseB9dHqdq2EKJBXM+auMaBkA7zPH5VGbXsqRf5fxpQsSJBAIHig+GFnFLLNFjzXs527uyIQPBAoioJv31qG29eT0elfbSCXMJafwH2Id0ogENyRFXuOwc/TiH+1DXd3VwSCB4bln/wBnUGHoZMfdXdXBJyICZVAILgjc9fthsVqw8T+nd3dFYHggeGPhdtgyjZj0MR+7u6KgBOhoboLGkkGabxDIIPw4iHTCA8bSn9F7BtT2ig9kcarq9LJ6nxaRZ1PS+ii9ERZANAT+/Y6wk/GLKsfPy2ltSI9Uqg0Si9F7PkT2ihaA0SkUfKDsihLxbrl1ACRmjFKf0DoZAJP2s2ftu+MQZ9eTfCwMQwrpavq+qg2SC0YZxqvDorStJHaLUpbRvWFKEuMFa1L49VBEfmobR1CDyfJxENE+faQehVO7Ral9yH0OYz4ziLjv5FplMaL+p51IpAL9YGjPgzU+0vozSTue6PGhU8XJAdWcvh519po9B7aHq16NsWRnScL9Y9zLaQCjgGtjaK+ryhtHnP4f1kjVqgEAsFd+fzLLWCMYfyLvdzdFYHggeGb91aDMYbnZwxzd1cEHIgJlUAguCtZWWb8c+giqlerhMaVg9zdHYHggSAjLRun/zmPei1rwT+47By/BfeG2PLjgNzKI5Y5FeKoqI7IZyaWI/XEOU8LtUVH7P1Q23vUNiBluWAk0kyEvYKJ2J4zE/YKAJBjU9sp6ImtPBORplXUc3wNsc1BWykQ2xK824DU1gx17Jq4ZWe2rOg0yq6BWN4mrQX4trt4j/jLpoJGPp21DstXTcAH3Xvj8WVLHTNSY0puXXLaP/BaKTizNUj2hW97j9xGJeqTqLLUFgn1py2ZxrlVRm4hEp2mbEVI2wTeo/ZEu1T4GGJcyKP2vPYKpJUC31jR9gqc4+KMjQBnfQv/twpzNr+JFz8bhZnPf2NPTE0jyt6/Y0BuXVLb1XmfQSLcUVkgVqgEAgEXiYkZOHXqOlqEhKKqj4+7uyMQPBCcjrqA2zeS0fmR1sJCoZwj3h2BQMDNnE82AAA+7BXp5p4IBA8OK+duhM6gxZCJfdzdFcEdEFt+AqcI1FfGrOazHdKWXlmL365tUOX10BgwtHpvdKncHEGGAGTbLDiVdgXLLv+Fw8nnXdqvOr6V0LVKbTT1D0WzyqGo5x8IrSxjdtTf+OLAAZe2VZjGVYLxQtd2aFszDD5GA26lZ2JXzAUs2HYAqSnZqvx92zTArFGPOKSNmb0SUWeulVofneHihVs4l5SILjVrwVdvQJrZ5O4uCQT3PWu/3YHR04dg0LhIrPhso7u7IygGMaG6CzKkYvRSxJFXyg6B0F/pqfrIEDXqfWDSIoEQFRgJHVQOU2ub+MPWqPNlSzZoc/Pm2HIQnXwQAHA9+zL0RfRVQRo/zGj2CsI8Q5BkSsXBpBPw0fqhQ2BDdAhsiC/OrsHv13bDLFFWCupxsBKaHblQvpENW2JUo/aqPJBYMfoXTg0Qeaze/v++4fUxe3A/6DQaHLsah+vJ19AkLATDO7dC3+YN8O8vluNqYqpD2evJaVhzwH4cunN4LVT29QKT1W3T+iE+DRAVkocYUloDRDyrH/+yA19PGIIZ7XthymL7xDm1Ief4OWOl4IwVBa9eyintFp8ujbZXIGxAKH0ToX8hw9tQ9hmkXUPpWylIlLaR0lVR8NorkPYABLxhV4gvCUZYUXDbCFAaJcraghpnTw8AwK61h9F7aARa9m6Ow7/tIfrnhJVCWYwB9b5xjgG3vULeZ8ZNtgliQiVwCZnWdPxweSEAIN1qVL3+Ur1hCPMMwdGU03j/1NcwKxakmQ2ICGyED5o9h5frD8TR5PM4bk5ySX9iU29h4an9OHErASeT4vFSs44YXK+pS+qmCPb2wozH+kKn0eCdP7Zi1b7jAABZkvDhkL4Y0Cocs4b3x9OfO4q5j1+Ow/HLcQCAb8cPQWVfr1Lro6v458w1xCWno3er+pj68yaYrW4KnCUQPEB88+7viHwiAs+//RheIiZUAvcjNFSCUifMowo6BLaAjdkw7+wvMCsFq2cHEk9jY9whaCQZT9dyncfRivNHMfPIdqy9GIPzqUlgnOas98q/O7SGp16HPecvY8Xh4/npCmN4d802pGXnoFmNUHRqULNU+1FWzF2zG1qNBv95rKu7uyIQPBBkpGbjdPQl1G1aTVgolFPEhEpQ6rQNaAUAiEm7gFumZNXr2+IPAwA6BTbOdaaveEQ2qgsAWHfitOq1LLMFO2Iu2PM1q1em/SotNkbFIiUjG493auburggEDwzfTPsdAPDSnGfd2xEBidjyu0ec8abitZyhQtTwhp6xEIZJRonQVUlqXZWB0l8pVD6rQ/iZPJ2VqYjeqrZXNQDAhczL0Bbys8nzprqYeQkA4KE1oI5PAC5lJjiUt3J7U3GE75CK8aYi9S/qbJSfkbdGh1qBAQCA43HxpAbqxM14DERjNAoLdtDfOGhxpIK0ohodWp9TBhogIi3gjD0czZJf9uGlsT0xvnVrvJ99iK8vLvaX4rVC4ve64vTJItOo8B18Y0r5VfGGspEozRNvOBpu/Qun/op6Q5wJR0OEniHHitObitYFEbovSu9DvR+8PkqU3oc3FEshDdrpI5eRGJeKzgPbQSoS4osplEle+RkD2puK+iAR9VG+UqTWKq8+EXpGcJ8SZLA7a98mVqcAIMtmQqbVfgKuikclMk95Jsy/YPn9Rmo6mScuNz2s0v2zVL/y14PIzjbjySci3N0VgeCBYcX8rdAZdBg6+VF3d0VQBDGhEpQ6Ro1dpJ5jK/6IfXbua15aQ5n0yZV46fX5/842q1f3APu2HwB4G/Xk6xWVNX9Ew8fHiCENm7i7KwLBA8Efi/+GKduMxyf2d3dXBEUQEyqBQHDPfLf4L1gsNvy3Q2d3d0UgeGDYtXIf/IN90bxbuLu7IiiE0FBxIBPzThvhEUXlo7ypKK0VqZci0izE/jLpJUUIRKh8vH5VVD6TrM3Xb0kSy9dQFfWsMtns23leWq2D5kpfKLaYh8aQn1cnOwoizESgKcqbiozvl6clydUDSDKDxBnfj5BvkLqbDIs5/99Gow4ZJrMqn4fRPqYZOY6vFacfKqrVomJt8XtT0W2o0/g8jmRLwTgrFgU7tp1E777N0KNqbey6fKlQWT6NF69+zfXeVOo0MuYf79hzxlHkfY+4/X0onydOPyja54kYQA01CJTuhugL0a5EamyI+nhj+VH3Qca2441jx+vVxAd3bDtK50aM38KpvyJyxEMYM2sEJvR4316fLUPdLvWclsUYUPdB6fpIvZkT3lR5aW7yoRIrVIJSJ9F8GwBQWU/rozw0Rnhp7eZ1cTmu8aEqS64X0k1V9aVj3FXxs6ffSCGCmlZw5s3bAoUx/K97D3d3RSB4IMhIycLpQxdQr0VN+AfdP7rMio6YUAlKnatZlwAAdbxrka/X964BwK6jupp1q4x65ToyzWZcSrIL7ptWCSHzNKlqTz91PYF8vSKTlWXGrkuXUNvfH02Dgt3dHYHggWDhW8sBAONmPuXmngjyEFt+9wjpl0QskVLbezKxbKojbBgspG2Cug3uEDWcaZS9gomwV9AVCj0jFaqraB9PpB7EwLAn0MCnLkKNfkjKdUPPs1DoHtIaAHAo6Thk2aKa5VPbezJxjFYmtqxkTd5Wn/1nSQK9vUdtT1HHwovZdtpy5jxe6NAW/2raCL+ePOWw1O6p06FHgzoAgC0x5xy3/ArXl9ecDNWfOrzbdmWxZUU8qvhowVb0mPUCZnSOxL8/tLvBJ7bg2/Ij++yMlQJ3Gmc4GqfKcm5nknYNfNt71PF7idNygd6G4dya4bVScMaagTf0DLU/T8G5jUWFcaFsBBjxfexUKBZOKwXJ0wOxp24iMS4FnR9tbQ9Pk5nFVx8FuT3qxBhQW8S81hG8dhJUfXlp1GtlgFihEpQ6cTnXEZUcDY2kwejaz0JXaHLW2r8xegZ3gI0p+PXaJlXZkbX6Yl+fmfiy7Zgy6WuHKtVx6YXXcOmF10pUbvGhw8gyW9C5dk0MbVEQ4kaWJEzt3xN+HkYcuxaHPecuu7rL5YJbKZk4dv4mGtcMQZVAsQUhEJQFK77cCp1eiydeinR3VwQQK1SCMmLRxR8Q5lEFTf2a4OMWM3Em/Qw8tf5o4lsPsiTj2wsrcTnrBgBHW4E8A1UrqUQtnsb+oXinZX+w3L/Ka3jbjTeH1WuFHlXq5+cbs/M33MrOzP85769Li1Ky9hIyMvHGuk2YM7A/PujXG080a4rrKWloVjUENSr541ZGJl5fsb5EdVY03v9pK1ZMG4m3R0bi5c9+c3d3KgQhIb5Y9tOLDmnff7cLv/zkGKutfce6aBgehgYNQlG/URUEBtk1eU8/OgeJ11Nc3q+6TcLQslN91GtaDfWaVUPVmpUhyzI+mvgTdvyuNnF1Fa26N8bjEx5Gw7Z1YPQ0IOHKbexefRDLPv4DOZlq25UnX/sXRn8wzCFtRJ3xiL9c8aQD98Ifi/7C6Lcfw6AxPbBs+nJ3d+eBR0yoBGVCujUdU0+8h39V7Y+2ldqgVUArmGwmHEmJwerr23A8NZYsV9+nOgDgj+sHS9Set9aAlpWqqdKrePqiimfBCopBdlyablbZrnVaEXscJWVj7Flc/WEpXuzUHm2rhaFxaBASMjLx8z9HMP/vA0hJJpbk7yMu3EzEhRuJaN+oBnw89UhEjru7VGHIzjbjr132sEUXzsWrXp8ydRC8fdRBx0uLpyf2Qac+ZRtWaNDLfTFu1tNQFAUn9p5BSkIamnRsgGFvDESXx9rh1V7vIS3R8STbxRNXsfnHXQBj6Dq4Azy8y26Mygu71kSh99AItOgWjqO7YtzdnQcaMaEqZSgrBQ2heVKIPXZe2wQ9oR/IIXVVhL0CmcantbKHnsnTUBWyTdCo6zQoVjBkYO2NFVh7YwUAINXiUdA3Oe//Be1oJQ2a+9fF6bSr2HUrGrpCcx8tsUdvLaS1ikq+iCZrpsFsVj/iVpNjWUkGlFzNSddqtZBlMWPOkT3ksX+J0roUynfidjxeXvsHZBOhnaO0QoXD0OQWUSSgqHyB0kEphE5Goo7pU9ooXtsEzpAovpfs1hFffLYFn370FN55rCeei1mnboQ3BIwzVgqcdhf8Ze9dL0Vq7grrO3LHIzU1Cx99VGi8ZMmh3d1/xeL6tSSci7mBc7FxWLlhcn4+MhwIoZci7QsoiwRJxukjV3DlbDzOnbyO8yev45WZQ9G8Yz27fi7vu4rSc5GhU+4eH6hus+oYM+Mp2Kw2TH1yLg5tsf9Bo9drMH3lK2jVowkmzhuF90d84WA3cHDTURzcdBTMZkPzbk3g4W2EJEuQZInUAJWJlYJToVg46ys09t+8txqRT0RgzKwRGN/p7bv2j9nUDzmplypPY8BpJ4G8nYUS7jC4CjGhErgET40vBoRNAgAcTjmEU2mHna6zsW8deGgM+PrcBqfr4sGg0aJdSBi+OXEQt7IzIZWBxLBZ9VA81aE5AKBOUMULu1OUI0evID4hDQ91aQB97AaYqS9CQYmZPetPAI4eYKXJygU7yqSdPJ58pT9kWcaGH3flT6YAwJRtxqcvfovFJz9B10HtUb1BFVyNuVamfSvvZKRmIzb6Mhq1rgX/YF+kJNx/1iwVBSFKF7gEvcYDLQN6oWVAL1Qx1nJJncdSz+KRvyYiKvmsS+q7GyabFQ1/nIPZh3eXSXsAUC3AF4+1aYLH2jRBZR+vMmu3NFn47U5otRr8X6fu7u6KoAKg1WnQvo/9j4odK/erXk+4mohT+84AADoNaFOmfasoLJz+OwBg3Mcj3dyTBxuxQnU3dBEASk+EWdFJtSTg3RMDHNLSbB7F5BYUZcOxM9hw7IxDmmwuJnMFYceu05g4vjeeCm+Gabu3u7s7gnJOWL0QGL3skRLORl8i85yJvoRmXRqhXvOaZdizisPp6EtIvJGMLo+1c3dXHmjEhOouSH7vAaZ+qnRKG0X58yiEKEMmMlJpGiJNR3h0mAmtFeVDZSG0VrRfFaGBIkPU0I+Plti/1spE27K6Ha2srrNoKBp7mnrrw0KkyYSHlUR47/CGo+ENz0KGWHGxtodbj0SFWCHeOkofRmqAiHxFt6KW/LIXL70UiQnNO2L+gQMF7eqpcSba4AwpQ46BM2mcXlzk+BG6JYXQ58iFxo/laycldduUDqqI1pJJEulxROqliM8H6UNFhv7I+79UoImifK2IvpDhaAp9j4XWDAIApKdkIjvT5FhH7r9vXbN714XUCrq7N5Uk5eZxQkdDPmx83kou96biDMWycu4GvPjRcAx94zGs/Mwuk2BZlDcV58ZUeRqDknpTCR+q8omkCQI8R7m7GwJBheLXVQeRaTbjhTZii0ZwZ/JO5lG2CHnkZNpPjHr6iNXv4ljz9TaYss0Y9FIfd3flgUVMqO4CUzIg+bwCsZgnEJSMn48eha/RiCeaNr17ZoFA4DS7fvsHAcG+aN61obu78kAiZgl3gaV/DMl/DlilZZBs5wBtE0BbF5KkhZI+B8j86o7lya08YplTIY6K6nXN4O0zHjp9B8iyD2y2BGTlbEFK2hwoSmJBvlyLBB+Pgaha2bE/Z+KHIsNkF3rqiL0fanuP2gakLBeMRBoAmIjtQROxlWcm7BX0xJFevUadZiLSSCsFIkSNjdgGVIgtEso2gdwGJK0U1EXpLaZ7z0eHbHEmnApvGp+VwvcL/sZzrVpjcrtO2Lb0BADgVvuSW1Hkp1mJz40ToXacC3njIiuFQuNWNK9Ehp6RVD9TY0+5IfCGrSHz5X2PSXKB/wdxv/QR/zsftc/Osq9MGb0M6rZzvw+MXvZVrKz0bKJvuWMlFfxbkqViLAOIwpxhV+gQP1R9nOPCbSPAZ08heXrgm/fWIHJYZ7zw4TBM7DsLyKa84KiHnG97j99iwtVjUEJrBuq1MkCsUN2NnLVgSjpkfXNIHo9D0jWEJJX+PFRn7I/AoD9h9PgXbLZryMneDECBr/dohIVsh1ZTS1XGYruC1MzlSM1cDovt/gvCK6hYKAqwcd9pBAV4o3Pz2u7ujqCcEn/F/sehj78XPLwNZJ6gapVy894us35VRDJSsxAbfQn1mlWHf2Ufd3fngUNMqDhgmT+BMQZmvQTl1sNg2atLtT1JDoGn/xxIkg6pya8h6dYjSE0eh9vxXZCRuQoaTTCCAueryuWYoxGX9Arikl5BjuV8qfZRIOBh9pIdsCkKXh3W3d1dEZRTrp2Ly9dP1W9Zi8zToLV9Qn7uyP0ZC9OVLHxnFQBg7LtD3NyTBw8xoeIh8zMw8wFAUxOQKwOEC7krMXiPhiR7wpTzF7Kzfin0ioLElDegKKkw6FvBaOheqv0QCJwlK8eMPUcvonqIP8Jrhbi7O4JyiNViwz+5Zp49hkSoXg+uHojGEfUAAHv/iCrTvlVETh++hMS4VHTu38LdXXngEBMqXlL+A0CB5D+72Cwy8Z9GUl/0f1L+pTM+DACwZK+GTpIdL2QjJ2czAMDHsz/0kgQ59yRz4Stvx1krMegkBTpJgV6yqS4diEuyEpeNuKh8xeVVX1ri0musqksrKapLJ9tUl1ZWVJdGZqpL1lCXorokDVNd0EB1MQ3jvKC+ZOJyJh9xKcTFZEl9aYiLykf1hcjnfd0C7+sWzJ1lP8L91rCeuaFLHC8mM/XFeW/cF9FnEBeVT9FIqsup961oP5A3DkXGUCIujaNmyv4eyaoLudfk6YOwKWo6Jk8flGsn4HhRbVD58r9QqC+bQlfk0AhsuD4Pi/dPK6Ye+yVJssO1Yt4mKIqCPsM7o02vJvn3Z/Qy4tX5o6HRavD36oO4di6ebptCkvkuXpjCd5FFFdUF8mLqixEXVVajyb9WfbUNOoMOQ//7aL6m7E4XNS5MYarLbWNAXkTZ/DHif1tdiRCl88KSwLKWQ/Z6GkxTt/Takbyg0dqXt62WY2QWi/kY4PkEdDpxekpQ/klKysSJk9fQtEk1VD/oh6vpqe7uUoVjxIhO6BBRV6WLfu/DJ2A12w93nD0Th3mzNzq8LucegLFZS7aq3q5bQzz9Uq/8n2vUC7b3Y2JvDBjZyZ7IGF4ZNNexvVxRudVaMg+o88eu4pu3V2Lsh0/ivRWTcHzPGaTcSkfTjvUQWCUAV8/cxLz//FiiOh9k1ny/C6P+bwAen9gfKz8lYmoKSgUxoSoJ6e+CeQwEdE3unvcekTXV8v+t2K6TeWy56RpNjVLrh0DgSj75bBMWfTMaM7r1wYg/V7q7OxWOqlUD0LhxmCq9foPQ/H+bzepJTP3wKgCAjWtKFlvTr5I3GrVUf79UrVkZVWtWLrZcvWbVAQCblqlDyNyN37/agkunruHx8X3QsHVtGD0NSLiWiGWz/8TyT9chO4M6sSYojr/+OIzIIe3RrGsjHP/7tLu780AgJlQlQgFL/xCy3wel1oIkeef/mzHC5bZQuix7k68LBOWNy1cScfHSbXSqVQP+BiNSTOKXY0n46KN1+OijdZCJlSapmIDJgUE+qFknGLt3xOD0ietUIIdi2fp7FLb+XkivRNiZwKq2PWndrSFu30zBmu92laC1AqJ3xSB6V8yd232ACKkeiB+OznRI+2HmWiybuyn/Z79Ab7Tt2RhtejZBgxY1EFQlAApjSLyZAgB46dNn8GK7KS7tV71WtdCye1PUb10b9dvUQdW6IZBlGbNGzcf2pXtc2lZhWj7UCI+/GIkGrWrB6KlHwvUk7Fl3BMs/34ycLLUxrF6vL7W+UJTahCopKQkTJkzAH3/8AVmWMXjwYMydOxfe3sVPAkwmEwwGA65cuYLGjRujb9++mD9/PkJCCsSsCQkJCA4Oxrhx47B//34cOXLEoY5Lly5Bp9MhLCwMw4YNw7JlywAA+/btQ4cOHZy/seyVYD7/B0n2AqR7C2Z7J28quZBHlSZfaFK4rGOKDEBHbBhLuWkyGDR5/yZ8VOjQM+o0o0SEnpF0qjSADlNjkNV5DUToGRORRvlQ6YnwNmYin5nwptKQ3lSEloIMM0P57FCeROps3J5JvN5UzvhGkfemLkv2WUvUR/xOl82OiZ9/vglzPh2ODzr2xsQ/C7YhbJ6EDxCntxcIKzRuHy/idzWvhxUd4ofvOSiclhdKxtfXA29Mtoe4+mvPGez95zwdBocI90L5VTGNhNYd68JmVbBowXZ7XVRZKnwMb1oRP73gsACE1Q7GZ68thdlkLXidCi9C6Z64Q5MUpLXv2wJdB7UDGOAXmGcTkKeNUr9x5FhxelNx+zJR3l7Ee8nly5T7c3ZmDnb/EQ0AuHj8qkO+MdMGo+eQ9rDZFFyOvYn9W47D6GlAgxY1IEkSajetjoeGdsbfqw/aq8yhHOlL5k014q3B6DSQiBvI7Forl45BLo+N7YWx7z8BRVFwcv85JN9OR9OIenhq0sPo3L8l/jvwE6Sl5jjUoVBhe0qRUptQDR8+HDdv3sSWLVtgsVgwatQojBkzBkuWLCm2zLvvvosPPvgAQUFB2LVrF8aPH4/HH38ce/YUzHhNJvvD0KVLF+zfTy8r503a5s+fjzlz5gAAAgMDXXVrgPkgYOwOyfgIWEbxIvV7gbGMgh8kT4Clq/JIkqc9r5Khek0gKK8cO3YN19PS0LdBfehlGeYy/rIrz3h66NEvshkA4PqNFOz9x3nbk83rjmLzuqNO18NLwvVk9Ks+qczaA4BaTaqhz4iuZdqmO0hLzMSnE34oSCj0B2B6SiZ++ugPbFp+AIlxBfpEo6ce//v6ObTu2QSvf/MConeeREYKvetRUmIOnMWlU9dwLvoizh29gskLx6BFt8YuqZuibrPqeOHdwbBZbZg2Yj4ObTsJaGQYPHSY+sOLaPVQI0z4aBg+GLvIoZyVWEUtTUplQhUTE4ONGzfi4MGDaNu2LQBg3rx56N+/P2bPno2qVauqyqSmpmLFihX44IMP4OHhgTZt2mDRokUIDw/H/v3781eXqle379HXqVOHbFuSJPj5+QEAAgICSuP2AGYP1AlNVUDbCrBGu67qQropWRMGxare+9Zo7FoKm+2qy9oVCMqCWbv+xrwBj2JK9+6Yvn27u7vjduJupaHroNmq1TwBHys+XYcVn64DiFXnB4UFb+dqEousxudkmTF92Of4/eYC6Aw6tO/bAtuX73NJm8s/XlvwQ0lOSt4jQyc9DFmWseGX3fbJVC6mbAs+e/VnfH/gXXR5tDWq1V2Pa+fjS70/xVEqI7Fv3z74+/vnT6YAIDIyErIs40ChyPOFiYqKgsXiuFXUqFEj1KhRA/v2qR8CalIGAAaD3Wk3MzMTVatWRZcuXbB27Voyb2FMJhPS0tIcrrvDIPnP4chXAlgGFOtFAIBW15zMotPb0y2W465tWyAoZdafOYOkrCwMbS5OqAoEpY0p24zb1+0LAD2GukDy4ga0Og3aR9q/L3b++o/q9YRrSTiVu6LbqR/9O7OsKJUVqri4OAQHBzs2pNWiUqVKiIuLK7YMJSALCQkhy9SoUQOhoaGqdH9/fwBAdnY21qxZg19//RWPPfYYVq9ejQEDBhTb5xkzZmD69Ol3ui0VzHISsr4ZmMdTQPYyMo9MzFlthDFo4Xy2nM2QvcdC7/kYrNmOJ6I0shcMxt4AAHPOBmgkKV8jVZg8mYHdh8r+up4QC1jIWH7qNAshLjESWikAyGFqvRSly9IRMb50RBAyPaWrktX90clUG8Q9E2U1VHw/SlfFGd+PkFsUo43i1N04E5+Oty/3oAEqSKM0Wer6go4AP/x6AK+M7IE3anXA96v3I47YsSFj+ZH3wafnIvVXvPfGq6typix1Hza+2IBUuxKvHomKYUm8lxIVt49amaCCCBI6RjrmH6WxUbchkfHfiD7zxvKj7oOM78cbx+7OsQvvDeYQ446MbUdsoWt9vWDwtP9eDY+oD8nTA5LZzNU/8jl1wxiE1Quxx3kEcObIxfxxKDwGZ49eRrOO9VG3SbUCTyo3UKIVqjfffBOSJN3xOn26bI5nyrKMadOmqdKNRnsQzcqVK6Ndu3aYOXMmRowYgY8//viO9U2ZMgWpqan519WrHNtppm1gLBuSz5sgh1LfHnLoGcihZzjuqABr5vdgSha0hq7QeQ4r9IoMH78ZkGV/WMzRsJju7SSNQOBOlm+KRlaOGcP6tXF3VwSC+5q+T3eGX6APbFYbvHyMaNahnru7VGJCawYBsGvFsjMoQT1w60ayPW8NF2ql74ESrVBNnjwZzz777B3z1KlTB6GhoUhIcAzOa7VakZSURK4qAUBoaCjMxMw5Pj6eLJOYmMh9ai8iIgJbtmy5Yx6DwZC/XahC2xiS77RCP9v9WSTPJwFbIiRtNSBoN1jiQEC5VaigfZLFGL2SUyxKAsypr0HvPxce/rOg83wSzHYNGl0LyNqaUGwJSEseX7I6BYJyxMrN0XhmQAQGdG+KhTbawFYgENw7tRpVxeh3BgEAln++CU/9px+enz4YEzZWrPA9eQGzc7KIlbVc8mJBenoby6RPxVGiCVVQUBCCgoLumq9jx45ISUlBVFQU2rSx/xW6fft2KIqCiAh1rCYAaNOmDXQ6x62i2NhYXLlyBR07dlTlX7hwIYKDgzF69Oi79ufIkSOoUqXKXfNRKGCA7A1J31L1mqSpUujflcGkYACFJlTaXAPQ7FUO5TQ8S8w5G5Bz+xJ03uOh0bcDdE3AbAnIyVyMrPTPAOV2vv2CjrBhyI8UUcg2gdpO0xHbj+T2HGdacenU9qBJUT9+BlmdZiLStNR2IWGboCW28rREPqtN/Z7IxLaTjXPLj9qaId923u04TisF7m0sZ2wdnNiy8kiwPwc/zt+Jp/u1wdhBnbBwlXpC5VSfObfjyLLUTgVZn/p+FeI5oPvCaWNB2iFQ/aPq49veU1mvoxiNMVUftSVOvnHUdiHx+SAaZtTxe257Bb77JS0DeCG3BoltSqJdRjxsqq3awvUX3m4jx6UgrXIVf0z7cRw8vY3Yt+kYfvpkA1p3D0fDVrXgH+KPlIS0YsveMY2icB9ZbngaSvNAbBdyjUHhcsWNQeFq8sLPuIFS0VCFh4fj4YcfxgsvvIAFCxbAYrFg/PjxeOqpp/LF5NevX0evXr3w448/on379vDz88PQoUMB2PVPp06dwoQJE9CxY0eHlahz584hIyMDly9fxuLFi/MnbI0bN4Zer8cPP/wAvV6PVq1aAQB+++03fP/99/j+++/v/YbM/0CJa1D867q2kCr9AslvCljSiPxkydAFTMkCy5h3T80q1hMwpYxzSMshRQACQcVCUYAt206h/8PN0bNGHWy/csHdXRII7gsCgnwxY+UkhFQPxKEdpzBjnN1KYOG03/DJmlcxdtZwzBr1lZt7yU+eQ77Rs3iTzjyNVZab3fRLzYfql19+wfjx49GrV698Y8/PP/88/3WLxYLY2FhkZRX4YrzzzjsAAJvNhpiYGLz//vv5E6M8nn/+eezaVaAdynv94sWLqFWrFgDgvffew+XLl6HVatGoUSMsX74cQ4YMKa1bBSyHAMsRQNcO0DYHrMcAGAB9GyDze0C5XXptF0KnawVPr3/b/62teHvlggeLefO3oG/vpvhfpx5iQiUQuAC/yt6Y+eskVKsXgsO7YvDu6G9gyY31GBN1CYlxqegyqF2FmlDFX7H//vTx94KHt4HUUQWF2S2S4q8mlmnfilJqE6pKlSrd0cSzVq1aqpMZeRomb29vjBgxgiqGnTt33rHdZ555Bs8880zJOusCWMoESEF/QfKfA3a7FwATWHzZHuHUaGvAw+vJMm1TILhXcnKs2H/gPDp1rIcWQaE4eos+ASwQCO6OX6A3Zq76D2o0qILov05j+jMLYCkSlmjVgq0YN30InnilP1bOWe+mnpaMa2fjkJNpgtHLgPota+HY7lhVnvotawIAzh1zrzejiOXnKpQEsOzfIXsOBvMYotJN3QuU5YKG0DwpuXvOlpy1uHXD7rmVU2hfW5e7JW0h9pX1hH5AR6RRVgo6Ig2gw9SYiDA1VOgZg4Zoh4gRoifsEKg0WldF2CYQmiyFslIgtTOUvoTPRoC0B3DCSoE3nArVZ4k8pk+064QGSC5iwDhn7kZ06jgeH3TqjUGLC/4As/gTN+KMFQW3dQRnPme0ZZxjRd4voUMhQx9R7y93mBlO7RG3bomKS8RpSUIev3etlQJpGUCNAa9eyhkbgWJCzxT9NytSn28lb8xYNQm1GlVF9F+nMW3kfJhzLIDW8Vf8mm934bkpA/D4xH5Y9cXmgvqImIl0mB51PqbI+X1juaFnaG3evY2B1WzFP5uP4aFB7dBjcHscyw30nDcGwdUqoXE7u9H33vXRbtVQlb7F6YNE2v9ybRTecndPBIIKQVJSJg5du44mIcGo6e/n7u4IBBUOb39PzPztFdQOD8PhXTEFk6li2PXrPwgI8UOzzg0x+avR2Ji2CP/9dmyZ9LX3yIewKecX/BD7WYnKrfhsAxRFQZ/hndGmV5P8dIOHHq/MfQYarQZ/r43CtXPuc0kHxAqVi7GCpc+B7Pt/YD7vAOnvurtDAkG5538bt2LD88/gvX6R+PfSX93dHYGgQvHKZ/9GnabVoSgK0lMyMX5WIe/CQiuB+zYew75Nx7DwrWWIHNYZL3z4JK6cvgEAsFpKdtipff/WGPH24NyfJNRobA+HNvKdJzDwpYfzj5lP6jrVoVzeqpethO2dO3YF37y9EmM/fBLvrZiE43vOICUxHU071EdgqD+uno3DvP/+UqI6SwMxoXI1WYvBvEZD8nwKLP1zACnu7pFAUK45n5iM2Fu3EVGjOip5GJGU7d6TOgJBRcInwAuA3ey628C2xeaLv5qIfZuOIT0pE7FRF9CwbR14eNl9mzYu2lmiNv2DfBHeQX3yPaxeKMLq0V6TAFC/VW17e4tL1h4A/P7VFlw6dQ2Pj++Dhq1rw+hpQML1JCybsx7L525AdoYJEuXKX4aICdVdsDEFyl1CxRSFpbwGqdIPQMBnQPKzfGUpKQMhypCJjFSahkjTEfvaZiJsjZ4wz7GQuqrifKgIbRThQ5XDCH8pUi9FhZ5Rp2kJvyo6HI36/bQQeikroSmSKK8hbm8qJ7yGONN4faj4w664WANEeCYFRSuY++U2zJ/2JGZE9MLbc/7AjV4u9vaiIlE44dnljLaMsF/j9s7i1UtR2ihGej9Rfab0TYRfEPFekn5VVBql3bqPvanu1ZcpXzPH4BhOpdC9vf6v2fYkajKhVadJHka7hcKf/0X1BlWwZ100Yg+dV2mm7hSmZ/OPf2Hzj38RGfKyUWFmNGgT2Qy3riVh9Rcb7Ukl9KaK3nES0TtygyNT95v3XlJawzJATKhKA8t+wHIckr4jmLYJYD159zICwQPM0dPXcfNWGh5qVw96vRZA8a7IAsGDhm+gNybPfw4AsHttFA5sOeFUfTGHLiAzLRtevh5Y/OEaV3TxroTUrIyweqGY8+I3d9R4uRKttmynOEKUXkqwlPEAGCT/z9zdFYGgQvDFz7ug1ciYOLKbu7siEJQrPLyN6D28C3oP74I6Tau7pM6fP/4TkiQhok8zl9R3N+Iv30Zf4/ASby86g0yttpZme2Xa2oOEEgeW8wckbU3AONDdvREIyj07D5xFcmoWHune1N1dEQjKBfFXbuNhv+ccrqWz/3RJ3Wu+2QFzjhmPj4t0SX3lESo+cGkitvzugkJojLhJfQvM+DAk36lgOSVfViW1UYQuQCG8PDhlM9AT+jALFbeP2FAvzoeK0lbRPlacMf8IbVQ2pw+VnvChMjnhTWWj/uKhNEW8uipSo6Qu6lRsO0LSQcft44vH54wGiNIZGVIKno2fft6DiS/3xqTWnTD38F7Hss54e1kp/zB1/3hjFzoV95A7Rh9vG3zaKH6/KnVRUo9ExvLj1DdRnyOqLK93kROSJ1LvQ3lTUW1w6qUo/zDar4ry4uJLK+pNBQAS9SEsNPZ//XEYvYZEoFnXcBwvapjJqy0rh2NAjUVZIFaoShUzWPrnkGRvwGeKuzsjEJR7fl97GFlZZoxu1sbdXREI7nsWTv0VYMCYmU+7uyv3BWJCVdpkfQNmuwXJcyQAb3f3RiAo9/y25hB8DUY8Hd7C3V0RCO5r0pOzEBt9CfVb14Z/sK+7u1PhEVt+d0GBAhu1fEitYhczP2Wpr0MK+B4ImAskjybz8FspqJc5ZWJbUkccD6bsH6izFjoiH7WNR20D2tvm3N7jDFFDlaXC1piJsDVaG5+VgpbYWtASR8VtRBq1bUKHo1En0dtYVD4n7BWInVmnthCJLSbuUCzE9mjRLaZFP/6NoU+0xyutOmPVroITsuZg9Y04ZUXBF9XEKdsJ7hA/ZHggdRq1HUfbNfDaKxChU4gxJbe1qfAi5NYgNfhEG85YKRBbiLzhaMjtQl57BTIcjRNhV8itsuJtBIokEm3cfQt24bRf8cnayRj78QjMem5BQTbOcDnk59zdYyBCz9zHmPcA1lOQ9F0AbSN390YgKNcoCrD6eAyCvL3Qs34dd3dHILiviTl0EYlxKehyB1NQAR9iQlVGsOQ8G4W57u6KQFDu+XDTTtgUBf/XW1goCASlzco566E36DB4Uj93d6VCIyZUZYVyHSxnI6CpBRj6u7s3AkG5JstqxfYzF1A9wA8tqhYfykIgEDjP6vmbYc4xY8iEh93dlQqN0FCVJamvA8ZekPzeBUtYf09VuNpKQUforyxEmkzoB4oPPUNZLPClUSFqDLJaV0VpqLIJgQllm6AnwtuYiXxmQvshUxogUmPDaaXAq/fhDLFCant49UOcR/JJ3RKvBojSghF9CTpiw9yLmxH57Yv4oHtPjHr9J1yjvuu5rRSIspS2jFOXRjxCJbBNoNKcsc/gHANCo0TKbkgdFGWvwBlShlMbxZ1G2Rw4Ux/5AVa/cZS9gsutFCidFqV944UKwUK0IXt44K81h9FraASa9WiGk/vPQUlPJ8reu7bMbWNQhogVqjLFDJYxH5LsC3i/5u7OCATlmuTULByNuY76tYMRFuLv7u4IBPc1C/+3wm6h8O4Qd3elwiImVGVN5ldgtkRIXs8C8HR3bwSCcs2srzcBAN54sY+beyIQ3N/kWyi0qAnfQGHxcy+ICZUbYKlTAGgB/znu7opAUK65cj0Z5y/fQqvG1VHJ6OHu7ggE9zXfvLMKkICx7z3h7q5USISG6i7YGKPDzxB7xDK5F09g3glYT0MydAfT1ANs58hslDeVjfCIovJR3lSU1kpDeVgRaXpCLGApNvSMOt0oqWMq5XB6TpFphKcJ6U0lq/fttRKfNxWVZiF0VZQ3lULpPDjD0dB+VUQaoW9yLiQKZxq31oq3LPG8pRd0cO7nm/H5J8PxYafeeHHbWseMrvb24vSm4vWh4ta0Ue8Hdz6+kEG83lTc2iji+4T0g6LMs0gtDpGP18OKDB9z795UEm/oGeo+eL2pyLArnF5NToWjIerT2jtz6vAlJMWnosuA1pjlYm1ZmY6B8KF6sGDJE2G3Ufjc3V0RCMo1J05eR1xcKnrXqg8jEddRIBC4jlVfbbVbKPxHWCiUFDGhchfKZTDTFkBbFzD0dXdvBIJyzfyF26GVZfyvYw93d0UguK9ZvXAHzDkWDJ4k7H1KiphQuZOU1wBYIPm95+6eCATlmt17z+J2diaGNGgqvrQEglLmrz+iUCnED006NXR3VyoUYv38Lsia2lBwRZ1OiDeoWHnFxfezkwOW8TVknwlg3v8BMj67a380VIw+YsOa0kvJxD60hvK1ImMDqu+Nivlnz8ungzISnlM5TK2rovKZFPWjayC2g7IJnRblTWUmjIWoWH5aoqxVQ2g1iDRKQ0D6KLnYm8opbZQz+isXa4Dmbz+Adx7picn1u2Dezv0AgJwwKr4fn56LjlNI9Y+zLPFx4I2FqBDPAdkX8t6ceIZI7Rvl/UQMIOnJRumgqIeD0r9w5uON70f5GTnjTUXVRwYC5ITTq4nykmLEw8Yd348cF8e0he+sQq/BERj30dOY0G16oWyEPoxXW0ZRWmNA1lv6iD/27oJX0J/wDDkOg//XkHVtXN9A5jwwJRmS1wsQNgoCQfEsOXgUGSYznunQ2t1dEQjua9KTsxB7+ALqt6oN38rCQoEXMaG6C5bsNWDMDI2xLzwq/wbP0NMwVFoC2RDpsjZY6luw2yjMdlmdAsH9yE8HouFtNGBYu+bu7opAcF+z8M2lgASMmznc3V2pMIgtv7uQlToFHux/gBwCnddYaI0PQ6PvBMnQGYxZwCwnYctaBiV71b03YtoKWM9CMvQCk2sByqUSFafC0dALqep8OmJJ2EaFoyGW3jXFbfkRy+CUlYKOtFcgtvcoewXC0kBLpBk0hJUCsV2oJbYqdMT2npY4xq0hypJWClZiW4LTSoE3nAr38XvecCrkVhmx5VIGW1aVj9mw9MTfGN2hDSZ07oAt30Xjag3qKLYTVhScW2WkHQLfyf0SbKPe+1hR9wtiG5UOfeRMmBnOrTLubTbiO4bcfuQ9au9aKwWqDUaNAWmlwDdW5DZqGVgpnDpyBUnxqej6WDt8PH6xvWwmZYHB12dmUz/k3GNA2isI24SKixIPS/q7yL7VCVlxTWFNnwNmuwpJ1xw6/5nQh8ZCE7gekucYAMYSV8+SJwAApIAvXNxxgeD+QVGAjTtPoZK/F7q2r+fu7ggE9zWrvtgMvVGHx19y3Y7M/YyYUN0TGbBlzIPlViTMcY1gSZ0GZokBtHWg8X0dmpDj0FTeAcn7vwD8+apULoKZdgDa+oALtxMFgvuNzxdth82mYPyz3d3dFYHgvub3BVvtFgovCWsfHsSEymmsULJ+hCVxAGzxjWBN/g9gOQhogqHxHgcp5ACkoN2Az7uAXP3OVaW8CsAKye+Dsui4QFAhyTFZsfvgOVQN8UOboKru7o5AcF/z99ooVArxRZMOYkX4bkiM2iwWIC0tDX5+frh4ugqCfNUaHsoOQVd0o1ffCbLnKEj69pBkL/u+PEsFM+0FMr8BrCfVDXu/Atn7RSjWG0DSSEC5ete+UnYNFipUDJVGlDURe+w5xGOSSYpLgCxFPV6ZTK9KS1fUsdnSbOrt0lSb+vRjukLks6rzpVnV+dIt6rQ0Ii3Dou5zptmgSssyq+83h0izmgirDQsxhiZC50GkyWbC8sJE2GWoZWmQ1ZGAoDER+YiyGqKsbCY0dlRZkzqfbKHKEjoKS8FzGeDviV+XjMfppAT0//1Hx4yUZMfMN350mro+egyINO7xI/JR40KMMz32fPlkK2H1YiY0NhYin4UIQ0KkgdDOSGa+fLAS4a2svGUJzQ6Vj9L2UO1SR/epslQb1DF+QjNGhXGRqPA7lN6MtG8hyhJpdD57fb6B3lh2fBbOHL2MiV3eUefjHAPSNoaAewzIcbGXtTIztmctQ2pqKnx9fbnadQVihao0Me8FUsaCJbSCcnsgWM46AAySsR/kyr9DCo4GAr4B9J3t+eXqkDwG2/+prQopaCuk4H8Av88AbRO33YZAUN5ITsnCsRNXER4YjNq+Ae7ujkBw35KWmIHYI5fRoEVNYaFwF8SEqqywxgCpr4IlRIDd6gklcwnAMiDpH4JcaRGkkBhIQVsBuTKUjK/vMAH7FtA/5O67EQjczuy5GwEAM7r2cXNPBIL7m2+mrbJbKMwa4e6ulGvEhModKNeB9Glgt7qCxUdAyfgKgA2SJIFlLAYyPikyAesOJfMXgKVD0neFXOlbSCEnIFVaBhgfc/PNCATu4dr1ZMQk3UL7KtVRyajePhYIBK7h1D8X8i0UBMUjNFTFkKehOn86FJV9CB+kQnopSVMHsqErNLqmkHTNIGnrQpK0sKZ/AmR+pSpLh6ORIQUfBiQJLL4NAGIPHwDgC3g/B8nYH9DUgCTJuZ4pDGBpYNnroGTOhcV2S1VSMj4CYxFbhozbT8Bq3u+QlkNorSgNlYnyQgGQydQ+T5TmidRV2dS/GKmyKYSuildDRaVlkLoqtV4qk9BVZZnVadkm9TNjNqvHxUboqpiFV0NFpBEaKm69D6nj4cxHppW+BqjqwBr4ZeyT2HrqHCb98icAILOuutO0hopKUyWRY8qtjaJ0acRHm3f8yHYJXRo9foQOysqZj9BQSYRGidJQkWmEdovURlmIG6byKVR9RFgiSt9k4cxno/ygOP2WqPoIePVSZD5ntEeU/krr+H312NieGDt9CL6eshS/zdtY8AIxLs5oy5wag9z7EBqqCozWazj0ftOg8RwCWdcQknQvfqkKWPoMSJIH4Dv9DvnSgIzPwNI/QX4cKZYDAJBkf8hew6EJ2getz3sAHB8kZrsKS9ZKWLJWQrEl3EMfBYLyxZErN3EtKRU9w+vCqBU+xQJBabH66+0w55gxZFI/d3el3CImVC5AsZyBJWMhLMn/gTkhEras3+6touzlYNbrkDweB+Sg4vPJwZD8ZkGSdFBS/wdbQjPY4hvAmvJfMFsiJEkDnfcIGEOPwBi8Dzrf9wE5DIrlCMyp/4U59b9g1vP31keBoJwxa8Nf0Mgy3ny0m7u7IhDc1/z1+0FUCvFDk04N3N2Vcon4k+4u2BiDQpzDLmxVoGQvBQBo8+M93Huka5byH0iBKyD5fw6WNIzMI3k+A0n2BDPtAbKXF7yQsxo202ZognZDkn2hWI5B1taD1ms4NJ5PAywFVtPfsGaotyELoyHCQmiIMShuNq4htgepMDM6wnaBzEfEMCFD1BD7ISZZ/YgbiD0XMxGiRm9TlzURoTCosDVaIvQMGY6GWGonA9iToVOoEDVEPs5wKmQasT1FhlPhTCPD1nDXp77fwFNWHD0Vi6RHM/FYy8b4cuYmZNVXlyVDxXCGcSHdQchQMUQaVZbaOeIdKyJ8DLHDToey0VJhUqi+8IWjocLMSMSYghpnKrwIFY6G2tbhDLFChbeRiIeNEaFseMPRkJ9VMoQO1WcqHI0ToWfIeEME1PYo9T1E3O/Cd1ah15OdMHbm05gU+aE9W3aOKh9lfUCG6SF3AXlDz9xhXEToGQEAwHrUbgyqa22/KIy9AQAs5w/1aywLzLTN/m/LSZjjm8F0awCUHLu+RGv8FzyCNkDWtwcAaHQiyKyg4rNo2V7o9Vo891QXd3dFILhvSUvMwJkjl9CgZS34BgoLhaKICVU5hKVMAqBA8vtU/aLkBUlby/5vywm6vOW4Pasu17vKegLWlEnIim+JrIQusGT+jLw/rTz8/ge/KufgHfgrdB6Pu/ZGBIIyYvXGI8jMMmHIo8X8ESIQCFzCwrdWABIw9oOh7u5KuUNs+ZVHlESwrJWQvZ4C83gayF5S8JomrODfthsO6dqgXQ7VSFpi70O5BnP6h9AYukPSVgNjNgBaaPTt4GVoD+b/CayWWJiyVyI78wcAxPGjEmDQ1oGP8SFU0beAl74ZPHX2E5BXUj5BbPK3TtV9J0KNddGp8hOo5tkURo0n0i0piEmPwtb4VUizqu+pQ6V2mFB/jEPalGNzcSL1XKn1UeBaVv4ZhWeHdsLIRq3w0+lod3dHILgvOXngnN1CYUBbfDzue3d3p1whJlR3QQFgo/RDxB6tnLtPnvcKA0AbC3CQPg3MYwAkn9fBspchX3gheRXkYdmqYkzJBLMchmzoCkiOR/dlyIAcCEPgCsjaagAAm/kfABI0+vZgDGC2W9DqGkGnnwYv33dgs12GKWsNLOlfA0h1qI/SVQEF4wAAlX1GorLP8+o8YNAT2ihKL1XStAY+XfCvam9AI2lxLessLmXGI8yjHjpX7ofmfh3w6Zn3cNsU71A2yZyAv2/tAQA082sKf70fdLICvcaxHZ2GaFdRp5kJ7YdMaElkQgdlI7VRlDbFCW0UJfPj1ACR0gUin8s1QER9hS0DfvxhN55+rD0mteyEn04cKVKWGD9yrDjHlHesqHEh7o3SiJBjQGrB1H1WqJPnpF6KetYorRWlRyLqoxIpLROpyeJM42yDTKO0Pc7UR+mlCGEVpSmi3l/6g0lYq1A6I0KnxYg3nbQboMLCUDo3nf3D+ttXW/HC9CEYPKEvVs1eqy7rjLaMgtSbEdq3vPqEhkrgiAKWPhOS7An43MlGoQgsGSwzb+VH/RDq/T6ErK0DptgnR6b0OchKHIrspFEAbJA0wUi+1RdpyZNgNR+GRhMGL9//oErVUwgJjYKf34eQ5TBVvcWRY4nFrbQFOH97Io7f6IXbmb/y38s94K2thP5hk6GRtNh043N8ff51rLj6CT478zKOJO+Ejy4Az9Z6WVXuQuZFfHvxe3x78XvczLlZqn0UlA6KAmzcegKVPbzQtyalTBcIBK7gtwXbYDZZ8PiLke7uSrlCTKjKM9nLwKw3IHkOAeRAexrLLHhdKsYdWso1uFQyHJO19aAx9gFjVigWR9sEq2kHLFkrIUkaeHqPhyl7FVISB+L2zdpISRwBk2knJMkXXt7PIiT0H4SEnkBQwHzotY3veAvJmUsRl/o+krLWIsd6vtT/cmhT6THoZSMuZRzG0ZQN+ekMCv648TWybZmo6VUXjXyalWo/BO7hi6+3w6ooeKtDd3d3RSC4r/l77WFhoVAEMaEq57CUVwDIkPw/tyfYrhe8qKlKF9JUsZctnBeAxtgXAKCYo0BpoyzZawAABmNvFN4Ntph2IClxOOJu1kdCfD9kZ/8BCRK8PR9DtdBtqFX1LEICf4LR0PWe7tGVNPDpBAA4lbpT9ZpZyUFs2j8AgBb+IoTC/YjZbMWWS2dR3ccPbUOK+XwIBAKnWTh1FcCAsTOedndXyg1CQ3UXzIzBQuzfUl5Ned5UDCz//wq1J05t7Rc3t7VGA5YoQNcW0LYCrNFg1kv2k366poD1TG7ZgvJynhWC9SQ0hfatJa391J9iOQ5Z11TVlM1yzJ5P9oJOWwc269lCr9rvyWo9ipTkcfZ/SzXg7/syPD16w9PYC14ekVCYCdmmI0jO+BFp2avzS2tyx0bKrUcGg0xtSZI+VHzeVN5aLQIM9u3IJHMMDLIFOYqjjiwu+ywQ0AM1vGrCUMh7yqAU/DtPA6aVbdAX8azSE75WJsInRier+2chtAukNxWlt6C0H9y6ICLNyqm7cbU3FakV4uwLpb8ihmXGD9vRd1oDvN+mN56Y8wsAIL0JFUOH0ktR3lREn0mdEZGPjjJ17/l4tVbc+ShtD68/F6GN4tVBEWmkHxQl/CK1OEQ+oj5yhZz0TLp3byqJ02+JNmDj9KYqqS+TQxqfjxejwsIU+oJJS8zAmaOX0aB1bfiF+CHtdsGOCNVnfm0ZEbaG0kvdaQyEhkpQHCxlAgAFkn+ujULOFgCAZPwXkVsGDD3s5XI2O7wi5QrRi65cFTSUAaak2WvRVL9rv6zKZdxOeR1XbrbC5ZtNkJz2BWzWm/A0tEO1yl8ivNpl1A7ZhErezwNQx7tzNb660Px/p1vo0DqpltsAgEr64FLvj8A9JGfm4ND5a2hYNQi1ggLc3R2B4L5l4Tsr7RYKH9Im1A8aYkLlRiR9BLSh56ANvcvRfCURLPtXSNowwONJsKwfwJQsSIbOgEcRLxDJH5LsB2Y+Cmb+u8hruUZsxOnAPBjLsmeVfUp0L4qSjOS0D3A1viNirtVHfPL7MFnOwqgLR2jAdLSqfgaNq+yEh75JieotCXq5QFNmUeh7tCh2V1+jXIz+THBfMG2l/Y+OqU8I0axAUFqc+ueC3ULhMSGhAMSEyiVodE3hXXkNjIG/wxj4O7SGngAArefT0AX+mn+p4/PZlzsZI7YjipL2DhjLhuQzBVBug6W+CcaskP3eh1RpBSSf/9lrlD3BbLdgS33VlbdYQnKQmPEVLsRHIuZaLVxPfAWZpmjotdXgqbeL2IN9xqFupanQy6F3qUsgKDlXE1Nx+vottK4dhkrenu7ujkBw3/Lr5xuhN+ow6OU+7u6K2xEaqruggI7MV9ibSpK8odWrHZplTVUH4TiTdGBgBbvQWruOiWWv5OoJS/sIst9UMJ+pQPpUsMSrgPc4u75KZ5+oMCUDLHEAoNxSV8Hse9yy5Jkv45IlycFfRco9ISgpGdAUEntxSjwAFPWnsiEjaznic+0S6gUtga9HF8iSAVV9R6KKzwhYlWSkZP+Fa6lfI8d8iaiP0CgRuiql0KlGT40eZiUTuiIaKA+tXVOVo2RBW0iHpS2kecq7aw0YtEW0GUV9qQBAR+z5U3opLVHWSsTQksg0zhhpLvam4tZBcXpTcXsr8XpEEd9gAWft7/ncuZvx1UfD8f7DPTEylghYTumlCD0X7bGlTiNkffxlifGj26C0R5y6L1Kbx/cMcT9rZFmiM0R9ZMw6zhhzpE6LDJBI6IKo+H6UP5Iz3lSk3xIlrOKE06+K8pcivam4701d328LtuHZdwZjyKR++P3r7fZE4jvRKW0ZBe8YlCFiQuUCrOZ9SLxRDTpiiqEjlXN2JEMXMCULSsbnfEuF2b+AeY2B5DkULGMuYD2Zq68CoAmDFLQDYCmAkkgWZ9brgK5Z8acDJW9Isi8AQLFd4+lRibHY7B5PcWlzcSt7L8J8X4C/sTMqew1EZa+BsLFMJOccwuXUH5Ccs79EdadbC8w6vbXBSDJfVOXx1dlXCZPNxIRTcF9x6sxN3IhLQad2deF5XossK6GmFwgETrN7bRR6PBGBJhH1cPLAgxtdolS3/JKSkjB8+HD4+vrCUDcQwwAAFIJJREFU398fo0ePRkZGxh3L5OTk4OWXX0ZgYCC8vb0xePBgxMfHq/KsXLkSP/30E1588UXMnTtXVc/OnTvRunVrGAwG1KtXD4sXL3blrbkAAyR9W7CsRYBym7sUS50MQIYU8HmJW2S5Ey2Nx0BI2rqq1/NOBzIlE4r1QonrLymZ5qM4c3s8/rnWCtE3eiI+YzlsShYqe3RD2yqL0LPmEbQN/RFhXn256rMoWUgx2yeCwcaGZJ5Qo93w8UZ26d+fwP188d0OaDQy3mkntFQCQWmx4K3lAANeeP8Jd3fFrZTqCtXw4cNx8+ZNbNmyBRaLBaNGjcKYMWOwZMmSYsu88sorWLduHVauXAk/Pz+MHz8ejz/+OPbs2ZOfx2g04okn7G9cs2bN0KlTJ2g0GowfPx4AcPHiRTzyyCMYN24cfvnlF2zbtg3PP/88qlSpgr59+X4552FlEhl6RuFMo7AxBUA2bHGFTDF5rRQshwBLNKBrB2hbANajZBtUWZv1sr0pTVChfBLy5tU6j8cAANacrZBhc+gUZRNRbOgZIk2Xu22XZ0kgS8yhvMV6CZeTpuAU00MrB6Cm32gEez4Mf2NbRHi0g8LeR5r5LK6kr8HFtFXQEEvPGii4lPE3WlYahga+vXA2bb2DvYJOMqK+T3sAwOm0fQ6vGWS1bYJeY3OwVgCAHJujDUNevqKYbOrlaC1hkaAhtjloKwViW4K0TeAMJUItnFJ2CLzbhU6FSSG2f8j6+LasZHNB2t6955CUlIlBdZvg/3Zudtg8Zlpq24nPioK2PuAcZ6os544VuYvl1NYgZcdBtEvURx55J8MNqeujNQScW2VkSBnOcCrk9iOv3YBrrRSoNnjD0VA2AmSoGNJagM82gdtKQaNBeko2zhy5jIatasMv2A8p14gdAGe2QksYkod6D8qCUptQxcTEYOPGjTh48CDatm0LAJg3bx769++P2bNno2pV9bZTamoqvvvuOyxZsgQ9e9qF3YsWLUJ4eDj279+PDh06qMq0bNkSo0aNwm+//ZY/oVqwYAFq166NTz75BAAQHh6O3bt3Y86cOcVOqEwmE0wmk0NfACAjQ4EH8cVhIT4f1GAaiP1qmZg9UaGt5OImaJkvQqq8EZDfBUt/1J5Xa4NvkP25S0un9+YtaQvhq2kLg3cfmDP/RuqN56Ew+6k3vWd3GD0GgzEbkhK+gMXk2G+vwFcQUPm/yM7ai5tXBwMAcsiHHDARXxQ5ubdi8bH/w2xiyExXl89kNgC3cd26EzX9XgAAHLg0C2FefeCjr4Va+pdQM3Ac0izXcC1jB2JTV8Gq2N3jsxUrDpqWoYnfY6jm1RZ1jf2wPyHXYgIy+tR+GUaND65knMHJuIOOfbYVfNiV3MmQJdsCc4ajAarFov4QW63q94mIvwyblZg8mYjJk5nQh5nVTxczE1/wRBosRJqJeOBM6iTSc4qKl02kkWctyHzEFzeZpi4rW4jPV5Gy87/diInjeuPFRq0xL3pvQX3UhMpEjBXh2UWNHzMTadQuIzXO1LhQZTnHjzdf0bEqNh/x7FJpEpmP0NMQf3DQacR3GZVGBUhUiAEkNFRUPkbVRwoFCc8k8q8LYlLEW5aEmtwR40f2hZj0cgeipLzC7Gnz3vwJH6yciOFv9sPcl4nA9+QY8I0LVZa637xxseZ+WZT5xIqVEt999x3z9/d3SLNYLEyj0bDffvuNLLNt2zYGgCUnJzuk16hRg3366afFtjV8+HA2ePDg/J+7du3KJk2a5JDn+++/Z76+vsXWMXXqVAagwl41a9ZkjDF28eLFO+YLCgpisbGxjDHGrl+/zpYtW8a2b9/ObDYbY4yxCRMmkOWmT5/OGGNs8+bNJepXq1at2L59+/KvhIQExhhjV65ccUgPDQ11KNejRw/GGGNms7nEYzFkyBBmsVgYY4zt27ePLV26lJ07d44xxtjNmzdZ3bp171h+x44djDHGunXr5vb3VVziEpe4xHVv1/nz54v9nV8alNoKVVxcHIKDHc0TtVotKlWqhLi4uGLL6PV6+Pv7O6SHhIQUW2bv3r1Yvnw51q1b51BPSEiIqo60tDRkZ2fDw0PtQTRlyhS8+mqB1UBKSgpq1qyJK1euwM/P7473Wh7IO6VRo0aN/NW1O2EymRASEoKhQ4eCMQZFUZCdnY33338f77//vip/3ph17tyZq/48NBoNvLy8VOnVq1dH9eoF5qFnzpxx+GtCr7cbgTLGStReHjk5OTAYDGjfvj0iIiLAGIPZbIaXlxcOHz58x7KenvaTjuvWrYON+mvYTaSlpaF69eq4evUqfH193d2dCo0YS9chxtI1iHF0HampqahRowYqVapUpu2WeEL15ptvYtasWXfMExMTc88dKgknTpzAwIEDMXXqVPTp45wHhsFggMFgUKX7+flVqIdbluUS91eSJMiyDK327o9D3mSjJKSlpcHPzw+pqanF9s3HhzYS1ev1+ZMrZ5AkqcR1URPB8oCvr2+FeibLM2IsXYcYS9cgxtF1yJRmrhQp8YRq8uTJePbZZ++Yp06dOggNDUVCgmP4D6vViqSkJISG0maOoaGhMJvNSElJcVilio+PV5U5deoUevXqhTFjxuDtt99W1VP0ZGB8fDx8fX3J1SmBQCAQCAQCZyjxhCooKAhBQUUdv9V07NgRKSkpiIqKQps2bQAA27dvh6IoiIiIIMu0adMGOp0O27Ztw+DBduFzbGwsrly5go4dO+bnO3nyJHr27IlnnnkGH3zwAdn2+vXrHdK2bNniUIdAIBAIBAKBqyi19bDw8HA8/PDDeOGFF/DPP/9gz549GD9+PJ566qn8E37Xr19Ho0aN8M8//wCwb6+NHj0ar776Knbs2IGoqCiMGjUKHTt2zD/hd+LECfTo0QN9+vTBq6++iri4OMTFxeHWrYJjmuPGjcOFCxfw+uuv4/Tp05g/fz5WrFiBV155hbv/BoMBU6dOJbcBBSVDjKVrEOPoOsRYug4xlq5BjKPrcNtYlqbiPTExkQ0bNox5e3szX19fNmrUKJaenp7/et6JtLxTVYwxlp2dzV566SUWEBDAPD092aBBg9jNmzfzXy/uNF7eKbc8duzYwVq2bMn0ej2rU6cOW7RoUWneqkAgEAgEggcYiTE3OWAJBAKBQCAQ3CeUrQReIBAIBAKB4D5ETKgEAoFAIBAInERMqAQCgUAgEAicREyoBAKBQCAQCJzkgZlQJSUlYfjw4fD19YW/vz9Gjx6NjIyMO5bJycnByy+/jMDAQHh7e2Pw4MEOhqFHjx7FsGHDUL16dXh4eCA8PBxz585V1bNz5060bt0aBoMB9erVw+LFi119e2VGaYwjAEycOBFt2rSBwWBAy5YtVXVcunQJkiSprv3797vy9soUd40lABw7dgxdu3aF0WhE9erV8dFHH7nqttxCaY3llStX8Mgjj8DT0xPBwcF47bXXYLUWBNPduXMn+VwWFyqrPPLll1+iVq1aMBqNiIiIyLexKY6VK1eiUaNGMBqNaNasmcrzjzGGd955B1WqVIGHhwciIyNx9uxZhzz38n6Vd9wxjrVq1VI9ezNnznT5vZU1rh7L3377DX369EFgYCAkScKRI0dUdfB8H9wV9x4yLDsefvhh1qJFC7Z//372999/s3r16rFhw4bdscy4ceNY9erV2bZt29ihQ4dYhw4dWKdOnfJf/+6779jEiRPZzp072fnz59lPP/3EPDw82Lx58/LzXLhwgXl6erJXX32VnTp1is2bN49pNBq2cePGUrvX0qQ0xpExe1DmL774go0cOZK1aNFCVUeexcbWrVvZzZs38y+z2ezK2ytT3DWWqampLCQkhA0fPpydOHGCLV26lHl4eLCvv/7albdXppTGWFqtVta0aVMWGRnJoqOj2fr161nlypXZlClT8vPs2LGDAWCxsbEOz2VesPHyzrJly5her2fff/89O3nyJHvhhReYv78/i4+PJ/Pv2bOHaTQa9tFHH7FTp06xt99+m+l0Onb8+PH8PDNnzmR+fn5s9erV7OjRo2zAgAGsdu3aLDs7Oz/Pvbxf5Rl3jWPNmjXZu+++6/DsZWRklPr9lialMZY//vgjmz59Ovvmm28YABYdHa2qh+e79W48EBOqU6dOMQDs4MGD+WkbNmxgkiSx69evk2VSUlKYTqdjK1euzE+LiYlhANi+ffuKbeull15iPXr0yP/59ddfZ02aNHHI8+STT7K+ffve6+24jbIYx6lTp95xQkV9ECoi7hzL+fPns4CAAGYymfLT3njjDdawYUMn7sh9lNZYrl+/nsmyzOLi4vLzfPXVV8zX1zd/7PImVMnJyaVwZ6VP+/bt2csvv5z/s81mY1WrVmUzZswg8w8dOpQ98sgjDmkRERFs7NixjDHGFEVhoaGh7OOPP85/PSUlhRkMBrZ06VLG2L29X+Udd4wjY/YJ1Zw5c1x4J+7H1WNZmOJ+j9zr7/uiPBBbfvv27YO/vz/atm2bnxYZGQlZlnHgwAGyTFRUFCwWCyIjI/PTGjVqhBo1amDfvn3FtpWamuoQ4Xrfvn0OdQBA375971hHeaUsx7E4BgwYgODgYHTp0gVr164t+U2UE9w5lvv27cNDDz3kECi6b9++iI2NRXJy8j3cjXsprbHct28fmjVrhpCQkPw8ffv2RVpaGk6ePOlQX8uWLVGlShX07t0be/bsceXtlRpmsxlRUVEOYyDLMiIjI4t9nu72fXbx4kXExcU55PHz80NERITDuJb0/SrPuGsc85g5cyYCAwPRqlUrfPzxxw5b0hWN0hhLHlz13VriWH4Vkbi4OAQHBzukabVaVKpUqVitQ1xcHPR6vUOQZgAICQkptszevXuxfPlyrFu3zqGewl/IeXWkpaUhOzu7QgVrLqtxpPD29sYnn3yCzp07Q5Zl/Prrr3jsscewevVqDBgwoMT34m7cOZZxcXGoXbu2qo681wICArjrKg+U1lgW99nNew0AqlSpggULFqBt27YwmUz49ttv0b17dxw4cACtW7d2xe2VGrdv34bNZiPv8fTp02SZ4sak8Jjlpd0pT0nfr/KMu8YRsOslW7dujUqVKmHv3r2YMmUKbt68iU8//dTp+3IHpTGWPLjqu7VCT6jefPNNzJo16455YmJiyqQvJ06cwMCBAzF16lT06dOnTNp0FeVpHIujcuXKePXVV/N/bteuHW7cuIGPP/64XE2oKsJYVhQqwlg2bNgQDRs2zP+5U6dOOH/+PObMmYOffvrJjT0TPAgU/k5s3rw59Ho9xo4dixkzZoiYgG6gQk+oJk+ejGefffaOeerUqYPQ0FAkJCQ4pFutViQlJSE0NJQsFxoaCrPZjJSUFIdZa3x8vKrMqVOn0KtXL4wZMwZvv/22qp6iJwXi4+Ph6+tbblanyss4lpSIiAhs2bLFqTpcTUUYy+KeybzXygvuHsvQ0FDV6SKecWrfvj127959x36XBypXrgyNRkM+C3catzvlz/t/fHw8qlSp4pAn78Tpvbxf5Rl3jSNFREQErFYrLl265DDRryiUxljy4LLfU9xqqwpMngjy0KFD+WmbNm3iEq2uWrUqP+306dMqkdqJEydYcHAwe+2118h6Xn/9dda0aVOHtGHDhlVoUXppjGMexQmpKZ5//nnWqlWrkt1EOcGdY5knSi98QnLKlCkVXpTu6rHME6UXPl309ddfM19fX5aTk1NsfyIjI9mgQYOcva0yoX379mz8+PH5P9tsNhYWFnZHAfCjjz7qkNaxY0eVmHr27Nn5r6emppKi9JK8X+Udd4wjxc8//8xkWWZJSUnO3I5bcfVYFuZuonTe79bieCAmVIzZj+m2atWKHThwgO3evZvVr1/f4ZjutWvXWMOGDdmBAwfy08aNG8dq1KjBtm/fzg4dOsQ6duzIOnbsmP/68ePHWVBQEBsxYoTDsdWEhIT8PHm2Ca+99hqLiYlhX375ZYW3TXD1ODLG2NmzZ1l0dDQbO3Ysa9CgAYuOjmbR0dH5p6kWL17MlixZwmJiYlhMTAz74IMPmCzL7Pvvvy+bGy8F3DWWKSkpLCQkhI0cOZKdOHGCLVu2jHl6elZ42wRXj2WebUKfPn3YkSNH2MaNG1lQUJCDbcKcOXPY6tWr2dmzZ9nx48fZpEmTmCzLbOvWrWVz406ybNkyZjAY2OLFi9mpU6fYmDFjmL+/f/7JxpEjR7I333wzP/+ePXuYVqtls2fPZjExMWzq1KnkcX9/f3+2Zs0aduzYMTZw4EDSNuFO71dFwx3juHfvXjZnzhx25MgRdv78efbzzz+zoKAg9u9//7tsb97FlMZYJiYmsujoaLZu3ToGgC1btoxFR0ezmzdv5ufh+W69Gw/MhCoxMZENGzaMeXt7M19fXzZq1CiWnp6e/3rezHXHjh35adnZ2eyll15iAQEBzNPTkw0aNMjhDZg6dSoDoLpq1qzp0PaOHTtYy5YtmV6vZ3Xq1GGLFi0q5bstPUpjHBljrFu3buRYXrx4kTFmn1CFh4czT09P5uvry9q3b+9wxLUi4q6xZIyxo0ePsi5dujCDwcDCwsLYzJkzS/t2S5XSGstLly6xfv36MQ8PD1a5cmU2efJkZrFY8l+fNWsWq1u3LjMajaxSpUqse/fubPv27aV+v65k3rx5rEaNGkyv17P27duz/fv357/WrVs39swzzzjkX7FiBWvQoAHT6/WsSZMmbN26dQ6vK4rC/ve//7GQkBBmMBhYr169WGxsrEOeu71fFZGyHseoqCgWERHB/Pz8mNFoZOHh4ezDDz+84+ppRcHVY7lo0SLyO3Hq1Kn5eXi+D+6GxBhj/BuEAoFAIBAIBIKiPBA+VAKBQCAQCASliZhQCQQCgUAgEDiJmFAJBAKBQCAQOImYUAkEAoFAIBA4iZhQCQQCgUAgEDiJmFAJBAKBQCAQOImYUAkEAoFAIBA4iZhQCQQCgUAgEDiJmFAJBAKBQCAQOImYUAkEAoFAIBA4iZhQCQQCgUAgEDjJ/wPx7mzy06u1IwAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.pcolormesh(xs,ys,sensor_values[:,:,2].T)\n",
+ "polytopes = get_polytopes(states, sliced_csim, minV, maxV)\n",
+ "plt.xlim(minV[0],maxV[0])\n",
+ "plt.ylim(minV[1],maxV[1])\n",
+ "plot_polytopes(plt.gca(),polytopes, skip_dots=[3,4,5], fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "8d00ce36-4eda-47a2-81f5-08e47af8cb57",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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bDYVNAAqFEk3uM+hKFtTpuUCBwrYHSvtBKNSxoOoACicwFGAoP4KhZD6GUsv9YJUub6F0vNNknTa9Yx3Xd30kUDWQvesOs3ddxSjdSqdWeAe0Ju7WGHoMDicgxIfYkd3oO6o70z66i6L8Es4kprFnQwJr5u4wtjpVx9blB7jvxRsI7x2Mp687WedMx6IaPKEXAPGrDlmc766piB7elekzHsBGpeSL6X+x7Nct1i7J6mb8eyH/++UxnrtvKK9/vcza5TRK7VxdWDf1IZN1H6/fwtebd5isG9QhkO6+3nTx8aKLd1u8nCvGNBvw2bdkFBTWeV3h3m2JCfCjq09bunh7EeDhhlKh4LmFy1h0KKFOz6UAItv5MKBDILEBfgS39sDJTk1hmYYjGZnMP3CExYctn/PNUcO4o2d3k3Uh//24TutrzvbmHKG3RzcADPoiykuXAqAvP2qynZ3Lq6gcJmIw6NBrj6EvXQ0KR2xsu2Pb6gFUDrdSmvMoes3mOqvN1vlJbOzrbsDnq7LxR91mLgAGfQ6G8kOgz0Nh44fSrj/Y9UdfOg597mSg3GRXg2YvekVFQ4XSYVLD1FtNEqjqkoVxNfQlJRbXpWadZ9auY8y6uM6+lT1Dbu9H7PgoOvUKpnPPILr07sA9z41FU1pOWlImp/afuXgeQHPZh0xta/xncmI6W5cfoO+o7jz94Z28+cBMNBdHS48aEs7wW6PR6fT8+cVKs7rumjaau58dw4Gtx3nxls+u91Wotm6xHXlv7lMAjG43tdr79R4azr++ebAiTL30J8t+a6EtU5e97wAH9iRzPquAuN4hfPrS32gvBubcThb67LXwrhBFGg0rjh4HICHjfOUDF7uNfDhhNC72DXcX7JT+0cSFNsxf2X7urvx53+0A5BSXcCg9g/zSMvzcXOkXFEC/oADGhocydd5iyq/oM7jvbBp2KhsAburepU7ras798PYeCgLg5dTdrLr1UqDKpjh3msl2Nhe/MQ36XDQFH6It/hODPqNyA4Ujdq7/Q+VwI/buX1CcORAM+XVSo16zF732OPryQxjKD2Pr+h42djGm29RZvyoD+rKtaItmYijbDJf1/7JRx2Dj/i1K+6EYWj2Goch0YmlD6d8YSv+uOPcVgepSPzJLdTYECVSNRGmxhmU/rGPZD+uM63qOiGDQLbF07RuKT1BbAjq3A6Ctnwe/HHiH4/vOsH3ZfjYuO2AyTMJnL/yOf4g3PQeG8cOW1zm04yRubZzoFtMRpVLJ16/OtTiPn1JZ8Y2h1dbszsQOXdsz5Z3KMVZ8AtoAMObufkTHVf7Qfeuh78jJrPzmV17s9K0tr/75XFs78cq3D2NrZ0vWuRw6RwXTOSrY4rbfvfU3+TlFNXouTd2smet5/pXxzPj5EV5+5ncyM+rmh21zk1NcwkuLzf+ouGRlwgnOZOdyOD2TwxmZxD/zf/Vaz76z6Rw/f4HDaZkczcjiv+OGEx1g+bJ9bRkMsO10Mt9t38WWpGT0l93s0tu/HTNvncDQTsE82rc3X26ON9n374NH+PvgEaDuA1VLcCL3AlnFRbRtdfVZHDT5b1h+wFBMWe4L2NgNRaF0x8Z+GLqSv+ukNm3RjDo5TrXokinPvtvyY+U70Bd9g43zNJQOE9FdEagaMwlUjdi+9UfZt76yKbhbvxA+WP0q5Rot9o52xIyKIHZ0JE8bDBQVlJCcmM6eDQmsnhPPU6Pf59Ypw+k3NpLYEd0oLdGwe30C879Zw77NiRbP17F7Rf+qlb9vq1Gdjs72hPUMMlvv6euOp6+78WtbtenHrWO3il8YK/6o/vnsHNSo7W2Nxx9+a9Xz2P364dIWF6jCu/lhMBgICPTkt7+fRKPRcraggP2paaxKOMH640loG9Gdio3Vy0tWVX7RAK0kM7ftrP+TXJSSm8d9s+dZfGxn8llmbtvJM4P6MaFruFmgErW34PgRHo3sAwoLLcfVUopeewobdQRKpQ/NcmAebUVox8bHunXUkASqxsLS5cIy02EN0k+mAZCdlsM9wZOxd7Rj0G196Tu+NyFRwYT1CCQ8Kpi7nx2DprSc9NOZHFh3iPV/bWPfusMWh2tQ2FUMr6CytaFbTEcS959hw6I9NSr94LYTNbpkd0nPQWGUFpfx24dLq71PZmr2dZ2ryXNzMVulb2U6fcW7X91LZO8gzp7N4cuvVxEb04luXdvj4+NGYPfOTIgIx2AwkF9Yyqm0C+w8kszybUdJyaoYDLSktXly0Fr4Q9pQzXuDm/MlnEavFkMpHE2vGMPNx6V5z4VZbyy89qr8ym+aP3ce4tHIPiiUzmTrTW9AaqO0vXJXC0MpqFDaVEwxZtBf/caiuh5KocHYBFb8/xrPr7GRQNUEubZx4fkfJgOwaf52Xp/4nvGxiMFdGHx7P7oNCMcnqC3+Ye0Y89BQ9Do9OZn5JO47zfZl+9kwf4fJZcLw3sE4tLLjx7cXNshzUNvb0qV3MPNnriUnq+DaO9SB3sO6MGBcD4BmNXGyWq3i69//j/b+rdm5K4kXp/8JwPb4UwCUtLHF3cWR4X1DiY0MoqO/JxEd29EjpD2PTuhLuVZHenYBB8+ms+bwSdYdPoGmCd+wIGonwMMNgMzCltW621AqW4hVKJUe6PXVv+kIQOV4Gwqb1hgMJWjL1td5fdZnj9LxXgAMpSusXEvNSKBqghyc7Blx/2AAzp5MY/uS3cbH9q8/zP71h1GoKv7S8QrwJO7uAfQa3o3ALn6Vlwk/vZfi/BLOHE9n74YEVs/ZzmifipBmcUDLOqYpLWdCx2fr/TyXCwzzveolwqaotacTM2Y/jrOrA4vn7uTjb9ZY3C4nv5i/lu/lr+V7AdA6KOgZ2p5hUSH0CGlH+7ZujPYMZUyPMAwGAwUlZZzKzGZ7SgqLDxzl5Pkci8cVzYu9SsW9URV/dKw8dsLK1TR/Hi4vcz73uWpvr1CFonZ+GYDygs9Af/4aezQ9Stc3Uaj8MejS0Rd+be1yakQCVROScSaL4cpbarzPb2/P57e356N0aoXa3paBE3vTd0wkIT0CjZcJ73p2DOVl5aSnXODwjlNsWLSHfZuO1dMzsY45X65izperrr1hE9ExzIePvnsAtVrFzI9XMv/37eBW/Vns9xxLZc+xyrFs1H4OjIoIYWBYEGHt2tLN35vIQF/+b0A0Wp2e9PwCDqVlsDbxFCuOHqdU2/wnn25p3hg1FD93VzIKCpmxdce1dxC1oMfJcXy1A5VC6Y29+/colE5oS1dSXvRVPdfX8GycpqB0mITBUIou90kw5Fq7pBqRQNVMGbTlZut0ubmUACu+XsqKryv7LXUb2Jmht/en28DO+AR70b6DF6Pu7FtxmTAjl+N7kti+4iAb5u8wTm0DoHCo4rZyG5u6fjoti4uz2aor+0v1HRzGK+9W3Fn56qvz2Lb1ODir0dmbv/YKC1fvbIvMO3oYEopZlrCPZewzrgsP8SYuNpQenf3w83ZnZOdOjAoP4d0bR1JUoiE5LYc9CaksPpRAwlnT+RM1bubn1Zt3EbGoUfW1MlzxbwNNc9iJa/SreqJfNDd170JpuZan/v6H3BLrT03VXPrhKXSVRSv0Ff/W6Euxs3Fmr/5hkooqwuujrkct76/0xL71bJQqP7Sl6ynLmXzdtTTWKWpsWj2EynkaBkMZ5Tn/h0Gzq/IxRRPo94UEKgEc3HiUgxsrv5HbBrZlxD2D6DWiOwHhfkSP7UnMuF489dl9FBeUkHwsjT1rD7NmwW7SWtAkxI3FLff046En4ygr0/LUk79w4kTGtXe6TodPpHP4RLrxa0d7NUP7hdIvMoiwIC9CAtsS3sGbu8dGodPpycwv5GhKJpsOJ7HoZCLFGs1Vji4aiwf69OTpQX0p02qZPG8Re1LNh1URdatMX4Ra6UA/z3uMgcoiZWvsPX5HqeqArmwTZTmPAs3r+0rpeC8ql39hMJShzXkCQ9lGa5d0XSRQCTNZKRf47b/z+e2/8wFQ26sZfNdAYsf2JKRnIKE9g+jcuwN3vTie8jIt6SkXOLLjBBsX7mHPBst/YYm68fQr4xl1Yw/ycot55JHvyc5u2I7DxaUaFm04xKINh4zrQvw9GTokjN6d/Ahs686Qbh0Y2r0jrxniKNaUc+ZCDrvOnGXpwWPszUi/ytGFNdwTFcn0uEFotFqmzlvCplNnrF1Si2Aw6MkoPYmXfUdU2KPFvEVQofTAweN3lLad0JVtpjT7IaB5TSuldLwHW9c3LoapyejL1l17p0ZKApW4Jk2phtW/b2X175Ujknfp24kht/WjW2xHfAI8ad+hLyPv7Iderycnq4ATB1OIX3WQ9Qt2U1LYvH4AWMu7M+4jMiqI5KQsHr9rBqX2jePbNzE5iwOLKjvH2qtVxHXvRP+eQXRr500nrzaE+3pxb2xPdHo95wuLOZqRyYbjSSw5nEB+afP6a7spuatXBK+OGIJGq2XK/CWsP5lk7ZJalG1ZvzLR/036tr2bjZnfmTymULrj2PoPlLahF8PUgzS/MHUntq5vXham1lq7pFppHD+RRaNi0JkPFacrMB3a4MCKPRxYUTleVVu/NsTdM5CokZEEdvWjz7AuRMd1Zer/bqc4v4SUxIrLhKt/38zZi5eolK0czU+uauQfSUt3QNZmoExn8+Eb9I6m/aXUdiq+njuZ9u092LXzFC8+/weobdA6mPeXKneo274Glvpf2WjM+0y0yrh8nYZ1KYdZ989h45qg9q0Z3i+MXt38CfT1YFDHIAZ3Cua10UMp1WhJycxl/4lzrNyVyJ7jqZS0MT+vzsF8ndX609RinKfG4vae3Xl95NDKMHVCwlRDKii357ltaob7lNGx1VjuOJDIowMutvArXHFs/Ts2tmFoyzZRfME0TJmPTVVJ7foBto63UF48B01e9e8gvF42DpNQu32AXptKWdaAau+ndLgNlctbzSZMgQQqUUcyU84z+7/zmf3f+aBQorZXM/DmaPrd2JuQ3h3o1DOIsN4duPPF8ZRrysk4c54ju5LYsHA3e9YdsXb5jVZrT2dm/PUEzq4OLFywm88+aVrjslySlHqBmX9uQT+vYhJrtUrJgKiODOzZkfBOPgT5eBDi58ktQyLQ6w1cKCrmWFoWm4+f5p/9x7hQWGzlZ1A9/xs3gpsiujB//2FeWlL11DZ1ZWL3cN69YSSpuXkM/fKHau93a2RX3hglYaoxWJ6SwKSg7kS4V44K7uD2Hja24RgMegz6XOzd/muyz6U4pStdia7sys/ZpT+qanYXrtJuCLZOlYMmK1QVc0uqnJ5CdXFcKICyCzdVcT7zG6GqolB1RuX6NgqFEr02BaX9KJT2oyxuq817odrHtTYJVKJeaEo1rP51E6t/3QSA0sGeLjGdGHxrLN37V8xNOLyjNyPu6Iteryc3q4ATB5OJX3WYdQt2yWVCICTclw9+eLBi4M4v1zBvbvO5jV2j1bNmeyJrtidS3qriB7Kfpyuj+oTRp7M/we1b069TAP1DAnlxzCDKtFpScvLYk5rGiiOJbElKrtf6nugfzeCO5tMpzbh1POW6ima7w+mZvLnc9K9q5cWWg5pO7zO4YxBP9K8cI61jGw8Apg6I5e6oSOP6W2f9Uevzdfby5K0xcSgVClJy8xgV1olRYZ0sbtsQobCle+/AWiYFdee5iKGgewMAhdKt4v8KJbYON1S5r0GXahaobGwrJl/WFv9ZozoUSg+U6h5m65WqQCCwyv2UF8+nK/6r+idTuqC4eOeeUtURVFVPDC6BSggLDm8/zuHtx41ft+3oy9Bbo4ka0oXAzr70HtaVPnHdmPK/2ygpLCP5RDr7Nh1jzdydpJ6svzvZGqP+w8J5+d1bwABvPPM7m/edtnZJ9S4lK49v/4nn23/iKWkDKqWSgWFBxIV3JMLPB38PNzq1bcNtPbuhNxjIKS7hWOZ5tpw6w+JDCaQXFNZZLf7urkS2N59HrIuPl/HfZTrzFoBLj8/Zd8jssatxd3Qgsp35+QI83AjArcr9unq3BWDuvsNVbnMlZzs7YxDr0KY1Hdq0rnJbCVT170JZMUkF2fTx9Kc4XQVoKb5w21X3qeqSn0LphdI2BG3pcvTle2tUh65kHiUllud4vBqlXX8MujS0RT9Wex+DJp6yNMuT2jdlCoPBYKk3QIuXn5+Pq6srg7kRlaKag+eIWlGpVQy8pS/9J/QmtHdHWvu6Y6Oq6CdUrtGScSaLI9uOs3FePLu3nrRwAAvjXzXAqO/V7kNlYS5Fg6P5QJyTHh/Cw48NoaxMy5NPzOLUiUzKncw/g+VOtRjvq4n194GKOQS9Wjszol9norsHEOzXBjdnBxQKBQaDgXKtjnPn89mXksbqg8fZlJBkfGvKL06F2M7VhTVPP8TZ3DyGfm5+iaw2fbK8XFqx6clHWZlwnCnzllz/gWpg5eP3Y69SMfzrHynT1tE0udV8DRJffgaAkP9+XDfnrYWmNDZVO1cX1j/xEEUaDcuOV/yBWaTRcF+PHuTn/Zfiwi9MtndUVK/dw0ahQOVwC2rX/1GSNQKDzsLPyBqozthUCpt22LfdjCb3JXQlV28Ru56xqSwex+EmlOoYFAoFSodJAGjTTVu48gv0tA5JIi8vDxcX83lQ64u0UIlGQ6vRsnb2ZtbO3mxcFx4bwrC7B9Ktfxi+HbwYfo83I+4diF5vIPd8PicPpBC/8gDr5u2guLj61/Abq6dfv5GRE3uRl1vMw/fPJDenafQdaigZFwr4ZdEOfllUcfnT4KAgumsgQ3p1IqKjL76ergR4uzOhTxcMBgN5xaWcSL/AluRklhw4iv7in49ujg68M34EACuPnmDd8VO1rq1fUABavZ6P1m+p9bGqo52rC4Ee7vzrn1V1F6auYWK3cKID2jfIuZq7Vmo1N3fpAsCHW7ZQptXi2Oo+s0BVE9qSOWhL5tRViddk0J2lJM380nh9Utr2xMbx5gY9Z3VJC1UVpIXKSiyMiKtUq43/bu3jRtxd/Yka3YOgzu1wcnM0tlCUFJWRcrziMuHqOfGknshoUi1U7377ABF9gkk+fZ5HH/oWbXnlcaWFqqKF6ko6O/Mn0iqwFWN6htEvNIAQX088nByNl7g0Wh3qK1oyP1m3ha83XwxotXldmuBratFVnsebo4ZxR8/uJuukher66OxNf/V+fcMNjOjQkayMAeh1lQG/Ji1Udak2o6dbPl7d1ne10dOt1UIlgaoKEqiaBpVaxYBJMfSf2IfQPp1oc8Vlwszk8xzZnsjGufHsWL4PAIWj+XANCju12TqLqjtsgqXw5GB+Dp2LA/b2Kmb8+Ajt/DzYGX+S51+fa7adxqUBGpOb4C+l6urVxY8hMSFEhLWjnZcbdmqVMYgXFJWRdO4Cuw+nsOhQAqczTCeCLvMwP56lKXQs/lJviq9pU6zZgsYesq78AyHQ3Y1Vj9xPVvFa9mdOMa6Ptqve4L0tLVBZcilkSaBqZCRQNUEXv5nCojsy7M7+dB8YTruO3qgd1CgUCvR6A3lZ+Zw8mMz25ftZOzee4vyK0YmtFajcgj2Z+dMjOLs4sPjv3Xz+0QrKnczDkwSquuXiZE/cwM7ERgQSEtCW1q6OKC++t+U6HRk5hRxOTmfDgVMsSz5hNhG0BKrGr6kFKoCD025HbePB2jORxnUSqKpPAlUjJYGqCbJ0udBWRWsfd4bd2Y+okREEdfHD2cOp8jJhYWnFZcLNiayZG0/K8WvcTViHgSqkiy/vz3oEtVrFjM9XM/+vistOEqgaht7W9Al3D/Fl8MAQenVoj19bNxzUtsbPSWGZhlNZ2ew4ncKSAwkkZF8wO54EqsalKQaqpQ/rCWn9LIezXuFcYcUddxKoqk8CVSMlgaoJqiJQXcnWzYV+43rQb1xPQnsF0cbHzXiZUFuuJTM1hyO7TrJ58V52rjmC/vLAVEeBqv/wLkx/r+LW6Df/NZdtmyuHk5BA1TCuDFQApW6V65zs1YzsFULfHsF08WmLp3MrbC6+/1q9noz8Qg6nZbDueBLLDh+j2MIwCk3yNW2KNVvQFAPV17d+x7DA/ZSUp7D17BhAAlVNSKBqpCRQtSyhfUIYdld/IgaF49vRG7srLhOe2JfE9n/2smHhbgpzTe+8UzjYmx3P0NrdbJ3OtWLulNvujuWhx4dSVqZl8jO/cOpUlul29uY/yLR2DdC53pJG/kupoYUFexHXN5Qe4X74+7rjaK82tmIVl5ZzJiObPcdSWbXzGIdPZ1DSxvwF1Jpn7cbfutWYaqmmxh6oLL2mOv8Sfh16J7FeAcT8/RlZpUXsGzzjuk/R0kKWBKpGSgJVC3NF65aHtxtx9w6i98gIgrv5m14mLCojNTGNvRuOsvaPrSSfyTY7XFWB6tnp4xg5LoK83GIeufsbzmvNh3qQQNX4GS7eKGivVjEsJpR+PYIJ6+CNp7sTKpuK90qn13O+oIgjZzPZeOw0S/cnUFiqkUDVQJpqoIpo7cP8Efcz79RBXohfIoGqBiRQNVISqFoYC5cLFTaVt9erVEr6TuzDgEkxhEUFm9xNWHGZMJsjO0+xefEedq45gs7d1ex47/7yKBE9AziTlMX/PfAtWo0erbP5Z0sCVeNnsDBiRbljxYvVsV1rRvYJIyrMjwDf1jhf1opVoinnTE4uu5LPsvRwIntSz1UcTwJVnWuqgQpg501PYWejovucDyVQ1YAEqkZKAlULc41AdYnysst7IT0CGXJbDBEDw/ENaoudw8VOzHoDuTlFnDx6jh0bjrFl9WHe/+kRfP1bs3P7SV6e9rvxGBKomqarBarLlbRRYK9SMbRLB4aEB9OtvQ/ebk7YXvxs6fV6zhcVczQji40nT/PP4QSyiyvuPG1Ur31jqqWamnKgeilyCI+Gx/LYhrm832nadZ9CApUEqkZBApWotovfxG5tXRh+z0B6j+pBcM9gXFwrp0UBWLBoD599udpkV4vhycK6RqWx/6Jq5AxKCPBxZ3i/zkR18SOofWucW9kbPytlGi2pmbnsSUlj9b7jbD9eORG0xs38eHpL47vW9XvUTN7zxhSy9Hbmv3p1jhU3uDiqVBx66CmOnM/kcN44s+1GO1ZvbtPGHqgsn6P2NecX6GkbekYCVWMhgUpUm6W7CyPDUKmUxA4K44W3bsLWTsVtd31F1nnTCXwlULU8lu7uUtjbMKBnMIN6dqRrBx+8WzujvniHqt5gIKewmMRz59l45gxLDh4lq7DyxggJVNXXVAIVwMKb7qKbpzd/nxyMFtMbYSRQXZ0EqkZGApWotioC1SXBIV58NftxtsWf5F+vmc7mLoGq5bE4hY7a/EX16ODK2KgwYkL86ejdBrfLWrE0Wh2pufnsTT3LyoSTbDyZhMngHRKoLGpKgWqwXxCzxk7iVP4/7Mx8w2Q7CVRXZ61AJZMjC1HPTiVmcC4lm+jewajVKjQaC+MVCXGFczn5fLtqB9+uqhjwVamEmF5BDO/ckR5+vrR3cyG4jTs39+iGwWAgp6SUxMzzbEk6w6JDCaTlF1j5GYjaKCovx2AwEOQ8hvatBpCvOU1myW6SC1cB1QtUomE1SAvVl19+yfvvv096ejoRERF8/vnn9OnTp8rt58yZw6uvvsrp06fp1KkT7777LmPGjDE+bjAYeP311/n222/Jzc2lX79+fP3113Tq1Mm4TWBgIGfOnDE57jvvvMNLL71UrZqlhUrUho2Tk8nXfW/oxWu/TeHvxXv4ZEZlP6ryVrWY4LgxaUR/+bckbdxbMWJQOH26BdDRzxM3F4fKiaDLtaRlF3AwKY11+0+w8lwS2isGodXZWThodd/LJvieW6uFymJrlL35gMA2HhoAXogYwqNhMegNBs6X5aNUKPCwczZ2utYbdBRrL5BVmkhKUTwnC9bS136v2fG8LN1YY+GNq01LVmPsqN5sL/n9+eef3HvvvcyYMYPo6Gg++eQT5syZw7Fjx2jbtq3Z9lu3bmXgwIG88847jBs3jtmzZ/Puu++yZ88eunbtCsC7777LO++8w08//URQUBCvvvoqBw8e5MiRI9jbV9yFFRgYyEMPPcQjjzxiPLazszOtWlkYBMYCCVSiNq4MVAB/pnyJnZ0tIyd9bFwngUrU1pUjvvfu4s/g/iFEBPvS3tMVO9vKiaDzSso4kXme7acqptA5VZhrfkAJVHWuuoHKtS38Oewewty9SC/O55bVP6O1TzU+Hu7SnmHe3Ynzaoub2g9bpaPxvTUYStBoz1BatouCkqUUl22QQNXcAlV0dDS9e/fmiy++ACpuE/bz82Pq1KkWW4tuu+02ioqKWLJkiXFdTEwMkZGRzJgxA4PBgK+vL88++yzPPfccAHl5eXh5eTFr1ixuv/12oCJQPf300zz99NPVqrOsrIyysjLj1/n5+fj5+UmgEtfFUqCa+Nn9PHb/ID6fuYa5C3cDEqhE7VmaQkfjXLnOw9mRkVGhxEQGEubjSetWjiiVFY+X63Sk5Rdw4Gw6qxNPsjrhBBqDhamULGmC73ljDlR9ff35ftxE7G1ULEk+wtPbFgLQ1iPfbN/7A7YBYIOaIOcBBDj1xd+hIyqVL0pFxRRXBoMegz4HvTaR8rLNlBcvQK9PlkBVj+o1UGk0GhwdHZk7dy4TJkwwrr/vvvvIzc1l4cKFZvv4+/szbdo0kyD0+uuvs2DBAvbv38+pU6fo0KEDe/fuJTIy0rjNoEGDiIyM5NNPPwUqAlVpaSnl5eX4+/tz55138swzz6BSWe429sYbb/Dmm2+arZdAJa6LhY7qKv92LDz6LkUFpdzZ53UA8nq3a+jK6kcT/OXakkWEtWNYbCgRndvR3ssde7vKVqzCkjKSzmWzKyGFFdsTOHXugsUpdMqdzY9rqcN9Y/9s1HXIqunlPYBXesRxf0hvtAY9bx38i1XpB4yPOduVmu3roi4zW6ek4rye6tb094yim2sY/q18cVE5o7gYmLR6Lfnl50ktOcGx/N0cyd+ORl/GKJcDZscLU5eYrXNUVK/btbVDVrPslH7+/Hl0Oh1eXl4m6728vEhISLC4T3p6usXt09PTjY9fWlfVNgBPPvkkPXv2xMPDg61btzJ9+nTS0tL46KOPLJ53+vTpTJtWOYDapRYqIerS2gW7GXV7DFGDwti1wfL3gBD1bX/CWfYnnDUOUOrsaEdc3zD6RgYREuRFl2Bvunf05cFx0Wh1OjLyCjmcmsH6I6dYeeg4pXJjRZ1wsbXnr7h76OTqSWpRLo/t+JILmsJr73gVWZoL/H12BX+fXYHapuJ96uIcSh+PKDo5d8DH3pNurv3o7tYfg+FJyvQllGtPkVe6k/NFSynQmPfFEtXTbO/yuzwcde/eHbVazWOPPcY777yDnZ15T0w7OzuL64WoSzP/s4ARt/ThkX/dKIFKNBoFxWX8vXo/f6/ej9a+ojWgS5AXI6LD6BHSHn8fd4Z368SI7iG8bRhJUZmGUxdy2HEmlX8OJ3AkPesaZxBXGuAdxIz+N2Nno2J+0kGej1+Mp3vtwlRVDhcc43DBMQACHS5gr3Skq1tfQpx70s6hAy7qMJztutLe9QEMBj0a3XnQHqG8bD1lJQvR6zPrpa7mpl4DVZs2bbCxsSEjw/QWz4yMDLy9vS3u4+3tfdXtL/0/IyMDHx8fk20uvwR4pejoaLRaLadPnyY0NPR6no4QtVZSpGHvlkR6DgjFr6MXedYuSIgqHE7K4HBSxc/ikjYKHNVqRkV0YmDnYMLbtSXcpy3d23nzcN8otHo9WQVFHErPYP2JJJYdSaRQo7nGGVqufw+M4+4uEZTr9UzeMp8Vqcca9Pyl+mJ2Za9mV3bFHcejXA7gqOqEp9M4XO1jcLTtgMpuEHb2g2nl8jpQhk6biqF8H9rSlejL1gDSSnmlBumU3qdPHz7//HOgolO6v78/U6ZMqbJTenFxMYsXLzau69u3L927dzfplP7cc8/x7LPPAhWX59q2bWvSKf1Kv/32G/feey/nz5/H3d39mnXLXX6iViwN9nlx5GufIE9+PPIxBzYd5YnPl5lt15gGH6yV5vI8hEUGJYQEehIXG0bPcD8CfD1o5XDZRNBl5aRk5LIrKZUVexPZfzrNuG+ZhR/Beks/ZhvgM1Sb7zeL/aUsrFN6VPR5clPbM3f4fQS7tCa1+AIPb/+G7Msu8bnam/dbstRfqrou9au6HmqlgR5uXYny6EEnp2A87Vpjq7Q1vr/FumIySs9RpNlCUsEq8jQnjfsOcDhjdjwPpfkbbKuo3k050ofqomnTpnHfffcRFRVFnz59+OSTTygqKuKBBx4A4N5776Vdu3a88847ADz11FMMGjSIDz/8kLFjx/LHH3+wa9cuZs6cCYBCoeDpp5/mP//5D506dTIOm+Dr62vs+L5t2zbi4+MZMmQIzs7ObNu2jWeeeYa77767WmFKiPqUlpTF6UMpdOsfhtMPayksuv4fmEJYU+LpLBJPV17uU7WyZWjvTgyIDKZzkDcd2rUmNKAtdw3uiU6v50J+MQlnM1l/+jRLDx0jt8S8w3VzNdS3I1/2vwm10oY/T+7lw+NzrF3SVenRszv3ALtzKzust3ewpbd7DOGu3WjvEEBgqw4onTrRzeN+9AYtJdrzZJcl4qBZTHHxYvQtrA2+QQb2/OKLL4wDe0ZGRvLZZ58RHR0NwODBgwkMDGTWrFnG7efMmcMrr7xiHNjzvffesziw58yZM8nNzaV///589dVXhISEALBnzx6eeOIJEhISKCsrIygoiHvuuYdp06ZVu5+UtFCJWrlKCxVA5OAu/G/ZdFZsOsK/P19usp20UImmwNIdfZaGcPAN82B0r870CfEj2MsDZwc7YytHabmW5Jxcdp05x7JjiexITjXduZm0UL03ahi3BEeg0euYvHk+a8+doI2F/lKNqYVKpdSZrXO1Na9vvKeaAOdheDlE4mIbYDY2llZ7mlLNLmxKl6PTbDbZt7m1UMlcflWQQCVq5RqBCuC3U1/g6unC0Ls/4fIBrCVQiaaguoGq1N10nVqlpH9MR4aGdqB7ey98XFxQqyp+seoNBi4UFZOQkcWmU6dZfPgYF4qLzY5Zl+ozULVxcGTepDsIcHXjVP4Fbl71E7maila55hKo+rkcv+KcakZ4dMXBYQR2tpGoVL4orhgbS1d+DJ1mM4aSJaBPuWYtEqiaOAlUos5dEbLGPTacp756mFmzt/Djr1uM68udmslgn5ZIyBIWtPNyZfjAzvTuEkBw+9a4OFVOBF1WruXs+Xz2nzzHmj3HWZdp3j9H62DhoLUYCslSyKppf6lRfqF8EjsBW6WSucnxvH90kcl2dR2eqqs2Iava51CYnsNT3ZpBbXvS1aUL7Rx9cb5sbCydvpx87XnOFSdyvGAH/oZvAdNLwcEq85rtLIyJdSl4SaBqZCRQiTpnodVqYeEv6HR6xt36mXGdBCrREukv+/2oUiqJjQxiYGwI3YK98W3tgvpiC6/eYCC3qITEjPNsOX6GJfsTOFtuYbgBKwaqD2NuYEJgV8p0Wh7bNJejZQfNtmtJgQrA3qbc5Osuzp0Z5tUZP8cuuKm9sVVUXgrWGwopLU+isGwHOcWL8dbvMTueBKomRAKVqHMWAtXDc57jtpt689a7i1mz4SgggUq0THoLt0hpnCq/Z7w9nBndO5TeEQGEeLfB3dHB+AtYo9NxNjefvWfTWJVwgvXHT6G3QqDycmzF/FvvoF0rVxJzs7h1zS/ka0ob5PJedTWWQAUQ0qpyiCR7pRNhLv3o5x6Cg7orahsvFBdDk8Ggw6A/j678CNqyjZSVLMLOkG1+XglUjZMEKlHnLAQq+44B/H34Hc6n5fLAwLeBZjQdTXVJyBLXQamE3t0CGBIdSrdQH3zbuqK+fCLoolJOnb3AjsNnWL49gbNZeZS2tjDvoav5sQ0W/qbRuJpPH3Pp8h7AOP9wPoy9AZVCyR9ntvJxwj/GxyxNH+NmYV1DaIhAVV2Wgtfl/Bx86demN11cO+Hr4EMrm8oO71pDOdma85wuSuRg3i4O5+1lgMsRAIoLdNzT46AEqsZCApWoc1XM7/fK1/fTd2Q3np74KYn7kyVQCVFDlwJQazdH4mLDiI0IomOAJ+7Ol00ErdWRlpvPgTPprD18kg1HTqLR6uskUH3edwJj/DtTqtPy7J6f2JV9ymQ7CVSWXStQXXKpg7wSJT3cutPLI5Iw5yA81G1QKSrHxirX51OgSSIpYyO9g56SQNVYSKASda6KQOXu6cxv8W+QeCCFpyd8IoFKiBqyFIC0dhUfrF6h7YnrE0pEJ1/ae7lhf1krVn5JGScvZLMtKZnFhxJIupBT5fEsBar27e2YM/xefBxdOJKTwW2rf8HB2fxSlAQqy2oaqC7XRl1xGdVF5UYP91g6u3Qn0LEd9jYeFBQU4erq2vwG9hRCXF1OVgEJe88Q1iOA1l4uLWwoPCHq1+5jqew+VjG+VWlrBa6O9oyODGVA5yDCfD2JaOdNTz9fJg+MoVynIyO/kANp6aw5fopVx05QqrU8xcpNoeG8N3QENgol3xzZxrv71wFg6YZDUX/ytblsyFrGhqxlDHKpmB9VrbHO9HLSQlUFaaESde4qY1N17BHIF1v/w7bFu3n+lw1m2zWbsamqq6U9X2E1BiV06+RDXGwYkZ3b0d7bHQe7ystIRSUazqRls7HkDAtPHyYhJ4tvBk1khF8IJToNT+/+iX05p43Hs9QaZamzeXVbZxpCU2y1upryIg1/D/9RWqiEaIlO7D1NelIm0WN6oP59Exqt+eUFIUT9OHg8jYPHK+cadHS2Y1h0CP0igwkLbEtYoBddbHx4vGsMBoMBhULBgQtpTNn9DaV6mQRaVJBAJUQj8f0rf/DK7Kd4/O6BfDprvbXLEaLFKizRsHD9IRauP2Rc1/YOD8YFdOaR8D7o9TrGL/2Jtp4SpkSlWozUIYSoS5vm76Agu5AbhnWzdilCiCscvJDOO3vW8b8967BR2nBnp0hrlyQaGQlUQjQUg95s0ZdrTZZ5n/yDg72aWwZ3x7ZQZ1xaHIOFRYh6oNCbLzYag9lif9IO+5N2/LpkP1qdnsdConG2KzVbXNRlZosleoPCbLEWPQqzxWq1NKLXpaYkUAnRiPz29nw0Gi333B5r7VKEEBboDbAtKRl/dze87N2tXY5oRCRQCdHIrFl/FA+PVkT1DLR2KUIIC95fuRGARzvcYOVKRGMigUqIRuaLb9ZgMMDkR4dYuxQhhAWJmRfILCgitnUXa5ciGhEJVEI0MvrdZ9i/7TiB/m3oWKrF4WjatXdqCaRflbAi90S9ybJg2T7sbGyJaxtDmU5lXGqjKfcfEhKohGiUvnxlLgCT/3OzlSsRQljy6+KdaPU67gwcbO1SRCMhgUqIRuhsUhbJx9OJiO2Io7O9tcsRQlxBq9ezL/cU/o6etFE33GjcovGSQCVEI6M7l4HuXAZfPz0LhULBY8+NQmHAbBFCNByFznx5Y902AB7wuYnUU56knvK0cpV1R4ZSqDkJVEI0UntWHyQnI5fBt/W1dilCCAsOXcggq6SIEYGdrF2KaAQkUAnRiP3+v4Wo7W25f1K0tUsRQljw+9H9ONjacmPHztYuRViZBCohGrGFX62kpKiUW8f2snYpQggLvty7HZ1ez5QeMhhvSyeBSohGxqDTmSzLf1iHq7MDI3p2atnT0VgiQykIK3I7oMJxn5K9J8/S0c2D4FMudd7fpzH1H5J+VVcngUqIRm7mC7+i0+l5+P4B1i5FCGHBpws3A/D0BPkebckkUAnRyGk1WrbGn8TX242Qjl7WLkcIcYWDp9PJKSwhrkdHa5cirEgClRBNwCdfrQLgqSfirFyJEMKSv7cewtFOTf82PaxdirASCVRCWJNBb75YULz5OIkHkgkP88Unq0Cmo7ka6VclGoh9rsG4/PRnRef0m9uPplhra7LUtcbUf6gx9auyNglUQjQRX702H4DJ/5bpaIRobDQaLYeOnyOwlQ+uqlbWLkdYgQQqIZqIxP3JZKRmEzO8KyqVfOsK0dh89ddmFAoFD3YYa+1ShBXIT2UhmpBZ7y9FpbLh/hfGWbsUIcQV9h87R155IYM9pR9VSySBSogm4NL8fmtnLKMgp4jRd8TI/H41If2qRD1Q6Axmy5yTB3FU2dPRNpbTWa05ndXa2mW2GNbuUyaBSogmZsGXy3F0dmB8XHdrlyKEuMLnhzegN+iZGj7Q2qWIBiaBSogm5rf//E15WTkP3hxj7VKEEFco1mvZn32Ozm5euKjsrV2OaEASqIRoAi6fikZXXs6GOdto4+FEdId2Mh3N9ZLLgKKWFHrzxXZfKz5bsA2FQsHzPsOx3deqQYY5kKEUrE8ClRBN0JdP/YjBAJMfG2rtUoQQV4g/lUpecSljI8OsXYpoQBKohGiCCnOLOXAoheBAT3y8Xa1djhDiCgv3HsHZ3o6BIUHWLkU0EAlUQjRRH39RMR3NtCkjrFyJEOJKn63eht5gYEpcrLVLEQ1EZe0ChBBXsDT9jML8b5/M5QdJOZlBr8gAPM6cp6RQgybGrwEKbMYs9aNqGd0/RB1xSr30ISojISmD8EAv1EmOFOtLTbZT29R/n0dL/aiUVhpfxVI/KmUz67goLVRCNGHfvrUQpY2SR1+daO1ShBBXmDFvC0qlgnuDZOT0lkAClRBN2K71R8k5X8DQiVHWLkUIcYXth85QUFzG4La9rV2KaAASqIRo4v76ajVqe1tufWKYtUsRQlxh+bajONs60sMt1NqliHomgUqIJkqXnokuPZN5/5lDaXEZNz08WKajEcLKbDQGk2Xm7E3oDQZu9x9PepGzcRHNjwQqIZqBFT9twLWNMwP7dLR2KUKIyxQWa0gsSCPctR32SrW1yxH1SAKVEM3Aty/ORqfV8cTdMn+YEI3NN8fXoFQoeTxELss3ZxKohGgGNKUadq7YT3tvNzoGeFq7HCHEZTZmJlBYXsq4dj2tXYqoRxKohGiiLp/fz6DT8ekT3wLw3ANDsS3QGhdRSzLnn6gBS/P75R5pzZKjibipHQnJjyD3SGurzb0nc/7VHwlUQjQT51OzSTyRQdfwdri6OFi7HCHEZd7ftBmDwcBz/ftZuxRRT2Sk9BbOK8CTX5O+Mln346u/M/vt+WbbOjjZc8f0ifS/KYa2/m0oLSolIf4Ecz9azL51h+q0rvYhvvQa0Z2Qnh3o1CsY/87tsFHZVFlbbTl7OBF7QxSdegXTqWcwHSIDsXe0Y8/qA7w44t9V7nfbixN4+J27TNbdHfQEGWey6rzG6vjsq1V88dHdPDV5OG+9s8gqNQghzOWWlnLs/HkifXywV8mv3uZI3lUBQElhKZvmbgfg1P4zZo+7ebrw0cZ/4xfqy4Vz2WxfvAt3Lzd6j46k9+hIvnr6RxZ+sbzO6rnh8RHc9FTDjS7cbUBnnv9xco33SzqYzMpZ6wEYcHMMDk72dVxZzZyat5us58YysG8IzokZaLV6mY6mPsgUNaIG3I5VfDi+n7ud9x+/gedDYphnOG62ndWmhZEpauqEBCoBQN75fN5/8MsqH3/6m8fwC/Vlz+oDvHbju5SVaADoM7oHby18kcc/foADG46QdDC5Tuo5fSiZvz5YxIm9SZzYc4o7pt/E8HsH1cmxLcnJyGPJNys5vqfifJ16BfP0jMeuud+OpXvYsXQPAN0Hh1s9UAH88sFSnv34bu55bgw//m+JtcsRQly0du8Jiks13Ni/K/P2WbsaUdcapA/Vl19+SWBgIPb29kRHR7Njx46rbj9nzhzCwsKwt7enW7duLF261ORxg8HAa6+9ho+PDw4ODsTFxXH8uGnaz87O5q677sLFxQU3NzceeughCgsL6/y5tQT+ndvTb0IfdFodHz78tTFMAexYtpeVP63HxkbJHS/V3Xxyy75fy7cv/MK63zeTcuwcBn39/oVydHsinz7+LUu/XU3i7lOUlzXdztyr5+2iKL+Esff0t3YpQogrrN59HDcnB8KcA61diqhj9R6o/vzzT6ZNm8brr7/Onj17iIiIYOTIkWRmZlrcfuvWrdxxxx089NBD7N27lwkTJjBhwgQOHarso/Pee+/x2WefMWPGDOLj42nVqhUjR46ktLRyNu+77rqLw4cPs2rVKpYsWcLGjRt59NFH6/vpNkv9J/YB4PCWY2Qmnzd7fO3szQDE3BCFjcqmQWsTli3+aROtnO0ZeVu0tUsRQlzm8/kVPy/vC7rRypWIulbvgeqjjz7ikUce4YEHHiA8PJwZM2bg6OjIDz/8YHH7Tz/9lFGjRvH888/TuXNn/v3vf9OzZ0+++OILoKJ16pNPPuGVV17hxhtvpHv37vz888+cO3eOBQsWAHD06FGWL1/Od999R3R0NP379+fzzz/njz/+4Ny5c/X9lJudDpFBACTuPmnx8cRdFesdnOxp18mnwepqUQx688WCS9PRzHr+Z8o1Wu56agQKvcFsEfVAhlcQVVAX6o1LYVohSWcv0NklmFJtKwo0dsalMQ1pIGquXgOVRqNh9+7dxMXFVZ5QqSQuLo5t27ZZ3Gfbtm0m2wOMHDnSuH1SUhLp6ekm27i6uhIdHW3cZtu2bbi5uREVFWXcJi4uDqVSSXx8vMXzlpWVkZ+fb7KICt5BbQEstk4BFBeUUJRXbLKtsC69Xs/m+Tto086DiM7trV2OEOIysxbEY6NQcnegjJzenNRroDp//jw6nQ4vLy+T9V5eXqSnp1vcJz09/arbX/r/tbZp29b0F7tKpcLDw6PK877zzju4uroaFz8/uTPqEkfnio7WpUWlVW5TUljxWCsZ/6jR+PLpWRgMBp5+cIi1SxFCXGbV1gRKdRpu8I2xdimiDsnAnhdNnz6dvLw845KSkmLtkoSolfzsQg5vTaRTYFt8PF2sXY4Q4jIbMw/grnYi1FlakJuLeg1Ubdq0wcbGhoyMDJP1GRkZeHt7W9zH29v7qttf+v+1trmy07tWqyU7O7vK89rZ2eHi4mKyiArFBRWtT/atqh4S4NJwAUX5JQ1Sk7DsyuloPpv8HQDPPTgM20KdcRENRPpVCUChM1/+tbFi3L/7/CZyOtWT06mNaw7OxtSfq6lMUVOvgUqtVtOrVy/WrFljXKfX61mzZg2xsbEW94mNjTXZHmDVqlXG7YOCgvD29jbZJj8/n/j4eOM2sbGx5Obmsnv3buM2a9euRa/XEx0tdz3VVMbpinDa1r+NxccdnR1o5eposq1oHE4fSiH1bA69ewZhby/DzgnRWGSVFnEyP5s+Xn6olHKxqDmo93dx2rRpfPvtt/z0008cPXqUxx9/nKKiIh544AEA7r33XqZPn27c/qmnnmL58uV8+OGHJCQk8MYbb7Br1y6mTJkCgEKh4Omnn+Y///kPixYt4uDBg9x77734+voyYcIEADp37syoUaN45JFH2LFjB1u2bGHKlCncfvvt+Pr61vdTbnZO7E0CIKRXB4uPh0RVrC8pLCU1Ma3B6hLV8/V367CxUfL4I0OtXYoQ4jJfHdiGSqnkia7Sl6o5qPdAddttt/HBBx/w2muvERkZyb59+1i+fLmxU3lycjJpaZW/hPv27cvs2bOZOXMmERERzJ07lwULFtC1a1fjNi+88AJTp07l0UcfpXfv3hQWFrJ8+XLs7SsvSf3222+EhYUxbNgwxowZQ//+/Zk5c2Z9P91macuCioFYu/QLxdPPvJVq6J0VA0huX7wLnVYuJzU2e3/YRO75AkYN7YLDkTQcjkjotSq5DCgAl31qVs8/TqmmnLuDe+CyT21xu0Z16a0x1dIILwM2SDvjlClTOHPmDGVlZcTHx5tcdlu/fj2zZs0y2f6WW27h2LFjlJWVcejQIcaMGWPyuEKh4K233iI9PZ3S0lJWr15NSEiIyTYeHh7Mnj2bgoIC8vLy+OGHH3Bycqq359icnTmSypYFO7BR2fDsd4+jtq/8xu89KpIR9w1Gp9Pz+//+Ntv3ntdvYZV+Dh+sfaNBau0+KJxV+jms0s9pkPM1FXNnrMXOQc2kR+WOPyEak/WHTuLp0oqwdo2rD5WoOelUIarlk8e+ISC8Pb2Gd+fnE59zcFMCbm1d6D4oHKVSyZdP/WBxHj/lxb4B2vKatVx17BHEk18+bPzap0PFzQRjHx1OzNhexvVv3PQ+2em5Fs5X86ljPtv6tvHfrhfvigvt3dFk/a//mWecu68pmTdzHfc8O5qb/28o82aus3Y5QoiLPly0iZE9QnnmhgH8L2u7tcsRtSCBSlRLblY+k3u/xO3TJzLgpmj63hhFaVEZu1bsZ+6Hi9i79pDF/Tr1DAZg+Q9rLD5eFUcXBzrHhJitb+vXhraXXXa0tbM1PV+vS+dbW6PzARbP18rV0WS9WxMefmD1vJ2Mvbsf0XHhrCwssHY5QgggI6+Q5PO59O7ohzJLiR7LsyCIxk8Clai24oISfnh5Nj+8PLta26tsVXQfFE7irpOs/3Nrjc51YMMRhitvqXGNvYZHUFJUys9v1PyS3/WcrynQpVfcefnNlG8ZdUcsD704jlX/Mn8PDUrr90FosSz1o5K3o1lzOF/5pv+2aCcvPzSCG3yH82eK6R+f9jZNd6L2lkbu1RQAuLZx4fkfJvP8D5OJGdfr2jtUQ5d+oTg42fPd9N/q5HjXorZX07V/GPM/+YecjNwGOWefMT2Nr5trm8bdelVarGH3qgO0D/EhyK+1tcsRQly0cP0hNOVabmg3wNqliFqQFioBVAzMOeL+wQCcPZnG9iW7r75DNexff7hBW300pRrGtbqrwc4HENTN3/i6NQWfT/2RnxM/4blHhjP5tT+sXY4Q4qLN+04xJKoTAY5enCnOuPYOotGRQNXCZZzJaraXuhrCn+8u4M93F1i7jGrLOJPFyf1n6N49ABcne/ILq56fUQjRcL74fRNDojrxSPCNvHJIhvhpiiRQCdFUGSx0XlWYX8U36EzvsPzqqR/5cP0bPHvvEN5+d4lxfZmr/DiwxNvThflfPmKybuYfm5k1P95kXWyPIMI7ehMa7EVokBeeHhXDtNz4f9+QlV1Y53WFBLWlVxd/woK9CA1qS3tvd5RKBW9+vpQVm4/W+fnatnYmtkcQYUFehAZ7EezXGrWtikVrD/K/b1ZWud/zD8cxcXiEybq+t31Y5/U1NUqtace5tLRcUotziHQPJTXb3dg1PcQzy2xfS+M/KRXWGdCsUdVyseOhtcakkp+gQrQwBzcnkHW+gEEDwnjn/SXo5aaiaiku1bBueyIAx8+Y/5J748kxOF9lvsu69uCkWAb27thg5xsc3Ymn76v5OGaHEs+htrUBYOzgrtfYumWbdXw7r0aO4d4OMcw6KUMoNDUSqIRogWb9soUXpo3m/nsG8MNPm6xdTpOQl1/C21+tqPLxDTtOkJKWQ2JSBsdOZbL0+yfqtZ7Dx9NISjnPsaRMEk9n8vL/jaRnF796O19aZh5zlu3hWFImx5IyGBYbyv03XXvKlGUbj7Bs4xFAAtW1zE7ayYvdRnBPx2gJVE2QBCohWqBlKw4w+f+GMnF8TwlUdeS/X1cdturDLwt3NOj5Nu06yaZdJ41fD+7TqUHP31JsyjzBUO9Qgpxak1R4wdrliBqQYROEaIHsj5xj6axNODnZMy68HfZHzlm7JHElmfOvWVPozRfDARfem7sNgOf8R2M4UP2hWBrVPHuNqJaGJIFKiBbq5/f/QVuu457nxlx7YyFEgziTncu5vHz6dwi0dimihiRQCdFCabV6ti4/gKevO517BVq7HCHERT/H70WtsuGu3hHX3lg0GhKohGiBdBlZ6DKy+PyxGWCAx1+bgEJvMFtEIyOXAZs159MGnE8bmP/HHsq1Oh7s3QutXmm2iMZJ3hkhWrC88wUciT9Oxx6BeLZ2snY5QoiLth86g28bF7zsZZqopkLu8hOihft86g98vfMdnn9kOC/8729rlyNEs+Xt6cL8L64YJPZP00Fi3Z0diO0WiOJiP+7ver+KRq8lqyyHvTkJ/J26juzy83VaV3ArP7q5htDByZ8OTn5423uiVCj5JHEWG7J21um5AFqr3enp3oUOrfwJdvLH39EHW6UtqzO28PXJqud+fTT4DkZ6m853OGlr/Q5PUhMSqIRo4U4dSObcyQxiegShVqvQaGR2eyHqk8kgsadNB4l95o7BjI7tjE6vR6vTo7RRcDD3OJ2c/RnfbhDDvWN45+i37M9NqLN6bvUbTXTrhuuvFdM6kgeDaj7lWWLBKdTKitgypG1sXZdVaxKohGhOrnM6mm9f/JU35j3Hk7cP4LMvVxvXy3Q0TYD0o2py8vJLeNvCuGWqUgMFeSV8O2cLi9cdYmT/zky+cyBr0o/xwt6/mN7lJkb4RPBc6AP83643KNKVXPU81Z0WJrEgiZTiNE4WpZBUmMKUTnfT1TXk+p/gNWrJKM1mybn1nC5K5lRRCn1b9+Rmv9HXPMb6rHjWZ1W05jXGQCV9qIQQbFmwk7y8YkaP6G7tUoRo0T7+aR0/zN9OVk4hs//ZiVav467AgZToNLx9aB5F2lJcbFsR5VF3o87PP7uK35IXs/3CPjLK6n8w0R3ZB/g+aQ7rsrZzpvgsOprH/FcSqIQQAPw1bwf29rZMmtDL2qW0KP96YiRb/3qWfz0xskHON2ZQF7b++SzzPn+4Qc4nrp9eD7sunKS9Y2u87F0p05dzpqii/1QbO3crVyeuJO35QggAZv8Zz7139efO22OZt2C3tctpcu6fFEPfHkFm6997YQLl2opLrIlJmXzw/RqTx5UXex/rdDX7K71vjyDun1Q5l15Q+4q7wR66JZZJoyKN6x995XeT/RQXz6et4flau7XinefGG79u6+EMwIBeHQj+zx3G9R98v4bEpMwaHVtU7YvEZUS36cTUkDG8fvBPfBzcAMjR5Fm3MGFGApUQAqiYjmbd/J2MuiOW/l6u7Fp3lLJYf2uX1WS083Kla4iv2frQYC/jvzXluiofX7z2UI3O5+biaPF87b3dae99WevFpS4zF7uwhF0835J1NTufra0NXTuZn8/d1RF3V0fj160c1DU6rqiksJBxt+zVkhFRRH/PcAbbDsZd7USZTsPunCPXdY7q9qtqCJdqMVw8fVPvDiiBSghhNPOt+Yy4NZpHXp3ArnVHrV1Ok/L2Vyt4+6uaTZDcxt2JYL82rI8/zuHjaTXad+mGwyzdcLhG+wD06R5A5oUC/ly6p0b7pWfl0/e2D2t8PlF7vx7cx3Ox/XltwBAA/kxZTl55gZWrEleSQCWEMCop1LB3cyI9B4bSvkNb5KJCJVcXB2M/p/Xxx9my+1StjxkdEYBWp+eb3zfX+ljV4e3pgp+PO+98sxJNecMMjzF6YDg9u/g1yLmaq4WJR3k2ph92KhWrTp1g3rmV1i5JWCCd0oUQJr54+S8Apvz3VitX0rg42qsZO7grYwd3pVOAZ50c85/1hxl4x8ecOZddJ8e7lkutTIvXHmyQ8wF0DfE1vm6i5to4OvLT+EkoFAoMBgNvbVxr7ZJEFaSFSggBVMzvB3A2I4ukQyl0i+mAs4OawqIyk+0MSvM+GM1ZelY+fW9tBpe6LHVQaYC38v3vVvP+d6uvvaEw4+7iwOejbyXY3YODSWl0DfTm5fCBfFVmHohr0w/qyn5Vhob4YDRD0kIlhDAz47lfUCgUPP3gUGuXIkSL5ObswOev3UqwT2viE5J59OM5XCgoZnBkB2uXJqoggUoIYWbfusNcSMthWN9QlPJTQogG5erswOev3UKwXxviE5J5+qsFlJXrmL/xAA5qWwZ5Rlm7RGGB/KgUQgAV09Fcvvz2n/mobVU8dEM06gKdcRFC1B/nVvZ89urNdPD3ZMeBM7zwv78xXChHna/n1znb0en03OI3isJytckC8GSnu1nY/wue7HR3g9Q6pG0Mf/f7km96vVVnx9QbFGZLUyF9qIQQFi35ZhWPfHgvkyb25qdft1q7HCFahOn/N4JOAW3R6w3kF5bywoPDTB7PLSjB37UtI7yjWJm+y+Qx4yCxhpr94dPLvQu3+Y0yfu3n6A3AbX5jGe0zyLj+pQMfmJ7vYl8rbQ3P527rwoudHzV+3VrtBkAf9+74d68c62zmyT85VZRSo2NbkwQqIUSVlizdx62T+jBsSGfWyLhUQtQ7l1b2ACiVCuL6hla53S1+A80CVYdWFQPxrsrYVqNzuto6EepiPsq/j4MnPlR9R2sHp4rhMNZk1OwPLpVSRaiz+flc1c64qp2NXzvY2NfouNYmgUoIUaVvf9jATTf24qH7B0qgEqIBTHnrL5Ov9RZ+Sy/78THaObYxWeehdsW/lQ/bzu8jseB0jc65NjOetZnxJuuqc9dghFtnzpflsCRtfY3Ol1WWzcQtk2u0T1MggUqI5s5gYT4LRfW6T6r2pxC/6hB9R3Wnu60NifuSKesbUMcFCqux0lAKLZ2riwP/evyyQWL3VD1IrNLC+Ku/Hz/AE1360tW2H/+kJAAwMcofnUHHL2cW10mN15qixtPOA1+Htnx54jc0+vI6Oee1ahniGU1X15B6PVdtSKd0IcRVffmvOWCAJ/5zi7VLEaJZMBkkNrDmg8R+cXAzOr2eKd36G9etydzOTVue4mxJRl2WWqVLrUyra3i5rzZCXYIY6hXDUK+Ya29sBdJCJYS4qpzMfBL2niasZyCtvVxlOhohrlNdzYdYqtey9/xZenm2x01tT66mtA6qa/xmnPyDGSf/AKw3ofPVSAuVEOKavnplDgBPvC2tVEI0Bu/vX4dCoeDFyCHWLkVcJIFKCFElXeZ5dJnnObZmH2lJmUTHdcFOqUChN5gsohkxWFhEo+K4z4HDqy6QU1TCuPZdcNzn0CDjNzXlMaIaggQqIUS1fP+vP7BR2fD43YOuvbEQot4t3HWEVvZqhobLdDSNgQQqIUS1bJoXT352ITfEdbN2KUII4ItVW9HrDTwxonF20m5pJFAJIart78+W4WCv5qZRkdYuRYgWr1Sj5WBKOqE+njipHK1dTosngUoIUaUr5/f79d9z0Wi03D8xBnW+zriIZk76VTUqrdINxmXm7M0oFAru9L8Bjd7GZGnOGmN/LglUQogaWbv+KB4erejZQwb4FMLadh1NIb+wlKFevaxdSosngUoIUSOff7UagwGmPD7s2hsLIerdsi1HaKVyoI9HuLVLadEkUAkhakS/K4n9WxMJDGhDUFEZ9kfOWrskYQ1yGdBqlOUGk2XmH1vQGwzc7jeW9AJn49IQGtOlN2vXIIFKCFFjX06vmMB16v9utXIlQojiUg1H887S2dUXJ5WdtctpsSRQCSFq7GxSFsmJ6UT0DcHR2d7a5QjR4n15bC1KhYLJoXIp3lokUAkhrsvMN+ejUCp47I2brF2KEC3elqzjFJSXMK5dpLVLabEkUAnREhn05ks1XZqOZufcLeRk5DL4xl5mU9HIdDQtlPSrahAKvflSfNCDRQcScVE7EFkQSfFBD6v1b2pM/aoakgQqIcR1+/1/C1Hb23L/zTJSsxDW9sH6zegNBp4Z2NfapbRIEqiEENdt4VcrKSkq5daxMgaOENZWqNGQkJFFd19vHFUqa5fT4kigEkLUyoof1+Pq7MCwviHWLkWIFu/TzdtQKhQ8Ka1UDa5eA1V2djZ33XUXLi4uuLm58dBDD1FYWHjVfUpLS5k8eTKtW7fGycmJSZMmkZGRYbJNcnIyY8eOxdHRkbZt2/L888+j1WqNj69fvx6FQmG2pKen18vzFKIluXI6mm+e/wWdTs//3T5ApqMR5qRfVYNwPWHA9YSB3ctOUlRSxqQu4S22L5O11Guguuuuuzh8+DCrVq1iyZIlbNy4kUcfffSq+zzzzDMsXryYOXPmsGHDBs6dO8dNN1XeRaTT6Rg7diwajYatW7fy008/MWvWLF577TWzYx07doy0tDTj0rZt2zp/jkK0dFqNlm3xJ/DxcSOko5e1yxGixVux4xhuTg50de1g7VJaFIXBYKiXvxeOHj1KeHg4O3fuJCoqCoDly5czZswYUlNT8fX1NdsnLy8PT09PZs+ezc033wxAQkICnTt3Ztu2bcTExLBs2TLGjRvHuXPn8PKq+OE9Y8YMXnzxRbKyslCr1axfv54hQ4aQk5ODm5tbteotKyujrKzM+HV+fj5+fn4M5kZUCttavhpCNAGK6//7yvXWgcyZ/QRHjp5jytO/AlDmJn04RBWkoaTOaZwqX1SXVvas+fRxjuYn8fz+T0y2Uymrf0dvXVIqGq5psrxIwz8jvyMvLw8XF5cGO2+9tVBt27YNNzc3Y5gCiIuLQ6lUEh8fb3Gf3bt3U15eTlxcnHFdWFgY/v7+bNu2zXjcbt26GcMUwMiRI8nPz+fw4cMmx4uMjMTHx4fhw4ezZcuWq9b7zjvv4Orqalz8/PwqHqjFLxkhWoqijQkk7j9DeJgv3hm52B9OtXZJQrQotiUG41JyvoSTKecJcwmkuNyZ3DIH42ItLeHyY72lhfT0dLNLbCqVCg8Pjyr7MqWnp6NWq81alby8vIz7pKenm4SpS49fegzAx8eHGTNmMG/ePObNm1fR0jR4MHv27Kmy3unTp5OXl2dcUlJSavR8hWjpvnplDihg8n9vs3YpQrR4P/69HaVCyYPBcdfeWNSJGrfJv/TSS7z77rtX3ebo0aPXXVBdCA0NJTQ01Ph13759OXnyJB9//DG//PKLxX3s7Oyws5M5kIS4Xol7k8lIySZmRDdUKmnZFcKa1sYnUqwtY1y73sw4sdza5bQINf6p9+yzz3L06NGrLsHBwXh7e5OZmWmyr1arJTs7G29vb4vH9vb2RqPRkJuba7I+IyPDuI+3t7fZXX+Xvq7quAB9+vThxIkTNX269J3QG6VSfjkIUR2z3l2MytaG+6ePt3YpQrR46zIO4mbbiq6u/tYupUWocVLw9PQkLCzsqotarSY2Npbc3Fx2795t3Hft2rXo9Xqio6MtHrtXr17Y2tqyZs0a47pjx46RnJxMbGwsALGxsRw8eNAkrK1atQoXFxfCw8OrrHvfvn34+PjU9Ony4qzJLNP8wR/nvuWNxS8z+MERqL29UNrZmy0olOaLEE1FbaajybqALusCa79dSUFOEaPv6ivT0YiqyVAKdU6hM19eX1vRX/lBv/GcPd2Gs6fbNKq+TI2plrpQb7fhdO7cmVGjRvHII48wY8YMysvLmTJlCrfffrvxDr+zZ88ybNgwfv75Z/r06YOrqysPPfQQ06ZNw8PDAxcXF6ZOnUpsbCwxMRVTW4wYMYLw8HDuuece3nvvPdLT03nllVeYPHmy8ZLdJ598QlBQEF26dKG0tJTvvvuOtWvXsnLlyho/j+/fXsjQ8X3o0NWPvmMi6Te2Bwa9geyMXI7Gn2D9nO1sWbALvd46d04I0dgs+HI597wyifHDu7No1QFrlyNEi3W+tJgTuReI8mqHWqlEI7+n6lW93tf822+/MWXKFIYNG4ZSqWTSpEl89tlnxsfLy8s5duwYxcXFxnUff/yxcduysjJGjhzJV199ZXzcxsaGJUuW8PjjjxMbG0urVq247777eOutt4zbaDQann32Wc6ePYujoyPdu3dn9erVDBkypMbPYekvW1j5U8Udho4u9gy/NYbY0RF06NKefjdG0X9C74qAlZnH0W2JrP9zC5vm7ZCAJVqs3/7zN7c/P54Hb46VQCWElX2+dxufDb2BKT1i+Wj31e92F7VTb+NQNXX5+fm4uroS5/MYKr35pTt9XgGOLg4Mv3sAfcf3okP3AJzcHFEoFJUtWNuPs/6PLWyat10ClmiaqnnZWmFjY/L1Cz8+zrA7+/PkG3+y57IhFAzKpt2kL+qRfDTq3Lm4it87R+5/mqJyDb1/+4puYclm2zXkGFHXUhe1WGscKglUVTAGKt/HUCmrd/efo7M9wyb1pu+o7nTo0h4nVwdjwMrJyidhz2k2LtzFpt82otWaBiy9RmN+wBr0YRGiXlxnP0AnN0fmZf1A0uksHnnsB+P6UncZJFfUgISsWiluW/H9+/a9oxgTFcZdH/xOQcw2s+0kUNUNCVRVuJ5AdSVHZ3uGTehJ3zERdOjqh5NrZQtWTmYeCTtPVvTB+nsHmuJS8wNIoBLWVosbK97f8z4R3f24+74ZpKXlARKoRA1JoKqVS4HK07UVK//9CDsTU/h35vtm20mgqhsSqKpQF4EKgPJy4z8dnOwZdksf+o7oRsfIAJzcWlUELIOBnIw8EuKPs/6vbWyau62iBUsClbC2WgSq9vfH8cN3D7NzVxIvvfwXIIFK1JAEqlq5FKgA/n7lPvzbuHHz1mfQYvq7RQJV3ZDJthpQSWEpS37cyKJP/gEqAlbcXf3od2NvOnQPIHZ8FH1v7I3h16mXBaytbJqzDa1WZ+XqhaiZM8kXSE65QK9egTg6qCkusXBZWwjRIH5ctZO37h7JJL8R/JkiA33WB2mhqkKdtVDVgIOTmqET+9BvdHc6dm1f2cndYCD3fAHH9pyu6OQ+d7tJHyzpfyXqTS1aqFTebYkaEs6/f32c5bO38unzv5PXL7DuahMtk7RaVZve1vTFWv/DVIoMxdy69U2T9Y6qcq7UlFut5JJfI2ONQHUlBzsbhk7qQ7+xEXTo5ofzZZcIczPzSdh1kg1z49kw27yTuwQqUSdqGagAZu97m1bODtzYYZoEKlF7Eqiq7cpA9e/JY4iLCeXRnR9wqijNuF4CVd2QQFWFxhCoLu9/BWDvqGbI2Aj6T4iiY2QQzh6XB6yKTu4b52xj/V/b0JbJ5RVRB+ogUE14ZAiPvXETP/53Ed/uO15XlYmWSgJVtV0ZqNp6OLHw00fYnXOMF/fPNK6XQFU3JFBVoVEEqmuwd1Qz9KYo+o2OoGM3P5yvuESYuPcMGxftZuOiPWg1WvR5BWbH0JdZuLtQiEtqEaguH5tqUe6PlBVrGPN/35htJ2NTiVqTj1C1zf7uftq3cqPr/P8au6aHemVedZ9LmkrIkk7posZKizUs/XUrS3/dClxswZrQi35jIunUrT19hnclekQ3nvv8voqAteskG+fFs35OPFqN1srVi5ZkxU8buPHxEQyM7sjG+JpPUi6EqBvfJ27n373G8nBoX2Ye22rtcpoVaaGqQlNoobqq8vKKgHVTH/qNi6RTd3+c3S+7RJiVT+LuU6z/Ywvr/9wiAUtYVkctVGp7NQuzv+dcVj63T/3eZDtpoRK1Jh+hajt/SzEHJ04nu6yYQUs/BaSFqq5IC1UzVlqsYdmvm1n262YA1OVlDLmtL/0n9qZTjyD6jIokenQPXpg1uSJg7TrBxjnbWfv7ZglYok5pSjXsXLGf6DE96BjgyYkzWdYuSYgWa13acUa2C6OjsycnCuR7sa5IC1UVmlQLleL6/jxT26sYMiGK/mMi6NTdD5fLWrDyLhSSuO8MmxbvYf3fu9Bk5ZjtL/2vWqjrnN+vTTsPfjv1OUf2nOG5O742rs+LbFun5QkBSKtVFXI7KfFzd2H5tAfZdPw0//fzAjqMPFWtfaWF6uqkhaoF05RqWfH7Nlb8XjG3kzFgje5Opwh/eg/rQp+4rkz75B7yzhdwfM8pNs7fydo/tkoLlqix82ezOXH4LOE9AnDxcCQ/u9jaJQnRIqXk5JOak09sB3+U139VX1xBWqiq0BJaqACw9PZfHK5Bba9iyMTe9L+hJ526++PS2qmyBet8QcUlwrnbWff7FjSlMkxDi3GdLVQAXSf248M/nmDjsgP87+nZgLRQiXoiLVQW5Xaq+P69IzqCV28YynvLNrDJaW619pUWqquTQFUFCVSm9HkFqO3VDLk9lgEX+2C5tnE2DVi7T7J57nbWzN4sAas5q0Wgsgn046f1L+Hh6cLEiFfQavUSqET9kEBl0aVABbDvjamcLyzmsYP/qta+EqiuTgJVFVpMoKoFtZ0Ng8f3ov/YSDpF+ON62UCj+dmFHN+fzObFe1j3905K0y+Y7W9xyhzR+FkIVJbCk0JtPhGyspUjcbdG8+zn95N6MoOXbvqYlPYeZttpHc2PZ5BfkKIm5PNikda+8oV5/6nxDOzRgUd3/ZfUEtM7/VSK6s220RhDlgSqRkYCVTVc8dFR26sqAtaY7nSKCMD1skuE+RcKOL73NJv/jmfN7K1oSjUSqJqqWgQq+9YuvLfgWUJ7BAJgMBgoLSvndPIFtu88yT8rD5B1vlAClag9+bxYdHmg8vNyY+67DxB/4TBvHvnWZDsJVDUngaoKEqiqoaqPzsVLhiq1isEToxhwQ09CIvxNLhHmXygkcfdJNs3bzppfN8klwqbkOgNV5+iOvLPweexb2bFn/RHmfLGKYbfG0GVQGF6eLtjYKI0BKyn1Atv2JLF47UGyLhQCEqhEDcnnxaLLAxXAwg8fpo2HI+M3P2uyXgJVzUmgqoIEqmq4RqC6nD43D5VaxZDbYhl4UwydegXh5uli0oKVuPuUBKym4DoC1QNv3Mxt08ah1+v57LnfWHnxzlKA4qggAHpG+DNyaFe6d21/RcDScjr1Alv3JrFozQGysgvr4UmJZkcClUVXBqo7RvTkmbsG89WJuSw+t8m4XgJVzUmgqkKTClSWWCtk1YBKrWLwDZH0H9eD0Ah/k0uEBTlFHD+QzJYl+1jz4xpKi00DloyBZT0KlfmlvKr6S9k7qvlg0XN06ObHhfRcnp30GRlnrxjTzM7C95dSQfc+wQyb0JNuUUG09XUzBqyy0nKST2WxY8txFm1PICPLdI5Krb154JPWLQFIyAIMFu4p2fjb02SW5jF+w4fGdb5O+dd9DmuHrPIiDctGfSuBqrGQQNXwVGoVg8d0o/8NPQmJDMDtskuEBdmFFX2wFuxk7e9bKM65/m92UTvVDVQRcRG89dtk7BzVbPlnH28/NBMc7M0PWEWgulLXgaEMHxdBt54BtPV2M2nBSk65wPbdp/hn5QHOFhSZ7SuBSgASqLAcqN7/dBz9PEO4ccOHnC2p+INHAlXNSaCqggQqK7mso7pKrWLg+J4MGBNBaFSwySXCguxCju85xeb58az+ZSOlxWVWLLplqU6geuTt25j05Gh0Wj0fP/0za+fuqHigFoFK38p0u249Axh6SxQRXf3wblt5ibBMU3GJcNveJBavOUjG+QIJVKKCfA4sBqp2j9swZ8BTrMs4wvN7K8aHk0BVcxKoqtDkA5UlTTVkXaRSqxg4LpKB4yIrWrAuv0SYW8yJA8ls+Wcva+fuMF4i1OcVmB1HLhdWX00u7wE4utjzwaLnCOrcjqxzOUyb9Cnn0/IqN7Q137euhmru1juIuIm96NYniLY+7tioKi8Rppw+z86tJ1i+YDcZF+sp9Wlldgy5XNgCyftLeoyCLQ8/goeDA50//wyALlFJ1308CVTChASqxk+lUjLghh4MHNeD0Eh/k0uEhbnFnDiYzOZ58ayZvYWSwsoQJYGq+moSqCIHhvLGz0+gtrdlw9+7eHfabPMD1mOgqjxexee8S69Ahk/oSbc+wXi1c69swSrTkpKUxbYDZ/hn5QHSMyv/EpdA1QLJ+0t6jILHoqJ4ccBA/rVqFb8fOiiB6jpIoKqCBKqmR6XXMuDGXgwc34vQnoGmASuniOP7TrNl4S5W/bjGJGCJqlU3UD3xyf2Mf2gw2nIdH0yexcZFuy1f3mvAQHU5fSs7uvTwZ8S4SLr3DMTLx62yBUuj5UzKBeJ3J7FowyHSskwvdUigaubk/SU9RoESSHjqKVLz8hk660cJVNdBAlUVJFA1QVcMFKpSKek3rDMDb4khLKoD7l6upgFrbxJb/o5n1c8bJGBV4VqBysnNkY/XvIp/qC/pyeeZNvZ9ci61+DSyQHWlTkM7MSquK5Fd/fHxcjXpg3XmbDbb9yaxeN0hzmXmme0rmpFm/mOxOtJjKl6EnybeRP+AAAZ8/x3uoQeu+3gSqIQJY6DyeQyVjQSqRsfCL81rUamU9BsTyaBxkYT2CMD98hasvGJOHExh69J9rJmzg5LCUsv9ryyN7m6o3ngtjV1N+0tFDe3Cqz8+iq2dLavm7uTj53833bAhwlMdCu8ZwPBJUXTvHVRxiVBlg8FgQFOmJfl0Fru2nmDFwr2knc2R/lfNXQt738pcKz67Ie3bMPvVu1m1K5FPS7402642QakhQ5YEqkZGAlUjdx2Bykhf8ZGvCFgRDLyhB2GRAbh7uqBQVgask/tOs2Xhblb9usnYgiWBqiJQTX3/Tkbf0x+tRsc7j33HtvXHzA/YxAIVADaV9YX38Gf4xF50i+6Al48rqssC1pmz2ezYk8SSFQdIy6howZJA1Yy0sPftUqACWPbeI7g62nPT9mlm20mgujoJVFWQQNXI1UGgMqHRVASscT0YeGMUYT0DTQNWbjEn959m8/wdrPxpveklwhYUqFzaOPHJ2tdpF9yWc0mZPDP2ffIvFFrv8l5dszGvT++oBqBLhD9xYyOIiArEq50bKpuLAUuj5UxqNtv3n2bR2oPGgAUSqJqsFva+XR6oHh4bzeM39uWTY7+xKiPeZDsJVFcngaoKEqgauXoIVGanKCqi38Q+DL4lhtDeHfBo63pZwCri5L4zbFm4g5Wz1lOcX3z99TQS1wpUMWN78K+fJ2OrVrH81y189txvlRu2gEB1uVKfVnTr7MvIYV3p0c0P77YVLVgAZZpyzpzNIX7/aRatOcDZDOmD1eQ0ox+V1XF5oFIplWz5cirpZVk8tuttk+0kUF2dBKoqNMtAZYmErGpTqZT0HdGdQeN7ENojAI+2lQONFuWXcPJQKtuW7WPVnHgKkzPN9m9Mlwsthidbldk6pVNFX6FnPrmH4bfHUl6m5b9PzCJ+7RHTDZtieKpjnXsFEjehJxF9gvFq714ZsMrKSTl9gd3bT7Bs4R6Sys0HoZXLhU1AM34/DFdMxfnZyzcT1cWfSZv/w3lN5V2vruq6vXmnvkKWBKpGRgJVE1TPgQowad1SqZT0HRXBoBsiCO0RaNKCVZRXzMkDyWxdtIuVv2yiOL+kSQYqj0BvPl76At4BbUg5ns6zN7xPQamFmiVQmbVudY70J+7mKLpHBeHt62YasM7msGNvEv+sPEjquRwJVE1BM34/rgxUYcFe/PDvu1idsZd/H6682UQC1dVJoKqCBKomqIEDlVF5ecXplUr6jolg8IRehPUMxMPLzTRg7TvNloU7WTFrQ+UlwkYcqAZM7M2LPzyOytaGxT9u4Ovpf1Y80Fwu79U1S5cLLxuuIaxrO4aPiyQiOhgf78svEWpJPpfNjn2nWbz2EClpFXOpSaBqZJrx+3FloAL458dHcFTZM2L9v4zrJFBdnQSqKkigaoKsHKhMNsvNR6lU0m9CFINviSGsT0c8vN1QXh6w9p9h68U+WIW55hP61qdrBaoXvv8/ht4Wi6a0nLfum8GeDUcrN5RAZdk1AtUlpd4Vw06Eh/owalhXenbzNwtYKedyiD9wmsVrDpJ8MWAJK2tGPyqvZClQPfqf7jwQPJx/H5rNqoy9gASqa5FAVQWTQKU075TabIJIc3ke0DCBqhaUSiWxI7syeHxPOvcIxL2tS2XAKijh1OGzbFu+n5UzV1GYa9rJvTaXC2vSX6q1tysf/fMCbdt7cProWZ699QuKC674ISrhqe7ZKAnp1p7hE3sSEd0Bn/YeqGwrfstpyspJTc5m97YTLNh2lJSzpgFLZyeXC62mGb/O52/Qc/TOZzmVn83wRd8D0C0otU7PIYGqhZBA1QQ18kB1JaVSSezQMAZPiCKsVxAeXq6VASu/hKSDyWxdvIeVP28gPzPX/AB1HKiG3DOI5z6/DxuVDQtmrmXma3OlNaqhWGjd6tQniOHjIomMCsKnnXtlwNJoSTmXzc69p1m88gCnz5vfRSiBqoE049f53Fgtf464gz5efvSe8wXnS4slUF2DBKoqSKBqgppYoAKgrLLlSalUEjOyG4PGRRIec+kSodIYsE7tP83WRbtY8eO6ihasOgxU//plMgMm9qGsRMPrd33Jga3HKx6QQNUwqnG5MKSzD0Nv603PCH98vd2wVV0WsNIqhmlYsvYQZ85mS6BqKM34dT43VktPT1/mjbqbeScP8dzWpRKorkECVRVaTKCypDk9t6YYsi5SKpXEDO/C4Bt70rlnxV2El1qwigtKSTpylu0rDrDyz20U5FRcItTn5ls4kPlrcGk4BM927nz0zwu08XHj5JGzPH/LZ5QUXXZ5UcJTo9WpaztG3BRFRGxHfPwuv0So5WzyBXbHn2T5or2kJJ0HoMTH0ewYcrmwlprxa1XYruKzsf6tx7C3VREz/Uv8JphPmFzXoagujieBqpGRQNVMNOFAdSWlUkl0XDiDx/civFegySXCSwFr68IdrPx5EwXZhZftaDlQxd0Ww1Mf3Y2NUsmcL1bw40crzE8qgarxu9i61THcl+ETexEZ0wEf/9bYXnaJ8GzyBXYcSmbJqoOcSblg3FUCVS0149fqUqB6ckw/Ho7rw/M//cORIPOfERKoKkmgqoIEqmaiGQWqKynLtUSP6Mrgib0J732pD1bFJcLi/BJOHUph25LdrPx1s2nAAl6fO43YURGUFpfxyu2fc2THKbm811RVcbmwY5gPI2+IJLJ3ED7tPUwCVuq5HHbuO83C9RWXCC8ngaoGmvFrdSlQ2atUbPvfZE6lX+DpE/82204CVSUJVFWQQNVMNONAdXn/K6howYqKDWLo7X0JjwmhtW9lH6ziglKSDiVzYv8Zht7WFxcPJxL3nebFiR9TWnzxOBKomqZqDtfQrl8AY+O60SsikHbebpUBq1xLalouOw9UjIN1KvWC2b6iCs34x8ulQAXw05RbiQzy5Y5t0ynQmt6BLIGqkgSqKlwzUFnSXIJIc3ke0LwDVTXep+i4Lgy5oQfhUUG09q5swfr981X88tEy040thSeVhQFqLLE0Ppdo1II7+zDi5t70uNgHy1ZdcaOCRqPlXEo2e+JPsnzhXs6cypL+VzXRTF4DrUPlE4kM8WXmy7fzx9EDvLRhpcl23bqcqfdaahqyrBWozO+dFkI0G/GrDxO/dJ/x669WvURQeDuO7jHvXCpallNH05jx3yXGr4PDfBhxUy8i+3Winb8HgR3actOdsZRrtKSm57Jr32n+WXWQU2fOW7FqYQ37Es+RW1jCuA6hZoFKVJIWqipIC1Uz0cJbqADQVI7k7uBkx9wj75F6KpPHhr9rup20ULU8V7lcGNzJi5HjI+nRJxhfv8pO7uWXLhEePMPitQc5lVx5iVBaqGiWLVQAT942kLtHR/Ho8gWsPH3CuF5aqCpJC5UQLUhJYRn7tyYS2T+U9sGepJ7KsnZJopE6dTyDrz+suKurxMeRjoGejBnejV4RAbT3cSPIvw23ju1FebmO1PQcdh5IZsn6Q5w4I5+p5mjm31u5c1Qvno6KNQlUopK0UFWhRbdQWdKcnltzabW6zvfEJ6A13697mQPxJ3npnpmVD1S3Naq6pNWqWQvs7MPIm6LoEdsR34DWxj5Y5eUVfbD27khi+aJ9HCk0HxutxfW/auTP7crWKACtnfm6WS/fTjd/bwa88hX5JRU3s/jcaN5CpVbq6r7IK1yt1UpaqIQQDSLtzAXOHM+gW59gnFzsKcyv2wlPRctwOjGDb/73j/HrwBAvht8WTc/oYHzbexAQ3JYJt0dTXq7jbHouu/ef5p/VBzkhraJN1sEzaXQP8GH9W//H1mNnWL43kf2koKV6szY0d9JCVQVpobpCc3puLbyFCiByYGf+O+th1izYw4cv/FmxUlqoRE1Y6H+lc6oceiOwoycjb+hBj74daO/rjtr2UguWjtSMXHYdTGbp2oMknq4IWNJCZT3XaqHycnPii6cm0sG3DXq9Ho1Wh52tCoWiYmDhHE0BCfmnWZe5l83n96NSlpsdr641xhYqCVRVuK5AZUlzCSLN5XlA8wlUllT3fVIo+XX7a7i4OTIh/GX0ej3YWeiUXtckZLVYAR29GHlLFD36dqJdYJvKS4QaLWkp2ezdc5rlC/dyMjHDuE+pj4PZcXTqZnK50Eo1lzuan1hn4fKe9uJIGU+P6899g3uhVChYfjCRl/5cjlavx0Gt4oYe4cRFdKSrtxcu9nYoFAr0BgNZRUXsTjvHwoQjrEo6CYBTkPkk3u1czdepFNVr7ZJA1YRIoLpCc3keIIEKQKFk3D19mfzmTfz66Up++3SlBCrRcGyUBHT0YvhNPenZtxPtAtqgtqtswUpLzWbvriQWb00g8VSGya4SqGqnuoEqNMSLzx4aj6erE1n5RTz53UIOZGWYbVfuVPF/J7Waid3DGRnWiTBvT5zVamPASi8sYF/eaRaf3c+WzOPGfSVQVVN2djZTp05l8eLFKJVKJk2axKeffoqTk1OV+5SWlvLss8/yxx9/UFZWxsiRI/nqq6/w8vIybvPkk0+yZcsWDh06ROfOndm3b5/ZcQ4cOMDkyZPZuXMnnp6eTJ06lRdeeKFG9UugukJzeR4ggQpAUfFLaf6ht9GW67i1x2sSqETDsXC5sH13P0beEEmv6GDa+bdGfakFS6vjbFouew6cYenqQxxNNe+DJYGq+q4VqJRK+O+DYxjeKwS9wcAvG/bw0eJNF7ezcDwLv9K1znrc7O25NbwrccEdCWvjSStbWxQKBTqDnoySPHZfOMOW7M3szztusq8EKgtGjx5NWloa33zzDeXl5TzwwAP07t2b2bNnV7nP448/zj///MOsWbNwdXVlypQpKJVKtmzZYtzmySefJDQ0lPj4eA4cOGAWqPLz8wkJCSEuLo7p06dz8OBBHnzwQT755BMeffTRatcvgeoKzeV5gAQqMAaqx14dz4QHBvK/J39hw6oj9VjYRRKoBFyz/xWAf2Ab4u6MondkIH6+HiYB61xGLrsPJrN43SEST2VKoKqBqwWqwREd+PcDo2hlr+Zk+gWemPk3abkFl21n4XhVBKor+YdqmeTfi0HeoXR09sLBpjJgZZZmcyD3BCvTt5NYeLJaz6PFBKqjR48SHh7Ozp07iYqKAmD58uWMGTOG1NRUfH19zfbJy8vD09OT2bNnc/PNNwOQkJBA586d2bZtGzExMSbbv/HGGyxYsMAsUH399df861//Ij09HbW6Igi99NJLLFiwgISEhGo/BwlU1dCcnltzCVk1fE9UahULDr9DVlouD8S9b75BQ7wuErJENfh18GTkzb3p2S+EdoGVlwi15TrOnc1h384kVi7ZR+LRNKCJ9r+q41rKW1noG2Vvvs7eTc0XD91Iz+B2lOt0vL9wI7/t3m+2nc78JbWoOq+pt7MTt/TsypCOwXRo44G9qqKTu1avJyUvj62pZ/jz8CEOZmXg3iHHbH8vpwKzdZdat8qLNKwYPbN5DJuwbds23NzcjGEKIC4uDqVSSXx8PBMnTjTbZ/fu3ZSXlxMXF2dcFxYWhr+/v8VAdbVzDxw40BimAEaOHMm7775LTk4O7u7uFvcrKyujrKzM+HV+vvnYKUI0N1qNlvg1R4gd0ZVOXdtx/NBZa5ckhEUpJ7P47t2lYLMcgPZBnoyc1Iue/TrRPrAN42/pzfhbeqMt15F2LofdR1L5Z/UBjp0w7/cjKt0xOJKnbxqIrY2SnSdSmfr9Ioo1GrAwV3pdSi8o5PNN2/l803YA2rm6cGtkVwaFBBHs7sFd3SK5q1sk5TodqSUXiD9/kvnJuzhR2Hjfz3oJVOnp6bRt29b0RCoVHh4epKenV7mPWq3Gzc3NZL2Xl1eV+1R1nKCgILNjXHqsqkD1zjvv8Oabb1b7PEI0F1+8OpfYEV154vUJPHPLl9YuR4hqSU3K4vsPlvP9B8vROdnjF9CakeMjiYrpSHt/DyYEtGHC6Ei0Wh3nMvLYcyCZJRsPc/RE9X+fNGc+Hi58NXUigV4eFJaW8fSPy9h01HpzfJ7Ny+fjDVt5b89mAILd3Lm9SzcG+gcS5O7B7YFtuT0wFo1eS3LRBfbnHmF5+lZSSzKtVvOVahSoXnrpJd59992rbnP06NFaFWQt06dPZ9q0acav8/Pz8fPzs2JFQjSMnKwCju1PJjTCH3dPJ3KyCq1dkhA1lnLmAt99vobvPl8DgGeUL+OGd6/og9XOgwmjIysDVmYeuw8l88+6wxw52fIC1nM3D+KOwZEoFAqW7jjK9L+Wo29kY3Oeys3hv1s28t8tG3HvkENHJy8m+PUipk1HAlq1oaPzECb5DUGjLye1OINd2QmszNhMRumFax+8ntQoUD377LPcf//9V90mODgYb29vMjNNU6NWqyU7Oxtvb2+L+3l7e6PRaMjNzTVppcrIyKhyn6qOk5Fh2iR46eurHcfOzg47Ows97mrLUhe15tT3qLmw1I+nKfarus7P21evzePThc8w5bUJ/HvKL5fvXHe1VaW6r7P0tRIAOvPf/DZ5xWbrstec4Oc1J/j54tftAlsz8pZoevXvRPugNkwcEcnEERUBK/1sLvt2JbHyn/0kHDprsf+V1sJ0OXXO0kfcwreHpWEOdBZu0tVd0V+qW6A3H//feFo7tyIjt4ApPywk4VyWxc7munq+5AdgaYB12zzz17lwT2v2oWUf8UA8AF18PLklshuxgf74u/kS7N+eW/yGUarVsi/5NCuYaXac+lajQOXp6Ymnp+c1t4uNjSU3N5fdu3fTq1cvANauXYteryc6OtriPr169cLW1pY1a9YwadIkAI4dO0ZycjKxsbHVrjE2NpZ//etflJeXY2tb8QlbtWoVoaGhVV7uE6KlO34wlYzUHKKHdkalVqHVaK1dkhB16uzpC/zw0XJ++KiiD1a7gNaMnNSbngNDaB/QmnGTohg3KQqtVkdaZh57D6awdPVBjiSmWbny2lMplfzvwTEMjeyI3mDg2zXxfLZsq7XLqpXDGVkcXrHW+HWPdj5M7N2F2HZ+hLa+dk6pD/U6bEJGRgYzZswwDpsQFRVlHDbh7NmzDBs2jJ9//pk+ffoAFcMmLF26lFmzZuHi4sLUqVMB2Lq18o0/ceIEhYWFzJgxg3Xr1vHnnxXTZoSHh6NWq8nLyyM0NJQRI0bw4osvcujQIR588EE+/vhj6wybYElzaaFqLs+jKk2xhcqSar5Pg26M4qVP7uTvHzcx850lFSst3N5uNdJCJWrrKsM1tPPzYNT4SHrFdMQvsDV2F8dluxSwdh9O4Z91hzjc0H2watlCNbxHJ964ZwQOaluOnzvP/33/N1n5RVccz9K+11twA905aeFHk8a1oslLX1JKyvOvNI9hE6BiYM8pU6aYDOz52WefGQf2PH36NEFBQaxbt47BgwcDlQN7/v777yYDe15+qW7w4MFs2LDB7HxJSUkEBgYCpgN7tmnThqlTp/Liiy/WqH4JVNXQXJ5HVVpYoMLWlr92vo5KZcNNPV6rWCeBSjQn1Rj/CiqGXGjn48a44d3p0yMQ/3YepgErK589h1NYuv4QB+u7Bes6A5Wzox2fPDOR7kE+aLQ63v1rHX9vPYTWwtAHEqjqhkw9UwUJVLXQXJ5fSwtUCiV3PTWCu58awRevzuOf37Y1zOjptSEhSzQQn8A2jLw5iqh+IbQP9sTOvjJgZaTlsn/3aVb8s5+9meadomvT/8rSxMV6C9+Wl29337AoptzQF5VSyfbjyUz9aRGlFy/j6y38Oqvr/lLWHttLV1rKqbdflkDVWEigqoXm8vxaYKBSKpUsOPJf8nOLuTvmLQlUQlxyReuWj58HI+6MISqmA34X5yJUKBRodTrSM/LZeyiZ5WsPcfDouQYLVH5tXPnyiYn4e7pTUFzKc7OXsuXYGdN9JVDVm3oZh0oI0TTp9Xo2LNnHsIm9iOzXiX27Tlu7JCEapbSUbH74ei0/fF3RMdqnnRsjb+hB1IAQ/Nt7MG54d24YEYFWpyctK4+9h1P5Z/0hDh47Vy/1TL9lKLf0745CAQu3H+b131aibYg79YSRtFBVoV5bqCxpLq060Lyey5VaQKuVo5M9c/b/h+TjGTw+4bMGLKqOSKuVaAS8/dwZeXMfogaE4NehrbEFS6fVkXE2hwP7U1j5z34O7U827lPibd7BqdjTvN3j8n5QPYN9+ejh8bg7OXAuJ5/JPyzgeEbFZUeLrVH1MDrQlaSFSgghgOLCUg7Gn6R7TEd8/FuTlmy9gfKEaKrSU3L46eMV/PTxCrBR4tXOjZGToogaEIpfsCcjb4hk1Pge6LR6MtJzObDnDIu3JXDgaGq1jq9WKXn/gXEM6hqMXm/gq5Xb+Gr19np+VuJqpIWqCtJCVQvN6blcqQW0UAG0D/Zk5uqX2LftBC8/9EMDFVVHpIVKNDYW7i5sE+rFqPE96BPbCb/ANthdasHS6UnLzGPfoRSWrT3M9vPmwzQM7x/KG3cMx85WRcLZLJ746m+ytOaDm0oLlbRQCSGsLPVUFiknMomI6YCjkz3FhaXWLkmIZiUjLY+fvlnPT9+sB8DLx5Vhd/QmpmcwgX6tGRvXjXHDu1cErOx8dh9LZevBJB4aG00nP0/KtDpe+20li3YcqTig9JeyOmmhqoK0UNWx5vL8WkgLFUDUoDD+PetRVs7dyccvz618oDGNTVUb0pIlGjFPHzdG3dKbyHFdCfLxwN62ogXLYDCw+dhpnv5lCaXayhkNLLVGWVrXEFpqC1Uz+ckohKhruzYkkHO+gME3RFq7FCFanKy0XH75bBV3vTubvk9/wZhXvqewtAyAl/5cbhKmROMggUoIUaU/v16L2s6W2x8fYu1ShGjR0nMKeObrRQBMG9PfytUISyRQCSGqtPCXrZQWa5j4wEBrlyJEi7freCo5hSWMjgizdinCAglUjYXBYL6IxkdvMF+aoup+3rQ6VszZgYubI/2HdwGtrmHrrE9KhfkiRCPTZl+RyfLPX7twVNsyvndnDLYYF73afLEWhcF8aQkkUAkhruqHd5eg0+p54IUx1i5FiBZv9k+b0er0/N+QaGuXIq4ggUoIcVWaUi071x/Fx781weG+1i5HiBZNq9Wz9eQZ/D3c8PNwtXY54jISqIQQ1/Tl6/MBmPzWTVauRAjx7tINAEwfM9i6hQgTMrCnaBiW+ug0l7GpmjODHoDz53I4fiiVzj0CcGvtRO6FQisXVk8s9aNqqv3kRLOgsvC9lrU3m3NZefTvFIDLWQN6PeR1lJ+n1iYtVEKIavn69XkAPPHKeCtXIoT4cfEOVDY23D9O+lI1FhKohBDVkrA3mcyzOcQOC0elkh8dQljToo2HKC0r55a4SGuXIi6Sn4qNmQyl0DS0oKEUfv5oGSpbG+57agTo9JVLc2ZpeAUZckFYkdvRQtyOFrJx9RE8XBzpp3azdkkCCVRCiBpY+/duCvNLGHO7XGYQwtq+/XQVAI9MHW7lSgRIoBJC1NDiX7fi6GTP6NskVAlhTXm5xZw4lk5Y1/Y4qa04kqcAJFAJIWro189WU67RcueUYdYuRYgW78ev1qJUKnhumMzvZ20ybEJT08iGH2jb3oOftr1usu6n9//hj89Wmm3r0MqOW6cMp//o7ni2c6e0WMOxvWeYP3Md+7cer9iojp5Lu+C29BoYSsdufnTs1h7/jl7YqGwqavt8VZ2cw5KO3dpz6xNxdO3TgVYu9mRn5rNjzRFmf76SPAu3Pw+6oQcvfX6vyboXbv+Cg9tP1luNtaXXlLN52QEGj+9B96hADmw/CTbyt5kMuSAaik1hqfHfe1cfJj+3mBu6debNxWtNtjPIb/gGJT8FRZ0oKSpj1Zx4Vs2JJ+nIWbPHXVs78dk/z3L7lOE4ONkTv+owyYnpRA3pzH9/f4Lx9w+o03rG3dOPx9+axPBb+hAU5ouNyqZOj29J/zERfLzgGQaMjSTzbDbbVh3CoDcw/v4BfLX8eXwC2pjtk55ygVVzd7Bq7g6ys/Lrvca68vWbCzAY4LFXb7R2KUK0eMvn7MTJTs2YriHWLqVFk/wq6kR+diEfTZtd5eNPvnsb7Tt4sXfTMd588FvKSssB6D0knNd/eJhH37iJA9tPcvpYWp3Uc/pYGnNnrOXk4VROHErltinDiZvUu06ObYmHlwvPfnQnKlsbPnvpT5b9sR0ApVLBtA/uZNhNUbz46d08PeETk/2O7Uvm2L5kAN79YzIeni71VmNdKsgt5vDOJLr2CcLbz4P0zAJrlyREi/XLl2uY9NAApgyOZemhRGuX02JJC5Wod/6dvOg7sjs6rY5Pnv/dGKYAdq47wqo5O7CxUXLblLg6O+eKP7bz/X8XsX7hHlJPZmKo50svEx4ahL2jHXs2HWPZ7G3G9Xq9gS9emUNhfgmhkQH0HBhar3U0pC8vDvQ5+d+TrFyJEC2bVqMlPimFoDbu+Lg6W7ucFksClah3fUd1B+DIriQyz+aYPb5+wW4AouO6YmOjaJJjb/UdWfEcLz2Xy8ekKi0sY/uqQwD0u/haNBmWxqYy6MGg58yxNFKTsujZrxP2DnKHkUUyXpWoD5ePAXdx+eS3ivn9XhkyCMcMcMywco0tkAQqUe86dGkPQOL/t3ffYVEd7d/AvyywC4hLky4giAIqCqIgih1L1MeaoiGJMdaIiRFfjSbmIY/52WKiSSyxRMUUS4g1oiIqGAVERVCpQcVKU+nS2Xn/QFZXFgTZ3bPl/lzXuRLmtHtm18PNOXNmrt+Xuj7jet0jL/02Atg6missLll5Me6MRutYV96xi63C4lKEHSuOgqfNw6wlo7kOhRCNdjPrCXLySzDA3Qk8+s3OCWp2IneWdmYAgEdS7k4BQFlpJZ4Wl0tsq0os25uK/z8vS3odHz8rt1LB+jUl7mwKCh+XYOg4T65DIUTj7T51Bbo62nhvaC+uQ9FIlFARudM3FAAAKsoqG92m/Nk6g7Z6ColJlurrBwAVZVVStxHXz1D16vcqoVvOgi/QxVszBnAdCiEa7c9z11BZXYN3h9IfOFyghEod0Jx/hEMHd5xDZXkVJk0boDnz+7UG9asicmCaUgbTlDJEn0uHuVEb9Kk14jokjUMJFZG78tK6uzN6BoJGt9F/tq6spKLRbZRVff0AQM9Aeudscf1KVa9+zRFxKB5Gpm3g69+F61AI0WhbNtYNXPyxDN+aJs1DCRWRu9wH+QAAc1sTqesNDAVoI9SX2FaV5D18HrOFjfQ6tntWror1a47tq4+htlaE6YtGcR0KIRotP/8pMm8/Qlf39tDXoaEmFYkSKiJ3t5Lq3nDr3N1O6vpO3e0