From e89dc0c215b27b814f68954242ff084db0ed4cc8 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Thu, 10 Sep 2020 23:58:30 -0400 Subject: [PATCH 001/125] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ba8c923..047f8f9 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Welcome to pyEPR :beers:! ## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR -* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 :bangbang: :beers: +* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers:
From 26bfb47ef86932227a8a26c7d3a1feda4251759d Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Tue, 15 Sep 2020 21:54:21 -0400 Subject: [PATCH 002/125] Update README.md --- README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 047f8f9..d0f8044 100644 --- a/README.md +++ b/README.md @@ -33,10 +33,19 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 * UC Berkeley, [Quantum Nanoelectronics Laboratory](https://physics.berkeley.edu/quantum-nanoelectronics-laboratory), Irfan Siddiqi, CA, USA * [Quantum Circuits, Inc.](https://quantumcircuits.com/), CT, USA * [Seeqc](https://seeqc.com/) (spin-out of Hypres) Digital Quantum Computing, USA -* Serge [Rosenblum Lab](https://www.weizmann.ac.il/condmat/rosenblum/) in the Weizmann Instatue, Israel -* Peter [Leek Lab](https://leeklab.org/), UK +* Serge [Rosenblum Lab] quantum circuits group (https://www.weizmann.ac.il/condmat/rosenblum/) in the Weizmann Instatue, Israel +* University of Oxford - LeekLab - Peter [Leek Lab](https://leeklab.org/), UK * Britton [Plourde Lab](https://bplourde.expressions.syr.edu/), Syracuse University, USA * Javad [Shabani Lab](https://wp.nyu.edu/shabanilab/) Quantum Materials & Devices, NYU, NY, USA +* UChicago Dave Schuster Lab, USA +* SQC lab - Shay Hacohen Gourgy, Israel +* Lawrence Berkeley National Lab +* Colorado School of Mines, USA +* Syracuse University, USA +* IPQC, SJTU, Shanghai, China +* Bhabha Atomic Research Centre, India +* Quantum Computing UK +* Alice&Bob, France * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) From 0569ab7400f1a2c1f859af2730b6f90328b5b6f7 Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 16:37:43 +0200 Subject: [PATCH 003/125] fix typo --- docs/source/installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index b6dad38..f1b3e21 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -35,7 +35,7 @@ Main installation method .. _install-via_pip: -Installing lcoally with via pip +Installing locally with via pip =============================== In the future, ``pyEPR`` can be installed using the Python package manager `pip `_. From 367a292d11598035d2cd228e73a14444f3b8efdb Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 16:50:20 +0200 Subject: [PATCH 004/125] update information on conda forge install --- docs/source/installation.rst | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index f1b3e21..7efc8ce 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -63,13 +63,16 @@ Now we can locally install the pyEPR module. Installing via conda ==================== -For Python 3.6+, installation via conda will be supported in a future version. +For Python 3.6+, installation via conda is supported since ``pyEPR`` v.0.8.03, through the ``conda-forge`` channel. You can download and install ``pyEPR`` typing in from the shell: +.. code-block:: bash -In the meantime, if you are using conda, you can locally install from the cloned repo. -Perform the steps in the :ref:`install-main` section. -Now, you can use + conda install -c conda-forge pyepr-quantum -.. code-block:: bash +The prefix ``-c conda-forge`` is required to activate the optional ``conda-forge`` channel. + +Note that the name of the recipe on the ``conda-forge`` channel is +`pyepr-quantum`_, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the examples. + +.. _pyepr-quantum: https://anaconda.org/conda-forge/pyepr-quantum - python -m pip install -e . \ No newline at end of file From cd65fd196b2922c461b3a7a7b61cdd1e3a06316b Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 16:51:22 +0200 Subject: [PATCH 005/125] add conda badge to readme --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index d0f8044..b9fd677 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,11 @@ Welcome to pyEPR :beers:! [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/zlatko-minev/pyEPR) [![star this repo](http://githubbadges.com/star.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR) [![fork this repo](http://githubbadges.com/fork.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR/fork) +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyepr-quantum/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
-## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR +## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers: @@ -38,20 +39,20 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 * Britton [Plourde Lab](https://bplourde.expressions.syr.edu/), Syracuse University, USA * Javad [Shabani Lab](https://wp.nyu.edu/shabanilab/) Quantum Materials & Devices, NYU, NY, USA * UChicago Dave Schuster Lab, USA -* SQC lab - Shay Hacohen Gourgy, Israel +* SQC lab - Shay Hacohen Gourgy, Israel * Lawrence Berkeley National Lab * Colorado School of Mines, USA * Syracuse University, USA * IPQC, SJTU, Shanghai, China * Bhabha Atomic Research Centre, India * Quantum Computing UK -* Alice&Bob, France +* Alice&Bob, France * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) ## How do I cite `pyEPR` when I publish? Cite the following and/or e-mail [`zlatko.minev@aya.yale.edu`](https://www.zlatko-minev.com/) or [`zaki leghtas`](http://cas.ensmp.fr/~leghtas/) -* [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. +* [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. * Z.K. Minev, Z. Leghtas, _et al._ (to appear soon on arXiv) (2020) From 3e1f88d36fd0011e887a90a8ce216aa9042024d3 Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 17:13:14 +0200 Subject: [PATCH 006/125] add information on pypi install and name --- docs/source/installation.rst | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 7efc8ce..1133217 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -35,7 +35,7 @@ Main installation method .. _install-via_pip: -Installing locally with via pip +Installing locally via pip =============================== In the future, ``pyEPR`` can be installed using the Python package manager `pip `_. @@ -71,8 +71,20 @@ For Python 3.6+, installation via conda is supported since ``pyEPR`` v.0.8.03, t The prefix ``-c conda-forge`` is required to activate the optional ``conda-forge`` channel. -Note that the name of the recipe on the ``conda-forge`` channel is -`pyepr-quantum`_, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the examples. +.. _install-via_pypi: + +Installing via pip from PyPI +============================ + +For Python 3.6+, installation via PyPI is supported since ``pyEPR`` v.0.8. You can download and install ``pyEPR`` typing in from bash with: + +.. code-block:: bash + + pip install pyEPR-quantum + +.. note:: + + Note that the name of the recipe on the ``conda-forge`` channel is `pyepr-quantum`_, and on PyPI it is `pyEPR-quantum`, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the guide and examples. .. _pyepr-quantum: https://anaconda.org/conda-forge/pyepr-quantum From 67ab9e8779f222dea2b547a730af92b2e3452ea4 Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 17:20:07 +0200 Subject: [PATCH 007/125] fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b9fd677..9e7e179 100644 --- a/README.md +++ b/README.md @@ -160,7 +160,7 @@ Use `pyEPR` directly from the source, and pull updates from the master git repo, Please keep up to date with `pyEPR` by using git. We like to make it simple using a git-gui manager, [SourceTree](sourcetree.com) or [GitHub Desktop](https://desktop.github.com/). **Quick setup** -We recommend the approach in the following section, which will be most up to date, but for quick use you can use the [conda forge chanel](https://anaconda.org/conda-forge/pyepr-quantum) to install +We recommend the approach in the following section, which will be most up to date, but for quick use you can use the [conda forge channel](https://anaconda.org/conda-forge/pyepr-quantum) to install ``` conda install -c conda-forge pyepr-quantum ``` From 997d72a0cea1d8a2be41eb617ca95ad1499c763a Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 17:20:27 +0200 Subject: [PATCH 008/125] add info on pypi install and note on package name --- docs/source/installation.rst | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 1133217..4968d88 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -63,7 +63,7 @@ Now we can locally install the pyEPR module. Installing via conda ==================== -For Python 3.6+, installation via conda is supported since ``pyEPR`` v.0.8.03, through the ``conda-forge`` channel. You can download and install ``pyEPR`` typing in from the shell: +For Python 3.6+, installation via `conda`_ is supported since ``pyEPR`` v.0.8.03, through the ``conda-forge`` channel. You can download and install ``pyEPR`` typing in from bash: .. code-block:: bash @@ -76,7 +76,7 @@ The prefix ``-c conda-forge`` is required to activate the optional ``conda-forge Installing via pip from PyPI ============================ -For Python 3.6+, installation via PyPI is supported since ``pyEPR`` v.0.8. You can download and install ``pyEPR`` typing in from bash with: +For Python 3.6+, installation via `PyPI`_ is supported since ``pyEPR`` v.0.8. You can download and install ``pyEPR`` typing in from bash: .. code-block:: bash @@ -84,7 +84,8 @@ For Python 3.6+, installation via PyPI is supported since ``pyEPR`` v.0.8. You c .. note:: - Note that the name of the recipe on the ``conda-forge`` channel is `pyepr-quantum`_, and on PyPI it is `pyEPR-quantum`, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the guide and examples. + Note that the name of the recipe on the ``conda-forge`` channel is ``pyepr-quantum``, and on PyPI is ``pyEPR-quantum``, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the guide and examples. -.. _pyepr-quantum: https://anaconda.org/conda-forge/pyepr-quantum +.. _conda: https://anaconda.org/conda-forge/pyepr-quantum +.. _PyPI: https://pypi.org/project/pyEPR-quantum/0.8/ From 5d4211229bb7fb41966cdd09d159a3521f2712f9 Mon Sep 17 00:00:00 2001 From: Nathan Shammah Date: Wed, 16 Sep 2020 17:26:22 +0200 Subject: [PATCH 009/125] add pypi badge to readme via fury.io --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 9e7e179..97f6e37 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ Welcome to pyEPR :beers:! [![star this repo](http://githubbadges.com/star.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR) [![fork this repo](http://githubbadges.com/fork.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR/fork) [![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyepr-quantum/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge) +[![PyPI version](https://badge.fury.io/py/pyEPR-quantum.svg)](https://badge.fury.io/py/pyEPR-quantum)
From e191f2d8db2dfbfce5b49903ed314c6f5c103bf6 Mon Sep 17 00:00:00 2001 From: SQClab <69586246+SQClab@users.noreply.github.com> Date: Thu, 24 Sep 2020 13:55:36 +0300 Subject: [PATCH 010/125] Merge remote-tracking branch 'upstream/master' into Asaf_branch --- README.md | 23 +++++++++++++++++------ docs/source/installation.rst | 28 ++++++++++++++++++++++------ 2 files changed, 39 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index ba8c923..97f6e37 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,14 @@ Welcome to pyEPR :beers:! [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/zlatko-minev/pyEPR) [![star this repo](http://githubbadges.com/star.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR) [![fork this repo](http://githubbadges.com/fork.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR/fork) +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyepr-quantum/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge) +[![PyPI version](https://badge.fury.io/py/pyEPR-quantum.svg)](https://badge.fury.io/py/pyEPR-quantum)
-## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR +## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR -* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 :bangbang: :beers: +* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers:
@@ -33,16 +35,25 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 * UC Berkeley, [Quantum Nanoelectronics Laboratory](https://physics.berkeley.edu/quantum-nanoelectronics-laboratory), Irfan Siddiqi, CA, USA * [Quantum Circuits, Inc.](https://quantumcircuits.com/), CT, USA * [Seeqc](https://seeqc.com/) (spin-out of Hypres) Digital Quantum Computing, USA -* Serge [Rosenblum Lab](https://www.weizmann.ac.il/condmat/rosenblum/) in the Weizmann Instatue, Israel -* Peter [Leek Lab](https://leeklab.org/), UK +* Serge [Rosenblum Lab] quantum circuits group (https://www.weizmann.ac.il/condmat/rosenblum/) in the Weizmann Instatue, Israel +* University of Oxford - LeekLab - Peter [Leek Lab](https://leeklab.org/), UK * Britton [Plourde Lab](https://bplourde.expressions.syr.edu/), Syracuse University, USA * Javad [Shabani Lab](https://wp.nyu.edu/shabanilab/) Quantum Materials & Devices, NYU, NY, USA +* UChicago Dave Schuster Lab, USA +* SQC lab - Shay Hacohen Gourgy, Israel +* Lawrence Berkeley National Lab +* Colorado School of Mines, USA +* Syracuse University, USA +* IPQC, SJTU, Shanghai, China +* Bhabha Atomic Research Centre, India +* Quantum Computing UK +* Alice&Bob, France * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) ## How do I cite `pyEPR` when I publish? Cite the following and/or e-mail [`zlatko.minev@aya.yale.edu`](https://www.zlatko-minev.com/) or [`zaki leghtas`](http://cas.ensmp.fr/~leghtas/) -* [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. +* [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. * Z.K. Minev, Z. Leghtas, _et al._ (to appear soon on arXiv) (2020) @@ -150,7 +161,7 @@ Use `pyEPR` directly from the source, and pull updates from the master git repo, Please keep up to date with `pyEPR` by using git. We like to make it simple using a git-gui manager, [SourceTree](sourcetree.com) or [GitHub Desktop](https://desktop.github.com/). **Quick setup** -We recommend the approach in the following section, which will be most up to date, but for quick use you can use the [conda forge chanel](https://anaconda.org/conda-forge/pyepr-quantum) to install +We recommend the approach in the following section, which will be most up to date, but for quick use you can use the [conda forge channel](https://anaconda.org/conda-forge/pyepr-quantum) to install ``` conda install -c conda-forge pyepr-quantum ``` diff --git a/docs/source/installation.rst b/docs/source/installation.rst index b6dad38..4968d88 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -35,7 +35,7 @@ Main installation method .. _install-via_pip: -Installing lcoally with via pip +Installing locally via pip =============================== In the future, ``pyEPR`` can be installed using the Python package manager `pip `_. @@ -63,13 +63,29 @@ Now we can locally install the pyEPR module. Installing via conda ==================== -For Python 3.6+, installation via conda will be supported in a future version. +For Python 3.6+, installation via `conda`_ is supported since ``pyEPR`` v.0.8.03, through the ``conda-forge`` channel. You can download and install ``pyEPR`` typing in from bash: +.. code-block:: bash -In the meantime, if you are using conda, you can locally install from the cloned repo. -Perform the steps in the :ref:`install-main` section. -Now, you can use + conda install -c conda-forge pyepr-quantum + +The prefix ``-c conda-forge`` is required to activate the optional ``conda-forge`` channel. + +.. _install-via_pypi: + +Installing via pip from PyPI +============================ + +For Python 3.6+, installation via `PyPI`_ is supported since ``pyEPR`` v.0.8. You can download and install ``pyEPR`` typing in from bash: .. code-block:: bash - python -m pip install -e . \ No newline at end of file + pip install pyEPR-quantum + +.. note:: + + Note that the name of the recipe on the ``conda-forge`` channel is ``pyepr-quantum``, and on PyPI is ``pyEPR-quantum``, as the name `pyepr` was already taken by another project. This does not change anything in the way the library is imported in Python as documented in the guide and examples. + +.. _conda: https://anaconda.org/conda-forge/pyepr-quantum +.. _PyPI: https://pypi.org/project/pyEPR-quantum/0.8/ + From 7a816e850022b8f29ea038be49abdb58530d27e7 Mon Sep 17 00:00:00 2001 From: SQClab <69586246+SQClab@users.noreply.github.com> Date: Thu, 24 Sep 2020 18:25:50 +0300 Subject: [PATCH 011/125] Update core_quantum_analysis.py allows to print result and print variation to be used togther where the variation to be printed is give by an integer --- pyEPR/core_quantum_analysis.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index d1ef012..8e6c489 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -734,11 +734,36 @@ def analyze_variation(self, self.n_modes = tmp_n_modes #TODO is this smart should consider defining the modes of intrest in the initilazaition of the quantum object self.modes[variation]=tmp_modes return result + def full_report_variations(self, var_list: list): + """see full_variation_report""" + for variation in var_list(): + self.full_variation_report(variation) + + def full_variation_report(self,variation): + """ + prints the results and paramters of a specific variation + + Parameters + ---------- + variation : int or str + the variation to be printed . + Returns + ------- + None. + + """ + self.print_variation(variation) + + self.print_result(variation) + + def print_variation(self, variation): """ Utility reporting function """ + if variation is int: variation = str(variation) + if len(self.hfss_vars_diff_idx) > 0: print('\n*** Different parameters') display(self._hfss_variables[self.hfss_vars_diff_idx][variation]) @@ -754,6 +779,7 @@ def print_result(self, result): """ Utility reporting function """ + if result is str or result is int: result = self.results[str(result)] # TODO: actually make into dataframe with mode labela and junction labels pritm = lambda x, frmt="{:9.2g}": print_matrix(x, frmt=frmt) From 52ef6e3f43fa81c6a27f1daeabde1f5908339f15 Mon Sep 17 00:00:00 2001 From: SQClab <69586246+SQClab@users.noreply.github.com> Date: Thu, 24 Sep 2020 18:28:27 +0300 Subject: [PATCH 012/125] Update core_quantum_analysis.py --- pyEPR/core_quantum_analysis.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 8e6c489..a1cacb4 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -734,8 +734,9 @@ def analyze_variation(self, self.n_modes = tmp_n_modes #TODO is this smart should consider defining the modes of intrest in the initilazaition of the quantum object self.modes[variation]=tmp_modes return result - def full_report_variations(self, var_list: list): + def full_report_variations(self, var_list: list=None): """see full_variation_report""" + if var_list is None: var_list =self.variations for variation in var_list(): self.full_variation_report(variation) From 55535df29efac76ef1a3203dd2d7d34b9b4c4595 Mon Sep 17 00:00:00 2001 From: SQClab <69586246+SQClab@users.noreply.github.com> Date: Thu, 24 Sep 2020 18:36:39 +0300 Subject: [PATCH 013/125] Update core_quantum_analysis.py --- pyEPR/core_quantum_analysis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index a1cacb4..4d8e6fd 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -737,7 +737,7 @@ def analyze_variation(self, def full_report_variations(self, var_list: list=None): """see full_variation_report""" if var_list is None: var_list =self.variations - for variation in var_list(): + for variation in var_list: self.full_variation_report(variation) def full_variation_report(self,variation): @@ -780,7 +780,7 @@ def print_result(self, result): """ Utility reporting function """ - if result is str or result is int: result = self.results[str(result)] + if type(result) is str or type(result) is int: result = self.results[str(result)] # TODO: actually make into dataframe with mode labela and junction labels pritm = lambda x, frmt="{:9.2g}": print_matrix(x, frmt=frmt) From 1ec9fdbfdaf7501dfc85fadc86771db1d9f1e6a9 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Mon, 28 Sep 2020 20:11:22 -0400 Subject: [PATCH 014/125] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 97f6e37..0d61002 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,7 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 * Bhabha Atomic Research Centre, India * Quantum Computing UK * Alice&Bob, France +* Centre for Quantum Technologies / Qcrew * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) From 1adad8673829b2aedfe14c7853ec9acb18937445 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Mon, 28 Sep 2020 20:26:28 -0400 Subject: [PATCH 015/125] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0d61002..c259ad5 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,9 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 * Yale University, Rob Schoelkopf lab [RSL](https://rsl.yale.edu/), CT, USA * [IBM Quantum](https://www.ibm.com/quantum-computing/) * [QUANTIC](https://team.inria.fr/quantic/people.html#) (QUANTUM INFORMATION CIRCUITS), PARISINRIA, ENS, MINES PARISTECH, UPMC, CNRS. Groups of Zaki Leghtas and team. France -* [Quantum Circuit Group](http://www.physinfo.fr/) Emanuel Flurin, Benjamin Huard, Ecole Normale Supérieure de Lyon, France +* [Quantum Circuit Group](http://www.physinfo.fr/) Benjamin Huard, Ecole Normale Supérieure de Lyon, France +* Emanuel Flurin, CEA Saclay, France +* Ioan Pop group, KIT Physikalisches Institut, Germany * UC Berkeley, [Quantum Nanoelectronics Laboratory](https://physics.berkeley.edu/quantum-nanoelectronics-laboratory), Irfan Siddiqi, CA, USA * [Quantum Circuits, Inc.](https://quantumcircuits.com/), CT, USA * [Seeqc](https://seeqc.com/) (spin-out of Hypres) Digital Quantum Computing, USA From 7980395656f103d45e8899d1c815e3e18d9b7d18 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:21:29 -0400 Subject: [PATCH 016/125] Update README.md --- README.md | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index c259ad5..1929526 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -Welcome to pyEPR :beers:! +Welcome to pyEPR :beers:!
 ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) =================== [![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.png?v=103)](https://github.com/zlatko-minev/pyEPR) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/zlatko-minev/pyEPR) @@ -14,18 +14,15 @@ Welcome to pyEPR :beers:! * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers:
+**Abstract:** Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. +([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) ### Automated Python module for the design and quantization of Josephson quantum circuits -**Abstract:** Superconducting circuits incorporating non-linear devices, such as Josephson junctions and nanowires, are among the leading platforms for emerging quantum technologies. Promising applications require designing and optimizing circuits with ever-increasing complexity and controlling their dissipative and Hamiltonian parameters to several significant digits. Therefore, there is a growing need for a systematic, simple, and robust approach for precise circuit design, extensible to increased complexity. The energy-participation ratio (EPR) approach presents such an approach to unify the design of dissipation and Hamiltonians around a single concept — the energy participation, a number between zero and one — in a single-step electromagnetic simulation. This markedly reduces the required number of simulations and allows for robust extension to complex systems. The approach is general purpose, derived ab initio, and valid for arbitrary non-linear devices and circuit architectures. Experimental results on a variety of circuit quantum electrodynamics (cQED) devices and architectures, 3D and flip-chip (2.5D), have been demonstrated to exhibit ten percent to percent-level agreement for non-linear coupling and modal Hamiltonian parameters over five-orders of magnitude and across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. - ## Documentation [Read the docs here.](https://pyepr-docs.readthedocs.io) -#### Legacy users -Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 2020; current branch: master \[to be made stable soon\]). If you used a previous version, you will find that all key classes have been renamed. Please, see the tutorials and docs. In the meantime, if you cannot switch yet, revert to use the stable v0.7. - ## Who uses pyEPR? * Yale University, Michel Devoret lab [QLab](https://qulab.eng.yale.edu/), CT, USA * Yale University, Rob Schoelkopf lab [RSL](https://rsl.yale.edu/), CT, USA @@ -55,9 +52,13 @@ Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 202 ## How do I cite `pyEPR` when I publish? -Cite the following and/or e-mail [`zlatko.minev@aya.yale.edu`](https://www.zlatko-minev.com/) or [`zaki leghtas`](http://cas.ensmp.fr/~leghtas/) -* [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. -* Z.K. Minev, Z. Leghtas, _et al._ (to appear soon on arXiv) (2020) +Cite the following: +* Z.K. Minev, Z. Leghtas, _et al._ ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) (2020) +or the earlier +* Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. ([arXiv:1902.10355](https://arxiv.org/abs/1902.10355)) (2018) [when using this, use the arXiv id] + +You can additionally drop us an e-mail [`zlatko.minev@aya.yale.edu`](https://www.zlatko-minev.com/) or [`zaki leghtas`](http://cas.ensmp.fr/~leghtas/) +
@@ -207,6 +208,10 @@ Follow the same instructions above. You shouldn't have to install mingw or modif +#### Legacy users +Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 2020; current branch: master \[to be made stable soon\]). If you used a previous version, you will find that all key classes have been renamed. Please, see the tutorials and docs. In the meantime, if you cannot switch yet, revert to use the stable v0.7. + + # HFSS Project Setup for `pyEPR` ------------- #### Eigenmode Design --- How to set up junctions @@ -299,7 +304,7 @@ This problem is due to pandas 0.20.1, update to 0.20.3 or better solves this iss This error happens when trying to read in an hdf file with numpy version 1.16, see [git issue here](https://github.com/numpy/numpy/issues/12791). A solution is to downgrade numpy to 1.15.4 or upgrade to newer versions of hdf and numpy. # Authors and Contributors -* _Authors:_ [Zlatko Minev](https://www.zlatko-minev.com/) & [Zaki Leghtas](http://cas.ensmp.fr/~leghtas/), with contributions from many friends and colleagues. +* _Authors:_ [Zlatko Minev](https://www.zlatko-minev.com/) & [Zaki Leghtas](http://cas.ensmp.fr/~leghtas/), with contributions from many friends and colleagues. ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) * 2015 - present. * Contributors: [Phil Rheinhold](https://github.com/PhilReinhold), Lysander Christakis, [Devin Cody](https://github.com/devincody), ... Original versions of pyHFSS.py and pyNumericalDiagonalization.py contributed by [Phil Rheinhold](https://github.com/PhilReinhold), excellent original [repo](https://github.com/PhilReinhold/pyHFSS). From 29422965f76fd7c8b6ab6072493f37be1f28e904 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:22:42 -0400 Subject: [PATCH 017/125] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1929526..7720c81 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -Welcome to pyEPR :beers:!
 ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) +Welcome to pyEPR :beers:!      ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) =================== [![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.png?v=103)](https://github.com/zlatko-minev/pyEPR) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/zlatko-minev/pyEPR) From f04ae241fafca5c8be2b53a07142faf5499f6a92 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:23:55 -0400 Subject: [PATCH 018/125] Update README.md --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7720c81..d6c02e1 100644 --- a/README.md +++ b/README.md @@ -9,15 +9,17 @@ Welcome to pyEPR :beers:!      ([arXiv:2010.00620](http
+### Automated Python module for the design and quantization of Josephson quantum circuits + ## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers:
-**Abstract:** Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. +##### Abstract +Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) -### Automated Python module for the design and quantization of Josephson quantum circuits ## Documentation From 74a2487d638518751d1d0c179dbf2ed13f953c7b Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:25:16 -0400 Subject: [PATCH 019/125] Update README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d6c02e1..9a0622f 100644 --- a/README.md +++ b/README.md @@ -11,14 +11,18 @@ Welcome to pyEPR :beers:!      ([arXiv:2010.00620](http ### Automated Python module for the design and quantization of Josephson quantum circuits +
+ ## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers:
+ ##### Abstract + Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. -([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) +[arXiv:2010.00620](https://arxiv.org/abs/2010.00620) ## Documentation From 642d81f0bf76e55f6fae16fe27b78d89c667df1c Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:25:46 -0400 Subject: [PATCH 020/125] Update README.md --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 9a0622f..8fd3aab 100644 --- a/README.md +++ b/README.md @@ -7,8 +7,6 @@ Welcome to pyEPR :beers:!      ([arXiv:2010.00620](http [![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyepr-quantum/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge) [![PyPI version](https://badge.fury.io/py/pyEPR-quantum.svg)](https://badge.fury.io/py/pyEPR-quantum) -
- ### Automated Python module for the design and quantization of Josephson quantum circuits
@@ -19,7 +17,7 @@ Welcome to pyEPR :beers:!      ([arXiv:2010.00620](http
-##### Abstract +#### Abstract Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) From 9711eda298de6404099342eb4897614e84811ae2 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:31:20 -0400 Subject: [PATCH 021/125] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8fd3aab..4cd4cbe 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -Welcome to pyEPR :beers:!      ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) +Welcome to pyEPR :beers:!      (see [arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) =================== [![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.png?v=103)](https://github.com/zlatko-minev/pyEPR) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/zlatko-minev/pyEPR) From 56b3f07385b5864e78181c289f848968a3175f7f Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:32:03 -0400 Subject: [PATCH 022/125] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4cd4cbe..dc13afa 100644 --- a/README.md +++ b/README.md @@ -84,7 +84,7 @@ You can additionally drop us an e-mail [`zlatko.minev@aya.yale.edu`](https://www 2. **Clone** :point_down: your forked repository locally. ([How to clone a GitHub repo?](https://help.github.com/en/articles/cloning-a-repository)). Setup the `pyEPR` python code by following [Installation and Python Setup](#installation-of-pyepr). 3. **Tutorials** Learn how to use using the [jupyter notebook tutorials](https://github.com/zlatko-minev/pyEPR/tree/master/_tutorial_notebooks) 4. **Stay up to date** Enjoy and make sure to git add the master remote branch `git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git` [(help?)](https://stackoverflow.com/questions/11266478/git-add-remote-branch). -5. **Cite `pyEPR`** [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) and enjoy! :birthday: +5. **Cite `pyEPR`** [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) / [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) and enjoy! :birthday: #### Start-up example From e9594dc127d0f0797838623489732236d59e4f3b Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:43:04 -0400 Subject: [PATCH 023/125] Update about.rst --- docs/source/about.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/about.rst b/docs/source/about.rst index e701291..f12260e 100644 --- a/docs/source/about.rst +++ b/docs/source/about.rst @@ -33,9 +33,9 @@ automated. References ~~~~~~~~~~ +- Z.K. Minev, Z. Leghtas, *et al.* (2020) [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) - Z.K. Minev, Ph.D. Dissertation, Yale University (2018), see Chapter 4. `arXiv:1902.10355`_ -- Z.K. Minev, Z. Leghtas, *et al.* (to appear soon on arXiv) (2019) .. _`arXiv:1902.10355`: https://arxiv.org/abs/1902.10355 @@ -46,4 +46,4 @@ References .. |star this repo| image:: http://githubbadges.com/star.svg?user=zlatko-minev&repo=pyEPR&style=flat :target: https://github.com/zlatko-minev/pyEPR .. |fork this repo| image:: http://githubbadges.com/fork.svg?user=zlatko-minev&repo=pyEPR&style=flat - :target: https://github.com/zlatko-minev/pyEPR/fork \ No newline at end of file + :target: https://github.com/zlatko-minev/pyEPR/fork From 9474010cb7d89d6bff71a174a9e61ca373a677af Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:44:43 -0400 Subject: [PATCH 024/125] Update about.rst --- docs/source/about.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/about.rst b/docs/source/about.rst index f12260e..fb9ab5d 100644 --- a/docs/source/about.rst +++ b/docs/source/about.rst @@ -33,7 +33,8 @@ automated. References ~~~~~~~~~~ -- Z.K. Minev, Z. Leghtas, *et al.* (2020) [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) +- Z.K. Minev, Z. Leghtas, *et al.* (2020) `arXiv:2010.00620 +`_. - Z.K. Minev, Ph.D. Dissertation, Yale University (2018), see Chapter 4. `arXiv:1902.10355`_ From 6d8c3e093addade1849f286084bfbb0811758656 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:45:55 -0400 Subject: [PATCH 025/125] Update about.rst --- docs/source/about.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/source/about.rst b/docs/source/about.rst index fb9ab5d..8ceb625 100644 --- a/docs/source/about.rst +++ b/docs/source/about.rst @@ -33,10 +33,9 @@ automated. References ~~~~~~~~~~ -- Z.K. Minev, Z. Leghtas, *et al.* (2020) `arXiv:2010.00620 -`_. +- Z.K. Minev, Z. Leghtas, *et al.* (2020) (`arXiv:2010.00620 `_). - Z.K. Minev, Ph.D. Dissertation, Yale University (2018), see Chapter - 4. `arXiv:1902.10355`_ + 4. (`arXiv:1902.10355 `_) .. _`arXiv:1902.10355`: https://arxiv.org/abs/1902.10355 From b585c243399d6eced66f16fad6908d122b771930 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:47:11 -0400 Subject: [PATCH 026/125] Update installation.rst --- docs/source/installation.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 4968d88..491bf78 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -22,7 +22,8 @@ Main installation method branch ``git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git`` `(help?)`_. -5. **Cite ``pyEPR``** `arXiv:1902.10355`_ and enjoy! 🎂 +5. **Cite ``pyEPR``** `arXiv:2010.00620 `_ and `arXiv:1902.10355 `_ enjoy! 🎂 + .. _``pyEPR top-level repository``: https://github.com/zlatko-minev/pyEPR .. _How to fork a GitHub repo?: https://help.github.com/en/articles/fork-a-repo From ca470d19181095e95ba92e91e08933c83b0a331d Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 4 Oct 2020 21:56:29 -0400 Subject: [PATCH 027/125] Update README.md --- docs/README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 859e8c0..f27e7fc 100644 --- a/docs/README.md +++ b/docs/README.md @@ -44,4 +44,10 @@ Notes for developers. sphinx-apidoc -f -o source/ ../pyEPR -o source/api --no-toc -M -e make html ``` -You can alos use this to update the doc tree. \ No newline at end of file +You can alos use this to update the doc tree. + +# Updating `readthedocs.org` + +The docs get update automatically each time you make a commit to the master branch. To check on the status of the docs build or failing, navigate to `https://readthedocs.org/projects/pyepr-docs/builds/` and select the latest build to see its progress and report. + +To manually initiate a rebuild: Navigate to `https://readthedocs.org/projects/pyepr-docs/`. Here, if you have write access you can initiate a build. From 39d914587938cd7aa7fc546444c5bd4572a922dc Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Mon, 5 Oct 2020 20:31:49 -0400 Subject: [PATCH 028/125] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index dc13afa..e9e3f86 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ Superconducting microwave circuits incorporating nonlinear devices, such as Jose * Quantum Computing UK * Alice&Bob, France * Centre for Quantum Technologies / Qcrew +* Quantum Device Lab ETHZ; Andreas Wallraff * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) From 2f1e573f620bffee0b0412c85b16588455131de3 Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Sun, 18 Oct 2020 10:48:02 +0300 Subject: [PATCH 029/125] Sorted Qs and freqs Instead of having the sorting of the dataframe as string indices (e.g. '0', '1', '10', '2'...) it is now sorted as integers ('0', '1', '2', ... , '10'). Only relevant for >10 variations. --- pyEPR/core_quantum_analysis.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 4f92de8..86f8f1c 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -807,11 +807,11 @@ def plot_hamiltonian_results(self, ############################################################################ ### Axis: Frequencies f0 = self.results.get_frequencies_HFSS( - variations=variations, vs=swp_variable).transpose() + variations=variations, vs=swp_variable).transpose().sort_index(key=lambda x : x.astype(int)) f1 = self.results.get_frequencies_O1( - variations=variations, vs=swp_variable).transpose() + variations=variations, vs=swp_variable).transpose().sort_index(key=lambda x : x.astype(int)) f_ND = self.results.get_frequencies_ND( - variations=variations, vs=swp_variable).transpose() + variations=variations, vs=swp_variable).transpose().sort_index(key=lambda x : x.astype(int)) # changed by Asaf from f0 as not all modes are always analyzed mode_idx = list(f1.columns) n_modes = len(mode_idx) @@ -842,7 +842,7 @@ def plot_hamiltonian_results(self, # Axis: Quality factors' Qs = self.get_quality_factors(swp_variable=swp_variable) Qs = Qs if variations is None else Qs[variations] - Qs = Qs.transpose() + Qs = Qs.transpose().sort_index(key=lambda x : x.astype(int)) ax = axs[1, 0] ax.set_title('Quality factors') From 98a0d255ba72529323d307f6405553ef40b8b8a3 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Fri, 23 Oct 2020 16:40:32 -0400 Subject: [PATCH 030/125] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e9e3f86..1d1ebd8 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,8 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( ## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers: +- See [pyEPR wiki](https://github.com/zlatko-minev/pyEPR/wiki) for notes from first meeting. +- We will schedule a follow-up meeting in 1-2 mo.
From 4cc0b3ca184ad80e2a27ec723709035c7b56ab47 Mon Sep 17 00:00:00 2001 From: willsALMANJ Date: Thu, 12 Nov 2020 18:07:59 -0500 Subject: [PATCH 031/125] Make package name in setup.py match name on PyPI --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index d10736e..ff52086 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ doclines = __doc__.split('\n') -setup(name='pyEPR', +setup(name='pyEPR-quantum', version='0.8', description = doclines[0], long_description = '\n'.join(doclines[2:]), From 297f8a68760bb9a2a0e5997fe18f7594a3765661 Mon Sep 17 00:00:00 2001 From: Will Shanks Date: Fri, 13 Nov 2020 08:12:35 -0500 Subject: [PATCH 032/125] Remove old conda recipe See https://github.com/conda-forge/pyepr-quantum-feedstock for conda recipe --- recepies/junk/conda_build_config.yaml | 25 --------- recepies/junk/meta copy.yaml | 42 -------------- recepies/junk/meta2.yaml | 45 --------------- recepies/meta.yaml | 50 ----------------- recepies/meta_zlatko.yaml | 79 --------------------------- 5 files changed, 241 deletions(-) delete mode 100644 recepies/junk/conda_build_config.yaml delete mode 100644 recepies/junk/meta copy.yaml delete mode 100644 recepies/junk/meta2.yaml delete mode 100644 recepies/meta.yaml delete mode 100644 recepies/meta_zlatko.yaml diff --git a/recepies/junk/conda_build_config.yaml b/recepies/junk/conda_build_config.yaml deleted file mode 100644 index 8b7a40b..0000000 --- a/recepies/junk/conda_build_config.yaml +++ /dev/null @@ -1,25 +0,0 @@ -python: - # - 3.5 - - 3.6 - # - 3.7 - -# channel_sources: -# - conda-forge/label/gcc7,defaults -# - conda-forge,defaults - -cross_compiler_target_platform: # [win] - - win-64 # [win] - -# The following are helpful variables to simplify go meta.yaml files. -target_goos: - - linux # [linux] - - darwin # [osx] - - windows # [win] -target_goarch: - - amd64 # [x86_64] - -# we build for the oldest version possible of numpy for forward compatibility -numpy: - - 1.14 # [not (aarch64 or ppc64le)] - - 1.15 # [aarch64 or ppc64le] - - 1.16 # [aarch64 or ppc64le] \ No newline at end of file diff --git a/recepies/junk/meta copy.yaml b/recepies/junk/meta copy.yaml deleted file mode 100644 index 48fea89..0000000 --- a/recepies/junk/meta copy.yaml +++ /dev/null @@ -1,42 +0,0 @@ -{% set data = load_setup_py_data() %} - -package: - name: pyepr-quantum - version: {{ data['version'] }} - -build: - number: 0 - script: python setup.py install --single-version-externally-managed --record=record.txt - -requirements: - build: - - python - - setuptools - requirements: - run: - {% for req in data.get('install_requires', []) %} - - {{ req }} - {% endfor %} - -test: - imports: - pyEPR - -about: - home: {{ data['url'] }} - license: {{ data['license'] }} - license_file: LICENSE - summary: {{ data['description'] }} - description: > - pyEPR is an open source, BSD-licensed library providing high-efficiency, - easy-to-use analysis functions and automation for the design of quantum - chips based on superconducting quantum circuits, both distributed and lumped. - pyEPR interfaces the classical distributed microwave analysis with that of - quantum structures and Hamiltonians. It is chiefly based on the energy participation - ratio approach; however, it has since v0.4 extended to cover a broad range of - design approaches. pyEPR stradels the analysis from Maxwell’s to Schrodinger’s - equations, and converts the solutions of distributed microwve (typically eignmode - simulations) to a fully diagonalized spectrum of the energy levels, couplings, - and key parameters of a many-body quantum Hamiltonian. - doc_url: https://pyepr-docs.readthedocs.io/en/latest/ - dev_url: https://github.com/zlatko-minev/pyEPR diff --git a/recepies/junk/meta2.yaml b/recepies/junk/meta2.yaml deleted file mode 100644 index f88521f..0000000 --- a/recepies/junk/meta2.yaml +++ /dev/null @@ -1,45 +0,0 @@ -{% set data = load_setup_py_data() %} - -package: - name: pyepr-quantum - version: {{ data.get('version') }} - -build: - number: 0 - script: python setup.py install --single-version-externally-managed --record=record.txt - -source: - url: https://github.com/zlatko-minev/pyEPR.git - -requirements: - build: - - python - run: - - python >=3.6 - - pip - - setuptools - - numpy >=1.15 - - attrdict - -test: - imports: - pyEPR - -about: - home: {{ data.get('url') }} - license: {{ data.get('license') }} - license_file: LICENSE - summary: {{ data.get('description') }} - description: > - pyEPR is an open source, BSD-licensed library providing high-efficiency, - easy-to-use analysis functions and automation for the design of quantum - chips based on superconducting quantum circuits, both distributed and lumped. - pyEPR interfaces the classical distributed microwave analysis with that of - quantum structures and Hamiltonians. It is chiefly based on the energy participation - ratio approach; however, it has since v0.4 extended to cover a broad range of - design approaches. pyEPR stradels the analysis from Maxwell’s to Schrodinger’s - equations, and converts the solutions of distributed microwve (typically eignmode - simulations) to a fully diagonalized spectrum of the energy levels, couplings, - and key parameters of a many-body quantum Hamiltonian. - doc_url: https://pyepr-docs.readthedocs.io/en/latest/ - dev_url: https://github.com/zlatko-minev/pyEPR diff --git a/recepies/meta.yaml b/recepies/meta.yaml deleted file mode 100644 index a292865..0000000 --- a/recepies/meta.yaml +++ /dev/null @@ -1,50 +0,0 @@ -{% set name = "pyepr-quantum" %} -{% set version = "0.8.01" %} -{% set data = load_setup_py_data() %} - -package: - name: {{ name|lower }} - version: {{ version }} - -source: - # url: https://github.com/zlatko-minev/pyEPR/archive/v0.7.tar.gz - # sha256: d25b940662abec11ba39eb5728cbb23757d6c40609da0808c921ea25ee74bcdf - path: .. - -build: - noarch: python - number: 0 - script: "{{ PYTHON }} -m pip install . -vv" - -requirements: - host: - - python - - pip - run: - - python - - ipython - - numpy>=1.15.0 - - pandas>=1.0.1 - - matplotlib>=3.1.0 - - scipy>=1.3.0 - - sympy>=1.3 - - pint - - qutip - - ipython>=7.0.0 - - seaborn>=0.10.0 - - attrdict -test: - imports: - - pyEPR -about: - home: {{ data.get('url') }} - license: {{ data.get('license') }} - license_family: BSD - license_file: LICENSE - summary: {{ data.get('description') }} - doc_url: https://pyepr-docs.readthedocs.io/ - dev_url: https://github.com/zlatko-minev/pyEPR - -extra: - recipe-maintainers: - - zlatko-minev diff --git a/recepies/meta_zlatko.yaml b/recepies/meta_zlatko.yaml deleted file mode 100644 index 17bb337..0000000 --- a/recepies/meta_zlatko.yaml +++ /dev/null @@ -1,79 +0,0 @@ -# PRELIM NOTES: -# Some of the packages we use arent in the stanrd conda channel, so try: -# conda config --add channels conda-forge -# Can also try -# conda build . --channel conda-forge - -{% set data = load_setup_py_data() %} - -package: - name: pyepr - version: 0.8.01 - -build: - number: 4 - script: python setup.py install --single-version-externally-managed --record=record.txt - -# https://github.com/python-packaging-tutorial/python-packaging-tutorial/blob/master/conda_build_recipes/02_local_source/meta.yaml -source: - path: . -# url: https://github.com/zlatko-minev/pyEPR.git -# path_url: ../ - -# extra: -# channels: -# - conda-forge - -# Packages required to run the package. -# These are the dependencies that are installed automatically whenever the package is installed. -# Package names should follow the package match specifications. -requirements: - # host: - # - python - # - pip - build: - - python - run: - - python - - ipython - - numpy>=1.15.0 - - pandas>=1.0.1 - - matplotlib>=3.1.0 - - scipy>=1.3.0 - - sympy>=1.3 - # - pint - # - qutip - - ipython>=7.0.0 - - seaborn>=0.10.0 - # - attrdict - # - pip: - # works for regular pip packages - # - attrdict - # WARNING: attrdict, pint, and qutip failed conda - -# test: - # imports: - # pyEPR - -about: - home: {{ data.get('url') }} - license: {{ data.get('license') }} - license_file: LICENSE - summary: {{ data.get('description') }} - description: > - pyEPR is an open source, BSD-licensed library providing high-efficiency, - easy-to-use analysis functions and automation for the design of quantum - chips based on superconducting quantum circuits, both distributed and lumped. - pyEPR interfaces the classical distributed microwave analysis with that of - quantum structures and Hamiltonians. It is chiefly based on the energy participation - ratio approach; however, it has since v0.4 extended to cover a broad range of - design approaches. pyEPR stradels the analysis from Maxwell’s to Schrodinger’s - equations, and converts the solutions of distributed microwve (typically eignmode - simulations) to a fully diagonalized spectrum of the energy levels, couplings, - and key parameters of a many-body quantum Hamiltonian. - doc_url: https://pyepr-docs.readthedocs.io/en/latest/ - dev_url: https://github.com/zlatko-minev/pyEPR - -# import sys -# sys.path.pop(1) -# import pyEPR \ No newline at end of file From 63ab2a955ece2370086c723525e3d39df34b4243 Mon Sep 17 00:00:00 2001 From: Will Shanks Date: Fri, 13 Nov 2020 08:12:12 -0500 Subject: [PATCH 033/125] Version 0.8.4 --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index b271c6b..58838f0 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -60,7 +60,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8 +@version: 0.8.4 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -91,7 +91,7 @@ __credits__ = ["Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", "Asaf Diringer", "Will Livingston", "Steven Touzard"] __license__ = "BSD-3-Clause" -__version__ = "0.8" +__version__ = "0.8.4" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 3d11740..16370cb 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ doclines = __doc__.split('\n') setup(name='pyEPR-quantum', - version='0.8', + version='0.8.4', description = doclines[0], long_description=long_description, long_description_content_type="text/markdown", From a8d1bdf5c3c287ea992028f3ad152ac43dd7a139 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Thu, 26 Nov 2020 11:52:07 -0500 Subject: [PATCH 034/125] Create greetings.yml --- .github/workflows/greetings.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 .github/workflows/greetings.yml diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml new file mode 100644 index 0000000..55a2b7c --- /dev/null +++ b/.github/workflows/greetings.yml @@ -0,0 +1,13 @@ +name: Greetings + +on: [pull_request, issues] + +jobs: + greeting: + runs-on: ubuntu-latest + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + issue-message: '👏👏👏 You are awesome! Thank you for making your first issue to pyEPR '' first issue' + pr-message: '👏👏👏 You are awesome! Thank you for making your first pull request to pyEPR! This team work makes the pyEPR dream work! '' first pr' From 06129972e87e41ebc31f4bd04fa95d11b6bcb9d8 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Thu, 26 Nov 2020 11:57:38 -0500 Subject: [PATCH 035/125] Create manual.yml --- .github/workflows/manual.yml | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 .github/workflows/manual.yml diff --git a/.github/workflows/manual.yml b/.github/workflows/manual.yml new file mode 100644 index 0000000..47f24e1 --- /dev/null +++ b/.github/workflows/manual.yml @@ -0,0 +1,30 @@ +# This is a basic workflow that is manually triggered + +name: Manual workflow + +# Controls when the action will run. Workflow runs when manually triggered using the UI +# or API. +on: + workflow_dispatch: + # Inputs the workflow accepts. + inputs: + name: + # Friendly description to be shown in the UI instead of 'name' + description: 'Person to greet' + # Default value if no value is explicitly provided + default: 'World' + # Input has to be provided for the workflow to run + required: true + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "greet" + greet: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Runs a single command using the runners shell + - name: Send greeting + run: echo "Hello ${{ github.event.inputs.name }}" From 3737d8b6b76022ffb9802e7e602b45f1c676f098 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Tue, 1 Dec 2020 19:05:07 -0500 Subject: [PATCH 036/125] Create Q3D setup within HfssDesign --- pyEPR/ansys.py | 35 +++++++++++++++++++++++++++++++---- 1 file changed, 31 insertions(+), 4 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 41dcfb5..1c6deae 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -637,6 +637,31 @@ def get_setup(self, name=None): return HfssDMSetup(self, name) elif self.solution_type == "Q3D": return AnsysQ3DSetup(self, name) + + def create_q3d_setup(self, freq_ghz=5., name="Setup", save_fields=False, enabled=True, + max_passes=15, min_passes=2, min_converged_passes=2, percent_error=0.5, + percent_refinement=30, auto_increase_solution_order=True, solution_order="High", + solver_type='Iterative'): + name = increment_name(name, self.get_setup_names()) + self._setup_module.InsertSetup( + "Matrix", [ + f"NAME:{name}", + "AdaptiveFreq:=", f"{freq_ghz}GHz", + "SaveFields:=", save_fields, + "Enabled:=", enabled, + [ + "NAME:Cap", + "MaxPass:=", max_passes, + "MinPass:=", min_passes, + "MinConvPass:=", min_converged_passes, + "PerError:=", percent_error, + "PerRefine:=", percent_refinement, + "AutoIncreaseSolutionOrder:=", auto_increase_solution_order, + "SolutionOrder:=", solution_order, + "Solver Type:=", solver_type + ] + ]) + return AnsysQ3DSetup(self, name) def create_dm_setup(self, freq_ghz=1, name="Setup", max_delta_s=0.1, max_passes=10, min_passes=1, min_converged=1, pct_refinement=30, @@ -877,7 +902,7 @@ class HfssSetup(HfssPropertyObject): min_freq = make_float_prop("Min Freq") basis_order = make_str_prop("Basis Order") - def __init__(self, design, setup): + def __init__(self, design, setup:str): """ :type design: HfssDesign :type setup: Dispatch @@ -1196,7 +1221,7 @@ class AnsysQ3DSetup(HfssSetup): """ prop_tab = "CG" max_pass = make_int_prop("Max. Number of Passes") - max_pass = make_int_prop("Min. Number of Passes") + min_pass = make_int_prop("Min. Number of Passes") pct_error = make_int_prop("Percent Error") frequency = make_str_prop("Adaptive Freq", 'General') # e.g., '5GHz' n_modes = 0 # for compatability with eigenmode @@ -1257,7 +1282,7 @@ def get_matrix(self, variation='', pass_number=0, frequency=None, return df_cmat, user_units, (df_cond, units_cond), design_variation @staticmethod - def _readin_Q3D_matrix(path): + def _readin_Q3D_matrix(path:str): """ Read in the txt file created from q3d export and output the capacitance matrix @@ -1321,7 +1346,9 @@ def _readin_Q3D_matrix(path): if len(var) <1: # didnt find var = re.findall(r'Design Variation:(.*?)\n', text) if len(var) <1: # didnt find - logger.error(f'Failed to parse Q3D matrix Design Variation:\nFile:{path}\nText:{text}') + # May not be present if there are no design variations to begin + # with and no variables in the design. + pass #logger.error(f'Failed to parse Q3D matrix Design Variation:\nFile:{path}\nText:{text}') var = [''] design_variation = var[0] From f63cf455127c1cd61fc186a4b8126c9b9357b662 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Tue, 1 Dec 2020 20:09:00 -0500 Subject: [PATCH 037/125] Create publish-to-pypi --- .github/workflows/publish-to-pypi | 43 +++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 .github/workflows/publish-to-pypi diff --git a/.github/workflows/publish-to-pypi b/.github/workflows/publish-to-pypi new file mode 100644 index 0000000..06b17f6 --- /dev/null +++ b/.github/workflows/publish-to-pypi @@ -0,0 +1,43 @@ +# This is a basic workflow to help you get started with Actions + +name: Publish Python 🐍 distributions 📦 to PyPI + +# Controls when the action will run. +on: + # Triggers the workflow on push or pull request events but only for the master branch + push: + branches: [ master ] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# A workflow run is made up of one or more jobs that can run sequentially or in parallel +jobs: + # This workflow contains a single job called "build" + build: + name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@master + - name: Set up Python 3.7 + uses: actions/setup-python@v1 + with: + python-version: 3.7 + + - name: Install pypa/build + run: >- + python -m + pip install + build + --user + + - name: Publish distribution 📦 to PyPI + if: startsWith(github.ref, 'refs/tags') + uses: pypa/gh-action-pypi-publish@master + with: + password: ${{ secrets.pypi_password }} + From d9d1a9c36e22b6e44179c2bc736f62764fd947c2 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Tue, 1 Dec 2020 20:10:15 -0500 Subject: [PATCH 038/125] Rename publish-to-pypi to publish-to-pypi.yml --- .github/workflows/{publish-to-pypi => publish-to-pypi.yml} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/workflows/{publish-to-pypi => publish-to-pypi.yml} (100%) diff --git a/.github/workflows/publish-to-pypi b/.github/workflows/publish-to-pypi.yml similarity index 100% rename from .github/workflows/publish-to-pypi rename to .github/workflows/publish-to-pypi.yml From bebf4902b7c78b91399c38cde98b62d1e9cc3804 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Tue, 1 Dec 2020 20:11:09 -0500 Subject: [PATCH 039/125] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1d1ebd8..dfe706a 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620](
-## :bangbang: :bangbang: pyEPR Working group meeting -- Planning for the future of pyEPR +## pyEPR Working group meeting -- Planning for the future of pyEPR * Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers: - See [pyEPR wiki](https://github.com/zlatko-minev/pyEPR/wiki) for notes from first meeting. From 3b3d2a1d534bc8f965b9b1f4fafc3a46fe75a5fa Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Thu, 3 Dec 2020 12:26:17 -0500 Subject: [PATCH 040/125] Update version from 0.8.4 to 0.8.4.2 --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 58838f0..1855673 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -60,7 +60,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4 +@version: 0.8.4.2 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -91,7 +91,7 @@ __credits__ = ["Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", "Asaf Diringer", "Will Livingston", "Steven Touzard"] __license__ = "BSD-3-Clause" -__version__ = "0.8.4" +__version__ = "0.8.4.2" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 16370cb..f362e5c 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ doclines = __doc__.split('\n') setup(name='pyEPR-quantum', - version='0.8.4', + version='0.8.4.2', description = doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 4dafcfc208a8ed5ff81c1227091c8f653123720f Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Wed, 6 Jan 2021 01:44:25 -0500 Subject: [PATCH 041/125] Fixed bug with port impedance in ansys.py --- pyEPR/ansys.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 1c6deae..18780e6 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -2231,7 +2231,7 @@ def _make_lumped_port(self, start, end, obj_arr, z0="50ohm", name="LumpPort"): "AlignmentGroup:=", 0, "RenormImp:=", "50ohm"]], "ShowReporterFilter:=", False, "ReporterFilter:=", [True], - "FullResistance:=", "50ohm", "FullReactance:=", "0ohm"] + "FullResistance:=", z0, "FullReactance:=", "0ohm"] self._boundaries.AssignLumpedPort(params) From c567ecb3c2844abc67d22f17b16124dfeaa3ac2e Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Mon, 25 Jan 2021 14:01:31 -0500 Subject: [PATCH 042/125] Modify how to connect to Ansys --- pyEPR/ansys.py | 36 ++++++++- pyEPR/core_distributed_analysis.py | 2 +- pyEPR/project_info.py | 117 ++++++++++++++++++++--------- 3 files changed, 115 insertions(+), 40 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 18780e6..41d3691 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -544,10 +544,20 @@ def get_active_design(self): raise EnvironmentError("No Design Active") return HfssDesign(self, d) - def new_dm_design(self, name): + def new_dm_design(self, name:str): + """Create a new driven model design + + Args: + name (str): Name of driven modal design + """ return self.new_design(name, "DrivenModal") - def new_em_design(self, name): + def new_em_design(self, name:str): + """Create a new eigenmode design + + Args: + name (str): Name of eigenmode design + """ return self.new_design(name, "Eigenmode") @property # v2016 @@ -599,6 +609,28 @@ def add_message(self, message:str, severity:int=0): oDesktop = desktop._desktop oDesktop.AddMessage(project.name, self.name, severity, message) + def save_screenshot(self, path:str=None, show:bool=True): + if not path: + path = Path().absolute() / 'ansys.png' # TODOL find better + self._modeler.ExportModelImageToFile(str(path), + 0,0, # can be 0 For the default, use 0, 0. For higher resolution, set desired and , for example for 8k export as: 7680, 4320. + [ + "NAME:SaveImageParams", + "ShowAxis:=" , "True", + "ShowGrid:=" , "True", + "ShowRuler:=" , "True", + "ShowRegion:=" , "Default", + "Selections:=" , "", + "Orientation:=" , "" + ]) + + if show: + from IPython.display import display, Image + display(Image(str(path))) + + return path + + def rename_design(self, name): old_name = self._design.GetName() self._design.RenameDesignInstance(old_name, name) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 87bc462..eb53d34 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -835,7 +835,7 @@ def get_Qseam_sweep(self, seam, mode, variation, variable, values, unit, U_H=Non """ if U_H is None: - U_H = self.calc_energy_(variation) + U_H = self.calc_energy_magnetic(variation) self.solutions.set_mode(mode+1, 0) self.fields = self.setup.get_fields() diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 0870259..649d0d8 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -220,23 +220,43 @@ def save(self): ports=pd.DataFrame(self.ports), ) - def connect(self): - """ - Do establihs connection to Ansys desktop. + def connect_project(self): + """Sets + self.app + self.desktop + self.project + self.project_name + self.project_path """ logger.info('Connecting to Ansys Desktop API...') self.app, self.desktop, self.project = ansys.load_ansys_project( self.project_name, self.project_path) - self.project_name = self.project.name - self.project_path = self.project.get_path() + + self.project_name = self.project.name # TODO: should be property? + self.project_path = self.project.get_path() # TODO: should be property? + + + def connect_design(self, design_name: str = None): + """Sets + self.design + self.design_name + """ + if not(design_name is None): + + self.design_name = design_name - # Design if self.design_name is None: - self.design = self.project.get_active_design() - self.design_name = self.design.name - logger.info(f'\tOpened active design\n\ -\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + #TODO: What if there is no active design? + try: + self.design = self.project.get_active_design() + self.design_name = self.design.name + logger.info(f'\tOpened active design\n'\ + '\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + except Exception as e: + self.design = None + self.design_name = None + logger.info(f'No active design found (or error getting active design). Note: {e}') else: try: @@ -250,35 +270,58 @@ def connect(self): raise(Exception(' Did you provide the correct design name?\ Failed to pull up design. \N{loudly crying face}').with_traceback(_traceback)) + + def connect_setup(self): + """Connect to the first avaialbe setup or create a new in eigenmode and driven modal + + Raises: + Exception: [description] + """ # Setup - try: - setup_names = self.design.get_setup_names() - - if len(setup_names) == 0: - logger.warning('\tNo design setup detected.') - if self.design.solution_type == 'Eigenmode': - logger.warning('\tCreating eigenmode default setup one.') - setup = self.design.create_em_setup() - self.setup_name = setup.name - elif self.design.solution_type == 'DrivenModal': - setup = self.design.create_dm_setup() # adding a driven modal design - self.setup_name = setup.name - else: - self.setup_name = setup_names[0] - - # get the actual setup if there is one - self.get_setup(self.setup_name) - - except Exception as e: - - _traceback = sys.exc_info()[2] - logger.error(f"Original error \N{loudly crying face}: {e}\n") - raise Exception(' Did you provide the correct setup name?\ - Failed to pull up setup. \N{loudly crying face}').with_traceback(_traceback) + if self.design is not None: + try: + setup_names = self.design.get_setup_names() + + if len(setup_names) == 0: + logger.warning('\tNo design setup detected.') + if self.design.solution_type == 'Eigenmode': + logger.warning('\tCreating eigenmode default setup one.') + setup = self.design.create_em_setup() + self.setup_name = setup.name + elif self.design.solution_type == 'DrivenModal': + setup = self.design.create_dm_setup() # adding a driven modal design + self.setup_name = setup.name + else: + self.setup_name = setup_names[0] + + # get the actual setup if there is one + self.get_setup(self.setup_name) + + except Exception as e: + + _traceback = sys.exc_info()[2] + logger.error(f"Original error \N{loudly crying face}: {e}\n") + raise Exception(' Did you provide the correct setup name?\ + Failed to pull up setup. \N{loudly crying face}').with_traceback(_traceback) + + else: + self.setup = None + self.setup_name = None + + def connect(self): + """ + Do establish connection to Ansys desktop. + Connects to project and then get design and setup + """ + self.connect_project() + self.connect_design() + self.connect_setup() # Finalize - self.project_name = self.project.name - self.design_name = self.design.name + if self.project: + self.project_name = self.project.name + if self.design: + self.design_name = self.design.name logger.info( '\tConnection to Ansys established successfully. \N{grinning face} \n') @@ -382,4 +425,4 @@ def validate_junction_info(self): def __del__(self): logger.info('Disconnected from Ansys HFSS') - self.disconnect() + # self.disconnect() From fe8c50b212388f5b137b0a828dc0472d36cc80a9 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Wed, 27 Jan 2021 15:08:20 -0500 Subject: [PATCH 043/125] Update config_user.py --- pyEPR/_config_user.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/_config_user.py b/pyEPR/_config_user.py index 745ce3e..c1334ce 100644 --- a/pyEPR/_config_user.py +++ b/pyEPR/_config_user.py @@ -21,7 +21,7 @@ # Folder to save result data to. # PLEASE CHANGE THIS - root_dir=r'D:\data-pyEPR', + root_dir=r'C:\data-pyEPR', # Not all machines have a D drive so substituting D with C here # Loss properties of various materials and surfaces dissipation=Dict( From 82b20aa4154709d71e66f488395b5c5b9d3eea6a Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Wed, 27 Jan 2021 15:24:41 -0500 Subject: [PATCH 044/125] Edit yml file --- .github/workflows/publish-to-pypi.