B1I22/vB2XsMNlPyRZllpJR5mPgIAdGq0jnXlN5MeSAypoJKkfB5VpZWI/ycdNh3aoUNHc6CmlusoVYu0x4D0aJC0gM6TMvHy67pw8Hg8LBjcD1oiSCxEfih9JXIXE34DUxePQZdejjC3McGjl96EGzTeCwAQdzoJtTWq+S8+Jvw63pozFIPGeyEi9JLEOj0DPnz8uwIAok9c5yI8hdj41QHsPv8lApdPwqJ3NnEdjsqxsDHG7jOfS5Tt/vEU9m2JlCjrPcAFnbvboVNXWzh3s4WZRd3o+u8PWInHubKfvqhjFxt49OkI5662cO5qCxsHM/B4PHy7aB8ijybK/Hz1PHydMXGaHzp3t4OePh95WQWIDk/C/m1RUl/+eGvmQHz0/96QKJs6ZI3Use/U3cWoNJQWl2OiR1esCj/HdTgag+5QEbm7928OYsKvQ1tHG5+tnQK+nq54Xa9Bbhj2ljdqa0XYv/F0g30DFozEifs/Ys2f8xQSq3sfZ5y49wNO3PuhRfsd3nEOFWWV6NnfBSOn9BGX83haCFzxFtoaGSA98S6u/pMm44iVx6OsQtxOeYguXh0gNG3DdTgqq/xpJSIOxSPiUDwy0xpOxbT4+8l47xN/+AxxEydT8vRu4FDM+Hw0Bo3xQHtHc/AUMMjR+Kl+WBUyA179O+NeRi7iIlPRxlAPkz8egp8OzIPQxKDBPnf+zUHEwXhEHIxH+dPG3yjWFOEH49FWT4Bhrh25DkVj0B0qohA/fb4f9p2s0HOAC3Ze+ApJl27B2Kwt3Pt0BI/Hw8//PYA7aVkN9uM9e8xRU92yO1cdu7XHvP97U/xz/cTEowL6wmdoV3H58lk7UZD3/K/65+dr2SOr/NxifL9wD5Zs+ADz10zGiHf6IPdBPjr3sIe1Qzvk5xVjzSe/teiYqujn5Uewdt9cfBw8AWsW7+c6HJVUXFiGdV/81ej6mIhkPLzzGDdTHuJWchb2XfxKrvGkJd7DvYxc3EzJwq2ULCxY+Sa6+zjJ7Xwd3Wwwc8ko1NbU4uuPd+PKP3Vz0wn0dBH88wfw7NsJn3w9ASvm/yGx3+Vz6bh8Lh0A0N3bCfptGn8JRhPs3hCB8R/0xSeD+yIi7RbX4WgESqjUlbR+Rlrc9cEoelKK+aO/w9uBw9BvVA/4DnNHRXkV4s+l4eDWSCRGS5/Q09m9ru/Rqf2xUtc3xsBQD649OzQoN7cxgfkLHcd1+ZL/BJzd60Z1D99/sUXnA4ALYdfw2b31eCdwGLp5O6Fj1/bIzyvG0ZDz2PtTOAofl7b4mCqD1SW8yZdu4XF2IfxGdsP3X4SiRkUf4SotnhbWLzug0FOGblfsI6O3Zw8Cj8fDib8uiZMpAKisqMYPXx7AztOL4TfSHe2dzPHg9iOFxqbMtF7qt1hdWouE21nw6mgLW1EbPCp+CgCoNJW2N5EFSqiIwpSVViJkzTGErDnWrO11dLXh3scZ/16/h3NHE1p0rhsXb+IN+89aHGPPAa6oKKvEH+tPtnhfALh54wFWzNn1Wvuqi99/CMeCbyfjvU+GIWR9ONfhEBWio6sN74GuAICoY4kN1udlFSLl6l2493ZEX/+u+HNblGIDVDFrj0RhX1AAFo0fiMW/Huc6HLVHfaiITAhNDRG07l0ErXtX3AG7tbr0coR+GwF2rfpbJsd7Fb5AF117O+LQL+dQ8KhEIed08bBH0HdTEPTdFLTvaKGQc8rbqdBLeFpSgTEv9CUjpDlsO7QTj+WWkfRA6jb15R272CgsLlWV+uAR8opKMbgb9aNSBEqoiEzotxFg2Fs+GPaWDxxlNAHw9dibeMNuPhIvSH8cKGtVldUY33kxfv1OcX/JWdmZYdib3hj2pjdMzeXfwVhRwn6PRpu2ehg+0YvrUIgKsXo2L2ZJUTnKn0qfxulRdtGzbaWP+UYk/RZ1FQJdHbzb34PrUNQePfJ7FSUb80jZ5D3Ixxt28xV/YiXrI/a6zh25inNHrkoWquJ4Qy99Hr99fxwTZwzCe/P8cSr08vMV2vQ3nNy8OFaVio5xpt+m7u5UZbn0ZAp4PieoOs6LKWtmKVUIS7mI+aP9MM3PC+FbLyHLT/psDvKmJeUryVTwUtcUuroRQmSupkaE6IgktLMyQtdeHbgOhxCNFnPxJizM28LJyZzrUNQaJVSEELnYvPwwwICPl43lOhSiIuof8wn0G7+Loqfm82LKw6YtdWP8Bc4eynEk6o0SKk2i5FO4EPVSnFeClKt34ORmA3OLtjQdjSKp6FQ2uc/mxWxrpC9+/Pcyc2ujZ9tq3gjoLSXILoEguwTFN7Jx//Yj9HC3g562DsAguRCZoISKECI3m/57EAAw75tJHEdCVMGDzMfiaWU6dWsvdZv68pvJDQcCJo37dfMZaGvzMG8gvX0rL5RQEULkJjMtG1l3HsNrgIv4dXhCGlNTXYtL5+qmZxo0xqPBegsbY3TxrJtMPeZ0siJDU3kXIpLxtLQCb/d05zoUtUUJFSFErnasDoO2Ng/Tl4zhOhSNFrTyTZxIXYWglW++emMZ8J/ghRPpqxHy0oTPr/LntiiIRCIMn+gFr/6dxeUCPV18tmIStHW0ceHkDRol/TWc+TsRRvp6GNjJketQ1BINm9AcavKKvtpTl89J2ivvStjf5ZWeTUcTe+o6ivJLMWxSL2xaoZhBWtXZlI+HwHugS4Py4M0fiOegvJmShU3Lj0is16qfp/LFvmzSvlcvff96D3TBu3Ofd2a2d64bgPa9ef4YG9BXXL7gnc0S+/Gkna8ZbqVkYfvq45j9xRgs3/YhblzOROGTUnTr5QgzCyHu387Dhq8PteiYmkrrpTlJQ747gTHv+CBoYF/EJGSKy6vbKjoy9UQJFSFE7g5sj8JHn4/BxA/9cDDkAtfhqDRrO1O49rBvUO78woC6VZU1DdZ36lq3/tSBKy06n5GpIVw9Gp7PxqEdbBwa38/52fnC/7rc+EaNOLz7Au78m4OJH/WHi3t76BnwkZdViH1bIvHntshGB/1UZxa2Jth9VvJu3+4fTmHfz2fFPxuZtkGv/i7w6t8Znd3bw9zaCCIRw6PsQlyNvomDIedx7V42PBysYWpogPzSslbH1cXKAn062KGrtQW6WlvCwdQYPC0tLDp8AkdvpLX6+I3p62iPaT490d3GCvq6usgqKkZ4Wga2Rl9GSQU3b4BSQkUIkbvQLWcRMH8E3poxkBKqVlr3xV9Y98VfLdrHzEIIB2dLREckIe3a/Rbte/pQPE4fim/RPgDQs18nPM4pwpFfo1u8LwAkxt5EYuzN19pXnZU/rcSF8CQAdX0UXzRr6RgMGeuJ2loR7mbk4uLZVOgZ8NG5W3uMe78vhk/0wg+nY+DhYI1FYwZg6b7Xm7P0RYEDfODv4tzq47TEh96e+GL4IIgYw5V7D/H4aRl62dniYz8fjHDthLe3/arQeOpRQkUIUYizh67gjSm+6D3QBZfPpXMdjtISGhuI+zlFn0pCXFTr/8rv2a8TamtqEbL+VKuP1RwWtiaw7dAOP3x5QOrdMnnoPdAF/Ud2BwAITQwUck4uFBeUYd3SUKnrSgrL8NtPEQgPvYwnecXiR7p6BnzMXz4Rg0b3wMf+ffCkpAzD3DvJJKFKfJCDjEdPkJKdh5ScR1j5n2Hw6WDX6uM2xs3SHEuGDUSNSIQ5+4/gn1t3AAB6OjrY8s449HW0xzdjR2LUlwvkFkNj5NYpPT8/HwEBARAKhTA2Nsb06dNRWlra5D4VFRUIDAyEmZkZDA0NMWnSJOTm5kps8+mnn8LLywsCgQAeHh4NjnHnzh1oaWk1WC5evCjL6qkPGpuKyMtL36tt3xyGqFaEmYtHAbWi5wuRoN9GgGETvDBsghccXa1lcsyIQ/EY474MDzKb0ZFbBuNV5T0swBsuS17rcd/r6tDZCsMmemHYRC/otxEo7LzKZMuKv7Fn05m6ZAqo6w8nYqgorcQPX/yFstIKGLfRx/Ub96Gnq4OpHbrCJKN148Ntj7mMHyJjcCrtJh4UFsmgFk2b3c8bPC0tHLyWLE6mAKCipgZfHDuFWpEIb3RzhYtLw36G8ia3O1QBAQHIzs5GREQEqqurMW3aNMyaNQt79uxpdJ8FCxYgLCwMoaGhMDIywrx58zBx4kRER0veMv7oo48QFxeH69evN3qs06dPo2vXruKfzczMWl8pQshrqyirwtWYDHj5dYaDsyXu3sx99U4aJC+rEG+4LeU6DJUVuv0cQref4zoMpVVZUY0HmY/Q2d0ONzPz0N+nE96b5IOjEY3/HlU2ujweBjnXvaH4d1LDO7dZRSW4+iALve3bY8KECYoOTz53qFJTU3Hy5En88ssv8PHxgZ+fHzZs2IB9+/YhK0v6YGxFRUXYsWMH1q1bhyFDhsDLywu7du1CTEyMxN2ln376CYGBgXBycmoyBjMzM1hZWYkXXV1dmdaRENJym74+DACY+/V4TuMgRNNo6/BgaWsCAHic/xRxVzNhZSGEQ3vVudnQwcwEBvy63+VJ2dL/IEvKqiv39PRUWFz15JJQxcbGwtjYGL169RKX+fv7g8fjIS4uTuo+8fHxqK6uhr+/v7jM1dUV9vb2iI2NbXEMY8eOhYWFBfz8/HD06NFXbl9ZWYni4mKJhRAiWzn385GZno1uvRxhKNTjOhxCNMaIN3vDyNQQFZXVuHg1Ez/tjAQAfPLRIG4Da4H2xnXTDhWVV+BpVbXUbbKLSwAAjo6KH2tLLglVTk4OLCwsJMp0dHRgamqKnJycRvfh8/kwNjaWKLe0tGx0H2kMDQ3x/fffIzQ0FGFhYfDz88P48eNfmVStWrUKRkZG4sXO7hWd6qjvkWpQl8/pWV8IiUUV1dRi6/Ij0NJ6Nmkyze+nGlR0bkBSp0NnS0xfPAoAsHdrFKoSc1B88T4e3nuCXu4O0OGpxhjfbZ7dnSqvlp5MAUDZs3VCoVAhMb2oRa24ZMkSqR2+X1zS0uQ37kRztGvXDkFBQfDx8UHv3r2xevVqvPfee1i7dm2T+y1duhRFRUXi5f79lr1aTAhpnusXb+FJbjH6j+oBnopcyAlRVe0shfj65w9h0EaA2DMp2L/zvHjdnm3noK3Dw7wBNL+fLLToarZw4UKkpqY2uTg5OcHKygp5eXkS+9bU1CA/Px9WVlZSj21lZYWqqioUFhZKlOfm5ja6T3P5+Pjg5s2mxzMRCAQQCoUSCyFEPvZsOAVdvg4C5g/jOhRC1JZJO0Os2j0Tlu1NcOV8OlbN/0Ni/dmwaygvq8I7PbtzFGHL1D/m02+iT7TBs3VcdNtpUUJlbm4OV1fXJhc+nw9fX18UFhYiPv75YHBnz56FSCSCj4+P1GN7eXlBV1cXZ86cEZelp6fj3r178PX1fc3q1UlMTIS1tWxeP9YI6vKYTN2p4mNAJgKYCCf2xKKstAJjp/pxHRGRJXoMqDSMTNtg9e6ZaO9ojqvRGVg+9zdUV9dCq7JaYok6lgjTNvroa9Me2hUQL8ro4bNhGYz09cSP/15mLaybR+fOnTuKCktMLvfb3dzcMHLkSMycOROXLl1CdHQ05s2bh8mTJ8PGxgYA8PDhQ7i6uuLSpUsAACMjI0yfPh1BQUGIjIxEfHw8pk2bBl9fX/Tp8/x25M2bN5GYmIicnByUl5cjMTERiYmJqKqqm4pg9+7d2Lt3L9LS0pCWloaVK1di586d+OSTT+RRVULIazqxNxaGQn0MGav4t3EIUWdGJnXJlL2zJRJiMvC/j3ejukr6AKs7vz8BEWNYOKK/gqNsucwnBSh7dpeqm7Wl1G262dSVX716VWFx1ZNbB4Y//vgDrq6uGDp0KEaNGgU/Pz9s27ZNvL66uhrp6ekoK3s+l9D69esxZswYTJo0CQMGDICVlRUOHjwocdwZM2bA09MTW7duxb///gtPT094enpKDMfwzTffwMvLCz4+Pjhy5Aj279+PadOmyauqhJDXEPJtGGpqavHBp/TYjxBZEZoYYNXumejQ2QoJMRn4es7uJkerLy2uQNKDXHSxtYSxgXK/eVstEiHqZt2kzv/p5tpgvY1RW3i2r7tpc+iQ4ifQltvAnqampk0O4tmhQwewlx4l6enpYdOmTdi0aVOj+0VFRTV53qlTp2Lq1KktipUQong1NSJcjEhCv5Hd4drDrsVzzBFCJBka6WNVyEw4uljhanTdnanmTP2zLvw8dk1/EzFffoxDV5Px5QH5T1E0oXsXrB43Ag8KizBk485m77ct5jJGunXCxB5dEZ6agfO37wKom3pm5Zjh0OHxcCIpDenpip/eiubykyVpfY20qA+B0qHPiTsvtf2mr/5Cv5Hd8fGysZg/ccPzFdr09p/akNaPShX6+6mgz/5vEpxcrSESiVBSVIZ5X0sfLTz2dDJiz6SIf06Luo+qD2oh4OuAVySC8C5DgUvzrokDnR0xt//zvtHO5qYAgHkDfBHQy0Nc/s6ufRL78Z5dc2tELZt+KiUnD6sjzuGL4YOwfcoEXLr7APlPy+BlbwvLtoa4/TgfXx1t/RyFr4MSKkIIZwoflyI18R7cPOzRzsoIj3PkPxcYIeqqrVHdpNA8Hg8DR/VodLvchwUSCRUAlFVUQcDXwZPCpy06p6mBPjzaN3zpy8HUGA6mxo3u19W6bqzKvxKTW3Q+AAi5lIB/Hz3BRz494W5jBQO+LrKKSrAl+hK2Rl9CcVl5i48pC5RQEUI4tenrQ9h4eD4Cvx6P/83ZzXU4hKiszz/Y9uqNpDA3MYSJ0AAixjDM1wXbDsQ0e99D11Nw6HrKqzd8ST8nB+QUl2D3pdfrPB6TeQ8xmfdea195oYSKEMKp26nZyL6fj94DXMHX00FVxav7fBCiqYQmBgha9RYAIPpUEuIiU1t9TG93B9TUinDj3yx4uNrC1tIIBZDfOE62RkJ0MDPBl8ciUKlGsyVQRwXSPDQ2lWpQxbGpamqxa00YtHV4+GjhGzQdjSagqWxem34bAYZN9MKwiV5wdJXN+IoXtl/E6D7fYONXhwEAi8bLd3y4h0XFcPlmPf5KSIIWg8SiyugOFSGEcxdOXEdxwVOMeMsbW/53mOtwCFE6eQ8L8IbLErme48HdJ8jJKkAvX2fwooCWdRcndIeKEKIUDu38B3oGfIyj0dMJ4czeneeho6ONOX7SZzUhjaOEihCiFPZtOoOqymq8PXcI16EQorHCjyaioqIaAb0bf0uQSEcJlbxR3yPVQJ8Td57N7wcmQtSRqzA1F6Jnv05cR0WUAfWrUgje00qJ5fzJGzBrYwAvaxvwqiFeSNMooSKEKI0tyw9BJBJh1pLRXIdCiMb6Ze1xAMDiEQM4jkS1UEJFCFEa5U+rcC0mA/YdLdDe0ZzrcAjRSMUFZUjNzkP39lYwFPC5DkdlUEJFXp+0x2T0qEz5KPtQCi99fzZ8GQoACPxqLFArer4QAtBjQHl48d/Zs2VzaDR4WlpY3M8PBjmAQQ7XQSo/SqgIIUol++4T3M3IRXcfJxgY6nEdDiEaKTb5LgpLyzGqjxvXoagMSqgIIUpn68q/oaWlhTlf/YfrUAjRWIfO30AbPT5GertwHYpKoISKEKJ0EmNuIv9RCQaN9gCPR5cpQriw7e9Y1NaKMHN0H65DUQl0peIC9TtSDfQ5caemFvs2nYYuXweTZw+k6WhI06hflcyZppTB8HopriXchYOVCVyf0MQqr0IJFSFEKR37LQblZZUY/xG9uk0IV37+KQIA8PGnwziORPlRQkUIUVrh++PQ1sgAA8fQqM2EcOHO7UfIyy2GT99OlDC8ArUPIURp7VhzHLU1tfhw0SiuQyFEY/35Ryx0dbUxzbcX16EoNUqoiOxR3yPlp+xjU4lqAVEtaioqEXcmGZbtTdGpmy3XURFVIq1fFfW1ajbtgjLxErbzPKoqqzHV2xNatZBYyHOUUBFClNrGZX8BAOZ+NY7jSAjRXNGnU2DRtg3cbSy5DkVpUUJFCFFqBY9KkH7tHlzc28OknSHX4RCikbZ9Wze/3+c0v1+jKKEihCi9zV8dAADMCx7PbSCEaKiCx6X4N+8xetrZQF+XhlCQhhIqZaHu/Y7UpX7qUg8Vk3HjPnIfFMBnkCt0tHk0vx+RHepXJZWWSNRg2Xw0BjweD/+vXz8ICgBBAddRKhdKqAghKiHk+5PQ1tHGRwtHch0KIRopKuk2issqMNanK9ehKCVKqAghKuHc8WsoKSrDyLe9uQ6FEI11OC4ZbfUFGNK9I9ehKB1KqAghdZR9KIWaWhwJuQD9NgKMfsebpqMh8kOPAaVeD8xSKrHnu0iIRCJ8OsQXZimVXEepVCihIoSojL0bTqO6qgZT5vlzHQohGqmqqgbXbzxAhw7tYGZGb92+iBIqQojKEIlEOHcsEaYWQnj068R1OIRopJ+3nAEAzJ0zlONIlAslVIQQlfLz14fBGMOsZWO5DoUQjZRxMxePH5egX1/6o+ZFlFApM3V/RV9d6qcu9VB2z6ajKSt+ihtxt9ChsxWs7c24jopoCprKBvycEvFyKOQC+HwdvN/bA2CQXDQUJVSEEJWz8ctQAMC8/9J0NIRw4eCv0aiuqsF0mjBZjBIqQojKeXD7Ee7fzIVHn47QN+BzHQ4hGuliVCqshIZwszTnOhSlQAkVIUQlbV9xBDxtHmYvHcN1KIRopK3P5vdbMpzm9wMooSJEPtSlX5USj011JSoNBY9LMPg/Hs+noqHpaAjX1LhflVaNSGJ58rAQt/Py0duhPdqW60C3BNAtaeU5WMNFVVBCRQhRWfu3RoIv0MXkjwdzHQohGmlTeCy0eTzMHeHLdSico4SKEKKyjuyORkVZFSZMo0cOhHDh1I0MlFZUYpJ3N65D4RwlVKpGXR4lNUbd60dkq6YW4aGXIDQ2gN+wrjQdDVFO6vIYUEoXANN/a3DqTDKMDPQwuq0dTP+t4TpKzlBCRQhRaTvXHENtjQjTFo/iOhRCNNK2X89DJGKY8V5/rkPhFCVUhBCVVlVRg8tRqbC2N4NTFxuuwyFE45RVVCE5PQvOjhYwMTLgOhzOUEJFCFF5m4IPAgACl0/kOBJCNNPmXVEAgLnTBnEZBqcooVIH1O9INajL56RMQyk8m47m8cN8ZNy4DzdPBxibGXIXDyHNpSZT2RjcLYHB3RJknkpHweNSDPLtzHVInKGEihCiFn4OPgAAmEuTJhPCiSN74yDQ08Xknt25DoUTlFARQtRCWsI95D0sgO/QLtDRoUsbIYoWGnIBNdW1mNlPM+f3o6sOIURt/Pr9CejoamPqZ8O5DoUQjSMSiXD5QgZsjYToaGbCdTgKRwkVUX7q0veIyN3ZQ1dQWlyOUe/40HQ0RD0oeb8qrcpqiWXbqqMAgC/8B0G7HOJFE1BCRQhRK3//HgMDQz2MeKs316EQonFy7hfg7pNC9OloDx2eZqUYmlVbQoja+/2n06iuqsF7nwzjOhRCNNLmMxeho83D3CF9uA5FoSihIoSoFZFIhAsnb8DMUgh3b0euwyFE4xy7loayyiq87aNZb/tRQqWuqN+RalCXz0mZxqaqqcXPXx8CY8CcL/5D8/sR9aNM/aqk/Ns3yhQhIjoNpm30MdTAFkaZmtGPkRIqQojaKSksQ/LlTDi62cDSVvPeNiKEa5v++AcixjD33QFch6Iwck2o8vPzERAQAKFQCGNjY0yfPh2lpaVN7lNRUYHAwECYmZnB0NAQkyZNQm5urnj9tWvXMGXKFNjZ2UFfXx9ubm748ccfGxwnKioKPXv2hEAggLOzM0JCQmRdPUKIEtv0bKDPef83ieNICNE8JWWVSL2VA1dHSwgN9bgORyHkmlAFBAQgOTkZEREROHbsGP755x/MmjWryX0WLFiAv//+G6GhoTh37hyysrIwceLz+bni4+NhYWGB33//HcnJyfjyyy+xdOlSbNy4UbxNZmYmRo8ejcGDByMxMRGfffYZZsyYgfDwcLnVlSiYujwqI7L3bDqau2lZeHD7ETz9OkNPn891VITIlxI9BhRmlECYUYKQtXW/cxeM8eUsFkXSYkw+v4lSU1PRpUsXXL58Gb161Y2aevLkSYwaNQoPHjyAjU3DWeGLiopgbm6OPXv24M033wQApKWlwc3NDbGxsejTR/obA4GBgUhNTcXZs2cBAJ9//jnCwsKQlJQk3mby5MkoLCzEyZMnpR6jsrISlZWVErHY29tjkMWH0OGpycVYS7nGL5E5dameunxOSjBeTq8Brli6+UOcOngFW1ce4zocQhSLo76MorbP70jtOvQp9PR10fen7a99PNbCS4mosgJ3vluOwsJCGBkZvfZ5W4zJyY4dO5ixsbFEWXV1NdPW1mYHDx6Uus+ZM2cYAFZQUCBRbm9vz9atW9fouQICAtikSZPEP/fv35/Nnz9fYpudO3cyoVDY6DGCg4MZAFpooYUWWmihRQ2WW7duNfo7Xx50ICc5OTmwsLCQKNPR0YGpqSlycnIa3YfP58PY2Fii3NLSstF9YmJisH//foSFhUkcx9LSssExiouLUV5eDn19/QbHWbp0KYKCgsQ/FxYWwsHBAffu3VNshquGiouLYWdnh/v370MoFHIdjsqidpQdakvZobaUDWpH2al/wmRqaqrQ87Y4oVqyZAnWrFnT5DapqamvHVBLJCUlYdy4cQgODsbw4a2bu0sgEEAgEDQoNzIyoi+3jAiFQmpLGaB2lB1qS9mhtpQNakfZ4Sl4pPYWJ1QLFy7Ehx9+2OQ2Tk5OsLKyQl5enkR5TU0N8vPzYWVlJXU/KysrVFVVobCwUOIuVW5uboN9UlJSMHToUMyaNQvLli1rcJwX3wysP4ZQKJR6d4oQQgghpDVanFCZm5vD3Nz8ldv5+vqisLAQ8fHx8PLyAgCcPXsWIpEIPj4+Uvfx8vKCrq4uzpw5g0mT6l51Tk9Px7179+Dr+/wtgeTkZAwZMgRTp07FihUrpJ77+PHjEmURERESxyCEEEIIkRW53Q9zc3PDyJEjMXPmTFy6dAnR0dGYN28eJk+eLH7D7+HDh3B1dcWlS5cA1D1emz59OoKCghAZGYn4+HhMmzYNvr6+4jf8kpKSMHjwYAwfPhxBQUHIyclBTk4OHj16JD73nDlzcPv2bSxevBhpaWnYvHkz/vzzTyxYsKDZ8QsEAgQHB0t9DEhahtpSNqgdZYfaUnaoLWWD2lF2OGtLefZ4f/LkCZsyZQozNDRkQqGQTZs2jZWUlIjXZ2ZmMgAsMjJSXFZeXs7mzp3LTExMmIGBAZswYQLLzs4Wr2/sbTwHBweJc0dGRjIPDw/G5/OZk5MT27VrlzyrSgghhBANJrdxqAghhBBCNAXN5UcIIYQQ0kqUUBFCCCGEtBIlVIQQQgghrUQJFSGEEEJIK2lMQpWfn4+AgAAIhUIYGxtj+vTpKC0tbXKfiooKBAYGwszMDIaGhpg0aZLEgKHXrl3DlClTYGdnB319fbi5ueHHH39scJyoqCj07NkTAoEAzs7OCAkJkXX1FEYe7QgAn376Kby8vCAQCODh4dHgGHfu3IGWllaD5eLFi7KsnkJx1ZYAcP36dfTv3x96enqws7PDt99+K6tqcUJebXnv3j2MHj0aBgYGsLCwwKJFi1BTUyNeHxUVJfV72dhUWcpo06ZN6NChA/T09ODj4yMexqYxoaGhcHV1hZ6eHtzd3RuM+ccYw3//+19YW1tDX18f/v7+yMjIkNjmdT4vZcdFO3bo0KHBd2/16tUyr5uiybotDx48iOHDh8PMzAxaWlpITExscIzmXA9eiduXDBVn5MiRrEePHuzixYvs/PnzzNnZmU2ZMqXJfebMmcPs7OzYmTNn2JUrV1ifPn1Y3759xet37NjBPv30UxYVFcVu3brFfvvtN6avr882bNgg3ub27dvMwMCABQUFsZSUFLZhwwamra3NTp48Kbe6ypM82pExxj755BO2ceNG9v7777MePXo0OEb9EBunT59m2dnZ4qWqqkqW1VMortqyqKiIWVpasoCAAJaUlMT27t3L9PX12datW2VZPYWSR1vW1NSwbt26MX9/f5aQkMCOHz/O2rVrx5YuXSreJjIykgFg6enpEt/L2tpaudVVlvbt28f4fD7buXMnS05OZjNnzmTGxsYsNzdX6vbR0dFMW1ubffvttywlJYUtW7aM6erqshs3boi3Wb16NTMyMmKHDx9m165dY2PHjmWOjo6svLxcvM3rfF7KjKt2dHBwYMuXL5f47pWWlsq9vvIkj7b89ddf2f/+9z+2fft2BoAlJCQ0OE5zrq2vohEJVUpKCgPALl++LC47ceIE09LSYg8fPpS6T2FhIdPV1WWhoaHistTUVAaAxcbGNnquuXPnssGDB4t/Xrx4MevatavENu+88w4bMWLE61aHM4pox+Dg4CYTKmn/EFQRl225efNmZmJiwiorK8Vln3/+OXNxcWlFjbgjr7Y8fvw44/F4LCcnR7zNzz//zIRCobjt6hOqgoICOdRM/ry9vVlgYKD459raWmZjY8NWrVoldfu3336bjR49WqLMx8eHzZ49mzHGmEgkYlZWVmzt2rXi9YWFhUwgELC9e/cyxl7v81J2XLQjY3UJ1fr162VYE+7Jui1f1Njvkdf9ff8yjXjkFxsbC2NjY/Tq1Utc5u/vDx6Ph7i4OKn7xMfHo7q6Gv7+/uIyV1dX2NvbIzY2ttFzFRUVScxwHRsbK3EMABgxYkSTx1BWimzHxowdOxYWFhbw8/PD0aNHW14JJcFlW8bGxmLAgAHg8/nishEjRiA9PR0FBQWvURtuyastY2Nj4e7uDktLS/E2I0aMQHFxMZKTkyWO5+HhAWtrawwbNgzR0dGyrJ7cVFVVIT4+XqINeDwe/P39G/0+vep6lpmZiZycHIltjIyM4OPjI9GuLf28lBlX7Vhv9erVMDMzg6enJ9auXSvxSFrVyKMtm0NW19YWz+WninJycmBhYSFRpqOjA1NT00b7OuTk5IDP50tM0gwAlpaWje4TExOD/fv3IywsTOI4L16Q649RXFyM8vJylZqsWVHtKI2hoSG+//579OvXDzweDwcOHMD48eNx+PBhjB07tsV14RqXbZmTkwNHR8cGx6hfZ2Ji0uxjKQN5tWVj/3br1wGAtbU1tmzZgl69eqGyshK//PILBg0ahLi4OPTs2VMW1ZObx48fo7a2Vmod09LSpO7TWJu82Gb1ZU1t09LPS5lx1Y5AXX/Jnj17wtTUFDExMVi6dCmys7Oxbt26VteLC/Joy+aQ1bVVpROqJUuWYM2aNU1uk5qaqpBYkpKSMG7cOAQHB2P48OEKOaesKFM7NqZdu3YICgoS/9y7d29kZWVh7dq1SpVQqUJbqgpVaEsXFxe4uLiIf+7bty9u3bqF9evX47fffuMwMqIJXrwmdu/eHXw+H7Nnz8aqVatoTkAOqHRCtXDhQnz44YdNbuPk5AQrKyvk5eVJlNfU1CA/Px9WVlZS97OyskJVVRUKCwslstbc3NwG+6SkpGDo0KGYNWsWli1b1uA4L78pkJubC6FQqDR3p5SlHVvKx8cHERERrTqGrKlCWzb2naxfpyy4bksrK6sGbxc1p528vb1x4cKFJuNWBu3atYO2trbU70JT7dbU9vX/zc3NhbW1tcQ29W+cvs7npcy4akdpfHx8UFNTgzt37kgk+qpCHm3ZHDL7PdXs3lYqrL4T5JUrV8Rl4eHhzeq0+tdff4nL0tLSGnRSS0pKYhYWFmzRokVSj7N48WLWrVs3ibIpU6aodKd0ebRjvcY6UkszY8YM5unp2bJKKAku27K+U/qLb0guXbpU5Tuly7ot6zulv/h20datW5lQKGQVFRWNxuPv788mTJjQ2mophLe3N5s3b57459raWmZra9tkB+AxY8ZIlPn6+jboTP3dd9+J1xcVFUntlN6Sz0vZcdGO0vz++++Mx+Ox/Pz81lSHU7Juyxe9qlN6c6+tjdGIhIqxutd0PT09WVxcHLtw4QLr1KmTxGu6Dx48YC4uLiwuLk5cNmfOHGZvb8/Onj3Lrly5wnx9fZmvr694/Y0bN5i5uTl77733JF5bzcvLE29TP2zCokWLWGpqKtu0aZPKD5sg63ZkjLGMjAyWkJDAZs+ezTp37swSEhJYQkKC+G2qkJAQtmfPHpaamspSU1PZihUrGI/HYzt37lRMxeWAq7YsLCxklpaW7P3332dJSUls3759zMDAQOWHTZB1W9YPmzB8+HCWmJjITp48yczNzSWGTVi/fj07fPgwy8jIYDdu3GDz589nPB6PnT59WjEVb6V9+/YxgUDAQkJCWEpKCps1axYzNjYWv9n4/vvvsyVLloi3j46OZjo6Ouy7775jqampLDg4WOrr/sbGxuzIkSPs+vXrbNy4cVKHTWjq81I1XLRjTEwMW79+PUtMTGS3bt1iv//+OzM3N2cffPCBYisvY/JoyydPnrCEhAQWFhbGALB9+/axhIQElp2dLd6mOdfWV9GYhOrJkydsypQpzNDQkAmFQjZt2jRWUlIiXl+fuUZGRorLysvL2dy5c5mJiQkzMDBgEyZMkPgAgoODGYAGi4ODg8S5IyMjmYeHB+Pz+czJyYnt2rVLzrWVH3m0I2OMDRw4UGpbZmZmMsbqEio3NzdmYGDAhEIh8/b2lnjFVRVx1ZaMMXbt2jXm5+fHBAIBs7W1ZatXr5Z3deVKXm15584d9sYbbzB9fX3Wrl07tnDhQlZdXS1ev2bNGtaxY0emp6fHTE1N2aBBg9jZs2flXl9Z2rBhA7O3t2d8Pp95e3uzixcvitcNHDiQTZ06VWL7P//8k3Xu3Jnx+XzWtWtXFhYWJrFeJBKxr776illaWjKBQMCGDh3K0tPTJbZ51eelihTdjvHx8czHx4cZGRkxPT095ubmxlauXNnk3VNVIeu23LVrl9RrYnBwsHib5lwPXkWLMcaa/4CQEEIIIYS8TCPGoSKEEEIIkSdKqAghhBBCWokSKkIIIYSQVqKEihBCCCGklSihIoQQQghpJUqoCCGEEEJaiRIqQgghhJBWooSKEEIIIaSVKKEihBBCCGklSqgIIYQQQlqJEipCCCGEkFb6/+7RJAS04aoKAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from qdarts.simulator import compensate_simulator_sensors\n",
+ "\n",
+ "compensated_tsim, _,m_comp = compensate_simulator_sensors(\n",
+ " tunneling_sim, #the simulator object to compensate\n",
+ " target_state=[1,1,1,5,5,5], #the electron configuration around which to compensate\n",
+ " compensation_gates= [6,7,8], #the ids of the sensor gate plungers.\n",
+ " sensor_ids = [3,4,5],#the ids of the sensor dots.\n",
+ " sensor_detunings = np.array([-0.0005,-0.0005,-0.0005]) #shift of the sensor dot plungers\n",
+ ")\n",
+ "sensor_values = compensated_tsim.sensor_scan_2D(P, m_comp, minV, maxV, resolution, target_state)\n",
+ "sliced_tsim, CSD_data, states = get_CSD_data(compensated_tsim.poly_sim, P,m_comp, minV, maxV, resolution, target_state)\n",
+ "polytopes = get_polytopes(states, sliced_tsim, minV, maxV)\n",
+ "\n",
+ "plt.pcolormesh(xs,ys,sensor_values[:,:,2].T)\n",
+ "plt.xlim(minV[0],maxV[0])\n",
+ "plt.ylim(minV[1],maxV[1])\n",
+ "plot_polytopes(plt.gca(),polytopes, skip_dots=[3,4,5], fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "73412425-e5f7-4690-80e3-51bcace00e2b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([2, 2, 2, 8, 8, 8])"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "compensated_tsim.poly_sim.find_state_of_voltage(m_comp,target_state)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "572e629e-7160-4dbe-ab0b-c136c4e80898",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:devel]",
+ "language": "python",
+ "name": "conda-env-devel-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "2a244a19",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "# add Code folder, if the package is not installed.\n",
+ "import sys\n",
+ "sys.path.append('../src')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "112fc52b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# import main class and plotting function\n",
+ "from qdarts.experiment import Experiment\n",
+ "from qdarts.plotting import plot_polytopes\n",
+ "\n",
+ "# import standard libraries\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c319dd1c",
+ "metadata": {},
+ "source": [
+ "## Device definition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45e86374",
+ "metadata": {},
+ "source": [
+ "In this cells we define the device from [Neyens et al.](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.12.064049). It consist of four target dots (indices 0-3) and two sensor dots (indices 4,5). The device is depicted in Fig below. The device is defined by the following parameters:\n",
+ "- C_DD - capacitance between the dots\n",
+ "- C_Dg - capacitance between the dots and the gates\n",
+ "- tc - tunneling rate between the dots\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6290f8a7",
+ "metadata": {},
+ "source": [
+ "<img src=\"figures/device.png\" alt=\"drawing\" width=\"600\"/>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "74fce5f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the system\n",
+ "\n",
+ "#All capacitances are given in aF\n",
+ "N = 6 #number of dots \n",
+ "C_DD=20* np.eye((N))/2 #The self-capacitance of each dot, NOTE: factor of 2 due to symmetrization\n",
+ "C_DD[0,1] = 10 #capacitance between dot 0 and dot 1 (Left double dot) \n",
+ "C_DD[2,3] = 7 #capacitance between dot 3 and dot 4 (Right double dot)\n",
+ "\n",
+ "C_DD[0,4] = 1.6 #capacitance between sensor dot 4 and dot 0\n",
+ "C_DD[1,4] = 1.4 #capacitance between sensor dot 4 and dot 1\n",
+ "C_DD[2,5] = 1.4 #capacitance between sensor dot 5 and dot 2\n",
+ "C_DD[3,5] = 2 #capacitance between sensor dot 5 and dot 3\n",
+ "C_DD[1,2] = 6 #capacitance between the middle dots 2 and dot 3\n",
+ "C_DD = C_DD + C_DD.T\n",
+ "\n",
+ "C_DG=11*np.eye(N) #dot-to-gate capacitances \n",
+ "#cross-capacitances\n",
+ "C_DG[0,1] = 1.5 #dot 0 from dot 1\n",
+ "C_DG[1,0] = 1.2 #dot 1 from dot 0\n",
+ "C_DG[2,3] = 1.3 #dot 2 from dot 3\n",
+ "C_DG[3,2] = 1.4 #dot 3 from dot 3\n",
+ "\n",
+ "# Definition of the tunnel couplings in eV \n",
+ "# NOTE: we use the convention that tc is the energy gap at avoided crossing H = tc/2 sx\n",
+ "tunnel_couplings = np.zeros((N,N))\n",
+ "tunnel_couplings[0,1] = 50*1e-6\n",
+ "tunnel_couplings[1,0] = 50*1e-6\n",
+ "tunnel_couplings[2,3] = 60*1e-6\n",
+ "tunnel_couplings[3,2] = 60*1e-6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "2ac93325",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "capacitance_config = {\n",
+ " \"C_DD\" : C_DD, #dot-dot capacitance matrix\n",
+ " \"C_Dg\" : C_DG, #dot-gate capacitance matrix\n",
+ " \"ks\" : 4, #distortion of Coulomb peaks. NOTE: If None -> constant size of Coublomb peak \n",
+ "}\n",
+ "\n",
+ "tunneling_config = {\n",
+ " \"tunnel_couplings\": tunnel_couplings, #tunnel coupling matrix\n",
+ " \"temperature\": 0.1, #temperature in Kelvin\n",
+ " \"energy_range_factor\": 5, #energy scale for the Hamiltonian generation. NOTE: Smaller -> faster but less accurate computation \n",
+ "}\n",
+ "sensor_config = {\n",
+ " \"sensor_dot_indices\": [4,5], #Indices of the sensor dots\n",
+ " \"sensor_detunings\": [-0.0005,-0.0005], #Detuning of the sensor dots\n",
+ " \"noise_amplitude\": {\"fast_noise\": 0.5*1e-6, \"slow_noise\": 1e-8}, #Noise amplitude for the sensor dots in eV\n",
+ " \"peak_width_multiplier\": 15, #Width of the sensor peaks in the units of thermal broadening m *kB*T/0.61.\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "903304ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 10. 0. 0. 1.6 0. ]\n",
+ " [10. 20. 6. 0. 1.4 0. ]\n",
+ " [ 0. 6. 20. 7. 0. 1.4]\n",
+ " [ 0. 0. 7. 20. 0. 2. ]\n",
+ " [ 1.6 1.4 0. 0. 20. 0. ]\n",
+ " [ 0. 0. 1.4 2. 0. 20. ]]\n",
+ " Dot-gate capacitances: \n",
+ " [[11. 1.5 0. 0. 0. 0. ]\n",
+ " [ 1.2 11. 0. 0. 0. 0. ]\n",
+ " [ 0. 0. 11. 1.3 0. 0. ]\n",
+ " [ 0. 0. 1.4 11. 0. 0. ]\n",
+ " [ 0. 0. 0. 0. 11. 0. ]\n",
+ " [ 0. 0. 0. 0. 0. 11. ]]\n",
+ " Size of Coulomb peaks V[n] scale as 0.701/(n + 6)\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: [4, 5]\n",
+ " Sensor detunings: [-0.5 -0.5] meV\n",
+ " Coulomb peak width: 0.41 meV\n",
+ " Slow noise amplitude: 0.01 ueV\n",
+ " Fast noise amplitude: 0.5 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [5.e-05 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 6.e-05 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 6.e-05 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]]\n",
+ " Temperature: 0.1 K\n",
+ " Energy range factor: 5\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create the experiment object from the configuration files\n",
+ "experiment = Experiment(capacitance_config, tunneling_config, sensor_config)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30bdf510",
+ "metadata": {},
+ "source": [
+ "## Figure 1: Four dot transition from Neyens et al. 2019"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "301631d5",
+ "metadata": {},
+ "source": [
+ "In the first example we reconstruct the four dot transition from Neyens et al. 2019. This is done by selecting:\n",
+ "- plane_axes = $[[0,0,-1,1,0,0],[1,-1,0,0,0,0]]$ - i.e. alligning x-axis with transition between 3 and 4 dot and y-axis with transition between 1 and 2 dot\n",
+ "- target_state = $[3,2,3,2,5,5]$ - i.e. selecting the state for the center transition\n",
+ "- target_transition = $[-1,1,-1,1,0,0]$ - finds energy offset, which moves four-dot transition between the states $[3,2,3,2,5,5]$ and $[2,3,2,3,5,5]$ to the origin\n",
+ "- x_voltages - voltage range for the x-axis\n",
+ "- y_voltages - voltage range for the y-axis\n",
+ "- compute_politopes - compute the regions of constant charge occupation for the device\n",
+ "- compensate_sensors - compensate for a drift of sensor dot\n",
+ "- use_virtual_gate - allign the CSD with the transitions selected in plane_axes \n",
+ "- use_sensor_signal - return conductance of the dots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "41c8b58a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.02893633 0.02977506 0.02942368 0.02924915]\n"
+ ]
+ }
+ ],
+ "source": [
+ "xexp, yexp, _, polytopesexp, sensor_signalexp, _ = experiment.generate_CSD(\n",
+ " plane_axes = np.array([[0,0,-1,1,0,0],[1,-1,0,0,0,0]]), # vectors spanning the cut in voltage space\n",
+ " target_state = [3,2,3,2,5,5], # target state for transition\n",
+ " target_transition = [-1,1,-1,1,0,0], #target transition from target state, here transition to [2,3,2,3,5,5]\n",
+ " x_voltages=np.linspace(-0.0022, 0.0018, 100), #voltage range for x-axis\n",
+ " y_voltages=np.linspace(-0.0021, 0.0019, 100), #voltage range for y-axis\n",
+ " compute_polytopes = True, #compute the corners of constant occupation\n",
+ " compensate_sensors=True, #compensate the sensor signals\n",
+ " use_virtual_gates=True, #use the virtual gates\n",
+ " use_sensor_signal=True) #use the sensor signals"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "3d5b41f0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.QuadMesh at 0x7fcaf025d520>"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Control plot\n",
+ "fig, ax = plt.subplots(2,2)\n",
+ "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
+ "#Conductance of the dot 4 (Left sensor dot)\n",
+ "ax[0,0].set_title(\"Sensor 4\", fontsize=14)\n",
+ "ax[0,0].pcolormesh(1e3*xexp,1e3*yexp,sensor_signalexp[:,:,0])\n",
+ "plot_polytopes(ax[0,0],polytopesexp, axes_rescale=1e3) # plot the polytopes to see the constant occupation regions\n",
+ "ax[0,0].set_xlim(xexp[0]*1e3,xexp[-1]*1e3)\n",
+ "ax[0,0].set_ylim(yexp[0]*1e3,yexp[-1]*1e3) \n",
+ "#NOTE: Default voltage unit is V, so with 1e3 we convert it to mV #TODO implement the voltage unit conversion\n",
+ "\n",
+ "#Conductance of the dot 5 (Right sensor dot)\n",
+ "ax[0,1].set_title(\"Sensor 5\", fontsize=14)\n",
+ "ax[0,1].pcolormesh(1e3*xexp,1e3*yexp,sensor_signalexp[:,:,1]) \n",
+ "\n",
+ "#Gradient of the left sensor along the y axis #NOTE: for np.array y-axis means axis=0.\n",
+ "ax[1,0].set_title(\"Gradient sensor 4\", fontsize=14)\n",
+ "ax[1,0].pcolormesh(1e3*xexp,1e3*yexp,np.gradient(sensor_signalexp[:,:,0],axis=0))\n",
+ "\n",
+ "#Gradient of the left sensor along the x axis #NOTE: for np.array x-axis means axis=1.\n",
+ "ax[1,1].set_title(\"Gradient sensor 5 \", fontsize=14)\n",
+ "ax[1,1].pcolormesh(1e3*xexp,1e3*yexp,np.gradient(sensor_signalexp[:,:,1],axis=1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "047f1e43",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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q6upgt9uxfft27NixA/fccw8uueQSPPjgg1rzCA0aNGg4jTilj/q6detw5513Yu/evXC73QgPD8d5552H8ePHY8SIEejZs+e3rtfa2opdu3Zh+/btWL58ObZu3YrXXnsN7777Lm655Rbcd999p6U4/K8dbx0bDr3ZD7f2pT461lcCR7eXXKjMqYOex9okqDopigHCLxulFvPUFKah76qjC/qXbHFR97QyTX99g0oTHie6Wq8eHQBGRhcp4xaXuIitDrqdTx6n1raPJ5Bb0857FhlH+mddtQTzykJIG9icTPv2puybfUhf3Pnu1cq4I0XoghkDqTWuaA8FAGQHkD65OI41tR/58CIAQL8Jx5W5fTvows87+z0AQEEng9TzBtFtvvSrGwAA+la6xb49GTS0JskxDQxm0HJ7I7XSY8MlQLm0npRNjY0UQHWrXKuIWAYKXSDVMSNWtPkb6/kOFayQ7fuMtihzs3tSP/2Zp3hZ9THe2wGDGHA+sEf07bpE8isZI4qU8daSVABAZz/q6eNSSeX4NsnxBwe285g/Z3BaN0ronRR/rvP0phnKODJBjjvcn5SLN6AMAFaX0IzNKnpjTMAxAECisUGZe7J2ujL+a448s33jWKKhoYO07sFioawmj2Lexjv1FARUt8l9eO/i55W5C766URmHJshzHO9vUebUwemnrdPknFL5++JjLINwXs8cAMCLZROVOWcgr29GsOj0IyL4Pi/Rx0JVpaPbOKWP+hlnCEc2ffp0XHfddZg5cyZ8fb+9RoMagYGBmDhxIiZOnIi7774bhYWFePvtt/HCCy/gqaeegtlsxgMPPPDjzkCDBg0aNCg4JfXL9OnTsXXrVnz55ZeYM2fOD/qgfxvS0tLw4IMPori4GE888QTCw8O7XkmDBg0aNHQJTaf+M8CrU79+47kwBfoi2kiqYku9UAThJrqo2UF08f+zTNxZQwbXuaiX6LiXFrPin8tFVz4uWJaN9KPbra7jfcASf9LvVlUqtZcWSFStMzSQbv0L+eKxqfXHbR1cv2eElCcotPCP9fU9SXU4PbbEokK6xepKdxf1lvNT65ObbPL7ZamkXNR4s0C0yDEqPb7NQUfUWyfdZqchMj9tszL2llYwqxQzc1SlGZ44LPTT3/tTu/7PUpYs8F6zI8XUxqfGs0zD1FhJ+Y9U6ey95RYAwOKpSf6PEUuUuV1tQp/saCCNVlTPa+o6KlRC0EDSH73CuM/t+4TK8Y0g5ZEaxWWL62Rb7uOkiezxpMQS42XZdtU18/UhJdXQKsfsKGGZAH0in+MHsz+X428hDbYil6qVXglCRXSo7lP/MMkd6OHPcgU9TCwtUNAh+vTnd7A/wJ0jWNV0a5Psy+EinzEylMqxRKOc0/05c5W5M1KPKeOvdokazRzD0gMDY1lv3bvd/VW8z27VuxcVIuuV55Hme2XGa8r49Wq55xPDSJ3+Y/FcODtsOP63+zSdugYNGjRoOBGnbKnPnTsXCxYswMyZM+Hjo/1N+CHwWupJLz0EH38/PD6KHW/u23QuAMAYSAtJXagqOUys5fGRzBT01oRWF+kKVGmmJ4VIkMjipAXl50MLdGerWICbqxjouzKVOnYv+qu04/mdzOAr7BArpLKDslRv5isApBjFyvp3CZs0l9TRwrwsU7oD2VXWVLODlvpnB8VqnjeYlvSqagkGnpdA63llDbMvDxWJ9zEug4HSvkE8pirPsR5voQU1IrxIGX9SLBZau6qO97hkWnhby1MBsHAYAPjqeJ8ifcVCs7l5T0aoCqa9UiVZifuXUkc/7oI9ynhnjQS6R8Qwk/Jgo1iDJj0DbX6q8bF1cv8MAxh8baug1e32lVfbP5yBTrUmfF+xaLKDQ/h7Sy6D2warWKBxo6k9L8pjkTYEyLHMyWI+wvJD9B7R6PHewvls9k7k/r1ZmSODeZ1KOuXZPqoqEOetNQ8A9/cQzfnhDjbtLu1gbfX+ZvFyF+44W5kb34vPhDfbuU8gA60xvrx+Xo9uVT2frUorBQGlJRIo9rHy2b1n6nJlvM7TrPzP8cxo/VjVDN3rpW+z8N3bWZIMl9WGwqsf+2Us9eXLl2POnDlITEzEvffei2PHjnW9kgYNGjRo+Flwyh/19PR0uN1uVFVV4amnnkJmZibGjRuHN998E1artesNaNCgQYOGnww/KlC6YcMGvP766/j444/R1ibBNp1Oh8DAQFx00UWYN28eRo4c2cVWfj/w0i9nr5wP3wAjgn3p7l4QKVTES+UTlTl1APJItbih9iK61TfPFB36VzUMOqkLRSWYLQCAvmbSD//6gO7oBedK0NJip753dhipgA2tkko+VFXn2qmqTX60Q2iBfCspmVWHSStcli1lBo63fXtt8/7B4s6/voWBwsQeDIyVlYk7HRDG69Q7Un4fGEJKqNBKzXSUSeiPpUf7K3NPDWERspfKhP5QF4rq6UcqYHuLuMNf7MhW5iYMZvGrzWultIJ/pkWZu60Pa8BXO052mV87OFoZX9lXArxvfj1RmXPFsOBXjwQ5v/PiSS89tV2Cs2mJDBSq2+m5S+X+Rffn7+r085oCobz84xgQt6nqqfdNlqDk8TpVu7tjpBoiBsl2G1r4nPju4nPoGC5B3w7VNuNiLMq4OleornMnMLhtVeUreIuYbTzOQKqPQQKxM3ry2l8dyXrlDxVLr4ELY9nge2sLtf2ri6SeebA/g8OzkkiZVdhCAQCV7bxfV8ezeFeDU87vm3q+WyUtocp4YS+hWra2Ubv+8RsTlfGoS3IAAJvKWAPfZuX1iY+2AADKq3kfx/c6js7WTrw/+Z1fLlA6fvx4LFq0CJWVlXj55ZcxevRouN1utLS04LXXXsOYMWPQr18/PPvss6itre16gxo0aNCg4bSgW5HOwMBALFiwAJs2bUJubi7+/Oc/IzY2Fm63G0eOHMGf//xnJCYm4vzzz8eKFSugqSc1aNCg4afFadepu1wufPnll3jttdewYsUK2O126HSeKHpcHP7whz/g6quvRnp6ehdb+u3AS79M+eJaGAJM6BNMt/+TPVL9r2cao/H5R+OVsX+MuM6TUqh+STBZAAA1nXSVmz1VAgFgc4lQCYPiqVrYkU+t8zOjRAt994dXcj/9qEn3pvG3qLZpUbWB89bE3ltLBcKCHlSq/CdPKuAlBlNVUNoUqozPTBbX+qsSUjZvD3xDGd+UdzEA1s4G2Frv3FjSEzY3XXlvnfUAH1IaeR1UUFR1yv6PNpMSUlff6xki+m51GvswlTa/2i7qmXX1vZU5L80FUKkSZiJlFGrk+KyIHADAPTvPU+ZiI3h9Ao2e1nQqGu1QmWwzIIDnlBzK+xRslGPdcpBUgG8jr5krgeeizKmqUCYmiGa7w07FTl0Dnymv/joxjin7tc2kXwyeNnVtzXxO8C1fk/Qk0kNtqmvubc13TiLVM5+UZgMAbk5nKY0XCqiiGhop6pazwvgc3Po62x5mz5Jny+WmvWpTVRv1Xt+dqvfBHMTrNDNN1l/3wghl7r57WXHxw9phAIDhIUXK3OdVrAeff1zuWUZvvnux/sxN8CLRj/dxZXkfOK0d2Hv+s79OnbqPjw9mzZqFTz75BOXl5XjmmWfQr18/uN1uVFRU4IknnkBGRkbXG9KgQYMGDaeM01ZP/dsQGRmJ2267DbfccgseeeQRPPLII3C5XF2v+BvFhMg8+AUaTqgJPjJTtNADghkAVAe7/tZTgn3r21gH+9/bxHK5ffRKZS5UT+XRgECxEpZVUDPsF0CtsNeavXAmA1CzQ2j5LG6QILdDVVis1B6qjM+LkCDVysPU8ob24v7Hx8s5zQijBfYCmAFY7glWBZh4TJfnzFPG3gDkkwdYyOma1E2e82TQ7/avLlfGwwaJFnlGJIuAqfHhLqltbgjkOWUn0Zo6WC8WVloIMy7/XTtRGVftE6v/8bnvKXP1Tlqt26vF8tPr+Hwf3E4tsmmCaLqjwmi11bcwj6DFVyzYflH02IakiFVa1MSg2qHDLNzmxQWjGDSsV3XDWrddLEgfG4PUPqrKHkFp8hyUFVK7b6riJ6EjRX5vbqcl7mfk9WtpFu8tfAut77ALmQ1duVoqsMarsqHr9Dxnr8f60pdTlbkn574LAHih6Awes0o44M3BKLcz78Hen89Epyd3I0zVINxs4Pk3e5qezxu0RZn7sIBF2Ew6Ob+WmcwovWMbi+3dMeQbAMAz38xS5rxduQAgKF56BOTb6BEeaKLnre785IW/0Q6H3X7S/I/FT/pRP378OF5//XW89dZbqKxk6zA/P7/vWUuDBg0aNPxYnPaPutVqxQcffIDXX38dW7bIX0MvbZ+dnY358+fjsssuO9271aBBgwYNOI2B0k2bNuH111/HRx99hLa2NuVDHhoaiksvvRTz58/HoEGDutjKbxPeQGnq63+Bj9kPyVEMPBUdFtcsJM2izGWEUwa6pywRABBgZrDsrgxx976spyb7aCPdvTlJbC/mhbo+tcUpLnqHi774+joG22bHCG3yzEdzlbmgwSwU5W0ZdqCBbuUlSaQAPq3MBgAY9Qz6nR3DY4o1WAAAUXpSEcua2Pqtr7/QIh9UMr3am1LertI5jwplenmEXtzlu7ewzdjDo5Yp4/crJcD152Smbz9TQnonr1YoiCEJpA/UhaCWVnjKCNipOZ6ZQP3ze0uEEpt1HsstfFnEQPCUZCngVNBKTfj1iWuVsbe2fe0+3sekIXIdqlcnKnP6ERZlPCBG8hDUQezD+xgANCYILdFZQcrDHcQyA9ExQgV0qgLSTRbSN/py8aid8aQLvXp6ALB6roVlEwPSoWMpAjgjVrLN3/9mrDJ33pStythLw+U1kv7x9TwzE2MoDMgOYG7BA/tEp94vljTVnj0UXUweKfTbkUYe08AI0myHG6XMQVKgBd8GhyfAOjmcOvn1jYwBjg2V44r3ZaDT24gdAP6WK/XWx8QzyL4ihzToTaOEWkwx8n368+YL4Wq3ofS6haclUNotS72yshKLFi3CokWLcPy4cJputxs6nQ6TJk3C/Pnzcd5558FkMnWxJQ0aNGjQcDpwyh91u92OZcuW4Y033sDKlSvhcrkUqzwxMVGRLKalpXWxJQ0aNGjQcLpxyh/1+Ph4NDSIK+92u+Hr64vZs2dj/vz5mD59uqJJ13Ay/P3t0Pv7oFcwXa/aYnGtz57ANmXq6odnposbuKeeLviyWqGxGlRu9/29v1DGfz0qkfmpCazZvLw2Wxknm+X+rS0n5aKud97iErfbkUad9YBIlhwY5amq93Uu1S9BqXTRF6ZJKrXFRVde3d7LWx0xSM91Pj7I47NmCsVyVFWbHB5WYU4sFTUvH6dbb3cIheB28PlbZ1G5zRFyzAds7Imrrid/2Cb72lPB65ylqvI4KVrc7vePD1bmSmxUYPgMESpjjeqatll4fyqiQgEA5S28t29UqsokBMr6fSaSvqjypLJ3DKCS4+JUqnt2NYoSpvpDUi7uLFJeAX6iLrL58TgMJv5eUyPb96bmA0BsFNUZ7khRrVTnkTIq0JEq0dWKB55+BlsMqq/p4gNCefUeyt+31NLYuyVNqIgvfEhPlLWFAgA+L2Ga/sqPeZ/fXPgCAGB5E6lc1yDec2/uRJ9wauO/2MplzxkjNGEvf17nvHZSNd7chFoH9fpDVZr0JE899i8bB/KYPa0WAeC//UW9o1ar/XXcUmVs81Ce71SylwDa9ED76etnd8of9fp6Oam+ffti/vz5uOKKKxAZGdnFWho0aPg1wnakANVPvqL8uxDAkCU3wTeEf5Bbtu1E+5GjsJdXwNnaApetE/ogM0zJaQiZMB7IPj16i9bKVpSsKkL9tkI05dXB0dIOvdmEgKBkRPcZjdDkrK438gPgtLtwZHcD9q+pQ95OC2pLO+B2A8HxZhw7IwBnXxMHhHS9nR+Cxop2rF9chMIDragt7UBLox16gw6mhMOIn5iOHhcMPGkdi8WCsLCwE+bWrl2LiRMn/qB9nvLdWLBgAebPn48RI0Z0vbCGEzA0tgTGQCNaVA2d51whWnGznoHQs8NzlPFeq1hhVfV8yrwdfZ7s84kyd/sBamnHJYpVOj4oV5n7ophW9aRwma+KYEBmnJk1p99tlHt7bj9axR9tGaaMU8aJpT80jRbY4orhynhqtHgXe5upqb4/5XNlnGOTc2p0MICnthC9gaXQaAZSkwIsAICnVlMfHJTI3xf0FqWVukvO0wXTlLE3w/CW2G+UuU0NtKpHpouFVtjE2tyvH2BBrhdHSFbhmx187mtVjaWtpWLZ3TZtqTIXkkELe0uL7CvKj8fcpnoOvE2qr49nJuUfP7gOAOCM57PxZRnvozcj06pyaIwNtPjMPcVS90+hZ1heqfIuDE7oPFmhhqgImNJTAbcONaWx8KkxITpdDDjHrjVoP16P4B7hcKckwsdogK28Htb9+2E9cAAp5hHoc55YpnWqaxIWJla7t+k2ADTn8WN1Z7E0C//HRNH+3399Ho7tboPO14CEAWEIiAxHU1kbqg4dRXPFUcy4OgZ+OrF01d3DPqnhh3F4gjyT6k5ej09nN6lYgwW7N7XhrgUSPPWLDUbG2CA4HW6U7GvEF6+1YOUnrbj2zeGITJNzOdLCC+wXLnrymg6ek9nAfIuHCiWQ6+0q1br7KCrfWAefkCD4xiYiqI8fnK02tOWWI/eV7Sj55jhiL78Neh3vm9FoxFVXXQVABCj5+RQE/BCc8kf95ZdfPtVVNGjQ8CuHKT0VkVdeDLhOpk8H3DYeQWnhMJiNKG0OVear3q9E5buLsPO5nUidnAq/0O7ln0TE+iJy3iwETxiEcwayT4NuRw7+cUMevnyjGmefEYnR47u3Hx8fHaIm9EbiBUMR3CcO0+MkocnWYsc/bihAy54CLHtgL+a/M66LLXUNvx7xiH34NhgTRHUTGS/Gi9nRhIMLP4NlTwka1q1C+CQaIGazGYsWLQIA/OEPf/jpP+rfB6fTiby8PDQ2NsLeRYbU+PHjT+euNWjQ8BMhrF/st84H9M2Cf1o62vPzULO/BsnjT852PRXc/nwq7t5zcsnuQZNCMeG8SKz5oBZfLbd2+6M+aLQZfWPOPmneL8gXSbecjcNXPY+yfY2wVFgRGm/+li38cBjCgmD0OVn9ZwgwIfXKUcjZU4L2/DxA9VHvLk7LR72srAz33XcfPvnkE7S3t3e5vE6ng8Ph6HK53xo2bsqCj58fnNF017Y3iNt62WSm7PvqeG18PBWSJvdk0LPUGgoAuP/oOcrcvF7U/04IEHrl6Yozlbnn+n+gjF+q9JQZiGfD3ov2LlDG/p5U8EtSdilzI7JJzwwyi244T1Uv/YJIBnrfKpUXc0YcddyrWsiHhhtEU77oMF/gPrEMXPUfINnHK3dRh1/uaaZ94ZjtylyDnfTNrmahdBbVkh7xBk8B4J+93wcAfN6crcylB1JzrYfQEAdr6Gr/aeA6ZfxOrVAxpgN8yVuj6eI/M+MdAMDtGy9W5kKjmGp+drJcnwpVUG1nET+CLpu8itdUsshaQF/RQnsbiQMnUhktR8TF12eomiTHM7hb3CxUh7oWvq6Jr3zmwHLU1zagCgBcOuicOrj8GDSty5VYmSOXwdGky2k17osLgtss2z5YnYyCo73hU8MPWERfoX2MBj7PtkYeywtzpIjbWzVjlLmeMXJP1ldQe/5R7WC0mLYCWIYNBdG48dgFcKvO6amBrJu/qEqCqtfFMQfA2x8AAAp95FzGRDEHQV33/uXxb+O8CD0s9U7kFAUhIjD2hF4AXmzPS1XGr49nMbprProWADB7CmvIq2m2MSEScH9s/wzYrELFxUc346Y5n+O2R07azY9Ctz/qBQUFGDNmDGpqan5waV2tBK8GDf/7aD98HLYj+fAJ8IcpPanrFboBR43EcXzDA7pYsntobXaipUkUQn7h3bPSvw+ujk40fCKKs6wJ4V0sfWro9kf9vvvuQ3V1NaKiovDkk09i+vTpiImJgV5/+iQ6GjRo+OVRn7sDdY/lwNnpgLWiGZbcGuj8/RB1w0XQB/h3vYEfCVdbO1o3S2euiFE9u1i6e1j2lgVOBxCcHo6A+O5ldqrR0dyBrc/uwmFjM1oa7Cjcsw+uFisChmdi6rxwOB2nz9Dt9kd91apV0Ol0eP/99zFp0qSuV/gdI2FAJQwBJsxPZvusB9ZJfe1IX6oiDrbT6ok3igv+2nZqdU2hooa4vi8pm3/u4bV/PUg0sAt6s8a5Wp/d6ZI/uFusVH/0CGd1wos8rcL+UzRBmYsz8/hu2XQJAODeEdTGf1pFLbA3BVtdhqCqgy/IqABxQa/IpIvqAl3ct1fKfkeMYrDMbJBz9qaWA8D+JVSCPHiD0B+dTqpwEs1M5f7UU4YgxUQliNmHNFiCQZY9HkVK6asaVRVKT+3yyy5hC7tPSqi6iDVIAOzB0Z8pc3/ddpYyhodpsTp4TS7LYmmFMs95bSoi7eBwimLHbGB8yldVb/3iqfIcralijfeccta4t3soHaM/13f78uNxtCoa7Q0eXbnRBXewA2qiQZ8g17zNU03SsrMQzeupqPIJ9EfUH+diwjkmAELJbW/mR9fPV/Y7O54lIsIvY3XEe3LPBQC07KYkOmWsqFcajnCu6dW34Wppg39CCtwDs1HfosNbQ0h5XPbWLcp44cVCsz1bSo460kTt/MBgKQOR08T34ezeQo3V5DbirX/JcxB++TQcbxAlVLuNNFtqgLwnaYmk7uZtuFoZm3sKVabOAem062Gva8bxFflg8QNg6IxIXPpgMJ5bdS5cNhuALTgd6PZH3Wazwd/fX/uga9DwG0fs3IvQ89HJcLZ3oqOsHqXv7UT1s+9h1/E+GHrPhK438CNgWbMGzUdyoPc3I3725T9ZcmNbvQ2f37EZjg4XhlzWE/ahp9cj8I0MRuanD+Gi9N1orrahfkcRlj5XjIVn70HQ5aPhexpzfbr9UU9LS0NBQUHXC2pA5e54+Pj5oSOJ1tqcoeJWHmqlhdXh4m3x8dTnjo63KHOP9/kUAPB6NSVXtw6hBVnp6fKj7v6iLkDk56mT/txm1rHWGWkBZgVLsG1uAnXquW0MIH448b8AgBsPX6rMpYfSAs4IkKBnhh/LLe9sYNZjaVDESedpddEaemS2BHXVBctiTOIpLD3O7MPQadSkL68TT2F0GAN5//6cgeKnzhWd+W1recwXDGMgeA9S5Th30Oq980xa3e+XSXExSydfdm9hMwC49KsbAAAjBjCgHBBycuehe5NXKOMrPv6TMnYGyfVPS2ehqkBf8STUOujIAFXG5noJMPYZyOOo38nsSD+Pgd6ZweDnkAF8V8tbQtAc0I4KALDrgFY99OHcl+GAWOghY3idI/zbgAAAkYFoD74cLtubKFh+BI6+WQgZnQl9BDX1JRVynz/oZLG2hmZy4sNTxLrfmsAcjD8lS4Dzfb/hKPoqDwVfrIPJ7IMH3kxC70FfYnebZKTuaefzZFdlMxd2SCBUXbc810Lvy9IpNNH+PFrqRWH+OHzXYlgr2mAe1h+1Uy6GTyGDm7PH8jkJMZwsBImLtSjjWoto21/KfkeZe7WGf/DaXH7QR/khY24vXJYehRcv2wbDijcw9eWpWHTSln8cut356KKLLoLNZsPq1au7XliDBg2/KQSMlj+mzduPdrHkqaF8UzG2P7oePgYf3PnvHug96KcJkLrtdhxd+DGsx6sRMjgNkdddCJ3PaW8I961I7B+KyNQANORZ0FrZ1vUKPxDdPvo77rgDAwcOxB//+EcUFhZ2vYIGDRp+M9AHysfW0WTtYskfjoPbW7HlfjESRz18BgaOO30BSzXcTifqXlyMlv0lCOybgF4PngOd4SftG3QSzGHitdssHV0s+cPR7TPw9/fHqlWrcM0116B///44//zzMWzYMAQFBX3veldeeeX3/v5bROqIEhgCTCfUZ95dKQWkxieRNkgzk8rwasHrLUy//k+FtPo6WEFK5JiqJnVDvkikApOpb25R1cn21hmPHEzrYFs13dkcixzTebF7lLkNxQzgnRsh7qhax67W1nvpn4cPMqX/+YHUye9tT8X/R4mVsq4rwiVgZAnlMdvdEtw9r2eOMndYlb5dYZUXf3YkA74pQ9ki8N7F8rzpUvnyhBl4/iYf4SruPpM12Ms6eUz39PgSAPBeDbX16nry54yQa9HQSYuys5Ov19pqCZytKGWhqsEjGTYz6uX61bTzPpd4mnVbqvhRCzzObV5wiVynFUXcptOPgdDwoRLMU+vEd+ewxZ6xUQ9rgTwjvgF2BCc1ozU/VPndG7tX67QPHVQlGPm7YMsVQ65XL2BK8hF8dJgBc6NZrmnjAT6b+nRq6rftlPdAF0bK55s9AXj1j4fgtDsxfeEwZM0Mx/vVDDrmfSjj8+dTh56VQm3+AH+hotRN2UdGsR57sF7ok/2dyWhY9BHacw4jOdOMe9+KRUDwZpR0Crf9cXG2ss7gQK7/8JcibJgymsFfb94IAJyTJZTlTYcuUeamJTK4/OEBKQh3VfY2dLTaUXGkBdDpsK90OIDTw3aclj9LRUVFqK6uhtVqxdtvv4233377e5fX6XS/y4+6Bg3/a7A1VMO64ShCR/WGjy9lym63G9btOWj+aj2gA/qdnXLCevbaRhTe8ncAQPrfnv1B+7JX1mLRk7vQ0erApLsHIWvODyvfnfP4KpR9lYtefxgGPPDDzqtxyXK0bd8DQ2w07nojEQHBXX8Ka8tsKLzlTvnHzh/2/ar/ei8s050ITQw8Yb65uh2f/3U/OtscMPftC31g4Hds4dTR7Y/6/v37MXHiRLS1idVjNBoRGRkJw8/sxpwOWK1WrF+/Hrt378aePXuwe/dulJTIX/6HHnoICxcu/GUPUIOGnxmOthYUPrEU+gATzD1j4fQNh7u9HZ1V1XDWNwI6HSbdMRBxWf8vgcbl8Rj0P5zhrf33++hs6ERAuBE1hxvx1QMieVUXwOtoLkH0sMknrOdNZvQx/LDcmNw1VWhZK3JSQ1gI3v8bA81tLin0VdxaipgLRsEviaoUtzfefAqce+PaA3jlhRJE9AhGeFoQalqPwtnQhH8Wl8HZ6UJUzyD4X3DBD97eD0G3v7wPPfQQWltb0aNHD7zyyiuYMGECfH6mQMPpxo4dOzBz5syfbPt1HyVDb/TDsX5UI3g7kb+dT311bBA14dF+4q5+MIaF1IwQpcST+hnKXJCBtEKOp3rf/DTq1P97nLV2njwobdyeyyYl8tlBKk1iA2X/f10zV5m7bDQ1tHe8MR8A0Gc6lR57cmmpXT5MWrpNTObv13w1XxnfdYYoQAJV9dT7mdly7OId1wAAFvTlPnuaRBWiVsl4NcMAUOCWl+/rRp5H2SYqHObMlm0t2UT6xOrktj4vlzIG1hVUj1x8LSs67msX2mFK+GFl7t+NE5Wxw/X9HxR/j9a8Vwj1zYXNrAh5fcp6AMCjh/n8eXXeagyYyzZrK1+V9HbDdLYqDMnkuHW1UHctvUm/+LbwOPUdgId1gtHgRKi5HWlDuf4+UyqccVEIcU1FR24BrCV1cLaWQ6fTQR8WjLCpAxExcygKI9NR6Cnz7vZo6zsKRAEVMHIITBlUolhb+YHWxwgV8tfBojK6xVGPOgBtDZ049FnRSecOAKZedvj1noiPCrOVudojOwC9D9oHjMLThXJ+/ipt/9AwfrQHmktQZ+M52o7kYRMvqQplMPYZA//2cDxmlfesdfthAHsRNCH7hLaKZ0azHMbySlFnXZS2GwCQe0MUPnkzFs3FxWjc2gB3RyX0ASYEZcYhenwvJJ6VhbJWHzitpKW6i25/1Lds2QKdTocPPvgAQ4YM6XqFXznCwsIwePBg5b/bbrsNVVVVXa+oQcNvEPqgQITOngzM9ljHenL2IZ7Sus2Wk9ezHSkA9HqEnDX55B+/A8+vy8YDu6R0rd6XEtub+5E///vaE40uR7MVnaU1CJo0GL4x4QCa0BUmnx+Oz/yZsHTPtOXKuKRD/tC+t2bsSetZDxUCej3Cz5v4g/YDAH0mRCOqc4ry78Qskfma9D9d7atuf9StVisCAgJ+Ex/0cePGKV2dvLjnnntO2/bt05rgMtvwTD8+RHutqQCAK9KZXTk1gNZgvl0s0L8UzlXm7k6RoN25UbuVuSXVtPSry6SQ0/tGNm5OCrYo4wJPptyThbT0J2VSklZhFd2wvo0e1+INLLoUNVqszf1lDBTeOnqVMn4hZyIAoH8SA1hjBnP7Xmt7cwM135MjaC4NShCrXd2cN0IvH5DlZdSpX5fGbk1LN0q995unsEjZ5ixysh/nZuP/o9nB1PYb0tYBANZfweJPKypYhMzaKcfcK4zHVFPLAKY9Sq5Vv0Ce88FAVje8KF4CqWsbuf2SGlIWd5dLAO6KQSxYtqxIvA5jCL2womau45omuQcthaHKnL6d92zuZeIxfZrLa+bo4O9+9T7Qe2KUDTlV2PPQGuj0bkTOOws+fiYElIjVaxpnUdYJ86fKpahGnqPgUM5NTZIs4DcfPQTz9CykDnFhXAyLvYUYuOzLq+WD/+CnFylzMyfLM311JLOlL9hynTJO9Dxmfe4V67vwSDGOGnSYdWsKguLyFKpmQDCD5Jvq+Jy9u02yrUcN4/O4tDpbGefXyvt29gRm+0YbxXN94fhhhMzsg/4DOrG1OFX5fdognt8fEsUj3NFCYYE632F9gwSHJ4Rz//ZYPWytDni/NFarFTfcIHkPmzYx+/yHotsf9Z49eyI3NxdOp/N/vt7L//rxa9DwY+GoqYejRuisiMvPBPy61yz+qo+mY2tN6mk4su9H2hkpSN9y+U++HwC46dPx2Nbw0/de7uzsxJtvvvmj1+/2R/3KK6/EHXfcgeXLl+Occ87pegUNGjT8ahCQ3BMpbzyp/Fvv//srif1rQ2hoaLcq2Xb7o37zzTdjxYoVuPbaaxEbG4tRo0Z1vdLvFDdnrIF/4ImXfFt9KgAgr4QBut29GXTcXy0Ux119Vypzb3jKAzhUZQCKmtgmTNcpuuI/p5GK8NI8agxRBZAW7eV98+qLE9eR14y/n5rq7TvEhXxi5vvK3L+LJyrjzASJQdyZ+JUy91UzKQBvUDg5xKLMPbuHvGNAoNANSWZqqvNbxS0eGV2kzL1RwjrYT86QY6lyMOU8yJ+0RasneJyRTXpkYCDP/4FNUlwqowd/nxZLffFAsyx7y5dXKHMZWaXK+I9REuh8okzVbs/I/b9WJPTVoEjSAnpVw+ebstcBAP5zkAFtu0WsZVMEU9P7h7P0wso9Qg+ZGvkcdIZxmx8fyAYA+PrxQ+0KoSbcOUrG+g4G/RxtHFuzJJDdVhSqzNni+fwmRFoAABF+pFTyWqhJrzwkz3RDGGNSextVjb2ziwBQjw8AJVZ5js/fRMrlo7EvKuNzG24GAARZeZ9vSqK++5kiKeRVaFDVgA9giYyqJNGvjwllEP/VfFKLg+KF+ovwZQ7DW0dJbY5MFM36fdl8ttc3kVLzQt2S8p0qvlt7torOPm0qabxlH42Bs8MGYP1J2/kx6PZH/bHHHsOoUaOwZ88ejB07FmPHjsXw4cO7TD568MEHu7vrXy06OjrQ0cEXurm5+XuW1qBBg4bTh25/1BcuXKhUTnO73di4ceMPIvd/yx/1J554Ag8//PAvfRgaNGj4HULn7mYbookTJ/6ocphr167teqFfAVJTU1FcXHxKyUffZqknJSXh0jWXwhhoxKWR25TfvHXEHyug235hAlUtfh4h8Z5WUjLnh0tkft5X1yhzZw/fq4wPN4nbm59LdUpiL1baKy0WKuPPY+hCrqihvrukUVzgs9MY1d9nYRXJdru46Gr6J8xEiqBHoLiWsSbKvt44Qhd0bk9JsV5dwYqIlmaWBEiNloDdGdGsp76kUNLPW1qoWPE1kVZwHxHPcMZMqoiK2qgDPyNSqJQ9zbyOmwpJ79w7SK5FDyOv02Ebz7nIU/3PB6Q3XKrSSfsa5VpfEk/VxL/zWZ3Pz5OqX3WANJsxjfkIQ+OFylFL3bytDA81UkXjp9Jfl24WHb6rt6rcgeqa2PJFneNS1VBXF0z3jRPaxOynomScPCf7nlAAgHsAj9NmYX/Q4EjZr48Pr0mgSbUtl2zr8hQqevJtrJi4bIMolsaMoPKprE1olfItpGmiR5Jympcs6pJdranK3MpVVN7Zw+X8DYG8TmdlHFDG3naCESbqwvfV8z6flSDPvLcsBcD7ALBsR10Hy0EcOcR8iD9NlNwGdS+BZJWK61C7nNfqcj77RoMTjrYO7DjneTQ1NSE4uHu1brptqa9bt667m/jNwWQywWTqnnpAgwYNGn4M/vdy+f+HUd8RCF+DUSnIBQC17fIX/5oUUlY5bSyatL5CNLYP96G2vdQuFuh/pi9S5tSB0IioVhTtrMOaG5iRWQzg1nUzYQ4z4eNmqTORs6wEBzZYUJprhaVuEzqtDpjDjIgZEIXBl/fGCh0TMGalMmvu/W3S3Nkvktb5eZk5ynhsgFjY9xWIGqq9qhnDDr2B/esaUZLbhr83OuAbaER033D0O783UickoTSAgd5p0aLTf7fo5Czby3qwiFh5R6gy9ukh1tSaD+pRu6MYzcfroGtqhs3qQnCYHscGRGPEFenYZKR+2GmjNfZeuViND6Z9rsy9W8L9T48Xa3LRZk+Qur4RxvLtaNxRgLb8Gjia2+EbaMTm1ESETBuOgKGZ+GMWM3rLO0LR0dKJLXc8BTVi/zIf/n17IMCTEayugV/YKpr02mbWBcmMYYPuzhixSgNU1nlWNIOSOwvF4jPE8j71jqEnklshHoDVzcxavZ5Wty1WAuUGB48pNIcWqP5M+b1N1RnItoc6+uBhks+gts7VTZhTsiQo7aOjJewdT5/J+7yqmFbtl3XiUeY1Mn2/18ginl+QnF+YL4O3mX4Mft+zWRqDvzjrNWVudw0t7eJ2ebcMqg5Tha30+LxF7tQe3bXVDJ6/8pHU8H/qMkoS1cH1EYNFcFBbyOuUnlkBh+Hk7OEfC+2j/htHZLIf0geHwOoywWA6UYe/+p0qlOZakdjbjJTBYTCYfFBX2Ibjq8twfE0Z4q/1Q/iMod+x5R+Og499hc0HK2Aw6pCeHYTQ4Ei0lregbGslyrZWov+lfZB07dSuN/QDUPTpfrQcr0NQjwj0GWqG0aRDeUEHcldVIHd1BSKvDkPw1BHd3k/d64vRkV8Ena8eQZnxCI4yoq2iGY37jqN933GEzBoNPHWiG+1j8MHU82Tu0K52VBSfvhdZgwYvtI/6bxzpg0Nw5ZOZKO8IO+m3Kx5KQ3y6Gf6BelTYQ5X5rStb8Pmdm1H5+koEj+kLQ3D3uqr7RQVi1gM9MHpuFPwDDfimQXp/WrbnYeUd63FgcS78BmcgalhyF1vqGlm3TkBgajgMZiNuTFqjzL/5WSA+vH0H6t/+AgEjsqAP7l7TBX1YCNJunILoqf2gN5sQHyAKp03LW1H198VoWrEFx+YMRe8xtCh9/Q3489PCvT/95wrto67hJ8EpfdSffvpp/OlPf4K//+nrHL5r1y7U1tZixowZXS/8P45hYUXwC/RFbisDX32jJQi0uIKufp2VH5y4QPlY/K2ArdnOS8gBADyawzoYc3ozGFRpC0Z1u7jkbujgcusQ6cvAkDdQuaF2OtAAoAE4I4ZByYGTI3FgaCSKtteiPbcEwSMyMCaQv7/vL9b74wM/VeZ2tJHW+GelaM69DZNH/HUqgvybcMANoIUa4Y+yhiDmzAZUrtiPI0vKUecr1MaMaRKsSgthyYbtuRLUnBjF4/jiOOuIJ0fKskNHVMJbl+P61Vcpv/s1GeCf0gBrQR5MFUcRltILUWZeE69W+pZDTFlX0wJLjkugdmC/Ipn4x1jMic4BIDSB3tN2MPHiZKw6noqDHxcg96sSjJ4gdEOcp52gl3rLta4G0IzBiWWI6+XAzhr5g6YOyvUOF/rCFc7o5pFqBlr1wRKUtDvoge3aQqoC8aIzd3Ty90O5pBqCc+X1V5UeR0e4KqjqaW1nNJLeaWP8Es5GeU7Dw3kdkyeTHvKWkVjt4DGlhFIzXlgmweeMfqQy+obI+q2qYmuOXHo8ZQFCJQX7sRjcBbEUFkQZ5H2psNOICdKTfho7TGi0P+1iW8OL+3D9VZWSg+Gl2wCgVxSP78kcKYZ3bgZbPQ5IIL3jiBOqanMLa8BH9OBzPD1Cnu2Rk9hWcHll/xNq1ncXp1RO8e6770aPHj3wj3/8AxaLpVs73rRpE8466yyMGDECO3fu7HqFnwmNjY2oq6tT/nO55GW1Wq0nzLe2nr6qar9G6A3ykOl+YDnTH4uAHvJiO38OLb+PnIvO96c9p6jeoQAAS03n9y+oQcNPgFOy1O+77z784x//wJ133on77rsPs2bNwiWXXIJx48YhOjr6e9e12+3IycnB8uXLsXjxYhQVFcHtdmP48OGYO3dud87htGLQoEEoLi4+af7pp5/G008/rfz7qquuwqJFi05p28faYmCEEbsqaC35JIhlVFjHYMzbQ19Xxu/US9bkpBAW+fpr7lkAgL8P+UiZ29jCbkoJfhY4jfJHx6BzwezTiX/nU1L4SB/p7hOkKn2r7vTid+wACnfUISjEBy+cux1BwbvwSSNlY3ExFgDAvftYFiIuhB/lNk/xK28JX+DEAOCnlbKv+cmb8F5bMY4DCEg0IjpDpF+HrWLhGX1oIcbGi4X30i7KBF8Zt0gZ/6tcikMF6ikl/e9kBque+jwVx4uPwRRsxKTxdvgFFWJYINsvPlAxGwDQN56BxgYbaae/ZEi5YPV1Xt2YqYy9hbYuS96BA1Vi2QVGmGBzS2Dx74fFe4kJlmvS6ZQ/LK12I5o6/aH3eAXj49gBa2WJ7Ku5QsXNGxnIhKd41/hhDGKv76CF6LLJ6603k+Zxt/MPWvsokSQ6quh5u8wMEOo81mOImc9JtYnH4m6Vc2vU0bMMNnHZmb3kmVVb3XtraOq7PQHYLe8OVuYmXSGS1AaVZNBtODmQel4CJbxeOg8AxoVJILLIRtrr5QIG/L3Nwv8z9F1l7vFCerx/SNkKAHh6H2M8+qOqY+ktAdi5obTubyi7TBl7pasGlczz7CSVNLhN3v1gA69T6e4EuGwnNyn/sTilj/qjjz6K66+/Hvfddx8WL16MTz75BJ9+Ki54UlISBg4ciKioKISHh8NkMqGxsRENDQ0oKCjAvn370Nkplovb7UZ6ejoeeeQRXHzxxaftZDT8OOz+tBTF60rhsjvQWdkIa14lDIFGPPB8JIKCfzqrtq3ZgU1L5UMeNrJXF0ufGvYtLcb2/eWwd7hQWdyJvAOH4BtoxIwnRsIvyNj1Bn4krM12bF8mFMKAyZFdLK1Bw+nHKQdKExIS8Oabb+KJJ57Ayy+/jNdffx1lZWUoKSlBSUnJtyYiefObDAYDZs2ahWuvvRbTp0//UUlLPzWKiop+6UP42VGytxENq1nHxDfYhIF3TcLwCfnfs1b3sejBQrQ0OJCeHYiwMb27XuEUUJZTj32fWpR/G4NNGHrPeKSODv/ulU4D3l94DK0NdqQNDMbAqdpHXcPPjx+tfomPj8fChQuxcOFCHDx4EBs2bMD27dtRUVGB2tpa2Gw2REREICoqCn379sX48eMxZsyYLmvC/JaR93of6I1+mPInum7BBgni+BpIybSpdMNeje7BEAZXvVl/d3zEQOBD57CLUWFHNPz14nIH+7Sjl7EKL/R9T/l9u1W07xs8daYjbu6JPn1T4OrsQGd9DdryV2DXX77C/J0ZGHGvBC93lDATc3SK0BZxscwY9Wa+AuxoNNi/iOfkoj758YaZKP9gG0q/aIB/iC/Oenw4DvmRNvEWUzonjNdpb1AqAKA0ih9ldQPrASFSiCnTX4JW/Z6KQfo/dLC2uVBSYMcNj0Ziy33f4MBnIxB5sbQPaxnN7MipPSXjtIc/s/++qWGhpts3iUfpq6Iy7h7IgmkHjInYtegIdn9RC72fGT6jbsIXNaSkzukhwek1VXI/nR466lh1NEqLk2Dyk+2uKCCVkBohAbaWZtIjMZHcZma4UEXrj9PLMfnz+Do8NpPTxte810D+8W5oF3rJkcZr37GL19fXsyvLOO4/pT87VNV+JVSKazRjB5UbSK9ccIloup/aziC/Kg6M6DiLHEcA6Y3jnoJgmSEMuM6Zk6OM/7pemmg0xZEa213OdyfAIMeSrSrWZjlEahMe+uWadVcrU38ayYJg3trqDwz6Qpl7pPh8Zfz+qFcAAGtbSb3d1pvrf147EAAbiQPMbQAAnV1uyuwx1OE7Al1wqfIDuovTImnMyspCVlaWUthdw/8mfIwm+MUlIfXSs3Cgcxnylx1F3IhEJJ9xemtI164+hNI31sPor8dl/x6B8KQAoK7r9X4MzAE+6NPfhOibLkG13YGWrdvh3ycDAdkDul75FJC7oghbXtgHna8RSRcugDEsAoBWyE3Dz4//zWaiGn5yxEwRS6Rs48lB4+5g55pmFDzzBXQGPS56biiSBp6sn/+pEDgmGwBgPXjo+xc8Rexb24BVD2+Hj8EHiedeDf+E1NO6fQ0aTgVa8tHPCFuoDnqTDis2Mdof1MMCABiTQCXGbQcvVMbeYkuBvnRxm1aEAgD+8ufFypy6jniisQGtBlFZHGpLwN/LJsPh4t/v8iViefe/gh+3yZewXnukoQW56RYcBlBV5oPNR3tiZhZ18C12oS3STGyi/FYJGzonB4lS5dUjrFN9RuoxVOyuxuc3HYbbDURffhmyxtYAELqgYDdd6OFT5Q9JhYMf/HcLRBs/IpZu9fIKFiHzavdfLB6vmqNCIjy8Fb7xOtQC6OF3HHfO+BAHrNznsEDRDW9R6YvPictRxukpQgd0uvnK/KdsEhpyyrDrz1vhdgOjHp6MP16wH4BQLatUqox3cySL1e1xv9vbhI6KDG1FSJRFKbKWncHzK20OlWXCqSJqaCXtsHm3eBv+KocgdjpdnqJq2aZ/Pum8QlVKvNNfuBCdg7EtdyRpAHugzDurSI8U1nBsHi30W4CqiFenqnH19ibJLbhwIKkGXx3VNV7kBJCyOVoVfcL/ASAii+d/8QgpDlbVSRXOwDhVC8Hn5JqsmcUYzd1nL1PGzxwUFVJqMrXnaoSbRN3yVhmfZ39V42xv0/fXc6kms7Xy+iYnSDG6cVGMRxX14HsS6il8N1SlvJp+5gFYW5y44FuP6NShWeoavhVHd8qXwjc6ooslfxhqjzTgy9vWw9npROTFF5x2+uOHoOWAfDCjk/26WPKHoelYDfbc9zlcdieG3jMBiZN6dL2SBg0/MTRL/WfEWZdthinQFzG+/Msf78k0fKmM+mvrQQardJ54y8FQzk29JgcAcP+nlyhzAZnM1GsuCkV73nEAB+Hr40Swrw1xftznlBtzUZ3filffSIN5UD/oDAbkxotl5Ha7cWb+Inz9ajV0OuDPNzQhfcAnONIu2nFLeRvemSHlRQcfppa3dygtnzqbFKBKjxSrsbWkEctuWg97mx1pN07GkEvDABzHf9ew21GKqiNRq1Os2PtutqDiqyNI/8NwzL1VMkmtThaU8lrnAJDhV4GS4x0YcuQ4hk8NgcHogw2ejjRutxthezdg/8dbAR3gmjQGy2ujMT1S9MN1ZTZcmC1Bvc1FvI4vVU9Sxk95snevHSslo2sLW7H99s/gautE2KWzETEtFc12YEkla+UcOsiyB736SoBxTqxkIr79eTl2AGhoCYCtMRhhsWKNHtzOPwyDR4nmel8FSyh3VtNSnzBFjn9raaoyV9emKn/g0XQHFzI62ZROq9yQLrkMtkqVDttES90dKOOAMGZkqrNXOzvl89GhKujVR6Xz31QgWcaTe7LJ8rEmdiQqypPg/4TBzN48bJftu+20N61OBtmrO0RoUdVOS12djRkwXzpLxdhVRcbcfGZGeDoXTQjjMT22gzr1gH3yB98+it7BfwdT037Opuvl+Fzcp1EVPLd5ylKrm5rflc6Aeq1Djn9VI7Ohd3ye5el8dB9OB7SP+u8ULfWdqH9pMRr8/WBMSUBrrAnOtg60l9Rhd3UTfHyAy+5NRvqAwBPWU6rv+/xwJy/n4a9gt1jhG2pG27Eq7HxUFBh19XTLdf39kHzJ/yu05ZKd+fzArFZLnQPP3VIEc5AePbLM6AypR0eLHQ2FTWipaIPOR4eBN41GeOaJiXLec9Kfwtvw/p05cLW0wScoAJ3F5dj/hHyAdSp5R5spDSHTzviuTWjQ8JNA+6j/ThHbMxAhc6bCdrQAjuo6NOa3Quejg29EEM68MBhzrghFcJ/Yk9arPurhUUcOOem374K9VSRzdosVNd98e5CywZZ40ke9Jb8OOr0P4ib3BtD+reupkdLLhAtvjcPh7a2oLLTB0lAKnU6HgBgzMmano9+FveGTlnLSeqW5YrFOPOeHa9jbm8WFcrW0oW3zbrR9yzKmXp3aR13Dzw7to/4z4uvyTOjNJsSo0ue99Zm9aeMAkDiczYnD/eRjNjdqjzL3n8KJAICrZ7IKoZrS8c1w4khoE/4GoN1pQFV7EBbEbFB+X9XSDwgOQNjksYBk1+OOqSuU38s6w1EEoM5CK73S6ikZu7EAOoMPEv4wCk6wMfOOj8iR37XgQwDUps9aNwRH2kgheNPGTaoyAA63Hl5d4/TQA2hudOCFgjqMPC8OF2SXINwgn011R5pPqhlwfr5QPp4P37AcMz3K2ufz+EEdHiNud08zCylZnEJl7N9qhc7gA+d5ZyPHRrXPzPD9yni9r6Tsh3iKQz22ZsQJx5LTIgHIZjvd7qGhRQDkHi3KlcDbG+0SYCtqrQNQBV+DE0ajAxZPc+fEgap66Ac8RdJUEubwNNJDW4ol4O2s5D5bHaRSho4SyirvEiZBqett2rZIvMRP5QjZ4ulpmMLlj7E6EJoew0CstwG5K4z0w/H1lL864uT+birjXHSQqmaSUyiMPoHsbJQ8QAKtX5dTB67OgZgYKrRJbSDzXdT34cMieSacXzMW9OyAacr4kmFShmBFLZ/XGX1paHT2kU/iziqV9l3H83d5iqPpWvjpVDczz1sj9NmXmbzS8Vm8Z1EeAcMxC2moATOOwt7WibwTS+3/aGgf9d84LAcqcOjJr/FEYBVueTga5oDuxcbr95YjdHI2jDGhp+cAvwMHtlmhN+hw5nWpP+l+ACB/RwMSZ2TCHNe9NmJdwdXRiaL/fgYAaDtU1sXSGjT8OHT7o+52u3+V6f4aBO0VTWivaEIlgOvvi4K5e2XEMX7RpSix/PTa8jEzgvGPMyb+5PsBgDuWjcba2oyuF+wm3A4nGlYd6HpBDRq6gW5/1DMzM/HQQw/hkksu6Xrh3zlGRhfDGOiLQYF08UP1QisMCKMLt7eBul1x4YH8DtbRDjQyrduL/+ZTnz0kqgzoA1y7i/TE7HDRbLe5RccOAAijW/nPQ1R63JG1CgDQ4eLjER5C1vjQIVF1hPZhy7Bpl7C58MoGieyrSwuc1YuV6nLr5FxSg6lpHhLCa7K8QWqXXxnJdnB7PCUBdrdwm8G+qsp2nop/asqloZza/fVb5VqkXPKNMne4hZTQI6lLAQAWF6mM+4+xCuW4vh4qo533QU0fHayLAwD0jWB6+7LSgcrY7dbBx2zGxNW3K3OFxbKttgZg0nC5Pmr6JmmgBQCQU8nGyPiMtIJjrJx/WE+69wOjmMa/4xOhGNpSVdpwlSbdOEiokJAV/Evvo6IB9UeF4qhLVf2eQXrG5XdyanvcaO6/sEgC0tYmnlOHquJjXC/Rbzc5SFV4lSznJzPHQHleAbxQIM9pZT4ppcvGsG3jo5lLAQBLoxnzmRrGZ+8/xRMBsEwDAFwQw4D9a6WSW2HU85rtbCd9FBwh74HVTHWNV9sOAMEj5Jy8fRAA4KXdfDcTYuVejYwuUuZKrOEnNHHvLrq9pfz8fFx++eXIysrCRx991OXyO3bsQPPPUTtbgwYNGn6H0LndbnfXi303CgsL8dhjj+Htt9+Gw+FA//79sXDhwu+skR4TE4P6+no4HI5v/f23iObmZoSEhOCcb66Gb4DxBKs0w0+CROo63R1OWsg7a8UqnpnAYM7rW6VA0L3j2SS5UtWO7s21YhlcNYnB0a/Kmd14R0/JHl1cxay5jCBamJ8cEwszLapemfNTFSiK8hMLb1c1g0mWUlrFN4yXAkfr65id2S+EwbDqjpO568IWKk/qWsQyfHXQW8rcvN1/AMCuTQCws5468KxQ2f4ZIdQ8h/rQgrr9iOTrnZvMjjVLSxgs89a8HmhmRufW1p7KuNkh+uUVOVwnOJLeyxlJYsmfFZqjzC1YPU8Zm0LFQr28DxvCHFF1wAozyrGqGzMfbRRLt24fJZiO2JO9tLhYizJuWk9Pwpok1qavhda3H5Mb0ZIuv5vjGby02/nseeusq43IgBL+I3lWEQAgf1OqMqeqowXz+XJPwkxULh2t5rmEeLoYNe1m0NCQJQH/6akMwh9u4nWqbZPgvTfwDQD5LbTay9bIM3HdpQz8v3z05Abquxv47Kp17gWFcv28eQMAcH1PvkfVdnnOB5mLlLlbtrN8+KPDJHt1vypb+eOj2crYXSjP9pBx1Mk3201wtHVg1cyX0NTUhODg7sV2um2pp6Wl4dVXX8W+ffswbtw47N+/H+eddx6GDh2KZcuW4fDhw8jLy0Nubi4eeOAB1NbWwmDQ4rMaNGjQ8FOg219Xm82GW2+9Fa+99prS+s3tdmPPnj0499xzv3Wds846q7u71aBBgwYN34Juf9Qff/xxvPzyywCAiIgIpKenw8/PDy6XC21tbdi7lwGPHj16YM6cOVi4cGF3d/s/iZ1HU+Hj74eCOFIN16eLm1fbQU34hmN0+4eni5vZ7GSwaUSWFAtSF0fKa6Nb6y3QFGmgC2k0kD45Zos76di86dcA4CgTF/FoDfcZ15P65CMbRD/94qUvKnP3+zKouLeZrqcXIwOPK+NaP3Ev1UXA5iSQVmmKkv2ubSVldG//rwCcWCSrsolu6oNpQkV92Ux65MvXWVCsZYS4+nVxvM5nJpKqCTcIBfHYMaaMT4zLU8Zf54luemQmde47d7Bo1LpV0ji8+QrWlQmOJq0xNE54ibe+YEB62hTWiy+3hsrxtTMo6W2M3WjjvTUeIz1jTZF72rSOlEt7nCp46WEVQvLIsNYPUrWG6xBH3d9EHbhDFSjtPUAyf/P2kuZqS+MzV7gmFQAQP47B0YCxDL4f2SO/l+u5T984UmK1xfIemFQEcEaU8EOf7GGQP/gg0/ynXCkB+ZwGBo/VQc/2WDm+twqZyOZr4DGPCxLaY3k+i8HZ2hj0HJ8lNNrxJlI66xpJjVa2yTP3xmGW9QjvyUDupmahHA83kjI6pzcpv71RIoJotvM+HjmUBFf7L9TO7tvw4YcfQqfT4YEHHsADDzwAvf7ElO7Vq1fjjjvuwP79+1FTU4OpU6f+rhtlaNCgQcNPiW5z6sXFxTCZTHjwwQdP+qADwOTJk7F7924sXLgQbW1tuOCCC7B79+5v2ZIGDRo0aOguuq1+SUhIgMViQWtra5dJSE8++STuu+8+jB49Gps2berObv+n4FW/TPz8ehgCTBio0qQnGEW3urKWadGBBrqwXlVE7wCmj/9ro1Q3PHMIKYujTXTRewULVZLfQk1zs420wIR4oW+GBZBKWNvE1m2TQw+fdA7vV9OdTTaLu+lwqTrTu+gi+3vSuqOMpH8aVVpks4+c3zu7SL/MHUSars1Tla+3mYqc7RbRCu/em67M3TmZCodnV84CABgTqUgZFM+szR5muSbv7uY+M9NJGySZLQCAeD8L99mQqoxTAuScMwOo4nlh2SxlfOUsqd74+kZqkoMSeP6txeK2mxJ4fNPTSP8s25cNAPArJBVw1rnbAADbaqnNb2zjdfT7XJQY9eP4vMDKe+L2FSpG56Dt1r8vVSOHdnn016rXNkplb9V6pN4+nVzAJ52Uku8OobICJlFSU1MZqoyj1wsRUD2O1N/YLFJaQQahHGpV1F/ucqEv2vpR5RMQTGrCli/XMXsk6by9xczruKSfaM7fX0nFy1mTqThatk1Oyi+GNJC3ZwEAhHjKcrhVipjaVlJ2V/US+uf9F1h6oCGb9E5qT3lPi/JJialx8UhZf8WbpAazLzyEzrZOLJn89q9D/TJu3DjYbDYsXbq0y2XvuOMOmEwm7Nu3r8tlNWjQoEHDqaPblvrmzZsxbtw4hIaG4r333sP06dO/c1mHw4Hg4GC4XC7YbKcvMPBrh9dS7/n2vdCb/fDgAFqY3zRK4G9MCC2PJ5dSNWToKdbe1FTqWmeHSXGvr1VBwb7+tDqfeeM8AIA1gUGzkUO5/vaCVAAnWiMXZLFg2NoKsZaMqgDTi31YU/rrVskYrVF1n1EXVaqyyfx1seuUuRVNzK5cXSEBxrGx9BTOVTWZ/leFVBlTN+/t6yn6tKeJQbs+gfRe9jdJ4Ox4HQNcl/TiNt/6ciKAE+vO357BhsHbW6QQ07oSBqmtqobPl2WLhXWslRZYURPLJbyZ9SYAYKeNVrW3YBgAHLVK4CzWyMS7d4+x9npnuQRIr560Xpl7fYNY/f2yKP4+VMQAoU+9eEduA19hd7Aq/8NT8zvgGL0o1S2D0yzrBRTzObAmcluBJTIfWMbnoGIil9W3yzjlS1rVJddz2fMycgAAH6xnlyB3CI9v3mDJGC5sp0e5v06yfBODWKDO2wEKAHSeGvH+vgzuOlVdvSxtcs/aWxiIDAjht8ZbDz7Qn8ecoNrXhbFi1b9fOVyZq2zlRWtqlnsaEUqPxazqSjYmSp7pdzePVubGDabm3puPUX6Ez1F4zwY4rR3Yd/4zvw5LfcyYMViwYAEsFgtmzpyJiy66CDt27PjWZZ955hnYbDbExHy7a6JBgwYNGrqH05IF9K9//Qv19fX49NNP8dFHH+Gjjz5CXFwcxowZg5SUFLhcLmzbtg1bt26FTqf7XmtegwYNGjT8eHSbflHjrbfewqOPPorjx0kleIOn3t0kJiZi+/btiIs7WSv9W4WXfunxl8eh9/PD05e8qfzm4ymW/WT+DGVO7U56tcrTo1gm4J8HRev8zOAPlbk7Fl+tjM+dLW7t8g8YjBk2h0WNLJ3iol4ayyJc9y1nQbbpE4WK+eJQljI3J4txkGU5UnArowcDvuMjec+9uuH38lhU6brMjcrY28LvSDupBPWyPtvE/bQNYnq5s1ECiOeMYvGlT3ZTy9w7XeiZ21JYsOvzxmxl/OVhoYzc7bRjDCF0wWPDhRapOEh98UtzXlHGjxRIwlyAytWO9qMLvv6wUEoZaQykxvozUDraQ6/l2bj9jzfQxTcny7JZ0aSUKq0SQCwpYBA86AiPvy3JQ0XUqAKZqioCvp7L51dPGq4tns9Wpyc+GVzMT0DNdG4geIcE19tUlIxLJSpPyZL7X3iEhdECEnnOfaMk0L1vHfX86uPzBmjtgdymXy+5D22VDE4amkntDRsvweXS1lBlrlcIA7XBnuDrsRZes7ztpMQC+8qzZ6nj9t1OXj+T55nw0fGYQgP4HDZvlO3ecsVSZW5bM1sQ5jcL/TckopS/16Qq48pC+T2jD4P41ucS4bDbsG3FA6eFfjmt+fpXXnklrrzySmzduhXffPMNdu/ejcLCQnR2diI6OhpnnHEG/vSnPyEyMrLrjWnQoEGDhlPGT1KEZdSoURg1alTXC2rQoEGDhtOKU6Zf1qxZg4kTJ8LnFBoP/97hpV8uXn05jIFGrDvAtOPR/cQtvz1+pTJ34fKblHGPfqJqifanq5/kLy5ksZWqAUsndeihRnFBo010hYcGFSrjh1ecL/+ftUSZK7OzdEG+VVzMCyIY8H6xguntqQFSvfGghRRaSiBVJTPDhKrxtrMDgMWV1LmXrEiV3/tQwTAqk/RNUbMcS/VhutCmFDmXO/qtUua8ndkBYE2NXNM7U9i5/b5cli64p7eUGVCXSFCXWdhYL6qXOH+qU2JMHHsrS14XvVaZq3LSTd7aKoqht7ZT9XDrGFJBr70u9JrpDJZbaMzl/ZsyTq7Z1vezlbnAaUJftHVQu+73UagytgcIbdA8XkVTNamWrfS0XlNVDog8wHO2RsrvdlVvcWMLPweesvjwbVUpXlSitbYecv8i4nidXF/wnBoHye8BEdSEqxVFPg2iylHdBkT3r5F1N1NMMXUOKbevCyWforOBzztU6p9hGfKc79pDFZMabh9PM/MOfr/80nj8HTa5fm7VNQvYzWP2myJUT6yqJeXBnFRlHHpEtjv+Gr47K/L6KWNfXzlZRy6fHZfJDZfNhqL77/9l1C9TpkxBdHQ0rrzySnz00Udoa/u2lrsaNGjQoOGXwClb6tnZ2di/XzIZdTodjEYjJk+ejNmzZ2P27NmIjT25A/3vHV5LPWvJn6E3m3BBKrMnvQ2jVzeoGu3qacF6Az/e/wNAu0usiVijRZn7105a0lcMkgBobSdNsPUq/fUz2RJgvWUX60CPTc1XxntrJEPPUkNLODmZwajyWtFnT+/NzNO1xayd/uTATwEAvjpqktVFl25afzkA4MwBbO02JpiZhk8ePhMA8GA/6vkr7LLPL6pp9bR20hOYHi8BtPU1PM/z4nmd1zeIJb+vkkG9c3sx+PvhN2JhDxjJ65DfQKvT2i7XXN14ufY9BuAuvUU8gb5+zBdQB2q9ddL7BTK4/OI3U5WxnyfYGT5JVXd+p3gVqgZL0PenprqjQCw6o+XbA6XRORLUtYWTZTU20Szu8Mybqxn8dRpVmu9eYkn71fMT4fUOAKBhjOzM3cFAZux6jqtHi7mb3ofnXLSH2Z/+1bKt0Gk853nJ0sUot50e1ZJ91POnJYolX1rHHIHwYFWRsDy5Z2OHM1t341E+m/CcirpxdGQ6+wbYPVnS3kbgAK1vABg9T3If1n/AwH6nqrujI0DO2dDGdfqMYT6Gt6NSg43Wf1VTMJxWG/Iuf/KXsdRzcnJQVFSE559/HhMnToTT6cQXX3yB66+/HomJiRg5ciSefPJJHDlypOuNadCgQYOG04ofRYwnJyfjpptuwurVq1FTU4O3334b5557LsxmM3bs2IH7778fWVlZ6N27N+666y5s2rQJp1E5qUGDBg0avgOnVafe2dmJNWvW4NNPP8Xnn3+Oykpxq3Q6HSIjI3H22Wdj9uzZmDZtGvz8/LrY2m8HXvol6ZlH4OPvB72q4fPknpK+PySoSJlTa5m9OvYttWx+G+UvcYxegTXK3NI8lgy4KEN05lvquE7pZtY4TxsnRZ3UOuoSle731lRJn3+9gkWRDm1mIa2oQRLAsy1jMMs1gzWln+r3MQDg+q2XK3PPj3xfGd+8VTTxs/uyINlnG+liuzyp5GP6MHh6YZSkb3eqyhE8m0/6wuEU+0RdU+6m9DXKeI2F9JYXxywMxFqs4g6/OPAdZa7KyRZ9n9dnn7R+lInB67wWack2Mpyu9otbJypjQ6BQak4LKSOYSIVkejT/FR+nKnOuyRYAQOe+UGUuuPDkQGavx+gVt4+hJtx8XO5JS6aKRorh9TO0e8oEVJLuswfy98aeMm7rSf4nejN/7wiRi63qT44Oxtvh9JftOwMYdTSXqhpbD5fza21WBfm3ynj6tWw6/uky5lt4G1sXV/GcgrdzfcsAOdZLhjMH40gz36eD20VTHpjBwH5rG9d3Nsj9mTSUeSH+Kjr0i92echd+vHe3DWPw3os38qn+a2kh1ZIcLVRPk4p+cbl1cFo7cPDCp38dZQLUMBqNOPPMM/HSSy+hvLwc27Ztwz333IM+ffqgtrYWr7/+Os455xxERkbinHPOwaJFi9DY2Nj1hjVo0KBBww/CT6pLHD58OB5//HEcOnQIx44dw9NPP43Ro0fDZrNh2bJlmD9/Pl544YWf8hA0aNCg4XeFn60DdM+ePXHHHXfgjjvuQF1dHZYvX47ly5fDbDZ3vfJvBFeN2Qi/QF8UtjOjdm2hROYTVdUDLwilxvWKXdKRXp227KVf1JURO1UtuQaYpaqfPopu75IsunT9Q4UWO9pM+kGNra2iIBkQQiVHaf9QZRxsEiXO9OvXKXNvfcP2Xk8FiHrlumyWBlhcwzrmdwwR/fabhZwbM4IUgreMwT3xXypzuZ1C9Rxqp3rCV6+qYx0sVMOtcdSG72wn/bS9XJQq8zK2qvbDZ69niKhaLl/zR2Xu1lF0q319ZF87Klkl8sxkHvO5MUJ5/ev4RGXOP0xVB7zVQ7s4yA9NUlXvW39cngNXH56Tn6eioKuPSjZcwGNOWC/LVl3GFn9R/96ijF3+ch0r50cpc4HMXod/nTxT1cP47OjINMDPw6iZ9vAzYTmblFPoZ1JZsm4Q13EG8PjNxbKeqYDP6ZRrtinj6cFCv127/Bquf6a8B0sOsgSEK4rbrNogpSV0GdTmWwbyoOOT5KA/2KCqDBlE+mjgCKHHChvJE7lqSL/4eCpb1rRT+XVjEmm8LxzZAADfMtJoS2KphDkrQRRdcUHUvlvqqUIL9JSZ8PY8AICv9/eDq52VNLuLn+2jrkZkZCTmzZuHefPm/RK716BBg4bfLH6Rj7oan332GXbu3Im//vWvv/Sh/OTw1bngq3MizZ9/pf3TxcqYEcyg4T4bg5peC91WRQvt0oFi7aiDd7GxFmW82iKW26ww6rCPxjKo2WwXy2ROTI4y9/RBBh2TEySYk9NCq3RBOgNX/Uxiwf9xDwOhd878TBm7PHXatzex0NG4MOrQn8mRzk0L+nOb3m5IAFDQLpblq3XjlLnd9XJN5ibwnPwNtNC8tdv/Xn4mj0PV0sdaI1ZlVVqoMhfsS0t6QqgErB39yEiqNfEzYyRwttHOc/ryLWaP5p0rQd25ybyPua0M0NXYxFprs9MqXreNBdMCSmS/kWfSO/Japb6qjEubShMdflD02R0htCp9BvOY4dFA9FhKq7Y1mVZpYIFYk+ZyWontsfzdGi0WtlNlRMYt4u/FF8j1H9KT3ZR2H6N232vpN4+njtwb+AeA67ZfIXMdvE/B/nJP2g/yRKMHUxBgS5JPlq2UQezAOHoP3uzbqJ7UnjftpKcydYTkVvyjfLIyZ6rjPbelyjlVfEAv74asPyjjXplyf+qtbBBetY/v1jJPMb7KUnoCaWns4HWgUDzNolBek4DjvnB2qG5yN/GL5/ofPnwYjz322C99GBo0aNDwm8Av/lHXoEGDBg2nD784/fJ7wmv7RsPH7Af/AOZyX9vn5AbcI/xZfOuRgctkwG5wsHoKZfUOoFt3vInB1xR/cT1v+uZKZW7CIAb1wo0SeHtk09nK3HuTX1TGtZ5CVf0jGVVrcDHYs2CnbNfeSirh08psZfzHpA0ASDkAQKqRZQbuypbiZW+VMFB6Y9o6ZfxhPYNkXniDw8etDO5eGs+A8j3HpAVguB/d2oPHGVS9aZwEPT8q4bZjAqjTf/qQ0E839eVx5JlULccM4uLrfUgfNGcyALc3X+ghRxrtpOMrSdU4BsjxhwXx+Ez1XLa1n9BP9i2sMd/jPamt3t6DrrxaR378DuFFEhbTdbdkMiBuCxNaI2Ybz9PYwuN3BMlzVJdNzbSxmQH58CNC21QNV/3eSJpsYA8JuB//gGn4Puncft8rherYvJsF7DY+yXuesEDOr0RVZqCiNhQAENDPoswNjuJzuLFM8iXunUS6b1U9A8W7CoQyvHzwBmXu5Wbq3P97VFoETk4/psxtDyDN2F4l169xAK+pbzhpOoeHXpmVRB374uZhyrj2oFA986evU+asLr4njcvk/kaew3e344x2ONo6gGdxWvCTfNRvueUWDBo0CNnZ2cjKyoLBoP3t0KBBg4afAz/J1/aFF15QOh75+voiMzNT+ch7/x8UFNTFVjRo0KBBw6niJ/moHzlyBHv37sXevXuRk5ODvXv3Yt8+US3odDrodDqkpqZi0KBBqK+v72Jrvx2MTC+EMcCITTuYsn4gUSiC57+aqcxdOYUd5WcEiZqiyE56pcIeCgAIM1C//GBPuqM2t6eiYG9Wx5sSRnfxgU1CVTw1nu3wrlzyJ2Uc3l/UObf2XK3MvVFGpUefWHEd1dp59fju7ecBAO4bSp35G5VUspwTLZruxnVUhxTGU6FwTYpQUoUdnPPWPl/8FlULw+eTpuofLlRAuZWqCN9AUgUvH5KSB8OSSpS5IFXly8O54tZ/dhddaccrVNfEGaU6YqAft2mv477ssUI7HN5B1YSzF2m21HALAKDzZVYf7BhHqiIgTKiO5IVsZ1d4ldACKrEUggtVFTw3CC3iVpVxNKmqMAaWy7zTrGqBpyoT4EiV+eAirp98L2mJnauF1lCV4ofjrywHcWCnh17qwXs/biQrd3Z6Kh7GqqogjlFVLPxon1Bh/vmkJ0LGiNIlM5zX4atNFML3GihUzAYLyyGEGUlp6RpkW//dO16Zu3ogcxNe3yLP4erj5DOdEbymvsFyz9KiecyW9hMrKgLApB48z7EjjirjG7aLImxtjaoypAp9LpFlvdcGAPaVRMJltX3r8j8GP8lHPSMjAxkZGbj4YpZ2LS8vVz7w3v9//LHUCNGpC3Zo0KBBg4YfjdNa0OtU0dzcrHzgb7nlll/qMH5y/P/OR8n+tHbGBspf7mu/YeNo/yhVMM0oVtS5KdRnv75HAj8XZ+9U5pYeZ0Gv83rmAABCDNQnf1RCa+fKVNG5qzv/mFWFuAs7oj3r8zj0Kn1xWWe4Zx1arYPMRcp4paU/ACBXlbEabOT2+weL1rfCFqrMrVlLyykmWzwBdQPumz3FuTY00UKLMlKf/PZe6ax07VAGyHKaGAC7M0Hqnb9Vx6DZnnoGUsfFSB317XXUWQ8Io6ezo1bmp8TRKvPuEwDiPHkCFSp9cg+VPrnokNRxV9fZjttKC7mun9hXEUd4T5pSxZqL2U6PrDOMVq3xS95/L+zTWBjNr0rWa+5Nj8JcparLHy2BUks6rUZ1lyS7J+bqUunUjSznDluURwc/mIHMvLx41cKysWuH0fOMMTDT8q1SCZqW5nCdSRPEM40zcUeLD6vOyU+s6sCPGRBuv8CijH08nY2aCkOVOXcILfE/DBKr/b1PJypzwUMZxPc3yrI2O0/aW6MdANxGTxG0WD571hZml7rbPN2cOmmoqloJ4N4pywEA75ax6XhJRQRc7TaUXvvwr6/x9KkiODgY48ePx/jx47teWIMGDRo0dIlu69S/+uorPPfccygqKjoNh/PrQEtLCxYuXIj+/fsjMDAQISEhGDZsGJ555hl0dnZ2vQENGjRo+IXQbfrl8ssvx3vvvYdPP/0Us2fPPl3H9YuhuLgYEydOVP5Imc1mOJ1OdHQIfTBo0CCsXr0aYWFh37OVE+GlX3rf+Tj0Jj9kzmTK/IhQiUK9uGqKMnfG6AMnbUMd1KtoDwUAjAtjUCvKQC3ywgNnAQCuy2RBLW/qPQB8WSABsGGJDBrOimB6u7dQWHGHKjjbQRfeW37gnWoGT/eWU19t9pc/fFMSeXwXhbG+da1T9Osf1jEoWafStB/aJcHG+dNYSOnto+KuPjuIzbK/aWKafZiHKvqoMFuZGxhN+mRnuVAxYYGklGJVOvWMIKFK6lQtAI818ZpV1Mv5X5K5m8e0i0WjJmVJHsDagwyCG2rpwkfvEiqi5XJVk+Ytoco4cZXQDRXjeZ1jn5PiXIZ0Bl/dRm7TGcoAHvfJc2oYITp7Qzs5FXsA7TiHv1AElgx+AtRNpu0hsl5IrqrF3Wg+h/6B8k60t5J+MJSxjEDv0fJsH95PSsutahI9aYgE76eGMej4wG75hkxK5zuyZjOpxaB0KfiVHGJR5oyqwm4VrUJdRPqTsjpYTHonPkbW8xbeAoAt9ewV4KcXSizWj/fp88N8zlyd8m4EHeF9sI/iNc+KlQBvq6ocxJxYUqc1Hk5rcS4ppYXZn8Pa4sQ1g3f/Ouqpe/uVDh8+vIslgRtuuAFz5szBnj17urvbnwQOhwNnn302ioqKEBcXh2+++QZtbW2wWq14//33ERQUhL179+Lyyy/vemMaNGjQ8Aug2x/1yspKGAyGExpOf/nll8jLyztp2QkTJuCzzz7DBx980N3d/iR48803ceCA/AX/+OOPMWWKWM8+Pj646KKL8NJLLwEAvvjiC6xevfo7t6NBgwYNvxS6HShtaWk5yV2YN28eamtr4XA4TpifOHEiAGDTppNT438NePPNNwEAkyZNwqhRo076/eKLL8b999+PwsJCvPXWW5g8efJJy3wfek0tgG+AETEq1y7NJLrc6AyKkc8Oz1HGR22ejvIqWUJNhyRu/X3bdG7cRgWDKVoohv5+VCV8Wp6tjGf3PAgAqFfVE/dq3wFgSbHUh/bzpWrgskSm5B+0iWokycwa8DlHmAruypb5S8OoD75g67XKeGpPqSO+qYhub49onn9QL1m/xUlX3uzRh9/wBVVC44ay9MGmStFMJ4RQNXGgjprwyGBRK7SrVA3H6kivNHcKhaDW21s+p9tu8njTb7XyuZgxkC78jldFcz3ocrbgO1TZUxnXDvLYT4dClblAPgZw7REqwncYKS2fIeL2O3x5b92+tMM8xTBhLKGaylXNiob1A4R+6fUsheYlV/GYvIxe0hrSF9ZIVbu7S0QV0pZGKsFt4TPjVSf5HVNVdkzmO98jULTeh3yoQgpPtijjtTlCA26PIj1jMskzV9oWyrlk0hvnpQqVcURVAfPiaD6bm1tEH76qnM/j9EzSO1srUwEArQ4e8xmRVDR5K3PuzOGzOWkYczxyG+WaXjx6lzKn7msQrhfax+bmc3akjc9RXadUdxyRxMqWC9+/GE6bDQCpve6g25Z6dHQ0WlpaTphzOp3f2mg6OjoaBoMBxcXFJ/32S8NqtWLzZikFO2PGjG9dRqfT4cwzpbTrypUrf7Zj06BBg4Yfim5b6qmpqUpiUXZ2NhwOh9J3tL6+HhER1HjqdDoEBgairq7uuzb3i+HIkSNwucQazsrK+s7lvL9VVVWhoaEB4eHh37ns/0f/4HKYAn1P0GffvUOyL32PM+h1MJH66aUlEiQy6GmpX5Yslsm2Bgbl7pjFjNJYgwUAsLqZtbVDTAxwfbRJ4h/njKHOOcZAC3dinFBnH6+iVbrGzH1Fm078Iw4APSYUKeNjlaJPv2TXAmXOXcXzW1khmvkzxjE422Kn5TTT40ksKWVHmdZ2saR79StT5uo6WNO6T7hYqBuPMpPvk4n/VsYv10pnprVfsKCXcaBFGRfXyn08owdpw/ALqfPfniMWblAuLbDDSxnAg8cpyP2KlnBnEq1Wc5G8aoHlNHZUTgGaL5drHVRM76ixn3hkPnZVILONz4HL4CnF0cBr54qljj/U01ipeg6PKfEbizIuPisUAGC5li6DOg/Q9rVYpa2D+ez4GFRB12IJKrtU5xmXRK/h8yPyrqT1qVTmKi0MBBs9hbKc+zn3xGVvAQC+aGTegrpBepa/eJ/vHmOgcfsOWuWjRshJD4+h4aj2vsbHS0brV+V8nuvrWLIkNlreg+QM5hicG0EL+qbdUsxuQyCvs7qGfHi0HGvTYdV3QXVNXR6de1CqSvB/mtFtS/3888+H2+3Gk08+CQBYsmQJnE5x5/bu3XvCsu3t7bBYLL/KAl8VFVRKJCQkfOdy6t/U66jR0dGB5ubmE/7ToEGDhp8D3f6oX3nllYiKisKHH36IHj16YN68eQpN4f3Qe/Hpp58CAOLj479tU78o1BTS9/VNVf/2/2knL5544gmEhIQo/yUlJX3rcho0aNBwutFtkzk0NBRLly7F3LlzFW332LFj8eKLLyIjIwNnnXUWbrjhBtTU1OCuu+6CTqfD6NGjv3+j/+O49957cfvttyv/bm5uRlJSEnRwwwfuE9qsXTfQoyVX1UtvcpCqcH8sWvGKkQxmtSSKu+0Mo9sboErzb3HJ+ptrWc87JYhBzYTeQlUUtFKH/ske0hIjM8VF/dPMr5S5f65mUPai8RIAXV9Ft75PGN3VTk8btMJKbv+duf9Sxn86dCn+P7YfZGBqh7+4sz411D87g+T8g+N4nvvLaRyEJ3v0560MWl24jU2kDR7a4MpzqVp6cykD3bEjhCKI97Moc7Ud1Kzrw2W/VhvvjUNVKKs9Ue5FxsukbGyxXNZ/pSewlkV6qD2B9FFnoNhXjgAev1+jnLPpMwYCDSo6s36WUABNWcyZaMiknZb8tVyT/Bv5vDn8Q5Wxr8cmsVSTftCpdOTmcUIR+BRQCHHWJFJ2fQeLp/rEDsagzk+mXPlfNZMAAIVHGbB+ZMpHyji/Q+idtx2UQz91XGJWU+PZlPvtzSzt0Ge83Cd7B6+9fwqNq83HPM+Ri+d854ivlXGRp0jczT3XKnNfhJJG85aTuCf/XB7zX//A8f2SJ/FayVhlLjiCmngvTRikaiSfEsLxRbFy/Z47zmdv5Jk56GztRMFpagB3WniQUaNGobCwEKtXr4bD4cCsWbNgNBrx8MMP4+6778aXX0q1PrfbDR8fH9xwww2nY7enFepSwFar9TuXU//2XeWDTSYTTCbTt/6mQYMGDT8lThu5bTabcfbZZ58w9+c//xlhYWF45plnkJeXh4SEBDz++OM/KFHp54aaEiovL8eAAQO+dbnycjYGPlUa6eOCbOjNJjgPMDB0ZL/Hgnx0uTL3n3X8K/63e94DABR0Un736pein7/rLAZHn887QxnXV4pl5V/CoF5hErv4RCeI5bAvj1IzvT+t/gMrJPC0YD4LMfUZwOzTBo8sq2cIA96BepZP8GblDUvjOnflna+M782QP/JPHKWF5xtKC9zbUUldCCm1h3gCfYMYdDMkMmi3ZUcfACc2HK4towXbK0MCrOUdocpcZxL32dwu3s9Hb01U5txjGMzyZskG7mRQspY10qDzeALHbuQf86j1tLrN6akAgOIZvPcpy3isvpFyTdviKB8MWiVSO108Ld3mUaqg3F65j9VjGJTzp8OEmqFCFQazvzccKmbRfIZIFhPNtDSPFPCZbi8VoyWkkFZv3Rh6L4chywYEM5BaZ1c1wfZck0enssTzowd5z42ejto9YvgcHS+XIPt6A73AG8bTu/rPdrH+9WYGlNuq6fHMHCqSx101fLbfKWbhtQBPYblmVWC+wMLrd3OLVJZ9JoOZy9ecww5iLxZJwL2iOlSZm92PAf9lOfJQXJ1JOW+Kkfd5v1Wo2DlJXKe8IxQuVUet7uIn71G6YMECHDlyBA6HAyUlJb/abMzMzEz4+MjlOHjw4Hcu5/0tNjb2lJQvGjRo0PBzQGs87YHZbMaYMcLdffXVV9+6jNvtxtdfCz83bdq0n+3YNGjQoOGH4hetp/5rw2uvvYYFCxZAp9Nh69atGDFixAm/L1myBBdddBEAYNWqVT84o9Rb0GvAh3dCbzahoZC0gLnSEyBT1WbqjDy56a29kW79kCzJEOx00r1Xd1LJr5YA5bSeDDat/ZSab2uquK59e5FKGh9Jffbn5VIPvayYgc7QWMoyWwpCAQCDhuYrc4e+ZgAwaKS49TXlPE8fFb1j9Jf9945iHethYdQVLzogdbbHpXP73hr0K/7NMs3Os6iJbmmRCzgrg9l/q0t4TDHBEkyrXM8cgImzGdTbtkgCxfYppFzsdlWmoKdhdFVlqDLnG0AKQJcnFICqGZUSiAQAl4foDM/lOuZCBtDcBnkO7BGkEnw2nCgJBoDmy5g7YLDJq9sap7LNVMPIffLsGFpIM/k8y33OjckBAPzjEJ/jYQmkzDbuFUrrnJHMnpwYzGfq9k+ElnBG85xG9GL2qtHTkUmdO3DFoG3KOEgvx7e+jr/nVkim6NPDVJTN0VnK+Koesv5/D/M5uLg3deTeHgHDzXx2NrT2Ucbv7BfqNzWOlE9qEJ+jCyIlkLm0nu9LmpnPaW6rUGEBBl7TdSWkiqamCGW2qoTaeZMvn/2LU+VYPynNVubOTcqBrdWBh0as+nUU9Pot4aqrrkL//v3hdrtx3nnnKfVdXC4XPvzwQ1xzzTUAJOP0VEsEaNCgQcPPgV9fFtAvCIPBgOXLl2PSpEkoKirClClTYDab4XK5YLOJVTFo0CC8++67v/CRatCgQcO3Q6NfvgUtLS34+9//jk8++QSFhYXw8fFB7969cckll+Cmm26C0WjseiMqeOmXlCceg4+fH56fvUj57bE8cS1TgukWbztMfbnOU6jLJ5zuXnqcuIO1bVQiTEukW2zyuL2flbDcQUY4Xcgdu8TdvXwiC6vlWEhLxPkL1bJhRbYyZ0ulukVfJ6oadcNe/xAqIC7pJS5mq5OUUXUHXcojDaLEseyhoufqud8o4xEe1/m1arrYPcziLi8r6q/MtR4PVcbx/aWOtTolXN2SrHWT7Ouay9gM+5+rqL03JQhvot9J9YZaKRJxUNQJFbN4zskfk57xIiCXSoe6MWznZ2iX4wrdRHrD2p/ZyU4/cZr9l7LuvM9AKXjVlqZSlDh5fqZaeSZs0bzONYNpp7k8j6lblaZuzCC9ZCvxlCGw0WH36cE2beNT5T5sKUtV5mamsYjaSg/FMCmR1N2y7aQt/jRB7mlNJ+99fzOLzHnbJqaYSIUcaxf6Rf3sjA5ikbSldaIuKW0NVeYGhJ+c2T1KtU5uOxU93r4A2UE8jtIOav9TTXxPvOhlYhNsH8j1v/E95kCMmkJhxRlhcn0WV1Dh1+7gc5gSKO/5niq+b22N/tLO7oaF//vt7H6tCAoKwsMPP4yHH374lz4UDRo0aDglaJb6zwCvpT79yz/CN8CIokZKIe/tK0qbPW2pytzZIQyQzdt+FQBgdgbLvH6602MN+TGg6m2oCwCHmsUy8WbHAcBfCucq44ovRcPrM8aizLU2U7fbI0Gslbo2Bu1amhnJjQgTa05d/Kn2OK2dvgMk6Fmj8iQaW2j2hnmCjpenMVPy3SJaNk1WOZa/9P9CmXvco28ergrkFbcwEKv36HyLqhncdTl5gC6Px9M7nTr3om3UMtvjxRMxFdELUwc6vWVqnSonLfIAvZfGDPkhuJhBMUu6ymbylslt4uvmDXQCQGCp7MC3lJZ+wzix5jqDeB5+DdQze8v5OhPoJelqaeEGplsAnNgByhv8BICgeDnB2al8tjbWMrM3wk/uU7Avt7+5iF2YZvWSoHSCyaLM7bSkKuNJEeI9/uvIBGVuYCyPZUCw5A68mTtSmfOWWB4YxSD+5pXMGYkaLlbz4AgWdluew3TsPumyfXW3ocrOUGV8rFW8RJuT9+ZwJcv4JoTLuUyPY7neED2zhJ/+RrqK3TFlhTKX184ckLNCcwAAu9p5ncaa2QHsinUSlxuXSe9mSvhhtLc6cP3gnVqgVIMGDRo0nAjto65BgwYNvyFonPrPiAhTK4x+RhwsSVXmPo0UffTO/XR7DaNJq5gOCG0xbii7sywLEHd0/sAtytyrayZxHU/Q76J8dhtKjKMWN2uubGv3RmppdYl0Mb2NfCMDKLrOimKwyJuSP3gItcBDR5MW+WqDnJOqWRMMqQzAeXW7LlUdgLb1DJr6eOJKf2lkUSXfepk8+jFrxDecw+PLiJYiZUNTeBwHPuP56UdaAACWN1kx0zGYBxjzjWy//UIGrG37SO+0ZEqA1FfVTLp4FsdGi3Kmypx/HekVb3OcsH0MVDYOZMmA5h5CbxkjGdTTd8j6gS08Tp8Ojh2xch19VJRLQCmpmoC+QmVszmfgPbkX6wgE+Uqg9fMSXlOv3h8A0ntKAHNiKJ+9KWHMA/jLBrk/Vw4n9dc/mLSJt+NPWwOpt4sHkHJ7rkhkwdf33aDMNTqE8jvQzCByyhje00viRUf+1+1nKXOzs0m1tHr4sa9reU5FFt7HiQkSQG1X8WhZGaSElhbKu/VpSbYyF+bPek/PzXwbAHD7jguVud7xqtoMofK/HY2kX3oYGXy9eLAc/zkh1NbfV3AOHG0dAFgsrTvo9kd93rx5p7S8n58fQkND0a9fP0yZMgUxMTFdr6RBgwYNGn4Quv1RX7RoEXTqiFkXcLvdyvIGgwFXXXUVnn32WQQGBnaxpgYNGjRo6Ard/qhfeeWV0Ol0WL58ORobG2E2mzFkyBClQ1B5eTl2794Nq9WK8PBwnHXWWbBYLNizZw/Kysrw2muvIS8vD6tWrYJef7Lu97eEjUd7w8ffD7owqiYaOjzurkqp8f5GVdPrRKFC1KnSGYni7hW3U3Hil0h6w+GQ6xiyk275NTdRk76xSepw2yOpuTaooisdj0gqdOfdpGw27yaVkdZP3NVduanK3D8mvKeMV1lFnZM+tkiZq26l1vq/GYsBAJf88w5lbsxFOcr4QIOn2fZaenHe4n9Vo0hp+JRRnXMUonkO9KeevyOCywZ49Ov1g1St4Zp40g2zhcqxl1F5EErGSeGEDLzM6FQZM6HHhBZx+HMuuJCUliFfrln1XFXj53YeS8TXQmW1jkhV5hovlmMyreExBVH0gZDdcp+be5GSsQ7nPv2ccn4hm0mpVExQUTme0gadSXwex2RQ372vVt7hklbSF8fzWTHy1jGiQ39u81RlTt3OzlsRcXw/qj+q7KScCo8IPROaRPrm35/Ic37p7HXK3KIN45TxB577eEU29fwWO+mdMk/D6ofTlilzr5iovvFSfnNVLeqcKhqwd1+56eqqp3VWPmd/9byH/xnxjjJ3/XK2bdwQJtRkZSvv2dEQXrNkk6ibgnx4zfuGVKNTbwdJqO6h24HSRYsWobOzExaLBQsXLkRVVRXWr1+PxYsXY/HixVi/fj2qq6vx8MMPw2KxAACWLl2KkpISLFq0CL6+vtiwYQPeeeed79+RBg0aNGjoEt3Wqb/yyiu47rrr8Pjjj+Puu+/+3mX/9re/4b777sNrr72GP/zhDwCAJ598Evfddx+mTZv2ndUR/9fh1amn3/c49H5+iBvNYFJhoVijEfEMoHnrjQPAFw0SuNlVSU21v1Es7KRgizK3p4ABQLdVHLDwRG6zoTRUGY/LFv1wegADOG8dYPEyXaXoxF0xtHqN+dSxx4/11Cbfxqw4exAtQF2ErBcQqOrGZKE1ZTSLldJZzbk7J1P3u75BvIJ8Cz2RJk8ALyHSosyV19KCdHjOWR3IRBoDqdEfyfoVZ9M78S2jJ+Pjme6Ipc5cr+qiZLSIBW6iIYqwPFpbnUGyrKWnqnMRJefo9HgaEYe5jltPq76xt8cTUPVnCS6WZWsGM6jXHqe6zg5Z3xXAwLq+hft3ejyxcX2oiR4RUqCMP/Y0AFcXtOodwKBfTpM8c4VNzKsYEc3Ca5/tzgYAXDaClnaIgZ7CKLPs945cBhUzwmqU8ZXRUui91kmr9v4vPcuqvNmhPRgovTpOOoV9qWpMHaza5x6LvAdH97Du/BljWbv8YL1YzZnhdMOq2rl/b9P0t0e+psytb6O2/+U1Etz989TPlTmjjs9Mi1Oes/JOPpvq2u1DgooAAJ9UsRh/cX04nFYbjl/xxK9Dp/7aa6/Bx8cHN954Y5fL3njjjfDx8cErr7yizHkDrTk5Od09FA0aNGj43aPbH/Xc3FyEhIT8oEBnYGAggoODcegQZVHR0dEICQlRqBkNGjRo0PDj0e1AqcvlgsViQWNjI8LCwr532cbGRjQ1NcFsNp8wb7fbfxfqF3uqDU4z0KFKUQ4+LGOzqmDWnWsvVsbGEKEwfFU1mWtqxT1raSd94G5T0Q4mccfVqdbbN5PKGDVRgnIvvjSb66SQhVt4trTyeqeC6dtTB7OQ095mccvbBpO+abbSxQwNEHe4rUOVU68qtBVolnPqP6xImfvnx2yF6PTz6LMzVM2yPbRL+zsMOk26kW71qoOZAIDwgTymuEDWgM/tL3kA3lruAHD1HIamlj0uLQKrw2jn+NWoNN9Vckx105gyb2jnOVs8HrqePyPwEO9ZQ5Dc55ZkXhO3ShfQlijbj97N69QZIus4VK9GSA8L91kvATxdE+/98DEs7LZ9h9BYMX6sd7C/lTRdRohQIUebWHgsUdV4e1yYBDhr2kkVfF1AKuK2cdIwJsmXPNPGFgbUP+kcKuura9DrSRVds0+89L9MZCvHv54pddSrHQyo9jKREnq3RkQEZ4aztMGqRmrSh4YJVXPeTNbKf62Ijasj/IWSOyOUz/PfqxnoDQqUG3jtviuUuat6sQa8b6zwYwfbqKMvbeN3r9YqN0tNMw0OJmW1s0X062fH8NltijTD1mrHozg96LalnpWVBbfbjSeeeKLLZZ988km4XC7068ebUF9fD6vViujo6O9ZU4MGDRo0/BB0+6N+zTXXwO1245lnnsG1116L4uLik5YpKSnBddddh7///e/Q6XT44x9ZtnLdunUApE65Bg0aNGjoHk5LlcaLL74YS5YsUZKKkpOTER8fD51Oh4qKCuVD73a7ccEFF+CDDz5Q1r300kvx5Zdf4oUXXvjVNqXuLrzqlzHL/gRDgAkOlS7W3yB0wDGV/jf4CN3p9uHiLrqqqDU+f4K4g58vGa3M+Y6igqGpVqQWVwylKuGtHdS+hxyS7fe/iJXoHKp2eCG+Qp/kNbPiYaQfZRkVbUL/hJqoOqhqow69vlp+T0ykW+5WFfWu3SlV8TqTqI5R6/R9q4SiCOjPc2r2VIkMW8vr0M7Dgy1aHuOYvnR7m1dT597WT+itcRlUgqhpiRUfC9XU3lN1TOAx6WvkmqnpkapZpMzil8rvQccsylz1OFJe3qz0sOOqKo5ppOE82fFo789rGrBHzlXd6rA9TqV08aiM/PaQzlRX3myxyIp6E9fxy+Gy1iyhGvomsXJl/1CmzO9tFHWTuuJhTgtVWAMCpSa5uoZ9iao2eZqnNvnKenrmewpJ/5yVKXXI4/1Is22pF5rsUCnfhw/GvqyMV7VKjflWB6mvLXVMyZ8RK890ipE12o/auK1B5iIAwLKGwcpcsh+fs/fyJMfiusyNPOZmKmn6exIF6ux83jP9ec28mvcXC9gLYEAkf2/slOs/OISKnpe3ToCr3Yaymx/89dRTX7x4MbKzs/Hkk0+iubkZxcXFJ1nswcHBuPvuu3HXXXedtK4GDRo0aDg9OK311K1WK1auXIk9e/agtlb+SkdFRWHw4MGYNm3aSQHS3wu8lnraG/fDx+x3Qh3yCzMkoLOynAGohkOqmuCxYo1Ff63qIjROLK/UdAaQKhoZWPIGInUqCyo2gFapt5b0lBgG1Qb603J47LhkzVXto6UbUM6D9jalcY+kDh67uP/2PmIB6o20EM3+tGrbD0lgSSXvBTKZqpkZU33CcQJAYa1YgE8P/liZe/J+BrMqpsi+QvfTy3GpYsctaaLvVhcZ6/M4NdtHHhRrL2YrvSjHJfQ0GhvFlA5bR7O5qRevr7FRrk9AJefa4lXeh0cy35LK3/0r+btX1uzX16LMObeFAgCsSbyObl/VPj1B9Hn9WNhtkao2eVyIBIrPimdQ8ZWPzlTGF86VQPGaqt7KXL8w6rfDjXLQkarC8v9ax6BibA+5PmcncvulNgYNvz4iVvW74yhhfqBgrjK+OWUNAKDWQat3baO8BwNVqbP/2TVRGev0cgNDQuk5qvM16trlPt3TkzkvW1uZxZvXKrE7deGx3n48Z++xvJFPz/bBPtSkP3Vcrt/VqbzmzxycooxDzOJpTYhnsbvBAUXK+LVSCdpa7QyYR/q3wd7Wia9nvPzrsdS9MJvNmDt3LubOnXs6N6tBgwYNGn4gtHrqGjRo0PAbwmmz1PPz87FkyRLs378fDQ0NsNvt37msTqfD6tWrT9eu/2cQFdoKQ4AdfgZeGy/toi4qpE9lervR424Gz2cAsONjCVb1GUL6JcyPAbajtVKb3KpqQZcawmCUNxj20mbWYFc3JzaFecTWSdymrZPUmcMsFECEiZRKXSYF2n8cJEGml/eyEFNbWSi330doG3Xw1G5noPbALqn/7RNPF9tHL/u8detFypxvP16zAb2LAABHgkgZqbevL5VrEZBhUeZqZ7GGPTz6afV1aMxVBTpDhCuqG8F751euCmh70vd921T0zbewjYZW7qCtH4OypiLhtNTlFPyHCX3ibuB99A3iNXeVyLJv6EgVGAykaibGSFD441Iqy9wqmuud/dJCsGcCn63dtSz9cGP6egBASSevQ0Zf0iLHyuRaD+hF6u7zcjY7v2eYUCB+Kp7N6eL1sTjl+I/beM9yG4QesXTwnBdPeEkZP1wkuRWtnaQj/fTcfnKQBQCwrpl05h/CNyvj5zqEKpkRRJ341XuvUsYp4fKepIeRertz+wXKOChY3gm7Ksngxn7rlfGGBqGyvijMVOa+AMd/yZISIMtqeU8ygyrR4WvH1zg9OC0f9YcffhiPPvooXC4XfghFfyqlejVo0KBBww9Htz/q7777Lh5++GEAQHx8PKZPn474+HgYDFpTJQ0aNGj4udFt9cvo0aOxbds2zJ49G0uWLIHRaOx6pd8ZvOqXHm/dB73ZD52qlH4fX3HbE6NJj6hd1JnxrJPjxdISqdzo+Jwt4ELOZTTf2wl90cdUKkSNohbZ9r7oxNtmUdVgVbv4ntZxA8dS0z0qjNH8Q61SB7vZznXSAuiufporx+fspIsaG02ljJd+errXR8rc+atuUMaXDxUd/sp/jFXm6j3p+fpi7rP36EJlfLxOFEPXZbJu/PPrpinj1N6icChvCFXmhidTdusDeQ12rKSm2shbAm8Z8NCRpCosW5kF7eeRRbeqyi0YrPRIbWlCtYSqatx3qkQO1l4eWsVH9Tp6uKC0JO7TW4McAHShnnUa+c5F7eI+a4bLszVoIK9ToYUVF72qjvveuVKZSxhfqoy96pRFlUyzvzXhG2Vc7xSlyb9LSOMF+pIemh4lz+6r+VzfoqKXdJ7H/KwMqmeOt8gz7VLlCJQ0UlFjLfUoZUK5n8l92G5vf51cH8sevhv9J/I5jjLJM68ujTApmr+b9XKf9OB96GmiOubpwukAgCERvE5rynop4+QQCwAg3KQqt6nC6BCpV59ni1XmLg3bitYWFyZklf86qjQePHgQOp0O//nPf7QPugYNGjT8wui2pR4SEgIfHx80NjZ2vfDvFF5L/Yz+d8OgNyHvKmq6zxwvOnWXKkLnr2cwbm+9BK4arLRwMiIkB2B3EbPzHhpGLe3WZgkA7qnl7+2raLm0Jcgtd5lVtbkDuc8+SWKZqANQfYNo6Xsz+NRWr+9OVp3qPVuskYMVzOSb05vW2K46Oa64ABbcyqlkgSSvpzIljRZYYasE63oH0Wr9sqCvMu6sEQt+zkh2tPnqs+HK2B4k5xx+UJmCZTqtKW+YJziQweG6Ct6nHmkSlC4sZlDPUKeiGD33z53Cbfoe5j1rT5br6xfGgLJDFRwO2ijLOpkoiSmXS3efLc/yPBoy+ZzEDZeA97goelHb6lOVcVG1eC9RYfTIQlQB9eNV8kyYTLz33kAhAPQLkefgo33MvpyUydyGtTly/dX11MN8GeQ/1ibW6LEmPnsjo4qUcaJRMjmTjMzo/E/pRAAnWvylzaHK+P7eXwA4MRBaYqX3kegvx59gsihzBarU4wMNYskPjWRwt38Ag7/bPO9Om4MGqjpjNkDP4/Kip7nmpLkPinnN1J53/0h5j3aWMzN3bHIBOls78fak934dlnqfPn1gtVrR0dHR9cIaNGjQoOEnRbc/6gsWLIDdbseHH354Oo5HgwYNGjR0A92WqFxzzTVYsWIFbr75ZiQnJ2P8+PFdr/Q7xfHLg+Dj74erJ61T5rwp2M8dZKPbW7PWKONlHi1xykAGQhPN4mKeM5w1o/+ygvptU72nzVk23e4AVcPky6aIjlydHu7rQ33zAymfAQAu/ZzdrPb4s6hRcKS42E4nbYLOLHpq8zwtxxY2s0Z6hCrVvE+oUBmrC1h729GhKi7uYRh6+dOt9RYcU7vKyyuGKmN9lOx/2bYhytyUmSxEtfUjaX9m6UNX2t5KF1vXLtuvt/GViIgjPVRYJoE1Hwt/d8bxnP2OCW/iVlEu9r4qnX2N/N6hijsFq1Ldm0bKtYz6hoHUT/eKC6/PBpHAdUKMQuV8VU4ddH0tU+7TEoWmOy9+rzL3RiE17U7PNbc2cJ/5Tt6H/iFC73w48b/K3E1HLlHGEYkW2aedjZnV+GarBMxHDiONtqWWxbdGe1iZgX6kQp5KkzIQ66x8Nl4om6iMv7aIDn5bVaoyd1UP1jv3Nq7OHE9KKtlMSqnyiNzHwTNYsOv9ymHK+Eiu0J0zh/LZyWsmfTQ1Wugnsw/v/TB/BqJvyZVeCFMTeM7qgl9La0SfflUfHvObS6bC2WEDwObt3UG3P+p//etfMXDgQGzcuBGTJk3CmDFjMGLECAQFBX3veg8++GB3d61BgwYNGv4fuv1RX7hwoZJM5Ha7sWnTJmzevLmLtbSPugYNGjT8FOi2+mXixIk/KkN07dq13dnt/xS86pfzVl0F3wAjtu2ka2mqE7c7bXKRMnekiKqR24avAgA8v3KGMpfcTyLoXj06AMQYSBVsaZaqdGvySK/MH8Cqcq/tkzrs12XTBVUrBEYFi+v6xAFW9LN38u//zN6iP441UXu+pb6HMs73aMZt1aQiolKpcLi3t6RKv1FBHXpuFVUlZ6ZLq7Ev8qhume3RMu+so2qgVwhb110TLana17x4kzI3eC41/mVtomQpquR5zsjk72EGoTXUVIalibRCbISca/0m6os7olQlHz1vUa/FpEeKZ1ARFHZUlnX78F1pSeLY7bm88ZOofy7aK1SAO46KmbhIXvPKQ3LNEvurchDszIHoFSbXp6CJaf6VFdR8zxwg11StkpoYR812kKc3X04Tfz8nmpTfU0clD2BQNCmxqWF8Jr2p9Ntb+GwcbOSzrfeUzCwsI71xyzChHp/fyByL5HTScA/0EJXXc6X8/VAxtfuXDdwBALC6SHOpdeT/zJJeDutbqZ5ZVc338Zx4oV2OWvk8frmvvzKOXSs3atbd6046D4A15NdZuP3DFm5rSqzQMvltPOeNh3vD1W5D6Y0P/TqqNHo7F2nQoEGDhl8eWi7/z4hp4QfhH2hATiI12e2+Yg0e28lAJFT68X8dmCiDSAZmvMW7Xtw6UZm7aTQLpA0OkkxJ/wzqj70WBADoPMWxnKqsva92D1DGe5LFMru3P2tSP7R5jjIeFSw6dLUF9ocEUm5/LpICSLdMXKnMPbeVNadvr5OgblQkvYuUF3gsWa+I5ddzIAuWHWqVaxag0i8PCGTw+G+lM2Xd2QxQlbaGKuNzEnLkOComK3MbFzOoGjFDttWqaubte4TZq+Wp8qq4k3lNTSG8J3ZPgLVoFq1zRwCd4JqzZNmQTdymukl1S08JVOcfo9VpTJaAtMnIfIHqHFp9V84Q78TqpFX6WQELahnC5TlqVjXIHtiTnoDJR7brdPHaH23h9lM9WcL7q2hdpwXQUwr2kxMYEsTM3D1tqcr4w91yfS8ZukOZuzCBeQTLqiR4na7KmP2iWjJ6e/VmcLFXMJ9dr4XeO5jrGNL4vry7WbzQSUPphT3VjzX4r1o/HwAwojeDm5UNzEf4r0WK0F3VZ7syF59IL/O8+yTovLGe1v/5MbuUsbcee7CvqkG5ypK3ewL+BpUwIXS3L5ydTvDOdA9a6V0NGjRo+A1B+6hr0KBBw28IpxQonTdvHgAgLi4Ojz322Alzp7RTnQ6vvfbaKa/3vwpvoHTOynnwDTCiup0u+hkxxwAAX5SxkFRGGF3LjYcl2PnZlH8qc0ssoqv18yEVsPgt0grt2ULP3D5olTL3cQXrN3trt/cJ4X5WHWOwyO+gUASD5jDoldfIwI6lTX4fkUi3u1eAquhUuwTm1m9mgMlbDgEAvtiWDQAYOKBImVOXSfAWQ+pUNcMeEiL7UtexLm5nADCnToKK6kCfmnL6xyG5Pmel0y3/aB+viZeSCsghVdHaU9XkucVj/6hqzDubVKnkbXJcLiNfJ52q6JRPpdA6EVlsiFzXyOfA5SkZYD7KbXYOFPrF0chj6t+X13z/MaHJdAbuc2A6nfh9BfL7LcNIzcX4MtD6QoEU4poefwTfBm+Rs3hVZbN/HmXxrs5dEnQdMYu1F7KDuP/KTqE1Ps7NVuayEhjUHRMuAfk0E5+dZ/OFXpkRz2dPXdzL++zWdvLaBRp4nc8OEXrkXyqaraCJZQRu7ikCjUdzZvI89aRHvDX4r85k6YNXVnFb3gbpxhQmfszowWP1lixQP8+WDt6/f/aSQO1dhecpc4ePJf5yjacXLVoEnU6HjIwM5aPunfuhddTdbvfv7qOuQYMGDT8XTumjfuWVV0Kn0yEuLu6kOQ0aNGjQ8Muj2zp1DV3DS78kvvAwfPz9lIp/APBY+qcAgNtzmeY/OY4KjhCDuPtq3e2iDRKhv3YiywmoW4b956CUakiOZNRejRuTxQV9rZzt5mZE04VucYm7uL+Zrc2GhxQp41ePicLg0p6M+r+yeYIyfnuatB97uXqiMqd2oS+M2nnSMW1sIf3z6aoRAABdEjXfKVFyLlmhdN+/+pzVC1+94t8AgEeLz1LmvO3WAOD6QRsAAC9+Q31zZMbJVIjRxOsY5E8FQ71Ffvc7QPWKLYavjitUqDBdM+2krOwiZXy4QvTtumJVW0BVmQGfBrm/7gjO+frJsSSEW9CYU4p9d5xYXynx+QehDwpAZno5XA4nGvaW4egXNbAdLYCjtgF6nRMh8WZgYBaizh0DQ0gAru3N3ARvnfN+kawXfrCWOvym4lAAwCszX1XmrvliARwNDWhetwk+tflor2yCs9kKH70O4SmBCB2bibQLBsLg74ur46W2famdNJm33RsAHFgjCpIHLvpAmWtwynXeZukBl8OF6t0VaNhyHOW7a9Bc1gqnWwffqFBEj05B8sXDYAw1IzGAlFKkSWiRr4uoE7+sF5/Tb6pkvrcqxyHc2IamijbsXpyPliOVqCntgKXRBZ1eh6DEEMRO7IkennN6NGUpAODLlv5ob7Zj4agT23KO/udcRA5KQLiJ1So3rCcN2XeEqG5sTj4nNa2BcFo7cOiip38dOnUNGjT8fDBEh8PUOxUAoPPl69uQU45ddy4DAOgjw+DfvzcSAutRtr8B7Uu3oWn9QaQ9eiXQ+9u2emroLK9Cy5oNMIYHwD8pHAlDQ2BrtqP8QAOqX92GitXHMOpf53Z7PzV7KrH+VklUC4oPQPKYeFQ3m2E7VoqyD3ejZnUusp+9AOij72JLXaM2rxk7385DaJQv4nv4IaB/DDpbOtBwqBq5r2xH+ao8jPn3OSeso/f1wZA58bC5jCjPqYOltPU7tv7zQrPUfwZ4LfXBH90OfYAJdbnMapwwRrL60vzZOejrSlWBphbRsXdYmSn4x8FibX1ZyeDq6CjqbqcFyzZrHfyL/+B+FtdyFIk1pGMcEEsuek4ZP1QsmvTqNtbvOSc5RxnXeFr2JJgYQDvQQqu+wiq/179HTfPwPzJQmt8i51+8hb8vmEtNe5NDrOFqVWugVYfFwro4m1bXAQs13QX1Yg0mq+qBq9FmF0u4sY2WstnIAFvvcLHcNuf2VOYy06iV7hkoVv3a91hETOU8wZokFzMkkVaj9QADdE4/Tw37QHXwlR8jU4N4Mm2p9BSy+0hQ9OzofTi63YK/X3kQ8dMzkXWvZHLm7ZNAqMvgRvvR42jdtA39r+mDkEyxtps7/OBo68Dxx5fBsqsQQX3jcfGbzBdI9JNrFakqtva3zcxcvn203JNWVZH3j4uzYW9ogaPFhpje8nw4PMFLR1sHKp94HyXbazD0yl6IXSBdgo5Ws8vQgr7MbPbizbwRynhGqgRtY40WHN9Wj21LypBy8RBE9ZNAvd2lR2drJ7Y+sArFW6oQNyAC89+hxzk2ULzcTa30/Dpc/OPXx188vUdW8gM9eeQBWOus6GjqQHAPuWdeDX9naye+uHMTqndVIOOS/ohbIJ5erJnXbH91PMqfX4qmtfsQ98B8+PftgfMyWUSttJ1ZvF7NequD+RAFjeGn1VLXJI0aNPwG4J/RE1HzL1c+6F4YAkzoeYeUe2g5XIGWyu5bk77hQfBPiTpp3hBgwpjrpLRDyY7ak34/VfQcGYHLnx2ofNC9MAYaMfUhod4q99fDUvHtreNOBeZIM8LSw06aNwYakbVAkqhqdlec9PuvEaeNfsnPz8eSJUuwf/9+NDQ0wG63f+eyOp0Oq1ev/s7fNWjQcPpgjAiCIcQMR5MV1tp2BMUFdr3Sj4SPQTwOve9Pay8GRvvDP8yE9sYOtNbaEBpv7nqlHwkfg5yLj2/3aZ6fA6flo/7www/j0Ucfhcvl+sHSxt8j4oKa4RtgxPwZTKl/uUCKWpX5hypzPYJJxTS0ysM6sifplQqbWBQlZaRxmm10kdN6ipXU5OSD3qGqHX7DmdI8uNRGemCPjWUKUgMkKPnHhPU8Dic/BJvqhKKoM3GuTyCDbQEGCfaNualAmVu0nnX2Rw0RF3ngLNaU/qQ0Wxn3CpXjTw+gtXfvCOFWP6pkm7CZMdSc/9PT/LjJzEDmzAT+/mGBaNJt+XRtg7Ooj/aWFAgOZ4Ardz/poVy3jP1Gs7TBCWgVd7qpXvXBTGTQ022XD4POzo+dK4a/W81Crxma+eE4tEVaqx2vT0dbyXEABxFg6ERKgNAmJUnyHBhUOuup0dScF7SLhfv1p33haJGgb4M5BjZPcbMzw/YDAG7fwBrp6pT+viYpnfBUMQu7NRSF8pSjJYg/MVXKRjhsDiz9t9RG9x2cicdTRQRwmYW5LD6qlPk380YCAKalsEVekkme/XX1pE/UOu8rEiV939rsQGeL0GcNAXHotAoV9HG+lB6wlpM6/GL2s8r4vFfuBADEjqVYYUNRujJ+JHs5AOD+jy8FALg6O+H4RoLwyWMT0Ow5FnW7vWcHLMHfwyuwCkBGTA2iUwz4QzhpphuO8frOiZdrbnPz0+ujc8Nu6sTJLeZ/HLr9UX/33Xfx8MMPAwDi4+Mxffp0xMfHw2DQYrAaNPwa0LRtE+BywRgTB3NcSNcr/EA4W9tRu+hLrAtsgc1iQ83BOnQ0dSB8TE/EXzAMQNFp29f/x5p3K+F0uJHY24zA+O/v3XAqaG1y4K3HSlFT/B6cba2wlZbAZW1D8oRk9LusHw7/OmKh34tuf3n//W/5KzZ79mwsWbIERlVnFw0aNPyyqM1tQMM68cwip5/VxdKnBldHJ1rW56BFNRcxIQPpf5oCvcn3O9frLooPt+Lz/0rRt/PvTMXJbZ9/PDqsLmz4tB4AveXUKakY+eeRMPgZgP+Bj3q31S/BwcFoa2tDaWkp4uPju17hVwqr1Yr169dj9+7d2LNnD3bv3o2SEnElH3roISxcuPBHb9urflm0dyDMQXpU2BmQWVkjgaWxkceVuQ4XX4gwg9ABfUwM0gT5iCt9xbb5yty1/TcpYy+VUVVIffDbZ76ojP9TJa3zyttotc1PJiUUa7AAACxO1hN/roCp0sEm2f941TG/tn+0MvaWJ1jbQK1w3yDqy73p+08fomb8ukzqpwM8rcKez2WLvz9lrAMAfFFLze+gUKakD/CXe6XW3hesYuu0jkyhCqLD+QmKMvMNLWkKBQD0DOfLvK+Mz3N4sATjmnYyaOforWotFyTbt6/mNbeP4b7aPan+pireW/+BzCM4I1HKG1S0hypzO3eKjjusVwNa9hcj7553kXFWD5zxsLSk+ypXnp2AQFVbPV/Gss4J2oBnLt6NhnIbIs4egbgF02Hr4P7P6c2Wbcoxq8owHG+Rc/WqWwAgJYDH3GQXquu2uK9RU+nE9o02/P1vVsANTHp2GhaMFfXObetIP8QlUp10f68VAE7Usesh9My7ZcxBUKP4uAEVD7wIR60FfS7uh6G3jsTxJtKQgyLLTjg2AJgcRkqqwfNMq9+xV3PGKOPLBwj99M6qsXBaLGg/dgzO1Z/A7QYefj0ZGyNEeXR+NFVYT+VNQ9Gzn6Fh1QHc/GY2eg0PO6GEx7OH+e60NQglGrqf+08+rwD2tk58PePlX4dOXafTITg4+H/6gw4AO3bswMyZM7teUIOG/wE4rR148c59aCi3IXhMX8TOm/aT7Uun0yEm3oDZFwViV+QYfH3NZ9j66EbM/zLptMbPHNZOVP1tMRy1FiRPTsOQm0d0vdKPhE6ngyEsDEEjRuDmi3bhjvML8Nxd5Rj8qvtXHxPs9ke9T58+yMnJQUdHB0wmU9cr/IoRFhaGwYMHK//ddtttqKqq6nrFH4gPqofDt9WoZKUBQEGQWENqzXlZNS15b4GhkekMlF4cLdZEbDiDdkPNDEq+axcttSGcGZGbrMw6CfAUQFIX9FrTSG18VbtwlI3tDLS2d9KyuD1d3Pk/b75AmTMFMnDk7Zhj1nNu8SE2981OFmtqlqoQktdjAYC7kqWOe0sj93+wTXTwWcH0WKJ9ef5e7yclkJbkwWR27LkpW4K+jaomyerG2/HBsq1qq6pQVAAtYIdLrFVnBgOpPgXcVkOCWLg+Saos0yYG+EyVcv064mnBuY7wPi9rlnr2uiquM22CaJ03lKajwy6vanl1OL5aL8HisydKZu76Cmrrawsi4LbbUf2fV9GR14reYyJw24uhMBgli3hRCRtPf3hAtuPNXAUAfz/es8gAOdf701Yoc/fkshBVwz55djfNLlLmjpmHwxC3DZbjNbjrm8thjA1HTIJF+T0piOM1zXLPRwexCFsfowQw94Xx3u2qSYar04FjDyxFZ2EF4kYk4PwnB0Hvuf+5DdTBt9jl+t0cy2J2l25boIzjPB2sZsUzg3p23/3K2OtFxvTlu/FK+xXwjf8PCnNrcbd9PWKTTXjsGA3AP/bYhEWBNdgGIMm3HhkmGw53sGeC1zoHgMEZ4r2MGql6X1+cBmenqrh+N9Ft3dGCBQtgt9vx4Ycfdr3wrxjjxo1DQ0MDVq1ahaeeegoXX3zx//wfKQ2/P7idTtS+/g468vKRMigUVz0/EAbjz5uOog+Sj5izufv6cQBwO13If2IpWvaXIKp/NCY8Ofknl0z+f+iD5ZyaGxxdLPnLo9tX5pprrsHs2bNx8803Y8OGDafjmH4R6PX/GxpUDRq+C273/7V33eFRVGv/ty1bk2x6hTRCgAAJLZQgRToioKIXRMDGZ8EuYOMqVtQLeK3XAoIgXgSuYKH33knoLaT3uimbbJ/vj7M778QkJEAIbX7Pk4fDmXNmzpyZfeftL4eSn39FzcnTUIQG44n/dIGbpmW90BzVJpjT8wCJBAp//TWfj+M4pM3/C4YDF6GJDMDA+UMhV18/I2x9sFebYUrNAySAf+jN7whyzU/8vffeQ1xcHHbv3o2BAwciMTERPXv2hLv75d2M3n777Wu99C2H10PXQecuxb5q8ov94zQTu/u1JaOjj4o4nAedpbL2V5CI/eLWiQCAqX3Ij3zG2XF8e3wkKxnm8mcHahemXmtgJc/ykyj6cMbo3/n2Ho4Z6Cx2+tC91XYd3z5jYvaT9uFk/HwgkNIAbCplqqSxvtTX05PETZd6ZkspqVzKLaR22GNkapGZvaic3uGKcABAgvslvu+btAF8u6CYGZe4UpKuXDnSASDfogcA6AQ15IJ1tCcRztJtFyvJEJp9gcR6OMP7Pb3JuFoVTjyR7hgzzNV0o2cnKaR7sniz+RIb6WNtGoGPgjOfukKQxOxkKdtnD7UZcKY0UOtNCOrIVBRrL7J9ntCePe+NH5+A8XAqFP7+CHn8KawosAMFgJeWztlOT2qFedErkZtlxf13MX/0GcnjsCuf3rPWOmbU/DKHUgtMi9qBnSsK0L6XJzKHMJVdppnFO1QVVsO2cDG4GjNC+4biu4HM53uXs8jzmreSsOqPLHiOGAKvkcPQth8zzs86TqUStU71j83O9jb3h40o2XYaimA/+L32BNz1OQBsaKsmtehJLdnzwjUlMOQY0atXOgDg32cH8UXLAcBXwZ7fMQPFZZzID0L5lqNQd4zA8RimNqnaxJLBWSsN0F38Nxw1FnQb6IFOgcz47aWmPT1cGYESaw6AApw1hcBc7QOFIAdH31hSL2mdKslvjlACPHlfIxzVJuA7NAuumajPnj2bNxxwHIc9e/Zg7969jcy6M4m6CBHXC+e35+HwL+zDKffUo2TtnzDsYfr7QjmpDPynRkMfTl5PLt83VyRoU7Dvj2IsmpUK78hU6MM9IJcDlQXVKDxbBrvFAX2kHn3e7AMgudY8l6OdpIlSccWB8yj5k9mP5D6eKFm2Hrv+YB/iM27kWSS7Lxy6MAqku5p7qth1AgX/+QOFYT7QtPKGtUgLa6UBNQXZ4Ow2tIpW4ZkPWwFoIPjsJsI1E/V+/frd9NbglobZbIbZTEa2igr2Ikw5+DikGhXua0+uZP9MYEYooStZUhVFMn5xibn1/SuGbBbanuzc7VVUeNlfS9x/qIIZC4XcgkwQyfeAswhzgT+5Tl0yEVeaWsFczDSCqLl0C7mNudzBHgykIsLC1MDJqxkH/sTTpI7bV0mFeh/0Yj9Uo5646j2HyFBb7MvcE/eU0D09G8oMfZ+kUnRjXga5wrWPYcZXfSRVJtp/juaXWJhOdE0auUQOiCTpaM151l/rVdYIMp45K96UF5EEKlMTsfQdxp5FVrLACyxQEFFaw35qukv0kzNG0Hytnq1br6H15+QxSUvraUK1he2vg5PA6pSg7o5kHODSA31QlXQEAIu2rLl4AQ0hc2QMjP6MqK8u74YLR3IA5CByRBsUmD1QaaJnEqoyAAAuGOjdyLXq0fPRaOhCC5B/ogQFR3NhMjqg1skQHa9FUfAgePTqjSNn5MiUMZdSo5mt/eLJA4BMCp972sItsBLxWmY03OtJBcyHBLLo0hyzHucUmXDVeao5yZ6Vi+cl3hcYOawIQRq2JzUON2SdY9JI+IhoHCqPQIInORl8fZpxyL1bpfN9Y6JO4tJTIbi4DUhLrkD58UzYqi1w07khIM4HtpA+cEvojY93ytG5H7tyhSDK1derCkoZe5YZNd4wVQWhlZpcNw1mcq/MsLBn+m7vP/i+d3bcB0dN8+VVvGaivmPHjmZYxu2FOXPm8FG2IkS0BHR9ukPXpzvi2lO5O70b+0DsTqGPm09oQa15mYeLIJVL0fnx+CZfq11/f7Tr788zD0LM/uOuemYAtopqmDIKoR/UBW6BdRNn1Xud0VFoNzoKG3ZRagi7Uw0WGUnqlyBBWg0AyDpcCKlcio6PdUFTEdUvCFH9gnCynH2UQzSUbXPnlrgmn+dmwC2bpdFVRu9q/zZs2ND4Ra4Sb7zxBsrLy/m/rKysxieJENEEVJ3OQuq8v5A67y9YqxtOmtdUZBwuQtSo6GYNta8PxlMZkMhk8H2wX+ODrxFZh4sQOartdb8nu8mKlLlrsfafR5CdVNz4hBaCmKDlOkCpVNbrDimROiCROnDSQOUAq2wusZq+rxEaSmRV48WOr62Ip+NOX1phxOf4IKomtKWMGdD6eJJ6ocBKetRfMxjnMzniIN+XXEl+wZ28mS94Gw0Z1YSqHJNT/bK2uDPfN9CbkjI9OYUl39pQTsezqwVRtHKm6jheTjnYH+5PdhiFlF1LJSP1xM8FzmpLreg+0/1IJfT7JXbOIE/SeSa2p/vPrGLXj/Anrm7reUoapXBWPPLxID/0/jE0/9eTLP0qZyA1kzyLvDAqPJg4Lgun+fYSEtElKnZPyjKB+qU9qcRC9QYAwMUcUnXofdi5DEU6mJwJ2cx5Bpjz2NhdD4+EzEML7xDiKpMvkOpuxaD/AAB2HqfIXmFB5DyTJ0YuG4dCkw6FTvuxQk7PeVVKPADg/ihSFwoN7rvL2f5dqiQ1WEIivQczghnjtMKQAIQBmDAWv1/y4nXefxQzTjpIS+dc8j8WfanrQUQyKJaki8fCWKKsFBNVtTpuIJ/wSa334d7N/kg2hQBgaheharCVM99+Dw9SyRwsJ/WPq9j29kukLpx53xq+7ee8/1dOPwxHdQ2KN5+CkJyHqg0I18ox1J2MsxP09Dv7NI+pD79No4+b5yk57ObmI8W3LFGfMGECRo26+lwWnp7Nl9hIhIjrDVW7KIQt+hgQEGWZh/kyM0Rcb0g1aoR9/ynm3/0L33eixvcyM1oG10zUH3/88cYH/Q0SiQQLFy68pus2xA2LECFCxJ2Ma07oJZVKIZFIGsyj/nfPGI5juRPsdnu9428mhIeHIyMjo9kSeiX+/hzkWiXuc3qfAIBKwvSi+8rJP/hQDonQ3YOZPt4iKMmVYmDi7uyYP/m+FUWUAKnYzNQyHgri5EoFFnhXLuhLpSQ2v95+I99emMUSHF3KIlXAwPZUDLu9lhmp7IJi0peqaeyWI8wP/sX+VKKup4ZUGV/nMxE7zj2b71t8gfJ4DAtnInyljdQXec4SeUID1sWKulyRr8DHv9pG6pFCIwv/rxJ4d/i4k6rE5vQoKSwlPaxKTd4/Fgvbf19PmpOfr6cLm9h8T4EqpKKcwsP9NrHrFg8jP3lHGa3FrZyp3xSxND/Qmbogs5jc9bQaeqbtfZha4kAKJS5TakjPHuhURUUIDInbz5HKaWA79kwP5ZLPdltfUv2FO333C820J9MCqbiNwulRtbaCDImr00nlZnKmlnglluZ8fnYg3w5zqkIG+ZHK5q9cpkab3Ipy7b+3bSzfDoli6xOqkZ6OIC+rJdksR7u3kryITmyhdBBdh7LkXl08yM5VaCUvMIMzEViUQPXYXU2qmu/zBgAAkrJJ5SMVxENYc9kzX3wvOZ1PWfd/fPvdIb8BAFbkUVlEg1kNm9GMQ/d9fnMk9Jo8efJlXRrLy8tx5MgRZGdnw8fH55pUJiJEiBAh4vK4ZqK+ePHiRsdwHIfFixfjmWeegaenJ/79739f62WvC8rKympJEA4H40Sqq6tRXEzmEJVKBZ3uykuCtdMXwk2nwLEK4oz8lCzCLd6dOAdlKzIQFpgYl5ToTZGULg50bxVxIB3cKdHV0jzGtVcbiDv3CyAOMM6Lje3iSdcUGlI93Bg3GBdJnHRhDXFrLm6vdzRFie6/QMYmvzDm6hagoGt+nktZAj0VjIsSFhx+tC0Zk1zVcb45S1F3rf3ZOfUK4sQzC0nSmNKRcXZ/ZnXk+2TSutIjJ+DwaqzEyYd7Mq5RLfDNL9hKhlyvvoxDLD1CEonfJTp/YR9nxKiAv3H3IG6xON7JlQuO+0cRB12YwbhxjcBQ6eJGhUJwtBe9hxnz2XOQdSMj+1Ojt/PtTUXM91/47Ia2pzS0/L0JIk4v/EkSY0Y/Zlx2SQQA8Nzph/n29LYssdvRMpIsQz3omeud3PKmEkpW91x7ioLupkoHALyXQUXRQ3Rs/repZEh0K6YYjsDOLOgot4q42YOV9O49GsoMqWlmek4+91AUcIyG3UupjZwM+rvTnhTY2O9AmPpXFUS/R4fzAXYKoWjqABUZevcq2Fq+zR/A97m4cwDYUMLeT4uD7mlk8GmYqqygmlPXhhZxaZRIJHjssccwZ84cfPnll/jtt98an3QD0KVLF/j5+fF/LlfEf/3rX7X6n3vuuRu8UhEiRIioHy3q/fLkk09ixowZ+PLLL3H//fe35KVvWmQdLsSKqTtq9U3a/BBUeqZPdtjsKD6WgzVHziD1cClKs6thd2yGJtADBQN80euxGGi8m8dgXJpTg90/Z+JU8ikYcytgKTdBIpNAG6pHYP82CB8X32zJlC78mYLsbUdhSi+AzVCFN01maLzcEBavR78prRHRtWkBKo3BXFCOsgMXUXboEqpTC2GrqIFMo4IyKhiBo+Oh79W2zhxrlQl7x3xTq6/txw/Do3NYnbEiRNxsaFGi7u7uDg8PDyQnJ7fkZZuM9PT063p+i0MGOOTYm06GrfFqJi66BXlB2575ig/wuwSVlolnP+73xoFXmb+vNkiHwF6toUU1Mo8bcHhJGU6uzcbgb+5B+xgSJ0dGMHHSLlA1CP3M47UsDN9dSuqB8+Yg5Fw0YudPmZB56aAM8UNcjxrUVFiRftyACwsOIGtzCmLnTYRUztQjbQSFoQf1IhH2oyMjAACnq8mYlGogY1+oRzmO/3oRVZeK4R/tAY82npC6yVGaXolTmwtweksBnn0vCLH9ieDGejJxd3MOGfqEhsz9pWxPO/qSWBzvnoXP3zyCjGPlkLrJoO8QAI1PEIy5FShLTkFqcgq6TGyL/tO7YEMaU1V0DsyDTWJF+Mi2KK3WoOZcJqz5pSgv08Jc6An4kShe3o6MnrJMtv8VgsLXcjfac7dy9ixkBXTcawQZ4woUegBA1SnaJ0VnNv/etpT7O1ZDqSE2v8z0MpVF5LP97/2UfEvjxZ5vsJ5UInIprWmUVzIAIK2KKlB5jiSjYE4lU0VU28jPu6s/qeROVLP3tZc3zam0k3G7t44Zx1cUUS7989WURK7YytRC9wUm8X3/y2cxFFF6UjP1GkeKCVc8xT3hlANdmGLD4VQ+/LiX1DeySlJIdBz1F4Dav4dTJlKz7XOmpujsRepMbzmpb7rr0wFQ9TF2TxR38lRbVsFrdV483yfck/QK9nwDtZS7JsvkBYuJ3uVrRYsS9dLSUhgMBmg0msYH32HQtm+FVi+PBgCotOQ1IpFKEDYoAu0f7gTfWJZBsJ0uH6ZKKxa+fAZ5B7Jx4INdaL/02iP1QmM98OrvfbDOwX7kU9qxEnmmKhs+mpqOiuQM5Px3H1QPjL3ma3V+pR/6xNVAqWWvoNWpY7ywIw+/vXIAP3yQjwFxNXDTqy93mkahD1DigVltYew7AHKNG9xk7Mecty8DB99Yj6RlFxCeGAQEUu4ZuUqBhFkDkZwXjPwvV8OaXzccXoSImxUtmibg9ddfBwDExMQ0MlKEC4Hdg3HXh3fzBN0FlbsCPd9ieTaKTxaiPNdY3/QrgoefEoHRdQ3AKp0coZP6AgDKj2de83UAwDs2gCfoQrQdEIROPbWwmDmUnrr2qlNT5nfCXRNbQa6pnQc7qE8YOoxh3P35DRn1TRUh4pbENXPqS5Ysuexxk8mErKwsrF69GmfPnuWNpnciOumyodbJ0akzibCKZOZ1keCVhjc6/goAkILCx6tsTF8u9Lk+uJn5eScOOwGVlwqmMhMCKjMQrWHi7DFjOABgdTIlNJrYlbxLdpUztYZUQm4VXnLygPigE8ut/nUm+RQPCt6LMwB8NSbYjWwtQhG20k4cdc/I9Dr3XpRBaoUpA5inilBsNQv88C9UBwO4iES/dHR0eu0EyNm/E2LpPt7LJq8JgzNr3vZkytGeHa3n24VV7GPVM5A+SrmaTgBSkZqqQLyWqSpO5AvWVKmE3cr4HnWaAjqbGzyHk3qnYh2NLe/gTDOgp30sryCJ1OLHnqkqiD6+wnKB0momqSjbkaqkKJ3tWbKSfNNTBPneL25hnhZWT3qOkfGknon3Zu0/tpAnxyU/UtXYYtk1B/hRzsM1mYLUDyHCXIgMwndqXBeWL18pKLKcUUMeSa7Mnvf4kKrk7WP0zB7pwFI+aKSketA5Sy0O8aZSh1sFpRY76Nj+C7OKtlaSF9EXKeydHdkjme8b7Hmab7ve2Y1F5JFzMpXUL9FhjJH4czf5kccMI+ZCJWHPeVUOJRnLKdXz7fRA9sw6eJLH0O/55Mdvd5ZFjNaR6u2vtFjYq5svOviaifqjjz7apNS7ruCkyZMni94jzQRzpRnmCvYy6H2vXzUYu8mKrd8zvWm7/r4418j4a0HOoTzUnEqFVKtGZPz1TchkK2JqFZnn9b2OCBEtiWsm6q1bt74sUZfL5fDy8kJcXBwmTJiAu++++1ovecvCxslh5eS1IjFzrH4ACnC0IgyvX7gHABDpSZyHK+qzqpKMLd3uZvmyTy+5AM7OQR/lhYPqXnBlFhrlmwwAGD+QuNp0K0VfGh2M+8+2EPcs5JRfXD8GZcv/Qge/5agqtSLzRDmMBis0sR1xIegRftzKVOLaHowkY1ei3pn7uoa4wll3U2WlDw6w+4yLzEbm2rMoSc5FoLQYhZkmZJyqgspdjvFzY3ECMXxNgp1pzH96ZBviup4OIp/sGWdY5Sc3LzJgCaMK7wtie7I0g/nG26pMsBxcAQDoNdoDEqdvfmEZEfh7O53AFq8ynAMgi6+ArHMpsjJoH6Vd6VpeXoxDL7tEXjvqAtJu1gQwTl2YMKvEQKout2DGwRsFxao1QcxA115PXN/aY8T1KTux44+3p+fsSrYGABeq2P537k0xDj5KkhSOFDKjozZYEHlcTmu6oGXccBt3MojPv2s53z5Rw/zTzxlJYrnf5wjf/i5nAADgXn9KCDZWUIXIVYXq0/MUw/BkFEvstjiLCmQPCyQj/KLTLGL0w670Pi0vIEnk3lbMqDxQR3N+K+vGtw1WJh250hIDwPgulCRuqAdb33YfkvjSTfTMz1YwQ++oYLqPwxpyfNDI2V728yTWZ7Ce3tn1ZSxitsBMfvYh+nLYFGY0nAX/ynDNRP16e4yIqB9lF4pxenEyACB+WsLlB18hOLMFxr1HcVjQp42Lg+/Y+yFVKOBA86V4KD2Zh6wN5+AKg9Lq5RgzuxPa9vXDpeapW1wv0r7YCFOZCX6d/BA2sDUyr90kIULETYFbNkvjnQxTaTX2v7kRdrMdMeM7IrhPK9gcjc9rKuTeeoT9+Ale6LkZhnwzLuwrwapPU5E9by4Cn3gSMn1Q4ydpIuJfvxvxr9+NIdpjKEirwYYF2fjlpST0GNcKnV+7PlJdzq8HULLzHJSeSgx47y6xcpeI2wrXTNQzMzMhk8kQEhLS+GAAubm5sNlsaN26deODbzPoZDVQy+QospGIr5cxFrGHRzpmxTB1wIcZlB/HZVgZF8vUGxajFWv+bwuq84zoOtwXT77tBan0AlblkeHGhfG7KZFQgB+FMlfsZmL1rEdJlE420vOIi2bGRDMUUAcqEHe/Dv06VeH5BzKh3Pg13psWB4lEgu8zqMrNz+fIF/njLixieEl6L77v94J4vj21O/PlLROEan9+YSRrPAaos5fj8KrTSOi/Hz2GMfXTvd2SAQBfCYy3+wvu49vDWzFxe00KGfoOXiSxONWPqZoKt55B1qKdkKoUGPdlHwRHSAEYsSWL5Rx3EyTE+nN/VxQXXQCQhgBdFXw9y1CequePQ0USS0UKU7sIbM8w+dF/JD5MLK80kHFUdaluZfr+95BPeqmZjd20hdQH/fqTKJ9SztQCyy6SUe/dTpTkzZUP3XaORP2Rw8nn22XIjYoko50wJUGmgd3TEyG7+b5Xtkzk2xN7sxgLXzfy415ZTO9BZ09mqP01l9ZXUk3PPM6PHb8/jNQzK3LYvb4asZnv+zmfVDGtfJljwVkTlQ0c5EOqlu2l7DnmC9Qb7bVk3N5Tw/Kk+yhpzecrSU2YZmR7emIdeeh98fj3fNsV4/FnManBngyk1AcbK9j7lyEo/5giSHaXV8N8/8sF5fByyzxhryZV3rXiml0aw8PDkZDQdPE/MTERkZGRjQ8UUQc2sx2/vbgPGaeN6NjXE49/2g5Sactwme3iVGgV6YbUcxYUZV3fPN7aHuwDlbSlef3DDQcvIvPff0IikyH8zXEI7uzT+CQRIm4xNIuf+pVm773GbL93JBw2B/587QCyjhShTVd3PP9VDORuLVuN0NOLucBVlNoaGXltkOkYN1fZjNepPJmJS3PWAByH1jPGwr1rVKNzRIi4FdHiOnWTyQS5/M5U5S/J6AW5Vom8Aj3f95DTRLgrvy0e3vEQAODzu/7LHy8J1IHjOHw7IxUpO/IR0l6Hp77tDJtKgX+fJJ3zS5228e01RYzTfbU7ibD/zSKxeMHULwEAx2rC+b4tgvD7AB0LYfaUkYfAu6cG4/SpBYAE+CH/fsj26fBo13388eMq8vV1eddEewnK8lnIK2NfCZPUTqeSyq5LNPmPZ5v2ogBAhj0W7/3OcgR17cN8pkO1BprjRf7+a39iOeAHPHyM71NK2Ueh8GwpVr23A5zVhtAXRsOjdwc4HMBv50mEltSX0VFlB5y5si/l+yI3PQTyQNoTaxWpT2QmJjH16E+qgKM5VCLQlu1UO6jI+FEjyMYp1zG1T241qQ2CNUxl5tGRvKHOl5EoX1jARPnAQAPf98ZRUknFBDK1SlZHUhMJQ+pfiGfvjNBnu2dEOt925fA/aiQ11rN3UW70r/czVdgTPffwfa5c+wCQY9YDqF2WMEJP0pcrH7swm+foYObT/k3WAL7vzbB1fHvKblaUp6OeruMqQQcAFmde/E0XyLc9J1TPt7MqWLtARV4+VWbKnfRaW1ZXwOc+Us+U2mnsrLX/AAAEx9L151vIe0fuvKc+weTLsrOY0l2092DzlJ60J5tt7WCTNp/026KsXm5uLoqKiuDjI4q9V4L/fpiGI3/mwz9Sg2cWdIHGo3GfdGNeBZ5vvx3Pt9/e6FgXijcmwZxXVqffUGDG6Q/Xw15tgU/PCMg8akedrnkrCWv6foOzC5uWPLQqvRR52y6Cs9XmxDmOQ/aWi0j+6QwgAXTdu9c6bsyrwOKExVicsLjJ91SWXoHfn9sJm9GCuBf7wmvQrVUZXoSIK8UVs8y7du3Cjh07avVVVVXhvffea3AOx3EwGAxYt24dOI5Dz549Gxx7O6Oo1B1SkwoegqjD/52KB3AKvQJT8Uq/pQCALAt99P757wgULWW+uyZPf3z2jgESJ2dic/yPH/dhn2FQBDIu7vH+O1FabsRGADK5BO4yEwYEUHTgHiPjyoX+8u19CrBl9zFkfbEOFW1UCI5UYZlCgpI8C1JPVcNq4eAb5YEH3++A8zLmNz1Ix4x2B2RlOA7AYNUhtcwbb6ePAQBMiaPqNVWCTJLuRRlY9u4eaNw3I6KjFp6+CmxM8Ye1oAC20lJAKkHUMwPA9fYHwAxIhy+Gw1ro5FhlUmw/2w5Tu5EBL/4htpYNB+L5PnW+FGk/zIO5zAyphxaXjhnRJ+tz/niZs3B3QKQGpcMfYM9I4KcNTgIXE+jtaYSHXwXsDtqzIiN9XDknR7//OHFlbiWCRFPORGAqH+L0XUWQASDlFJN0/COIQ9yXwThklZKMt8JfbOtQFpiQlU/xBlwVrancmxnjXMZ2ACi3UuTvT6nMkG0QVGgSSizdWzMpctkx+r2+2JM49SFxLOpz0XbKe//k3cRErE9lvt6JrSnvvoecDIKuJGp5fiSdnDzHDPYegZTw6vHfnubbzw5n0ud3GygJWWAn8uNv7c721GGlvW+lpX0e4c8M0fOSBPN9SFJ49xSLoaguoPegPJ72LKA9k37Ucnomb7Zey7dd8SC/FtKeua4JUOHqxTmJfF+MVyEsCguOonlwxUR9+/btePfdd2u5gRmNRrz77ruNzuU4DiqVCm+88caVXvaOhaOaiEBFUnqD45Qde/NEHQAKLrCXp8/YphfCbf9IZ+hCPWA5m4nTBythMtqhcZchOl6LoIFt0PmBCMjdZMDf7JcpZy2ATAp9/9j6T/w3+EZ5oN+z7VGSlIO8NBMuHK2EjauATO8JXc8ExDwaDfe2AcgsrD3PmsW8GLSJdT19GoLdxD6gjgojqnYlYVOto2yPonroETC8yacUIeKmxhUT9fDwcPTvT1/mnTt3QqFQoHfv3g3OkUql8PDwQMeOHTFlyhS0adOmwbEiakPXuwd0vXvw1YQAquhjNJNOtzKrdl3DtEMlkCkkGP1sKJqKkMTWCElsjdE+yQCoAhEAHK6q32OpvMyO1PMWeA+JhzKwaTnQtd5K9H2qHWI15No5669/8G33tjn1TYPpbCogk0E/uun+622e/ycAwNyBfRwX9P6JP3bSRDrvFVkQcYvg4yE7IO2hhGc/lpl039BPEfPOffBJjL7BK7s5cMVEfcqUKZgyZQr/f6lUCm9vb2zf3nTd7Z0KRYoaMpUKAf0FxX2DLiAVwM6dMhz8BxPz+r7fjs8q+PbdawCQPzsAnKphxChNkDxpahz5yubbPLHwSB66jQ2BIyAABVagyEIE1BU+LvTVHetLBsYfc1hGRo1AxAxWkYj6QDDzmf8o8x7k70wB5JnQjhqMqiq2/oc71U3UVFhN4uwvyUz0DIimnNlPDyWjbhd1OgBg2n7ys3/5gT/x0YfHETY2DIMHZQDI4A1xALDnEBPlB/em8G25IOFYP09muDpYTV4v1XZSCVmdBjZrvgYOiwUlv/4GiR0wOyOmK01K2I1qhHoZ+DllRSQFuaLzhYWnzX6kCvHexPa/NJ5UHfmClAHFK5ehalcS3J9OQPvJLP2CIkmLygsnkfK/Rei67k0AQEcfMtB5u7F3oou3wGC8hQziroRtHfxIPSE03s7syOSWtcXk2y8s6NxLz9QmKQH0ngkNrWUWdi+6MIqB8JSRarF9ALtugJJUKZ019PUMdCYv+24vxR78624WO+Evozm/hZKffoCiHFIJB9/QMoR1Y8b1gZvGQOkhh9wtE2uOMilOaiKV0wg9JRT78CJTr/h705pjvWhPz0mZtLt/6ufwGNEHniMSEamh9/RwFlMP5RpoT37UUrzG9mNMWtUG0m/rYEo43/b2Yf2ylaQyK3yo+OZK6LVo0SKo1deW81oEYCsuQVUx0xk7LHHANaac/2xdO6SYAxsfeI0I7N8Ggf2fw4Xc5qlUdDm8+UdCrex81wuczQ7joSOND2xmSBRynFuWjKixHeDm0TzVrADAYbVDqpA1PrCFwHEcHM2XaQJaX5H+CHHNRF3ItYu4ckQm+OCj0yPwwyaqWOOmT7vMDBHXGzKNGuFfzoXURFyrPLLqMjOaB+pOUZCVFODs0iTETevV4LiMbelI/v4YqrIroPFVIW58W3Sd1I6Oz/kA7j16onJTJkr2pSC0fzj8ugTj+Bf74P/8OBT+tBG24gp8M9ADT3wSjYx153B24RFYjWa0GhaDTs8nQiJrumNc9dks5H63DubsYnzaVoNhT4dhwfOn0P9HP3hG+yHzcBH+++QujPs6Eb9+cwpZF2rw+qIYmHzUWPfpOWQd2Q3OYoEi0B8nlCp0TiRVYlmxDX+8sgtZhwqg9VHB9ErdFBVfdF2Oe+b1RdRApmq0lRpgWP4Xas5dgEQiwQe/yzH1n8EICGXS76W5f8FuNMM9NgR5vx1Gkt2KqCER6P0qk3JOvLIctmIDSpeuQ+nSdfgAwKwTY5u8HzcazeYwnp2djfnz52Pjxo3IyMiAyWSCTeCyVlZWhv/85z+QSCSYMWPGHemrPusfv0LjLsO+StL9uVQUbq2JaJw8SyH7Z3IZt/2v7qv4viqn2qCXB3kVXLQQV/5HUTyA2uHT3dzT+fbnZ5m4268VXXNeCvnavt92DQDg4/QRfN8LwVv49uIipj4xmIhDer87Zc17cz/zlZ7Vk7wCZkf/wbffsDHf85ltNvB9/zw1hm8f9gsHAJgD6P353OmTL5ORnn90FKlahvVJBgBU2ij8uoOOxGaXf3FvQdk9g5nWX3iJidOPDtjF9/2UTITV5vSmEHqSSC2CcoFObYFtD4nVHgNIzVbYhYleKj9ST9jsdC7OJgXnkEE19h5c+OZXGBOGQx7kgLmIqVBKS5j6KjWnCnve2o62jyUgYXAkSk/mY9+83ciXBcF/aCcEd81DtpsdFXu2YcDTbdH2pf74/VhXpKWkwVpjB7dtB4Z/0gdWoxWbpu/CK4+UwNMPiP94DGpyy3HinbXo3EOFDsNDUepM49DBm9Q35Tbas/RyL9irzcif8wM69vPF4C+640SaGivnMj+O3EoPlBm80N35ju/5/AQcY8YjYIo3fipXQ1lcgJp27njj5QLI3aQ4+HsBPpyajqivnoMqgBH29Pd+gaexAi8ujoNMIcWfH51BZYkF0kte4HZRzdhCkztkRh9IqjgUzV+AXndxGPpeW8hkEnz7byNemVSAoUvH4dU2m/C1Ry727KuC0keDznMfQj/rXnz90kX0jLPgrtHx6P5VHL64txIePXvDvWdPvD1yFbw06QCAqjbst1dsJnWi0KPntxFfAADmZN/D9x0qIntUQgAryNL7TYor2WGIgaXKAspzem1oFj/1zZs3o1OnTvj8889x9uxZVFdX14ka9fLywpo1azBr1iysW7eugTOJEHFnQ9s9Fm5hQTD8b0u9x1N/TYZv11C0ndID7q31CLunHQJHd0XOytoxAvourdFzSjS8Wumg8HPq/u129HqtF3xifBDYNRCaHp1gvpCO2BmDoQv3gV+fSIT18EXG4aJ6rlw/SrefhkQiwT/eb4fANjqEJYYgflKHescOeq4t1B2joQjwgUyngToiEN7DuyG4rQ7+4Rrc+2IE3AK8UHn4PADAnFOCqqMpmPBeDCLiPdE61h0vfBwCi6nhiHTjsWRwHIfHP4xCqxgtgtto0OOtAaguqELRMao7KndXoc3zg6Bp7YMuA70QP0CPM/uZjl/t6QaJVAqpSgm5hwe8/BqPC7mZcM3sclZWFsaNG4fKykqMHj0akydPxtSpU2EwGOqMffzxx3HkyBGsXbsWo0ePvtZL33KQwQEZJJAJsj6l1rBKNqZi4oC6dST1i0rGjJXvnaMkX66KNJ1UZHQyOARVdpxGP2GR32F64mrdFM7qL2fpx/dCNzJ0z89iXHuEO3ncTN72JN++vyszqvqqyXi7KLsPra8d+1Ea7LSmD7YQJ/6vYSxidnUxGcDuCadEVZuymSohIoI4RJOz8lNFDemahZWX9ueFAwDe60AJrS6aKVFTbFe2p0l5FMU6KoKuecaZefLnM2RoVAqSe1lM7KeSfoH2tPcg4vr3X2AGWFUaeSRV7KcqRR7dma90hBftaVoZcfVypQ12mx0Oiwye949E4dwf4DG0Px+BqvNg3OCltHJ49mqLnEpPeKqYR0+HnlpsW12GGI88mKHEMYkDIR098VcmM9pJ3K2Ayg6JUoGjhb0Bp6vo2I5/Yne2BC/EUGTwMzofVBZYcKg0DHHO4svHCyl51iErSZHhPqUoLsiHPbgVvjnN/L4ndjqMtvEa7AQwKPgCgsILEGi8hB8BPNIzFzJQvEQbSSY2f5OClzYZYTNUAnYHOKsV8vJivNxuK5KyS5Aql6BvFzOkUiZ1GcK9ofGQIbZjBrqNZnEQ894GtAoL9G41MBfkwFZcgqfiaZ9t3BE4LDZcPA+sS+yMbHM51K0BK+cG2IACmyfcfHTIu1jJS8Ee6hr073AaA0dV1IqoXbObvR9j76LE1NtzSPJ2GZKPpNE+KQU5/tOqmES4eTvVImizrBw2+01kKJ03bx4qKyvx0EMPYflyZrmeNm1avWOHDRsGADh8+HC9x0WIEAGoYiKhim2Lsv9tgC6xe+MT6oFcrUCdzDmyvxlLJSw47e99cFyf3ExqtRSgsAusnXseF/cXQ//g/VAE+kCiUMDwzRI4rFefR5ozW+AWFoKnvyJCuz6LMS9yDw1cXzSJ/G9KCgnANWP66huJa1a/bNy4ERKJBO+//36jYyMiIqBUKpGWJhoCRYi4HPTjhqMm+SzMl2oXxVa18oXxTHatvoLjhfBo7QHpFRg3mwvaVl6wZOWDs9InJPdU3VQT9SEjqQzdxoRA2yMWbq0CIdPrYCogV8OgSDXsNg6XTtGXIC+1BtUVDbvOuLUOga2gGFpvN/i01sKntRbKIG8og7wh06oanPd3yBQSOK7Tx+16o1nyqavVakRHN83xX6fToby8vPGBtyHWlsbBzeKG/VnhfF+I0+/5ubtIh+ouJXEtqYqJcW/EkFHRFWr8zGfP833PPrOGb08IYvrVHzL78n3CItOupEopAkOdt5yMpk+HMp/38ybyNIjoSUZHV8m0UhOpV16KpPWvKWa+wscryCd6+t1kR/k5jwWqndlNPuOqWAPfNhqZCGwyky5zSLhTpSMIcz9USoayzn4s2lQhIeLy/Wm6f72OEYbqCpq/K5+C4AJ82Z6WVFC+b2HpOQ9v9kwMSpp/MC2cb4cGM3fUbI78lxV5pIqpStEDAE4oPPk+iZW4ZDe5HXaZHWo9u45a74WqLl1RuZmliKgsYevq9Egsdv3fSlT/thm2XvGoOpuD9F/PI/jpkTiYG4bnYnZgg9SKKHUhLjhP7+dTCYnOBIOEg9SdYgc2HeqGiuJTePsMFYP2UPwCs9yCaI8irDreBWUrNqCbYhNenseepTC51Yq87ggd0hYpC/fD/MtyhD/cA3+ul+Dij8x/fFdBG2i0AQgtrwawFctKeyPUiwg+F+iPgxsM6BB3ChIJkPLjfshhQ5SuCGnmrkAIEJWYgw9eM6DnzA6QyKXYP+8wpEo5jueEIi3ZFVm8AkM8T+Mu/3QcH/sAjFvV+O8Lh3H3tBh4BKjRrWQLzm7JQ+JjbfBnej8UVh1HlCYD/xfB0kz8L78rsmsKUGG180bPSvcQbNoix9FWPdAquBIKT/au901gKreN6eRt5OtOasgCU+0gQKB2moSxPswcesSbVDrZ/fWwVFmAZqoJc82fdqlUCoejaXKLzWZDRUUFPDzq3rgIESJqw3vo8DoOB/oYP3R/dxhyt17EqWcWIOfn3Qh4eAC8BsVflzXYyytRlGdt8LhC64a4D0ejMqUIB6YuQ+rCvQifxPKeSN0uzzO2n3YXFO4qHHpuBZLe/AM+PcIQ0qE2bbj3/a7Q+Gmw6dl12PX6FoSM6gQ3fcN+6VKVAtGfTIJnkBrLXz6Cr8Zsxx9vJ8NmdkCpazoP6zVuEGzFBmS/Mh/7HvhPk+fdDLhmTj0sLAxnz55FZmZmo9WMdu3aBavV2mSuXoSIOwXRM1jlpxIKpITC2xtRcz6Fzbe2djx4QBSCB0Qhr4oRQGFaYwD4aBsjqhdSqM93SGf4DumMYkHenoDewxHQeziEyXzufrd2ug/fqQ/io76/XHbt+o7B6LWQFSR3cBLkbzkLiVwKN2eirtDuAXju6MPO0aRK0QR5oOe/74ONI95yyFNMuil33rLOV4WB88jdNr/GHUFD2+NiBhmsd6XVzo2v8NZh7IdkiHSgtt0gYNr9eD5uQ62+Ds/3c7aY84EqujVC5jBJuHVA8xZrud64ZqI+ePBgnD17Ft9++y0++uijBsdZrVa89dZbkEgkGDFiRIPjbmfsPdYOUrUKQ3tS2PJwL9Y+ZKQXs4M75T5ZXs4MZWoZcUt93JkHQb/JFPX4adIwvq1wY7+IvgKxb5I3eTgsBfNUEVaW1wvCu0tsTMSOUFJGrWoHeZ1sKmVeFfeFJvN9aWaK9HSFUi9P+IHvey31Ab7dUc9UJedjaI6Xlq5faWCcWKA/6VdTqphb3lA/8jjZZKac2bsvMFXKUG/KiPd/sZTn+7tTLJS7R5t0vk+oksoxMgKk1ZAXQidfKoO2fye7Z6Xg922JpbFeSkaslK3JYyf/DKmfpE5Vi2447WlpFamvcvKYJ0xIkOAC8ez5lBmJMz15mHyeHRomIQ/tRp5NO8ooL77r/iI86ZxhHqT+qA5lH4NIHeVr76glfb0mjr1zLx4Yz/e1D6WQ+nSn907EqbXQh2ih81fjt82+KP1pHzQJnTHImZt9xV7KWBjXOZ1vuz5KMd60J4UW1tdaSWs6ZSPvmwid815I84ZdRlKFhHmy+0sqp72XC3IYLeixBADw2P5H+b4JsfQ7+u96Rtwnj9zB9wnTXXz/F/vA3DOEXEjXbqLKb+pezAbyR7+v+b6nz1EJwJcvspoJ7/aguI01i/vBbr6Jytm9/PLLcHNzw7x587Bw4cJ6xxw7dgyDBw/GwYMH4e7ujmefffZaLytChIibBMZiE/586zAW3LcJZcvWQtuzE3yeGHujl3XHQsI1Q225ZcuWYcqUKeA4Dr6+vigvL4fVakXPnj2RkZGB/Px8cBwHuVyOVatW3XE+6hUVFfD09ETk4rcg1ajQJ4y8f/rrmQGwXODT3VNDcvOMCw8CAPKPk8+1WxST0c2ZlKRr+nDyz1ZJGIe1tYw42cIaMnC5iv+ObU0SQ4WduMG73NmahEZHHylx9Zk2xqGV2uicZ6qJm/J3Y+vzlZMu4dNkEqEfbM+MRa4ivwBQKojufK4185mfJYgybePDDLXDfYkTz7Pq+XauibV3ZZLE4+tBxt/O3sznet3BeL5PFUDSgdXChFaXlAMAMf7EQR5PYZwfZyM+SJ1Dgq7J32lUVZNxVXuODKWuGtsPjaWI1WVbKREUnJk3teEknZhPsHvSdSWuVack6aCVzgAA2HeYONVl9xKH6Kps1VC+nNUHmBQo86Jz9gpP59vuTqNhTrWe73OT0f25kn/FeRJ3L3zmlQ7mbbJkOeUuN7cj9Uv/aCZxxuooKOiniyyKNzGEpEyhRHWmjKld0lNI/SLRkRTbL5r9dgwWQQ50Fa3pLk/2bn96liRboZTYwVlRaW9OZL3HJ4YyDn2XgfLmTwukHPNry+MBAClG2nM3Kb1TLlXTIG+SOD9deT/sJhNSP3gT5eXl12xzbJZY/YkTJ8Lf3x/Tpk1DSgoRpAMHqEhCmzZt8O233+Luu5vJxCtCxG0Ah9UK2O08URdm67ObTHX6bDZqWyVMLeCoIdHdWCkol2dixMRsqt/Q6ZonEXworEZSNVhkrG2toT5JPUTdLFAN1siJgJkcrF+oWnBUU9tSxc5rEnjUu+7VdQwAhLXVbUZzrbUDgEQqCBJzzrMKimRYbHSuGmdpvVp7Cmpb5JZ6j0sVskYNvzcLmm2VQ4YMwfnz57Fr1y7s3bsXubm5sNvtCAwMRGJiIgYOHAjZ34MfRIi4g+GwWmGe+xbyC5svZeHQmcL/ZTQ0zInf6vSkX8U11zY6ov6UB/WvjuVEOVvvscaR2chxyqZff52h/Q3MU3hrkfDz1KtbVAujWT89EokE/fv3r1VEQwTBXWeCTMPhYT96db7KHgQAuC+A0vkU2Un8cpVXa5eQzvedPsmsROMHkvHzcEU4304qZNnqugdQGoFnWu2os57t5ZQmQCkoDrw4z5mwy0zBGpNCD/Lt/1xixqRSgU/3M51JrfD10QEAaqsqYiLI6Lh8LxOx5ZX0kbfp6fr/sjFVzQORyXzf8XJ2T6V2uubOQvIzfzOC+cEfLaKiIDkFlA7YR8VE6Kh2JOpfyhaoJYxO9UuBIMlXbxK7XZy0VEXr1PSgeAvlBuafXt6JVAWmeFI1KE+x865Kief7OnS+hPWFdqQcaQWlzgEbHLByDpg4DjZIYOKksHJS2DgZTJwcFk4GM6eAlZPBxLnBBilrOxSwcXKYOBmsDjlsnAxmhxxWTgaLQw6bg53Hyslhc8hgcchYn0MKm0MKu0MKq0MGu0MKG8f+73BIYLOzfx0OCTiHBA67FJxDAo6TAA72x9klkDjA/u9sSzgJ4ABru47bnf93AOBcx/72L+dsc38/xkHiAKR251w7x/dLXcdtHJvnajs4SOwO51gHJA4OcHCQ2ByAw8H+tTtYGKnNxto2O+BwgLPbAZvN+a8dVrsJu0v/RKw2h1dleSvo3fg8l1SLLlVLN0/6ZH1zhOjh+73XAACW5JCXkcXbDkdN833Ybw15QoSI2xge7lIodYAVgJUDFBwHKySQcy5iLIWMk0HBySDjZLByMkid/8o5GSQOOaycHODkkDmJORxySF3/OmSQcFLAIYfEIQPnkEHiYFkh4ZDybfav1NknAWdnxyROwg4XUXc0QtQddYm6REjUHY0Q9TrHiKhLHKyOqkTqJOrOfyUSznl+DhI4ibrEwfoljLhD4oCEc9C/YAQeUhkj7lIbAAc4zg5IpOAkdkBiA4dmTP7eArhmom4wGLBmzRrs3LkTly5dQmkpczny8fFBVFQUBgwYgLFjx4oBRyJEiBDRArgmov7JJ5/g448/RkUFWexdzjQSiQR79uzBTz/9hJdeeglvvvkmpk+ffm2rvcVRWuQOqVoFeztSSzwWzHyp7QLv0h9zKbz94XbMh3bRIao+/s6Q1QCAbAtl+XslkMrBbdExtUqlndQnrhJ4AHDRaZkf4UP+zSoJGZNyalgo++OtSO+4Ip+yF85suxEA8N5pyhzZQUW+9ZEhLHWrt5LUD2Fa8pVOD2DrdpST987M/pRGYHsJ8+YIUhj4vo1G5snj6UvnHB9C/sVT1z8BAIiJJU8MkyAop8SZ0kCYebLEm1Q5FndnOTsf+kkUnCH1jE8M80DxVpPYnbGPgu1svdm65IJ870K4EkqaBakPTuY0vX6siBuPTZdiEN+WvT8BKqJ5rrJ/AFDtYB5P3+whh5AeHen4L3nMZz9YQ/MvagOcUkLz4KqJ+qRJk/DLL7/wRFwmkyEyMhLe3k53t9JSpKamwm63w2Aw4LXXXsPp06exaNGi5lm5CBEiRIiog6si6t999x2WLVsGAOjSpQvefPNNDB8+HFqtttY4o9GI9evXY86cOUhKSsKSJUuQmJiIJ598sr7T3vaYGH8QSp0Ci/LIP7mrnhlUhLnBu+nJhu/yHx/S+QzfF+3GfGkPVJBP9lI7GV50MuaO1VFNhtJWCookjNPoAQBFNuKUVxd35dsPBTBfXCEn/lzMDr69qpBx7cIc6O4ScjFzceixHmSULBQUvn7NWfD4sz0UZSpErjO685OD5KfuVs782pK9iTu+WEF+7lHt2bWMVvIN93cn/+S0DObnX+pF8QA1RoqS9fZmPu0WC/0k/NsLKheVsPULqxVZWglyYJex6zoEDl5+rUkqqJKz34a9gjh1Zd7f0t6KuKkxNuYEOvkyic3FkQPA1hKKB7lUygzm8wdRaoUvM4hrfzCE1SL4PoWkcY8kJezm5ssIecVE3Wq1YtasWZBIJBg/fjwWL14MhaL+yiBarRbjxo3DmDFjMGXKFCxfvhxvvfUWHn300TuynJ0IEfWhotIBJVef9wsznNo4CUycHRYOMHNSWDnAxNlhAwcrx8HkkMDGASaOg9XBwcY5YHZwsHIOWBwcbA670+DqgM0hg9Xp/WITeL/YnN4vdoH3i/0m837hmsP7xe70frELvF8cNmYwdTi9Xxx2gLMxgylnhw0NJzS7GXHFlPWPP/5ASUkJIiMjsXDhwgYJuhAKhQI//vgjDh48iPT0dPz555+47777rmrBIkTcLpDIZJDp3NGme1bjg0XcUCg0sjoZM29WXDFR3759OyQSCZ577jmoVE1POq9SqTBt2jRMnz4dW7duvSOJeoHZHW4KNxQIQvb7BLMI3H3V5HPdU0tRuZcsTG1QJifVVr6dGTLbaCiM/Wi5oAhvDRsbGkrif66VfLajlUx9IzS0Giz0LOecZwnXIr0pPH1bKYmYrhJ7uwtI/bM2PZZvV6ew9VkSSBfxbAgV2n12NVO/2WKJA3LliAeAAQEsfLzAi5JjnS5lYeGTfPfyfT9ypMa6WM5Kx+UW6vk+rpTUKxP7sXmucmUAsOEvSsTEebE0CNZKEqvzjdTWejMDqVpBBi2Dme7PrYK1JdGUmqCihvbU/ySbVxAviKh0qBHy0RvgbHbAzlQxmhA2315tRsqTn2HCXw9gT2Ynfk7nKDIEG0zs/DkldM+STFIvjRvK7jnfTJ5nB3LoPbHb2Jpf6UTBQZ9uodzqbwxhxcT/tZp+q0+PXs+3XWki4tXkky0su/iPCKZqyLfQ9duqKSHYiSpmKFYI0gAcL2M5/IVxE+5uZMR3vXsuFR0A9PUjQ+T/UuIAANF+lP//Hn9Kh2F3hulvKOrI9wWrDXw7RsveuYMGShNQU2XDhvuWIrXaHwfTmRNCnA85BmSU02/LWM3eL2GCvHhvGisDM6QnBJCKdYevN+zNl8/ryol6UhILkhkyZEgjI+ti2LBhmD59On8OESLudEgUCkgUCp6oyzS1RX03rQJSNX0cFFr60MhljIBIBR8PiYDRUuqYFO2moDkyDR3nnERdLcgzLryWq1+mqtsHAEpn3ViNhj5uMg19NFXOsUqBF5JaTfPdwNalEGRRlFuUznujcwqWD4WM7ZMcdB3XfQrvT7hPwjW7iLqimo67aait0jqD0AS2GWFq4FsBV7zazMxMSCQSdOhQf8Xwy6FDhw6QSqXIzGwsmFeECBEiRFwNrphTr6iogLu7OySSK7fcSyQSeHh41PJrv5OwfU8cpCoVHh9OqohVpcyTxCrgBjxl5It9yhgCALhUSZ4e51RMFeHKIQ4AK/t/y7cf/Os5dj2toOSWktQCKmcCpG4ayhbZrTW1Z59nIvjZAsoMGRdEnixjfJhYXeRJIrBQlXPBj81Lyg3h+1qFUUi9ZzumFjKkkNj6fVY/vn23P8ukt+p3Uq94pDER/adnBSXqFLRP7fRMFVVtIQ6rQk4coMtbYZQ+me9bJyf1S+Vx5rUgC6Nz+njRnlU6ueG8LLpPVR79fNRd2T1Vm+j65nLicAsS2FhLEaUhuDeBJNYNl5h6y1jGjjtq2O9rW1IspGriWpNSyPvn3k5MrRDhQWoyv3a05n66cwCAf5eRVK1WkiTQtRV75l9cHMj3Lbz3e75tcGYOXfnIfL7v9fT7+fb5JKbKWaSlZ/Lz4O/49lPJrHBGe0G2y9waPd8+V8JUZiNaU6aXqWEsbmNRZh++L1xL95dSyebc5XeJ71MJEno91p6l4LhUTTEGwndzyUnmJx7kS+/j2f2kajkQy57j0NBzfN/KbazgxslDkfj6YebV8tzvj/PHg2JJTfh4HEvdsaiA3t0zC0g1Wfgk82LTykilFLTPCpvVClIiXRuumFOvqqqCWt1wOanGoFQqYTQaGx8oQsQdDIlcDs97hkAieondcEjkcuiHDL1lnsUVr7I5LMC3ihW5uSEPq4JMY6sV6bkjm3HbL7Yj7t1V2Bkg//W0Iipo/FxXNvZ0CeWU3mmkijc/jmQVh1aXduf71pyO49ul0cyQujedit++Ekc5odUKxvm83/V3vm9vJeWPNnGMG9VIyU+7jaqgTruNlji0IkEirg/arwEAHA8nrjO5nNque7a1JWPTIw+yHOu7Smkdh/JozvuxrJLM4XxBtaEUuuaFQMa5bcl8iO9LuJt8/z2dXP+JUsoLn3+MCm8Hd2OSSp7AT92qJQ7RXMyMhhJBRKlfoIFvF8mY8VhY2ejPo/F8G0p2z88m7OC7vpYwDjs8goyLuQYqXO3i7tsG0D7vOE/749uVce2D/Yjr1ATQM9tjYGUl/xFBkcP/d2gS33Y4E7K5inIDgK+aGLL+fVhEsslO7+vbqRRbsKALqzIkTHh18ARJl1P7snu1C6TUAiu7v15+6XzfgaJwvu3nvH6EimIIhPhgL5MyA4MpLuOC3I9vL+q9GEDtakamaCKDuTYmPf4ng5Jw/f7wF8DDAHAaWTY9AKBnL9rTPIHR9qszAwAA87us4Pue7UPPJKeK3V9mIUkPjoc4OGo4gILCrwm3lgVAhAgRIkRcFlclTxQUFFx1bnSO465KHy9ChAgRIhrHVRH121F9UlJSgj/++ANbt27FsWPHkJGRAZvNBj8/P3Tv3h1Tpky5Zt96z790kLmp4PsGha8viGMiql1Q8fxwDRlulM5EPw4HHXdzlplb1JFS/pc4yD95V1W7Oud8Mp78u386x4xFkztQ8VxhqHMfP2ZAm358HN8X5k3ibLyW+SX7CAy6/yuhhF9mB3ut7vclsX6PkUTQH3YPAAB060ymoZ56MtQO0TK1yM86MmSer2aqpqOXSOUSEUIiuEbC1AodfEkNtD+U4gFiPVk+90orucLtOUlrkrlUKQWkGgvrTj7huXuZT7VNT+oVqZn2V6Jkvwmh91tRDRmCFXq2vpw0MnhDSee6K4bFJiw8R+keXO7bGWdIDcQJ5rj5MOfmBwKP0TWryeAc5EwNMWsnpWOY3JNy8LtKvh0xhPN9rXzpOT8QzAy5Jo7UK98kkVoi2N8AAMgrIZWQvZrGrvRmz+9wGj2zuNh0vv3DUaeBVfBuv5vIVH5SgZtjiI6MmkHORFpSEA0Sll2UlbJ3Tx1G6hVXCToAWFvO1JDCUopHUml9XSNZIJhE4Dt/7x8v8+3H++8EAHT2oHfj4P7BfLtnb6aWWZhHz+GbAUv49rS/HgMAhHWg+gKVv4bAbnGguULQrpiov/POO8106ZsLgYGBsNno5VCpVFAoFMjJyUFOTg5+//13jBgxAqtWrYJGo7nMmUSIECHixqFZCk/fDpBIJEhISMCjjz6KYcOGITKSccvp6en44IMPsHDhQgDAI488gqVLl17RuV2Fp1/dOwpKnQIlVjLgGW2MczyQGc73+XiQMeqRMFZxyGWAAYBhEcwFrJOWuIVl2cTV9vFlXG+shiLZ3v1lPN92izcAAMZGUKSdRkYGtCob41aX7Se3sumC1LiRSmaYW1pAx9vryJj3VzaL1gvQkkTyUOBhvj37GDNmTYmlakrnjeQ+ufes05hmJbZX6cOkgu4hxM/EudP97yxmRj+5lDi8zp50/4dKmfudsIhx+uZwvo2ujAPUa0n6MBjJy8tbx4y2wohVaT5x/e7tyuqcvzSNOHWplXGjkiA6v0JBSdyC9IwbTU3zhuG3zTDuOwaHsQaK0CD0fKEzAhKYAfhwCkWEuqo1TUwk7jvXTFzz7jQW8SvkvqM9KNJS76ze4+9GhtBzRpIKXNxwgYmSsU0JIolvkTNF9NthVPT8g0yKKE03sPvvF0wS2eZ0Muh3CWbPr0ogPVU7A5r6+1FU9VEDcdIns5ghe0IspV3elk8SVxtPdn8ZVYIoT4Gbq4eSSTfCKNTUauLaXZGqY4OPw2y0YduPmUg9Xonsk+WoqbDi3ve7IG5MWK3fy74SiqwO0RgAAB219O7tLqP1RWjZ+pYfI8kWVikcNSZkvfh2sxSeFg2lTmzbtg0HDx7EM888wxN0AAgPD8eCBQvw1FNPAQB+/vlnZGWJuTpEXB8U/7ASFRt3Q9u7C7wmjIZEKsGe6etRfDyv8ckimhVVBis2fpOGotQqBMa4Nz7hJoFI1J0YOHDgZY8/8cQTfPvIkSOXGSlCxNWh4lweqg8eh37ccHiNHwn3AT0RMPP/oAnU4cQ3B2708u44ePop8d6uvpi5ZSBGTG/X+ISbBLeGN/1NAGHyMrv96moWrjjYE1K1Cl06kVEwWsdUGX3CqK+HB7VdFYW8VpHKptU/ma/zxmJKSpRRQH7sLhVEtolE0A6DBEnC/sfUG0f0JNY+HESqkNnrmC/39FF/8X0/ppKq5dk2zFg00ofUN3mChGHd/VkaiGg1+U9/m04GtpHRzBAa7EZqgY15ZKj9sh/L1b8gl4xNblK25y8EUvKpRcUUyei65wSvdL5PaGyz2Jm3ltFCon6boSSCpxQzETzSk6IXT1koDiAnnR13K6CfjE1Hqha9iqlV0rPIJxoe5Mcuy2bXtQqSgHF2MhBmFnmjZMMhSGQSdJ/SCnJtLkqq2TOvjOuL0nXrcGi9LzRdSeyf0Ys9nzyrnu/TCSIVH+nAVF4/rxvA9/UfSe/BaA9mCP22kBiaghriSF8IZXv9zOGJfF+pPxmfu3mx5zzl2KN8n81G9zc4kkUG784jyffBaIqiXZvFIi1r9tO7+/TEtQAArSAG4rSMYgf6t2HJ3hRS+g0ODjrPt5ccZobmJXcv4PsK7XRPrhiQ/ZXkLy9U/7jUMt+fp3ernawQZZXsXd6b2gbHD3VHQIiBPz4xnAyx6Sb2ngj94D0Fkc8u/Osu8mP/PG0QbEZzsxlKRU69idixYwff7tSpU8MDRYi4SljS86AJ1UOuVdbqV7Vi+nRzbm5900SIqAWRU28CDAYD5syZAwC46667EBMTc9nxZrMZZjNxGndqrhsRVwZbWSXc/bV1+mVOw5ldfI9ENAEiUW8EDocDkyZNQl5eHlQqFb766qtG58yZMwfvvvtunf5Heu+FSqeoVWZuXioLoX6rzVq+zyAIqXcl/Gr7EoW0r86OBwBY7STq6j0ppP7tcOaNcLCGrPL/y+rCt239mKfF660pN/b8bArlnjScqVeExbCjvchr4kINU0tsz43m+zr4kH94ZpUeALBpSze+L6ALeceM8DoOANhcTuqjaRE7+Pb7F5gHRVEJic2RwcwnfZuR1DQbTtL8WX2YKuJIZXi9x91ymQdEj0G0j0dzKKWAzBnen/QHZR/1GkBrloWwPStT07MJDjDw7fRU5r0j0ZBbLCfwv7a5s/NLFaQSclSRT3fryELk2s0wcZ5ILWbqCD8PFuavi2HP1s2rHBoVifWrC9gzFXr8JJ2m+7+3ezIA4J37fuX7ztWQKmNRMUs6dcZAnketdHRPSwpZsfPeYel8X4qJxh4tZfsXF0gSRKCgIHO+iX2MXKopADhWRntecZqFyn84hUq//Tt1EADg6YhdfF+sO51/ZxFTmwz3OsX3bSij5+zyeV9aTOrC7bs60/3FsXN18KJn+3AgqR6/zhgAAHi/I6XIOGqMQJ6GqQrjWmehXRc3DPSkJGRCuEuZd825mqB6j7tg5Yj0Vq8Mgt3SfAnVb1n1y+LFiyGRSK76b8OGDU26zosvvoi//mIE4+uvv0bnzp0bmQG88cYbKC8v5/9EbxkRTYFUKQcniJVwgbOwPqmbyIOJaBziW3IZTJ8+nefMP/vsMzz++OONzGBQKpVQKpV1+g+WhkNuViIsmLheQzXzhT5kJK5aKUgl6uAY57HrEHGQTw5kya2yBIZQYRWkpxZMAwD0GE3czF0BlKrUFU132kypcUf6neTbX50fAAAwndbzfW0TyXi7t5AZvkoMZDRLFkgNo1qzgtQDHiTfdmFU4uunWPrWboH0sVNJiAP9v8jdAIAFEjJWDQtgnJFwbz7q+xvfPl3N7sXG0Tp6tE3n25f8GfdbKKg6JeR62zsljdJhZLxNKyYDnjWdzVOVEfcdHi2oLKXWAwC0yWRQl/ajc1XJWL/QOCpREwFXymxQ+WhRVVDFxwHnFbNzWg4yf39HWTAUAq48SM244v3L4/m+iJHku99ey7jSI1WUuK2DhrjeDZWMw/V0Iy7xwGFSLX44ghnzvrhEhZOFFYm6ebPnt3IzccV2P0FKWWdCs7Z6ivzdL4jHmDaKMVZvHKZI7YhA9tsQpt4dF0IRs//Xir0bmw2UzvZ4Eb3Ham8mFbTVkOSY1Y3qN/iqWAyIl5wkW28ZpSt2GWDfOT2a77sn/DQvtV40+KM4J7LW/DOVxJX382aG3JPlJBHp5LQnvTzY73BXOfmulw+sgaPaBCxGs+CWJeoTJkzAqFGjGh/YADw9PS97fObMmZg3bx4AYO7cuXjppZeu+loiRDQF7m38UJqUBHu1qVaFoppsRpSUgcHALVYEWUTL45Yl6g1xw82BGTNmYO7cuQCATz/9FK+++up1uY4IEUL492uDjF+PonzzUXiPYfpszmpDRdIhqEJbQ+HpBaDw8icRccfjliXq1wvTp0/nOfRPP/0UM2bMaLZzWz8JACdX4dhcCvX2d2eidZUgx3qJhVQEG44xHX6PruRfvC6XiZ7z25Kv61f5g/i2K8/W2VIyaqUpKH/zx23+BwCYk3kP32d8j0RYPM9OIBWod88cpzVzesYt+vuRUUz5LamClo3pBQA4/F08zfmYVBHjIpIBAIuOU/Iqv44kAm/NZaLp8BAyRp01MuNsUmEo32e20OvrMio+HUYGtjd2UEKytv/HfIkr15N/slJON7jvEAsu0aWRmcnUjo5Hd2Wqhox95Nt/cAepxDhPJrabBPm63DfQnmiHGQAA0m16vk8QaY60iNYAWsO9z0kULdsCe0UVZNIQVB49DKuhFH4PPQS7moObYM2bTrPrS8IEScaqKS9RoZUZKg8V0bPrGkFFojOWsr1QP0BGw8cG7uTbH/7AUksYI8knfHgIGZp1Mqa2mTmKjIpbSmhPXEWiq20Upv965418+z9zmBpOPZbeo4eCWRK4cjvdxxe/UTFsRSwzWHuoSWVUXkFjJ3VkRs8fTifyffKTZNxOdQ5NotcNa3uTKidAx36P5lN6AIBh/26sOFMFs4WpbSTHT0Bak42NUgc6/qMd3HRuKKqh8xdb2W83bXs43xe6lVQ1aV+zve7neYHvK2mtg9VoQTqaByJRF0BI0OfOnSty6CJaHEEv3AfFf7ejfMcJOKoOwS0wCEGPPQF1ZFTjk0U0O8r27ECRgRiSrB3pyNqRDgCIHhEJN51bAzNvHESi7oRQhz5//ny8/PLLjcxoOlw502w2xpqZq0gvajM6+wS1Iy12ilR01DCOxGq01JljrBRETAqO281sjt1IrKBNQed3zat1ThtxPvZqNs9uoj5HDV2Lc6611vmtwrGu+6U+TjDWpGLzHdV0XLgnrusL+yxmS61jAGC3ENdqc7K91ZXEVbr2DgBsHDuXTbAOYXpV11i7WSroE5zfWHdPBDZZONzYdR0m6rTT9vLr5swC1zXBcYezW2qzw+fBfvB5sB9sl5yJnSQcHM7rCtfvWrOkhn7Gwv1x7Z9wTk0V3ZPLjU543CR8Ds61OmpoT4XPRO40msoFIp3wnZI6OXWr4H2uUdW9vnDNrvWZ7IJxgj2XOsfaHIJ9qOc9EvbZBVG8DqmrD3RccH2bpPZzbv38DITspuNB/2QRp2qnwd5SZam1f67rC9cs/B24jlfbaE+tRgu/b82RX1HM0gggMzMTYWHOLH5SKfz8/C47fvr06Zg+fXqTz5+dnY1WrVo1PlCECBF3NLKyshAaGtr4wMtA5NTBAoyE7YKCgsuMZsW3rwTBwcHIysqCu7v7FVd9qqioQKtWrZCVlXXNKTlF3NoQ34XbFxzHobKyEsHBwY0PbgQip36Tw5WLvTnyLIu4tSG+CyKagls2olSECBEiRNSFSNRFiBAh4jaCSNRvciiVSrzzzjvXLdBKxK0D8V0Q0RSIOnURIkSIuI0gcuoiRIgQcRtBJOoiRIgQcRtBJOoiRIgQcRtBJOoiRIgQcRtBJOq3IEpKSrBo0SI88sgj6NChA7RaLZRKJUJDQzF27FisXr36Ri9RRDOisrISs2fPRqdOnaDT6eDp6YkePXpg3rx5sFgsjZ9AxB0F0fvlFoRCoYBNUPZMpVJBJpPBaDTyfSNGjMCqVaug0WjqO4WIWwQZGRkYMGAA0tPTAQAajQZ2u50vbN6lSxds3boVXl5elzmLiDsJIqd+C8JmsyEhIQHffPMNLl26hJqaGlRVVSEtLQ1PPPEEAGD9+vV46qmnbvBKRVwLbDYb7r33XqSnpyMoKAibN2+G0WhEdXU1li9fDnd3dyQlJeGRRx650UsVcTOBE3HLYdu2bZc9/tRTT3EAOABcZmZmC61KRHNjwYIF/HPct29fneO//PILf3zLli03YIUibkaInPotiIEDB172uItbB4AjR45c7+WIuE746aefALDn3bt37zrHx48fj4gIVlR6yZIlLbo2ETcvRKJ+G0KlotJ4drv9MiNF3Kyorq7G3r17ATD7SH2QSCQYPnw4AGDTpk0ttjYRNzdEon4bYseOHXy7U6dON24hIq4aZ8+e5fP8d+zYscFxrmP5+fkoLS1tkbWJuLkhEvXbDAaDAXPmzAEA3HXXXYiJibnBKxJxNcjNzeXbISEhDY4THhPOEXHnQiTqtxEcDgcmTZqEvLw8qFQqfPXVVzd6SSKuEpWVlXz7cm6pwmPCOSLuXIhEvQWwePFiSCSSq/7bsGFDk67z4osv4q+//gIAfP311+jcufP1vC0RIkTchBCJ+m2C6dOn85z5Z599hscff/wGr0jEtcDd3Z1vV1dXNzhOeEw4R8SdC7HwdAtgwoQJGDVq1FXP9/T0vOzxmTNnYt68eQCAuXPn4qWXXrrqa4m4OSAsQJyTk9Og1JWTk1PvHBF3LkSi3gJQKpXXrVrNjBkzMHfuXADAp59+ildfffW6XEdEy6J9+/aQSqVwOBw4depUg26Np06dAgAEBgbC29u7JZco4iaFqH65hTF9+vRaBH3GjBk3eEUimgsajQaJiYkA0KBNheM4bNy4EQAwdOjQFlubiJsbIlG/RTF9+vRaKheRoN9+mDJlCgBg+/btOHjwYJ3jK1euRGpqKgBg8uTJLbo2ETcxbnSeAhFXjhkzZvA5P+bPn3+jlyPiOsFqtXKdOnXiAHAhISF8fhe73c6tWLGC8/Dw4ABwI0aMuMErFXEzQUy9e4shMzMTYWFhAACpVAo/P7/Ljp8+fTqmT5/eEksTcR2Qnp6OgQMH1kq963A4YDKZAIipd0XUhWgovcXgCh13tQsKCi47vqqq6novScR1RHh4OE6cOIG5c+fit99+Q1paGhQKBWJjYzFhwgQ8//zzcHNzu9HLFHETQeTURYgQIeI2gmgoFSFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRFyFChIjbCCJRF3Fb4dFHH4VEIsGjjz56o5ciogmYNWsWJBIJPvnkkxu9lKvG8OHDIZFIsG3bthu9FAAiUb/pMXv2bEgkklp/UqkUHh4eCA0NRZ8+fTBt2jSsWrUKFovluq1j8eLFmD17Nnbs2HHdriHixiA+Ph4SiQRbtmxp0etmZ2dj/vz58PPzw3PPPdei1546dSokEgl8fHxgNpubPC86OhoSiQSjR4/m+2bPng2ApbkWZlG9URCJ+i2EgIAABAQEwN/fHxKJBLm5udi/fz+++eYbPPjggwgODsa33357Xa69ePFivPvuuzc9UQ8KCkJMTAyCgoJu9FJuCWRkZOD48ePQ6/UYMGBAi177rbfeQk1NDWbOnAmtVtui137iiScAAKWlpfj999+bNGfnzp1ISUmpNR8AevXqhWHDhiEpKQk///xz8y/2SnFja3SIaAzvvPMOX+Xo77DZbNyJEye4efPmcREREfy4hx9+mHM4HM26jv79+3MAuHfeeadZzyvixuLzzz/n35mWRHZ2NieTyTg3NzeupKSkRa/tQocOHTgA3PDhw5s0fsqUKRwALiAggLNarbWOrV69mgPAxcbGXo+lXhFETv0WhkwmQ6dOnfDKK6/g1KlTGD9+PADgl19+wccff3yDVyfiVoCLSx0zZkyLXveHH36A3W7HyJEj4e3t3aLXdsHFbW/atAk5OTmXHVtZWYlVq1YBYPVg5fLa9YVc93H69Gns3bv3+iy4qbjRXxURl8flOPW/w2w2c126dOEAcB4eHg1yQGlpadyLL77IdejQgdNqtZxareZiYmK4F154gcvIyKg1dtGiRfz1G/pLS0vjx4eFhXEAuEWLFjW4ThfHM2XKlDrHhBKBw+Hgvv/+ey4hIYFzd3fndDod16tXL27p0qU35NwOh4P78ccfuV69enE6nY7z8PDgEhISuO+++45zOByXvXZTUVpayn3++edc3759udDQUM7NzY3z8fHhOnbsyD388MPc2rVrr/rc9V1LLpdzbm5uXEVFRb1jdu/ezf3jH//ggoKCOJVKxcXExHCzZs3iqqurOY7juEuXLnFyuZyLjo7mbDZbk67rcDi40NBQDgC3YsWKBscJn5fVauXmz5/PxcfHc1qtlvPz8+PGjBnDJScn8+ONRiP3/vvvc7GxsZxGo+G8vb25hx56iEtJSan3/IWFhZxCoeAAcB988MFl1/zDDz/w7/vZs2frHTN16lQOADd58uQm7ML1g0jUb3JcCVHnOI5buXIlP37hwoV1jv/888+cUqnkxyiVSk6tVvP/d3d35zZu3MiPX758ORcQEMC//FqtlgsICKj1l5mZyY9vLqI+a9YsbsyYMRwATi6X80WWXX9vv/12i57bZrNx//jHP/gxEomE8/Ly4qRSKQeAmzBhwjUT9X379nH+/v78NRQKBeft7c3J5XK+b968eVd17vqwdOnSBtUPJpOJJ1IAOJVKVeu9mTp1KsdxHPfEE09wAC77Mfw7Tpw4wZ8nLy+vwXGu5/Xmm29ygwYN4gBwbm5unFar5efrdDru8OHDXHFxMc/QqFSqWu+0v79/HWbFhQceeIADwLVp0+aya+7Tpw8HgOvTp0+DY1z7GRAQ0LSNuE4QifpNjisl6pWVlZxMJquXY9i0aRMnlUo5uVzOzZw5k0tLS+McDgfncDi4c+fOcQ8++CDP5f/9R9BUnXpzEXUvLy/O09OTW7x4Mc8VZmVlcffeey8HgJNKpdyFCxda7Nxz5szhn8Mrr7zCFRcXcxzHceXl5dxHH33EE/mrJepVVVU8QR83bhx39OjRWsdzcnK41atXc5cuXbriczeEcePGcQC4//znP7X6HQ4Hd//993MAOE9PT27lypWczWbjrFYr989//pPfozNnznAKhYJr164dZ7fbm3zdr776igPAtWrV6rLjXM9Lr9dzPj4+3MqVKzmLxcI5HA7u0KFDXGRkJE9o77vvPi48PJzbuHEjZ7fbObvdzm3ZsoXz8/PjAHATJ06s9xrr1q3jn+vOnTvrHXPu3LnLMkouXLhwoVFuviUgEvWbHFdK1DmO46KjozkAXGJiIt9nt9v5/u+++67BuaNHj+YAcC+++GKt/pYm6gC4bdu21TluMpm44ODgBkXm63Huqqoqnpt/4okn6r0n4XO6GqL++++/cwC4sLCwZjdy1weTycTpdDpOIpFwOTk5tY799NNP/L38Xd1jtVo5Hx8fDgDXr18/DgC3fPnyK7r2pEmTOADcqFGjLjtO+Lx2795d5/jWrVv542q1mrt48WKdMQsXLuSPWyyWOsftdjuvCmrouc2cOZOXCiorKy+7Zp1OxwHgfvzxx8uOu54QDaW3IVyGp9LSUr5v165duHjxInx9ffHkk082OHfy5MkAgI0bN17fRTaCxMREDBw4sE6/UqnEJBRH8QAACx9JREFUsGHDAAAnTpxokXNv2rQJFRUVAJgbXn149dVXodFormo9AGCz2QAAubm5+OWXX2C1Wq/6XE3Btm3bUFVVhe7duyM4OJjv5zgO77//PgDggQcewMiRI2vNk8vliIqKAsDeqY4dO+Khhx66omvn5uYCAPz8/Jo0vm/fvujbt2+d/v79+0OpVAIAxo0bhzZt2tQZ43qeNTU1uHjxYp3jUqmUD1RbtWoVqqqqah232+1YunQpAOChhx6CTqe77Fp9fHwA0D3eCIhE/Q6ByyJfXl6O4OBgBAYG1vs3depUAMx/+UaiZ8+eDR5zESHhR+t6nvvYsWMAgNatWyMiIqLeee7u7ujWrdtVrQcA7rnnHiQmJsJqteKRRx6BWq2Gn58fAgMDsXbt2gbnpaSk1AlOCwgIwKhRo3D8+PEG561ZswZAXa+X/fv3877Y06dPr3euSqXi267guCtBUVERADTZ6yUhIaHefplMBl9fXwBAjx496h0TEBDAt8vKyuod89hjj0EikcBoNOLXX3+tdWz9+vXIy8sDUNs3vSG47sl1jzcC8saHiLjV4CJILq4BIM7BarWioKCg0XPU1NRcn8U1Ee7u7g0ec7mTXS03e6Xndv1AhRxtfQgJCbmq9QBMSvjll1/w9NNPY/369bDb7SguLgaAejlQF5KSkgAA06ZNQ69evWC323Hy5El8/fXXOHDgAE6dOoXAwMBacziOw59//gkAGDt2bK1jmzdvBsCCuBr6+LmiJuPj43H//fdf8b2aTCYA4LnsxtCU59XQGKHrYUPvS2RkJAYMGIDt27fjxx9/rEW8f/zxRwBAu3bt0KdPn0bXqlarAdA93giInPpthqqqKqSmpgIALyYDTIwEGJfKMVtKo38iauNKOdIrwb/+9S/ExMTAZDJh48aNKCws5J9DTExMg/NcRH3q1Kl45JFHMGXKFMydOxevvfYaSkpKsH79+jpzDh06hLy8PERFRSE2NrbWsaNHjwJgKqqG7tdoNAIA/vnPf17VnriYjYY45xsBFyHft28fLly4AIB9zP/66y8AwOOPP96k89THULU0RKJ+m2HDhg08AReGfbu4teutVnFxRpfjVMrLy6/rGpobLt1vY3rSxgJYGsKCBQswc+ZMDBgwAFu2bMHQoUObrG9OSkqCm5sbOnToUKs/MjISQP17fbmAo7S0NABAWFhYvdfbunUr/yFp27Ztk9b4d7ju7WrVZ9cDDzzwAPR6PQDizn/++WdYrVbI5XLe1tQYXPfU1Od3PSAS9dsIFosFH330EQDA09OzlmidmJgIAMjPz8eRI0eu+NxSKXtVGuPgvby8AABZWVn1Hnc4HFd1/RuJrl27AmAfxPT09HrHVFVV8VzulWLevHkAmArFtc9NRVJSEjp27AiFQlGr38Wh9+rVq86chvTpAPikcPUlprLZbHjllVf4///9mk2F6wPkkihvBqhUKjz88MMAgCVLlsBut2PRokUAgFGjRtXSzTeEyspKXmXWvn3767fYRiAS9dsENTU1ePTRR3ku6o033uA5DwAYOHAgr5t9+eWXG83o+HcuysPDAwBgMBguOy8uLg4AsHr16no/AD/99BOys7Mve46bDUOHDuXv3/XR/Ds+++wzVFdXX9X5XeJ+YWHhFc3Ly8tDQUEBYmJiUFxcjKKiIiQnJ2PatGlYvnw5pkyZUoeoX7x4EWfPnoWvry//oRfC398fAHDgwIE6xz766KNaXkEuifBK0a9fPwDA8ePHryhD4vWGSwWTl5eH999/HydPngTQdNXLkSNH4HA4IJfL693bloJI1G9hOBwOnDp1CvPnz0dsbCz++9//AgAmTZqEmTNn1horl8vx7bffQi6XY8+ePejXrx+2bt1ay3iUmpqKb7/9Fj169MA333xTa37Hjh0BAOvWrbusmmHChAkAgLNnz+L//u//UFJSAgCoqKjAZ599hqeffvqG5fq4Wmi1Wrz22msAWM6SmTNn8h+9yspKfPLJJ5g9ezYvpVwpXAbY1157DcuWLeM/Dg6HA/n5+fj111/xzDPP1Jnn+oD/97//hZ+fH/z9/dGlSxcsX74cX3/9Na9GEMKlehk1ahRkMlmd43fffTcA5gXz8ccfw2q1wmKx4OOPP8bs2bOh1Wp5D6Bly5Zd1f0mJiZCLpfDYrEgOTn5qs5xPdC1a1fEx8cDAO/WGRQUVMetsyEcPHiQP09jro/XEyJRv4UgdD308vKCQqFAp06d8OqrryItLQ2+vr749ttvsWTJknoNWIMGDcLKlSvh7u6OgwcPYvDgwdBqtfD19YVKpUJUVBSeeeYZHDlypM78KVOmQKVSISUlBa1bt0ZgYCDCw8MRHh5ei/MeNGgQJk2aBIDpin19feHl5QUvLy+88soreOqpp3Dvvfde3426Dpg5cybGjRsHgBk1/fz84O3tDS8vL7z++uuYOHEif19Cl7+m4KOPPoJEIkFxcTEeeeQRaLVaeHl5QalUIigoCOPHj69XunER9e+++w6bN2/GmjVrMHHiRJSWlqKwsLBeVU5jCbyee+45hIaGAmDSnoeHBzw8PPDGG29ApVJhxYoVvFrvo48+gr+/f71c/eXg4eGBe+65BwDwxx9/XNHc6w0Xt+5SP02ZMqXej199cN2LS41zoyAS9VsIBQUFKCgoQGFhIWw2GwIDA9GrVy8888wzWLVqFXJycvDUU09d9hxjx45FSkoK3nnnHSQkJECn08FgMECpVCIuLg5PPvkkVq9ejRkzZtSaFx0dje3bt2P06NHw8/NDSUkJMjIykJGRwQfOuLB48WJ8/vnniI+Ph1qthsPhQGJiIlasWIEvvvii2felJSCXy7FixQosWLAACQkJUKvVsNls6N69OxYsWIAlS5bwqimh2qspmDRpEvbt24dJkyYhKioKarUaRqMRfn5+6NWrF1599VW88847deYlJSVBJpNh8uTJGDx4MMaMGYOlS5eiW7dumDt3Lu+l4kJRURH27dsHtVqNoUOH1rsWPz8/HDhwAE888QRCQ0Nht9shk8nQq1cv7NixAyNHjsTs2bNx3333QaFQoKioCO3atbui+wXAv6e//PLLTeVpNXHixFof5aaqXlJTU7F//36o1eomG1WvG1o8hlWEiNsQwsyDS5YsaZFrRkZGcu3atavT/+WXX3IAuF9//bVWvytk/t57722R9V0Odrudi4qKumzOlVsJ7777LgeAe+yxx270UsQ0ASJENAeWLl2K7OxsyOVyDB48+Lpfr7y8HGlpabxhWohRo0YBIC8XF1yql78HHN0ISKVSXm99q+f+NxqN+PLLL6FUKuuVqFoaIlEXIaKJmDBhAlatWsW7rQFMJfbxxx/z6RUmT57cIqX0kpOTwXFcvUQ9PDwc7du3x7p162oZwhMTE/HOO++0eEGMhjB+/HgkJCRg/fr1OHTo0I1ezlXjq6++QnFxMV544YUG/ftbEmKaABEimoj169dj+fLlAACNRgOFQlEruOeuu+7CZ5991iJrcRlJO3fuXO/xESNGYP78+di5cycvOfzdI+pGQyKR4LvvvsOaNWtuaK6Ua4VWq8Xs2bPx0ksv3eilAAAkHHcTWSlEiLiJsWTJEqxfvx5JSUkoLCxEVVUV9Ho94uPjMX78eEyaNOmqA3JEiGguiERdhAgRIm4jiDp1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiNIBJ1ESJEiLiN8P/n+PG/j5GdcQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 450x340 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot main figure\n",