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index 06b17f6..b871ac8 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -6,6 +6,7 @@ name: Publish Python 🐍 distributions 📦 to PyPI on: # Triggers the workflow on push or pull request events but only for the master branch push: + tags: [ '*' ] branches: [ master ] # Allows you to run this workflow manually from the Actions tab From 2da100b29b43cba671f41d2d3ff834bd85f1c9f0 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Wed, 27 Jan 2021 15:53:03 -0500 Subject: [PATCH 045/125] Update publish-to-pypi.yml --- .github/workflows/publish-to-pypi.yml | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index b871ac8..a76c503 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -4,10 +4,8 @@ name: Publish Python 🐍 distributions 📦 to PyPI # Controls when the action will run. on: - # Triggers the workflow on push or pull request events but only for the master branch - push: - tags: [ '*' ] - branches: [ master ] + release: + types: [created] # Allows you to run this workflow manually from the Actions tab workflow_dispatch: @@ -36,6 +34,15 @@ jobs: build --user + - name: Build a binary wheel and a source tarball + run: >- + python -m + build + --sdist + --wheel + --outdir dist/ + . + - name: Publish distribution 📦 to PyPI if: startsWith(github.ref, 'refs/tags') uses: pypa/gh-action-pypi-publish@master From d37c393f1c783f6e18309e7b1bc185c3e09a7489 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Wed, 27 Jan 2021 15:58:46 -0500 Subject: [PATCH 046/125] Updated version number --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 1855673..1692f4d 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -60,7 +60,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4.2 +@version: 0.8.4.3 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -91,7 +91,7 @@ __credits__ = ["Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", "Asaf Diringer", "Will Livingston", "Steven Touzard"] __license__ = "BSD-3-Clause" -__version__ = "0.8.4.2" +__version__ = "0.8.4.3" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index f362e5c..807412e 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ doclines = __doc__.split('\n') setup(name='pyEPR-quantum', - version='0.8.4.2', + version='0.8.4.3', description = doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 090d381b11f68d232adbc688060e01ee8a5ce70d Mon Sep 17 00:00:00 2001 From: Priti Ashvin Shah <74020801+priti-ashvin-shah-ibm@users.noreply.github.com> Date: Wed, 3 Feb 2021 14:15:17 -0500 Subject: [PATCH 047/125] =?UTF-8?q?If=20a=20project=20or=20design=20is=20m?= =?UTF-8?q?issing=20in=20Ansys=20app,=20then=20return=20None=20and=20?= =?UTF-8?q?=E2=80=A6=20(#68)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * If a project or design is missing in Ansys app, then return None and give a warning. * Update warnings to be more accurate. * Revert accidental change. --- pyEPR/ansys.py | 1335 ++++++++++++++++++++++------------------- pyEPR/project_info.py | 89 ++- 2 files changed, 789 insertions(+), 635 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 41d3691..dbd9702 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -54,17 +54,18 @@ from pint import UnitRegistry ureg = UnitRegistry() Q = ureg.Quantity -except(ImportError, ModuleNotFoundError): +except (ImportError, ModuleNotFoundError): ureg = "Pint module not installed. Please install." - ############################################################################## ### -BASIS_ORDER = {"Zero Order": 0, - "First Order": 1, - "Second Order": 2, - "Mixed Order": -1} +BASIS_ORDER = { + "Zero Order": 0, + "First Order": 1, + "Second Order": 2, + "Mixed Order": -1 +} # UNITS # LENGTH_UNIT --- HFSS UNITS @@ -89,7 +90,10 @@ def increment_name(base, existing): if not base in existing: return base n = 1 - def make_name(): return base + str(n) + + def make_name(): + return base + str(n) + while make_name() in existing: n += 1 return make_name() @@ -146,7 +150,7 @@ def fix_units(x, unit_assumed=None): return x elif isinstance(x, Number): - return fix_units(str(x)+unit_assumed, unit_assumed=unit_assumed) + return fix_units(str(x) + unit_assumed, unit_assumed=unit_assumed) elif isinstance(x, Iterable): # hasattr(x, '__iter__'): return [fix_units(y, unit_assumed=unit_assumed) for y in x] @@ -176,7 +180,8 @@ def unparse_units(x): [HFSS UNITS] ----> [USER UNITS] ''' - return parse_entry(fix_units(x, unit_assumed=LENGTH_UNIT), LENGTH_UNIT_ASSUMED) + return parse_entry(fix_units(x, unit_assumed=LENGTH_UNIT), + LENGTH_UNIT_ASSUMED) def parse_units_user(x): @@ -286,15 +291,25 @@ def make_str_prop(name, prop_tab=None, prop_server=None): def make_int_prop(name, prop_tab=None, prop_server=None): - return make_prop(name, prop_tab=prop_tab, prop_server=prop_server, prop_args=["MustBeInt:=", True]) + return make_prop(name, + prop_tab=prop_tab, + prop_server=prop_server, + prop_args=["MustBeInt:=", True]) def make_float_prop(name, prop_tab=None, prop_server=None): - return make_prop(name, prop_tab=prop_tab, prop_server=prop_server, prop_args=["MustBeInt:=", False]) + return make_prop(name, + prop_tab=prop_tab, + prop_server=prop_server, + prop_args=["MustBeInt:=", False]) def make_prop(name, prop_tab=None, prop_server=None, prop_args=None): - def set_prop(self, value, prop_tab=prop_tab, prop_server=prop_server, prop_args=prop_args): + def set_prop(self, + value, + prop_tab=prop_tab, + prop_server=prop_server, + prop_args=prop_args): prop_tab = self.prop_tab if prop_tab is None else prop_tab prop_server = self.prop_server if prop_server is None else prop_server if isinstance(prop_tab, types.FunctionType): @@ -303,12 +318,16 @@ def set_prop(self, value, prop_tab=prop_tab, prop_server=prop_server, prop_args= prop_server = prop_server(self) if prop_args is None: prop_args = [] - self.prop_holder.ChangeProperty( - ["NAME:AllTabs", - ["NAME:"+prop_tab, - ["NAME:PropServers", prop_server], - ["NAME:ChangedProps", - ["NAME:"+name, "Value:=", value] + prop_args]]]) + self.prop_holder.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:" + prop_tab, ["NAME:PropServers", prop_server], + [ + "NAME:ChangedProps", + ["NAME:" + name, "Value:=", value] + prop_args + ] + ] + ]) def get_prop(self, prop_tab=prop_tab, prop_server=prop_server): prop_tab = self.prop_tab if prop_tab is None else prop_tab @@ -322,19 +341,28 @@ def get_prop(self, prop_tab=prop_tab, prop_server=prop_server): return property(get_prop, set_prop) -def set_property(prop_holder, prop_tab, prop_server, name, value, prop_args=None): +def set_property(prop_holder, + prop_tab, + prop_server, + name, + value, + prop_args=None): ''' More general non obj oriented, functionatl verison prop_args = [] by default ''' if not isinstance(prop_server, list): prop_server = [prop_server] - return prop_holder.ChangeProperty( - ["NAME:AllTabs", - ["NAME:"+prop_tab, - ["NAME:PropServers", *prop_server], - ["NAME:ChangedProps", - ["NAME:"+name, "Value:=", value] + (prop_args or [])]]]) + return prop_holder.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:" + prop_tab, ["NAME:PropServers", *prop_server], + [ + "NAME:ChangedProps", + ["NAME:" + name, "Value:=", value] + (prop_args or []) + ] + ] + ]) class HfssApp(COMWrapper): @@ -500,21 +528,29 @@ def get_variable_names(self): def get_variables(self): """ Returns the project variables only, which start with $. These are global variables. """ - return {VariableString(s): self.get_variable_value(s) for s in self._project.GetVariables()} + return { + VariableString(s): self.get_variable_value(s) + for s in self._project.GetVariables() + } def get_variable_value(self, name): return self._project.GetVariableValue(name) def create_variable(self, name, value): - self._project.ChangeProperty( - ["NAME:AllTabs", - ["NAME:ProjectVariableTab", - ["NAME:PropServers", "ProjectVariables"], - ["Name:NewProps", - ["NAME:" + name, - "PropType:=", "VariableProp", - "UserDef:=", True, - "Value:=", value]]]]) + self._project.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:ProjectVariableTab", + ["NAME:PropServers", "ProjectVariables"], + [ + "Name:NewProps", + [ + "NAME:" + name, "PropType:=", "VariableProp", + "UserDef:=", True, "Value:=", value + ] + ] + ] + ]) def set_variable(self, name, value): if name not in self._project.GetVariables(): @@ -531,9 +567,10 @@ def get_path(self): Either there is no HFSS project open, or it is not saved.''') def new_design(self, name, type): - name = increment_name(name, [d.GetName() - for d in self._project.GetDesigns()]) - return HfssDesign(self, self._project.InsertDesign("HFSS", name, type, "")) + name = increment_name( + name, [d.GetName() for d in self._project.GetDesigns()]) + return HfssDesign(self, + self._project.InsertDesign("HFSS", name, type, "")) def get_design(self, name): return HfssDesign(self, self._project.GetDesign(name)) @@ -544,7 +581,7 @@ def get_active_design(self): raise EnvironmentError("No Design Active") return HfssDesign(self, d) - def new_dm_design(self, name:str): + def new_dm_design(self, name: str): """Create a new driven model design Args: @@ -552,7 +589,7 @@ def new_dm_design(self, name:str): """ return self.new_design(name, "DrivenModal") - def new_em_design(self, name:str): + def new_em_design(self, name: str): """Create a new eigenmode design Args: @@ -566,7 +603,6 @@ def name(self): class HfssDesign(COMWrapper): - def __init__(self, project, design): super(HfssDesign, self).__init__() self.parent = project @@ -579,7 +615,8 @@ def __init__(self, project, design): self.solution_type = design.GetSolutionType() except Exception as e: logger.debug( - f'Exception occured at design.GetSolutionType() {e}. Assuming Q3D design') + f'Exception occured at design.GetSolutionType() {e}. Assuming Q3D design' + ) self.solution_type = 'Q3D' if design is None: @@ -593,11 +630,11 @@ def __init__(self, project, design): self._modeler = design.SetActiveEditor("3D Modeler") self._optimetrics = design.GetModule("Optimetrics") self._mesh = design.GetModule("MeshSetup") - self.modeler = HfssModeler(self, self._modeler, - self._boundaries, self._mesh) + self.modeler = HfssModeler(self, self._modeler, self._boundaries, + self._mesh) self.optimetrics = Optimetrics(self) - def add_message(self, message:str, severity:int=0): + def add_message(self, message: str, severity: int = 0): """ Add a message to HFSS log with severity and context to message window. @@ -609,19 +646,17 @@ def add_message(self, message:str, severity:int=0): oDesktop = desktop._desktop oDesktop.AddMessage(project.name, self.name, severity, message) - def save_screenshot(self, path:str=None, show:bool=True): + def save_screenshot(self, path: str = None, show: bool = True): if not path: - path = Path().absolute() / 'ansys.png' # TODOL find better - self._modeler.ExportModelImageToFile(str(path), - 0,0, # can be 0 For the default, use 0, 0. For higher resolution, set desired and , for example for 8k export as: 7680, 4320. + path = Path().absolute() / 'ansys.png' # TODOL find better + self._modeler.ExportModelImageToFile( + str(path), + 0, + 0, # can be 0 For the default, use 0, 0. For higher resolution, set desired and , for example for 8k export as: 7680, 4320. [ - "NAME:SaveImageParams", - "ShowAxis:=" , "True", - "ShowGrid:=" , "True", - "ShowRuler:=" , "True", - "ShowRegion:=" , "Default", - "Selections:=" , "", - "Orientation:=" , "" + "NAME:SaveImageParams", "ShowAxis:=", "True", "ShowGrid:=", + "True", "ShowRuler:=", "True", "ShowRegion:=", "Default", + "Selections:=", "", "Orientation:=", "" ]) if show: @@ -630,7 +665,6 @@ def save_screenshot(self, path:str=None, show:bool=True): return path - def rename_design(self, name): old_name = self._design.GetName() self._design.RenameDesignInstance(old_name, name) @@ -660,8 +694,8 @@ def get_setup(self, name=None): if name is None: name = setups[0] elif name not in setups: - raise EnvironmentError( - "Setup {} not found: {}".format(name, setups)) + raise EnvironmentError("Setup {} not found: {}".format( + name, setups)) if self.solution_type == "Eigenmode": return HfssEMSetup(self, name) @@ -669,77 +703,83 @@ def get_setup(self, name=None): return HfssDMSetup(self, name) elif self.solution_type == "Q3D": return AnsysQ3DSetup(self, name) - - def create_q3d_setup(self, freq_ghz=5., name="Setup", save_fields=False, enabled=True, - max_passes=15, min_passes=2, min_converged_passes=2, percent_error=0.5, - percent_refinement=30, auto_increase_solution_order=True, solution_order="High", + + def create_q3d_setup(self, + freq_ghz=5., + name="Setup", + save_fields=False, + enabled=True, + max_passes=15, + min_passes=2, + min_converged_passes=2, + percent_error=0.5, + percent_refinement=30, + auto_increase_solution_order=True, + solution_order="High", solver_type='Iterative'): name = increment_name(name, self.get_setup_names()) - self._setup_module.InsertSetup( - "Matrix", [ - f"NAME:{name}", - "AdaptiveFreq:=", f"{freq_ghz}GHz", - "SaveFields:=", save_fields, - "Enabled:=", enabled, - [ - "NAME:Cap", - "MaxPass:=", max_passes, - "MinPass:=", min_passes, - "MinConvPass:=", min_converged_passes, - "PerError:=", percent_error, - "PerRefine:=", percent_refinement, - "AutoIncreaseSolutionOrder:=", auto_increase_solution_order, - "SolutionOrder:=", solution_order, - "Solver Type:=", solver_type - ] - ]) + self._setup_module.InsertSetup("Matrix", [ + f"NAME:{name}", "AdaptiveFreq:=", f"{freq_ghz}GHz", "SaveFields:=", + save_fields, "Enabled:=", enabled, + [ + "NAME:Cap", "MaxPass:=", max_passes, "MinPass:=", min_passes, + "MinConvPass:=", min_converged_passes, "PerError:=", + percent_error, "PerRefine:=", percent_refinement, + "AutoIncreaseSolutionOrder:=", auto_increase_solution_order, + "SolutionOrder:=", solution_order, "Solver Type:=", solver_type + ] + ]) return AnsysQ3DSetup(self, name) - def create_dm_setup(self, freq_ghz=1, name="Setup", max_delta_s=0.1, max_passes=10, - min_passes=1, min_converged=1, pct_refinement=30, + def create_dm_setup(self, + freq_ghz=1, + name="Setup", + max_delta_s=0.1, + max_passes=10, + min_passes=1, + min_converged=1, + pct_refinement=30, basis_order=-1): name = increment_name(name, self.get_setup_names()) - self._setup_module.InsertSetup( - "HfssDriven", [ - "NAME:"+name, - "Frequency:=", str(freq_ghz)+"GHz", - "MaxDeltaS:=", max_delta_s, - "MaximumPasses:=", max_passes, - "MinimumPasses:=", min_passes, - "MinimumConvergedPasses:=", min_converged, - "PercentRefinement:=", pct_refinement, - "IsEnabled:=", True, - "BasisOrder:=", basis_order - ]) + self._setup_module.InsertSetup("HfssDriven", [ + "NAME:" + name, "Frequency:=", + str(freq_ghz) + "GHz", "MaxDeltaS:=", max_delta_s, + "MaximumPasses:=", max_passes, "MinimumPasses:=", min_passes, + "MinimumConvergedPasses:=", min_converged, "PercentRefinement:=", + pct_refinement, "IsEnabled:=", True, "BasisOrder:=", basis_order + ]) return HfssDMSetup(self, name) - def create_em_setup(self, name="Setup", min_freq_ghz=1, n_modes=1, max_delta_f=0.1, - max_passes=10, min_passes=1, min_converged=1, pct_refinement=30, + def create_em_setup(self, + name="Setup", + min_freq_ghz=1, + n_modes=1, + max_delta_f=0.1, + max_passes=10, + min_passes=1, + min_converged=1, + pct_refinement=30, basis_order=-1): name = increment_name(name, self.get_setup_names()) - self._setup_module.InsertSetup( - "HfssEigen", [ - "NAME:"+name, - "MinimumFrequency:=", str(min_freq_ghz)+"GHz", - "NumModes:=", n_modes, - "MaxDeltaFreq:=", max_delta_f, - "ConvergeOnRealFreq:=", True, - "MaximumPasses:=", max_passes, - "MinimumPasses:=", min_passes, - "MinimumConvergedPasses:=", min_converged, - "PercentRefinement:=", pct_refinement, - "IsEnabled:=", True, - "BasisOrder:=", basis_order - ]) + self._setup_module.InsertSetup("HfssEigen", [ + "NAME:" + name, "MinimumFrequency:=", + str(min_freq_ghz) + "GHz", "NumModes:=", n_modes, "MaxDeltaFreq:=", + max_delta_f, "ConvergeOnRealFreq:=", True, "MaximumPasses:=", + max_passes, "MinimumPasses:=", min_passes, + "MinimumConvergedPasses:=", min_converged, "PercentRefinement:=", + pct_refinement, "IsEnabled:=", True, "BasisOrder:=", basis_order + ]) return HfssEMSetup(self, name) def delete_setup(self, name): if name in self.get_setup_names(): self._setup_module.DeleteSetups(name) - def delete_full_variation(self, DesignVariationKey="All", del_linked_data=False): + def delete_full_variation(self, + DesignVariationKey="All", + del_linked_data=False): """ DeleteFullVariation Use: Use to selectively make deletions or delete all solution data. @@ -771,17 +811,24 @@ def create_variable(self, name, value, postprocessing=False): else: variableprop = "VariableProp" - self._design.ChangeProperty( - ["NAME:AllTabs", - ["NAME:LocalVariableTab", - ["NAME:PropServers", "LocalVariables"], - ["Name:NewProps", - ["NAME:" + name, - "PropType:=", variableprop, - "UserDef:=", True, - "Value:=", value]]]]) - - def _variation_string_to_variable_list(self, variation_string: str, for_prop_server=True): + self._design.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:LocalVariableTab", + ["NAME:PropServers", "LocalVariables"], + [ + "Name:NewProps", + [ + "NAME:" + name, "PropType:=", variableprop, + "UserDef:=", True, "Value:=", value + ] + ] + ] + ]) + + def _variation_string_to_variable_list(self, + variation_string: str, + for_prop_server=True): """Example: Takes "Cj='2fF' Lj='13.5nH'" @@ -798,7 +845,7 @@ def _variation_string_to_variable_list(self, variation_string: str, for_prop_ser local, project = [], [] for arr in s: - to_add = [f'NAME:{arr[0]}', "Value:=", arr[1]] + to_add = [f'NAME:{arr[0]}', "Value:=", arr[1]] if arr[0][0] == '$': project += [to_add] # global variable else: @@ -825,26 +872,22 @@ def set_variables(self, variation_string: str): #print('\nlocal=', local, '\nproject=', project) if len(project) > 0: - self._design.ChangeProperty( - ["NAME:AllTabs", - ["NAME:ProjectVariableTab", - ["NAME:PropServers", - "ProjectVariables" - ], - content + project - ] - ]) + self._design.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:ProjectVariableTab", + ["NAME:PropServers", "ProjectVariables"], content + project + ] + ]) if len(local) > 0: - self._design.ChangeProperty( - ["NAME:AllTabs", - ["NAME:LocalVariableTab", - ["NAME:PropServers", - "LocalVariables" - ], - content + local - ] - ]) + self._design.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:LocalVariableTab", + ["NAME:PropServers", "LocalVariables"], content + local + ] + ]) def set_variable(self, name: str, value: str, postprocessing=False): """Warning: THis is case sensitive, @@ -878,15 +921,17 @@ def get_variable_value(self, name): def get_variable_names(self): """ Returns the local design variables. Does not return the project (global) variables, which start with $. """ - return [VariableString(s) for s in - self._design.GetVariables()+self._design.GetPostProcessingVariables()] + return [ + VariableString(s) for s in self._design.GetVariables() + + self._design.GetPostProcessingVariables() + ] def get_variables(self): """ Returns dictionary of local design variables and their values. Does not return the project (global) variables and their values, whose names start with $. """ local_variables = self._design.GetVariables( - )+self._design.GetPostProcessingVariables() + ) + self._design.GetPostProcessingVariables() return {lv: self.get_variable_value(lv) for lv in local_variables} def copy_design_variables(self, source_design): @@ -912,11 +957,16 @@ def _evaluate_variable_expression(self, expr, units): except SyntaxError: return Q(expr).to(units).magnitude - sub_exprs = {fs: self.get_variable_value(fs.name) - for fs in sexp.free_symbols} + sub_exprs = { + fs: self.get_variable_value(fs.name) + for fs in sexp.free_symbols + } - return float(sexp.subs({fs: self._evaluate_variable_expression(e, units) - for fs, e in sub_exprs.items()})) + return float( + sexp.subs({ + fs: self._evaluate_variable_expression(e, units) + for fs, e in sub_exprs.items() + })) def eval_expr(self, expr, units="mm"): return str(self._evaluate_variable_expression(expr, units)) + units @@ -934,7 +984,7 @@ class HfssSetup(HfssPropertyObject): min_freq = make_float_prop("Min Freq") basis_order = make_str_prop("Basis Order") - def __init__(self, design, setup:str): + def __init__(self, design, setup: str): """ :type design: HfssDesign :type setup: Dispatch @@ -1001,55 +1051,69 @@ def solve(self, name=None): name = self.name return self.parent._design.Solve(name) - def insert_sweep(self, start_ghz, stop_ghz, count=None, step_ghz=None, - name="Sweep", type="Fast", save_fields=False): + def insert_sweep(self, + start_ghz, + stop_ghz, + count=None, + step_ghz=None, + name="Sweep", + type="Fast", + save_fields=False): if not type in ['Fast', 'Interpolating', 'Discrete']: logger.error( - "insert_sweep: Error type was not in ['Fast', 'Interpolating', 'Discrete']") + "insert_sweep: Error type was not in ['Fast', 'Interpolating', 'Discrete']" + ) name = increment_name(name, self.get_sweep_names()) params = [ - "NAME:"+name, - "IsEnabled:=", True, - "Type:=", type, - "SaveFields:=", save_fields, - "SaveRadFields:=", False, + "NAME:" + name, + "IsEnabled:=", + True, + "Type:=", + type, + "SaveFields:=", + save_fields, + "SaveRadFields:=", + False, # "GenerateFieldsForAllFreqs:=" - "ExtrapToDC:=", False, + "ExtrapToDC:=", + False, ] # not sure hwen extacyl this changed between 2016 and 2019 if self._ansys_version >= '2019': if count: params.extend([ - "RangeType:=", 'LinearCount', - "RangeStart:=", f"{start_ghz:f}GHz", - "RangeEnd:=", f"{stop_ghz:f}GHz", - "RangeCount:=", count]) + "RangeType:=", 'LinearCount', "RangeStart:=", + f"{start_ghz:f}GHz", "RangeEnd:=", f"{stop_ghz:f}GHz", + "RangeCount:=", count + ]) if step_ghz: params.extend([ - "RangeType:=", 'LinearStep', - "RangeStart:=", f"{start_ghz:f}GHz", - "RangeEnd:=", f"{stop_ghz:f}GHz", - "RangeStep:=", step_ghz]) + "RangeType:=", 'LinearStep', "RangeStart:=", + f"{start_ghz:f}GHz", "RangeEnd:=", f"{stop_ghz:f}GHz", + "RangeStep:=", step_ghz + ]) if (count and step_ghz) or ((not count) and (not step_ghz)): - logger.error('ERROR: you should provide either step_ghz or count \ + logger.error( + 'ERROR: you should provide either step_ghz or count \ when inserting an HFSS driven model freq sweep. \ YOu either provided both or neither! See insert_sweep.') else: params.extend([ - "StartValue:=", "%fGHz" % start_ghz, - "StopValue:=", "%fGHz" % stop_ghz]) + "StartValue:=", + "%fGHz" % start_ghz, "StopValue:=", + "%fGHz" % stop_ghz + ]) if step_ghz is not None: params.extend([ - "SetupType:=", "LinearSetup", - "StepSize:=", "%fGHz" % step_ghz]) + "SetupType:=", "LinearSetup", "StepSize:=", + "%fGHz" % step_ghz + ]) else: - params.extend([ - "SetupType:=", "LinearCount", - "Count:=", count]) + params.extend(["SetupType:=", "LinearCount", "Count:=", count]) self._setup_module.InsertFrequencySweep(self.name, params) @@ -1058,6 +1122,7 @@ def insert_sweep(self, start_ghz, stop_ghz, count=None, step_ghz=None, def delete_sweep(self, name): self._setup_module.DeleteSweep(self.name, name) + # def add_fields_convergence_expr(self, expr, pct_delta, phase=0): # """note: because of hfss idiocy, you must call "commit_convergence_exprs" # after adding all exprs""" @@ -1093,30 +1158,26 @@ def get_sweep(self, name=None): if name is None: name = sweeps[0] elif name not in sweeps: - raise EnvironmentError( - "Sweep {} not found in {}".format(name, sweeps)) + raise EnvironmentError("Sweep {} not found in {}".format( + name, sweeps)) return HfssFrequencySweep(self, name) def add_fields_convergence_expr(self, expr, pct_delta, phase=0): """note: because of hfss idiocy, you must call "commit_convergence_exprs" after adding all exprs""" assert isinstance(expr, NamedCalcObject) - self.expression_cache_items.append( - ["NAME:CacheItem", - "Title:=", expr.name+"_conv", - "Expression:=", expr.name, - "Intrinsics:=", "Phase='{}deg'".format(phase), - "IsConvergence:=", True, - "UseRelativeConvergence:=", 1, - "MaxConvergenceDelta:=", pct_delta, - "MaxConvergeValue:=", "0.05", - "ReportType:=", "Fields", - ["NAME:ExpressionContext"]]) + self.expression_cache_items.append([ + "NAME:CacheItem", "Title:=", expr.name + "_conv", "Expression:=", + expr.name, "Intrinsics:=", "Phase='{}deg'".format(phase), + "IsConvergence:=", True, "UseRelativeConvergence:=", 1, + "MaxConvergenceDelta:=", pct_delta, "MaxConvergeValue:=", "0.05", + "ReportType:=", "Fields", ["NAME:ExpressionContext"] + ]) def commit_convergence_exprs(self): """note: this will eliminate any convergence expressions not added through this interface""" args = [ - "NAME:"+self.name, + "NAME:" + self.name, ["NAME:ExpressionCache", self.expression_cache_items] ] self._setup_module.EditSetup(self.name, args) @@ -1132,13 +1193,14 @@ def get_convergence(self, variation="", pre_fn_args=[], overwrite=True): temp = tempfile.NamedTemporaryFile() temp.close() temp = temp.name + '.conv' - self.parent._design.ExportConvergence( - self.name, variation, *pre_fn_args, temp, overwrite) + self.parent._design.ExportConvergence(self.name, variation, + *pre_fn_args, temp, overwrite) # Read File temp = Path(temp) if not temp.is_file(): - logger.error(f'''ERROR! Error in trying to read temporary convergence file. + logger.error( + f'''ERROR! Error in trying to read temporary convergence file. `get_convergence` did not seem to have the file written {str(temp)}. Perhaps there was no convergence? Check to see if there is a CONV available for this current variation. If the nominal design is not solved, it will not have a CONV., but will show up as a variation Check for error messages in HFSS. @@ -1149,8 +1211,10 @@ def get_convergence(self, variation="", pre_fn_args=[], overwrite=True): # Parse file text2 = text.split(r'==================') if len(text) >= 3: - df = pd.read_csv(io.StringIO( - text2[3].strip()), sep='|', skipinitialspace=True, index_col=0).drop('Unnamed: 3', 1) + df = pd.read_csv(io.StringIO(text2[3].strip()), + sep='|', + skipinitialspace=True, + index_col=0).drop('Unnamed: 3', 1) else: logger.error(f'ERROR IN reading in {temp}:\n{text}') df = None @@ -1165,17 +1229,24 @@ def get_mesh_stats(self, variation=""): temp.close() # print(temp.name0 # seems broken in 2016 because of extra text added to the top of the file - self.parent._design.ExportMeshStats( - self.name, variation, temp.name + '.mesh', True) + self.parent._design.ExportMeshStats(self.name, variation, + temp.name + '.mesh', True) try: - df = pd.read_csv(temp.name+'.mesh', delimiter='|', skipinitialspace=True, - skiprows=7, skipfooter=1, skip_blank_lines=True, engine='python') + df = pd.read_csv(temp.name + '.mesh', + delimiter='|', + skipinitialspace=True, + skiprows=7, + skipfooter=1, + skip_blank_lines=True, + engine='python') df = df.drop('Unnamed: 9', 1) except Exception as e: print("ERROR in MESH reading operation.") print(e) - print('ERROR! Error in trying to read temporary MESH file ' + temp.name + - '\n. Check to see if there is a mesh available for this current variation.\ + print( + 'ERROR! Error in trying to read temporary MESH file ' + + temp.name + + '\n. Check to see if there is a mesh available for this current variation.