+ "fig, ax = plt.subplots(figsize=(4.5,3.4))\n",
+ "plt.subplots_adjust(left=0.1, right=0.99, top=0.98, bottom=0.2)\n",
+ "left_dot_factor = 2.5 #factor to make the intensities of both sensor equal (similairy to the experiment)\n",
+ "sum_sensor_gradient = left_dot_factor*np.abs(np.gradient(sensor_signalexp[:,:,0],axis=0)\n",
+ " ) + np.abs(np.gradient(sensor_signalexp[:,:,1],axis=1)) #sum of gradients\n",
+ "\n",
+ "pc = ax.pcolormesh(1e3*xexp,1e3*yexp,sum_sensor_gradient/np.max(sum_sensor_gradient),\n",
+ " cmap=\"viridis_r\",vmin=-0.05, vmax=0.8) #plot the sum of gradients\n",
+ "ax.set_aspect('equal', 'box')\n",
+ "\n",
+ "#plot polytopes. NOTE: only_labels=True -> plot only the labels of the polytopes, skip_dots=[4,5] -> skip the sensor indices\n",
+ "plot_polytopes(ax, polytopesexp, axes_rescale=1e3, only_labels=True, fontsize=15, color=\"black\", skip_dots = [4,5])\n",
+ "\n",
+ "#labels\n",
+ "plt.xlabel(r'Detuning $\\epsilon_{R}/\\alpha$ (mV)')\n",
+ "plt.ylabel(r'Detuning $\\epsilon_{L}/\\alpha$ (mV)')\n",
+ "cbaxes = inset_axes(ax, width=\"40%\", height=\"3%\", loc='lower right', borderpad=1)\n",
+ "cb = plt.colorbar(pc,ticks = [0,0.8], cax=cbaxes, orientation='horizontal')\n",
+ "cb.ax.set_xticklabels([\"0\",\"1\"])\n",
+ "\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "cb.ax.set_xlabel('Norm. gradient', fontsize=10)\n",
+ "cb.ax.xaxis.set_label_position('top')\n",
+ "\n",
+ "\n",
+ "plt.savefig(\"figures/fig1.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59cc821a",
+ "metadata": {},
+ "source": [
+ "### Figure 4: Charge stability diagram with varying Coulomb peak size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77e6ec79",
+ "metadata": {},
+ "source": [
+ "In this example we test the effect of varying Coulomb peak size on the charge stability diagram. This is controlled by the capacitance configuration file, through the parameter ks. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "c2487482",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate the simplest CSD diagram\n",
+ "xks, yks, csd_dataks, polytopesks, _, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(0, 0.06, 500), #V\n",
+ " y_voltages = np.linspace(0, 0.03, 100), #V\n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]], #NOTE: with defauls use_virtual_gates = False, this means voltages.\n",
+ " compute_polytopes = True) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "79423fac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Create a figure\n",
+ "fig = plt.figure(figsize=(12, 4))\n",
+ "\n",
+ "\n",
+ "# Define the grid\n",
+ "gs = gridspec.GridSpec(1, 2, width_ratios=[4, 1.3])\n",
+ "plt.subplots_adjust(wspace=0.3, top = 0.98, bottom = 0.16, left = 0.08, right = 0.99)\n",
+ "\n",
+ "# Create subplots\n",
+ "ax0 = plt.subplot(gs[0])\n",
+ "ax1 = plt.subplot(gs[1])\n",
+ "\n",
+ "\n",
+ "#plot the csd with background polytopes\n",
+ "ax0.pcolormesh(1e3*xks,1e3*yks,csd_dataks) #plot the background\n",
+ "plot_polytopes(ax0,polytopesks, axes_rescale = 1e3, skip_dots=[2,3,4,5], fontsize=16) #plot the polytopes\n",
+ "\n",
+ "# plot customisation\n",
+ "ax0.set_xlabel('Relative Voltage $v_0$ (mV)')\n",
+ "ax0.set_ylabel('Relative Voltage $v_1$ (mV)')\n",
+ "ax0.set_xlim(xks[0]*1e3,xks[-1]*1e3)\n",
+ "ax0.set_ylim(yks[0]*1e3,yks[-1]*1e3)\n",
+ "\n",
+ "\n",
+ "# computation of the line-cut data\n",
+ "cut_voltage = 14*1e-3 #select the voltage at which the line-cut is taken\n",
+ "indx = np.argmin(np.abs(yks-cut_voltage)) #find corresponding index\n",
+ "labels = set(csd_dataks[indx,:])\n",
+ "addition_energies = [np.sum(csd_dataks[indx,:]==label) for label in labels]\n",
+ "\n",
+ "#plot the line-cut data\n",
+ "ax1.plot(np.arange(1,6),addition_energies[1:-1]/addition_energies[1], \"o\")\n",
+ "ax1.set_ylabel(r\"Diamond size $\\Delta v_{n}/\\Delta v_{1}$\")\n",
+ "ax1.set_xlabel(r\"Diamond number $n$\")\n",
+ "ax1.set_xticks(np.arange(1,6))\n",
+ "ax0.hlines(yks[indx]*1e3, xks[0]*1e3, xks[-1]*1e3, color=\"r\", linestyle=\"--\")\n",
+ "\n",
+ "\n",
+ "plt.savefig(\"figures/fig4.png\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad4f1534",
+ "metadata": {},
+ "source": [
+ "### Figure 5: Finding particulair transition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79fb803d",
+ "metadata": {},
+ "source": [
+ ".Here we show the ability to select the transition in high-dimensional voltage space. We compare two-dot and four-dot transition"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "d05b293c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.01825755 0.01984214 0.05132545 0.03493506 0.04909741 0.04986671]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Two-dot transition\n",
+ "x2, y2, csd_data2, polytopes2, _, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.015, 0.015, 100), #V\n",
+ " y_voltages = np.linspace(-0.015, 0.015, 100), #V \n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]],\n",
+ " compute_polytopes = True,\n",
+ " target_state=[2,1,5,3,4,4], #target state for the transition\n",
+ " target_transition=[-1,1,0,0,0,0]) #target transition from the target state\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "20abf1d4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.01871567 0.02037499 0.04581756 0.03699487 0.05022946 0.05079063]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Four-dot-transition\n",
+ "x4, y4, csd_data4, polytopes4, _, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.005, 0.005, 100), #V\n",
+ " y_voltages = np.linspace(-0.005, 0.005, 100), #V\n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]],\n",
+ " compute_polytopes = True,\n",
+ " target_state=[2,1,5,3,4,4],\n",
+ " target_transition=[-1,1,-1,1,0,0]) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "ec6de142",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a figure\n",
+ "fig = plt.figure(figsize=(10, 4))\n",
+ "\n",
+ "\n",
+ "# Define the grid\n",
+ "gs = gridspec.GridSpec(1, 2, width_ratios=[1.3, 1.3])\n",
+ "plt.subplots_adjust(wspace=0.25, top = 0.98, bottom = 0.16, left = 0.06, right = 0.99)\n",
+ "\n",
+ "#Two-dot transition plotting\n",
+ "ax0 = plt.subplot(gs[0])\n",
+ "ax0.pcolormesh(1e3*x2,1e3*y2,csd_data2) \n",
+ "plot_polytopes(ax0,polytopes2, axes_rescale = 1e3, skip_dots=[4,5], fontsize=11)\n",
+ "ax0.set_xlabel('Voltage $v_0$ (mV)')\n",
+ "ax0.set_ylabel('Voltage $v_1$ (mV)')\n",
+ "ax0.set_xlim(x2[0]*1e3,y2[-1]*1e3)\n",
+ "ax0.set_ylim(y2[0]*1e3,y2[-1]*1e3)\n",
+ "ax0.set_aspect('equal')\n",
+ "\n",
+ "#Four-dot transition plotting\n",
+ "ax1 = plt.subplot(gs[1])\n",
+ "ax1.pcolormesh(1e3*x4,1e3*y4,csd_data4) \n",
+ "plot_polytopes(ax1,polytopes4, axes_rescale = 1e3, fontsize=12, color=\"white\")\n",
+ "ax1.set_xlabel('Voltage $v_0$ (mV)')\n",
+ "ax1.set_ylabel('Voltage $v_1$ (mV)')\n",
+ "ax1.set_xlim(x4[0]*1e3,x4[-1]*1e3)\n",
+ "ax1.set_ylim(y4[0]*1e3,y4[-1]*1e3)\n",
+ "ax1.set_aspect('equal')\n",
+ "plt.savefig(\"figures/fig5.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a8c84df",
+ "metadata": {},
+ "source": [
+ "### Fig 6: Virtualisation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e83156fb",
+ "metadata": {},
+ "source": [
+ "We now showcase gate virtualisation, i.e. allignment of the transition with selectaed axes in plane_axes. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "9ff3ab0d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.05306243 0.04244193 0.04984212 0.04984418 0.0561569 0.05628286]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Show the transition without gate virtualisation\n",
+ "xnV, ynV, csd_datanV, polytopesnV, _, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.005, 0.015, 100), #V\n",
+ " y_voltages = np.linspace(-0.001, 0.019, 100), #V\n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]],\n",
+ " compute_polytopes = True,\n",
+ " target_state=[5,4,5,5,5,5],\n",
+ " target_transition=[1,0,0,0,0,0], #Transtiion from [5,4,5,5,5,5] -> [6,4,5,5,5,5] at v=0.\n",
+ " compensate_sensors=False) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "c4f8da6b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.05306243 0.04244193 0.04984212 0.04984418 0.0561569 0.05628286]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Show the same transition with gate virtualisation\n",
+ "xV, yV, csd_dataV, polytopesV, _ ,_ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.005, 0.015, 100), #V\n",
+ " y_voltages = np.linspace(-0.001, 0.019, 100), #V\n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]],\n",
+ " compute_polytopes = True,\n",
+ " target_state=[5,4,5,5,5,5],\n",
+ " target_transition=[1,0,0,0,0,0] , \n",
+ " use_virtual_gates= True) # Now we change it to True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "beb42a81",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x400 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Create a figure\n",
+ "fig = plt.figure(figsize=(10, 4))\n",
+ "\n",
+ "\n",
+ "# Define the grid\n",
+ "gs = gridspec.GridSpec(1, 2, width_ratios=[1.3, 1.3])\n",
+ "plt.subplots_adjust(wspace=0.2, top = 0.96, bottom = 0.18, left = 0.06, right = 0.99)\n",
+ "\n",
+ "# Create subplots\n",
+ "ax0 = plt.subplot(gs[0])\n",
+ "ax1 = plt.subplot(gs[1])\n",
+ "\n",
+ "\n",
+ "#plot the csd with background polytopes\n",
+ "ax0.pcolormesh(1e3*xnV,1e3*ynV,csd_datanV) \n",
+ "plot_polytopes(ax0,polytopesnV, axes_rescale = 1e3, skip_dots = [4,5], fontsize=10)\n",
+ "\n",
+ "ax1.pcolormesh(1e3*xV,1e3*yV,csd_dataV)\n",
+ "plot_polytopes(ax1,polytopesV, axes_rescale = 1e3, skip_dots = [4,5], fontsize=11)\n",
+ "\n",
+ "# plot customisation\n",
+ "ax0.set_xlabel('Voltage $v_0$ (mV)')\n",
+ "ax0.set_ylabel('Voltage $v_1$ (mV)')\n",
+ "ax0.set_xlim(xnV[0]*1e3,xnV[-1]*1e3)\n",
+ "ax0.set_ylim(ynV[0]*1e3,ynV[-1]*1e3)\n",
+ "ax0.set_aspect('equal')\n",
+ "\n",
+ "# plot customisation\n",
+ "ax1.set_xlabel('Virtual voltage $u_0$ (mV)')\n",
+ "ax1.set_ylabel('Virtual voltage $u_1$ (mV)')\n",
+ "ax1.set_xlim(xV[0]*1e3,xV[-1]*1e3)\n",
+ "ax1.set_ylim(yV[0]*1e3,yV[-1]*1e3)\n",
+ "ax1.set_aspect('equal')\n",
+ "plt.savefig(\"figures/fig6.png\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46cbca07",
+ "metadata": {},
+ "source": [
+ "### Fig 7: Sensor parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5a3f96f",
+ "metadata": {},
+ "source": [
+ "We now show the consequence of different source of the noise:\n",
+ "- Fast noise: noise that is present in the sensor signal and is fast compared to the measurement time.\n",
+ "- Slow noise: noise that is present in the sensor signal and is slow compared to the measurement time.\n",
+ "- Temperature (relevant for tunnel coupling simulations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "0fb9511e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 10. 0. 0. 1.6 0. ]\n",
+ " [10. 20. 6. 0. 1.4 0. ]\n",
+ " [ 0. 6. 20. 7. 0. 1.4]\n",
+ " [ 0. 0. 7. 20. 0. 2. ]\n",
+ " [ 1.6 1.4 0. 0. 20. 0. ]\n",
+ " [ 0. 0. 1.4 2. 0. 20. ]]\n",
+ " Dot-gate capacitances: \n",
+ " [[11. 1.5 0. 0. 0. 0. ]\n",
+ " [ 1.2 11. 0. 0. 0. 0. ]\n",
+ " [ 0. 0. 11. 1.3 0. 0. ]\n",
+ " [ 0. 0. 1.4 11. 0. 0. ]\n",
+ " [ 0. 0. 0. 0. 11. 0. ]\n",
+ " [ 0. 0. 0. 0. 0. 11. ]]\n",
+ " Size of Coulomb peaks V[n] scale as 0.701/(n + 6)\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: [4, 5]\n",
+ " Sensor detunings: [0.3 0.3] meV\n",
+ " Coulomb peak width: 0.55 meV\n",
+ " Slow noise amplitude: 0.0 ueV\n",
+ " Fast noise amplitude: 0.0 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [5.e-05 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 6.e-05 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 6.e-05 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]]\n",
+ " Temperature: 0.1 K\n",
+ " Energy range factor: 5\n",
+ " \n",
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 10. 0. 0. 1.6 0. ]\n",
+ " [10. 20. 6. 0. 1.4 0. ]\n",
+ " [ 0. 6. 20. 7. 0. 1.4]\n",
+ " [ 0. 0. 7. 20. 0. 2. ]\n",
+ " [ 1.6 1.4 0. 0. 20. 0. ]\n",
+ " [ 0. 0. 1.4 2. 0. 20. ]]\n",
+ " Dot-gate capacitances: \n",
+ " [[11. 1.5 0. 0. 0. 0. ]\n",
+ " [ 1.2 11. 0. 0. 0. 0. ]\n",
+ " [ 0. 0. 11. 1.3 0. 0. ]\n",
+ " [ 0. 0. 1.4 11. 0. 0. ]\n",
+ " [ 0. 0. 0. 0. 11. 0. ]\n",
+ " [ 0. 0. 0. 0. 0. 11. ]]\n",
+ " Size of Coulomb peaks V[n] scale as 0.701/(n + 6)\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: [4, 5]\n",
+ " Sensor detunings: [0.3 0.3] meV\n",
+ " Coulomb peak width: 0.55 meV\n",
+ " Slow noise amplitude: 0.0 ueV\n",
+ " Fast noise amplitude: 20.0 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [5.e-05 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 6.e-05 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 6.e-05 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]]\n",
+ " Temperature: 0.1 K\n",
+ " Energy range factor: 5\n",
+ " \n",
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 10. 0. 0. 1.6 0. ]\n",
+ " [10. 20. 6. 0. 1.4 0. ]\n",
+ " [ 0. 6. 20. 7. 0. 1.4]\n",
+ " [ 0. 0. 7. 20. 0. 2. ]\n",
+ " [ 1.6 1.4 0. 0. 20. 0. ]\n",
+ " [ 0. 0. 1.4 2. 0. 20. ]]\n",
+ " Dot-gate capacitances: \n",
+ " [[11. 1.5 0. 0. 0. 0. ]\n",
+ " [ 1.2 11. 0. 0. 0. 0. ]\n",
+ " [ 0. 0. 11. 1.3 0. 0. ]\n",
+ " [ 0. 0. 1.4 11. 0. 0. ]\n",
+ " [ 0. 0. 0. 0. 11. 0. ]\n",
+ " [ 0. 0. 0. 0. 0. 11. ]]\n",
+ " Size of Coulomb peaks V[n] scale as 0.701/(n + 6)\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: [4, 5]\n",
+ " Sensor detunings: [0.3 0.3] meV\n",
+ " Coulomb peak width: 0.55 meV\n",
+ " Slow noise amplitude: 30.0 ueV\n",
+ " Fast noise amplitude: 0.0 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [5.e-05 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 6.e-05 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 6.e-05 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]]\n",
+ " Temperature: 0.1 K\n",
+ " Energy range factor: 5\n",
+ " \n",
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 10. 0. 0. 1.6 0. ]\n",
+ " [10. 20. 6. 0. 1.4 0. ]\n",
+ " [ 0. 6. 20. 7. 0. 1.4]\n",
+ " [ 0. 0. 7. 20. 0. 2. ]\n",
+ " [ 1.6 1.4 0. 0. 20. 0. ]\n",
+ " [ 0. 0. 1.4 2. 0. 20. ]]\n",
+ " Dot-gate capacitances: \n",
+ " [[11. 1.5 0. 0. 0. 0. ]\n",
+ " [ 1.2 11. 0. 0. 0. 0. ]\n",
+ " [ 0. 0. 11. 1.3 0. 0. ]\n",
+ " [ 0. 0. 1.4 11. 0. 0. ]\n",
+ " [ 0. 0. 0. 0. 11. 0. ]\n",
+ " [ 0. 0. 0. 0. 0. 11. ]]\n",
+ " Size of Coulomb peaks V[n] scale as 0.701/(n + 6)\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: [4, 5]\n",
+ " Sensor detunings: [0.3 0.3] meV\n",
+ " Coulomb peak width: 0.55 meV\n",
+ " Slow noise amplitude: 0.0 ueV\n",
+ " Fast noise amplitude: 0.0 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [5.e-05 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 6.e-05 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 6.e-05 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n",
+ " [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]]\n",
+ " Temperature: 0.5 K\n",
+ " Energy range factor: 5\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "# We define the baseline configuration files:\n",
+ "sensor_config_base = {\n",
+ " # Specify list of indices of dots that are sensors\n",
+ " \"sensor_dot_indices\": [4,5],\n",
+ " # Specify a list of the same length, with detuning for each sensor \n",
+ " \"noise_amplitude\": {\"fast_noise\": 1e-99, \"slow_noise\": 1e-99}, # sigma of the noise in eV, typically 10 ueV\n",
+ " \"peak_width_multiplier\": 10, # broedening of the peak due to tunnel coupling to reservoir, width = p_w_m*0.12meV*T/0.1K\n",
+ " \"sensor_detunings\": [3*1e-4,3*1e-4], #detuning from the center of coulomb peak in eV, typically ~ peak width\n",
+ "\n",
+ "}\n",
+ "\n",
+ "tunneling_config_base = {\n",
+ " \"tunnel_couplings\": tunnel_couplings,\n",
+ " \"temperature\": 0.1,\n",
+ " \"energy_range_factor\": 5,\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "#Here we define the parameters to be scanned in four different experiments\n",
+ "experiment_params = [{\"slow_noise_amp\": 1e-99, \"fast_noise_amp\": 1e-99, \"temperature\": 0.1, \"width\": 20},\n",
+ " {\"slow_noise_amp\": 1e-99, \"fast_noise_amp\": 20*1e-6, \"temperature\": 0.1, \"width\": 20},\n",
+ " {\"slow_noise_amp\": 30*1e-6, \"fast_noise_amp\": 1e-99, \"temperature\": 0.1, \"width\":20},\n",
+ " {\"slow_noise_amp\": 1e-99, \"fast_noise_amp\": 1e-99, \"temperature\": 0.5, \"width\": 4},]\n",
+ "#NOTE: In the last experiment we keep width of Coulomb peak constant by compensating temperature with peak_width_multiplier.\n",
+ "\n",
+ "# Generation of four different experiment objects:\n",
+ "sensor_experiment = []\n",
+ "for params in experiment_params:\n",
+ " sensor_config = sensor_config_base.copy()\n",
+ " tunneling_config = tunneling_config_base.copy()\n",
+ "\n",
+ " sensor_config[\"noise_amplitude\"] = {\"fast_noise\": params[\"fast_noise_amp\"], \"slow_noise\": params[\"slow_noise_amp\"]}\n",
+ " tunneling_config[\"temperature\"] = params[\"temperature\"]\n",
+ " sensor_config[\"peak_width_multiplier\"] = params[\"width\"] \n",
+ " sensor_experiment.append(Experiment(capacitance_config, tunneling_config, sensor_config))\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "87cfa416",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.02099275 0.03913514 0.01315959 0.04491494]\n",
+ "v_offset found: [0.02099275 0.03913514 0.01315959 0.04491494]\n",
+ "v_offset found: [0.02099275 0.03913514 0.01315959 0.04491494]\n",
+ "v_offset found: [0.02099275 0.03913514 0.01315959 0.04491494]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute CSD in four experiments defined in the previous cell (It takes about 4x2min)\n",
+ "sensor_signal_list = []\n",
+ "polytopes_list = []\n",
+ "\n",
+ "for k in range(4):\n",
+ " x,y, CSD_data, polytopes1, sensor_signal, _ = sensor_experiment[k].generate_CSD(\n",
+ " x_voltages = np.linspace(-0.01, 0.010, 100),\n",
+ " y_voltages = np.linspace(-0.015, 0.015, 100),\n",
+ " plane_axes = [[1,0,0,0,0,0],[0,1,0,0,0,0]],\n",
+ " target_state = [2,3,1,4,5,5],\n",
+ " target_transition = [0,1,0,0,0,0],\n",
+ " compensate_sensors = True, #Sensors are conmpensated to not affects noise effects\n",
+ " use_virtual_gates = False,\n",
+ " compute_polytopes = True,\n",
+ " use_sensor_signal = True\n",
+ " )\n",
+ " sensor_signal_list.append(sensor_signal)\n",
+ " polytopes_list.append(polytopes1)\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "7199e085",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/wj/x78z9z710q7csr068fhd10bxk7mx3x/T/ipykernel_9566/3274068000.py:25: UserWarning: Adding colorbar to a different Figure <Figure size 400x600 with 5 Axes> than <Figure size 400x600 with 4 Axes> which fig.colorbar is called on.\n",
+ " cb = plt.colorbar(pc1,ticks = [], cax=cbaxes, orientation='horizontal')\n",
+ "/var/folders/wj/x78z9z710q7csr068fhd10bxk7mx3x/T/ipykernel_9566/3274068000.py:25: UserWarning: Adding colorbar to a different Figure <Figure size 400x600 with 6 Axes> than <Figure size 400x600 with 5 Axes> which fig.colorbar is called on.\n",
+ " cb = plt.colorbar(pc1,ticks = [], cax=cbaxes, orientation='horizontal')\n",
+ "/var/folders/wj/x78z9z710q7csr068fhd10bxk7mx3x/T/ipykernel_9566/3274068000.py:25: UserWarning: Adding colorbar to a different Figure <Figure size 400x600 with 7 Axes> than <Figure size 400x600 with 6 Axes> which fig.colorbar is called on.\n",
+ " cb = plt.colorbar(pc1,ticks = [], cax=cbaxes, orientation='horizontal')\n",
+ "/var/folders/wj/x78z9z710q7csr068fhd10bxk7mx3x/T/ipykernel_9566/3274068000.py:25: UserWarning: Adding colorbar to a different Figure <Figure size 400x600 with 8 Axes> than <Figure size 400x600 with 7 Axes> which fig.colorbar is called on.\n",
+ " cb = plt.colorbar(pc1,ticks = [], cax=cbaxes, orientation='horizontal')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x600 with 8 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x600 with 8 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot of the four experiments and the gradients\n",
+ "#Generate 2x2 plot number 1 (signals)\n",
+ "fig, axs = plt.subplots(2,2, figsize = (4,6))\n",
+ "plt.subplots_adjust(wspace=0.3, hspace=0.3, top = 0.98, bottom = 0.16, left = 0.06, right = 0.99)\n",
+ "\n",
+ "#Generate 2x2 plot number 2 (gradients)\n",
+ "fig, axs2 = plt.subplots(2,2, figsize = (4,6))\n",
+ "plt.subplots_adjust(wspace=0.3, hspace=0.3, top = 0.98, bottom = 0.16, left = 0.06, right = 0.99)\n",
+ "\n",
+ "\n",
+ "for k in range(4):\n",
+ " ax = axs[k//2,k%2]\n",
+ " ax2 = axs2[k//2,k%2]\n",
+ " pc1 = ax.pcolormesh(1e3*x,1e3*y,sensor_signal_list[k][:,:,0], cmap=\"viridis_r\") \n",
+ " pc2= ax2.pcolormesh(1e3*x,1e3*y,\n",
+ " np.gradient(sensor_signal_list[k][:,:,0],axis=1),cmap=\"viridis_r\") #gradient along x-axis\n",
+ " if k%2==0:\n",
+ " ax.set_ylabel('$v_1$ (mV)')\n",
+ " ax2.set_ylabel('$v_1$ (mV)')\n",
+ " if k//2==1:\n",
+ " ax.set_xlabel('$v_0$ (mV)')\n",
+ " ax2.set_xlabel('$v_0$ (mV)')\n",
+ " \n",
+ " cbaxes = inset_axes(ax, width=\"40%\", height=\"3%\", loc='lower center', borderpad=1.2)\n",
+ " cb = plt.colorbar(pc1,ticks = [], cax=cbaxes, orientation='horizontal')\n",
+ " #cb.ax.set_xticklabels([\"0\",\"1\"])\n",
+ " cb.ax.tick_params(labelsize=12)\n",
+ " cb.ax.set_xlabel('Norm. conductance', fontsize=11)\n",
+ " cb.ax.xaxis.set_label_position('top')\n",
+ "\n",
+ "\n",
+ " cbaxes = inset_axes(ax2, width=\"40%\", height=\"3%\", loc='lower center', borderpad=1.2)\n",
+ " cb = plt.colorbar(pc2,ticks = [], cax=cbaxes, orientation='horizontal')\n",
+ " #cb.ax.set_xticklabels([\"0\",\"1\"])\n",
+ "\n",
+ " cb.ax.tick_params(labelsize=12)\n",
+ " cb.ax.set_xlabel('Norm. gradient', fontsize=11, color=\"k\")\n",
+ " cb.ax.xaxis.set_label_position('top')\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "071315e1",
+ "metadata": {},
+ "source": [
+ "### Fig 8: Tunnel coupling and sensor compensation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91187b14",
+ "metadata": {},
+ "source": [
+ "In the last example we show the effect of tunnel coupling on the charge stability diagram, manifesting itself as a level repulsin. It is controlled by the parameter tc in the device configuration file. Additionally, we show how the sensor compensation works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "0fe0e069",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n",
+ "v_offset found: [0.02796662 0.01679969 0.03875483 0.01674422 0.04986768 0.04964416]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Plot without sensor compensation\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "xtc, ytc, CSD_data, polytopestc, sensor_signaltc, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.004, 0.002, 100),\n",
+ " y_voltages = np.linspace(-0.003, 0.003, 100),\n",
+ " plane_axes = [[0,0,1,0,0,0],[0,0,0,1,0,0]],\n",
+ " target_state = [2,1,3,2,4,4], \n",
+ " target_transition = [0,0,1,-1,0,0],\n",
+ " compute_polytopes = True,\n",
+ " compensate_sensors = False,\n",
+ " use_virtual_gates = False,\n",
+ " use_sensor_signal=True\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "9c85c6c4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [0.02791077 0.01643804 0.02826617 0.02896927]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Plot with sensor compensation\n",
+ "xtcC, ytcC, CSD_data, polytopestcC, sensor_signaltcC, _ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.004, 0.002, 100),\n",
+ " y_voltages = np.linspace(-0.003, 0.003, 100),\n",
+ " plane_axes = [[0,0,1,0,0,0],[0,0,0,1,0,0]],\n",
+ " target_state = [2,1,3,2,4,4], \n",
+ " target_transition = [0,0,-1,1,0,0],\n",
+ " compute_polytopes = True,\n",
+ " compensate_sensors = True,\n",
+ " use_virtual_gates = False,\n",
+ " use_sensor_signal=True\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "692be297",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x400 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "# Create a figure\n",
+ "fig = plt.figure(figsize=(10, 4))\n",
+ "\n",
+ "\n",
+ "# Define the grid\n",
+ "gs = gridspec.GridSpec(1, 2, width_ratios=[1.3, 1.3])\n",
+ "plt.subplots_adjust(wspace=0.3, top = 0.98, bottom = 0.16, left = 0.06, right = 0.99)\n",
+ "\n",
+ "#plot without sensor compensation\n",
+ "ax0 = plt.subplot(gs[0])\n",
+ "pc0 = ax0.pcolormesh(1e3*xtc,1e3*ytc,sensor_signaltc[:,:,0])\n",
+ "plot_polytopes(ax0, polytopestc, axes_rescale=1e3, fontsize = 12) \n",
+ "ax0.set_xlim(xtc[0]*1e3,xtc[-1]*1e3)\n",
+ "ax0.set_ylim(ytc[0]*1e3,ytc[-1]*1e3)\n",
+ "ax0.set_xlabel('Voltage $v_2$ (mV)')\n",
+ "ax0.set_ylabel('Voltage $v_3$ (mV)')\n",
+ "ax0.set_aspect('equal')\n",
+ "\n",
+ "\n",
+ "cbaxes = inset_axes(ax0, width=\"40%\", height=\"3%\", loc='upper left', borderpad=0.8)\n",
+ "cb = plt.colorbar(pc0,ticks = [0,1], cax=cbaxes, orientation='horizontal')\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "cb.ax.set_xlabel('Norm. conductance', fontsize=10)\n",
+ "cb.ax.xaxis.set_label_position('top')\n",
+ "cb.ax.set_xticklabels([\"0\",\"1\"])\n",
+ "\n",
+ "#plot with sensor compensation\n",
+ "ax1 = plt.subplot(gs[1])\n",
+ "pc1 = ax1.pcolormesh(1e3*xtcC,1e3*ytcC,sensor_signaltcC[:,:,0])\n",
+ "plot_polytopes(ax1, polytopestcC, axes_rescale=1e3, fontsize = 12)\n",
+ "ax1.set_xlim(xtcC[0]*1e3,xtcC[-1]*1e3)\n",
+ "ax1.set_ylim(ytcC[0]*1e3,ytcC[-1]*1e3)\n",
+ "ax1.set_xlabel('Voltage $v_2$ (mV)')\n",
+ "ax1.set_ylabel('Voltage $v_3$ (mV)')\n",
+ "ax1.set_aspect('equal')\n",
+ "\n",
+ "\n",
+ "cbaxes = inset_axes(ax1, width=\"40%\", height=\"3%\", loc='upper left', borderpad=0.8)\n",
+ "cb = plt.colorbar(pc1,ticks = [0,1], cax=cbaxes, orientation='horizontal')\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "cb.ax.set_xlabel('Norm. conductance', fontsize=10, color = \"w\")\n",
+ "cb.ax.xaxis.set_label_position('top')\n",
+ "cb.ax.set_xticklabels([\"0\",\"1\"], color=\"w\")\n",
+ "\n",
+ "\n",
+ "plt.savefig(\"figures/fig8.png\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:devel]",
+ "language": "python",
+ "name": "conda-env-devel-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/insitu_reflectometry_beta.ipynb b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/insitu_reflectometry_beta.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c1b548d50b5d5a1fd9fbc9848560966e3cc9a887
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/examples/insitu_reflectometry_beta.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e27c8b66",
+ "metadata": {},
+ "source": [
+ "## In-situ reflectometry notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76a8c9be",
+ "metadata": {},
+ "source": [
+ " In order to make the code more versatile and useful for a wider range of applications, we have implemented the vannila version of in-situ reflectometry and provided its description in the repository, providing a separate example file. For the time being we have not included description of this mechanism in the manuscript, as the corresponding user interface is likely to change, once barrier gates are added (work in progress). Nevertheless, below we provide a brief description of the implemented feature from the corresponding notebook example ```insitu_reflectometry.ipynb```:\n",
+ "\n",
+ "Following Vigneau et al. (2023), we have implemented the adiabatic version of the quantum capacitance sensing. We use the fact that capacitance is proportional to $\\partial P_\\mathbf{n}/\\partial \\epsilon$, where $P_\\mathbf{n}$ is the probability of finding charge occupation $\\mathbf{n}$ that minimizes electrostatic energy $E_\\mathbf{n}$ at given voltage $\\mathbf{v}$, and $\\epsilon_{\\mathbf{n}\\mathbf{m}}$ is the energy detuning to the next energy state with charge configuration $\\mathbf{m}$. The insitu signal is activated by adding the insitu_axis argument (default: None):\n",
+ "```python\n",
+ "\n",
+ "model = CapacitanceModel(\n",
+ " ...\n",
+ " insitu_axis=[1,0],\n",
+ " plane_axes=[[1,0],[0,1]]\n",
+ " ...\n",
+ ")\n",
+ "```\n",
+ "where by defining the vector ```insitu_axis``` we select which gate is to be modulated. In the example above, the oscilattory signal is coupled to the first axis, which according to ```insitu_axis``` is corresponds to the first plunger gate $v_0$. We compute the relative signal strenght using finite difference approximation to the derrivative:\n",
+ "$$\n",
+ "S(\\mathbf v) \\propto \\frac{\\partial P_\\mathbf{n}}{\\partial \\epsilon_{\\mathbf{n}\\mathbf{m}}} \\approx \\frac{P_\\mathbf{n}(\\mathbf v + \\delta \\mathbf v) - P_\\mathbf{n}(\\mathbf v)}{\\epsilon_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v} + \\delta \\mathbf v)- \\epsilon_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v})}\n",
+ "$$\n",
+ "where $\\delta \\mathbf v = \\delta v$ (```insitu_axis```$\\cdot$ ```plane_axes```), $e\\alpha$ is a constant irrelevant for normalized signal, and we keep $\\delta v = 0.01$mV is sufficiently small and fixed. \n",
+ "\n",
+ "To compute the signal $S(\\mathbf{v})$ at every point in voltage space we contruct two-level Hamiltonian, using the subspace of two lowest lying energy states,\n",
+ "$$\n",
+ "H_{2\\text{-level}}(\\mathbf{v}) = \\begin{pmatrix}\n",
+ "E(\\mathbf{n},\\mathbf{v}) & t_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v}) \\\\\n",
+ "t_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v}) & E(\\mathbf{m},\\mathbf{v})\n",
+ "\\end{pmatrix}\n",
+ "$$\n",
+ "where $E(\\mathbf{n},\\mathbf{v})$ and $E(\\mathbf{m},\\mathbf{v})$ are the ground and excited state electrostatic energies corresponding to charge occupation $\\mathbf{n}$ and $\\mathbf{m}$, and $t_{\\mathbf{n}\\mathbf{m}}$ is the tunnel coupling between them, which is either constant or zero if two states are not coupled. Using the Hamiltonian one can compute relevant quantities, such as the energy detuning $\\epsilon_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v})=E(\\mathbf{n},\\mathbf{v})-E(\\mathbf{m},\\mathbf{v})$ and the probability of being in on of the charge configurations \n",
+ "$$\n",
+ "P_\\mathbf{n}(\\mathbf{v}) = \\frac{1}{2} \\left(1 - \\frac{\\epsilon_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v})}{\\sqrt{\\epsilon_{\\mathbf{n}\\mathbf{m}}(\\mathbf{v})^2 + 4t_{\\mathbf{n}\\mathbf{m}}^2(\\mathbf{v})}}\\right)\n",
+ "$$\n",
+ "Intuitively we assume the signal is proportional to the change in quantum capacitance, caused by motion of the electron between the dots induced by the modulation of the gate voltages. At this point, we have not included the other effects contributing to in-situ signal including sisyphus resistance and tunneling capacitance, however their contribution is expected to be relatively weaker [Vigneau et al. (2023)].\n",
+ "\n",
+ "Below we are attaching the notebook reconstructing the in-situ signal for three dot system.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "112fc52b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add Code folder\n",
+ "import sys\n",
+ "sys.path.append('../src')\n",
+ "\n",
+ "\n",
+ "# import main class and plotting function\n",
+ "from qdarts.experiment import Experiment\n",
+ "from qdarts.plotting import plot_polytopes\n",
+ "\n",
+ "#reload file\n",
+ "\n",
+ "\n",
+ "# import standard libraries\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.gridspec as gridspec"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e5d1236e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the system\n",
+ "\n",
+ "#All capacitances are given in aF\n",
+ "N = 3 #number of dots \n",
+ "C_DD=20* np.eye((N))/2 #The self-capacitance of each dot, NOTE: factor of 2 due to symmetrization\n",
+ "C_DD[0,1] = 4 \n",
+ "C_DD[0,2] = 2 \n",
+ "C_DD[1,2] = 3\n",
+ "C_DD = C_DD + C_DD.T #symmetrize\n",
+ "\n",
+ "C_DG=4*np.eye(N) #dot-to-gate capacitances \n",
+ "C_DG[0,1] = 0.7\n",
+ "C_DG[1,2] = 0.6\n",
+ "C_DG[0,2] = 0.5\n",
+ "\n",
+ "\n",
+ "# NOTE: we use the convention that tc is the energy gap at avoided crossing H = tc/2 sx\n",
+ "tunnel_couplings = np.zeros((N,N))\n",
+ "tunnel_couplings[0,1] = 50*1e-6\n",
+ "tunnel_couplings[1,2] = 50*1e-6\n",
+ "tunnel_couplings[0,2] = 50*1e-6\n",
+ "tunnel_couplings = tunnel_couplings + tunnel_couplings.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cdce310b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "capacitance_config = {\n",
+ " \"C_DD\" : C_DD, #dot-dot capacitance matrix\n",
+ " \"C_Dg\" : C_DG, #dot-gate capacitance matrix\n",
+ " \"ks\" : None #distortion of Coulomb peaks. NOTE: If None -> constant size of Coublomb peak \n",
+ "}\n",
+ "\n",
+ "tunneling_config = {\n",
+ " \"tunnel_couplings\": tunnel_couplings, #tunnel coupling matrix\n",
+ " \"temperature\": 0.1, #temperature in Kelvin\n",
+ " \"energy_range_factor\": 5, #energy scale for the Hamiltonian generation. NOTE: Smaller -> faster but less accurate computation \n",
+ "}\n",
+ "sensor_config = {\n",
+ " \"sensor_dot_indices\": [], #Indices of the sensor dots\n",
+ " \"sensor_detunings\": [], #Detuning of the sensor dots\n",
+ " \"noise_amplitude\": {\"fast_noise\": 0.8*1e-6, \"slow_noise\": 1e-8}, #Noise amplitude for the sensor dots in eV\n",
+ " \"peak_width_multiplier\": 15, #Width of the sensor peaks in the units of thermal broadening m *kB*T/0.61.\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "87cfa416",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EXPERIMENT INITIALIZED\n",
+ "-----------------------\n",
+ "\n",
+ " Capacitance model deployed with the following parameters:\n",
+ " Dot-dot capacitances: \n",
+ " [[20. 4. 2.]\n",
+ " [ 4. 20. 3.]\n",
+ " [ 2. 3. 20.]]\n",
+ " Dot-gate capacitances: \n",
+ " [[4. 0.7 0.5]\n",
+ " [0. 4. 0.6]\n",
+ " [0. 0. 4. ]]\n",
+ " Size of Coulomb peaks V[n] is constant\n",
+ " \n",
+ "\n",
+ " Sensor model deployed with the following parameters: \n",
+ " Sensor dot indices: []\n",
+ " Sensor detunings: [] meV\n",
+ " Coulomb peak width: 0.41 meV\n",
+ " Slow noise amplitude: 0.01 ueV\n",
+ " Fast noise amplitude: 0.8 ueV\n",
+ " Signal noise scale: 0.0\n",
+ " \n",
+ "\n",
+ " Tunneling model deployed with the following parameters:\n",
+ " Tunneling matrix:\n",
+ " [[0.e+00 5.e-05 5.e-05]\n",
+ " [5.e-05 0.e+00 5.e-05]\n",
+ " [5.e-05 5.e-05 0.e+00]]\n",
+ " Temperature: 0.1 K\n",
+ " Energy range factor: 5\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "experiment = Experiment(capacitance_config, tunneling_config, sensor_config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6f90402e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "v_offset found: [ 0.13350857 0.27012592 -0.82873128]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Show the same transition with gate virtualisation\n",
+ "xV, yV, csd_dataV, polytopesV, sensors ,_ = experiment.generate_CSD(\n",
+ " x_voltages = np.linspace(-0.02, 0.05, 200), #V\n",
+ " y_voltages = np.linspace(-0.02, 0.05, 200), #V\n",
+ " plane_axes = [[1,0,0],[0,0,1]], #we use v0, v1\n",
+ " target_transition = [-1,1,0], #we target at the [1,1,0] -> [0,2,0] transition\n",
+ " target_state = [1,1,0],\n",
+ " compute_polytopes = True, \n",
+ " use_virtual_gates= False,\n",
+ " use_sensor_signal = True, #this has to be active to allow for insitu_axis\n",
+ " insitu_axis = [1,0]) #in_situ reflectometry is activated when insitu_axis is not None it is a vector in the basis of plane_axes\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "32fac162",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([], shape=(200, 200, 0), dtype=float64)"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sensors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "7199e085",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, '$v_1$ (V)')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "fig,ax = plt.subplots(1,1,figsize=(8,5))\n",
+ "plt.pcolormesh(xV, yV, sensors[:,:,0], cmap = \"seismic\", vmin=-0.5, vmax=0.5) \n",
+ "plot_polytopes(ax, polytopesV, axes_rescale=1, fontsize = 12, alpha = 0.2, color=\"k\")\n",
+ "plt.xlabel(\"$v_0$ (V)\")\n",
+ "plt.ylabel(\"$v_1$ (V)\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "458da244",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.19"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/pyproject.toml b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..bdcc1119e9944a40edf546536a1b640270a1f6a4
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/pyproject.toml
@@ -0,0 +1,29 @@
+[project]
+name = "QDarts"
+version = "1.0.3"
+authors = [
+ { name="Jan Krzywda", email="j.a.krzywda@liacs.leidenuniv.nl" },
+ { name="Evert van Nieuwenburg", email="e.p.l.van.nieuwenburg@liacs.leidenuniv.nl" },
+ { name="Oswin Krause", email="oswin.krause@di.ku.dk" },
+]
+description = "A package for simulating realistic quantum dot arrays and their charge transitions."