\ If the nominal design is not solved, it will not have a mesh., \ but will show up as a variation.') df = None @@ -1184,8 +1255,13 @@ def get_mesh_stats(self, variation=""): def get_profile(self, variation=""): fn = tempfile.mktemp() self.parent._design.ExportProfile(self.name, variation, fn, False) - df = pd.read_csv(fn, delimiter='\t', skipinitialspace=True, skiprows=6, - skipfooter=1, skip_blank_lines=True, engine='python') + df = pd.read_csv(fn, + delimiter='\t', + skipinitialspace=True, + skiprows=6, + skipfooter=1, + skip_blank_lines=True, + engine='python') # just borken down by new lines return df @@ -1205,16 +1281,23 @@ def setup_link(self, linked_setup): ''' type: linked_setup ''' - args = ["NAME:" + self.name, - ["NAME:MeshLink", - "Project:=", "This Project*", - "Design:=", linked_setup.parent.name, - "Soln:=", linked_setup.solution_name, - self._map_variables_by_name(), - "ForceSourceToSolve:=", True, - "PathRelativeTo:=", "TargetProject", - ], - ] + args = [ + "NAME:" + self.name, + [ + "NAME:MeshLink", + "Project:=", + "This Project*", + "Design:=", + linked_setup.parent.name, + "Soln:=", + linked_setup.solution_name, + self._map_variables_by_name(), + "ForceSourceToSolve:=", + True, + "PathRelativeTo:=", + "TargetProject", + ], + ] self._setup_module.EditSetup(self.name, args) def _map_variables_by_name(self): @@ -1224,11 +1307,13 @@ def _map_variables_by_name(self): design_variables = self.parent.get_variable_names() # build array - args = ["NAME:Params", ] + args = [ + "NAME:Params", + ] for name in project_variables: - args.extend([str(name)+":=", str(name)]) + args.extend([str(name) + ":=", str(name)]) for name in design_variables: - args.extend([str(name)+":=", str(name)]) + args.extend([str(name) + ":=", str(name)]) return args def get_solutions(self): @@ -1273,11 +1358,15 @@ def get_convergence(self, variation=""): ''' return super().get_convergence(variation, pre_fn_args=['CG']) - def get_matrix(self, variation='', pass_number=0, frequency=None, - MatrixType='Maxwell', - solution_kind='LastAdaptive', # AdpativePass - ACPlusDCResistance=False, - soln_type="C"): + def get_matrix( + self, + variation='', + pass_number=0, + frequency=None, + MatrixType='Maxwell', + solution_kind='LastAdaptive', # AdpativePass + ACPlusDCResistance=False, + soln_type="C"): ''' Arguments: ----------- @@ -1299,14 +1388,14 @@ def get_matrix(self, variation='', pass_number=0, frequency=None, temp = tempfile.NamedTemporaryFile() temp.close() - path = temp.name+'.txt' + path = temp.name + '.txt' # , , , , , , # , , , , , , # self.parent._design.ExportMatrixData(path, soln_type, variation, f'{self.name}:{solution_kind}', - "Original", "ohm", "nH", "fF", "mSie", - frequency, MatrixType, + "Original", "ohm", "nH", "fF", + "mSie", frequency, MatrixType, pass_number, ACPlusDCResistance) df_cmat, user_units, (df_cond, units_cond), design_variation = \ @@ -1314,7 +1403,7 @@ def get_matrix(self, variation='', pass_number=0, frequency=None, return df_cmat, user_units, (df_cond, units_cond), design_variation @staticmethod - def _readin_Q3D_matrix(path:str): + def _readin_Q3D_matrix(path: str): """ Read in the txt file created from q3d export and output the capacitance matrix @@ -1357,30 +1446,34 @@ def _readin_Q3D_matrix(path:str): text = Path(path).read_text() - s1 = text.split('Capacitance Matrix') assert len(s1) == 2, "Copuld not split text to `Capacitance Matrix`" s2 = s1[1].split('Conductance Matrix') - df_cmat = pd.read_csv(io.StringIO( - s2[0].strip()), delim_whitespace=True, skipinitialspace=True, index_col=0) + df_cmat = pd.read_csv(io.StringIO(s2[0].strip()), + delim_whitespace=True, + skipinitialspace=True, + index_col=0) units = re.findall(r'C Units:(.*?),', text)[0] if len(s2) > 1: - df_cond = pd.read_csv(io.StringIO( - s2[1].strip()), delim_whitespace=True, skipinitialspace=True, index_col=0) + df_cond = pd.read_csv(io.StringIO(s2[1].strip()), + delim_whitespace=True, + skipinitialspace=True, + index_col=0) units_cond = re.findall(r'G Units:(.*?)\n', text)[0] else: df_cond = None - var = re.findall(r'DesignVariation:(.*?)\n', text) # this changed circe v2020 - if len(var) <1: # didnt find + var = re.findall(r'DesignVariation:(.*?)\n', + text) # this changed circe v2020 + if len(var) < 1: # didnt find var = re.findall(r'Design Variation:(.*?)\n', text) - if len(var) <1: # didnt find - # May not be present if there are no design variations to begin - # with and no variables in the design. - pass #logger.error(f'Failed to parse Q3D matrix Design Variation:\nFile:{path}\nText:{text}') + if len(var) < 1: # didnt find + # May not be present if there are no design variations to begin + # with and no variables in the design. + pass #logger.error(f'Failed to parse Q3D matrix Design Variation:\nFile:{path}\nText:{text}') var = [''] design_variation = var[0] @@ -1455,7 +1548,6 @@ def list_variations(self, setup_name: str = None): class HfssEMDesignSolutions(HfssDesignSolutions): - def eigenmodes(self, lv=""): ''' Returns the eigenmode data of freq and kappa/2p @@ -1471,11 +1563,11 @@ def eigenmodes(self, lv=""): if np.size(np.shape(data)) == 1: # in Python a 1D array does not have shape (N,1) data = np.array([data]) - else: # but rather (N,) .... + else: # but rather (N,) .... pass if np.size(data[0, :]) == 6: # checking if values for Q were saved # eigvalue=(omega-i*kappa/2)/2pi - kappa_over_2pis = [2*float(ii) for ii in data[:, 3]] + kappa_over_2pis = [2 * float(ii) for ii in data[:, 3]] # so kappa/2pi = 2*Im(eigvalue) else: kappa_over_2pis = None @@ -1545,33 +1637,27 @@ def set_mode(self, n, phase=0, FieldType='EigenStoredEnergy'): if self._ansys_version >= '2019': # THIS WORKS FOR v2019R2 self._solutions.EditSources( - [ - [ - "FieldType:=", "EigenPeakElectricField" - ], - [ - "Name:=", "Modes", - "Magnitudes:=", ["1" if i + 1 == - n else "0" for i in range(n_modes)], - "Phases:=", [str(phase) if i + 1 == - n else "0" for i in range(n_modes)] - ] - ]) + [["FieldType:=", "EigenPeakElectricField"], + [ + "Name:=", "Modes", "Magnitudes:=", + ["1" if i + 1 == n else "0" for i in range(n_modes)], + "Phases:=", + [ + str(phase) if i + 1 == n else "0" + for i in range(n_modes) + ] + ]]) else: # The syntax has changed for AEDT 18.2. # see https://ansyshelp.ansys.com/account/secured?returnurl=/Views/Secured/Electronics/v195//Subsystems/HFSS/Subsystems/HFSS%20Scripting/HFSS%20Scripting.htm self._solutions.EditSources( - "EigenStoredEnergy", - ["NAME:SourceNames", "EigenMode"], - ["NAME:Modes", n_modes], - ["NAME:Magnitudes"] + [1 if i + 1 == - n else 0 for i in range(n_modes)], - ["NAME:Phases"] + [phase if i + 1 == - n else 0 for i in range(n_modes)], - ["NAME:Terminated"], - ["NAME:Impedances"] - ) + "EigenStoredEnergy", ["NAME:SourceNames", "EigenMode"], + ["NAME:Modes", n_modes], ["NAME:Magnitudes"] + + [1 if i + 1 == n else 0 + for i in range(n_modes)], ["NAME:Phases"] + + [phase if i + 1 == n else 0 for i in range(n_modes)], + ["NAME:Terminated"], ["NAME:Impedances"]) def has_fields(self, variation_string=None): ''' @@ -1586,9 +1672,16 @@ def has_fields(self, variation_string=None): if variation_string is None: variation_string = self.parent.parent.get_nominal_variation() - return bool(self._solutions.HasFields(self.parent.solution_name, variation_string)) + return bool( + self._solutions.HasFields(self.parent.solution_name, + variation_string)) - def create_report(self, plot_name, xcomp, ycomp, params, pass_name='LastAdaptive'): + def create_report(self, + plot_name, + xcomp, + ycomp, + params, + pass_name='LastAdaptive'): ''' pass_name: AdaptivePass, LastAdaptive @@ -1605,10 +1698,10 @@ def create_report(self, plot_name, xcomp, ycomp, params, pass_name='LastAdaptive setup = self.parent reporter = setup._reporter - return reporter.CreateReport(plot_name, "Eigenmode Parameters", "Rectangular Plot", - f"{setup.name} : {pass_name}", [], params, - ["X Component:=", xcomp, - "Y Component:=", ycomp], []) + return reporter.CreateReport( + plot_name, "Eigenmode Parameters", "Rectangular Plot", + f"{setup.name} : {pass_name}", [], params, + ["X Component:=", xcomp, "Y Component:=", ycomp], []) class HfssDMDesignSolutions(HfssDesignSolutions): @@ -1660,10 +1753,8 @@ def get_network_data(self, formats): if list: fn = tempfile.mktemp() self.parent._solutions.ExportNetworkData( - [], self.parent.name + " : " + self.name, - 2, fn, ["all"], False, 0, - data_type, -1, 1, 15 - ) + [], self.parent.name + " : " + self.name, 2, fn, ["all"], + False, 0, data_type, -1, 1, 15) with open(fn) as f: f.readline() colnames = f.readline().split() @@ -1672,11 +1763,11 @@ def get_network_data(self, formats): if freq is None: freq = array[:, 0] for i, j in list: - real_idx = colnames.index( - "%s[%d,%d]_Real" % (data_type, i, j)) - imag_idx = colnames.index( - "%s[%d,%d]_Imag" % (data_type, i, j)) - c_arr = array[:, real_idx] + 1j*array[:, imag_idx] + real_idx = colnames.index("%s[%d,%d]_Real" % + (data_type, i, j)) + imag_idx = colnames.index("%s[%d,%d]_Imag" % + (data_type, i, j)) + c_arr = array[:, real_idx] + 1j * array[:, imag_idx] ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr return freq, ret @@ -1689,8 +1780,8 @@ def create_report(self, name, expr): for v_name in var_names], []) self.parent._reporter.CreateReport( name, "Modal Solution Data", "Rectangular Plot", - self.solution_name, ["Domain:=", "Sweep"], [ - "Freq:=", ["All"]] + var_args, + self.solution_name, ["Domain:=", "Sweep"], + ["Freq:=", ["All"]] + var_args, ["X Component:=", "Freq", "Y Component:=", [expr]], []) return HfssReport(self.parent.parent, name) @@ -1733,7 +1824,6 @@ class Optimetrics(COMWrapper): Note that running optimetrics requires the license for Optimetrics by Ansys. """ - def __init__(self, design): super(Optimetrics, self).__init__() @@ -1762,11 +1852,16 @@ def solve_setup(self, setup_name: str): """ return self._optimetrics.SolveSetup(setup_name) - def create_setup(self, variable, swp_params, name="ParametricSetup1", swp_type='linear_step', + def create_setup(self, + variable, + swp_params, + name="ParametricSetup1", + swp_type='linear_step', setup_name=None, - save_fields=True, copy_mesh=True, solve_with_copied_mesh_only=True, - setup_type='parametric' - ): + save_fields=True, + copy_mesh=True, + solve_with_copied_mesh_only=True, + setup_type='parametric'): """ Inserts a new parametric setup. @@ -1780,7 +1875,8 @@ def create_setup(self, variable, swp_params, name="ParametricSetup1", swp_type=' """ setup_name = setup_name or self.design.get_setup_names()[0] print( - f"Inserting optimetrics setup `{name}` for simulation setup: `{setup_name}`") + f"Inserting optimetrics setup `{name}` for simulation setup: `{setup_name}`" + ) if setup_type != 'parametric': raise NotImplementedError() @@ -1792,37 +1888,25 @@ def create_setup(self, variable, swp_params, name="ParametricSetup1", swp_type=' else: raise NotImplementedError() - self._optimetrics.InsertSetup("OptiParametric", - [ - f"NAME:{name}", - "IsEnabled:=" , True, - [ - "NAME:ProdOptiSetupDataV2", - "SaveFields:=" , save_fields, - "CopyMesh:=" , copy_mesh, - "SolveWithCopiedMeshOnly:=", solve_with_copied_mesh_only, - ], - [ - "NAME:StartingPoint" - ], - "Sim. Setups:=" , [setup_name], - [ - "NAME:Sweeps", - [ - "NAME:SweepDefinition", - "Variable:=" , variable, - "Data:=" , swp_str, - "OffsetF1:=" , False, - "Synchronize:=" , 0 - ] - ], - [ - "NAME:Sweep Operations" - ], - [ - "NAME:Goals" - ] - ]) + self._optimetrics.InsertSetup("OptiParametric", [ + f"NAME:{name}", "IsEnabled:=", True, + [ + "NAME:ProdOptiSetupDataV2", + "SaveFields:=", + save_fields, + "CopyMesh:=", + copy_mesh, + "SolveWithCopiedMeshOnly:=", + solve_with_copied_mesh_only, + ], ["NAME:StartingPoint"], "Sim. Setups:=", [setup_name], + [ + "NAME:Sweeps", + [ + "NAME:SweepDefinition", "Variable:=", variable, "Data:=", + swp_str, "OffsetF1:=", False, "Synchronize:=", 0 + ] + ], ["NAME:Sweep Operations"], ["NAME:Goals"] + ]) class HfssModeler(COMWrapper): @@ -1856,15 +1940,16 @@ def get_all_properties(self, obj_name, PropTab='Geometry3DAttributeTab'): PropTab, PropServer, key) return properties - def _attributes_array(self, - name=None, - nonmodel=False, - wireframe=False, - color=None, - transparency=0.9, - material=None, # str - solve_inside=None, # bool - coordinate_system="Global"): + def _attributes_array( + self, + name=None, + nonmodel=False, + wireframe=False, + color=None, + transparency=0.9, + material=None, # str + solve_inside=None, # bool + coordinate_system="Global"): arr = ["NAME:Attributes", "PartCoordinateSystem:=", coordinate_system] if name is not None: arr.extend(["Name:=", name]) @@ -1890,7 +1975,11 @@ def _attributes_array(self, def _selections_array(self, *names): return ["NAME:Selections", "Selections:=", ",".join(names)] - def mesh_length(self, name_mesh, objects: list, MaxLength='0.1mm', **kwargs): + def mesh_length(self, + name_mesh, + objects: list, + MaxLength='0.1mm', + **kwargs): ''' "RefineInside:=" , False, "Enabled:=" , True, @@ -1904,14 +1993,16 @@ def mesh_length(self, name_mesh, objects: list, MaxLength='0.1mm', **kwargs): ''' assert isinstance(objects, list) - arr = [f"NAME:{name_mesh}", - "Objects:=", objects, - 'MaxLength:=', MaxLength] - ops = ['RefineInside', 'Enabled', 'RestrictElem', - 'NumMaxElem', 'RestrictLength'] + arr = [ + f"NAME:{name_mesh}", "Objects:=", objects, 'MaxLength:=', MaxLength + ] + ops = [ + 'RefineInside', 'Enabled', 'RestrictElem', 'NumMaxElem', + 'RestrictLength' + ] for key, val in kwargs.items(): if key in ops: - arr += [key+':=', str(val)] + arr += [key + ':=', str(val)] else: logger.error('KEY `{key}` NOT IN ops!') @@ -1929,25 +2020,24 @@ def mesh_get_all_props(self, mesh_name): # TODO: make mesh tis own class with preperties prop_tab = 'MeshSetupTab' prop_server = f'MeshSetup:{mesh_name}' - prop_names = self.parent._design.GetProperties( - 'MeshSetupTab', prop_server) + prop_names = self.parent._design.GetProperties('MeshSetupTab', + prop_server) dic = {} for name in prop_names: - dic[name] = self._modeler.GetPropertyValue( - prop_tab, prop_server, name) + dic[name] = self._modeler.GetPropertyValue(prop_tab, prop_server, + name) return dic def draw_box_corner(self, pos, size, **kwargs): - name = self._modeler.CreateBox( - ["NAME:BoxParameters", - "XPosition:=", str(pos[0]), - "YPosition:=", str(pos[1]), - "ZPosition:=", str(pos[2]), - "XSize:=", str(size[0]), - "YSize:=", str(size[1]), - "ZSize:=", str(size[2])], - self._attributes_array(**kwargs) - ) + name = self._modeler.CreateBox([ + "NAME:BoxParameters", "XPosition:=", + str(pos[0]), "YPosition:=", + str(pos[1]), "ZPosition:=", + str(pos[2]), "XSize:=", + str(size[0]), "YSize:=", + str(size[1]), "ZSize:=", + str(size[2]) + ], self._attributes_array(**kwargs)) return Box(name, self, pos, size) def draw_box_center(self, pos, size, **kwargs): @@ -1959,7 +2049,7 @@ def draw_box_center(self, pos, size, **kwargs): pos (list): Coordinates of center of box, [x0, y0, z0] size (list): Width of box along each direction, [xwidth, ywidth, zwidth] """ - corner_pos = [var(p) - var(s)/2 for p, s in zip(pos, size)] + corner_pos = [var(p) - var(s) / 2 for p, s in zip(pos, size)] return self.draw_box_corner(corner_pos, size, **kwargs) def draw_polyline(self, points, closed=True, **kwargs): @@ -1982,31 +2072,35 @@ def draw_polyline(self, points, closed=True, **kwargs): pointsStr = ["NAME:PolylinePoints"] indexsStr = ["NAME:PolylineSegments"] for ii, point in enumerate(points): - pointsStr.append(["NAME:PLPoint", - "X:=", str(point[0]), - "Y:=", str(point[1]), - "Z:=", str(point[2])]) - indexsStr.append(["NAME:PLSegment", "SegmentType:=", - "Line", "StartIndex:=", ii, "NoOfPoints:=", 2]) + pointsStr.append([ + "NAME:PLPoint", "X:=", + str(point[0]), "Y:=", + str(point[1]), "Z:=", + str(point[2]) + ]) + indexsStr.append([ + "NAME:PLSegment", "SegmentType:=", "Line", "StartIndex:=", ii, + "NoOfPoints:=", 2 + ]) if closed: - pointsStr.append(["NAME:PLPoint", - "X:=", str(points[0][0]), - "Y:=", str(points[0][1]), - "Z:=", str(points[0][2])]) - params_closed = ["IsPolylineCovered:=", - True, "IsPolylineClosed:=", True] + pointsStr.append([ + "NAME:PLPoint", "X:=", + str(points[0][0]), "Y:=", + str(points[0][1]), "Z:=", + str(points[0][2]) + ]) + params_closed = [ + "IsPolylineCovered:=", True, "IsPolylineClosed:=", True + ] else: indexsStr = indexsStr[:-1] - params_closed = ["IsPolylineCovered:=", - True, "IsPolylineClosed:=", False] + params_closed = [ + "IsPolylineCovered:=", True, "IsPolylineClosed:=", False + ] name = self._modeler.CreatePolyline( - ["NAME:PolylineParameters", - *params_closed, - pointsStr, - indexsStr], - self._attributes_array(**kwargs) - ) + ["NAME:PolylineParameters", *params_closed, pointsStr, indexsStr], + self._attributes_array(**kwargs)) if closed: return Polyline(name, self, points) @@ -2017,22 +2111,16 @@ def draw_rect_corner(self, pos, x_size=0, y_size=0, z_size=0, **kwargs): size = [x_size, y_size, z_size] assert 0 in size axis = "XYZ"[size.index(0)] - w_idx, h_idx = { - 'X': (1, 2), - 'Y': (2, 0), - 'Z': (0, 1) - }[axis] - - name = self._modeler.CreateRectangle( - ["NAME:RectangleParameters", - "XStart:=", str(pos[0]), - "YStart:=", str(pos[1]), - "ZStart:=", str(pos[2]), - "Width:=", str(size[w_idx]), - "Height:=", str(size[h_idx]), - "WhichAxis:=", axis], - self._attributes_array(**kwargs) - ) + w_idx, h_idx = {'X': (1, 2), 'Y': (2, 0), 'Z': (0, 1)}[axis] + + name = self._modeler.CreateRectangle([ + "NAME:RectangleParameters", "XStart:=", + str(pos[0]), "YStart:=", + str(pos[1]), "ZStart:=", + str(pos[2]), "Width:=", + str(size[w_idx]), "Height:=", + str(size[h_idx]), "WhichAxis:=", axis + ], self._attributes_array(**kwargs)) return Rect(name, self, pos, size) def draw_rect_center(self, pos, x_size=0, y_size=0, z_size=0, **kwargs): @@ -2048,31 +2136,34 @@ def draw_rect_center(self, pos, x_size=0, y_size=0, z_size=0, **kwargs): y_size (int, optional): Width along the y direction. Defaults to 0. z_size (int, optional): Width along the z direction]. Defaults to 0. """ - corner_pos = [var(p) - var(s)/2. for p, - s in zip(pos, [x_size, y_size, z_size])] - return self.draw_rect_corner(corner_pos, x_size, y_size, z_size, **kwargs) + corner_pos = [ + var(p) - var(s) / 2. for p, s in zip(pos, [x_size, y_size, z_size]) + ] + return self.draw_rect_corner(corner_pos, x_size, y_size, z_size, + **kwargs) def draw_cylinder(self, pos, radius, height, axis, **kwargs): assert axis in "XYZ" - return self._modeler.CreateCylinder( - ["NAME:CylinderParameters", - "XCenter:=", pos[0], - "YCenter:=", pos[1], - "ZCenter:=", pos[2], - "Radius:=", radius, - "Height:=", height, - "WhichAxis:=", axis, - "NumSides:=", 0], - self._attributes_array(**kwargs)) + return self._modeler.CreateCylinder([ + "NAME:CylinderParameters", "XCenter:=", pos[0], "YCenter:=", + pos[1], "ZCenter:=", pos[2], "Radius:=", radius, "Height:=", + height, "WhichAxis:=", axis, "NumSides:=", 0 + ], self._attributes_array(**kwargs)) def draw_cylinder_center(self, pos, radius, height, axis, **kwargs): axis_idx = ["X", "Y", "Z"].index(axis) edge_pos = copy(pos) - edge_pos[axis_idx] = var(pos[axis_idx]) - var(height)/2 + edge_pos[axis_idx] = var(pos[axis_idx]) - var(height) / 2 return self.draw_cylinder(edge_pos, radius, height, axis, **kwargs) - def draw_wirebond(self, pos, ori, width, height='0.1mm', z=0, - wire_diameter="0.02mm", NumSides=6, + def draw_wirebond(self, + pos, + ori, + width, + height='0.1mm', + z=0, + wire_diameter="0.02mm", + NumSides=6, **kwargs): ''' Args: @@ -2086,86 +2177,61 @@ def draw_wirebond(self, pos, ori, width, height='0.1mm', z=0, ''' p = np.array(pos) o = np.array(ori) - pad1 = p-o*width/2. - name = self._modeler.CreateBondwire(["NAME:BondwireParameters", - "WireType:=", "Low", - "WireDiameter:=", wire_diameter, - "NumSides:=", NumSides, - "XPadPos:=", pad1[0], - "YPadPos:=", pad1[1], - "ZPadPos:=", z, - "XDir:=", ori[0], - "YDir:=", ori[1], - "ZDir:=", 0, - "Distance:=", width, - "h1:=", height, - "h2:=", "0mm", - "alpha:=", "80deg", - "beta:=", "80deg", - "WhichAxis:=", "Z"], - self._attributes_array(**kwargs)) + pad1 = p - o * width / 2. + name = self._modeler.CreateBondwire([ + "NAME:BondwireParameters", "WireType:=", "Low", "WireDiameter:=", + wire_diameter, "NumSides:=", NumSides, "XPadPos:=", pad1[0], + "YPadPos:=", pad1[1], "ZPadPos:=", z, "XDir:=", ori[0], "YDir:=", + ori[1], "ZDir:=", 0, "Distance:=", width, "h1:=", height, "h2:=", + "0mm", "alpha:=", "80deg", "beta:=", "80deg", "WhichAxis:=", "Z" + ], self._attributes_array(**kwargs)) return name - def draw_region(self, Padding, PaddingType="Percentage Offset", name='Region', + def draw_region(self, + Padding, + PaddingType="Percentage Offset", + name='Region', material="\"vacuum\""): """ PaddingType : 'Absolute Offset', "Percentage Offset" """ # TODO: Add option to modify these RegionAttributes = [ - "NAME:Attributes", - "Name:=" , name, - "Flags:=" , "Wireframe#", - "Color:=" , "(255 0 0)", - "Transparency:=" , 1, - "PartCoordinateSystem:=", "Global", - "UDMId:=" , "", - "IsAlwaysHiden:=" , False, - "MaterialValue:=" , material, - "SolveInside:=" , True + "NAME:Attributes", "Name:=", name, "Flags:=", "Wireframe#", + "Color:=", "(255 0 0)", "Transparency:=", 1, + "PartCoordinateSystem:=", "Global", "UDMId:=", "", + "IsAlwaysHiden:=", False, "MaterialValue:=", material, + "SolveInside:=", True ] - self._modeler.CreateRegion( - [ - "NAME:RegionParameters", - "+XPaddingType:=" , PaddingType, - "+XPadding:=" , Padding[0][0], - "-XPaddingType:=" , PaddingType, - "-XPadding:=" , Padding[0][1], - "+YPaddingType:=" , PaddingType, - "+YPadding:=" , Padding[1][0], - "-YPaddingType:=" , PaddingType, - "-YPadding:=" , Padding[1][1], - "+ZPaddingType:=" , PaddingType, - "+ZPadding:=" , Padding[2][0], - "-ZPaddingType:=" , PaddingType, - "-ZPadding:=" , Padding[2][1] - ], - RegionAttributes) + self._modeler.CreateRegion([ + "NAME:RegionParameters", "+XPaddingType:=", PaddingType, + "+XPadding:=", Padding[0][0], "-XPaddingType:=", PaddingType, + "-XPadding:=", Padding[0][1], "+YPaddingType:=", PaddingType, + "+YPadding:=", Padding[1][0], "-YPaddingType:=", PaddingType, + "-YPadding:=", Padding[1][1], "+ZPaddingType:=", PaddingType, + "+ZPadding:=", Padding[2][0], "-ZPaddingType:=", PaddingType, + "-ZPadding:=", Padding[2][1] + ], RegionAttributes) def unite(self, names, keep_originals=False): self._modeler.Unite( self._selections_array(*names), - ["NAME:UniteParameters", "KeepOriginals:=", keep_originals] - ) + ["NAME:UniteParameters", "KeepOriginals:=", keep_originals]) return names[0] def intersect(self, names, keep_originals=False): self._modeler.Intersect( self._selections_array(*names), - ["NAME:IntersectParameters", "KeepOriginals:=", keep_originals] - ) + ["NAME:IntersectParameters", "KeepOriginals:=", keep_originals]) return names[0] def translate(self, name, vector): - self._modeler.Move( - self._selections_array(name), - ["NAME:TranslateParameters", - "TranslateVectorX:=", vector[0], - "TranslateVectorY:=", vector[1], - "TranslateVectorZ:=", vector[2]] - ) + self._modeler.Move(self._selections_array(name), [ + "NAME:TranslateParameters", "TranslateVectorX:=", vector[0], + "TranslateVectorY:=", vector[1], "TranslateVectorZ:=", vector[2] + ]) def get_boundary_assignment(self, boundary_name: str): # Gets a list of face IDs associated with the given boundary or excitation assignment. @@ -2186,14 +2252,17 @@ def append_PerfE_assignment(self, boundary_name: str, object_names: list): object_names = list(object_names) # enforce list # do actual work - if boundary_name not in self._boundaries.GetBoundaries(): # GetBoundariesOfType("Perfect E") + if boundary_name not in self._boundaries.GetBoundaries( + ): # GetBoundariesOfType("Perfect E") # need to make a new boundary self.assign_perfect_E(object_names, name=boundary_name) else: # need to append objects = list(self.get_boundary_assignment(boundary_name)) - self._boundaries.ReassignBoundary(["NAME:" + boundary_name, - "Objects:=", list(set(objects + object_names))]) + self._boundaries.ReassignBoundary([ + "NAME:" + boundary_name, "Objects:=", + list(set(objects + object_names)) + ]) def append_mesh(self, mesh_name: str, object_names: list, old_objs: list, **kwargs): @@ -2207,7 +2276,8 @@ def append_mesh(self, mesh_name: str, object_names: list, old_objs: list, object_names = [object_names] object_names = list(object_names) # enforce list - if mesh_name not in self.mesh_get_names(): # need to make a new boundary + if mesh_name not in self.mesh_get_names( + ): # need to make a new boundary objs = object_names self.mesh_length(mesh_name, object_names, **kwargs) else: # need to append @@ -2216,7 +2286,7 @@ def append_mesh(self, mesh_name: str, object_names: list, old_objs: list, return objs - def assign_perfect_E(self, obj:List[str], name:str='PerfE'): + def assign_perfect_E(self, obj: List[str], name: str = 'PerfE'): ''' Assign a boundary condition to a list of objects. @@ -2227,43 +2297,54 @@ def assign_perfect_E(self, obj:List[str], name:str='PerfE'): if not isinstance(obj, list): obj = [obj] if name == 'PerfE': - name = str(obj)+'_'+name + name = str(obj) + '_' + name name = increment_name(name, self._boundaries.GetBoundaries()) self._boundaries.AssignPerfectE( - ["NAME:"+name, "Objects:=", obj, "InfGroundPlane:=", False]) + ["NAME:" + name, "Objects:=", obj, "InfGroundPlane:=", False]) def _make_lumped_rlc(self, r, l, c, start, end, obj_arr, name="LumpRLC"): name = increment_name(name, self._boundaries.GetBoundaries()) - params = ["NAME:"+name] + params = ["NAME:" + name] params += obj_arr - params.append(["NAME:CurrentLine", - # for some reason here it seems to swtich to use the model units, rather than meters - "Start:=", fix_units(start, unit_assumed=LENGTH_UNIT), - "End:=", fix_units(end, unit_assumed=LENGTH_UNIT)]) - params += ["UseResist:=", r != 0, "Resistance:=", r, - "UseInduct:=", l != 0, "Inductance:=", l, - "UseCap:=", c != 0, "Capacitance:=", c] + params.append([ + "NAME:CurrentLine", + # for some reason here it seems to swtich to use the model units, rather than meters + "Start:=", + fix_units(start, unit_assumed=LENGTH_UNIT), + "End:=", + fix_units(end, unit_assumed=LENGTH_UNIT) + ]) + params += [ + "UseResist:=", r != 0, "Resistance:=", r, "UseInduct:=", l != 0, + "Inductance:=", l, "UseCap:=", c != 0, "Capacitance:=", c + ] self._boundaries.AssignLumpedRLC(params) - def _make_lumped_port(self, start, end, obj_arr, z0="50ohm", name="LumpPort"): + def _make_lumped_port(self, + start, + end, + obj_arr, + z0="50ohm", + name="LumpPort"): start = fix_units(start, unit_assumed=LENGTH_UNIT) end = fix_units(end, unit_assumed=LENGTH_UNIT) name = increment_name(name, self._boundaries.GetBoundaries()) - params = ["NAME:"+name] + params = ["NAME:" + name] params += obj_arr - params += ["RenormalizeAllTerminals:=", True, "DoDeembed:=", False, - ["NAME:Modes", ["NAME:Mode1", - "ModeNum:=", 1, - "UseIntLine:=", True, - ["NAME:IntLine", - "Start:=", start, - "End:=", end], - "CharImp:=", "Zpi", - "AlignmentGroup:=", 0, - "RenormImp:=", "50ohm"]], - "ShowReporterFilter:=", False, "ReporterFilter:=", [True], - "FullResistance:=", z0, "FullReactance:=", "0ohm"] + params += [ + "RenormalizeAllTerminals:=", True, "DoDeembed:=", False, + [ + "NAME:Modes", + [ + "NAME:Mode1", "ModeNum:=", 1, "UseIntLine:=", True, + ["NAME:IntLine", "Start:=", start, "End:=", + end], "CharImp:=", "Zpi", "AlignmentGroup:=", 0, + "RenormImp:=", "50ohm" + ] + ], "ShowReporterFilter:=", False, "ReporterFilter:=", [True], + "FullResistance:=", z0, "FullReactance:=", "0ohm" + ] self._boundaries.AssignLumpedPort(params) @@ -2301,14 +2382,16 @@ def set_working_coordinate_system(self, cs_name="Global"): Use: Sets the working coordinate system. Command: Modeler>Coordinate System>Set Working CS """ - self._modeler.SetWCS( - [ - "NAME:SetWCS Parameter", - "Working Coordinate System:=", cs_name, - "RegionDepCSOk:=" , False # this one is prob not needed, but comes with the record tool - ]) + self._modeler.SetWCS([ + "NAME:SetWCS Parameter", + "Working Coordinate System:=", + cs_name, + "RegionDepCSOk:=", + False # this one is prob not needed, but comes with the record tool + ]) - def create_relative_coorinate_system_both(self, cs_name, + def create_relative_coorinate_system_both(self, + cs_name, origin=["0um", "0um", "0um"], XAxisVec=["1um", "0um", "0um"], YAxisVec=["0um", "1um", "0um"]): @@ -2326,33 +2409,22 @@ def create_relative_coorinate_system_both(self, cs_name, origin, XAxisVec, YAxisVec: 3-vectors You can also pass in params such as origin = [0,1,0] rather than ["0um","1um","0um"], but these will be interpreted in default units, so it is safer to be explicit. Explicit over implicit. """ - self._modeler.CreateRelativeCS( - [ - "NAME:RelativeCSParameters", - "Mode:=" , "Axis/Position", - "OriginX:=" , origin[0], - "OriginY:=" , origin[1], - "OriginZ:=" , origin[2], - "XAxisXvec:=" , XAxisVec[0], - "XAxisYvec:=" , XAxisVec[1], - "XAxisZvec:=" , XAxisVec[2], - "YAxisXvec:=" , YAxisVec[0], - "YAxisYvec:=" , YAxisVec[1], - "YAxisZvec:=" , YAxisVec[1] - ], - [ - "NAME:Attributes", - "Name:=" , cs_name - ]) + self._modeler.CreateRelativeCS([ + "NAME:RelativeCSParameters", "Mode:=", "Axis/Position", + "OriginX:=", origin[0], "OriginY:=", origin[1], "OriginZ:=", + origin[2], "XAxisXvec:=", XAxisVec[0], "XAxisYvec:=", XAxisVec[1], + "XAxisZvec:=", XAxisVec[2], "YAxisXvec:=", YAxisVec[0], + "YAxisYvec:=", YAxisVec[1], "YAxisZvec:=", YAxisVec[1] + ], ["NAME:Attributes", "Name:=", cs_name]) def subtract(self, blank_name, tool_names, keep_originals=False): - selection_array = ["NAME:Selections", - "Blank Parts:=", blank_name, - "Tool Parts:=", ",".join(tool_names)] + selection_array = [ + "NAME:Selections", "Blank Parts:=", blank_name, "Tool Parts:=", + ",".join(tool_names) + ] self._modeler.Subtract( selection_array, - ["NAME:UniteParameters", "KeepOriginals:=", keep_originals] - ) + ["NAME:UniteParameters", "KeepOriginals:=", keep_originals]) return blank_name def _fillet(self, radius, vertex_index, obj): @@ -2361,15 +2433,17 @@ def _fillet(self, radius, vertex_index, obj): to_fillet = [int(vertices[v]) for v in vertex_index] else: to_fillet = [int(vertices[vertex_index])] + + # print(vertices) # print(radius) - self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], - ["NAME:Parameters", - ["NAME:FilletParameters", - "Edges:=", [], - "Vertices:=", to_fillet, - "Radius:=", radius, - "Setback:=", "0mm"]]) + self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], [ + "NAME:Parameters", + [ + "NAME:FilletParameters", "Edges:=", [], "Vertices:=", + to_fillet, "Radius:=", radius, "Setback:=", "0mm" + ] + ]) def _fillet_edges(self, radius, edge_index, obj): edges = self._modeler.GetEdgeIDsFromObject(obj) @@ -2378,22 +2452,22 @@ def _fillet_edges(self, radius, edge_index, obj): else: to_fillet = [int(edges[edge_index])] - self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], - ["NAME:Parameters", - ["NAME:FilletParameters", - "Edges:=", to_fillet, - "Vertices:=", [], - "Radius:=", radius, - "Setback:=", "0mm"]]) + self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], [ + "NAME:Parameters", + [ + "NAME:FilletParameters", "Edges:=", to_fillet, "Vertices:=", + [], "Radius:=", radius, "Setback:=", "0mm" + ] + ]) def _fillets(self, radius, vertices, obj): - self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], - ["NAME:Parameters", - ["NAME:FilletParameters", - "Edges:=", [], - "Vertices:=", vertices, - "Radius:=", radius, - "Setback:=", "0mm"]]) + self._modeler.Fillet(["NAME:Selections", "Selections:=", obj], [ + "NAME:Parameters", + [ + "NAME:FilletParameters", "Edges:=", [], "Vertices:=", vertices, + "Radius:=", radius, "Setback:=", "0mm" + ] + ]) def _sweep_along_path(self, to_sweep, path_obj): """ @@ -2405,45 +2479,49 @@ def _sweep_along_path(self, to_sweep, path_obj): whose length is the desired resulting thickness path_obj (polyline): Original polyline; want to broaden this """ - self.rename_obj(path_obj, str(path_obj)+'_path') + self.rename_obj(path_obj, str(path_obj) + '_path') new_name = self.rename_obj(to_sweep, path_obj) - names = [path_obj, str(path_obj)+'_path'] - self._modeler.SweepAlongPath(self._selections_array(*names), - ["NAME:PathSweepParameters", - "DraftAngle:=" , "0deg", - "DraftType:=" , "Round", - "CheckFaceFaceIntersection:=", False, - "TwistAngle:=" , "0deg"]) + names = [path_obj, str(path_obj) + '_path'] + self._modeler.SweepAlongPath(self._selections_array(*names), [ + "NAME:PathSweepParameters", "DraftAngle:=", "0deg", "DraftType:=", + "Round", "CheckFaceFaceIntersection:=", False, "TwistAngle:=", + "0deg" + ]) return Polyline(new_name, self) def sweep_along_vector(self, names, vector): - self._modeler.SweepAlongVector(self._selections_array(*names), - ["NAME:VectorSweepParameters", - "DraftAngle:=" , "0deg", - "DraftType:=" , "Round", - "CheckFaceFaceIntersection:=", False, - "SweepVectorX:=" , vector[0], - "SweepVectorY:=" , vector[1], - "SweepVectorZ:=" , vector[2] - ]) + self._modeler.SweepAlongVector(self._selections_array(*names), [ + "NAME:VectorSweepParameters", "DraftAngle:=", "0deg", + "DraftType:=", "Round", "CheckFaceFaceIntersection:=", False, + "SweepVectorX:=", vector[0], "SweepVectorY:=", vector[1], + "SweepVectorZ:=", vector[2] + ]) def rename_obj(self, obj, name): - self._modeler.ChangeProperty(["NAME:AllTabs", - ["NAME:Geometry3DAttributeTab", - ["NAME:PropServers", str(obj)], - ["NAME:ChangedProps", ["NAME:Name", "Value:=", str(name)]]]]) + self._modeler.ChangeProperty([ + "NAME:AllTabs", + [ + "NAME:Geometry3DAttributeTab", ["NAME:PropServers", + str(obj)], + ["NAME:ChangedProps", ["NAME:Name", "Value:=", + str(name)]] + ] + ]) return name class ModelEntity(str, HfssPropertyObject): prop_tab = "Geometry3DCmdTab" model_command = None - transparency = make_float_prop( - "Transparent", prop_tab="Geometry3DAttributeTab", prop_server=lambda self: self) - material = make_str_prop( - "Material", prop_tab="Geometry3DAttributeTab", prop_server=lambda self: self) - wireframe = make_float_prop( - "Display Wireframe", prop_tab="Geometry3DAttributeTab", prop_server=lambda self: self) + transparency = make_float_prop("Transparent", + prop_tab="Geometry3DAttributeTab", + prop_server=lambda self: self) + material = make_str_prop("Material", + prop_tab="Geometry3DAttributeTab", + prop_server=lambda self: self) + wireframe = make_float_prop("Display Wireframe", + prop_tab="Geometry3DAttributeTab", + prop_server=lambda self: self) coordinate_system = make_str_prop("Coordinate System") def __new__(self, val, *args, **kwargs): @@ -2454,8 +2532,8 @@ def __init__(self, val, modeler): :type val: str :type modeler: HfssModeler """ - super(ModelEntity, self).__init__( - ) # val) #Comment out keyword to match arguments + super(ModelEntity, + self).__init__() # val) #Comment out keyword to match arguments self.modeler = modeler self.prop_server = self + ":" + self.model_command + ":1" @@ -2479,7 +2557,7 @@ def __init__(self, name, modeler, corner, size): self.prop_holder = modeler._modeler self.corner = corner self.size = size - self.center = [c + s/2 for c, s in zip(corner, size)] + self.center = [c + s / 2 for c, s in zip(corner, size)] faces = modeler.get_face_ids(self) self.z_back_face, self.z_front_face = faces[0], faces[1] self.y_back_face, self.y_front_face = faces[2], faces[4] @@ -2488,6 +2566,7 @@ def __init__(self, name, modeler, corner, size): class Rect(ModelEntity): model_command = "CreateRectangle" + # TODO: Add a rotated rectangle object. # Will need to first create rect, then apply rotate operation. @@ -2496,7 +2575,7 @@ def __init__(self, name, modeler, corner, size): self.prop_holder = modeler._modeler self.corner = corner self.size = size - self.center = [c + s/2 if s else c for c, s in zip(corner, size)] + self.center = [c + s / 2 if s else c for c, s in zip(corner, size)] def make_center_line(self, axis): ''' @@ -2504,22 +2583,28 @@ def make_center_line(self, axis): ''' axis_idx = ["x", "y", "z"].index(axis.lower()) start = [c for c in self.center] - start[axis_idx] -= self.size[axis_idx]/2 + start[axis_idx] -= self.size[axis_idx] / 2 start = [self.modeler.eval_expr(s) for s in start] end = [c for c in self.center] - end[axis_idx] += self.size[axis_idx]/2 + end[axis_idx] += self.size[axis_idx] / 2 end = [self.modeler.eval_expr(s) for s in end] return start, end def make_rlc_boundary(self, axis, r=0, l=0, c=0, name="LumpRLC"): start, end = self.make_center_line(axis) - self.modeler._make_lumped_rlc( - r, l, c, start, end, ["Objects:=", [self]], name=name) + self.modeler._make_lumped_rlc(r, + l, + c, + start, + end, ["Objects:=", [self]], + name=name) def make_lumped_port(self, axis, z0="50ohm", name="LumpPort"): start, end = self.make_center_line(axis) - self.modeler._make_lumped_port( - start, end, ["Objects:=", [self]], z0=z0, name=name) + self.modeler._make_lumped_port(start, + end, ["Objects:=", [self]], + z0=z0, + name=name) class Polyline(ModelEntity): @@ -2538,6 +2623,8 @@ def __init__(self, name, modeler, points=None): else: pass # TODO: points = collection of points + + # axis = find_orth_axis() # TODO: find the plane of the polyline for now, assume Z @@ -2555,27 +2642,36 @@ def make_center_line(self, axis): # Expects to act on a rectangle... center = [0, 0, 0] for point in self.points: - center = [center[0]+point[0]/self.n_points, - center[1]+point[1]/self.n_points, - center[2]+point[2]/self.n_points] - size = [2*(center[0]-self.points[0][0]), - 2*(center[1]-self.points[0][1]), - 2*(center[1]-self.points[0][2])] + center = [ + center[0] + point[0] / self.n_points, + center[1] + point[1] / self.n_points, + center[2] + point[2] / self.n_points + ] + size = [ + 2 * (center[0] - self.points[0][0]), + 2 * (center[1] - self.points[0][1]), + 2 * (center[1] - self.points[0][2]) + ] axis_idx = ["x", "y", "z"].index(axis.lower()) start = [c for c in center] - start[axis_idx] -= size[axis_idx]/2 - start = [self.modeler.eval_var_str( - s, unit=LENGTH_UNIT) for s in start] # TODO + start[axis_idx] -= size[axis_idx] / 2 + start = [ + self.modeler.eval_var_str(s, unit=LENGTH_UNIT) for s in start + ] # TODO end = [c for c in center] - end[axis_idx] += size[axis_idx]/2 + end[axis_idx] += size[axis_idx] / 2 end = [self.modeler.eval_var_str(s, unit=LENGTH_UNIT) for s in end] return start, end def make_rlc_boundary(self, axis, r=0, l=0, c=0, name="LumpRLC"): - name = str(self)+'_'+name + name = str(self) + '_' + name start, end = self.make_center_line(axis) - self.modeler._make_lumped_rlc( - r, l, c, start, end, ["Objects:=", [self]], name=name) + self.modeler._make_lumped_rlc(r, + l, + c, + start, + end, ["Objects:=", [self]], + name=name) def fillet(self, radius, vertex_index): self.modeler._fillet(radius, vertex_index, self) @@ -2589,8 +2685,9 @@ def rename(self, new_name): These names are not checked; they require modifying get_objects_in_group. ''' - new_name = increment_name(new_name, self.modeler.get_objects_in_group( - "Sheets")) # this is for a clsoed polyline + new_name = increment_name( + new_name, self.modeler.get_objects_in_group( + "Sheets")) # this is for a clsoed polyline # check to get the actual new name in case there was a suibtracted ibjet with that namae face_ids = self.modeler.get_face_ids(str(self)) @@ -2602,8 +2699,9 @@ def rename(self, new_name): class OpenPolyline(ModelEntity): # Assume closed polyline model_command = "CreatePolyline" - show_direction = make_prop( - 'Show Direction', prop_tab="Geometry3DAttributeTab", prop_server=lambda self: self) + show_direction = make_prop('Show Direction', + prop_tab="Geometry3DAttributeTab", + prop_server=lambda self: self) def __init__(self, name, modeler, points=None): super(OpenPolyline, self).__init__(name, modeler) @@ -2613,6 +2711,8 @@ def __init__(self, name, modeler, points=None): self.n_points = len(points) else: pass + + # axis = find_orth_axis() # TODO: find the plane of the polyline for now, assume Z @@ -2620,6 +2720,7 @@ def __init__(self, name, modeler, points=None): # X, Y, Z = (True, True, True) # for point in points: # X = + def vertices(self): return self.modeler.get_vertex_ids(self) @@ -2634,8 +2735,11 @@ def fillets(self, radius, do_not_fillet=[]): list_vertices = [] for vertex in raw_list_vertices[1:-1]: # ignore the start and finish list_vertices.append(int(vertex)) - list_vertices = list(map(int, np.delete(list_vertices, - np.array(do_not_fillet, dtype=int)-1))) + list_vertices = list( + map( + int, + np.delete(list_vertices, + np.array(do_not_fillet, dtype=int) - 1))) #print(list_vertices, type(list_vertices[0])) if len(list_vertices) != 0: self.modeler._fillets(radius, list_vertices, self) @@ -2650,10 +2754,11 @@ def rename(self, new_name): Warning: The increment_name only works if the sheet has not been stracted or used as a tool elsewher. These names are not checked - They require modifying get_objects_in_group ''' - new_name = increment_name( - new_name, self.modeler.get_objects_in_group("Lines")) + new_name = increment_name(new_name, + self.modeler.get_objects_in_group("Lines")) # , self.points) - return OpenPolyline(self.modeler.rename_obj(self, new_name), self.modeler) + return OpenPolyline(self.modeler.rename_obj(self, new_name), + self.modeler) def copy(self, new_name): new_obj = OpenPolyline(self.modeler.copy(self), self.modeler) @@ -2682,7 +2787,8 @@ def __init__(self, setup): self.ComplexMag_Jvol = NamedCalcObject("ComplexMag_Jvol", setup) self.P_J = NamedCalcObject("P_J", setup) - self.named_expression = {} # dictionary to hold additional named expressions + self.named_expression = { + } # dictionary to hold additional named expressions def clear_named_expressions(self): self.parent.parent._fields_calc.ClearAllNamedExpr() @@ -2742,14 +2848,14 @@ def __mul__(self, other): return self._bin_op(other, "*") def __rmul__(self, other): - return self*other + return self * other def __div__(self, other): return self._bin_op(other, "/") def __rdiv__(self, other): other = ConstantCalcObject(other, self.setup) - return other/self + return other / self def __pow__(self, other): return self._bin_op(other, "Pow") @@ -2816,9 +2922,8 @@ def integrate_line(self, name): def integrate_line_tangent(self, name): ''' integrate line tangent to vector expression \n name = of line to integrate over ''' - self.stack = self.stack + [("EnterLine", name), - ("CalcOp", "Tangent"), - ("CalcOp", "Dot")] + self.stack = self.stack + [("EnterLine", name), ("CalcOp", "Tangent"), + ("CalcOp", "Dot")] return self.integrate_line(name) def line_tangent_coor(self, name, coordinate): @@ -2826,9 +2931,8 @@ def line_tangent_coor(self, name, coordinate): name = of line to integrate over ''' if coordinate not in ['X', 'Y', 'Z']: raise ValueError - self.stack = self.stack + [("EnterLine", name), - ("CalcOp", "Tangent"), - ("CalcOp", "Scalar"+coordinate)] + self.stack = self.stack + [("EnterLine", name), ("CalcOp", "Tangent"), + ("CalcOp", "Scalar" + coordinate)] return self.integrate_line(name) def integrate_surf(self, name="AllObjects"): @@ -2937,7 +3041,9 @@ def get_report_arrays(name: str): return r.get_arrays() -def load_ansys_project(proj_name: str, project_path: str = None, extension: str = '.aedt'): +def load_ansys_project(proj_name: str, + project_path: str = None, + extension: str = '.aedt'): ''' Utility function to load an Ansys project. @@ -2950,7 +3056,8 @@ def load_ansys_project(proj_name: str, project_path: str = None, extension: str project_path = Path(project_path) # Checks - assert project_path.is_dir(), "ERROR! project_path is not a valid directory \N{loudly crying face}.\ + assert project_path.is_dir( + ), "ERROR! project_path is not a valid directory \N{loudly crying face}.\ Check the path, and especially \\ charecters." project_path /= project_path / Path(proj_name + extension) @@ -2962,9 +3069,10 @@ def load_ansys_project(proj_name: str, project_path: str = None, extension: str "ERROR! Valid directory, but invalid project filename. \N{loudly crying face} Not found!\ Please check your filename.\n%s\n" % project_path) - if (project_path/'.lock').is_file(): + if (project_path / '.lock').is_file(): logger.warning( - '\t\tFile is locked. \N{fearful face} If connection fails, delete the .lock file.') + '\t\tFile is locked. \N{fearful face} If connection fails, delete the .lock file.' + ) app = HfssApp() logger.info("\tOpened Ansys App") @@ -2980,8 +3088,17 @@ def load_ansys_project(proj_name: str, project_path: str = None, extension: str else: project = desktop.open_project(str(project_path)) else: - project = desktop.get_active_project() - logger.info( - f"\tOpened Ansys Project\n\tFolder: {project.get_path()}\n\tProject: {project.name}") + projects_in_app = desktop.get_projects() + if projects_in_app: + project = desktop.get_active_project() + else: + project = None + + if project: + logger.info( + f"\tOpened Ansys Project\n\tFolder: {project.get_path()}\n\tProject: {project.name}" + ) + else: + logger.info(f"\tAnsys Project was not found.\n\t Project is None.") return app, desktop, project diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 649d0d8..f9b9eae 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -21,8 +21,10 @@ from . import Dict, ansys, config, logger from .toolbox.pythonic import get_instance_vars +diss_opt = [ + 'dielectrics_bulk', 'dielectric_surfaces', 'resistive_surfaces', 'seams' +] -diss_opt = ['dielectrics_bulk', 'dielectric_surfaces', 'resistive_surfaces', 'seams'] class ProjectInfo(object): """ @@ -104,7 +106,6 @@ class ProjectInfo(object): http://google.github.io/styleguide/pyguide.html """ - class _Dissipative: """ Deprecating the _Dissipative class and turning it into a dictionary. @@ -138,7 +139,8 @@ def __getitem__(self, attr): def __setattr__(self, attr, value): logger.warning( - f"DEPRECATED!! use pinfo.dissipative['{attr}'] = {value} instead!") + f"DEPRECATED!! use pinfo.dissipative['{attr}'] = {value} instead!" + ) self[attr] = value def __getattr__(self, attr): @@ -158,8 +160,12 @@ def data(self): """Return dissipatvie as dictionary""" return {str(opt): self[opt] for opt in diss_opt} - def __init__(self, project_path: str = None, project_name: str = None, design_name: str = None, - setup_name: str = None, do_connect: bool = True): + def __init__(self, + project_path: str = None, + project_name: str = None, + design_name: str = None, + setup_name: str = None, + do_connect: bool = True): """ Keyword Arguments: @@ -205,8 +211,10 @@ def __init__(self, project_path: str = None, project_name: str = None, design_na self.connect() self.dissipative['pinfo'] = self - _Forbidden = ['app', 'design', 'desktop', 'project', - 'dissipative', 'setup', '_Forbidden', 'junctions'] + _Forbidden = [ + 'app', 'design', 'desktop', 'project', 'dissipative', 'setup', + '_Forbidden', 'junctions' + ] def save(self): ''' @@ -232,22 +240,28 @@ def connect_project(self): self.app, self.desktop, self.project = ansys.load_ansys_project( self.project_name, self.project_path) - - self.project_name = self.project.name # TODO: should be property? - self.project_path = self.project.get_path() # TODO: should be property? + if self.project: + # TODO: should be property? + self.project_name = self.project.name + self.project_path = self.project.get_path() def connect_design(self, design_name: str = None): """Sets self.design self.design_name """ - if not(design_name is None): + if not (design_name is None): self.design_name = design_name + #TODO: What if there is no active design? + designs_in_project = self.project.get_designs() + if not designs_in_project: + self.design = None + return + if self.design_name is None: - #TODO: What if there is no active design? try: self.design = self.project.get_active_design() self.design_name = self.design.name @@ -256,7 +270,9 @@ def connect_design(self, design_name: str = None): except Exception as e: self.design = None self.design_name = None - logger.info(f'No active design found (or error getting active design). Note: {e}') + logger.info( + f'No active design found (or error getting active design). Note: {e}' + ) else: try: @@ -267,11 +283,11 @@ def connect_design(self, design_name: str = None): except Exception as e: _traceback = sys.exc_info()[2] logger.error(f"Original error \N{loudly crying face}: {e}\n") - raise(Exception(' Did you provide the correct design name?\ - Failed to pull up design. \N{loudly crying face}').with_traceback(_traceback)) + raise (Exception(' Did you provide the correct design name?\ + Failed to pull up design. \N{loudly crying face}'). + with_traceback(_traceback)) - - def connect_setup(self): + def connect_setup(self): """Connect to the first avaialbe setup or create a new in eigenmode and driven modal Raises: @@ -285,11 +301,13 @@ def connect_setup(self): if len(setup_names) == 0: logger.warning('\tNo design setup detected.') if self.design.solution_type == 'Eigenmode': - logger.warning('\tCreating eigenmode default setup one.') + logger.warning( + '\tCreating eigenmode default setup one.') setup = self.design.create_em_setup() self.setup_name = setup.name elif self.design.solution_type == 'DrivenModal': - setup = self.design.create_dm_setup() # adding a driven modal design + setup = self.design.create_dm_setup( + ) # adding a driven modal design self.setup_name = setup.name else: self.setup_name = setup_names[0] @@ -302,7 +320,8 @@ def connect_setup(self): _traceback = sys.exc_info()[2] logger.error(f"Original error \N{loudly crying face}: {e}\n") raise Exception(' Did you provide the correct setup name?\ - Failed to pull up setup. \N{loudly crying face}').with_traceback(_traceback) + Failed to pull up setup. \N{loudly crying face}' + ).with_traceback(_traceback) else: self.setup = None @@ -313,18 +332,34 @@ def connect(self): Do establish connection to Ansys desktop. Connects to project and then get design and setup """ + self.connect_project() - self.connect_design() + if not self.project: + logger.info('\tConnection to Ansys NOT established. \n') + if self.project: + self.connect_design() self.connect_setup() # Finalize - if self.project: + if self.project: self.project_name = self.project.name if self.design: self.design_name = self.design.name - logger.info( - '\tConnection to Ansys established successfully. \N{grinning face} \n') + if self.project and self.design: + logger.info( + '\tConnection to Ansys established successfully. \N{grinning face} \n' + ) + + if not self.project: + logger.info( + '\t Project not detected in Ansys. Is there a project in your desktop app? \N{thinking face} \n' + ) + + if not self.design: + logger.info( + '\t Design not detected in project. Is there a design in your project? \N{thinking face} \n' + ) return self @@ -370,6 +405,7 @@ def disconnect(self): assert self.check_connected() is True,\ "It does not appear that you have connected to HFSS yet.\ Use the connect() method. \N{nauseated face}" + self.project.release() self.desktop.release() self.app.release() @@ -390,7 +426,8 @@ def get_dm(self): def get_all_variables_names(self): """Returns array of all project and local design names.""" - return self.project.get_variable_names() + self.design.get_variable_names() + return self.project.get_variable_names( + ) + self.design.get_variable_names() def get_all_object_names(self): """Returns array of strings""" @@ -422,7 +459,7 @@ def validate_junction_info(self): """pyEPR ProjectInfo user error found \N{face with medical mask}: Seems like for junction `%s` you specified a %s that does not exist in HFSS by the name: `%s` """ % (jjnm, name, jj[name]) - + def __del__(self): logger.info('Disconnected from Ansys HFSS') # self.disconnect() From 0b362179d85a42064ae5a70d9bf7c5eba6f18883 Mon Sep 17 00:00:00 2001 From: Marco Facchini Date: Wed, 3 Feb 2021 22:14:28 +0100 Subject: [PATCH 048/125] typo and logger add for no active design --- pyEPR/project_info.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index f9b9eae..11d31a7 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -251,23 +251,27 @@ def connect_design(self, design_name: str = None): self.design self.design_name """ - if not (design_name is None): - + if design_name is not None: self.design_name = design_name - #TODO: What if there is no active design? designs_in_project = self.project.get_designs() if not designs_in_project: self.design = None + logger.info( + f'No active design found (or error getting active design).' + ) return if self.design_name is None: + # Look for the active design try: self.design = self.project.get_active_design() self.design_name = self.design.name - logger.info(f'\tOpened active design\n'\ - '\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + logger.info( + '\tOpened active design\n' + f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') except Exception as e: + # No active design self.design = None self.design_name = None logger.info( @@ -277,8 +281,9 @@ def connect_design(self, design_name: str = None): try: self.design = self.project.get_design(self.design_name) - logger.info(f'\tOpened active design\n\ -\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + logger.info( + '\tOpened active design\n' + f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') except Exception as e: _traceback = sys.exc_info()[2] @@ -288,7 +293,7 @@ def connect_design(self, design_name: str = None): with_traceback(_traceback)) def connect_setup(self): - """Connect to the first avaialbe setup or create a new in eigenmode and driven modal + """Connect to the first available setup or create a new in eigenmode and driven modal Raises: Exception: [description] From 29a2747865127233f43ed8bcaefd2b18ca757194 Mon Sep 17 00:00:00 2001 From: Priti Ashvin Shah <74020801+priti-ashvin-shah-ibm@users.noreply.github.com> Date: Fri, 5 Feb 2021 09:38:36 -0500 Subject: [PATCH 049/125] Add method to allow user to add a new Q3d to project referenced in ProjectInfo --- pyEPR/ansys.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index dbd9702..eb39fef 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -597,6 +597,18 @@ def new_em_design(self, name: str): """ return self.new_design(name, "Eigenmode") + def new_q3d_design(self, name: str): + """Create a new Q3D design. + + Args: + name (str): Name of Q3D design + """ + name = increment_name( + name, [d.GetName() for d in self._project.GetDesigns()]) + + return HfssDesign( + self, self._project.InsertDesign("Q3D Extractor", name, "", "")) + @property # v2016 def name(self): return self._project.GetName() From 8e31179ca3497cecea298307ef3df9428c2cef08 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Mon, 8 Feb 2021 14:32:05 -0500 Subject: [PATCH 050/125] Updated version number --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 1692f4d..7bacc34 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -60,7 +60,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4.3 +@version: 0.8.4.4 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -91,7 +91,7 @@ __credits__ = ["Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", "Asaf Diringer", "Will Livingston", "Steven Touzard"] __license__ = "BSD-3-Clause" -__version__ = "0.8.4.3" +__version__ = "0.8.4.4" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 807412e..ebf5d9b 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ doclines = __doc__.split('\n') setup(name='pyEPR-quantum', - version='0.8.4.3', + version='0.8.4.4', description = doclines[0], long_description=long_description, long_description_content_type="text/markdown", From ff52e2a739396e6af7700984b1d40a4a9c29188e Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Tue, 9 Feb 2021 23:15:17 -0500 Subject: [PATCH 051/125] Fix get_setup method --- pyEPR/project_info.