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent",
+]
+dependencies = [
+ 'cvxpy >= 1.5',
+ 'matplotlib >= 3.8',
+ 'numpy < 2.0',
+ 'scipy <=1.12.0,>= 1.6.0'
+]
+[project.urls]
+Homepage = "https://github.com/condensedAI/QDarts"
+Issues = "https://github.com/condensedAI/QDarts"
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_comp.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_comp.png
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_comp.png:Zone.Identifier b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_comp.png:Zone.Identifier
new file mode 100644
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+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_comp.png:Zone.Identifier
@@ -0,0 +1,3 @@
+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_grid.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_grid.png
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diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_grid.png:Zone.Identifier b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_grid.png:Zone.Identifier
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@@ -0,0 +1,3 @@
+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png
new file mode 100644
index 0000000000000000000000000000000000000000..6e7dd443aea7b12d93d3a55e2ff0dc5327d3d96f
Binary files /dev/null and b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png differ
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png:Zone.Identifier b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png:Zone.Identifier
new file mode 100644
index 0000000000000000000000000000000000000000..2d45b2b2fdb7bbe49418900f19c3cf6a7d3b94ce
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/_static/api_tunnel_raster.png:Zone.Identifier
@@ -0,0 +1,3 @@
+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api.rst b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c6da2729323c05fde359a82343a6cb5de201f576
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api.rst
@@ -0,0 +1,94 @@
+API
+===
+.. currentmodule:: qdarts
+
+In the following, we will describe the core components of our API
+
+Experiment Class
+----------------
+For a quick start for uing the simulator, there is the Experiment class that is also used
+in the example notebook. We refer to the notebook for a detailled usage example.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ experiment.Experiment
+
+
+Simulation Components
+---------------------
+The main components to build your own simulations are shown below.
+We first need to create a Capacitance model that generates a
+capacitive energy function :math:`E(v,n)`. With this, we can start
+to generate a purely capacitive simulation.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ capacitance_model.CapacitanceModel
+ simulator.CapacitiveDeviceSimulator
+
+This simulation is rather basic and can only compute areas of voltages that lead to the same ground
+state, so called coulomb diamonds. This can already be used to generate
+Charge Stability diagrams by computing 2D slices through the set of polytopes,
+but those will not look very realistic, nor will they include tunneling.
+
+To move beyond this simulation, we need to drastically improve the modeling.
+This next step is based on the full tunneling simulator that requires a simulation
+of the sensor dot and a simulation of tunnel barriers - the latter can be simplified by
+just providing a matrix of constant tunnel couplings.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ tunneling_simulator.ApproximateTunnelingSimulator
+ tunneling_simulator.TunnelBarrierModel
+ tunneling_simulator.NoisySensorDot
+
+
+The simulation of the sensor dot can optionally make use of a noise model in order
+to generate dependent noise. For this, we currently offer the following two classes
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ noise_processes.OU_process
+ noise_processes.Cosine_Mean_Function
+
+
+
+Data Classes
+-------------
+Finally, both levels of simulations have their own data classes. The polytope class is returned by the
+boundaries method by any simulator and the local system is returned by the tunneling simulator. Both describe
+the local state of the simulator in some region.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ tunneling_simulator.LocalSystem
+ polytope.Polytope
+
+Interfaces
+----------
+QDarts offers a set of interfaces and base classes that can serve as a primer to how to extend the simulator by
+replacing existing components. In most cases, only a few specialized functions need to be implemented as the base
+class implements most of the important logic. Please note that QDarts is under active development, all of these
+interfaces are subject to change.
+
+.. autosummary::
+ :toctree: interfaces
+ :recursive:
+
+ capacitance_model.AbstractCapacitanceModel
+ simulator.AbstractPolytopeSimulator
+ simulator.AbstractCapacitiveDeviceSimulator
+ noise_processes.AbstractNoiseProcess
+ tunneling_simulator.AbstractSensorSim
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api_tutorial.rst b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api_tutorial.rst
new file mode 100644
index 0000000000000000000000000000000000000000..dcef4fe5573d63ff497c7804c6f0aa0e1e12e9d9
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/api_tutorial.rst
@@ -0,0 +1,202 @@
+API Usage Tutorial
+==================
+
+QDarts comes with two API-levels, the ``Experiment`` class that simplifies many of the most common applications at the cost
+of flexibility. It is build on top of the low-level API, that gives access to the full power of QDarts. It is possile to mix both
+APIs, for example, it is possible to construct a simulator using the ``Experiment`` class and then modify it using the low-level API.
+In the following, we will explain the low-level API (The usage of the high-level api is described in the ipython notebook located in ``examples/figures_scipost.ipynb``
+that also reproduces all figures of our publication).
+
+The code for this tutorial is provided in the ipython notebook ``examples/api_tutorial.ipynb``. To keep the tutorial concise,
+we only include the most important lines here that are needed to create the simulator,
+but we skip definition of capacitance parameters etc. For this, directly reading the code is best.
+
+The simulated device is a dcevice with 6 dots, of which three are sensor dots. It is controlled by 9 plunger gates, 1 for each dot and
+three that govern barrier gates between the three dots that are not sensor dots. The device is defined using a set of parameter matrices
+that govern the capacitances between the 9 gates and 6 dots, as well as the effect the 9 gates have on the barrier potential and therefore the tunnel coupling.
+
+
+Construction of the capacitive simulation
+-----------------------------------------
+
+As first step, we need to initialize the capacitive simulation.
+It depends on a capacitive model, which creates the capacitive energy function and which depends on a number of
+parameters, the capacitanc matrices between plunger gates and dots, ``C_DG``, the interdot capacitances ``C_DD``,
+a lower bound on the gate voltages ``bounds_limits`` and optionaly a parameter ``ks`` that governs the deviation from the constant interaction
+model and which can be set to ``None``. With these parameters, creating the capacitive simulation is straight forward::
+
+ from qdarts.capacitance_model import CapacitanceModel
+ from qdarts.simulator import CapacitiveDeviceSimulator
+
+ capacitance_model = CapacitanceModel(C_DG, C_DD, bounds_limits, ks=ks)
+ capacitive_sim = CapacitiveDeviceSimulator(capacitance_model)
+
+The capacitance simulation is purely classical and fits to the simulation of typical GaAs spin-qubit devices.
+It can only be used to query the ground state of the electron configuration given a set of gate voltages,
+as well as for each electron configuration the polytope of gate voltages that lead to the ground state configuration. Both features together are enough
+to plot a charge stability diagram and to exactly compute the boundaries of ground states within the diagram.
+
+
+Plotting the Charge Stability Diagram
+-------------------------------------
+For ploting, we have two helper functions. First, ``get_CSD_states`` computes for a 2D grid of gate voltages the ground state. This is done by computing a standard 2D grid
+that is then transformed into gate-voltage space. This transformation is done via a linear transformation :math:`v=m+P@v'`, where v is the full vector of gate plunger voltages,
+in our case 9 values. The variables :math:`m` and :math:`P` are projection parameters, where :math:`m` is a 9 dimensional vector and :math:`P` a 9x2 matrix.
+This defines a local 2D coordinate system transformation from the 2D grid :math:`v'` to :math:`v`. With this, it is possible to create a plot in any two coordinate axes.
+If we pick the grid of :math:`v'` values as symmetric around 0, then :math:`m` can be interpreted as the midpoint of the scan that we compute next.
+
+the question is, how we can obtain these values. We can use the capacitive simulation to query points of interest. For example, when we are interested in the scan around
+a point on the state where there is a single electron on each inner dot and 5 electrons on each sensor dot, we can query it simply via::
+
+ m = capacitive_sim.boundaries([1,1,1,5,5,5]).point_inside
+
+And if we want to perform a scan over the axes of the first and third plunger gate (which in our simulation are strongest coupled to the first and third inner dot) we can
+set::
+
+ P=np.zeros((N_gates,2))
+ P[0,0]=1
+ P[2,1]=1
+
+
+Now that we have the projection, we can define the minimum and maximum values of :math:`v'` in both directions and choose an image resolution.
+Then, we can use ``get_CSD_data``::
+
+ from qdarts.plotting import get_CSD_data
+
+ minV = [-0.01,-0.01]
+ maxV = [ 0.01, 0.01]
+ resolution = 100
+
+ sliced_csim, CSD_data, states = get_CSD_data(capacitive_sim, P,m, minV, maxV, resolution, target_state)
+
+The last parameter is needed to provide the simulation with an initial guess of which might be the ground state of the bottom left corner of the plot.
+This guess does not need to be exact. This function returns three things: first, a simulation ``sliced_csim`` that is defined on the transformed space :math:`v'` and serves as a cache for
+computed values. We will talk more about these slices later in the tutorial. The second result, ``CSD_data`` is a plottable 2D grid of values in which each ground state configuration is assigned
+a different value. Finally, ``states`` is an array of all ground states for each grid point. We can plot this directly with matplotlib::
+
+ xs = np.linspace(minV[0],maxV[0],resolution)
+ ys = np.linspace(minV[1],maxV[1],resolution)
+ plt.pcolormesh(xs,ys,CSD_data.T)
+
+The resulting grid is a rastered low resolution image.
+
+.. image:: /_static/api_csd_raster.png
+ :scale: 50 %
+ :align: center
+
+We can improve on this by plotting the exact boundaries between the two ground states using ``get_polytopes`` and ``plot_polytopes``::
+
+ polytopes = get_polytopes(states, sliced_csim, minV, maxV)
+ plt.xlim(minV[0],maxV[0])
+ plt.ylim(minV[1],maxV[1])
+ plot_polytopes(plt.gca(),polytopes, skip_dots=[3,4,5], fontsize=16)
+
+Note that this function computes the polytopes based on the low dimensional sliced simulation returned by ``get_CSD_data`` as well as its computed array of states. The function also prints the labels
+of each state, and the option ``skip_dots`` allows to skip the part of the electron configuration we are not interested in. In this case, we skipped the 3 sensor dots.
+Adding the last two plotting commands together, the resulting plot should look like this:
+
+.. image:: /_static/api_csd_grid.png
+ :scale: 50 %
+ :align: center
+
+
+Creation of a tunneling simulation
+----------------------------------
+Now that we are able to generate a CSD, we want to also simulate tunneling and simulate a more realistic sensor signal.
+For this, we need to create a few objects. First, we have to decide on a sensor simulator. The default choice is the ``NoisySensorDot``
+which simulates a sequence of sensor reflectometry peaks on one or more sensor dots::
+
+ sensor_model = NoisySensorDot(sensor_dots) #a model of the sensor dots that just needs which dot has which index
+ sensor_model.config_peak(g_max = 1.0, peak_width_multiplier = 20) #make the sensor peak broader
+
+The constructor takes the indexes of the dots we want to use as sensor dots and each entry in this list will produce one sensor signal, the quantum capacitance measured
+at the sensor dot. In the next line we add configuration options. ``g_max`` is just a scaling factor of the signal, which we leave at 1. The default sensor peak is simulated using only the
+temperature of the dot and does not take broadening due to RF power into account. To compensate for this, we introduce a broadening factor that can make the peak wider. in our experience, values between
+5 and 20 work well. There are more options to add here, for example the noise model of the signal, but for this tutorial, we do not add noise to keep it simple. You can check the documentation
+of ``NoisySensorDot`` for more details on that.
+
+Now, with our configured sensor dot, we create the simulation using tunnel coupling::
+
+ tunneling_sim = ApproximateTunnelingSimulator(capacitive_sim, #the underlying polytope simulation
+ tunnel_couplings, #symmetric tunnel coupling matrix T_ij which stores the tunnel coupling between dots i and j
+ temperature, #electron temperature, should be <=200mK
+ sensor_model) #our sensor model simulation
+
+The tunneling simulation is very expensive, and by default it tries to minimize the number of basis elements for the computed Hamiltonians by excluding states with energies that are large compared to
+a chosen slack. By default the slack is 0, and thus all states that are not direct neighbours of our polytope (i.e., they do not share a facet) will be excluded. By increasing the slack proportional to
+the inverse temperature, we add more states that might still be relevant for tunneling. Further, the sensor dot simulation relies on the existence of states that add or remove an electron from the sensor dot.
+These can easily be excluded from the simulation and therefore we enfore in the simulation that states with additional electrons added/subtracted on the sensor dot are always present.
+
+ capacitive_sim.set_maximum_polytope_slack(5/tunneling_sim.beta) #adding slack to keep more states that are likely to affect the hamiltonian
+ tunneling_sim.num_additional_neighbours[sensor_dots] = 2 #adding additional states for the sensor dots
+
+Finally, we can create a similar plot as before. We use the same values for ´´m´´ and ´´P´´ as for the previous simulation, so that we can compare the two plots.
+Note that since the device has three sensor dots, the output is a 3 dimensional tensor where the last dimension is the number of sensor dots and we plot below only
+the output of the third sensor::
+
+ sensor_values = tunneling_sim.sensor_scan_2D(P, m, minV, maxV, resolution, target_state)
+ plt.pcolormesh(xs,ys,sensor_values[:,:,2].T)
+
+
+This plots results in the sensor signal
+
+.. image:: /_static/api_tunnel_raster.png
+ :scale: 50 %
+ :align: center
+
+which again can be overlayed with our grid as before:
+
+.. image:: /_static/api_tunnel_grid.png
+ :scale: 50 %
+ :align: center
+
+
+Slicing and sensor compensation
+-------------------------------
+
+The last plot shows that most of the sensor signal is very low and there is only a small area with a peak. This is because of the missing sensor compensation: the sensor peak
+is capacitely affected by the plunger gates and thus changing the voltages changes the sensor signal. On the previous plot we already configured a wide width of the peak (``peak_width_multiplier = 20```) to make
+this effect small, but in reality many devices have very narrow and clear peaks.
+What we need is sensor compensation, i.e., a change of coordinate system where the sensor plunger voltage is corrected for the cross-talk from the other gates so that we stay on the sensor peak as well as possible.
+Moreover, the sensor peak is not centered. This is because we used as mid-point a point inside the polytope, but the sensor peaks are only on the boundaries between polytopes with different number of
+electrons on the sensor dots. Thus, we need to change our coordinate system and tune the sensor to a better point.
+
+This change of coordinate system is achieved in the simulator via slicing. In the previous examples, we defined a matrix ``P`` and vector ``m`` to generate the plot. We said that this changed the coordinate
+system via :math:`v=m+Pv'`, where ``v'`` was a 2D case. This operation is called slicing and a simulation can be sliced via::
+
+ sliced_sim = sim.slice(P,m)
+
+This works on all simulator objects and it returns a new simulation with gate voltages ``v'``. This works in any dimensions, not just 2 and it is used in most simulation transformation code.
+QDarts makes use of this in all its transformation code. For example, compensating the sensor dot::
+
+ compensated_tsim, _,m_comp = compensate_simulator_sensors(
+ tunneling_sim, #the simulator object to compensate
+ target_state=[1,1,1,5,5,5], #the electron configuration around which to compensate
+ compensation_gates= [6,7,8], #the ids of the sensor gate plungers.
+ sensor_ids = [3,4,5],#the ids of the sensor dots.
+ sensor_detunings = np.array([-0.0005,-0.0005,-0.0005]) #shift of the sensor dot plungers
+ )
+
+This function returns the sensor compensated simulator object, which has its gates transformed such, that changing the gate voltages of any gate plunger other the sensor dot plungers does not change the
+chemical potential of the sensor dot and thus leaves the sensor signal constant, except when a transition happens. This function relies on the user to supply the ids of the sensor dots and which elements of the voltage
+vector are the sensor plungers (for example, the sensor dot with id 3 has plunger 6 as its plunger gate). Since in our model the capacitance parameters of the model vary with the occupation number, we have to
+indicate the target state. Further, we have to supply a vector of sensor detunings that indicate on which position we are on the snesor detuning axis, i.e., the sensor peak. This value is relative to the peak maximum,
+a value of 0 will return the exact peak max, which is a value that is typically avoided in practice due to the low signal to noise ratio. Additional to the sensor compensation object, the function returns also as third
+return value the exact tuning point, i.e., the voltage vector that identifies the flank point for the given state. We can use this to plot a 2D charge stability diagram with the returned point as its center.
+We also show in the point below how to query the internal modified capacitance simulator using ``compensated_tsim.poly_sim`` in order to compute the state boundaries::
+
+ sensor_values = compensated_tsim.sensor_scan_2D(P, m_comp, minV, maxV, resolution, target_state)
+ sliced_tsim, CSD_data, states = get_CSD_data(compensated_tsim.poly_sim, P,m_comp, minV, maxV, resolution, target_state)
+ polytopes = get_polytopes(states, sliced_tsim, minV, maxV)
+
+
+Plotting the computed ```sensor_values`` and ``polytopes`` as before, leads to
+
+
+.. image:: /_static/api_tunnel_comp.png
+ :scale: 50 %
+ :align: center
+
+As you can see, the sensors are almost perfectly compensated, leading to almost perfectly flat polytopes with no visible shape of a sensor peak. The [1,1,1] state indicates a color in the middle of the peak
+and we can see how single electron transitions change the position on the peak due to the change of color on the polytopes. It is similarly possible to compensate any other gates and thus to compute virtual coordinates,
+for this we refer to the documentation of ``axis_align_transitions``.
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/conf.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..c44443f3d412407b735f6a72a60995cdffa34a6f
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/conf.py
@@ -0,0 +1,44 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'QDarts'
+copyright = '2024, Oswin Krause, Jan Krzywda, Weikun Liu, Evert van Nieuwenburg'
+author = 'Oswin Krause, Jan Krzywda, Weikun Liu, Evert van Nieuwenburg'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.autosummary',
+ 'sphinx.ext.napoleon'
+ ]
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'alabaster'
+html_static_path = ['_static']
+
+
+# -- Package options -------
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath('../src'))
+autosummary_generate = True
+autosummary_imported_members = True
+
+autodoc_default_options = {
+ 'members': True,
+ 'undoc-members': True
+}
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/index.rst b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/index.rst
new file mode 100644
index 0000000000000000000000000000000000000000..ed00c6157f0399baf0e0b40c0eba2e28d08d46aa
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/sphinx/index.rst
@@ -0,0 +1,23 @@
+QDarts
+======
+
+Installation
+------------
+Installation can be done via ``python build``. In the QDarts directory, run::
+
+ make build
+ python3 -m pip install QDarts/dist/QDarts-<Version>-py3-none-any.whl
+
+where ``<Version>`` is the current QDarts version.
+
+
+Documentation
+-------------
+A usage example is provided in the ipython notebook in the ``examples/`` folder.
+Below you find the `detailed documentation of ``QDarts``
+
+.. toctree::
+ :maxdepth: 1
+
+ api_tutorial
+ api
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/__init__.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/capacitance_model.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/capacitance_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c86376b871a368e8a2ded75531c9e591ee59b602
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/capacitance_model.py
@@ -0,0 +1,435 @@
+import numpy as np
+from qdarts.util_functions import compute_maximum_inscribed_circle, compute_polytope_slacks
+from qdarts.polytope import Polytope
+from abc import ABCMeta, abstractmethod
+
+def is_sequence(seq):
+ if isinstance(seq, str):
+ return False
+ try:
+ len(seq)
+ except Exception:
+ return False
+ return True
+
+
+#internal unit conversion of capacitances from attoFarrad to Farrad/eV
+eV = 1.602e-19
+to_Farrad_per_eV = 1e-18/eV
+
+class AbstractCapacitanceModel(metaclass=ABCMeta):
+ """Base Class for all capacitance models.
+
+ This class provides all required meta information to compute capacitive energies of a system with electron configuration
+ n and gate voltages v, E(v,n). The only required to this model is that E(v,n)-E(v,n') is a linear function in v and that energies
+ are measured in eV.
+ The class provides basic abilities: enumeration of possible transition states from a given state and computation/verification of a polytope P(n) for a state.
+ For this, the user only needs to provide functions to generate transition equations and a function that allows to slice the voltage space.
+
+ As a base class it only needs to know the number of gates/inputs and the number of dots of the array. Additionally
+ the user must supply voltage bounds that ensure that all computed polytopes are bounded. In practical devices these would be voltage limits
+ e.g., for device protection.
+
+ Attributes
+ ----------
+ num_dots: int
+ the number of discrete dot locations on the device. This is the number of elements in the electron state n.
+ num_inputs: int
+ The number of gate voltages of the device.
+ bounds_limits: N np.array of float
+ right hand side of the bound inequalities.
+ set of linear inequalities A that provide bounds for the voltage space.
+ """
+ def __init__(self, num_dots, num_inputs, bounds_limits, bounds_normals):
+ """ Initializes the model.
+
+ Parameters
+ ----------
+ num_dots: int
+ the number of discrete dot locations on the device. This is the number of elements in the electron state n.
+ num_inputs: int
+ The number of gate voltages of the device.
+ bounds_limits: N np.array of float
+ right hand side of the bound inequalities. If bounds_normals is none, this is interpreted as lower bounds and thus N=num_inputs
+ bounds_normals: N x num_inputs np.array of float
+ set of linear inequalities A that provide bounds for the voltage space. A valid voltage v fulfils Av+b<0, where b is
+ bounds_limits. Can be none, in which case A=-Id.
+ """
+ self.num_dots = num_dots
+ self.num_inputs = num_inputs
+
+ if not is_sequence(bounds_limits):
+ bounds_limits = bounds_limits * np.ones(num_inputs)
+
+ if bounds_normals is None:
+ if num_inputs != len(bounds_limits):
+ raise ValueError("if bounds_normals is not given, bounds_limits must be either a scalar or a sequence of length same as number of gates")
+ bounds_normals = -np.eye(self.num_inputs)
+
+
+ self.bounds_normals = np.asarray(bounds_normals)
+ self.bounds_limits = np.asarray(bounds_limits)
+
+ @abstractmethod
+ def compute_transition_equations(self, state_list, state):
+ """
+ For a given state n and a list of other states (n_1,...n_N), computes the set of
+ linear equations E(v,n)-E(v,n_i). Must be implemented by derived classes.
+
+ Parameters
+ ----------
+ state: K np.array of int
+ State n
+ state_list: NxK np.array of int
+ list of other states (n_1,...n_N)
+
+ Returns
+ -------
+ (A,b) set of linear equations represented by matrix A and offset b. The ith element computes
+ E(v,n)-E(v,n_i) as a function of v.
+ """
+ pass
+
+ @abstractmethod
+ def slice(self, P, m):
+ """ Restricts the model to the affine subspace v=m+Pv'
+
+ Computes the slice through the device by setting v=m+Pv', where v is the plunger gate voltages of the
+ original device and v' is the new coordinate system. Must be implemented by derived classes. Note
+ that derived classes also need to apply the affine transformation to the bounds variables
+
+ Parameters
+ ----------
+ P: MxK np.array of floats
+ The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+ and K the dimensionality of the subspace.
+ m: M np.array of floats
+ offset of the affine trnsformation.
+
+ Returns
+ -------
+ A model object describing the simulation on the affine subspace.
+ """
+ pass
+
+ def enumerate_neighbours(self, state):
+ """ Generates a state_list object for compute_transition_equations,
+
+ Enumerates the set of neighbours of a transition to return all possible
+ state transitions. In this class it is implemented by returning all possible
+ states that can be reached by any combintion of adding or removing an electron on any dot.
+ As a result, this list has 3^D-1 elements, where D is the number of dots.
+
+ Derived classes may overwrite this if they want to consider a more restricted set of transitions.
+
+ Parameters
+ ----------
+ state: D np.array of int
+ The state for which to enumerate all neighbours
+
+ Returns
+ -------
+ List of lists of neighbours. By default only a list including a list of all neighbours is returned.
+ Derived classes may decide to instead return several lists each representing a batch of transitions.
+ Aong those lists, states do not need to be unique but instead represent groups of elements that can
+ filtered efficiently by the solver, e.g., by adding all states that can be used to filter a lot of other
+ states in the list quickly. This is only ever relevant when larger arrays need to be computed.
+ """
+ d = state.shape[0]
+ # Compute list of possible state transitions for the provided state
+ # For simplicity, we restrict to only single electron additions/subtractions per dot
+ # This leaves 3^d-1 states
+ state_list=np.zeros((1,d),dtype=int)
+ for i in range(d):
+ state_list1 = state_list.copy()
+ state_listm1 = state_list.copy()
+ state_listm1[:,i] = -1
+ state_list1[:,i] = 1
+ state_list = np.vstack([state_list,state_list1])
+ if state[i] >= 1:
+ state_list = np.vstack([state_list,state_listm1])
+
+ # First element is all-zeros, we don't want it
+ state_list=state_list[1:]
+
+ return [state_list+state]
+
+ def compute_polytope_for_state(self, state, maximum_slack):
+ """For a given state, computes P(N)
+
+ Calls enumerate_neighbours and compute_transition_equations to obtain a list of
+ possible transitions and then removes from this list iteratively
+ all transitions that are not sufficiently close to the polytope.
+
+ This is computed by computing the slack. The slack is 0 if the ith transition is a facet of
+ the polytope, otherwise it is a positive number computed as
+
+ :math:`s_i = min_v A_i^Tv +b_i, v \in P(n)`
+
+ This function retains all facets that have slack smaller than maximum_slack.
+ Since enregy differences are measure din eV, the slack represents the minimum
+ energy difference between the ground state and the state represented by the transition
+ for any point inside the polytope.
+
+ Parameters
+ ----------
+ state: D np.array of int
+ The electron configuration n for which to compute P(n)
+ maximum_slack: float
+ The maximum distance in eV after which the transition is discarded
+
+ Returns
+ -------
+ A Polytope object representing P(n). See documentation of Polytope.
+ """
+ #get the potentially bacthed list of states
+ state_lists = self.enumerate_neighbours(state)
+ As = []
+ bs = []
+ transition_slacks = []
+ states = []
+ # Now for each of those, get the list of transitions...
+ for idx,state_list in enumerate(state_lists):
+ A,b = self.compute_transition_equations(state_list, state)
+
+ #check, whether there are superfluous transitions
+ #TODO: Oswin: i don't remember what the significance of this was.
+ zero_const = np.all(np.abs(A)<1.e-8,axis=1)
+ if np.any(zero_const):
+ A = A[~zero_const]
+ b = b[~zero_const]
+ state_list = state_list[~zero_const]
+ # ... and check for this batch whether we can filter out non-touching ones
+ slacks = compute_polytope_slacks(A,b, self.bounds_normals, self.bounds_limits, maximum_slack)
+ keep = slacks <= maximum_slack+1.e-8
+
+ #if we have kept nothing, this means there is a set of equations that is not fullfillable
+ #this happens often when slicing, e.g, a polytope is not within the sliced subspace.
+ if not np.any(keep):
+ return Polytope(state)
+
+ As.append(A[keep])
+ bs.append(b[keep])
+ transition_slacks.append(slacks[keep])
+ states.append(state_list[keep])
+
+
+ #TODO: this code is currently untested and might break.
+ # Keep iterating over the list, merging facets, until they are all merged
+ while len(As)>1:
+ # Take the next set of As, bs and states, and merge them into a new set
+ A = np.vstack(As[:2])
+ b = np.concatenate(bs[:2])
+ max_slack = np.concatenate(max_slacks[:2])
+ state = np.vstack(states[:2])
+ # Update the lists; we've now taken care of another set of two
+ As=As[2:]
+ bs=bs[2:]
+ states = states[2:]
+ max_slacks = max_slacks[2:]
+ transition_slacks = transition_slacks[2:]
+
+ # Handle possible duplicate transitions
+ state, indxs = np.unique(state, axis=0, return_index = True)
+ A = A[indxs]
+ b = b[indxs]
+
+ # Find transitions in the merged sets
+ slacks= self._check_transition_existence(A,b, max_slack)
+ keep = slacks <= maximum_slack+1.e-8
+
+ #if we have kept nothing, this means there is a set of equations that is not fullfillable
+ #this happens often when slicing, e.g, a polytope is not within the sliced subspace.
+ if not np.any(keep):
+ return Polytope(state)
+
+ # Add the merged ones back to the list
+ As.append(A[keep])
+ bs.append(b[keep])
+ max_slacks.append(max_slack[keep])
+ transition_slacks.append(slacks[keep])
+ states.append(state[keep])
+
+ #create final polytope
+ poly = Polytope(state)
+ touching = transition_slacks[0]<1.e-8
+ point_inside, _ = compute_maximum_inscribed_circle(As[0][touching], bs[0][touching], self.bounds_normals, self.bounds_limits)
+ poly.set_polytope(states[0] - state, As[0], bs[0], transition_slacks[0], point_inside)
+ return poly
+
+ def verify_polytope(self, polytope, maximum_slack):
+ """Verifies a polytope.
+
+ After slicing, polytopes that have been computed earlier also need to be sliced. It is inefficient to recompute
+ the polytopes from scratch, as slicing can only remove, but never add transitions. verify_polytope allows to take
+ a polytope that has been modified via polytope.layz_slice and verify/filter all transitions. This recomputes
+ all slack variables and removes all transitions that have slack larger than maximum slack
+
+ Note that this does not touch any other internal information stored in the polytope.
+ This function doe snothing if polytope.must_verify=False
+
+ TOOD: this should be moved somewhere else.
+
+ Parameters
+ ----------
+ polytope: Polytope
+ The polytope P(n) to be verified
+ maximum_slack: float
+ The maximum distance in eV after which a transition of the polytope is discarded
+
+ Returns
+ -------
+ The updated polytope after filtering out transitions.
+ """
+ if not polytope.must_verify:
+ return polytope
+ slacks = compute_polytope_slacks(polytope.A, polytope.b, self.bounds_normals, self.bounds_limits, maximum_slack)
+ keep = slacks <= maximum_slack + 1.e-8
+ touching = slacks <= 1.e-6
+ point_inside, _ = compute_maximum_inscribed_circle(polytope.A[touching], polytope.b[touching], self.bounds_normals, self.bounds_limits)
+
+ verified_polytope = Polytope(polytope.state)
+ verified_polytope.set_polytope(
+ polytope.labels[keep],
+ polytope.A[keep], polytope.b[keep],
+ slacks[keep],
+ point_inside
+ )
+ return verified_polytope
+
+class CapacitanceModel(AbstractCapacitanceModel):
+ """Implementation of a slight generalization of the constant interaction model.
+
+ The constant interaction model defines
+ :math:`E(v,n)=1/2 n^T C_{DD}^{-1}n - n^T C_{DD}^{-1}C_{DG}v`
+
+ where :math:`C_{DD}` and :math:`C_{DG}` are part of the maxwell capacitance matrix created by the system
+ of plunger gates G and quantum dots D. Thus, :math:`C_{DD}` are the interdot capacitances (mawell) and
+ :math:`C_{DG}` the dot to gate capacitances.
+
+ This model is a generalization of the constant interaction model as it makes :math:`C_{DD}` and :math:`C_{DG}` a function
+ of electron state n. The speed of this change from the constant interaction is governed by a parameter k for each dot. The larger
+ k is, the smaller the deviation. if k=None, this is exactly the constant interaction model.
+ """
+ def __init__(self, C_g, C_D, bounds_limits, bounds_normals=None, ks = None, transform_C_g = None, offset = None):
+ """ Initializes the model
+
+ The parameters here are normal capacitances and not maxwell capacitances given in atto Farrad.
+
+ Parameters
+ ----------
+ C_g: DxK np.array of float
+ Capacitances in atto Farrad between the K gates and D dots.
+ C_D: DxD np.array of float
+ Capacitances in atto Farrad between the D dots. Self capacitances are possible via the diagonal matrix elements.
+ bounds_limits: N np.array of float
+ right hand side of the bound inequalities. If bounds_normals is none, this is interpreted as lower bounds and thus N=num_inputs
+ bounds_normals: N x num_inputs np.array of float
+ set of linear inequalities A that provide bounds for the voltage space. A valid voltage v fulfils Av+b<0, where b is
+ bounds_limits. Can be none, in which case A=-Id.'
+ ks: D np.array of float or None.
+ How quickly the capacitances change as deviation from the constant interaction model for each dot. Can be None in which this is just the constant interaction model.
+ Larger integers give smaller changes. Realistic values are 3-5.
+ transform_C_g:
+ Internal. Used to implement slicing. Should be None.
+ offset:
+ Internal. Used to implement slicing. Should be None.
+ """
+ # Set the transformation matrix to the identity if not provided
+ if transform_C_g is None:
+ transform_C_g = np.eye(C_g.shape[1])
+
+ super().__init__(C_D.shape[0], transform_C_g.shape[1], bounds_limits, bounds_normals)
+
+ # Set instance properties
+ self.C_g_atto = np.asarray(C_g)
+ self.C_D_atto = np.asarray(C_D)
+ self.transform_C_g = np.array(transform_C_g)
+
+ # Check that an offset is provided for every gate
+ self.offset = np.zeros(self.transform_C_g.shape[0])
+ if offset is not None:
+ if len(offset) != self.transform_C_g.shape[0]:
+ raise ValueError("The offset you provided does not have an offset for every gate of the device (prior to slicing).")
+ self.offset = np.array(offset)
+
+ # Convert units from attoFarrad to Farrad per eV
+ self.C_g = self.C_g_atto * to_Farrad_per_eV
+ self.C_D = self.C_D_atto * to_Farrad_per_eV
+
+ # Check if value for non-constant capacitance is provided
+ self.ks = ks
+ if ks is not None:
+ self.ks = np.array(ks)
+ #if np.any(ks<1):
+ # raise ValueError("The ks values must be larger than 1")
+
+ # TODO: What are S values?
+ # Cache S values for up to 1000 total dots
+ self.S_cache=np.zeros((1000,self.num_dots))
+ self.S_cache[0,:] = 1
+ self.S_cache[1,:] = 1
+
+ r = 2.6
+ alphas = 1-0.137*(1+r)/(ks+r)
+ for n in range(2,self.S_cache.shape[0]):
+ Sprev = self.S_cache[n-2]
+ self.S_cache[n] = n/(2*alphas*(ks+2)/(n+ks)+(n-2)/Sprev)
+
+ def _compute_capacitances(self, state):
+ N = len(state)
+
+ S = np.eye(N)
+ if self.ks is not None:
+ S_values = self.S_cache[state,range(N)]
+ S = np.diag(S_values)
+
+ sum_C_g = np.sum(self.C_g,axis=1)
+
+ # General transform by changing dot capacitances
+ Cn_g = S @ self.C_g
+ Cn_D = S @ self.C_D @ S
+ Csum = S @ S @ sum_C_g + np.sum(Cn_D,axis=1)+np.diag(Cn_D)
+
+ Cn_inv = np.linalg.inv(np.diag(Csum) - Cn_D)
+ return Cn_inv, Cn_g
+
+ def compute_transition_equations(self, state_list, state_from):
+ # Get number of targets
+ N = state_list.shape[0]
+
+ # Compute normal and offset for the from_state
+ C0_inv, C0_g = self._compute_capacitances(state_from)
+ q0 = state_from @ C0_inv
+ A0 = q0 @ C0_g
+ b0 = q0 @ state_from -2*q0 @ C0_g @ self.offset
+
+ # Now compute the normals and offsets for the target states
+ A = np.zeros((N, self.num_inputs))
+ b = np.zeros(N)
+ for i,n in enumerate(state_list):
+ # Compute the capacitances for the target state
+ Cn_inv, Cn_g = self._compute_capacitances(n)
+ qn = n @ Cn_inv
+ An = qn @ Cn_g
+
+ # Compute the normal
+ A[i] = (An - A0)@self.transform_C_g
+ # Compute the offset
+ b[i] = (b0 - qn @ n)/2 + qn @ Cn_g @ self.offset
+
+ return A,b
+
+ def slice(self, P, m):
+ new_offset = self.offset+self.transform_C_g@m
+ new_transform = self.transform_C_g@P
+
+ new_boundsA = self.bounds_normals@P
+ new_boundsb = self.bounds_limits + self.bounds_normals@m
+
+ #throw out almost orthogonal bounds
+ sel = np.linalg.norm(new_boundsA,axis=1)>1.e-7*np.linalg.norm(self.bounds_normals,axis=1)
+ new_boundsA = new_boundsA[sel]
+ new_boundsb = new_boundsb[sel]
+
+ return CapacitanceModel(self.C_g_atto, self.C_D_atto, new_boundsb, new_boundsA, self.ks, new_transform, new_offset)
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/experiment.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/experiment.py
new file mode 100644
index 0000000000000000000000000000000000000000..d21549aa3307d1ceca2a9da60a9c94d7cf1ddcae
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/experiment.py
@@ -0,0 +1,437 @@
+from qdarts.simulator import *
+from qdarts.tunneling_simulator import *
+from qdarts.noise_processes import OU_process,Cosine_Mean_Function
+
+#for the algorithm
+import numpy as np
+
+#for plotting
+from matplotlib import pyplot as plt
+from qdarts.plotting import get_CSD_data, get_polytopes
+
+#for measuring runtime
+import time
+plt.rcParams.update({'font.size': 18})
+
+# SIMULATION CONSTANTS
+SLOW_NOISE = {"tc": 50}
+
+
+
+class Experiment(): #TODO: change name to the simulator name
+ """Experiment class that helps setup the different parts of the simulator into an easy to ue interface.
+ The usage of this class is best described by the tutorials in the example folder.
+ """
+ def __init__(self, capacitance_config, tunneling_config = None, sensor_config = None, print_logs = True):
+ '''
+ capacitance_config: dictionary containing the capacitance model parameters
+ tunneling_config: dictionary containing the tunneling model parameters
+ sensor_config: dictionary containing the sensor model parameters
+ print_logs: bool, whether to print logs
+ '''
+ # Store configurations
+ self.capacitance_config = capacitance_config
+ self.tunneling_config = tunneling_config
+ self.sensor_config = sensor_config
+ self.print_logs = print_logs
+
+ print("EXPERIMENT INITIALIZED")
+ print("-----------------------")
+
+ # Deploy simulators
+ self.capacitance_sim = self.deploy_capacitance_sim(capacitance_config)
+ self.has_sensors = False
+
+ # Check requirements for sensor and tunneling configurations
+ if tunneling_config != None and sensor_config == None:
+ raise ValueError("Specifying a tunneling configuration also requires a sensor configuration.")
+ if (tunneling_config == None and sensor_config != None):
+ raise ValueError("Specifying a sensor configuration also requires a tunneling configuration.")
+ if tunneling_config != None:
+ self.sensor_model = self.deploy_sensor_model(sensor_config)
+ self.tunneling_sim = self.deploy_tunneling_sim(self.capacitance_sim, tunneling_config)
+ self.has_sensors = True
+
+
+
+# DEPLOYMENT FUNCTIONS
+#--------------------------
+ def deploy_capacitance_sim(self,config):
+ '''
+ Function that deploys a capacitance simulator.
+
+ Arguments
+ ---------
+ config: dictionary containing the capacitance model parameters
+
+ Returns
+ -------
+ sim: CapacitiveDeviceSimulator object
+ '''
+ capacitance_model = CapacitanceModel(config["C_Dg"], config["C_DD"], -1, ks= config["ks"])
+ sim = CapacitiveDeviceSimulator(capacitance_model)
+
+ #Save the parameters
+ self.N = len(config["C_Dg"]) #number of dots
+ self.inner_dots = list(np.arange(self.N)) #indces of the dots. NOTE: no sensor at this point
+
+ if self.print_logs:
+ if not config["ks"] is None:
+ # Print log of capacitance parameters
+ log = """
+ Capacitance model deployed with the following parameters:
+ Dot-dot capacitances:
+ {}
+ Dot-gate capacitances:
+ {}
+ Size of Coulomb peaks V[n] scale as {}/(n + {})
+ """.format(config["C_DD"], config["C_Dg"], np.round((1-0.137*3.6/(config["ks"]+2.6)*config["ks"]),3), 2+config["ks"])
+ print(log)
+ else:
+ # Print log of capacitance parameters
+ log = """
+ Capacitance model deployed with the following parameters:
+ Dot-dot capacitances:
+ {}
+ Dot-gate capacitances:
+ {}
+ Size of Coulomb peaks V[n] is constant
+ """.format(config["C_DD"], config["C_Dg"])
+ print(log)
+
+
+ return sim
+
+
+ def deploy_tunneling_sim(self, capacitance_sim, tunneling_config):
+ '''
+ Function that deploys a tunneling simulator.
+
+ Arguments
+ ---------
+ capacitance_sim: CapacitiveDeviceSimulator object
+ tunneling_config: dictionary containing the tunneling model parameters
+
+ Returns
+ -------
+ tunneling_sim: ApproximateTunnelingSimulator object
+ '''
+ tunneling_matrix = tunneling_config["tunnel_couplings"]
+ tunnel_barrier_offsets = np.log(tunneling_matrix + 1e-20)
+ barrier_levers = tunneling_config["barrier_levers"] if "barrier_levers" in tunneling_config.keys() else None
+ barrier_sim = TunnelBarrierModel(tunnel_barrier_offsets, barrier_levers)
+ tunneling_sim = ApproximateTunnelingSimulator(capacitance_sim,
+ barrier_sim,
+ tunneling_config["temperature"],
+ self.sensor_model)
+
+ # explore the neighboring polytopes
+ slack = tunneling_config["energy_range_factor"]*np.maximum(1.0/tunneling_sim.beta,np.max( tunneling_matrix))
+ tunneling_sim.poly_sim.set_maximum_polytope_slack(slack)
+ tunneling_sim.num_additional_neighbours[self.sensor_model.sensor_dot_ids] = 2
+
+
+ if self.print_logs:
+ # Print log of tunneling parameters
+ log = """
+ Tunneling model deployed with the following parameters:
+ Tunneling matrix:
+ {}
+ Temperature: {} K
+ Energy range factor: {}
+ """.format(tunneling_matrix, tunneling_config["temperature"], tunneling_config["energy_range_factor"])
+ print(log)
+
+
+ return tunneling_sim
+
+
+ def deploy_sensor_model(self, sensor_config):
+ '''
+ Function that deploys a sensor model.
+
+ Arguments
+ ---------
+ config: dictionary containing the sensor model parameters
+
+ Returns
+ -------
+ sensor_sim: NoisySensorDot object
+ '''
+ if not "noise_amplitude" in sensor_config.keys():
+ sensor_config["noise_amplitude"]={}
+ if not "fast_noise" in sensor_config["noise_amplitude"]:
+ sensor_config["noise_amplitude"]["fast_noise"]=0.0
+ if not "slow_noise" in sensor_config["noise_amplitude"]:
+ sensor_config["noise_amplitude"]["slow_noise"]=0.0
+ if not "signal_noise" in sensor_config["noise_amplitude"]:
+ sensor_config["noise_amplitude"]["signal_noise"]=0.0
+ # Separate between inner and sensor dots
+ self.inner_dots = list(set(self.inner_dots) - set(sensor_config["sensor_dot_indices"]))
+
+ # Define slow-noise generator
+ slow_noise_gen = OU_process( #TODO: LATER: implement 1/f noise
+ sigma = sensor_config["noise_amplitude"]["slow_noise"],
+ tc = SLOW_NOISE["tc"],
+ dt = 1, # the unit of time is the single measurment
+ num_elements = len(sensor_config["sensor_dot_indices"])
+ )
+
+ if "mean_field" in sensor_config.keys():
+ mean_field_config = sensor_config["mean_field"]
+ if mean_field_config["type"] == "Cosine_Mean_Function":
+ b = mean_field_config["b"] if "b" in mean_field_config.keys() else None
+ slow_noise_gen = Cosine_Mean_Function(slow_noise_gen, mean_field_config["a"], mean_field_config["W"], b)
+
+ # Deploy sensor model
+ sensor_sim = NoisySensorDot(sensor_config["sensor_dot_indices"])
+
+ # Configure sensor model
+ sensor_sim.config_noise(
+ sigma = sensor_config["noise_amplitude"]["fast_noise"],
+ signal_noise_scale = sensor_config["noise_amplitude"]["signal_noise"],
+ slow_noise_gen = slow_noise_gen
+ )
+ sensor_sim.config_peak(
+ g_max = 1.0,
+ peak_width_multiplier = sensor_config["peak_width_multiplier"]
+ )
+
+
+ # Pring log of sensor parameters
+ if self.print_logs:
+ log = """
+ Sensor model deployed with the following parameters:
+ Sensor dot indices: {}
+ Sensor detunings: {} meV
+ Coulomb peak width: {} meV
+ Slow noise amplitude: {} ueV
+ Fast noise amplitude: {} ueV
+ Signal noise scale: {}
+ """.format(sensor_config["sensor_dot_indices"], np.array(sensor_config["sensor_detunings"])*1e3,
+ np.round(2*sensor_config["peak_width_multiplier"]*self.tunneling_config["temperature"]*86*1e-3/0.631,2),
+ np.round(sensor_config["noise_amplitude"]["slow_noise"]*1e6,4),np.round(sensor_config["noise_amplitude"]["fast_noise"]*1e6,4)
+ ,np.round(sensor_config["noise_amplitude"]["signal_noise"],5))
+ print(log)
+ return sensor_sim
+
+
+ def center_transition(self, simulator, target_state, target_transition, plane_axes, use_virtual_gates = False, compensate_sensors = False):
+ '''
+ Function that center the CSD at a given facet (transition) of the polytope (occupation state).
+
+ Arguments
+ ---------
+ simulator: any simulator object
+ target_state: int, the state at which the transition happens, e.g. [2,2]
+ target_transition: list of integers, the transition point e.g. [1,-1] would be the transition from [2,2] to [1,1]
+ plane_axes: 2xN array, the axes of the transition which span the plane
+ use_virtual_gates: bool, whether to use virtual gates
+ compensate_sensors: bool, whether to compensate the sensors
+
+ Returns
+ -------
+ plane_axes: 2xN array, the axes spanning the cut through volage plane
+ transition_sim: CapacitanceSimulator object, the transition simulator
+ '''
+
+ if compensate_sensors:
+ # fix the sensor gate if we compensate
+ simulator = fix_gates(simulator,self.sensor_config["sensor_dot_indices"], np.zeros(len(self.sensor_config["sensor_dot_indices"])))
+ # reduce dimension of the voltag space
+ complement_gates = [i for i in range(plane_axes.shape[1]) if i not in self.sensor_config["sensor_dot_indices"]]
+ plane_axes = plane_axes[:,complement_gates]
+ #find boundaries of the selected polytope
+ poly = simulator.boundaries(target_state)
+ #get_labels of the transitions
+ inner_labels = poly.labels[:,self.inner_dots]
+
+ #Find the index of the transition point
+ transition = np.array(target_transition)[self.inner_dots].T
+ try:
+ idx_multidot_transition = [find_label(inner_labels, transition)[0]]
+ except ValueError:
+ raise "The transition point is not in the polytope. Please choose a different transition"
+
+ #Find the offset of V
+ v_transition = find_point_on_transitions(poly,idx_multidot_transition)
+ print("v_offset found:",v_transition)
+
+
+ if use_virtual_gates:
+ # Compute the normals
+ pair_transitions=np.array(
+ [np.array(transition).T for transition in plane_axes],dtype=int)
+
+ idxs = [find_label(inner_labels,t[self.inner_dots])[0] for t in pair_transitions]
+ normals = -poly.A[idxs]
+ normals /= np.linalg.norm(normals,axis=1)[:,None]
+ P_transition = normals.T
+ transition_sim = axis_align_transitions(
+ simulator.slice(P_transition, v_transition),
+ target_state,poly.labels[idxs],[0,1])
+ else:
+ transition_sim = simulator.slice(plane_axes.T, v_transition)
+
+
+ return np.eye(2), transition_sim
+
+# GETTERS
+#--------------------------
+
+ def get_virtualised_sim(self, simulator, target_state):
+ '''
+ Function that takes a simulator and virtualises the gates specified by inner_dots.
+
+ Arguments
+ ---------
+ simulator: AbstractPolytopeSimulator object
+ target_state: int, the initial corner state guess
+
+ Returns
+ -------
+ simulator: AbstractPolytopeSimulator object, the virtualised simulator
+ '''
+ gate_transitions = np.eye(self.N,dtype=int)[self.inner_dots]
+ #TODO: Default target state is lower left corner state (initial guess). In future user could specify!
+ self.target_state = target_state
+ simulator = axis_align_transitions(simulator, self.target_state, gate_transitions, self.inner_dots)
+ return simulator
+
+ def get_compensated_sim(self,simulator, target_state):
+ '''
+ Function that takes a capacitance simulator and compensates the sensors.
+
+ Arguments
+ ---------
+ simulator: AbstractPolytopeSimulator object
+ target_stater: int, the state at which sensor compensation happens
+
+ Returns
+ -------
+ simulator: AbstractPolytopeSimulator object, the compensated simulator
+ '''
+ if not self.has_sensors:
+ raise ValueError("Compensating sensors requires a sensor model.")
+
+ return compensate_simulator_sensors(simulator,
+ target_state=target_state,
+ compensation_gates=self.sensor_config["sensor_dot_indices"],
+ sensor_ids = self.sensor_config["sensor_dot_indices"],
+ sensor_detunings = self.sensor_config["sensor_detunings"])[0]
+
+ def get_plot_args(self, x_voltages, y_voltages, plane_axes, v_offset = None):
+ '''
+ Function that returns the arguments for plotting the CSD.
+
+ Arguments
+ ---------
+ x_voltages: list of floats, the x-axis voltages
+ y_voltages: list of floats, the y-axis voltages
+ plane_axes: 2xN array, the axes of the plane in which the CSD is to be rendered
+ v_offset: Nx1 array, the offset voltage of all of the gates, which defines the origin of the plot
+
+ Returns
+ -------
+ v_offset: Nx1 array, the offset voltage of all of the gates
+ minV: 2x1 array, the minimum voltage of selected axes
+ maxV: 2x1 array, the maximum voltage of selected axes
+ resolution: list of integers, the resolution of the plot
+ '''
+
+ if v_offset is None:
+ v_offset = np.zeros(plane_axes.shape[1], dtype=float)
+ else:
+ v_offset = np.array(v_offset,dtype=float)
+
+ xout = x_voltages + np.dot(v_offset,plane_axes[0])
+ yout = y_voltages + np.dot(v_offset,plane_axes[1])
+
+
+ minV = np.array([x_voltages[0], y_voltages[0]])
+ maxV = np.array([x_voltages[-1], y_voltages[-1]])
+
+ resolution = [len(x_voltages), len(y_voltages)]
+
+ return v_offset, minV, maxV, resolution, xout, yout
+
+
+# RENDER FUNCTIONS
+#--------------------------
+
+ def generate_CSD(self, x_voltages, y_voltages, plane_axes, target_state = None,
+ target_transition = None, use_virtual_gates = False,
+ compensate_sensors = False, compute_polytopes = False,
+ use_sensor_signal = False, v_offset = None, insitu_axis = None):
+ '''
+ Function that renders the capacitance CSD for a given set of voltages and axes.
+
+ Arguments
+ ---------
+ x_voltages: list of floats, the x-axis voltages
+ y_voltages: list of floats, the y-axis voltages
+ plane_axes: 2xN array, the axes of the plane in which the CSD is to be rendered
+ target_state: int, the guess state or the state at which the transition happens
+ target_transition: list of integers, the transition point e.g. [1,-1] would be the transition from [2,2] to [1,1]
+ use_virtual_gates: bool, whether to use virtual gates
+ compensate_sensors: bool, whether to compensate the sensors
+ compute_polytopes: bool, whether to compute the polytopes
+ use_sensor_signal: bool, whether to use the sensor signal
+ v_offset: Nx1 array, the offset voltage of all of the gates, which defines the origin of the plot
+
+ Returns
+ -------
+ xout, yout: list of floats, the x and y voltages
+ CSD_data: 2D array, the CSD data
+ polytopes: dictionary, the polytopes of the CSD. None if compute_polytopes is False
+ sensor_values: 3D array, the sensor signal [size(xout),size(yout),num_sensors]. None if use_sensor_signal is False
+ v_offset: Nx1 array, the offset voltage of all of the gates
+ '''
+ # check required parameters
+ if target_state is None:
+ #if not target state use [0,0,0,0,0,0]
+ target_state = [0]*self.N
+
+ plane_axes = np.array(plane_axes)
+ # prepare plot
+ v_offset, minV, maxV, resolution, xout, yout = self.get_plot_args(x_voltages, y_voltages, plane_axes, v_offset)
+
+ # pick the simulator
+ if use_sensor_signal:
+ simulator = self.tunneling_sim
+ else:
+ simulator = self.capacitance_sim
+ if compensate_sensors:
+ simulator = self.get_compensated_sim(simulator,target_state= target_state)
+ if target_transition is not None:
+ plane_axes, simulator = self.center_transition(simulator, target_state, target_transition,
+ plane_axes, use_virtual_gates, compensate_sensors)
+ v_offset = np.zeros(2) #TODO: how to do it nicer?
+
+
+ elif use_virtual_gates:
+ simulator = self.get_virtualised_sim(simulator, target_state)
+
+ if use_sensor_signal:
+ csimulator = simulator.poly_sim
+ else:
+ csimulator = simulator
+
+ # Part for the electrostatic CSD:
+ CSD_data = None
+ polytopes = None
+ if not use_sensor_signal or compute_polytopes:
+ backend, CSD_data, states = get_CSD_data(csimulator, np.array(plane_axes).T,v_offset, minV, maxV, resolution, target_state)
+ CSD_data = CSD_data.T
+ if compute_polytopes:
+ polytopes = get_polytopes(states, backend, minV, maxV)
+
+
+ # Part for the sensor signal:
+ sensor_values = None
+ if use_sensor_signal:
+ sensor_values = simulator.sensor_scan_2D(plane_axes, v_offset, minV, maxV, resolution, target_state,insitu_axis=insitu_axis)
+
+ return xout, yout, CSD_data, polytopes, sensor_values, v_offset
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/noise_processes.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/noise_processes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a28177c62848d39d62146293938bd21eb4fc2c7
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/noise_processes.py
@@ -0,0 +1,166 @@
+import numpy as np
+from abc import ABCMeta, abstractmethod
+
+class AbstractNoiseProcess(metaclass=ABCMeta):
+ """
+ Models a noise process that produces possibly dependent samples :math:`\epsilon(v)_t`. The noise process
+ can depend on the device voltages v and the noise can depend on all previous samples in the sequence. We assume
+ that :math:`\epsilon(v)_t` is vector valued and the number of elements is stored in the ``num_elements`` attribute
+
+ A sequence is started by calling ``start_sequence``, at which point the newly sampled points are independent from
+ all previous samples.
+
+ Note that currently, the elements in the noise process are assumed to be drawn with equal time difference between samples.
+
+ Attributes
+ ----------
+ num_elements: int
+ the dimensionality of the noise variable
+ """
+
+ def __init__(self, num_elements):
+ self.num_elements = num_elements
+
+ @abstractmethod
+ def slice(self, P, m):
+ """ Restricts the noise to the affine subspace :math:`v=m+Pv`.
+
+ Parameters
+ ----------
+ P : MxK np.array of floats
+ The linear coefficient matrix.
+ m: M np.array of floats
+ offset of the affine transformation.
+
+ Returns
+ -------
+ A noise process object describing the noise on the affine subspace. The current noise object remains unchanged.
+ """
+ pass
+
+ @abstractmethod
+ def start_sequence(self):
+ """
+ Restart the sequence so that the next sample drawn is independent from all previous samples.
+ """
+ pass
+
+ @abstractmethod
+ def __call__(self,v):
+ """ Returns the next element of the noise process.
+
+ Parameters
+ ----------
+ v : K np.array of floats
+ The current voltage parameter of the device
+
+ Returns
+ -------
+ An element of the noise process :math:`\epsilon(v)_t`. This is assumed to be a vector of M elements,
+ """
+ pass
+
+class OU_process(AbstractNoiseProcess):
+ """Implements the Ornstein-Uhlenbeck noise process
+
+ This noise is independent of v. We have that
+
+ .. math::
+ \epsilon(v)_{t+1}=a \epsilon(v)_t + \sqrt{1-a^2} \sigma \epsilon_t
+
+ Where :math:`\epsilon_t` is independent gaussian noise and
+
+ .. math::
+ a=e^{-\\frac{\Delta t}{t_c}}
+
+ It is possible to generate multiple independent samples of the same process simultaneously.
+ """
+ def __init__(self, sigma, tc, dt, num_elements):
+ """ Instantiates the OU process with its process parameters.
+
+ Arguments
+ ---------
+ sigma: float
+ Standard deviation of the OU process
+ tc: float
+ time correlation parameters, the higher the more samples are correlated
+ dt: float
+ time step between samples. The higher, the less samples are correlated
+ num_elements: int
+ How many independnet samples are computed simultaneously
+ """
+ super().__init__(num_elements)
+ self.sigma = sigma
+ self.tc = tc
+ self.dt = dt
+ self.a = (np.exp(-self.dt/self.tc))
+ self.b = np.sqrt(1-np.exp(-2*self.dt/self.tc))*self.sigma
+
+ def slice(self, P,m):
+ return self
+
+ def start_sequence(self):
+ self.x = self.sigma * np.random.randn(self.num_elements)
+ def next_val(self):
+ n = np.random.randn(len(self.x))
+ self.x = self.x*self.a +self.b*n
+ return self.x
+
+ def __call__(self,v):
+ return self.next_val()
+
+class Cosine_Mean_Function(AbstractNoiseProcess):
+ """ Decorator of an random process that models an additive mean term that depends on the gate voltages.
+ This term is added to noise values sampled from the decorated noise model
+
+ The mean term of the ith noise element is given as a set of cosine functions:
+
+ :math:`\mu_i(v)= \sum_j a_{ij} \cos(2\pi (w_{ij}^T v+b_{ij}))`
+
+ and the resulting noise is
+
+ :math:`\epsilon(v)_{t}=\mu(v) + \epsilon(v)^D_{t}`
+
+ where :math:`\epsilon(v)^D_{t}` is the decorated noise process.
+
+ The user supplies the weight tensor W with elements :math:`W_{ijk}` so that W[i,j] is the vector :math:`w_{ij}`
+ and a matrix a with the amplitude values :math:`a_{ij}`. Finally, b is the matrix of offsets :math:`b_{ij}`, which can be left as None,
+ in which case it is sampled uniformly between 0 and 1.
+ """
+ def __init__(self, noise_model, a, W, b=None):
+ """ Initialized the cosine mean function.
+
+ Parameters
+ ----------
+ noise_model: AbstractNoiseProcess
+ The noise process with N dimensions to decorrate.
+ a: NxM np.array of float
+ Amplitudes of the M overlapping cosine functions
+ W: NxMxK np.array of float
+ K dimensional weights of the M consine functions for the N outputs.
+ b: NxM np.array of float or None
+ Phases of the M overlapping cosine functions. If none, it is drawn uniformly between 0 and 1.
+ """
+ super().__init__(noise_model.num_elements)
+ self.noise_model = noise_model
+ self.a = a
+ self.W = W
+ self.b = b if not (b is None) else np.random.uniform(size=a.shape)
+ def slice(self, P,m):
+ new_W = np.einsum('ijk,kl->ijl',self.W,P)
+ new_b = self.b + np.einsum('ijk,k->ij',self.W,m)
+ return Cosine_Mean_Function(self.noise_model, self.a, new_W, new_b)
+ def start_sequence(self):
+ self.noise_model.start_sequence()
+ def __call__(self,v):
+ #compute noise
+ noise_values = self.noise_model(v)
+
+ #add the mean
+ activation = 2*np.pi*(np.einsum('ijk,k->ij',self.W,v)+self.b)
+ mean = np.sum(2*np.pi*self.a*np.cos(activation),axis=1)
+ noise_values += mean.reshape(1,-1)
+
+ return noise_values
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/plotting.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/plotting.py
new file mode 100644
index 0000000000000000000000000000000000000000..b275fd42bd3e093d7664c48e2a01046a055a8a57
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/plotting.py
@@ -0,0 +1,214 @@
+import numpy as np
+from scipy.spatial import HalfspaceIntersection, ConvexHull
+from scipy.optimize import linprog
+from matplotlib import pyplot as plt
+#all code here is required for plotting in the provided notebook
+def is_sequence(seq):
+ if isinstance(seq, str):
+ return False
+ try:
+ len(seq)
+ except Exception:
+ return False
+ return True
+
+
+
+def find_feasible_point(halfspaces):
+ """Computes a feasible point by a polytope defined in halfspace format. internal."""
+ norm_vector = np.reshape(np.linalg.norm(halfspaces[:, :-1], axis=1), (halfspaces.shape[0], 1))
+ c = np.zeros((halfspaces.shape[1],))
+ c[-1] = -1
+ A = np.hstack((halfspaces[:, :-1], norm_vector))
+ b = - halfspaces[:, -1:]
+ res = linprog(c, A_ub=A, b_ub=b, bounds = (None,None))
+ return res.x[:-1]
+
+
+def plot_2D_polytope(ax,A,b,color,lower_bounds, label=None, linestyle='-',linewidth=1):
+ """Plots a single 2D polytope. internal. """
+ eqs=np.hstack([A,b.reshape(-1,1)])
+ #add constraints to polytope
+ eqs = np.vstack([eqs,lower_bounds])
+
+ # Get the corners
+
+ feasible_point = find_feasible_point(eqs)
+ if not np.all(eqs[:,:-1]@feasible_point+eqs[:,-1] < 1.e-5):
+ return None, None
+ corners = HalfspaceIntersection(eqs, feasible_point).intersections
+
+ # Easiest way to get proper line-segments of the boundaries in 2D is via the convex hull
+ # Form a convex hull based on the corners
+ hull = ConvexHull(corners)
+ # Plot lines between the points forming the hull (they are assumed to come in counterclockwise order)
+ for i,simplex in enumerate(hull.simplices):
+ label_trace = label
+ if i != 0:
+ label_trace = None
+ ax.plot(corners[simplex, 0], corners[simplex, 1], linestyle, c=color, lw=linewidth,label=label_trace)
+ return feasible_point, (np.min(corners,axis=0),np.max(corners,axis=0))
+
+
+def get_2D_polytope(A,b,color,lower_bounds, label=None, linestyle='-',linewidth=1):
+ """computes the corners of a 2D polytope from the provided polytope. internal"""
+ eqs=np.hstack([A,b.reshape(-1,1)])
+ #add constraints to polytope
+ eqs = np.vstack([eqs,lower_bounds])
+
+ # Get the corners
+
+ feasible_point = find_feasible_point(eqs)
+ if not np.all(eqs[:,:-1]@feasible_point+eqs[:,-1] < 1.e-5):
+ return None, None
+ corners = HalfspaceIntersection(eqs, feasible_point).intersections
+
+ # Easiest way to get proper line-segments of the boundaries in 2D is via the convex hull
+ # Form a convex hull based on the corners
+ return corners
+
+
+
+def raster_CSD_states(simulation, P, v_0, minV, maxV, resolution, state_hint_lower_right):
+ """Creates a grid of points in 2D space and computes for each point the ground state. Internal."""
+ if not is_sequence(resolution):
+ resolution = [resolution,resolution]
+ states=np.zeros((resolution[0],resolution[1], simulation.num_dots),dtype=int)
+ line_start = simulation.find_state_of_voltage(v_0+P@np.array([minV[0],minV[1]]), state_hint_lower_right)
+ for i,v1 in enumerate(np.linspace(minV[0],maxV[0],resolution[0])):
+ state = line_start
+ for j,v2 in enumerate(np.linspace(minV[1],maxV[1],resolution[1])):
+ v = v_0+P@np.array([v1,v2])
+ state = simulation.find_state_of_voltage(v,state)
+ if not simulation.inside_state(v,state):
+ print("error")
+ if j == 0:
+ line_start = state
+ states[i,j] = state
+ return states
+
+
+
+def get_CSD_data(simulation, P, v_0, lower_left, upper_right, resolution, state_hint_lower_left):
+ """
+ Function that computes a Charge Stability Diagram from a simulation of a device.
+ The function plots the states at voltages v=v_0+P*x where x is a vector with elements
+ in a box defined by its lower-left and upper-right corners given by lower_left and upper_right and
+ number of values given by the resolution. if upper_right=-lower_left then v_0 is the center
+ pixel of the rastered plot.
+
+ By default, a background image is plotted
+ which is based on the charge onfiguration at a position. This is overlayed with a line plot indicating the
+ exact transition points between states. Optionally each region is labelled using the exact electron state.
+
+ Parameters
+ ----------
+ simulation: the device simulation to raster
+ ax: matplotlib axis object to plot into
+ v_0: the origin of the coordinate system to plot.
+ P: coordinate system. A nx2 axis where n is the number of gates in simulation.
+ lower_left: minimum value in x for both axes
+ upper_right: maximum value of x in both axes
+ resolution: number of sampled points in each direction of x. either single number or one per axis.
+ state_hint_lower_left: starting point to guess the initial state of the lower left corner. must not be empty within sim.
+ draw_labels: whether to draw the label of the state in a region
+ draw_background: whether to draw a color map of the states of the CSD.
+ """
+ minV = np.array(lower_left)
+ maxV = np.array(upper_right)
+
+ #find the true corner state before slicing the simulation. otherwise we might have trouble
+ #finding it if the state hint does not touch the projection
+
+ lower_left_corner = v_0 + P@minV
+ corner_state = simulation.find_state_of_voltage(lower_left_corner, state_hint_lower_left)
+ simulation_slice = simulation.slice(P, v_0, proxy=True)
+
+ #compute CSD
+ states = raster_CSD_states(simulation_slice, np.eye(2), np.zeros(2), minV, maxV, resolution, corner_state)
+ color_weights = np.linspace(1,2.7,simulation_slice.num_dots)
+ CSD_data = 1+np.sum(color_weights.reshape(1,1,-1)*states,axis=2)
+ return simulation_slice, CSD_data, states
+
+
+def get_polytopes(states, simulation_slice, minV, maxV):
+ """For each unique state in the provided state list, computes the corners of the polytope. of the 2D sliced simulation.
+
+ This function is used for plotting of the exact state lines of the underlying capacitive model.
+ """
+ #iterate over the list of different states and plot their sliced polytope
+
+ states = [tuple(s) for s in states.reshape(-1,simulation_slice.num_dots).tolist()]
+ state_list = set(states)
+ polytope_list = {}
+ for state in state_list:
+ #get the polytope
+ polytope=simulation_slice.boundaries(state)
+ A=polytope.A
+ b=polytope.b
+
+ #check if polytope is empty and continue otherwise (should never trigger)
+ if A.shape[0] == 0:
+ continue
+ lower_bounds_graph = np.hstack([-np.eye(2), (minV-0.05*abs(minV))[:,None]])
+ upper_bounds_graph = np.hstack([np.eye(2), -(maxV+0.05*abs(maxV))[:,None]])
+ lower_bounds = np.vstack([lower_bounds_graph, upper_bounds_graph])
+ corners = get_2D_polytope(A,b,"white",lower_bounds)
+ polytope_list[str(state)] = np.array(corners)
+ return polytope_list
+
+def plot_polytopes(ax, polytopes, fontsize = 10, color = "w",
+ axes_rescale = 1, only_labels = False, only_edges = False,
+ skip_dots = [], alpha = 1, lw = 1
+ ):
+ """Plot the polytopes computes by get_polytopes"""
+ xlim = ax.get_xlim()
+ ylim = ax.get_ylim()
+
+ for polytope in polytopes:
+ corners = polytopes[polytope]
+ hull = ConvexHull(polytopes[polytope])
+ if not only_labels:
+ for i,simplex in enumerate(hull.simplices):
+ ax.plot(corners[simplex, 0]* axes_rescale , corners[simplex, 1]* axes_rescale , "-", c=color, lw=lw, alpha = alpha)
+ if not only_edges:
+ box_mid = (np.array(np.min(corners, axis=0)) + np.array(np.max(corners, axis=0)))/2*axes_rescale
+ # skip skip_dots entries to polytope
+ res = np.array([int(ele) for ele in str(polytope[1:-1]).split(",")])
+ inds = np.array(list(set(np.arange(len(res))) - set(skip_dots)), dtype=int)
+
+
+ if( box_mid[0] > xlim[0] and box_mid[0] < xlim[1] and box_mid[1] > ylim[0] and box_mid[1] < ylim[1] ):
+ ax.text(box_mid[0], box_mid[1], str(list(res[inds])), c=color,ha='center', va='center', fontsize=fontsize)
+
+
+'''
+def get_polytopes(states, simulation_slice, minV, maxV):
+ #iterate over the list of different states and plot their sliced polytope
+ draw_labels = True
+ states = [tuple(s) for s in states.reshape(-1,simulation_slice.num_dots).tolist()]
+ state_list = set(states)
+ polytope_list = []
+ for state in state_list:
+ polytope_list.append(simulation_slice.boundaries(state))
+ print(polytope_list[-1])
+ #get the polytope
+ polytope=simulation_slice.boundaries(state)
+ A=polytope.A
+ b=polytope.b
+
+ #check if polytope is empty and continue otherwise (should never trigger)
+ if A.shape[0] == 0:
+ continue
+ lower_bounds_graph = np.hstack([-np.eye(2), (minV-0.05*abs(minV))[:,None]])
+ upper_bounds_graph = np.hstack([np.eye(2), -(maxV+0.05*abs(maxV))[:,None]])
+ lower_bounds = np.vstack([lower_bounds_graph, upper_bounds_graph])
+ point_inside,box = plot_2D_polytope(ax,A,b,"white",lower_bounds)
+ print(box)
+ if draw_labels and not box is None:
+ box_mid = (box[0]+box[1])/2
+ # Add charge state text
+ if( box_mid[0] > minV[0] and box_mid[0] < maxV[0] and box_mid[1] > minV[1] and box_mid[1] < maxV[1] ):
+ ax.text(box_mid[0], box_mid[1], str(state), c="white",ha='center', va='center')
+'''
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/polytope.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/polytope.py
new file mode 100644
index 0000000000000000000000000000000000000000..741ebca0ffb088d064195deca472e111e176d74c
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/polytope.py
@@ -0,0 +1,123 @@
+import numpy as np
+
+class Polytope:
+ """ Represents the polytope P(n) defined by all gate voltages v in a device that have
+ capacitive ground state n. They are stored as a set of linear inequalities (A,b), and
+ A point v in P(n) fulfills
+
+ :math:`Av+b <0`
+
+ Each inequality represents a facet of the polytope and each facet marks a transition from P(n) to
+ some other ground state polytope P(n'). The state difference t=n'-n is stored as label for each
+ inequality.
+
+ Not each inequality stored must touch the polytope. There might be others that could be removed without changing P(n).
+ The distance from the polytope is given by the slack variable s that for each inequality either is 0 if the side is touching
+ (or some number numerically close to 0, e..g, 1.e-8) and otherwise we have
+
+ :math:`s_i = min_v A_i^Tv +b_i, v \in P(n)`
+
+ If the inequalities measure difference in capacitive energy from the ground state (which is default in the simulator) the slack
+ therefore indicates the minimum energy gap between the transition state indicated by the inequality and the ground state.
+
+ Finally, for optimization reason, not every polytope might be fully computed and must be verified. This should never happen to a user
+ and is mostly an internal detail of the simulator. This holds as well for additional information that can be stored inside a dict in the
+ polytope. The simulators can store additional info in the polytope via this way.
+
+ Attributes
+ ----------
+ state: D np.array of int
+ The D dimensional array that stores the electron configuration n of the current ground state polytope. All points inside
+ the polytope have this state as ground state.
+ labels: NxD np.array of int
+ Matrix of transitions. Each of the N inequalities is labeled by the state difference t=n'-n.
+ A: NxK np.array of float
+ Linear factors of the N inequalities for a K-dimensional gate space.
+ b: N np.array of float
+ constant offsets of the N linear inequalities
+ slacks: N np.array of float
+ Measures the distance of the inequality from the boundary of the polytope. ~0 if touching.
+ point_inside: K np.array of float
+ A point inside the polytope.
+ must_verify: bool
+ Internal variable for bookkeeping whether the polytope needs recomputing (mostly after slice). Should ALWAYS be False for polytopes
+ queried from the simulator. TODO: can we remove this?
+ additional_info: dict
+ Internal additional information that later steps of the simulation can store inside a polytope for bookkeeping.