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 11d31a7..c595a89 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -375,14 +375,15 @@ def get_setup(self, name: str): Args: name (str): Name of the setup. - If the setup does not exist, then throws a loggger error. + If the setup does not exist, then throws a logger error. Defaults to ``None``, in which case returns None """ + print('hello!') if name is None: return None else: - self.setup = self.design.get_setup(name=self.setup_name) + self.setup = self.design.get_setup(name=self.setup_name if not name else name) if self.setup is None: logger.error(f"Could not retrieve setup: {self.setup_name}\n \ Did you give the right name? Does it exist?") From 7fa1272a2e3b3db6325740be93ffd231a0125ba4 Mon Sep 17 00:00:00 2001 From: Marco Facchini Date: Wed, 17 Feb 2021 22:51:07 +0100 Subject: [PATCH 052/125] correction to enable q3d design setup default + removing error in get_objects_in_group() (#70) * correction to enable q3d design setup default * prevent unnecessary error when modeler is not defined in get_objects_in_group --- .gitignore | 1 + pyEPR/ansys.py | 19 +++++++++---------- pyEPR/project_info.py | 14 ++++++++++---- 3 files changed, 20 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 4d76a0d..7e0556c 100644 --- a/.gitignore +++ b/.gitignore @@ -112,3 +112,4 @@ pyEPR/core.py.rej pyEPR/core.py.rej pyEPR/core.py.rej .vscode/ +.idea/ \ No newline at end of file diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index eb39fef..cb5ea97 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -566,11 +566,11 @@ def get_path(self): raise Exception('''Error: HFSS Project does not have a path. Either there is no HFSS project open, or it is not saved.''') - def new_design(self, name, type): - name = increment_name( - name, [d.GetName() for d in self._project.GetDesigns()]) + def new_design(self, design_name, solution_type, design_type="HFSS"): + design_name = increment_name( + design_name, [d.GetName() for d in self._project.GetDesigns()]) return HfssDesign(self, - self._project.InsertDesign("HFSS", name, type, "")) + self._project.InsertDesign(design_type, design_name, solution_type, "")) def get_design(self, name): return HfssDesign(self, self._project.GetDesign(name)) @@ -603,11 +603,7 @@ def new_q3d_design(self, name: str): Args: name (str): Name of Q3D design """ - name = increment_name( - name, [d.GetName() for d in self._project.GetDesigns()]) - - return HfssDesign( - self, self._project.InsertDesign("Q3D Extractor", name, "", "")) + return self.new_design(name, "Q3D", "Q3D Extractor") @property # v2016 def name(self): @@ -2387,7 +2383,10 @@ def get_objects_in_group(self, group): One of , , "Non Model", "Solids", "Unclassi­fied", "Sheets", "Lines" """ - return list(self._modeler.GetObjectsInGroup(group)) + if self._modeler: + return list(self._modeler.GetObjectsInGroup(group)) + else: + return list() def set_working_coordinate_system(self, cs_name="Global"): """ diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 11d31a7..eaca822 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -307,12 +307,18 @@ def connect_setup(self): logger.warning('\tNo design setup detected.') if self.design.solution_type == 'Eigenmode': logger.warning( - '\tCreating eigenmode default setup one.') + '\tCreating eigenmode default setup.') setup = self.design.create_em_setup() self.setup_name = setup.name elif self.design.solution_type == 'DrivenModal': - setup = self.design.create_dm_setup( - ) # adding a driven modal design + logger.warning( + '\tCreating drivenmodal default setup.') + setup = self.design.create_dm_setup() + self.setup_name = setup.name + elif self.design.solution_type == 'Q3D': + logger.warning( + '\tCreating Q3D default setup.') + setup = self.design.create_q3d_setup() self.setup_name = setup.name else: self.setup_name = setup_names[0] @@ -375,7 +381,7 @@ def get_setup(self, name: str): Args: name (str): Name of the setup. - If the setup does not exist, then throws a loggger error. + If the setup does not exist, then throws a logger error. Defaults to ``None``, in which case returns None """ From 231e8183e6d17f5bae274a7d074c4f63c022fd2e Mon Sep 17 00:00:00 2001 From: Priti Ashvin Shah <74020801+priti-ashvin-shah-ibm@users.noreply.github.com> Date: Fri, 19 Feb 2021 15:03:22 -0500 Subject: [PATCH 053/125] 209 if design exists add it (#71) * Save before switching branches. * Add changes which were part of merge. * Update where the flag needs to be passed. * Need method instead of getting a list of designs. * Remove the previous solution, Decide with different solution. * Change to version 0.8.4.5 . * Remove reference to yapf. --- pyEPR/__init__.py | 60 ++++++++++++++++++++++++++----------------- pyEPR/ansys.py | 14 ++++++---- pyEPR/project_info.py | 20 +++++++-------- setup.py | 41 +++++++++++++++-------------- 4 files changed, 74 insertions(+), 61 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 7bacc34..94f90e2 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -31,7 +31,6 @@ # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ############################################################################### - """ **pyEPR** @@ -60,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4.4 +@version: 0.8.4.5 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -88,10 +87,12 @@ __author__ = "Zlatko Minev, Zaki Leghas, and the pyEPR team" __copyright__ = "Copyright 2015-2020, pyEPR team" -__credits__ = ["Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", - "Asaf Diringer", "Will Livingston", "Steven Touzard"] +__credits__ = [ + "Zlatko Minev", "Zaki Leghtas,", "Phil Rheinhold", "Asaf Diringer", + "Will Livingston", "Steven Touzard" +] __license__ = "BSD-3-Clause" -__version__ = "0.8.4.4" +__version__ = "0.8.4.5" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' @@ -102,7 +103,6 @@ from ._config_default import get_config config = get_config() - ############################################################################## # Set up logging -- only on first loading of module, not on reloading. logger = logging.getLogger('pyEPR') # singleton @@ -111,22 +111,20 @@ set_up_logger(logger) del set_up_logger - - ############################################################################## # # Check that required packages are available. If not raise log warning. try: import pandas as pd - warnings.filterwarnings('ignore', category=pd.io.pytables.PerformanceWarning) + warnings.filterwarnings('ignore', + category=pd.io.pytables.PerformanceWarning) del pd except (ImportError, ModuleNotFoundError): if config.internal.warn_missing_import: logger.warning("IMPORT WARNING: `pandas` python package not found. %s", config.internal.error_msg_missing_import) - # Check for a few usually troublesome packages if config.internal.warn_missing_import: @@ -135,7 +133,8 @@ import qutip del qutip except (ImportError, ModuleNotFoundError): - logger.warning("""IMPORT WARNING: `qutip` package not found. + logger.warning( + """IMPORT WARNING: `qutip` package not found. Numerical diagonalization will not work. Please install, e.g.: $ conda install -c conda-forge qutip %s""", config.internal.error_msg_missing_import) @@ -144,7 +143,8 @@ import pythoncom del pythoncom except (ImportError, ModuleNotFoundError): - logger.warning("""IMPORT WARNING: + logger.warning( + """IMPORT WARNING: Python package 'pythoncom' could not be loaded It is used in communicting with HFSS on PCs. If you wish to do this, please set it up. For Linux, check the HFSS python linux files for the com module used. It is equivalent, @@ -156,18 +156,20 @@ del Dispatch del CDispatch except (ImportError, ModuleNotFoundError): - logger.warning("""IMPORT WARNING: Could not load from 'win32com.client'. + logger.warning( + """IMPORT WARNING: Could not load from 'win32com.client'. The communication to hfss won't work. If you want to use it, you need to set it up. %s""", config.internal.error_msg_missing_import) try: - import pint # units + import pint # units del pint except (ImportError, ModuleNotFoundError): - logger.error("""IMPORT ERROR: + logger.error( + """IMPORT ERROR: Python package 'pint' could not be loaded. It is used in communicting with HFSS. Try: - $ conda install -c conda-forge pint \n%s""", config.internal.error_msg_missing_import) - + $ conda install -c conda-forge pint \n%s""", + config.internal.error_msg_missing_import) # remove unused del Path, warnings, logging @@ -184,12 +186,22 @@ from .core import ProjectInfo, DistributedAnalysis, QuantumAnalysis,\ Project_Info, pyEPR_HFSSAnalysis, pyEPR_Analysis # names to be depricated - -__all__ = ['logger', 'config', - 'toolbox', 'calcs', 'ansys', 'core', - 'ProjectInfo', 'DistributedAnalysis', 'QuantumAnalysis', - 'Project_Info', 'pyEPR_HFSSAnalysis','pyEPR_Analysis', # names to be depricated - 'parse_units', 'parse_units_user', 'parse_entry' - ] +__all__ = [ + 'logger', + 'config', + 'toolbox', + 'calcs', + 'ansys', + 'core', + 'ProjectInfo', + 'DistributedAnalysis', + 'QuantumAnalysis', + 'Project_Info', + 'pyEPR_HFSSAnalysis', + 'pyEPR_Analysis', # names to be depricated + 'parse_units', + 'parse_units_user', + 'parse_entry' +] # TODO: Add "about" method. Add to tutorial diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index cb5ea97..850f03d 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -501,6 +501,9 @@ def make_active(self): def get_designs(self): return [HfssDesign(self, d) for d in self._project.GetDesigns()] + def get_design_names(self): + return [d.GetName() for d in self._project.GetDesigns()] + def save(self, path=None): if path is None: self._project.Save() @@ -567,10 +570,12 @@ def get_path(self): Either there is no HFSS project open, or it is not saved.''') def new_design(self, design_name, solution_type, design_type="HFSS"): - design_name = increment_name( + design_name_int = increment_name( design_name, [d.GetName() for d in self._project.GetDesigns()]) - return HfssDesign(self, - self._project.InsertDesign(design_type, design_name, solution_type, "")) + return HfssDesign( + self, + self._project.InsertDesign(design_type, design_name_int, + solution_type, "")) def get_design(self, name): return HfssDesign(self, self._project.GetDesign(name)) @@ -585,7 +590,7 @@ def new_dm_design(self, name: str): """Create a new driven model design Args: - name (str): Name of driven modal design + name (str): Name of driven modal design """ return self.new_design(name, "DrivenModal") @@ -599,7 +604,6 @@ def new_em_design(self, name: str): def new_q3d_design(self, name: str): """Create a new Q3D design. - Args: name (str): Name of Q3D design """ diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index eaca822..3c550fc 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -181,7 +181,7 @@ def __init__(self, Defaults to ``None``, which will get the current active one. do_connect (bool) [additional]: Do create connection to Ansys or not? Defaults to ``True``. - + """ # Path: format path correctly to system convention @@ -258,8 +258,7 @@ def connect_design(self, design_name: str = None): if not designs_in_project: self.design = None logger.info( - f'No active design found (or error getting active design).' - ) + f'No active design found (or error getting active design).') return if self.design_name is None: @@ -269,7 +268,8 @@ def connect_design(self, design_name: str = None): self.design_name = self.design.name logger.info( '\tOpened active design\n' - f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]' + ) except Exception as e: # No active design self.design = None @@ -283,7 +283,8 @@ def connect_design(self, design_name: str = None): self.design = self.project.get_design(self.design_name) logger.info( '\tOpened active design\n' - f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]') + f'\tDesign: {self.design_name} [Solution type: {self.design.solution_type}]' + ) except Exception as e: _traceback = sys.exc_info()[2] @@ -306,18 +307,15 @@ def connect_setup(self): if len(setup_names) == 0: logger.warning('\tNo design setup detected.') if self.design.solution_type == 'Eigenmode': - logger.warning( - '\tCreating eigenmode default setup.') + logger.warning('\tCreating eigenmode default setup.') setup = self.design.create_em_setup() self.setup_name = setup.name elif self.design.solution_type == 'DrivenModal': - logger.warning( - '\tCreating drivenmodal default setup.') + logger.warning('\tCreating drivenmodal default setup.') setup = self.design.create_dm_setup() self.setup_name = setup.name elif self.design.solution_type == 'Q3D': - logger.warning( - '\tCreating Q3D default setup.') + logger.warning('\tCreating Q3D default setup.') setup = self.design.create_q3d_setup() self.setup_name = setup.name else: diff --git a/setup.py b/setup.py index ebf5d9b..1c11835 100644 --- a/setup.py +++ b/setup.py @@ -29,32 +29,31 @@ doclines = __doc__.split('\n') -setup(name='pyEPR-quantum', - version='0.8.4.4', - description = doclines[0], - long_description=long_description, - long_description_content_type="text/markdown", - author='Zlatko K. Minev', - packages=find_packages(), - author_email='zlatko.minev@aya.yale.edu', - maintainer='Zlatko Minev, pyEPR team', - license='BSD-3-Clause', - url=r'https://github.com/zlatko-minev/pyEPR', - classifiers=[ +setup( + name='pyEPR-quantum', + version='0.8.4.5', + description=doclines[0], + long_description=long_description, + long_description_content_type="text/markdown", + author='Zlatko K. Minev', + packages=find_packages(), + author_email='zlatko.minev@aya.yale.edu', + maintainer='Zlatko Minev, pyEPR team', + license='BSD-3-Clause', + url=r'https://github.com/zlatko-minev/pyEPR', + classifiers=[ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Operating System :: Microsoft :: Windows", - "Operating System :: MacOS", - "Operating System :: POSIX :: Linux", + "Operating System :: MacOS", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", - "Topic :: Scientific/Engineering", - "Environment :: Console", - "License :: OSI Approved :: Apache Software License"], - python_requires=">=3.5, <4", - # install_requires=['numpy','pandas','pint','matplotlib','attrdict','sympy','IPython'], - install_requires=requirements - ) + "Topic :: Scientific/Engineering", "Environment :: Console", + "License :: OSI Approved :: Apache Software License" + ], + python_requires=">=3.5, <4", + # install_requires=['numpy','pandas','pint','matplotlib','attrdict','sympy','IPython'], + install_requires=requirements) From d6dbae5bde7afa451b7646de681b7a8560e234f8 Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Fri, 19 Feb 2021 16:24:26 -0500 Subject: [PATCH 054/125] Ansys version update for Z matrices --- pyEPR/ansys.py | 24 ++++++++++++++++-------- pyEPR/project_info.py | 1 - 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index eb39fef..a448322 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -1757,7 +1757,6 @@ def get_network_data(self, formats): for f in formats: fmts_lists[f[0]].append((int(f[1]), int(f[2]))) - ret = [None] * len(formats) freq = None @@ -1774,13 +1773,22 @@ def get_network_data(self, formats): # WARNING for python 3 probably need to use genfromtxt if freq is None: freq = array[:, 0] - for i, j in list: - real_idx = colnames.index("%s[%d,%d]_Real" % - (data_type, i, j)) - imag_idx = colnames.index("%s[%d,%d]_Imag" % - (data_type, i, j)) - c_arr = array[:, real_idx] + 1j * array[:, imag_idx] - ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr + if self._ansys_version < '2020': + for i, j in list: + real_idx = colnames.index("%s[%d,%d]_Real" % + (data_type, i, j)) + imag_idx = colnames.index("%s[%d,%d]_Imag" % + (data_type, i, j)) + c_arr = array[:, real_idx] + 1j * array[:, imag_idx] + ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr + elif self._ansys_version == '2020': + for i, j in list: + real_idx = colnames.index("%s[%d,%d]_Re" % + (data_type, i, j)) + imag_idx = colnames.index("%s[%d,%d]_Im" % + (data_type, i, j)) + c_arr = array[:, real_idx] + 1j * array[:, imag_idx] + ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr return freq, ret diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index c595a89..d1727b5 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -379,7 +379,6 @@ def get_setup(self, name: str): Defaults to ``None``, in which case returns None """ - print('hello!') if name is None: return None else: From 3b3d1943ef00cd79fa50ea719bd0162f9b398dab Mon Sep 17 00:00:00 2001 From: Dennis Wang Date: Thu, 25 Feb 2021 19:29:57 -0500 Subject: [PATCH 055/125] Update ansys.py for Ansys 2020 --- pyEPR/ansys.py | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index f1f356c..6bc5d52 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -1773,22 +1773,15 @@ def get_network_data(self, formats): # WARNING for python 3 probably need to use genfromtxt if freq is None: freq = array[:, 0] - if self._ansys_version < '2020': - for i, j in list: - real_idx = colnames.index("%s[%d,%d]_Real" % - (data_type, i, j)) - imag_idx = colnames.index("%s[%d,%d]_Imag" % - (data_type, i, j)) - c_arr = array[:, real_idx] + 1j * array[:, imag_idx] - ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr - elif self._ansys_version == '2020': - for i, j in list: - real_idx = colnames.index("%s[%d,%d]_Re" % - (data_type, i, j)) - imag_idx = colnames.index("%s[%d,%d]_Im" % - (data_type, i, j)) - c_arr = array[:, real_idx] + 1j * array[:, imag_idx] - ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr + # TODO: If Ansys version is 2019, use 'Real' and 'Imag' + # in place of 'Re' and 'Im + for i, j in list: + real_idx = colnames.index("%s[%d,%d]_Re" % + (data_type, i, j)) + imag_idx = colnames.index("%s[%d,%d]_Im" % + (data_type, i, j)) + c_arr = array[:, real_idx] + 1j * array[:, imag_idx] + ret[formats.index("%s%d%d" % (data_type, i, j))] = c_arr return freq, ret From c3cc66638c4cdcd14e254b3324b0b9c87686665a Mon Sep 17 00:00:00 2001 From: Marco Facchini Date: Mon, 1 Mar 2021 14:02:16 +0100 Subject: [PATCH 056/125] get_setup fixed to use the passed setup name + typos --- .gitignore | 4 +--- pyEPR/ansys.py | 29 +++++++++++++++-------------- pyEPR/project_info.py | 23 +++++++++++------------ 3 files changed, 27 insertions(+), 29 deletions(-) diff --git a/.gitignore b/.gitignore index 7e0556c..08f0b6c 100644 --- a/.gitignore +++ b/.gitignore @@ -109,7 +109,5 @@ pyEPR/.pylintrc *.aedtresults/* *.aedtresults* pyEPR/core.py.rej -pyEPR/core.py.rej -pyEPR/core.py.rej .vscode/ -.idea/ \ No newline at end of file +.idea/ diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 6bc5d52..8a84c99 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -255,19 +255,19 @@ def _add_release_fn(fn): def release(): ''' - Release COM connection to HFSS. + Release COM connection to Ansys. ''' global _release_fns for fn in _release_fns: fn() time.sleep(0.1) - # Note that _GetInterfaceCount is a memeber + # Note that _GetInterfaceCount is a member refcount = pythoncom._GetInterfaceCount() # pylint: disable=no-member if refcount > 0: print("Warning! %d COM references still alive" % (refcount)) - print("HFSS will likely refuse to shut down") + print("Ansys will likely refuse to shut down") class COMWrapper(object): @@ -403,7 +403,8 @@ def close_all_windows(self): self._desktop.CloseAllWindows() def project_count(self): - return self._desktop.Count() + count = len(self._desktop.GetProjects()) + return count def get_active_project(self): return HfssProject(self, self._desktop.GetActiveProject()) @@ -1080,17 +1081,12 @@ def insert_sweep(self, name = increment_name(name, self.get_sweep_names()) params = [ "NAME:" + name, - "IsEnabled:=", - True, - "Type:=", - type, - "SaveFields:=", - save_fields, - "SaveRadFields:=", - False, + "IsEnabled:=", True, + "Type:=", type, + "SaveFields:=", save_fields, + "SaveRadFields:=", False, # "GenerateFieldsForAllFreqs:=" - "ExtrapToDC:=", - False, + "ExtrapToDC:=", False, ] # not sure hwen extacyl this changed between 2016 and 2019 @@ -1404,6 +1400,11 @@ def get_matrix( # , , , , , , # , , , , , , # + logger.info(f'Exporting matrix data to ({path}, {soln_type}, {variation}, ' + f'{self.name}:{solution_kind}, ' + '"Original", "ohm", "nH", "fF", ' + f'"mSie", {frequency}, {MatrixType}, ' + f'{pass_number}, {ACPlusDCResistance}') self.parent._design.ExportMatrixData(path, soln_type, variation, f'{self.name}:{solution_kind}', "Original", "ohm", "nH", "fF", diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index bf59530..c7be9ee 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -357,7 +357,7 @@ def connect(self): if self.project and self.design: logger.info( - '\tConnection to Ansys established successfully. \N{grinning face} \n' + f'\tConnected to project \"{self.project_name}\" and design \"{self.design_name}\" \N{grinning face} \n' ) if not self.project: @@ -367,7 +367,7 @@ def connect(self): if not self.design: logger.info( - '\t Design not detected in project. Is there a design in your project? \N{thinking face} \n' + f'\t Connected to project \"{self.project_name}\". No design detected' ) return self @@ -385,16 +385,15 @@ def get_setup(self, name: str): """ if name is None: return None - else: - self.setup = self.design.get_setup(name=self.setup_name if not name else name) - if self.setup is None: - logger.error(f"Could not retrieve setup: {self.setup_name}\n \ - Did you give the right name? Does it exist?") + self.setup = self.design.get_setup(name=name) + if self.setup is None: + logger.error(f"Could not retrieve setup: {name}\n \ + Did you give the right name? Does it exist?") - self.setup_name = self.setup.name - logger.info( - f'\tOpened setup `{self.setup_name}` ({type(self.setup)})') - return self.setup + self.setup_name = self.setup.name + logger.info( + f'\tOpened setup `{self.setup_name}` ({type(self.setup)})') + return self.setup def check_connected(self): """ @@ -409,7 +408,7 @@ def check_connected(self): def disconnect(self): ''' - Disconnect from existing HFSS design. + Disconnect from existing Ansys Desktop API. ''' assert self.check_connected() is True,\ "It does not appear that you have connected to HFSS yet.\ From 8485220f9f9d9730860b98fafa2c44b7d3480507 Mon Sep 17 00:00:00 2001 From: marcolincs Date: Tue, 2 Mar 2021 22:08:21 +0100 Subject: [PATCH 057/125] up-rev --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 94f90e2..b88272b 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4.5 +@version: 0.8.4.6 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -92,7 +92,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.4.5" +__version__ = "0.8.4.6" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 1c11835..a2796bd 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.4.5', + version='0.8.4.6', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 16f6107b1e22aa9a1574064421f7811c4453e941 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 17 Mar 2021 17:27:02 -0400 Subject: [PATCH 058/125] Create pyEPR.bib --- pyEPR.bib | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 pyEPR.bib diff --git a/pyEPR.bib b/pyEPR.bib new file mode 100644 index 0000000..b13f166 --- /dev/null +++ b/pyEPR.bib @@ -0,0 +1,5 @@ +@misc{pyEPR, +author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Diringer, Asaf and Devoret, Michel H.}, +title = {{pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design}}, +year = {2018} +} From a8374145b4f7af938823f7e8840e8e8a1ad50d98 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 17 Mar 2021 17:30:41 -0400 Subject: [PATCH 059/125] Update pyEPR.bib --- pyEPR.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR.bib b/pyEPR.bib index b13f166..59ca1a3 100644 --- a/pyEPR.bib +++ b/pyEPR.bib @@ -1,5 +1,5 @@ @misc{pyEPR, -author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Diringer, Asaf and Devoret, Michel H.}, +author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Reinhold, Philip and Diringer, Asaf and Devoret, Michel H.}, title = {{pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design}}, year = {2018} } From 0701d2f884401ef5e37774d3d251968079af482f Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 17 Mar 2021 17:36:45 -0400 Subject: [PATCH 060/125] Update README.md --- README.md | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index dfe706a..f7e9dbf 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,12 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620](
-#### Abstract +### Relevant work: +* Minev, Z. K., Leghtas, Z., Mudhada, S. O., Reinhold, P., Diringer, A., & Devoret, M. H. (2018). [pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design.](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) +* Minev, Z. K., Leghtas, Z., Mundhada, S. O., Christakis, L., Pop, I. M., & Devoret, M. H. (2020). Energy-participation quantization of Josephson circuits. ArXiv. Retrieved from http://arxiv.org/abs/2010.00620 (2020) +* Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. ([arXiv:1902.10355](https://arxiv.org/abs/1902.10355)) (2018) + -Superconducting microwave circuits incorporating nonlinear devices, such as Josephson junctions, are one of the leading platforms for emerging quantum technologies. Increasing circuit complexity further requires efficient methods for the calculation and optimization of the spectrum, nonlinear interactions, and dissipation in multi-mode distributed quantum circuits. Here, we present a method based on the energy-participation ratio (EPR) of a dissipative or nonlinear element in an electromagnetic mode. The EPR, a number between zero and one, quantifies how much of the energy of a mode is stored in each element. It obeys universal constraints---valid regardless of the circuit topology and nature of the nonlinear elements. The EPR of the elements are calculated from a unique, efficient electromagnetic eigenmode simulation of the linearized circuit, including lossy elements. Their set is the key input to the determination of the quantum Hamiltonian of the system. The method provides an intuitive and simple-to-use tool to quantize multi-junction circuits. It is especially well-suited for finding the Hamiltonian and dissipative parameters of weakly anharmonic systems, such as transmon qubits coupled to resonators, or Josephson transmission lines. We experimentally tested this method on a variety of Josephson circuits, and demonstrated agreement within several percents for nonlinear couplings and modal Hamiltonian parameters, spanning five-orders of magnitude in energy, across a dozen samples. Here, in this package, all routines of the EPR approach are fully automated. -[arXiv:2010.00620](https://arxiv.org/abs/2010.00620) ## Documentation @@ -58,16 +60,6 @@ Superconducting microwave circuits incorporating nonlinear devices, such as Jose * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) -## How do I cite `pyEPR` when I publish? -Cite the following: -* Z.K. Minev, Z. Leghtas, _et al._ ([arXiv:2010.00620](https://arxiv.org/abs/2010.00620)) (2020) -or the earlier -* Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. ([arXiv:1902.10355](https://arxiv.org/abs/1902.10355)) (2018) [when using this, use the arXiv id] - -You can additionally drop us an e-mail [`zlatko.minev@aya.yale.edu`](https://www.zlatko-minev.com/) or [`zaki leghtas`](http://cas.ensmp.fr/~leghtas/) - - -
# Contents: @@ -89,6 +81,8 @@ You can additionally drop us an e-mail [`zlatko.minev@aya.yale.edu`](https://www 4. **Stay up to date** Enjoy and make sure to git add the master remote branch `git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git` [(help?)](https://stackoverflow.com/questions/11266478/git-add-remote-branch). 5. **Cite `pyEPR`** [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) / [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) and enjoy! :birthday: + + #### Start-up example [Jupyter notebook tutorials](https://github.com/zlatko-minev/pyEPR/tree/master/_tutorial_notebooks) @@ -318,6 +312,10 @@ Original versions of pyHFSS.py and pyNumericalDiagonalization.py contributed by * Terms of use: Use freely and kindly cite the paper (arXiv link to be posted here) and/or this package. * How can I contribute? Contact [Z. Minev](https://www.zlatko-minev.com/) or [Z. Leghtas](http://cas.ensmp.fr/~leghtas/). +## How do I cite `pyEPR`? +Use this [bibtex](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) for `pyEPR` and for the method use the energy-participation-ratio paper [arXiv:2010.00620](https://arxiv.org/abs/2010.00620). + + [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/zlatko-minev/pyEPR/graphs/commit-activity) [![Twitter](https://github.frapsoft.com/social/twitter.png)](https://twitter.com/zlatko_minev) From 4110d6c9003fb926271f81011aba37e5c20ecf59 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 17 Mar 2021 17:37:30 -0400 Subject: [PATCH 061/125] Update README.md --- README.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f7e9dbf..7de2644 100644 --- a/README.md +++ b/README.md @@ -11,25 +11,23 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620](
-## pyEPR Working group meeting -- Planning for the future of pyEPR +## Documentation -* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers: -- See [pyEPR wiki](https://github.com/zlatko-minev/pyEPR/wiki) for notes from first meeting. -- We will schedule a follow-up meeting in 1-2 mo. +[Read the docs here.](https://pyepr-docs.readthedocs.io) -
-### Relevant work: +## Scientific work: * Minev, Z. K., Leghtas, Z., Mudhada, S. O., Reinhold, P., Diringer, A., & Devoret, M. H. (2018). [pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design.](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) * Minev, Z. K., Leghtas, Z., Mundhada, S. O., Christakis, L., Pop, I. M., & Devoret, M. H. (2020). Energy-participation quantization of Josephson circuits. ArXiv. Retrieved from http://arxiv.org/abs/2010.00620 (2020) * Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. ([arXiv:1902.10355](https://arxiv.org/abs/1902.10355)) (2018) +## pyEPR Working group meeting -- Planning for the future of pyEPR +* Please sign-up here: https://github.com/zlatko-minev/pyEPR/issues/45 or [directly here](https://docs.google.com/forms/d/e/1FAIpQLScd3WyfzDS47D0WB9skkSPQAXCnKLf7JMxsZ7BnMwK0LjE3Sw/viewform?usp=sf_link) :bangbang: :beers: +- See [pyEPR wiki](https://github.com/zlatko-minev/pyEPR/wiki) for notes from first meeting. +- We will schedule a follow-up meeting in 1-2 mo. - -## Documentation - -[Read the docs here.](https://pyepr-docs.readthedocs.io) +
## Who uses pyEPR? * Yale University, Michel Devoret lab [QLab](https://qulab.eng.yale.edu/), CT, USA From 97d0906a82c336461656bca6f305f7cd21d56128 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 17 Mar 2021 17:37:55 -0400 Subject: [PATCH 062/125] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7de2644..f6f5244 100644 --- a/README.md +++ b/README.md @@ -9,12 +9,11 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( ### Automated Python module for the design and quantization of Josephson quantum circuits -
-## Documentation +### Documentation [Read the docs here.](https://pyepr-docs.readthedocs.io) - +
## Scientific work: * Minev, Z. K., Leghtas, Z., Mudhada, S. O., Reinhold, P., Diringer, A., & Devoret, M. H. (2018). [pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design.](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) From 6c4ed5a70bf5c79275c313b020b63a67be8074f8 Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Thu, 8 Apr 2021 13:59:00 +0300 Subject: [PATCH 063/125] Added ansys calculator functions Two functions that return vectors tangent and normal to a surface. I used it to calculate surface participation ratios, e.g.: p_metal = t* (E_surface_metal / UE) print(f'Metal-Air/Metal-Substrate participation ratio, p_MA = p_MS = {p_metal:.3}') #SA participation ratio vecE_normal = vecE.normal2surface('chip_holder1').__div__(epsilon_r) #Complex Vector vecE_tangent = vecE.tangent2surface('chip_holder1').__mul__(epsilon_r) #Complex Vector vec_E_total = vecE_normal.__add__(vecE_tangent) E_squared = vec_E_total.dot(vecE).__abs__().real().__pow__(2) E_surface = E_squared.integrate_surf(name='chip_holder1').evaluate() E_surface -= E_surface_metal p_SA = t* (E_surface / UE) print(f'Air-Substrate participation ratio, p_SA = {p_SA:.3}') --- pyEPR/ansys.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 8a84c99..a114bcd 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -2936,6 +2936,30 @@ def getQty(self, name): def integrate_line(self, name): return self._integrate(name, "EnterLine") + def normal2surface(self, name): + ''' return the part normal to surface. + Complex Vector. ''' + stack = self.stack + [("EnterSurf", name), + ("CalcOp", "Normal")] + stack.append(("CalcOp", "Dot")) + stack.append(("EnterSurf", name)) + stack.append(("CalcOp", "Normal")) + stack.append(("CalcOp", "*")) + return CalcObject(stack, self.setup) + + def tangent2surface(self, name): + ''' return the part tangent to surface. + Complex Vector. ''' + stack = self.stack + [("EnterSurf", name), + ("CalcOp", "Normal")] + stack.append(("CalcOp", "Dot")) + stack.append(("EnterSurf", name)) + stack.append(("CalcOp", "Normal")) + stack.append(("CalcOp", "*")) + stack = self.stack + stack + stack.append(("CalcOp", "-")) + return CalcObject(stack, self.setup) + def integrate_line_tangent(self, name): ''' integrate line tangent to vector expression \n name = of line to integrate over ''' From 492bca697855d3d2cfd0069543e90d19f079361e Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Mon, 19 Apr 2021 16:43:24 +0300 Subject: [PATCH 064/125] Typos mainly Also one change where an error is raised when `pint` is not properly imported. --- pyEPR/ansys.py | 3 ++- pyEPR/core_distributed_analysis.py | 18 ++++++++---------- pyEPR/core_quantum_analysis.py | 2 +- 3 files changed, 11 insertions(+), 12 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index a114bcd..eab3157 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -55,7 +55,8 @@ ureg = UnitRegistry() Q = ureg.Quantity except (ImportError, ModuleNotFoundError): - ureg = "Pint module not installed. Please install." + raise NameError ("Pint module not installed. Please install.") + ############################################################################## ### diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index eb53d34..ed09215 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -151,14 +151,14 @@ def __init__(self, *args, **kwargs): # Modes and variations - the following get updated in update_variation_information self.n_modes = int(1) # : Number of eigenmodes self.modes = None - #: List of variation indecies, which are strings of ints, such as ['0', '1'] + #: List of variation indices, which are strings of ints, such as ['0', '1'] self.variations = [] - self.variations_analyzed = [] # : List of analyzed variations. List of indecies + self.variations_analyzed = [] # : List of analyzed variations. List of indices # String identifier of variables, such as "Cj='2fF' Lj='12.5nH'" self._nominal_variation = '' self._list_variations = ("",) # tuple set of variables - # container for eBBQ list of varibles; basically the same as _list_variations + # container for eBBQ list of variables; basically the same as _list_variations self._hfss_variables = Dict() self._previously_analyzed = set() # previously analyzed variations @@ -356,7 +356,6 @@ def _get_lv(self, variation=None): ['Lj1:=','13nH', 'QubitGap:=','100um'] ''' - if variation is None: lv = self._nominal_variation # "Cj='2fF' Lj='12.5nH'" lv = self._parse_listvariations(lv) @@ -369,7 +368,7 @@ def _get_lv(self, variation=None): @property def n_variations(self): - """ Number of **solved** variaitons, corresponding to the + """ Number of **solved** variations, corresponding to the selected Setup. """ return len(self._list_variations) @@ -477,7 +476,6 @@ def update_ansys_info(self): n_modes, _list_variations, nominal_variation, n_variations ''' - # from oDesign self._nominal_variation = self.design.get_nominal_variation() @@ -888,7 +886,7 @@ def get_Qdielectric(self, dielectric, mode, variation, U_E=None): def get_Qsurface_all(self, mode, variation, U_E=None): ''' - caculate the contribution to Q of a dieletric layer of dirt on all surfaces + calculate the contribution to Q of a dielectric layer of dirt on all surfaces set the dirt thickness and loss tangent in the config file ref: http://arxiv.org/pdf/1509.01854.pdf ''' @@ -1027,10 +1025,10 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): #print('\tV_peak=', V_peak) # ------------------------------------------------------------ - # Calcualte participations from the peak voltage and currents + # Calculate participation from the peak voltage and currents # - # All junction capactive and inductive lumped energies - all peak + # All junction capacitive and inductive lumped energies - all peak U_J_inds = {j_name: 0.5*Ljs[j_name] * I_peak_[j_name] ** 2 for j_name in self.pinfo.junctions} U_J_caps = {j_name: 0.5*Cjs[j_name] * V_peak_[j_name] @@ -1423,7 +1421,7 @@ def save(self, project_info: dict = None): pickle.dump(to_save, handle) # , protocol=pickle.HIGHEST_PROTOCOL) def load(self, filepath=None): - """Utility function to load reuslts file + """Utility function to load results file Keyword Arguments: filepath {[type]} -- [description] (default: {None}) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 4d8e6fd..541d7d4 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -445,7 +445,7 @@ def analyze_all_variations(self, Specific params: -------------------- - variations : None returns all_variations otherwis this is a list with number + variations : None returns all_variations otherwise this is a list with number as strings ['0', '1'] nalyze_previous :set to true if you wish to overwrite previous analysis ''' From 0d985599bdc8880ef02f44902b85a76a2b203edb Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Mon, 19 Apr 2021 18:13:18 +0300 Subject: [PATCH 065/125] Update ansys.py --- pyEPR/ansys.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index eab3157..be9c496 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -41,14 +41,14 @@ try: import pythoncom except (ImportError, ModuleNotFoundError): - pass + raise NameError ("pythoncom module not installed. Please install.") try: # TODO: Replace `win32com` with Linux compatible package. # See Ansys python files in IronPython internal. from win32com.client import Dispatch, CDispatch except (ImportError, ModuleNotFoundError): - pass + raise NameError ("win32com module not installed. Please install.") try: from pint import UnitRegistry From 3e979be443a62b95f6ecf9481d36176ffbca6f6f Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 9 May 2021 10:00:14 -0400 Subject: [PATCH 066/125] Update ansys.py --- pyEPR/ansys.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index be9c496..29530f8 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -41,21 +41,21 @@ try: import pythoncom except (ImportError, ModuleNotFoundError): - raise NameError ("pythoncom module not installed. Please install.") + pass #raise NameError ("pythoncom module not installed. Please install.") try: # TODO: Replace `win32com` with Linux compatible package. # See Ansys python files in IronPython internal. from win32com.client import Dispatch, CDispatch except (ImportError, ModuleNotFoundError): - raise NameError ("win32com module not installed. Please install.") + pass #raise NameError ("win32com module not installed. Please install.") try: from pint import UnitRegistry ureg = UnitRegistry() Q = ureg.Quantity except (ImportError, ModuleNotFoundError): - raise NameError ("Pint module not installed. Please install.") + pass # raise NameError ("Pint module not installed. Please install.") ############################################################################## From 8876e93cd90d43f80954a7835a42b0954cbc9c44 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 9 May 2021 10:06:49 -0400 Subject: [PATCH 067/125] Update README.md --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index f6f5244..ebf4a0f 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,8 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( [![fork this repo](http://githubbadges.com/fork.svg?user=zlatko-minev&repo=pyEPR&style=flat)](https://github.com/zlatko-minev/pyEPR/fork) [![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyepr-quantum/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge) [![PyPI version](https://badge.fury.io/py/pyEPR-quantum.svg)](https://badge.fury.io/py/pyEPR-quantum) - +[![DOI](https://zenodo.org/badge/101073856.svg)](https://zenodo.org/badge/latestdoi/101073856) + ### Automated Python module for the design and quantization of Josephson quantum circuits @@ -16,7 +17,7 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620](
## Scientific work: -* Minev, Z. K., Leghtas, Z., Mudhada, S. O., Reinhold, P., Diringer, A., & Devoret, M. H. (2018). [pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design.](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) +* Minev, Z. K., Leghtas, Z., Mudhada, S. O., Reinhold, P., Diringer, A., & Devoret, M. H. (2018). [pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design.](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) [![DOI](https://zenodo.org/badge/101073856.svg)](https://zenodo.org/badge/latestdoi/101073856) * Minev, Z. K., Leghtas, Z., Mundhada, S. O., Christakis, L., Pop, I. M., & Devoret, M. H. (2020). Energy-participation quantization of Josephson circuits. ArXiv. Retrieved from http://arxiv.org/abs/2010.00620 (2020) * Z.K. Minev, Ph.D. Dissertation, Yale University (2018), Chapter 4. ([arXiv:1902.10355](https://arxiv.org/abs/1902.10355)) (2018) @@ -76,7 +77,7 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( 2. **Clone** :point_down: your forked repository locally. ([How to clone a GitHub repo?](https://help.github.com/en/articles/cloning-a-repository)). Setup the `pyEPR` python code by following [Installation and Python Setup](#installation-of-pyepr). 3. **Tutorials** Learn how to use using the [jupyter notebook tutorials](https://github.com/zlatko-minev/pyEPR/tree/master/_tutorial_notebooks) 4. **Stay up to date** Enjoy and make sure to git add the master remote branch `git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git` [(help?)](https://stackoverflow.com/questions/11266478/git-add-remote-branch). -5. **Cite `pyEPR`** [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) / [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) and enjoy! :birthday: +5. **Cite `pyEPR`** [arXiv:2010.00620](https://arxiv.org/abs/2010.00620) / [arXiv:1902.10355](https://arxiv.org/abs/1902.10355) and enjoy! :birthday: [![DOI](https://zenodo.org/badge/101073856.svg)](https://zenodo.org/badge/latestdoi/101073856) @@ -307,9 +308,10 @@ This error happens when trying to read in an hdf file with numpy version 1.16, s * Contributors: [Phil Rheinhold](https://github.com/PhilReinhold), Lysander Christakis, [Devin Cody](https://github.com/devincody), ... Original versions of pyHFSS.py and pyNumericalDiagonalization.py contributed by [Phil Rheinhold](https://github.com/PhilReinhold), excellent original [repo](https://github.com/PhilReinhold/pyHFSS). * Terms of use: Use freely and kindly cite the paper (arXiv link to be posted here) and/or this package. -* How can I contribute? Contact [Z. Minev](https://www.zlatko-minev.com/) or [Z. Leghtas](http://cas.ensmp.fr/~leghtas/). +* How can I contribute? Contact [Z. Minev](https://www.zlatko-minev.com/) or [Z. Leghtas](http://cas.ensmp.fr/~leghtas/). [![DOI](https://zenodo.org/badge/101073856.svg)](https://zenodo.org/badge/latestdoi/101073856) ## How do I cite `pyEPR`? + [![DOI](https://zenodo.org/badge/101073856.svg)](https://zenodo.org/badge/latestdoi/101073856) Use this [bibtex](https://github.com/zlatko-minev/pyEPR/blob/master/pyEPR.bib) for `pyEPR` and for the method use the energy-participation-ratio paper [arXiv:2010.00620](https://arxiv.org/abs/2010.00620). From e2dff8e3819a22900e9ea3dced1655033a76d5b6 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 9 May 2021 11:03:20 -0400 Subject: [PATCH 068/125] Update pyEPR.bib --- pyEPR.bib | 29 +++++++++++++++++++++++++---- 1 file changed, 25 insertions(+), 4 deletions(-) diff --git a/pyEPR.bib b/pyEPR.bib index 59ca1a3..43a8c95 100644 --- a/pyEPR.bib +++ b/pyEPR.bib @@ -1,5 +1,26 @@ -@misc{pyEPR, -author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Reinhold, Philip and Diringer, Asaf and Devoret, Michel H.}, -title = {{pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design}}, -year = {2018} +@software{pyEPR, + author = {Zlatko K. Minev and + Zaki Leghtas and + Philip Reinhold and + Shantanu O. Mundhada and + Asaf Diringer and + Daniel Cohen Hillel and + Dennis Zi-Ren Wang and + Marco Facchini and + Priti Ashvin Shah and + Michel Devoret}, + title = {{pyEPR: The energy-participation-ratio (EPR) open- + source framework for quantum device design}}, + month = may, + year = 2021, + note = {{https://github.com/zlatko-minev/pyEPR https + ://pyepr-docs.readthedocs.io/en/latest/}}, + publisher = {Zenodo}, + doi = {10.5281/zenodo.4744447}, + url = {https://doi.org/10.5281/zenodo.4744447} } +%@misc{pyEPR, +%author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Reinhold, Philip and Diringer, Asaf and Devoret, Michel H.}, +%title = {{pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design}}, +%year = {2018} +%} From ad93ee5f400b32dfbf9b2d9bbf44dfe2b96c3683 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 9 May 2021 11:03:35 -0400 Subject: [PATCH 069/125] Update pyEPR.bib --- pyEPR.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR.bib b/pyEPR.bib index 43a8c95..d151b2c 100644 --- a/pyEPR.bib +++ b/pyEPR.bib @@ -19,7 +19,7 @@ @software{pyEPR doi = {10.5281/zenodo.4744447}, url = {https://doi.org/10.5281/zenodo.4744447} } -%@misc{pyEPR, +%@misc{pyEPR_old, %author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Reinhold, Philip and Diringer, Asaf and Devoret, Michel H.}, %title = {{pyEPR: The energy-participation-ratio (EPR) open-source framework for quantum device design}}, %year = {2018} From b13ef8f8f78b63e4aa1f711146dbe8650b21b1df Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Sun, 9 May 2021 11:09:36 -0400 Subject: [PATCH 070/125] Update pyEPR.bib --- pyEPR.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR.bib b/pyEPR.bib index d151b2c..ef57eeb 100644 --- a/pyEPR.bib +++ b/pyEPR.bib @@ -17,7 +17,7 @@ @software{pyEPR ://pyepr-docs.readthedocs.io/en/latest/}}, publisher = {Zenodo}, doi = {10.5281/zenodo.4744447}, - url = {https://doi.org/10.5281/zenodo.4744447} + url = {https://doi.org/10.5281/zenodo.4744447 https://github.com/zlatko-minev/pyEPR}, } %@misc{pyEPR_old, %author = {Minev, Zlatko K. and Leghtas, Zaki and Mudhada, Shantanu O. and Reinhold, Philip and Diringer, Asaf and Devoret, Michel H.}, From 8a766d98a96a178d097aeddf4a2b3e18dd86604f Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Tue, 1 Jun 2021 11:55:27 +0300 Subject: [PATCH 071/125] Update core_quantum_analysis.py Get numeric frequencies results should retrieve f_ND, not f_1. Some typos. --- pyEPR/core_quantum_analysis.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 541d7d4..5c7f202 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -605,7 +605,7 @@ def analyze_variation(self, print_result: bool = True, junctions: List = None, modes: List = None): - # TODO avoide analyzing a previously analyzed variation + # TODO avoid analyzing a previously analyzed variation ''' Core analysis function to call! @@ -620,10 +620,10 @@ def analyze_variation(self, ---------------- f_0 [MHz] : Eigenmode frequencies computed by HFSS; i.e., linear freq returned in GHz f_1 [MHz] : Dressed mode frequencies (by the non-linearity; e.g., Lamb shift, etc. ). - If numerical diagonalizaiton is run, then we return the numerically diagonalizaed - frequencies, otherwise, use 1st order pertuirbation theory on the 4th order + If numerical diagonalization is run, then we return the numerically diagonalized + frequencies, otherwise, use 1st order perturbation theory on the 4th order expansion of the cosine. - f_ND [MHz] : Numerical diagonalizaiton + f_ND [MHz] : Numerical diagonalization chi_O1 [MHz] : Analytic expression for the chis based on a cos trunc to 4th order, and using 1st order perturbation theory. Diag is anharmonicity, off diag is full cross-Kerr. chi_ND [MHz] : Numerically diagonalized chi matrix. Diag is anharmonicity, off diag is full @@ -1004,7 +1004,7 @@ def get_frequencies(self, swp_variable='variation', numeric=True, variations: li index: eigenmode label columns: variation label """ - label = 'f_1' if numeric else 'f_ND' + label = 'f_ND' if numeric else 'f_1' return self.results.vs_variations(label, vs=swp_variable, to_dataframe=True, variations=variations) def get_quality_factors(self, swp_variable='variation', variations: list = None): From dd61375c73f2b49b8a8166e8eae567e2661261ee Mon Sep 17 00:00:00 2001 From: Ashish Panigrahi Date: Tue, 22 Jun 2021 20:06:45 +0530 Subject: [PATCH 072/125] Updates linguist's calculation for pyEPR --- .gitattributes | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5be91f9 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-vendored From ad58d2b3a87991caaff06d493aeb43681df7ac06 Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Wed, 21 Jul 2021 16:39:05 +0300 Subject: [PATCH 073/125] Typos --- pyEPR/core_quantum_analysis.py | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 5c7f202..052c95b 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -52,11 +52,11 @@ class HamiltonianResultsContainer(OrderedDict): def __init__(self, dict_file=None, data_dir=None): """ input: - dict file - 1. ethier None to create an empty results hamilitoninan as + dict file - 1. ethier None to create an empty results hamiltonian as as was done in the original code 2. or a string with the name of the file where the file of the - previously saved HamiltonianResultsContainer instatnce we wish + previously saved HamiltonianResultsContainer instance we wish to load 3. or an existing instance of a dict class which will be @@ -834,7 +834,7 @@ def plot_hamiltonian_results(self, """Plot results versus variation Keyword Arguments: - swp_variable {str} -- Variable against which we swept. If noen, then just + swp_variable {str} -- Variable against which we swept. If none, then just take the variation index (default: {None}) variations {list} -- [description] (default: {None}) fig {[type]} -- [description] (default: {None}) @@ -842,7 +842,6 @@ def plot_hamiltonian_results(self, Returns: fig, axs """ - x_label = x_label or swp_variable # Create figure and axes @@ -852,7 +851,7 @@ def plot_hamiltonian_results(self, axs = fig.axs ############################################################################ - ### Axis: Frequencies + # Axis: Frequencies f0 = self.results.get_frequencies_HFSS( variations=variations, vs=swp_variable).transpose() f1 = self.results.get_frequencies_O1( @@ -866,7 +865,7 @@ def plot_hamiltonian_results(self, ax = axs[0, 0] ax.set_title('Modal frequencies (MHz)') - # TODO: shouldmove these kwargs to the config + # TODO: should move these kwargs to the config cmap = cmap_discrete(n_modes) kw = dict(ax=ax, color=cmap, legend=False, lw=0, ms=0) @@ -886,7 +885,7 @@ def plot_hamiltonian_results(self, plt_me_line.plot(**{**kw, **dict(lw=1, alpha=0.6, color='grey')}) ############################################################################ - # Axis: Quality factors' + # Axis: Quality factors Qs = self.get_quality_factors(swp_variable=swp_variable) Qs = Qs if variations is None else Qs[variations] Qs = Qs.transpose() @@ -899,14 +898,14 @@ def plot_hamiltonian_results(self, ax.set_yscale('log') ############################################################################ - ### Axis: Alpha and chi + # Axis: Alpha and chi axs[0][1].set_title('Anharmonicities (MHz)') axs[1][1].set_title('Cross-Kerr frequencies (MHz)') def plot_chi_alpha(chi, primary): """ - Intenral function to plot chi and then also to plot alpha + Internal function to plot chi and then also to plot alpha """ idx = pd.IndexSlice kw1 = dict(lw=0, ms=4, marker='o' if primary else 'x') @@ -930,13 +929,11 @@ def plot_chi_alpha(chi, primary): chi_element = chi.loc[idx[:, mode], mode2].unstack(1) chi_element.plot( ax=ax, label=f"{mode},{mode2}", color=cmap[i], **kw1) - if primary: chi_element.plot(ax=ax, **kw2) def do_legends(): - legend_translucent(axs[0][1], leg_kw=dict( - fontsize=7, title='Mode')) + legend_translucent(axs[0][1], leg_kw=dict(fontsize=7, title='Mode')) legend_translucent(axs[1][1], leg_kw=dict(fontsize=7)) chiO1 = self.get_chis(variations=variations, From e74d94d1415f3aabcc96861a3e5ee866a59620ab Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Wed, 28 Jul 2021 09:39:33 +0300 Subject: [PATCH 074/125] Mostly typos, some corrections In core_quantum_analysis: Rows 671-674: update to modes that are not the first. Row 917: trying to get pandas to plot with the right label. Unsuccessfully. --- pyEPR/ansys.py | 4 ++-- pyEPR/core_distributed_analysis.py | 16 +++++++------- pyEPR/core_quantum_analysis.py | 35 +++++++++++++++--------------- 3 files changed, 27 insertions(+), 28 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 29530f8..f4fc609 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -1223,7 +1223,7 @@ def get_convergence(self, variation="", pre_fn_args=[], overwrite=True): df = pd.read_csv(io.StringIO(text2[3].strip()), sep='|', skipinitialspace=True, - index_col=0).drop('Unnamed: 3', 1) + index_col=0).drop('Unnamed: 3', axis=1) else: logger.error(f'ERROR IN reading in {temp}:\n{text}') df = None @@ -1248,7 +1248,7 @@ def get_mesh_stats(self, variation=""): skipfooter=1, skip_blank_lines=True, engine='python') - df = df.drop('Unnamed: 9', 1) + df = df.drop('Unnamed: 9', axis=1) except Exception as e: print("ERROR in MESH reading operation.") print(e) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index ed09215..7f71f01 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -964,14 +964,13 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): Potential errors: If you dont have a line or rect by the right name you will prob - get an erorr o the type: + get an error of the type: com_error: (-2147352567, 'Exception occurred.', (0, None, None, None, 0, -2147024365), None) ''' # ------------------------------------------------------------ - # Calcualte all peak voltage and currents for all junctions in a given mode + # Calculate all peak voltage and currents for all junctions in a given