+ """
+ def __init__(self, state):
+ #empty polytope
+ self.state = state
+ self.labels = np.array([])
+ self.A = np.array([])
+ self.b = np.array([])
+ self.slacks = np.array([])
+ self.point_inside = np.array([])
+ self.must_verify = False
+ self.additional_info={}
+
+ def set_polytope(self, labels, A, b, slacks, point_inside, must_verify = False):
+ """ Sets the internal variables of the polytope.
+
+ Helper function to ensure thateverything is set as it should be.
+ """
+ self.labels = labels
+ self.A = A
+ self.b = b
+ self.slacks = slacks
+ self.point_inside = point_inside
+ self.must_verify = must_verify
+
+ def lazy_slice(self, P, m):
+ """
+ Slices a polytope lazyily, i.e., without recomputing the slacks and boundaries.
+
+ As a result, after this must_verify is True. P is not required to be invertible.
+
+ Parameters
+ ----------
+ P: KxK np.array of float
+ Invertible linear transformation matrix
+ m: K np.array of float
+ Affine offset of the transformation.
+ """
+ sliced = Polytope(self.state)
+ if self.A.shape[0] == 0:
+ sliced.set_polytope(self.labels, self.A, self.b, np.array([]), None, False)
+ else:
+ sliced.set_polytope(
+ self.labels,
+ self.A@P, #we know the line equations
+ self.b+self.A@m, #and their offsets
+ None, None, #but nothing else
+ True #user must verify this later.
+ )
+ return sliced
+
+ def invertible_transform(self, P, m):
+ """
+ Apply an invertible affine transformation to the polytope. This can be done without changing slacks and thus no verification is needed.
+
+ Changes the space of the polytope via the transformation :math:`v=Av'+b`. Returns the polytope in the coordinate system of v'
+
+ Parameters
+ ----------
+ P: KxK np.array of float
+ Invertible linear transformation matrix
+ m: K np.array of float
+ Affine offset of the transformation.
+ """
+ if self.must_verify:
+ return self.lazy_slice(P, m)
+
+ transformed = Polytope(self.state)
+
+ transformed_point_inside = np.linalg.inv(P)@(self.point_inside - m)
+ transformed.set_polytope(
+ self.labels,
+ self.A@P, #we know the line equations
+ self.b+self.A@m, #and their offsets
+ self.slacks, #slacks are constant under invertible transforms.
+ transformed_point_inside
+ )
+ return transformed
\ No newline at end of file
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/simulator.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/simulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd7887504065ca98a9018f0a84f3ee759967aee4
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/simulator.py
@@ -0,0 +1,448 @@
+import numpy as np
+from qdarts.util_functions import is_invertible_matrix
+from qdarts.capacitance_model import CapacitanceModel
+from qdarts.util_functions import compensate_simulator_sensors, fix_gates, find_point_on_transitions, axis_align_transitions
+from abc import ABCMeta, abstractmethod
+
+class AbstractPolytopeSimulator(metaclass=ABCMeta):
+ """ Base class for all simulation objects that can compute and return polytopes.
+
+ The class only has a single method boundaries which returns the boundary description of the polytope
+ with meta information as well as two attributes:
+
+ Attributes
+ ----------
+
+ num_dots: int
+ number of dots in the device, i.e., number of entries in the state vector of the polytope
+ num_inputs: int
+ number of gate voltages in the device. The polytope lives in a space that is num_inputs dimensonal.
+ """
+ def __init__(self, num_dots, num_inputs):
+ self.num_dots = num_dots
+ self.num_inputs = num_inputs
+
+ @abstractmethod
+ def boundaries(self, state):
+ """
+ Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.
+
+ Parameters
+ ----------
+ state: list of ints
+ The state n for which to compute the polytope P(n)
+
+ Returns
+ -------
+ The polytope P(n)
+ """
+ pass
+
+ @abstractmethod
+ def slice(self, P, m, proxy=False):
+ """ Restricts the simulator to the affine subspace v=m+Pv'
+
+ Computes the slice through the device by setting v=m+Pv', where v is the plunger gate voltages of the
+ original device and v' is the new coordinate system. Must be implemented by derived classes.
+
+ Parameters
+ ----------
+ P : MxK np.array of floats
+ The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+ and K the dimensionality of the subspace.
+ m: offset of the affine trnsformation.
+ proxy: bool
+ Whether a proxy is returned. A proxy can share computation between instances, if supported by the derived class
+
+ Returns
+ -------
+ A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.
+ """
+ pass
+
+class AbstractCapacitiveDeviceSimulator(AbstractPolytopeSimulator):
+ """Base class for all objects that create device simulations from a Capacitive Model.
+
+ This class includes all tools to compute and cache polytopes from the provided capacitive model.
+ Polytopes are queried using a call to boundaries() which queries the internal cache and then
+ computes the polytope on demand.
+
+ The computed polytope P(n) is the set of voltages v for which n is the ground state of the capacitance
+ energy function E(v,n), i.e., n=min_n' E(v,n'). A facet of the polytope is given by the equality
+ E(v,n')-E(v,n) = 0 for a suitable choice of n'. As a result, when shooting a ray through
+ a facet of the polytope created by state n', there is a transition from state n->n' in the ground state.
+
+ The computation of the polytope discards by default all states and inequalitis that do not form a facet of P(n).
+ However, this can be relaed by allowing a maximum slack, which also keeps facets for which
+ min_v E(v,n')-E(v,n) < max_slack, where v is restricted to elements in the polytope P(n). i.e., max slack keeps facet
+ in which the energy difference is small.
+
+ This class supports slicing of the voltage space into affine subspaces.
+ """
+ def __init__(self, capacitance_model):
+ super().__init__(capacitance_model.num_dots, capacitance_model.num_inputs)
+ self.capacitance_model = capacitance_model
+ self.cache = {}
+
+ @abstractmethod
+ def slice(self, P, m, proxy=False):
+ """ Restricts the simulator to the affine subspace v=m+Pv'
+
+ Computes the slice through the device by setting v=m+Pv', where v is the plunger gate voltages of the
+ original device and v' is the new coordinate system. Must be implemented by derived classes.
+
+ Parameters
+ ----------
+ P : MxK np.array of floats
+ The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+ and K the dimensionality of the subspace.
+ m: offset of the affine trnsformation.
+ proxy: bool
+ Whether a proxy is returned. A proxy shares the cache, if possible. This is the case when P is invertible,
+ especially this entails M=K. If cache sharing is possible, the simulation computes the original polytope and then
+ applies the affine transformation. This can reduce run time a lot if several slices need to be computed for the
+ same simulation.
+
+ Returns
+ -------
+ A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.
+ """
+ pass
+
+ @abstractmethod
+ def compute_polytope(self, state):
+ """
+ Computes the polytope for a given state.
+
+ Is implemented by the derived class and called when the polytope for a state is not found in cache.
+
+ Parameters
+ ----------
+ state : list of ints
+ the state identifying the polytope
+
+
+ Returns
+ -------
+ A Polytope object containing the full computed polytope.
+ """
+ pass
+
+ def compute_transition_equations(self, state_list, state_from):
+ """ Computes the energy difference equations from target states to all states in the list.
+
+ For a given state and list of neighbour states, computes the linear equations Av+b that compute the energy differences
+ Between the target state_from and the other states. That is, if state_list contains a list of states n', this
+ function constains linear equations E(v,n')-E(v,state_from)
+
+ Parameters
+ ----------
+ state_list: numpy array of ints of size NxK
+ A list containing N states for which to compute the energy differences
+
+ Returns
+ -------
+ A: NxK np.array, containing the linear factors for each state in state_list
+ b: np.array, containing the N offsets, one for each equation.
+ """
+ return self.capacitance_model.compute_transition_equations(state_list,state_from)
+
+ @abstractmethod
+ def get_maximum_polytope_slack(self):
+ """Returns the maximum slack value for inclusing of a facet into the polytope.
+
+ Returns the maximum energy distance the closest point of a transition can have to the polytope
+ before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+ are kept.
+ """
+ pass
+
+ @abstractmethod
+ def set_maximum_polytope_slack(self, maximum_slack):
+ """Sets the maximum slack value for inclusing of a facet into the polytope.
+
+ Sets the maximum distance the closest point of a transition can have to the polytope
+ before it is discarded. Setting to 0 means that only transitions that actually touch the polytope
+ are kept.
+
+ Note that changing this value clears the cache.
+ """
+ pass
+
+ def cached_polytopes(self):
+ """
+ Returns a sequence including all computed and cached polytopes for inspection and modification.
+ """
+ return self.cache.values()
+
+ def boundaries(self, state):
+ """
+ Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.
+
+ If the polytope is not cached, it needs to be computed. This can take some time for large devices.
+
+ Parameters
+ ----------
+ state: list of ints
+ The state n for which to compute the polytope P(n)
+
+ Returns
+ -------
+ The polytope P(n)
+ """
+ # Convert to array to be sure
+ state = np.asarray(state).astype(int)
+
+ # lookup key of this state
+ dict_key = tuple(state.tolist())
+ # See if we already have this key in our prepared list
+ if not dict_key in self.cache.keys():
+ self.cache[dict_key] = self.compute_polytope(state)
+
+ #obtain polyope from dict
+ polytope = self.cache[dict_key]
+
+ #slice is allowed to be lazy but then we need to verify the polytope now.
+ if polytope.must_verify:
+ polytope = self.capacitance_model.verify_polytope(polytope,self.get_maximum_polytope_slack())
+ self.cache[dict_key] = polytope
+
+ return polytope
+
+
+ def inside_state(self, v, state):
+ """ Returns true if a point v is fully within the polytope of a given state.
+
+ Parameters
+ ----------
+ state: list of ints
+ The state n identifying the polytope P(n)
+ v: np.array of floats
+ The point v
+
+
+ Returns
+ -------
+ The truth value of whether v is element of P(n)
+ """
+ polytope = self.boundaries(state)
+ if len(polytope.labels) == 0:
+ return False
+ f = polytope.A@v + polytope.b
+ return np.all(f < 1.e-8)
+
+ def find_boundary_intersection(self, old_v, new_v, state, epsilon=1.e-6, deep_search=True):
+ """Computes an intersection of a ray with the boundary of a polytope and computes the new state
+
+ For a given state and a voltage old_v within the polytope of this state and a point new_v outside the polytope,
+ computes the intersection of the ray old_v+t*(new_v-old_v) with the boundary of the polytope.
+ the intersection point and new target state is computed.
+
+
+ Parameters
+ ----------
+ old_v: np.array of floats
+ A point within the current polytope
+ new_v: np.array of floats
+ Another point on the ray
+ state: list of ints
+ The ground state n of old_v. It is assumed that v is element of P(n)
+ epsilon: float
+ slack value added to the ray t to ensure that the point is numerically clearly outside the polytope.
+ deep_search: bool
+ whether an iterative search is performed for the new point in case none of the direct neighbours of the polytope match.
+ If false, will throw an exception in that case. An exception is also raised when the deep search failed.
+
+
+ Returns
+ -------
+ The first intersection point of the ray with the polytope, together with the new state
+ """
+ if not self.inside_state(old_v,state):
+ raise ValueError("old_v must be in the provided state.")
+
+ polytope = self.boundaries(state)
+
+ direction = new_v - old_v
+ direction /= np.linalg.norm(direction)
+
+ A_line = polytope.A @ direction
+ b_line = polytope.b + polytope.A @ old_v
+ positive = np.where(A_line > 0)[0]
+ ts = -b_line[positive]/A_line[positive]
+ transition_idx = np.argmin(ts)
+
+ #construct point of cosest hit
+ transition_state = state + polytope.labels[positive[transition_idx]]
+ v_intersect = old_v + (1+epsilon)*ts[transition_idx]*direction
+ if self.inside_state(v_intersect, transition_state):
+ return transition_state, v_intersect
+
+ #the new point might have went through a corner, so we check all states whose transitions are now violated
+
+ rel_energy = polytope.A@v_intersect+polytope.b
+ idx_order = np.argsort(rel_energy)
+ for idx in idx_order:
+ #pass 1: ignore transitions that don't touch the polytope.
+ if polytope.slacks[idx]>1e-6:
+ continue
+ if rel_energy[idx] < -1.e-8:
+ continue
+ transition_state = state + polytope.labels[idx]
+ if self.inside_state(v_intersect, transition_state):
+ return transition_state, v_intersect
+
+ for idx in idx_order:
+ #pass 2: now try the near-hits
+ if polytope.slacks[idx]<1e-6:
+ continue
+ if rel_energy[idx] < -1.e-8:
+ continue
+ transition_state = state + polytope.labels[idx]
+ if self.inside_state(v_intersect, transition_state):
+ return transition_state, v_intersect
+ if not self.inside_state(v_intersect, transition_state):
+ if deep_search == False:
+ print(old_v, new_v, state)
+ raise LookupError()
+
+ transition_state = self.find_state_of_voltage(new_v, state, deep_search= False)
+
+ return transition_state, v_intersect
+
+ def find_state_of_voltage(self,v,state_hint, deep_search=True):
+ """ Searches the ground state for a given voltage, given an initial guess.
+
+ For a given state voltage, computes the state for which is within the polytope of the state.
+ Note that the choice of the hint is not arbitrary, since the search starts from a point in state_hint
+ in order to find iteratively intersections with the boundary that are closer to v. A specific requirement
+ is that the polytope must not be empty, i.e., in case of a sliced simulator, the polytope must intersect
+ with the affine space. This can sometimes be tricky and we recommend perform this type of computations
+ only on spaces where all plungers are available and then perform the slicing through v.
+
+ Parameters
+ ----------
+ v: np.array of floats
+ Voltage vector for which to find the ground state
+ state_hint: list of ints
+ a likely candidate for the state.
+ deep_search: bool
+ whether an iterative search is performed in case none of the direct neighbours of a polytope match.
+ If false, will throw an exception in that case. An exception is also raised when the deep search failed.
+ """
+ state = state_hint
+ polytope = self.boundaries(state)
+ if len(polytope.labels) == 0:
+ raise ValueError("polytope of state_hint does not intersect with plane")
+
+ # Check if hint was correct
+ # If not we have to search.
+ # We hope that the solution is close enough and find the transitions
+ v_inside = polytope.point_inside.copy()
+ while not self.inside_state(v,state):
+ state,v_inside = self.find_boundary_intersection(v_inside, v, state, deep_search = deep_search)
+
+ return state
+
+class CapacitiveDeviceSimulator(AbstractCapacitiveDeviceSimulator):
+ """
+ This class simulates a quantum dot device based on a capacitance model.
+
+ The simulator interally keeps track of the Coulomb diamonds (polytopes) and their transitions (facets),
+ and takes care of keeping track of which transitions are feasible, with what precision, etc.
+ This allows one to ask questions such as: "which transition does this facet correspond to?" and
+ "what is the orthogonal axis in voltage space (i.e. virtual gate) that tunes across it?".
+ The simulator will return, for each transition, a point on the transition line and the virtual gate.
+
+ It also has the ability to take 2D slices through high dimensional voltage spaces to construct 2D
+ projections of charge stability diagrams. See documentation of AbstractCapacitiveDeviceSimulator for more details.
+ """
+
+ def __init__(self, capacitance_model):
+ super().__init__(capacitance_model)
+ self.maximum_slack = 0.0
+
+ def compute_polytope(self, state):
+ return self.capacitance_model.compute_polytope_for_state(state,self.maximum_slack)
+
+ def get_maximum_polytope_slack(self):
+ return self.maximum_slack
+
+ def set_maximum_polytope_slack(self, maximum_slack):
+ self.maximum_slack = maximum_slack
+ self.cache={}
+
+ def slice(self, P, m, proxy=None):
+
+ #if proxy is not set, we check whether P is invertible
+ #if it is invertible, then reusing the cache is the most efficient
+ #in the general case where we don't know whether the original simulator
+ #will be used still.
+
+ #checking invertibility also allows us to quickly transform the cache
+ is_invertible = is_invertible_matrix(P)
+ if proxy is None:
+ proxy = is_invertible
+
+ if proxy == True:
+ sliced_proxy = CapacitiveDeviceSimulatorProxy(self, P, m)
+ return sliced_proxy
+ else:
+ sliced_simulator = CapacitiveDeviceSimulator(self.capacitance_model.slice(P,m))
+ sliced_simulator.maximum_slack = self.maximum_slack
+ #slice all precomputed polytopes in a lazy manner.
+ for key, polytope in self.cache.items():
+ if is_invertible:
+ sliced_simulator.cache[key] = polytope.invertible_transform(P, m)
+ else:
+ sliced_simulator.cache[key] = polytope.lazy_slice(P, m)
+
+ return sliced_simulator
+
+class CapacitiveDeviceSimulatorProxy(AbstractCapacitiveDeviceSimulator):
+
+ """
+ This class is a slice proxy for the CapacitiveDeviceSimulator class. It gets returned by
+ any slice operation, when a proxy is requested. This is unlikely to be used by the user
+ directly and mostly used during plotting. The advantage of a proxy is that it can make better use of
+ caching at the expense of higher computation cost: all queries for polytopes are computed by the original simulator
+ and thus if several different slices of the same simulator are needed, they can share computed polytopes.
+
+ For the methods, see the documentation of AbstractCapacitiveDeviceSimulator
+ """
+ def __init__(self, simulator, P, m):
+ super().__init__(simulator.capacitance_model.slice(P,m))
+ self.simulator = simulator
+ self.P = P
+ self.m = m
+
+ def get_maximum_polytope_slack(self):
+ return self.simulator.get_maximum_polytope_slack()
+
+ def set_maximum_polytope_slack(self, maximum_slack):
+ self.simulator.set_maximum_polytope_slack(maximum_slack)
+ self.cache={}
+
+ def compute_polytope(self, state):
+ #query or compute original polytope
+ polytope = self.simulator.boundaries(state)
+
+ #transform lazyly
+
+ polytope_sliced = polytope.lazy_slice(self.P, self.m)
+ polytope_sliced = self.capacitance_model.verify_polytope(polytope_sliced,self.get_maximum_polytope_slack())
+ return polytope_sliced
+
+ def slice(self, P, m, proxy=None):
+ if proxy is None:
+ proxy = True
+
+ if proxy == True:
+ return CapacitiveDeviceSimulatorProxy(self,P, m)
+ else:
+ new_P = self.P@P
+ new_m = self.m + self.P@m
+ return self.simulator.slice(new_P, new_m, False)
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/tunneling_simulator.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/tunneling_simulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..49b93c064e04ce8bc01c2f1c0eff89cdd866a9f1
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/tunneling_simulator.py
@@ -0,0 +1,844 @@
+import numpy as np
+from abc import ABCMeta, abstractmethod
+from qdarts.util_functions import find_label
+from qdarts.simulator import AbstractPolytopeSimulator
+
+def softmax(v,axis=None):
+ max_v = np.max(v)
+ y = np.exp(v-max_v)
+ return y/np.sum(y,axis)
+
+class AbstractSensorSim(metaclass=ABCMeta):
+ """ Base class defining the interface for all sensor simulations"""
+ def __init__(self, num_sensors):
+ """Initialized a sensor configuration with num_sensors sensor dots"""
+ self.num_sensors = num_sensors
+
+ @abstractmethod
+ def slice(self, P, m):
+ """Takes an affine subspace of the simulated model."""
+ pass
+
+ @abstractmethod
+ def start_measurement(self):
+ """Reinitializes the sensor as to generate independent noise samples"""
+ pass
+
+ @abstractmethod
+ def precompute_sensor_state(self, state, A, b, basis_labels):
+ """ Allows the sensor to precompute internal information that is valid for a whole ground state polytope.
+
+ This allows the sensor to precompute and cache information that is valid for all voltages v that are inside a
+ ground state polytope P. The returned values are cached in the polytope objects of the simulator and
+ supplied as sensor_state argument during a call of sample_sensor_equilibrium and sample_sensor_configuration.
+ The supplied information provides all information of the basis labels considered by the simulation for P(n),
+ and the linear functions defining the facets of P(n) Av+b. Note that as everywhere else, these linear functions define
+ energy differences for each considered basis label to the ground state.
+
+ Parameters
+ ----------
+ state: np.array of ints
+ the state of N dots, identifying the ground state polytope for which to generate the sensor state information
+ A: LxK np.array of floats
+ LxK linear parameters of the energy difference function for the K sensor gates
+ b: np.array of floats
+ The affine offsets of the energy difference function for the L basis states
+ basis_labels: LxN np.array of ints
+ The labels of the L basis states
+ """
+ pass
+
+ @abstractmethod
+ def sample_sensor_equilibrium(self, v, H, mixed_state, sensor_state, beta):
+ """ Computes a noisy average of the sensor response for a given mixed state.
+
+ This is intended to simulate a long (>1mus) time integration of the sensor signal, thus
+ we can assume that states are thermalized but the signal is still affected by noise.
+
+ Parameters
+ ----------
+ v: np.array of floats
+ vector of K gate voltages defining the current system
+ H: LxL np.array of floats
+ Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state
+ mixed_state: LxL np.array of floats
+ Mixed state matrix computed via the Hamiltonian using expm(-beta*H)
+ sensor_state:
+ Cached information returned by precompute_sensor_state. All information therein are internal to the
+ sensor simulator
+ beta: float
+ scaled inverse temperature parameter
+ """
+ pass
+
+ @abstractmethod
+ def sample_sensor_configuration(self, sampled_configuration, v, H, mixed_state, sensor_state, beta):
+ """ samples a sensor response for a given sampled elecron configuration
+
+ This is intended to simulate a short (<<1mus) time integration of the sensor signal,
+ where we can not assume that electrons transitions during the measurement. In this case,
+ the user supplied the relevant configuration and the sensor returns a sampled signal for this configuration.
+ Care should be taken that the configuration sampled has all information needed in the base to compute the sensor
+ signal, e.g., there should be a state with one more or less electrons on each sensor dot.
+
+
+ Parameters
+ ----------
+ sampled_configuration: np.array of ints
+ vector of N elements describing the sampled electron configuration.
+ v: np.array of floats
+ vector of K gate voltages defining the current system
+ H: LxL np.array of floats
+ Hamiltonian of the system defined by v. Labels and basis are the same as in precompute_sensor_state
+ mixed_state: LxL np.array of floats
+ Mixed state matrix computed via the Hamiltonian using expm(-beta*H)
+ sensor_state:
+ Cached information returned by precompute_sensor_state. All information therein are internal to the
+ sensor simulator
+ beta: float
+ scaled inverse temperature parameter
+ """
+ pass
+
+class NoisySensorDot(AbstractSensorSim):
+ """ Simulates a sensor signal by computing the conductance of the sensor dots.
+
+ This class implements the interface of AbstractSensorSim and for most points, the
+ documentation there should be referred to. This simulation combines a simple
+ estimation of the conductance g with two noise sources. A fast noise source
+ that simulates gaussian white noise that is drawn for each query of the sensor response
+ and a slow noise source that models time dependent noise between different invocations
+ of the sensor.
+
+ The shape of the simulated sensor peak can be configured ´via config_peak, in which
+ height and with of the peak can be adapted. Currently, all sensor dots share these
+ parameters.
+
+ The noise can be configured via config_noise. The noise is modeled as an additive
+ noise on the sensor peak position in voltage space. Thus, at peaks or valleys, the noise
+ is small while on the sides of the peak, where the derivatives are largest, the noise will
+ affect measurements the most. Additional signal noise is then modeled by adding white gaussian noise.
+ """
+ def __init__(self, sensor_dot_ids):
+ super().__init__(len(sensor_dot_ids))
+ self.sensor_dot_ids = sensor_dot_ids
+ self.g_max = 1
+ self.fast_noise_var = 0.0
+ self.peak_width_multiplier = 1
+ self.slow_noise_gen=None
+ self.signal_noise_scale = 0.0
+
+ def config_noise(self, sigma, signal_noise_scale, slow_noise_gen = None):
+ self.fast_noise_var = sigma**2
+ self.slow_noise_gen = slow_noise_gen
+ self.signal_noise_scale=signal_noise_scale
+ #initialize noise
+ self.start_measurement()
+
+ def config_peak(self, g_max, peak_width_multiplier):
+ self.g_max = g_max
+ self.peak_width_multiplier = peak_width_multiplier
+
+ def start_measurement(self):
+ if not self.slow_noise_gen is None:
+ self.slow_noise_gen.start_sequence()
+ def slice(self, P, m):
+ #if there is no slow noise, there is nothing to slice
+ if self.slow_noise_gen is None: return self
+
+ #otherwise create a copy of this with a sliced slow noise model
+ sliced_sensor_dot = NoisySensorDot(self.sensor_dot_ids)
+ sliced_sensor_dot.g_max = self.g_max
+ sliced_sensor_dot.fast_noise_var = self.fast_noise_var
+ sliced_sensor_dot.peak_width_multiplier = self.peak_width_multiplier
+ sliced_sensor_dot.signal_noise_scale = self.signal_noise_scale
+ sliced_sensor_dot.slow_noise_gen = self.slow_noise_gen.slice(P,m)
+ return sliced_sensor_dot
+
+ def precompute_sensor_state(self, state, A, b, labels):
+ sensor_state ={}
+ for i, sensor_id in enumerate(self.sensor_dot_ids):
+ labels_nosens = np.delete(labels,sensor_id, axis=1)
+ labels_unique, inverse_index = np.unique(labels_nosens, return_inverse=True,axis=0)
+
+ labels_sens = labels[:,sensor_id]
+ sorted_ind = np.lexsort((labels_sens,inverse_index))
+
+ relevant_label_indices = []
+ prev = []
+ next = []
+ cur = -1
+ last = None
+ last_2 = None
+ for ind in sorted_ind:
+ l = labels_nosens[ind]
+ if np.any(l != cur):
+ cur = l
+ last = None
+ last_2 = None
+ else:
+ if not last_2 is None:
+ relevant_label_indices.append(last)
+ prev.append(last_2)
+ next.append(ind)
+ last_2 = last
+ last = ind
+ terms = np.array(relevant_label_indices,dtype=int)
+ prev = np.array(prev,dtype=int)
+ next = np.array(next,dtype=int)
+ terms_labels = labels[terms,:]
+ sensor_state[sensor_id] = (terms,prev,next,terms_labels)
+ return sensor_state
+
+ def _precompute_g(self, v, H, sensor_state, beta):
+ results = np.zeros(len(self.sensor_dot_ids))
+ gs={}
+ slow_noise = np.zeros((results.shape[0],1))
+ if not self.slow_noise_gen is None:
+ slow_noise = self.slow_noise_gen(v)
+ for i, sensor_id in enumerate(self.sensor_dot_ids):
+ terms,neighbour_prev, neighbour_next, _ = sensor_state[sensor_id]
+
+ #compute sensor detuning between every state and their neighbour
+ eps_prev = np.abs(np.diag(H)[terms]-np.diag(H)[neighbour_prev])
+ eps_next = np.abs(np.diag(H)[terms]-np.diag(H)[neighbour_next])
+ eps = np.minimum(eps_prev,eps_next)
+ #add noise
+ eps = eps + slow_noise[i:i+1]
+ if self.fast_noise_var > 0:
+ fast_noise = np.random.randn(*eps.shape)*np.sqrt(self.fast_noise_var)
+ eps += fast_noise
+ eps *=beta
+
+ #we approximate the logistic peak of g with the peak of a normal distribution of same width
+ #todo: we can fully go back to the logistic peak
+ var_logistic = (1/0.631*self.peak_width_multiplier)**2
+ norm_pdf = lambda x, mu,var: 1/np.sqrt(2*np.pi*var)*np.exp(-(x-mu)**2/(2*var))
+ gs[sensor_id] = self.g_max*4*norm_pdf(0,eps, var_logistic)
+ return gs
+ def sample_sensor_equilibrium(self, v, H, mixed_state, sensor_state, beta):
+ results = np.zeros(len(self.sensor_dot_ids))
+ gs = self._precompute_g(v, H, sensor_state, beta)
+
+ for i, sensor_id in enumerate(self.sensor_dot_ids):
+ terms,neighbour_prev, neighbour_next, _ = sensor_state[sensor_id]
+ g = gs[sensor_id]
+ p = np.diag(mixed_state)[terms]
+ results[i] = np.sum(p*g)/np.sum(p)
+ var_logistic = (1/0.631*self.peak_width_multiplier)**2
+ scale = self.g_max*self.signal_noise_scale*4/np.sqrt(2*np.pi*var_logistic)
+ results += scale*np.random.randn(len(results))
+ return results
+
+ def sample_sensor_configuration(self, sampled_configuration, v, H, mixed_state, sensor_state, beta):
+ results = np.zeros(len(self.sensor_dot_ids))
+ gs = self._precompute_g(v, H, sensor_state, beta)
+
+
+ for i, sensor_id in enumerate(self.sensor_dot_ids):
+ terms,neighbour_prev, neighbour_next,terms_labels = sensor_state[sensor_id]
+ label_pos = find_label(terms_labels, sampled_configuration)
+ results[i] = gs[sensor_id][label_pos]
+ scale = self.g_max*self.signal_noise_scale*4/np.sqrt(2*np.pi*var_logistic)
+ results += scale*np.random.randn(len(results))
+ return results
+
+class TunnelBarrierModel:
+ """ Model of the tunnel barriers of a device
+
+ This class defines a mapping between gate voltages of the device and
+ the tunnel coupling between the dots. To be more exact, the tunnel
+ coupling between dots i, and j and the gate voltages v is given by
+
+ T_ij = exp(W_ij^Tv+b_ij)
+
+ where W_ij is a vector of couplings and b_ij is an offset.
+ """
+ def __init__(self, gate_offsets, gate_levers = None):
+ """Creates a tunnel barrier model.
+
+ Parameters
+ ----------
+ gate_offsets : NxN np.array of floats
+ the offsets b_ij provided as matrix.
+ gate_levers : NxNxK np.array of floats or None
+ Here, K is the number of plunger gates. The first two indices describe the index of the tunnel coupling matrix ij.
+ If None, it is assumed to be 0.
+ """
+ self.gate_offsets = gate_offsets
+ self.gate_levers = gate_levers
+
+ def slice(self, P, m):
+ """Takes an affine subspace of the simulated model.
+
+ Let v=Pv'+m. Computes a new parameterization such, that
+ T_ij = exp(W'_ij^Tv'+b'_ij)
+
+ Parameters
+ ----------
+ P : KxM np.array of floats
+ The linear transformation matrix
+ m : np.array of floats or None
+ Array of size K storing the affine offset.
+ """
+ if self.gate_levers is None:
+ return self
+ sliced_levers = self.gate_levers.reshape(-1,self.gate_levers.shape[-1])@P
+
+ sliced_levers = sliced_levers.reshape(self.gate_levers.shape[0],self.gate_levers.shape[1],P.shape[1])
+ m_applied = self.gate_levers.reshape(-1,self.gate_levers.shape[-1])@m
+ sliced_offsets = self.gate_offsets + m_applied.reshape(self.gate_levers.shape[0],self.gate_levers.shape[1])
+ return TunnelBarrierModel(sliced_offsets, sliced_levers)
+
+ def get_tunnel_matrix(self, v):
+ """Returns the tunnel matrix for a given gate voltage
+
+ Parameters
+ ----------
+ v : np.array of floats or None
+ Array of size K storing the gate voltages
+
+ """
+ if self.gate_levers is None:
+ return np.exp(self.gate_offsets)
+ else:
+ barrier_potentials = self.gate_levers@v
+ return np.exp(barrier_potentials+self.gate_offsets)
+
+
+
+class LocalSystem:
+ """ Class describing a quantum system defined by the gate voltages of a simulated device.
+
+ For a given set of gate voltages, the simulator first computes a set of core states that are most
+ likely relevant for the computation of the hamiltonian and then extends it by adding additional
+ states. These are then used to define a basis of the vector space for the Hamiltonian, which
+ is then used to compute the mixed state. Finally, the mixed state is then used to simulate a sensor signal.
+
+ This class stores all this information and includes some minimal tools to query information on
+ different sub-bases. This class is tightly coupled to tunneling_simulator.
+
+ Attributes
+ ----------
+
+ v: np.array of floats
+ gate voltages that define the parameters of this system
+ state: np.array of ints
+ the ground state configuration of v
+ beta: float
+ the scaled inverse temperature 1/k_bT
+ H: LxL np.array of floats
+ Hamiltonian over the subspace spanned by the L basis state of the extended basis.
+ See methods basis_labels and core_basis_indices
+ """
+ def __init__(self, v, H, state, sim):
+ """ Creates the LocalSystem.
+
+ This is an internal function used by the tunneling simulator.
+ """
+ self.v = v
+ self.H = H
+ self.state = state.copy()
+ self._sim = sim
+ self.beta = self._sim.beta
+ def _compute_mixed_state(self, H):
+ diffs = np.diag(H)-np.min(np.diag(H))
+ sel = np.where(diffs<2*self._sim.poly_sim.get_maximum_polytope_slack())[0]
+ H_sel = H[:,sel][sel,:]
+
+ eigs, U = np.linalg.eigh(H_sel)
+ ps = softmax(-eigs * self.beta)
+ rho_sel = U @ np.diag(ps) @ U.T
+
+ rho = np.zeros(H.shape)
+ indizes = H.shape[0]*sel[:,None]+sel[None,:]
+ np.put(rho,indizes.flatten(), rho_sel.flatten())
+ return rho
+ @property
+ def mixed_state(self):
+ """ Computes an approximate mixed state matrix over the full basis.
+
+ the mixed state matrix, defined as expm(-beta*H)
+
+ Note that this function approximated the true mixed state matrix by inly taking basis eleemnts into account
+ that have a small energy difference to the ground state. This is a multiple of the polytope slack used by the
+ capacitive simulation.
+ """
+ return self._compute_mixed_state(self.H)
+
+
+ def compute_mixed_state_of_subset(self, subset_indices):
+ """Computes the mixed state for a subset, ignoring the existance of any other state entirely.
+
+ The result is a KxK matrix where K is the length of subset_indices.
+ This function is not equivalent to selecting a subset of mixed_state, since this assumes that
+ the states not referenced by subset_indices are ruled out for some other reason, i.e., they are
+ assigned probability 0 and probabilities are renormalized to sum to 1 over the elements in the subset.
+
+
+ Parameters
+ ----------
+ subset_indices : np.array of ints
+ The L' indices into the basis element matrix as returned by basis_labels
+ m : np.array of floats or None
+ """
+
+ return self._compute_mixed_state(self.H[subset_indices,:][:, subset_indices])
+ @property
+ def basis_labels(self):
+ """The labels of the basis elements, indentified by their ground state electron configuration"""
+ return self._sim.boundaries(self.state).additional_info["extended_polytope"].labels
+
+ @property
+ def core_basis_indices(self):
+ """Indices into basis_labels that define the subset of core basis elements."""
+ return self._sim.boundaries(self.state).additional_info["extended_polytope"].core_basis_indices
+
+ def sample_sensor_equilibrium(self):
+ """ Samples a boisy averaged sensor response from the current system over all basis elements.
+
+ This returns the average signal with added sensor noise. This is an approximation to long
+ average measurements at a single point.
+ """
+ sensor_state = self._sim.boundaries(self.state).additional_info["sensor_state"]
+ return self._sim.sensor_sim.sample_sensor_equilibrium(self.v, self.H, self.mixed_state, sensor_state, self.beta)
+ def sample_sensor_configuration(self, sampled_configuration):
+ """ Samples the sensor signal for a given sampled electron configuration.
+
+ For a short time simulation it is more prudent to externally sample a state from the basis and then generate a sensor signal
+ from it. This function allows this. Note that only selecting states from the set of core_basis_indices is safe as otherwise
+ the sensor might miss information required to correctly compute the response.
+
+ Parameters
+ ----------
+ sampled_configuration: list of ints
+ the sampled state for which to generate the sensor response.
+ """
+ sensor_state = self._sim.boundaries(self.state).additional_info["sensor_state"]
+ return self._sim.sensor_sim.sample_sensor_configuration(sampled_configuration, self.v, self.H, self.mixed_state, sensor_state, self.beta)
+
+class ApproximateTunnelingSimulator(AbstractPolytopeSimulator):
+ """Simulator for approximate charge tunneling in a quantum dot device.
+
+ The simulator extends the supplied capacitive simulation by creating a Hamiltionian H,
+ where on the diagonals are the capacitive energies of the simualation, while the off-diagonals
+ have added tunnel coupling parameters. Locally the hamiltonian is approximated via L basis states,
+ where each state is an electron configurtion on the dots. This mixed state is then used to create a sensor simulation.
+
+ It is possible to query the state of single hamiltonian, their mixed state and their sensor simulation via the class
+ LocalSystem, returned by compute_local_system, but the primary use of tis class lies in its ability to compute
+ 1D or 2D sensor scans via sensor_scan and sensor_scan_2D.
+
+ For computing the tunnel coupling parameters, this class can make use of an additional Tunnel barrier simulation, but it
+ is also possible to just supply a NxN constant matrix of tunnel couplings between all D dots in the array.
+
+ Finally, the class follows the interface of AbstractPolytopeSimulator, which means it is possible to directly query the information
+ of the underlying polytopes of the simulation. This is there to unify slicing between simulators.
+
+ Implementation details:
+
+ The basis used for a gate voltage v is queried by finding its ground states n and then the facets of the
+ ground state polytope P(n) create the basis. Thus, this basis becomes extended as the slack variable in the underlying
+ capacitance simulation is increased. This is called the core state set.
+ Additionally, the simulation allows to add additional states. For example, for most sensor simulations to work, we also need
+ other higher energy states to compute correct conductance. These additional states can be added by modifying the vector
+ num_additional_neighbours. if the ith element in this vector is R>0, and s is a state in the core basis, then
+ the extended basis will also include the states :math:`s+ke_i` where :math:`|k|<=R` and :math:`e_i` is the ith basis vector.
+
+ The tunnel couplings T are included into the Hamiltonian the following way: let :math:`s_i` and :math:`s_j` be two states in the basis of the Hamiltonian
+ that differ only in the value of the electron configuration at dots i and j.
+ More exactly, we have that :math:`s_i` and :math:`s_j` are related by moving an electron from state :math:`s_i` to :math:`s_j` or vice versa.
+ Let :math:`H_{kl}` be the off-diagonal matrix element of those states. Then we have :math:`H_{kl} = T_{ij}`.
+ In all other cases, tunnel coupling is 0.
+
+ The mixed state is then again computed approximately, for more info on that, see documentation of LocalSystem.
+
+ The sensor signal of the computed mixed state is computed via the sensor_sim.
+
+ Attributes
+ ----------
+
+ beta: float
+ Scaled inverse temperature 1/k_BT
+ T: float
+ Temperature
+ poly_sim:
+ the capacitive simulation object
+ barrier_sim:
+ the barrier simulation object. Note that even if the supplied object to init was a matrix, this will be a TunnelBarrierModel.
+ sensor_sim:
+ the sensor simulation object
+ num_additional_neighbours: np.array of ints
+ for each dot defines how many additional states should be added for each state in the core basis. This is done
+ by adding or subtracting electrons on the ith element where the maximum is given by the ith element of num_additional_neighbours.
+ We advise to set this to 2 for sensor dots. Note that computation time can quickly explode when increasing this parameter.
+ Outside of sensor dots, we advise therefore to increase the slack in the capacitive simulation.
+ """
+ def __init__ (self, polytope_sim, barrier_sim, T, sensor_sim):
+ """ Creates a tunneling simulation
+
+ Parameters
+ ----------
+ polytope_sim:
+ capacitance simulator object that computes ground state polytopes and capacitive energy differences
+ barrier_sim: Object or Matrix
+ Either a DxD basis that describes a constant tunnel coupling between all D dots. Note that the diagonal of this matrix is zero.
+ Alternatively an object with a method barrier_sim.get_tunnel_matrix(v) returning a DxD matrix, and which supports the slice operation.
+ T: float
+ Temperature in Kelvin. Good values are < 0.1
+ sensor_sim: Derived from AbstractSensorSim
+ A sensor simulation that follows the interface of AbstractSensorSim and which computes the sensor signal.
+ """
+ self.poly_sim = polytope_sim
+ #compatibility to earlier code that uses matrices
+ if isinstance(barrier_sim, np.ndarray):
+ self.barrier_sim = TunnelBarrierModel(np.log(barrier_sim+1.e-20))
+ else:
+ self.barrier_sim = barrier_sim
+ eV = 1.602e-19
+ kB = 1.380649e-23/eV
+ self.beta=1.0/(kB*T)
+ self.T = T
+ self.sensor_sim = sensor_sim
+
+ #clean up potentially stored conflicting data
+ for poly in self.poly_sim.cached_polytopes():
+ poly.additional_info.pop("features_out_info",None)
+
+ self.num_additional_neighbours=np.zeros(self.poly_sim.num_dots, dtype=int)
+
+ super().__init__(self.poly_sim.num_dots, self.poly_sim.num_inputs)
+
+ def slice(self, P, m, proxy=False):
+ """ Restricts the simulator to the affine subspace v=m+Pv'
+
+ Computes the slice through the simulated device by setting v=m+Pv', where v is the plunger gate voltages of the
+ original device and v' is the new coordinate system. This is implemented here by slicing all the different parts
+ of the simulation, capacitance model, barrier model and sensor model.
+
+ Parameters
+ ----------
+ P : MxK np.array of floats
+ The linear coefficient matrix. Here M is the number of voltage elements in v in the full simulation
+ and K the dimensionality of the subspace.
+ m: offset of the affine trnsformation.
+ proxy: bool
+ Whether a proxy is returned. A proxy shares the cache, if possible. This is the case when P is invertible,
+ especially this entails M=K. If cache sharing is possible, the simulation computes the original polytope and then
+ applies the affine transformation. This can reduce run time a lot if several slices need to be computed for the
+ same simulation.
+
+ Returns
+ -------
+ A simulator object describing the simulation on the affine subspace. The current simulation object remains unchanged.
+ """
+ sliced_poly_sim = self.poly_sim.slice(P,m, proxy)
+ sliced_barrier_sim = self.barrier_sim.slice(P, m)
+ sliced_sensor_sim = self.sensor_sim.slice(P,m)
+ sliced_tunneling_sim = ApproximateTunnelingSimulator(sliced_poly_sim, sliced_barrier_sim, self.T, sliced_sensor_sim)
+ sliced_tunneling_sim.num_additional_neighbours = self.num_additional_neighbours.copy()
+ return sliced_tunneling_sim
+
+ def _compute_tunneling_op(self, state_list):
+ """Computes the mapping between tunnel coupling and hamiltonian off diagonal elements
+ and also also computes a multiplicative weight for each tunnel coupling based on the number
+ of affected electrons during the state transitions.
+
+ We currently only add tunnel coupling between two states n,m if they describe the transition of a single
+ electron between two dots i and j. In this case the tunnel coupling is w*T_ij where w=1 if the
+ total number of electrons on dots i and j is odd, otherwise w=sqrt(2).
+ Parameters
+ ----------
+ state_list: list of vectors of ints
+ The list of states that describe a subset of the fokh basis of the Hamiltonian.
+
+ Returns
+ -------
+ TOp: a mapping TOP(n,m)=i*num_dots+j, the index in the flattened matrix of tunnel couplings
+ TOpW: weight matrix W(n,m).
+
+ """
+ N = state_list.shape[0]
+ n_dots = state_list.shape[1]
+ TOp = np.zeros((N,N),dtype=int)
+ TOpW = np.ones((N,N))
+
+ sums = np.sum(state_list,axis=1)
+ for i,s1 in enumerate(state_list):
+ for j,s2 in zip(range(i+1, len(state_list)),state_list[i+1:]):
+ if sums[i] != sums[j]:
+ continue
+
+ if np.sum(np.abs(s1-s2)) == 0:
+ continue
+ abs_diff = np.abs(s1-s2)
+ if np.sum(abs_diff) != 2:
+ continue
+
+ #compute lookup indices in tunneling strength matrix
+ idxs = np.where(abs_diff>0)[0]
+ if len(idxs) == 1:
+ ind = idxs[0]*n_dots + idxs[0]
+ else:
+ ind = idxs[0]*n_dots + idxs[1]
+ TOp[i,j] = ind
+ TOp[j,i] = ind
+
+
+ #compute weight. If the total number of electrons on the affected dots
+ #is even, the tunneling strength is multiplied by sqrt(2)
+ pos_changes = (s1 != s2)
+ num_electrons_affected = np.sum(pos_changes*s1)
+ if num_electrons_affected % 2 == 0:
+ TOpW[i,j] = np.sqrt(2)
+ TOpW[j,i] = TOpW[i,j]
+ return TOp, TOpW
+
+ def _create_state_list(self, state, direct_neighbours):
+ """ Creates the extended basis
+ """
+ state_list = np.vstack([direct_neighbours, [np.zeros(len(state),dtype=int)]])
+
+ additional_states = []
+ for i in range(self.poly_sim.num_dots):
+ e_i = np.eye(1, self.poly_sim.num_dots, i,dtype=int)
+ for k in range(1,1+self.num_additional_neighbours[i]):
+ additional_states.append(state_list+k*e_i)
+ additional_states.append(state_list-k*e_i)
+
+ if len(additional_states) > 0:
+ for add in additional_states:
+ state_list = np.vstack([state_list,add])
+ state_list = np.unique(state_list, axis=0)
+ state_list += state[None,:]
+ state_list = state_list[np.all(state_list>=0,axis=1)]
+
+ #mark the subset of original polytope transitions + the current state
+
+ core_index_set = [int(find_label(state_list,state)[0])]
+ for core_transition in direct_neighbours:
+ core_index_set.append(int(find_label(state_list,core_transition+state)[0]))
+ return state_list, np.array(core_index_set,dtype=int)
+ def boundaries(self, state):
+ """
+ Returns the polytope P(n) of a given state n with all its boundaries, labels and meta information.
+
+ If the polytope is not cached, it needs to be computed. This can take some time for large devices.
+
+ Parameters
+ ----------
+ state: list of ints
+ The state n for which to compute the polytope P(n)
+
+ Returns
+ -------
+ The polytope P(n)
+ """
+ state = np.asarray(state)
+ polytope = self.poly_sim.boundaries(state)
+ #cache features_out info in polytope structure
+ if not "extended_polytope" in polytope.additional_info.keys():
+ #create a list of all neighbour states of interest for use in the Hamiltonian
+ state_list,polytope_base_indx = self._create_state_list(state, polytope.labels)
+
+
+ #create full set of transition equations
+ A,b = self.poly_sim.compute_transition_equations(state_list, state)
+
+ TOp,TOpW = self._compute_tunneling_op(state_list)
+ extended_polytope = status=type('',(object,),{})()
+ extended_polytope.A = A
+ extended_polytope.b = b
+ extended_polytope.TOp = TOp
+ extended_polytope.TOpW = TOpW
+ extended_polytope.labels = state_list
+ extended_polytope.core_basis_indices = polytope_base_indx
+ polytope.additional_info["extended_polytope"] = extended_polytope
+
+ #also compute the sensor info
+ polytope.additional_info['sensor_state'] = self.sensor_sim.precompute_sensor_state(state, A, b, state_list)
+ return polytope
+ def _create_hamiltonian(self, v, A, b, TOp,TOpW):
+ """ Computes the hamiltonian at the given gate voltages
+ """
+ tunnel_matrix = self.barrier_sim.get_tunnel_matrix(v)
+ N = A.shape[0]
+ energy_diff = -(A@v+b)
+ diags = np.sort(energy_diff)
+ if tunnel_matrix is None:
+ return np.diag(energy_diff)
+ else:
+ t_term = ((tunnel_matrix.reshape(-1)[TOp.reshape(-1)]).reshape(N,N))*TOpW
+ return np.diag(energy_diff)-t_term
+
+
+ def get_displacement(self, H, dH):
+ """ Computes the displacement of the ground state"""
+ ind0 = np.argsort(np.diag(H))[:2]
+ tc = H[ind0[0],ind0[1]]
+ eps = H[ind0[0],ind0[0]] - H[ind0[1],ind0[1]]
+
+ dind0 = np.argsort(np.diag(dH))[:2]
+ dtc = dH[dind0[0],dind0[1]]
+ deps = dH[dind0[0],dind0[0]] - dH[dind0[1],dind0[1]]
+
+ return deps/np.sqrt(dtc**2 + deps**2) - eps/np.sqrt(tc**2 + eps**2)
+
+ def compute_local_system(self, v, state, search_ground_state = True):
+ """ Computes a full description of the local quantum system and returns the LocalSystem object.
+
+ This is a full locla simulation of the device and can be used to query sensor values but also the mixed state matrix.
+ See LocalSystem for more info.
+
+ Note that unlike in most other places, v does not need to belong to the ground state polytope of state.
+ This might be useful for the computation of signals in which the device is far out of equilibrium.
+
+ Parameters
+ ----------
+ v: np.array of floats
+ The vector of gate voltages of the device
+ state: np.array of ints
+ The ground state polytope relative to which the local system is computed. This is in most cases the ground state.
+ search_ground_state: bool
+ If True, verifies that state is the ground state of v and searches it otherwise. If you know that this is the case,
+ you can safely set it to False for a speed-up. In the general case, setting this to false will compute the
+ LocalSystem relative to a different basis state.
+ """
+ if search_ground_state:
+ state = self.poly_sim.find_state_of_voltage(v, state_hint = state)
+ polytope = self.boundaries(state)
+ extended_polytope = polytope.additional_info['extended_polytope']
+ H = self._create_hamiltonian(v, extended_polytope.A, extended_polytope.b, extended_polytope.TOp, extended_polytope.TOpW)
+ system = LocalSystem(v, H, state, self)
+ return system
+
+
+ def sensor_scan(self, v_start, v_end, resolution, v_start_state_hint, cache=True, start_new_measurement=True, insitu_axis = None):
+ """ Computes a 1D sensor ramp scan.
+
+ Computes a linear set of points between v_start and v_end and for each point computes the sensor signal.
+ To be more exact, for each point, the ground state polytope is computed which is then used to define the local_system.
+ Returns the sensor signal for each sensor and dot
+
+ Parameters
+ ----------
+ v_start: np.array of floats
+ Vector of gate voltages of the device describing the first measurement point
+ v_end: np.array of floats
+ Vector of gate voltages of the device describing the last measurement point
+ resolution: int
+ number of measured points on the linear scan between v_start and v_end, including both end points.
+ v_start_state_hint: np.array of int
+ Guess for the state n for which holds that v_start is element of P(n). The simulator will use this
+ guess as a starting point for the search of the correct state if this guess is wrong. Note that P(n)
+ must intersect with the affine slice, if slicing was used.
+ cache: bool
+ Whether the simulation should try to cache the computed polytopes. This might lead to a slower computation time
+ for a scan compared to not using caching, but consecutive scans with similar ranges tend to be quicker.
+ start_new_measurement: bool
+ Whether the seimulated sensor measurement should be independent of any previous measurements.
+ insitu_axis: list or None
+ The axis along which the modulated signal is applied, insitu_axis @ plane_axes. If none, the code uses standard sensor dot approach. If a list it computes changes in quantum capacitance
+ """
+ #prepare start state
+ state = self.poly_sim.find_state_of_voltage(v_start, state_hint = v_start_state_hint)
+
+ P=(v_end - v_start).reshape(-1,1)
+ if cache:
+ sim_slice = self.slice(P, v_start, proxy=cache)
+ else:
+ sim_slice = self
+
+ if start_new_measurement:
+ sim_slice.sensor_sim.start_measurement()
+
+ if insitu_axis is None:
+ values = np.zeros((resolution, self.sensor_sim.num_sensors))
+ else:
+ values = np.zeros((resolution,1))
+ for i,v0 in enumerate(np.linspace([0.0],[1.0], resolution)):
+ if cache:
+ v = v0
+ else:
+ v = v_start + P@v0
+ if not sim_slice.poly_sim.inside_state(v, state):
+ state = sim_slice.poly_sim.find_state_of_voltage(v,state_hint=state)
+
+ system = sim_slice.compute_local_system(v, state, search_ground_state = False)
+ if insitu_axis is None:
+ values[i] = system.sample_sensor_equilibrium()
+ else:
+ # inisitu reflecometry
+ dv = 0.0001
+ system2 = sim_slice.compute_local_system(np.array(v)+dv*np.array(insitu_axis), state, search_ground_state = False)
+ a = sim_slice.get_displacement(system.H, system2.H)
+ values[i] = a
+ return values
+
+ def sensor_scan_2D(self, P, m, minV, maxV, resolution, state_hint_lower_left,cache=True, insitu_axis = None):
+ """ Computes the sensor signal on a 2D grid of points.
+
+ For the exact computation of points, see sensor_scan.
+
+ The grid is defined the following way: Let w_ij be a 2D vector that is part of a regular
+ rectangular grid spanned by the lower left corner given by minV and the upper right corner given
+ by maxV and let (m,n) be the number of points in both grid directions. We have that w_00=minV and w_m-1,n-1=maxV.
+
+ This grid is then affinely transformed into the K-dimensional space of gate vectors via
+ v_ij = m+ P w_ij
+
+ and thus P must be a Kx2 matrix and m a K-vector.
+
+ Parameters
+ ----------
+ P: Kx2 np.array of floats
+ linear transformation of grid-points into the K-dimensional voltage space
+ m: np.array of floats:
+ affine offset of the grid
+ minV: np.array of floats
+ 2D vector describing the minimum value of the grid points
+ maxV: np.array of floats
+ 2D vector describing the maximum value of the grid points
+ resolution: int or list of ints
+ if integer, describes the same number of points in both grid directions. If a list of 2 elements,
+ describes the number of points along each axes of the grid.
+ state_hint_lower_left: np.array of int
+ Guess for the state n for point described by the grid position minV. The simulator will use this
+ guess as a starting point for the search of the correct state if this guess is wrong. Note that P(n)
+ must intersect with the affine slice, if slicing was used.
+ cache: bool
+ Whether the simulation should try to cache the computed polytopes. This might lead to a slower computation time
+ for a scan compared to not using caching, but consecutive scans with similar ranges tend to be quicker.
+ insitu_axis: list or None
+ The axis along which the modulated signal is applied. If none, the code uses standard sensor dot approach. If a list it computes changes in quantum capacitance.
+ """
+ if P.shape[1] != 2:
+ raise ValueError("P must have two columns")
+ if isinstance(resolution, int):
+ resolution = [resolution, resolution]
+
+ #obtain initial guess for state
+ line_start = self.poly_sim.find_state_of_voltage(m+P@minV, state_hint = state_hint_lower_left)
+
+
+ #now slice down to 2D for efficiency
+ sim_slice = self.slice(P, m, proxy=cache)
+ if insitu_axis is None:
+ sim_slice.sensor_sim.start_measurement()
+ values=np.zeros((resolution[0],resolution[1], self.sensor_sim.num_sensors))
+
+
+ else:
+ values=np.zeros((resolution[0],resolution[1],1))
+
+
+
+ for i,v2 in enumerate(np.linspace(minV[1],maxV[1],resolution[1])):
+ v_start = np.array([minV[0],v2])
+ v_end = np.array([maxV[0],v2])
+ line_start = sim_slice.poly_sim.find_state_of_voltage(v_start, state_hint = line_start)
+ values[i] = sim_slice.sensor_scan(v_start, v_end, resolution[0], line_start, cache=False, start_new_measurement=False, insitu_axis=insitu_axis) #changed cache to False?
+ return values
+
+
+
diff --git a/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/util_functions.py b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/util_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6de85276726a9ad7f7658e5a52239d878fe956ba
--- /dev/null
+++ b/QDarts-0781fb4eb93ceb8e37e27f4c6c2a8a2afbeade8c/src/qdarts/util_functions.py
@@ -0,0 +1,472 @@
+import cvxpy as cp
+import numpy as np
+from scipy.spatial import HalfspaceIntersection
+
+def is_invertible_matrix(A,max_cond=1.e8):
+ """ Returns true if A is an invertible matrix.
+
+ Parameters
+ ----------
+ max_cond: float
+ conditioning of A (fraction of maximum and minimum absolute eigenvalue) above which
+ it is assumed that A is numerically rank-deficient.
+ """
+ if A.shape[0] != A.shape[1]:
+ return False
+ else:
+ return np.linalg.cond(A)<max_cond
+def solve_linear_problem(prob):
+ """Internal helper function to solve supplied linear cvxpy problems"""
+ try:
+ prob.solve(verbose=False, solver=cp.CLARABEL, max_iter=100000)
+ except cp.SolverError:
+ prob.solve(solver=cp.GLPK)
+
+
+def _compute_polytope_slacks_1D(A,b):
+ """Special case called by compute_polytope_slacks when A has a single column"""
+ w=A.reshape(-1)
+ close_to_zero = (np.abs(w)<1.e-6*np.max(w))
+ #compute the single point fulfilling the constraint
+ x=-b/(w+close_to_zero*1.e-6*np.max(w))
+
+ #check whether the constraint is a lower or upper bound
+ is_lower = (w < 0)
+ #count the constraints for checking whether we have both lower and upper bounds
+ num_lower = np.sum(is_lower)
+
+ #now find the lower and upper bounds of the interval that fulfill all constraints
+ if num_lower > 0:
+ lower_x = np.max(x[is_lower])
+ else:
+ lower_x = -np.inf
+ if num_lower < len(A):
+ upper_x = np.min(x[~is_lower])
+ else:
+ upper_x = np.inf
+ #handling of infeasibility
+ #since the slacks are defined as smallest violation of the constraints,
+ #if the polytope is infeasible, the slack for the lower bound constraints
+ #must be computed based on the upper bound and vice versa
+ if lower_x> upper_x:
+ temp = lower_x
+ lower_x = upper_x
+ upper_x = temp
+
+ #compute slacks:
+ slacks_lower = -(is_lower*(w*lower_x+b))
+ slacks_upper = -((~is_lower)*(w*upper_x+b))
+
+ slacks = slacks_lower + slacks_upper
+ return slacks
+
+
+def _compute_polytope_slacks_2D(A,b, bounds_A, bounds_b):
+ """Special case in 2D solved via halfspace intersection"""
+
+ #first we use halfspace intersection to compute all corners of the final polytope
+ #find a point fulfilling all constraints
+ feasible_point, _ = compute_maximum_inscribed_circle(A, b, bounds_A, bounds_b)
+ #bring all constraints together in matrix form
+ halfspaces_poly = np.concatenate([A,b.reshape(-1,1)],axis=1)
+ halfspaces_bounds = np.concatenate([bounds_A,bounds_b.reshape(-1,1)],axis=1)
+ halfspaces = np.concatenate([halfspaces_poly,halfspaces_bounds],axis=0)
+ hs = HalfspaceIntersection(halfspaces, feasible_point)
+ intersections = hs.intersections
+
+ #now compute slacks. since the slack is computed as the solution of a linear programming problem,
+ #the solution must lie on one of the vertices.
+ slacks_intersections = A@(intersections.T)+b.reshape(-1,1)
+
+ min_slacks = np.min(-slacks_intersections,axis=1)
+ slacks = np.maximum(min_slacks, np.zeros(min_slacks.shape))
+
+ return slacks
+
+
+def compute_polytope_slacks(A, b, bounds_A, bounds_b, maximum_slack):
+ """Computes the slacks of each candidate transition of a ground state polytope.
+
+ The polytope is given by all points x, such that :math:`Ax+b<0`. There might be boundaries
+ such that for no x holds that :math:`A_ix+b_i = 0`. In this case the definition of the polytope is
+ the same when it is removed. However, sometimes we are interested in keeping transitions that
+ are near misses - i.e., there exists an x such that, the inequality is almost fulfilled.
+ In this case, we can relax this by allowing a positive slack and
+ accept transitions to still be relevant for the polytope when we find an x, such that
+
+ :math:`A_ix+b_i <= s`
+
+ if this inequality holds exactly with slack :math:`s=0`, we say that the ith transition touches
+ the polytope and the larger slack is, the more distant is the polytope
+
+ Computing the slack can be difficult in the presence of unbounded polytopes. For this reason additional linear bound
+ constraints need to be provided that ensure that all polytopes are bounded.
+
+ The function computes the minimum slack for all transitions in :math:`A_i` and :math:`b_i`.
+ The default slack is needed if for some reason it is not possible to compute the slack due to numerical
+ difficulties.
+
+ Parameters
+ ----------
+ A: NxK np.array of floats
+ The linear coefficients of the N affine linear equations in K dimensions
+ b: N np.array of floats
+ The constant offsets of the N affine linear equations
+ bounds_A: N'xK np.array of floats
+ The linear coefficients of N' additional constraint that ensure that polytopes are bounded.
+ bounds_b: N' np.array of floats
+ The constant offsets of the N' bounds
+ maximum_slack: float
+ Value for the maximum acceptable slack for transitions to be considered near the polytope.
+ TODO: review whether this parameter is needed.
+ """
+
+ #to find whether an equation a^Tx+b<= 0 touches the polytope, we need to find
+ #a point that is on the intersection between polytope and the line where
+ #a^Tx+b=0. For this we systematiclly go throuh the full list of
+ #equations and for each solve an LP with this goal. if an equation
+ # is found not to touch the polytope it is removed from the list
+ # of candidates for all future solves.
+
+ #we slightly generalize this problem by weakening the equation via
+ # a slack variable
+ #a^Tx+b = eps
+ #eps > 0
+ #and minimize for eps.
+ #if eps = 0, then the equation touches the polytope. If not, then
+ #it can be removed. this allows us to introduce a maximum slack value
+ #which allows us to keep close matches for later stages in the simulation, e.g., to keep
+ # neighbours that are possibly relevant for tunneling equations. Note: for eps>0 this formulation
+ # produces the x value in the polytope closest to the equation in function value a^Tx+b.
+
+ #we return the vector of eps values for all equations so that the user can filter transitions afterwards
+
+ #first special cases.
+ #only one constraint? feasible qwith slack 0
+ if len(b) == 1:
+ return np.zeros(1)
+
+ #1D and 2D problems can be solved efficiently
+ if A.shape[1] == 1:
+ return _compute_polytope_slacks_1D(A,b)
+ if A.shape[1] == 0:
+ return _compute_polytope_slacks_2D(A,b, bounds_A, bounds_b)
+
+
+ #now we know there is a polyope and we can compute its sides
+ N = len(A)
+ touching = np.ones(N, dtype=bool) #equations with eps~=0. At the beginning we assume all are touching
+ slacks = (maximum_slack + 1)*np.ones(N) #slack value (updated when equation is computed)
+ for k in range(N):
+ # take all previous tested and verified touching eqs and all untested eqs, except the current
+ touching[k] = False
+ Ak = A[touching,:]
+ bk = b[touching]
+
+
+ #the current equation to test
+ A_eq = A[k]
+ b_eq = b[k]
+
+ #setup optimisation problem
+ x = cp.Variable(A.shape[1])
+ eps = cp.Variable()
+ prob = cp.Problem(cp.Minimize(eps),
+ [A_eq @ x + b_eq + eps == 0, Ak@ x + bk <= 0, eps >= 0])
+ solve_linear_problem(prob)
+ if prob.status not in ["infeasible", "infeasible_inaccurate"]:
+ slacks[k] = eps.value
+ if eps.value < 1.e-6:
+ touching[k] = True
+ return slacks
+
+def compute_maximum_inscribed_circle(A, b, bounds_A, bounds_b):
+ """Computes the maximum inscribed circle in a polytope intersected with a set of linear inequalities.
+
+ The maximum inscribed circle is a crude measure for position and size of a polytope.
+ It computes the circle with maximum radius r and midpoint m, such that all its points
+ lie inside the polytope. The function returns the (m,r) maximizing this. This choice is very often not unique.
+
+ Since the polytope given by linear equations A,b might be unbounded, the function takes another
+ set of linear equations for establishing lower and upper bounds. In essence, this is the same as
+ adding the additional equalities to A and b and computing the maximum inscribed circle for that polytope.
+
+
+
+ Parameters
+ ----------
+ A: NxK np.array of floats
+ The linear coefficients of the N affine linear equations in the K-dimensional polytope
+ b: N np.array of floats
+ The constant offsets of the N affine linear equations of the polytope
+ bounds_A: MxK np.array of floats
+ The linear coefficients of the M added linear inequality constraints
+ bounds_b: M np.array of floats
+ The constant offsets of the M added linear inequality constraints
+ """
+ if len(A)==0:
+ return None, 0.0
+
+ norm_A = np.linalg.norm(A, axis=1)
+ norm_bounds = np.linalg.norm(bounds_A, axis=1)
+
+ r = cp.Variable()
+ v = cp.Variable(A.shape[1])
+ constraints = [
+ A @ v + b + r * norm_A <= 0, #linear boundaries are only allowed to intersect the sphere once
+ bounds_A @ v + bounds_b + norm_bounds*r <= 0, #also stay away from bound constraints
+ r >=0 # Radius is strictly positive
+ ]
+ prob = cp.Problem(cp.Maximize(r), constraints)
+ solve_linear_problem(prob)
+ return v.value, r.value
+
+def find_label(labels, label):
+ """helper function that finds the position of a state in a matrix of states"""
+ dist = np.sum(np.abs(labels-np.array(label)),axis=1)
+ return np.where(dist<1.e-5)[0]
+
+def find_point_on_transitions(polytope, indizes):
+ """Finds a point on a facet (or intersection point of multiple facets) of a polytope
+
+ Given a precomputed polytope with facets (A,b) and their slacks,
+ computes a point where a set of facet inequalities are exactly equal.
+ Among all points that fulfill this, we pick the mid point defined
+ by the maximum inscribed circle on the facet (or the subfacet
+ created by the intersection of facets).
+
+ TODO: it is not quite clear what happens when the indizes are not touching.
+
+ Parameters
+ ----------
+
+ polytope: A polytope object
+ The polytope for which the intersections are computed
+ indices: list of int
+ The subset of facets for which a common point is to be found.
+ note that it is quietly assumed that the facets are touching the polytope.
+ """
+ slacks = np.delete(polytope.slacks, indizes)
+ A = np.delete(polytope.A, indizes,axis=0)
+ b = np.delete(polytope.b, indizes)
+ A = A[slacks<1.e-8]
+ b = b[slacks<1.e-8]
+ A_eq = polytope.A[indizes,:]
+
+ b_eq = polytope.b[indizes]
+
+ norms = np.linalg.norm(A,axis=1)
+
+ eps = cp.Variable()
+ x = cp.Variable(A.shape[1])
+ prob = cp.Problem(cp.Maximize(eps),
+ [A @ x + b+norms*eps <=0, A_eq@x+b_eq == 0,eps >= 0])
+ solve_linear_problem(prob)
+ return x.value
+
+def fix_gates(simulator, gate_ids, gate_values, proxy=False):
+ """Fixes a number of gate voltages in the simulator object
+
+ Returns a new simulation where the values of the given gates are fixed to a constant.
+ This is done by computing the apropriate parameters for slice, and therefore the
+ operation can not be undone in the returned simulation.
+
+ Please keep in mind that by doing this, all indices of gate voltages at entries
+ after the deleted entries change, i.e., in a device with 4 plungers, removing the
+ third plungers will lead to a simulator with 3 plungers where the last plunger has
+ index 3. It is therefore advisable to order parameters such that fix_gates is always
+ applied to the end.
+
+ Parameters
+ ----------
+ simulator: AbstractPolytopeSimulator
+ The simulator object for which gates are to be fixed
+ gate_ids: list of int
+ The indices of the gates in the voltage vector
+ gate_values: np.array of float
+ The values of the fixed gates
+ proxy: bool
+ whether or not the returned simulator should be a proxy, i.e., share cache if possible
+ Todo: is this ever possible?
+ """
+ v = np.zeros(simulator.num_inputs)
+ v[gate_ids] = gate_values
+
+ P=np.zeros((simulator.num_inputs,simulator.num_inputs-len(gate_ids)))
+ pos = 0
+ for i in range(simulator.num_inputs):
+ if i not in gate_ids:
+ P[i,pos] = 1
+ pos += 1
+ return simulator.slice(P, v, proxy)
+
+def axis_align_transitions(simulator, target_state, transitions, compensation_gates,proxy=True):
+ """Transform the simulators coordinate system such that transitions are aligned with coordinate axes
+
+ Takes a set of transitions from a target state and a set of gate indices of same length.
+ Computes a linear transformation such, that the normal of the ith transition is parallel to the ith gate axis supplied as
+ argument.
+
+ For example, to align the transition from state [1,1,1] to [1,1,2] with the first plunger gate, we set
+ target_state=[1,1,1], transitions=[[1,0,0]] and compensation_gates=[0]
+
+ Parameters
+ ----------
+ simulator: AbstractPolytopeSimulator
+ The simulator object which is to be transformed
+ target_state: list of int
+ The state from which the transitions are extracted
+ transitions: NxD np.array of int
+ The set of N transitions (given as D-dimensional difference vectors state-target_state) to align
+ compensation_gates: list of int
+ The indices of the plunger gates that should be transformed to align with the transition normals
+ proxy: bool
+ whether or not the returned simulator should be a proxy, i.e., share cache if possible
+ """
+ compensation_gates = np.array(compensation_gates, dtype=int)
+ #get the polytope of the target state
+ polytope = simulator.boundaries(target_state)
+
+ #find the transitions inside the polytope
+ transition_idxs = []
+ for transition in transitions:
+ idx = find_label(polytope.labels, transition)[0]
+ transition_idxs.append(idx)
+
+ #get normals of the transitions
+ normals = -polytope.A[transition_idxs,:]
+ #normalize to ensure that we do not change the spacing of transitions
+ normals /= np.linalg.norm(normals,axis=1)[:,None]
+
+ #compute compensation matrix
+ B = normals[:,compensation_gates]
+ compensation = -B.T@np.linalg.inv(B@B.T)
+
+ #compute coordinate transform
+ P=np.eye(simulator.num_inputs)
+
+ #get the indizes of the elements in the submatrix of the compensation parameters
+ P_sub_ids = simulator.num_inputs * compensation_gates[:,None] + compensation_gates[None,:]
+ np.put(P,P_sub_ids, compensation.flatten())
+
+ return simulator.slice(P,np.zeros(simulator.num_inputs), proxy=proxy)
+
+def compensate_simulator_sensors(simulator, target_state, compensation_gates, sensor_ids, sensor_detunings, sensor_slope_detuning=0.0):
+ """Transforms the simulation to compensate the sensors against all other gates.
+
+ This function allows for perfect or imperfect sensor compensation as well as the exact position on the sensor peak.
+ This is done by finding the compensation values of the sensor plunger gates to compensate for the linear cross-talk of all other
+ plungers. This compensation is computed for a given target state as the compensation parameters might depend on the capacitances
+ in the state if they are variable.
+
+ The position on the sensor peak is given by sensor_detunings which move the position as a direct modification of the sensor potential.
+
+ Parameters
+ ----------
+ simulator: AbstractPolytopeSimulator
+ The simulator object which is to be transformed
+ target_state: list of int
+ The state from which the transitions are extracted
+ compensation_gates: list of int
+ The gates to be used for compensation of the sensor. Typically the sensor plunger gates in the device
+ sensor_ids: list of int
+ The indices of the sensor ids.
+ sensor_detunings: np.array of float
+ detuning parameter for each sensor which allows to move the sensor on a pre-specified point of the peak.
+ sensor_slope_detuning: float
+ (Experimental) scaling factor that moves the compensation linearly from perfect compensation (0) to no compensation (1).
+
+ Returns
+ -------
+ sliced_sim: the sliced simulation that is created from the computed compensation parameters
+ compensation_transform: a linear function that for any point v in the original coordinate system finds the point with the compensation applied in the new coordinate system
+ tuning_point: a vector of gate voltages that indicates the exact compensation point of the simulation
+ """
+ if len(sensor_ids) != len(compensation_gates):
+ raise ValueError('Number of gates for compensation must equal number of sensors')
+
+ if len(sensor_ids) != len(sensor_detunings):
+ raise ValueError('Number of gates for compensation must equal number of sensors')
+
+ for sensor in sensor_ids:
+ if target_state[sensor] <= 0:
+ raise ValueError('Target state must have at least one electron on each sensor dot')
+
+ compensation_gates = np.array(compensation_gates,dtype=int)
+ other_gates = np.delete(np.arange(simulator.num_inputs),compensation_gates)
+ sensor_detunings = np.array(sensor_detunings)
+
+
+ # by default we assume that for the sensor dots,
+ # we compute the transition between dots K and K+1
+ # where K is the electron occupation on the target state,
+ # which means that we take the polytope at the target state
+ # and compute the transition for the electron K->K+1 on the sensor dot.
+ # However, we will change the computed polytope based
+ # on the sensor detuning. if it is positive, we will instead compute
+ # the polytope for the K+1 electron and then search for the transition K+1->K
+
+
+ target_state = target_state.copy()
+ transitions = []
+ detunings = []
+ for detuning, sens_id in zip(sensor_detunings, sensor_ids):
+ if detuning > 0:
+
+ transitions.append(-np.eye(1,simulator.num_dots,sens_id))
+ detunings.append(-detuning)
+ else:
+ target_state[sens_id] -= 1
+ transitions.append(np.eye(1,simulator.num_dots,sens_id))
+ detunings.append(detuning)
+
+ #get geometry of the target state to compensate for
+ polytope = simulator.boundaries(target_state)
+
+
+ #find the sensor transitions inside the polytope
+ transition_idxs = []
+ for transition in transitions:
+ idx = find_label(polytope.labels, transition)[0]
+ transition_idxs.append(idx)
+
+ #get normals of sensor transitions
+ normals = polytope.A[transition_idxs,:]
+ #compute point on the intersection of the transition
+ v = find_point_on_transitions(polytope, transition_idxs)
+ #apply sensor detunings. First compute virtual gates for the
+ #two sensor voltages
+ comp_det = normals.T@ np.linalg.inv(normals @ normals.T)
+ #now use the compensation to define sensor detunings
+ v_detuning = comp_det @ sensor_detunings
+ v_detuned = v - v_detuning #use detuning to move the point away from the transition
+
+
+
+ #compute compensation matrix
+ normals /= np.linalg.norm(normals,axis=1)[:,None]
+ A1 = normals[:,compensation_gates]
+ A2 = normals[:,other_gates]
+ compensation = -np.linalg.inv(A1)@A2
+
+
+ #now create the P-matrix
+ P=np.eye(simulator.num_inputs)
+ #get the indizes of the elements in the submatrix of the compensation parameters
+ P_sub_ids = simulator.num_inputs * compensation_gates[:,None] + other_gates[None,:]
+ np.put(P,P_sub_ids, compensation.flatten())
+
+ #add errors to P-matrix
+ P = (1-sensor_slope_detuning)*P+sensor_slope_detuning*np.eye(simulator.num_inputs)
+
+ #If we compensate now with v as central point, our gates would compute
+ #relative voltages to this (arbitrary) point. Tis would make it impossible
+ #to plot the same region with different compensation points
+ #instead, we will now take v and move it such, that the other gates are 0.
+ v_zero = v_detuned - P[:,other_gates]@v_detuned[other_gates]
+
+ P_inv = np.linalg.inv(P)
+ def compensation_transform(v):
+ #find the linear transformation such, that v is mapped on v_detuned
+ return P_inv@(v-v_zero-v_detuning)
+ return simulator.slice(P, v_zero,True), compensation_transform, compensation_transform(v)