mode method = self.pinfo.options.method_calc_P_mj - I_peak_ = {} V_peak_ = {} Sj = pd.Series({}) @@ -992,12 +991,12 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): variation, line_name, Lj, Cj) logger.debug( - f'Differnece in I_Peak calculation ala the two methods: {(_I_peak_1,_I_peak_2)}') + f'Difference in I_Peak calculation ala the two methods: {(_I_peak_1,_I_peak_2)}') V_peak = _V_peak_2 # make sure this is signed I_peak = _I_peak_1 - elif method == 'line_voltage': # new preffered method + elif method == 'line_voltage': # new preferred method I_peak, V_peak, _ = self.calc_current_using_line_voltage( variation, line_name, Lj, Cj) @@ -1011,7 +1010,7 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): V_peak_[j_name] = V_peak Sj['s_' + j_name] = _Smj = 1 if V_peak > 0 else - 1 - # REPORT prelimnary + # REPORT preliminary pmj_ind = 0.5*Ljs[j_name] * I_peak**2 / U_E pmj_cap = 0.5*Cjs[j_name] * V_peak**2 / U_E #print('\tpmj_ind=',pmj_ind, Ljs[j_name], U_E) @@ -1155,6 +1154,7 @@ def do_EPR_analysis(self, eprd = epr.DistributedAnalysis(pinfo) eprd.do_EPR_analysis(append_analysis=False) """ + if not modes is None: assert max(modes) < self.n_modes, 'Non-existing mode selected. \n'\ f'The possible modes are between 0 and {self.n_modes-1}.' @@ -1175,7 +1175,7 @@ def do_EPR_analysis(self, self.pinfo.save() # Main loop - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # TODO: Move inside of loop to funciton calle self.analyze_variation + # TODO: Move inside of loop to function calle self.analyze_variation for ii, variation in enumerate(variations): print(f'\nVariation {variation} [{ii+1}/{len(variations)}]') @@ -1202,7 +1202,7 @@ def do_EPR_analysis(self, # This could fail if more varialbes are added after the simulation is compelted. self.set_variation(variation) except Exception as e: - print('\tERROR: Could not set the variaiton string.' + print('\tERROR: Could not set the variation string.' '\nPossible causes: Did you add a variable after the simulation was already solved? ' '\nAttempting to proceed nonetheless, should be just slower ...') diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 052c95b..3ea3d5c 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -447,7 +447,7 @@ def analyze_all_variations(self, -------------------- variations : None returns all_variations otherwise this is a list with number as strings ['0', '1'] - nalyze_previous :set to true if you wish to overwrite previous analysis + analyze_previous :set to true if you wish to overwrite previous analysis ''' result = OrderedDict() @@ -461,6 +461,7 @@ def analyze_all_variations(self, else: result[variation] = self.analyze_variation(variation, **kwargs) + self.results.save() return result @@ -637,10 +638,9 @@ def analyze_variation(self, modes = list(range(self.n_modes)) tmp_n_modes = self.n_modes - tmp_modes =self.modes[variation] + tmp_modes = self.modes[variation] self.n_modes = len(modes) - self.modes[variation]= modes - + self.modes[variation] = modes if (fock_trunc is None) or (cos_trunc is None): fock_trunc = cos_trunc = None @@ -668,10 +668,10 @@ def analyze_variation(self, if modes is not None: freqs_hfss = freqs_hfss[range(len(self.modes[variation])), ] - PJ = PJ[modes, :] - SJ = SJ[modes, :] - Om = Om[modes, :][:, modes] - PHI_zpf = PHI_zpf[modes, :] + PJ = PJ[range(len(modes)), :] + SJ = SJ[range(len(modes)), :] + Om = Om[range(len(modes)), :][:, range(len(modes))] + PHI_zpf = PHI_zpf[range(len(modes)), :] PJ_cap = PJ_cap[:, junctions] # Analytic 4-th order @@ -691,8 +691,7 @@ def analyze_variation(self, f1_ND, CHI_ND = None, None result = OrderedDict() - result['f_0'] = self.freqs_hfss[variation][modes] * \ - 1E3 # MHz - obtained directly from HFSS + result['f_0'] = self.freqs_hfss[variation][modes] * 1E3 # MHz - obtained directly from HFSS result['f_1'] = pd.Series(f1s)*1E3 # MHz result['f_ND'] = pd.Series(f1_ND)*1E-6 # MHz result['chi_O1'] = pd.DataFrame(CHI_O1) @@ -731,18 +730,19 @@ def analyze_variation(self, self.print_variation(variation) self.print_result(result) - self.n_modes = tmp_n_modes #TODO is this smart should consider defining the modes of intrest in the initilazaition of the quantum object + self.n_modes = tmp_n_modes # TODO is this smart should consider defining the modes of intrest in the initilazaition of the quantum object self.modes[variation]=tmp_modes return result + def full_report_variations(self, var_list: list=None): """see full_variation_report""" if var_list is None: var_list =self.variations for variation in var_list: self.full_variation_report(variation) - def full_variation_report(self,variation): + def full_variation_report(self, variation): """ - prints the results and paramters of a specific variation + prints the results and parameters of a specific variation Parameters ---------- @@ -757,8 +757,7 @@ def full_variation_report(self,variation): self.print_variation(variation) self.print_result(variation) - - + def print_variation(self, variation): """ Utility reporting function @@ -782,7 +781,7 @@ def print_result(self, result): """ if type(result) is str or type(result) is int: result = self.results[str(result)] - # TODO: actually make into dataframe with mode labela and junction labels + # TODO: actually make into dataframe with mode labels and junction labels pritm = lambda x, frmt="{:9.2g}": print_matrix(x, frmt=frmt) print('*** P (participation matrix, normalized.)') @@ -915,6 +914,7 @@ def plot_chi_alpha(chi, primary): ax = axs[0, 1] for i, mode in enumerate(mode_idx): # mode index number, mode index alpha = chi.loc[idx[:, mode], mode].unstack(1) + alpha.columns = [mode] alpha.plot(ax=ax, label=mode, color=cmap[i], **kw1) if primary: alpha.plot(ax=ax, **kw2) @@ -927,8 +927,7 @@ def plot_chi_alpha(chi, primary): for i, mode2 in enumerate(mode_idx): if int(mode2) > int(mode): chi_element = chi.loc[idx[:, mode], mode2].unstack(1) - chi_element.plot( - ax=ax, label=f"{mode},{mode2}", color=cmap[i], **kw1) + chi_element.plot(ax=ax, label=f"{mode},{mode2}", color=cmap[i], **kw1) if primary: chi_element.plot(ax=ax, **kw2) From 389cddecbd67afd3d269a1494768a6cedf92022f Mon Sep 17 00:00:00 2001 From: Barkay Guttel <51173082+bguttel@users.noreply.github.com> Date: Thu, 29 Jul 2021 18:58:16 +0300 Subject: [PATCH 075/125] clean_up_solutions functions and some typos clean_up_solutions function for an Ansys design (to avoid analysis of all variations). Also removed index name for frequencies and typos --- pyEPR/ansys.py | 3 +++ pyEPR/core_quantum_analysis.py | 10 +++++----- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index f4fc609..595c960 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -988,6 +988,9 @@ def eval_expr(self, expr, units="mm"): def Clear_Field_Clac_Stack(self): self._fields_calc.CalcStack("Clear") + def clean_up_solutions(self): + self._design.DeleteFullVariation('All', True) # Delete existing solutions + class HfssSetup(HfssPropertyObject): prop_tab = "HfssTab" diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 3ea3d5c..be6085c 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -157,7 +157,7 @@ def vs_variations(self, QUANTITIES: `f_0` : HFSS Frequencies - `f_1` : Analyutical first order PT on the p=4 term of the cosine + `f_1` : Analytical first order PT on the p=4 term of the cosine `f_ND` : Numerically diagonalized `chi_O1`: chi matrix from 1st order PT @@ -169,7 +169,7 @@ def vs_variations(self, vs {str} -- Swept against (default: {'variation'}) to_dataframe {bool} -- convert or not the result to dataframe. Make sure to call only if it can be converted to a DataFrame or can - be concatinated into a multi-index DataFrame + be concatenated into a multi-index DataFrame Returns: [type] -- [description] @@ -192,7 +192,7 @@ def vs_variations(self, z = sort_df_col(pd.DataFrame(z)) if self.sort_index: z = self._do_sort_index(z) - z.index.name = 'eigenmode' + # z.index.name = 'eigenmode' z.columns.name = vs return z @@ -549,7 +549,7 @@ def _get_participation_normalized(self, variation, _renorm_pj=None, print_=False if np.any(Pm < 0.0): print_color(" ! Warning: Some p_mj was found <= 0. This is probably a numerical error,'\ 'or a super low-Q mode. We will take the abs value. Otherwise, rerun with more precision,'\ - 'inspect, and do due dilligence.)") + 'inspect, and do due diligence.)") print(Pm, '\n') Pm = np.abs(Pm) @@ -568,7 +568,7 @@ def get_epr_base_matrices(self, variation, _renorm_pj=None, print_=False): :Om: Omega_mm matrix (in GHz) (\hbar = 1) Not radians. :EJ: E_jj matrix of Josephson energies (in same units as hbar omega matrix) :PHI_zpf: ZPFs in units of \phi_0 reduced flux quantum - :PJ_cap: capactive particiaption matrix + :PJ_cap: capacitive participation matrix Return all as *np.array* PM, SIGN, Om, EJ, Phi_ZPF From f79941d91ea8458f61523c23de2ce0f281d33fcd Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Fri, 27 Aug 2021 07:50:56 -0400 Subject: [PATCH 076/125] Update core_quantum_analysis.py Fixed #83 --- pyEPR/core_quantum_analysis.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 5ce11f5..0e8869a 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -621,10 +621,10 @@ def analyze_variation(self, ---------------- f_0 [MHz] : Eigenmode frequencies computed by HFSS; i.e., linear freq returned in GHz f_1 [MHz] : Dressed mode frequencies (by the non-linearity; e.g., Lamb shift, etc. ). - If numerical diagonalization is run, then we return the numerically diagonalized - frequencies, otherwise, use 1st order perturbation theory on the 4th order + Result based on 1st order perturbation theory on the 4th order expansion of the cosine. - f_ND [MHz] : Numerical diagonalization + f_ND [MHz] : Numerical diagonalization result of dressed mode frequencies. + only available if `cos_trunc` and `fock_trunc` are set (non None). chi_O1 [MHz] : Analytic expression for the chis based on a cos trunc to 4th order, and using 1st order perturbation theory. Diag is anharmonicity, off diag is full cross-Kerr. chi_ND [MHz] : Numerically diagonalized chi matrix. Diag is anharmonicity, off diag is full From 1339bd55f1afc2e773f148052c425925240d1652 Mon Sep 17 00:00:00 2001 From: Xinyu Date: Tue, 26 Oct 2021 17:44:17 +0200 Subject: [PATCH 077/125] Tutorial2: Correct import of CalcObject to pyEPR.ansys --- ...ations - dielectric energy participation ratios (EPRs).ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_tutorial_notebooks/Tutorial 2. Field calculations - dielectric energy participation ratios (EPRs).ipynb b/_tutorial_notebooks/Tutorial 2. Field calculations - dielectric energy participation ratios (EPRs).ipynb index 52a9810..6ba5c1f 100644 --- a/_tutorial_notebooks/Tutorial 2. Field calculations - dielectric energy participation ratios (EPRs).ipynb +++ b/_tutorial_notebooks/Tutorial 2. Field calculations - dielectric energy participation ratios (EPRs).ipynb @@ -397,7 +397,7 @@ ], "source": [ "from pyEPR.core import *\n", - "from pyEPR.core import CalcObject\n", + "from pyEPR.ansys import CalcObject\n", "\n", "self, volume = eprh, 'AllObjects'\n", "\n", From 7f374920edb0c176e445b8a1deb29128b4716fa3 Mon Sep 17 00:00:00 2001 From: Xinyu Date: Wed, 27 Oct 2021 17:19:51 +0200 Subject: [PATCH 078/125] Indentation after was wrong and variable swp_var was never defined --- docs/source/examples_quick.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/examples_quick.rst b/docs/source/examples_quick.rst index cdb1e14..9a8a009 100644 --- a/docs/source/examples_quick.rst +++ b/docs/source/examples_quick.rst @@ -34,7 +34,7 @@ succinctly plotted. # 3. Perform microwave analysis on eigenmode solutions eprd = epr.DistributedAnalysis(pinfo) - if 1: # automatic reports + swp_var = 'Lj_alice' # Sweep variable from optimetric analysis that should be used on the x axis for the frequency plot eprd.quick_plot_frequencies(swp_var) # plot the solved frequencies before the analysis eprd.hfss_report_full_convergence() # report convergen eprd.do_EPR_analysis() From b8a76c7c6ce49e38eb3ec13cdce4c6304991a7ec Mon Sep 17 00:00:00 2001 From: CHAO ZHOU <44282719+hatlabcz@users.noreply.github.com> Date: Thu, 4 Nov 2021 16:36:57 -0400 Subject: [PATCH 079/125] fixed mode number error in QuantumAnalysis fixed KeyError when '0' is not in variations for QuantumAnalysis. updated type hint for QuantumAnalysis.analyze_variation fix mode number error in QuantumAnalysis --- pyEPR/core_quantum_analysis.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 0e8869a..8b949bd 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -265,7 +265,7 @@ def __init__(self, data_filename, self.convergence = results['convergence'] self.convergence_f_pass = results['convergence_f_pass'] - self.n_modes = len(self.modes['0']) + self.n_modes = len(self.modes[self.variations[0]]) self._renorm_pj = config.epr.renorm_pj # Unique variation params -- make a get function @@ -600,7 +600,7 @@ def get_epr_base_matrices(self, variation, _renorm_pj=None, print_=False): return PJ, SJ, Om, EJ, PHI_zpf, PJ_cap, n_zpf # All as np.array def analyze_variation(self, - variation: List[str], + variation: str, cos_trunc: int = None, fock_trunc: int = None, print_result: bool = True, From d507810f181446b862802c553bf51fa8775d20e6 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Thu, 18 Nov 2021 09:20:34 -0800 Subject: [PATCH 080/125] Update back_box_numeric.py --- pyEPR/calcs/back_box_numeric.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/pyEPR/calcs/back_box_numeric.py b/pyEPR/calcs/back_box_numeric.py index 794fe08..5f6e298 100644 --- a/pyEPR/calcs/back_box_numeric.py +++ b/pyEPR/calcs/back_box_numeric.py @@ -43,7 +43,8 @@ def epr_numerical_diagonalization(freqs, Ljs, ϕzpf, cos_trunc=8, fock_trunc=9, use_1st_order=False, - return_H=False): + return_H=False, + non_linear_potential=None): ''' Numerical diagonalizaiton for pyEPR. Ask Zlatko for details. @@ -63,7 +64,8 @@ def epr_numerical_diagonalization(freqs, Ljs, ϕzpf, ), "Please input the inductances in Henries. \N{nauseated face}" Hs = black_box_hamiltonian(freqs * 1E9, Ljs.astype(np.float), fluxQ*ϕzpf, - cos_trunc, fock_trunc, individual=use_1st_order) + cos_trunc, fock_trunc, individual=use_1st_order, + non_linear_potential = non_linear_potential) f_ND, χ_ND, _, _ = make_dispersive( Hs, fock_trunc, ϕzpf, freqs, use_1st_order=use_1st_order) χ_ND = -1*χ_ND * 1E-6 # convert to MHz, and flip sign so that down shift is positive @@ -73,7 +75,8 @@ def epr_numerical_diagonalization(freqs, Ljs, ϕzpf, -def black_box_hamiltonian(fs, ljs, fzpfs, cos_trunc=5, fock_trunc=8, individual=False): +def black_box_hamiltonian(fs, ljs, fzpfs, cos_trunc=5, fock_trunc=8, individual=False, + non_linear_potential = None): r""" :param fs: Linearized model, H_lin, normal mode frequencies in Hz, length N :param ljs: junction linerized inductances in Henries, length M @@ -119,10 +122,13 @@ def tensor_out(op, loc): def cos(x): return cos_approx(x, cos_trunc=cos_trunc) + + if non_linear_potential is None: + non_linear_potential = cos linear_part = dot(fs, mode_ns) cos_interiors = [dot(fzpf_row/fluxQ, mode_fields) for fzpf_row in fzpfs] - nonlinear_part = dot(-fjs, map(cos, cos_interiors)) + nonlinear_part = dot(-fjs, map(non_linear_potential, cos_interiors)) if individual: return linear_part, nonlinear_part else: @@ -297,4 +303,4 @@ def black_box_hamiltonian_nq(freqs, zmat, ljs, cos_trunc=6, fock_trunc=8, show_f H = black_box_hamiltonian(f0s, ljs, fzpfs, cos_trunc, fock_trunc) return make_dispersive(H, fock_trunc, fzpfs, f0s) -black_box_hamiltonian_nq = black_box_hamiltonian_nq \ No newline at end of file +black_box_hamiltonian_nq = black_box_hamiltonian_nqzZ From 1483e7f12bb6f9cf6b19a1299c7eb1597a65539b Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Fri, 7 Jan 2022 11:39:56 -0500 Subject: [PATCH 081/125] fix log plot bug if q__coupling isnot there --- pyEPR/core_quantum_analysis.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 8b949bd..f199023 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -894,7 +894,8 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=0, marker=markerf1, ms=4, legend=True, zorder=20, color=cmap) Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) - ax.set_yscale('log') + if not (len(df) == 0): + ax.set_yscale('log') ############################################################################ # Axis: Alpha and chi From a52a8501a13cecea449948c6a761555b0469b690 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Fri, 7 Jan 2022 11:40:50 -0500 Subject: [PATCH 082/125] Update core_quantum_analysis.py --- pyEPR/core_quantum_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index f199023..558a09f 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -894,7 +894,7 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=0, marker=markerf1, ms=4, legend=True, zorder=20, color=cmap) Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) - if not (len(df) == 0): + if not (len(Qs) == 0): ax.set_yscale('log') ############################################################################ From 8a29dbb91b85ce77c95a42eddec34dcd910c82f1 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Fri, 7 Jan 2022 15:08:26 -0500 Subject: [PATCH 083/125] Update the init and setup files before creating a tag. --- pyEPR/__init__.py | 5 +++-- setup.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index b88272b..ad07f1f 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.4.6 +@version: 0.8.5.2 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -92,7 +92,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.4.6" +__version__ = "0.8.5.2" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' @@ -101,6 +101,7 @@ ############################################################################## # Config setup from ._config_default import get_config + config = get_config() ############################################################################## diff --git a/setup.py b/setup.py index a2796bd..8f20d1a 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.4.6', + version='0.8.5.2', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 9290bcd15a3b2f3e84b190611d79e2133e751e0f Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Mon, 10 Jan 2022 10:59:21 -0500 Subject: [PATCH 084/125] Update back_box_numeric.py Fix typo --- pyEPR/calcs/back_box_numeric.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/calcs/back_box_numeric.py b/pyEPR/calcs/back_box_numeric.py index 5f6e298..57e904f 100644 --- a/pyEPR/calcs/back_box_numeric.py +++ b/pyEPR/calcs/back_box_numeric.py @@ -303,4 +303,4 @@ def black_box_hamiltonian_nq(freqs, zmat, ljs, cos_trunc=6, fock_trunc=8, show_f H = black_box_hamiltonian(f0s, ljs, fzpfs, cos_trunc, fock_trunc) return make_dispersive(H, fock_trunc, fzpfs, f0s) -black_box_hamiltonian_nq = black_box_hamiltonian_nqzZ +black_box_hamiltonian_nq = black_box_hamiltonian_nq From 7c968a40f0bcadd80dbabde0e14ff6566f674941 Mon Sep 17 00:00:00 2001 From: Will Shanks Date: Mon, 10 Jan 2022 14:20:50 -0500 Subject: [PATCH 085/125] Add pylint CI check --- .github/workflows/ci.yaml | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/workflows/ci.yaml diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 0000000..9fbce14 --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,37 @@ +name: CI + +on: + push: + branches: + - master + pull_request: ~ + +env: + # Increment this to invalidate the cache without modifying requirements.txt + PIPCACHEVERSION: 0 + +jobs: + pylint: + runs-on: ubuntu-latest + steps: + - name: Check out repo + uses: actions/checkout@v2 + - name: Set up Python + id: setup-python + uses: actions/setup-python@v2 + with: + # qutip does not support 3.10 yet + python-version: '3.9.x' + - name: Set up cache + id: cache + uses: actions/cache@v2 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('requirements.txt') }}-${{ env.PIPCACHEVERSION }} + restore-keys: | + ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('requirements.txt') }}- + ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}- + - name: Install package and pylint + run: python -m pip install . pylint + - name: Run pylint + run: pylint --errors-only --jobs=0 pyEPR From b24c42a46cf62f709654888495c6269bec47258b Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Mon, 7 Feb 2022 17:12:45 +0200 Subject: [PATCH 086/125] Fix old keyword argument name in first tutorial --- .../Tutorial 1. Startup example.ipynb | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/_tutorial_notebooks/Tutorial 1. Startup example.ipynb b/_tutorial_notebooks/Tutorial 1. Startup example.ipynb index c72caef..13d7edb 100644 --- a/_tutorial_notebooks/Tutorial 1. Startup example.ipynb +++ b/_tutorial_notebooks/Tutorial 1. Startup example.ipynb @@ -578,7 +578,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -596,7 +596,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAADaCAYAAAA2YqSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydeXwbxdnHv49s+XYS4gRCEhLHISTcJAQoUK5yvEDhhUKhBVrCFdq+QGmhLVdbekNbjkKhBzcUKGehXC2EK5RS7itATpwQ0gTI7duSpef9Y3bttSytVpZkyc58Px997N2ZnXme3dVqZnZ+84iqYrFYLBaLxWKxWCxBCBXaAIvFYrFYLBaLxTJ4sB0Ii8VisVgsFovFEhjbgbBYLBaLxWKxWCyBsR0Ii8VisVgsFovFEhjbgbBYLBaLxWKxWCyBsR0Ii8VisVgsFovFEhjbgbBYLEMHEUXkL57tUkRWI/JYDsreH5GNiLyFyEJEXkDkiADHnYLIdc7/RyOyXYb1nuL48LbzuaNf9mdW568RebdXXSJfR+TcvNdtsVgslqLHdiAsFstQohXYAZFKZ/tg4L85LP9fqE5HdSrwbeA6RA7M4Pijgcw6EIZ7Ud3F+ZzcJ1WktB9lJkdkOLAXqjsBJYjs6JzPU4A/9K9ILhHhfRHeFeFtEfZIk/95EWb2p66Eck4R4boM8u8vQvadzX4gQr0IJ2Z4jIjwrAjDnO1bRPhMhPcS8o0UYY4Ii52/m3mOv1aEJc61meE5ZpaTf7EIs9LYcYUIX8jEdovFMrixHQiLxTLU+AfwRef/E4C/dqeI7I7IS85bhJcQmersPw+RW5z/d0TkPUSqfGtRfRv4GXC2c9xoRB5E5DXns3ev/CJ7Af8L/NZ5kzAZkdlO3necY/3r7F3e84j8CpG5wLkp6xepQ+Qpx+c/I/IRIqN8So4DZYgIUAlEge8D16IaDWxft5nsCRwBzFBlJ+Ag4ONMy9kEqIfMOhDA4cA7qjQ527cBhybJdyHwjCpTgGecbYDDgCnO50zgj2A6HMClwB7A7sClbqcjBb/3lGmxWDYBbAfCYrEMNe4BvopIBbAT8IonbQGwL6rTgR8Dv3L2/w7YGpEvAbcC30C1LUBdbwLTnP+vAa5GdTfgWOCmXjlVXwIeAb7vvEn4EPgbqruhujMwHzg9RT1f8UxhOtWzfwSq+6F6pU/9lwIvOj4/Akzw9Ui1GXgQeAtYCmwEdkP172nORSq2BNao0mmKZ40qKwFEOFCEt0SY54yel3sPFOFbIvzGs32KCL93/v+aCK86bzT+LEKJs/9UERaJMBfo3YnrKafaqe81p/6jguZxbHhYhEdFWCrC2SKc5+R52Wl8I8JkEf4pwhsi/EvE3Cci3OaM+r8kQqMIX3aqvBzYx/HnuyJs7/HvXRGmJHHlJKD7uqjyArAuSb6jgNud/2/HvAlz99+hiqryMjBChC2B/wHmqLJOlfXAHOBQEUoc+99zrtl3nXo/AupEGJPsfFsslqFH7l57WywWSzGg+i4i9Zi3D08kpA4HbkdkCqBA2DkmjsgpwLvAn1H9d8DaxPP/QcB2SPeuYYjUpjl+B0R+AYwAaoAnU+S7F9Wzk+5PX/++wDEAqD6OyPo0NoHqb8BpuIvcBPwYkTOAQ4B3Uf1F2jJ6eAr4sQiLgKeBe1WZK0IFZsT8QFUWiXAH8C1MZ87lAeA/wA+c7a8AvxRhW+f/vVWJivAH4CQR5gA/BXbFdHyew3SEErkEeFaV00QYAbwqwtMZ5NkBmA5UAEuAC1SZLsLVwMmODzcA31RlsTNl6w/QPc1nS+DzmM7nI46fFwLfU+UIAKejdI0qd4lQBqaDlMDewDeS7E9kC1VWAaiySoTNnf3j6P02aIWzL9X+XYBxquzg2DjCk+dNx54HA9hjsVgGObYDYbFYhiKPAFcA+wN1nv0/B55D9UtOJ+N5T9oUoAUYm0E90zFvDsC80d0T1fZeOUQSj/FyG3A0qu84HZj9M6gbjObDxa9+zbBc99jpzn+LgGtQ3ReRexCZguriIEWo0iLCrsA+wAHAvSJciPOGQ5VFTtbbgbPwdCBUWe2M0n8OWAxMBf7t5NsVeM05vZXAZ5gpN8+rstpx/V5gmyRmHQL8rwjfc7Yr6Ptmxi/Pc6o0A80ibAQedfbPA3YSoQbYC7jfc/m9b1ceViUOfCDCFklPnOk4XSLCeOBvqiQ73yMdO/pLsptTffY3Ag1O5+ZxTOfQ5TMy++5YLJZBjJ3CZLFYhiK3AD9DdV7C/uH0iKpP6d5rhMPXYEbr6xD5MukQ2Qn4EXC9s+cpXD2ESd8lyVHNgPetRC2wCpEwZjpKNqSq/4XuskUOA9+57In8HDPVK0zPCHgcCK7VAFSJqfK8Kpc6Nh5L8kZqMu4FjneOeUi1u4F7uyq7OJ+pqvzErS5AmQIc6zl+gmp3RzBInk5PvrhnO44ZmAsBGzzH7qLKtp5jvMcnPQ+q3I3RzLQDT6YQKXeJBPod/9SZmoTz9zNn/wpgK0++8cDKVPud6Uw7YzreZ9F7ml6FY6vFYtkEsB0Ii8Uy9FBdgeo1SVJ+A1yGyL/pPSXkauAPqC7C6BAuR2TzJMfvg7uMq+k4fBvVZ5y0bwMzneVPPwC+meT4e4DvO2VMxnRAXsHMMV/QD0+9pKr/p8C+iLyJGVVf3n2EyBOIJB81FjkaeA3VlahuAP6DyDxAUX0nqFEiTE2Yv78L8BHG33oRtnb2fx2Ym6SIv2Hm7J9Az5StZ4Avu1NxnFWGJmLO5f4i1IkQBo5LYdaTwDkipvEuwvR+5kmKI2peKmLqd1Y72jnNYb06lyI0AI2qXIt5o7ZTkmMWAg0BTHoEuldSmkWPbuIR4GTHvs8BG52pTk8Ch4iwmSOePgTTiRkFhFR5EHPvzvDUsQ30Xv3JYrEMXUS1f2+2LRaLxTIIEVkGzER1zcBUx66YVXpGAF0YzcCZqqwR4UDMVLNS4DXgW6p0ivA8Rg/wulPGY8B2qj2NZRG+AlyEGQiLAmep8rIIpzr7VwFvAyWq9NKPiFCJmSq1F+YNwDJVjhBhf6feI3zynALMdMsUYZmzvcabJsIkzKpGW2Le4Nyjys9EuA14TJUHnONbVKlxOjz/BEZhprZVAF9zfPsEOFG1t0BahB8Bq1TNmwAR/oqZBjcK+BS4VJWbRagD7sNMwVoOHKfKOqdzdB1m5aY24FTPOT8NuNip6peq3Op0gm6lZ/DxIlX+4dj+LrCjKl1YLJYhj+1AWCwWy6bEAHcgLPnDmY50hyoHF9iOL2GW6f1RIe2wWCwDx5CYwiQiJ4nIU+lzFi8iUi8iKv0ICCWGW0VkvYi8mg/7ig0RuU1EImIaQwNV5zYi0iIiMTEr0lgsgw/Vett5GBo4041uFCeQXAEpBa4ssA2WIYqI/ERE7iy0HZbeDJoOhIgsE5F2pwHnfq4DUNW7VPWQQttYQD6Pibg7XlV3L7QxA8hvVLXeu0NEDhaR50SkWUTWisjbInKBmJgAKR9ETudt68T9XlR1karWAP/KpRMWi8XSX1S5zxNIrlA23K/KhkLaYAmG05aKSEIwSee3UsWsTpfL+tzBUbfd9qmIPCYi/Xprls1gq6cMFZFWj00FuXdF5EQRWSUiS0Vkf8/+ySLykogkW7q5aBg0HQiHI1W1xvNJti76pshEYJmqtqbNOYQRkeMw66nfDUxU1TrMWvHj6b2iiMVisVgsmypLMYsSACAiO2KWQs4nI5wBuJ0xi0Y8JGbp6kKxs6ctOSJZhmw6Kelwyr4csxDBORgtksu1wHmqGstX/blgsHUgkiIip4jIi57tQ0RkoYhsFJE/iMhc75QTETlNROY7U36eFJGJnjQVkW+KyGIn/XoREU89/xaRq0Vkg4g0ishezv6PReQzEZnlKWu4iNwhIqtF5CMR+aGIhJy0EhG5QkTWiEgj8MUEn4aLyM1O7/S/IvKLZL1RETkds5Tenk5P+qcJ6eWOrTt49o123uZsLiKjnNGADSKyTkT+5dqY5pzvLyIrRORix4dlInKSJ/2LIvKWiDQ55+YnnrQKEbnTeUOwQUReE5EtPOe40XmDsNRbZhp7BLgK+Jmq3qiq6wBUdaGqnqMB16x3ytrgGZlozceojMVisVgsBeIvmICHLrOAO7wZ0vyGf8X5nR7mbB8mIp+IyOh0FavqJ2pWyPsJ8GtPm2isiDzotJeWisi3UxTxgvPX/Z3e0xmxf9ZpU6wRkbtEJGmnwA9Pu+YCEfkEs2AAInKEmDc0G8S8GdjJc8x0EXnTabPcKyL3iAkOmo464L+qugoTYLPBKe/Lzv6XM7V/oBkSHQgvYl7LPYBZhaMOs8zdXp70ozErSxwDjMZMR/lrQjFHALthesrHA//jSdsDs9pEHWak+x4n79aYFTOuE5EaJ+/vMevONwD7Yb6wpzpps516pgMzgcR152/HrFiytZPnEKDPvHtVvRmzXON/nJ70pQnpnZhlEE/w7D4emKuqnwHnY9b8Hg1s4ZyboMr6MZjVPsZhHkA3iMhUJ63V8XcEpnP0Lefc4+QdjnkrUOfY3y4i1Zie92GqWou5bm8HtGUq5k1D1lFQVXWEOzKBiQ3wL3piB1gsFovFMph5GRgmIts6A5NfARKn9qb8DVfVezGBDq8VkTrgZuAMVV2dgQ1/AzYHpjqdiEeBdzDtiQOB74jI/yQ5bl/nr/s7/R/MKmmXYQIZbotpW/wkA1u8jAFGYmZ2nCkiMzBxhb6Baa/8GXjEGZwtAx7GdMhGAvdj4tUEYTVQJyLjMVPQ33fajj/EtF+LnsHWgXjY6QG6n9lJ8hwOvK+qf1PVLkyD9BNP+jeAy1R1vpP+K2AX8byFAC5X1Q2quhx4DrNuuctSVb3VebV0L+ZG/ZmqdqrqU0AE2NrzpbxIVZtVdRlGZPZ1p5zjgd+p6sfOaPllbgXOaPxhwHdUtdVp6F8NfLUf5wxMR8fbgTjR2QdmicAtMVN+oqr6L81saa4fOb7PxUQmPR5AVZ9X1XmqGlfVdzGdtP08ddYBW6tqTFXfUFV3Dm8c2EFEKlV1laq+H9AOdz5n97V2RgI2iEibiHzdk/f4hPso6fxHEfkK5lwdq6rRgHZYLBaLxVLsuG8hDsbEZOk1SJbmNxxMIMEvYIIKPqqqj2VY/0rn70jMIOxoVf2ZqkZUtRG4kYBtHlVdoqpznLbIasxshP3SHPampw1wrWd/HLjUKasdM9j7Z1V9xWmv3I4JBPk55xPGtOWiqvoAZjnqIDbHgW9hBry/59TzM8zA845itJxPemePFBt5m9+VJ45W1afT5BkLfOxuqKqKyApP+kTgGhHxrhghmF7vR862t8PRBtR4tj/1/N/u1JG4rwbToC3zlInz/7hkdibkm4i5KVeZmTmA6ex582fCs0CliOyB8W0X4CEn7beYnvpTTl03qOrlActdn6C7+AjjF05dlwM7YM5DOaZ3DubBtRVwj/Oa8U7gElVtdRrt3wNuFhPs63xVDRJga63zd0vM/E5U9auOLS/SO2jYfar6Ne/BIqIJ29MxcxIPyXBUxWKxWCyWYucvmOlAk0iYvgRpf8NR1Q0icj9wHsFH3b24baF1wI7A2ITBvBICLlgiJujntcA+mGCMIWB9msNmqOqSJPtXq2qHZ3siMEtEzvHsK8O0dRQz3cjbfvC25XxRE4T0GceHnTCzUb4PLMMsjrMVZor654KWOZAMtjcQQViFmcoCdM+NH+9J/xj4hjNNxf1UqupLObZjDWak3ftmYwI9vfxV9Bb2TkiwsRMY5bFxmKpu3x9DnJ7ufZi3ECcCj6lqs5PWrKrnq2oDcCRwnogcGLDozZxpR14f3FGFuzFRTrdS1eHAnzAdNZye+k9VdTvMNKUjcOZjquqTqnowpiOwADMKEQR3BOWYgPlT4szjfAg4W1XfyrY8i8VisViKCVX9CDPYdjhmOlEiKX/DAURkF+A0zJuJa5Mcn44vAZ9hppl/jJnd4W2X1arq4clMT7LvMmf/Tqo6DDOdXJLkC0Ji+R8Dv0ywrUpV/4ppx40Tz0gvvdtygXCOvw74NmbwucS5Pq+RPAJ9UTAUOxCPY17/HC1G5X4WZk6by5+Ai0Rke+gWKx+XayOcKU73Ab8UkVpnitR59MwzvA/4toiMF5HNgAs9x64CngKuFJFhIhJyRELpXsn5cTdmStVJ9ExfcsVBWzs3cBMQcz5B+amIlInIPpiOgDtCUQusU9UOEdkd03Fx6zxARHZ0pnk1YTpaMRHZQkT+1+mUdAItQW1xRgDOBy4VkdkispkYpmC0HYFw7pkHgbuceZ4Wi8VisQxFTge+oMlXcPT7Da/AtGUuxug6x4nI/wWp0PmdPxu4FDPFOw68CjQ54uVKMYvM7CAiuyUpYjVmmlGDZ18tpr2wQUTGYUbxc8WNwDdFZA+nTVEtRmBei9GBdGHacqUicgzQn6X0zwDeUtW3MbMpKkVkO+AAoDFHfuScwdaBeFR6x4F4KDGDmgBJxwG/wVyI7YDXMQ1SVPUh4NeY6TNNwHsYvUE+OAcjRGoEXsQ03G9x0m4EnsSIht6k7wjAyZjXZB9gXsU9gBmV7xeq+opjy1jgH56kKZgVAFowX4Y/qOrzACLyDxG52KfYTxzbVgJ3Ad/0TDf6P+BnItIM/BjTYXIZ4/jTBMwH5mIeRiFMJ2Al5rXmfk45QX28F6PB+Bpm1GCNU+8NeF69pmE85jXodxLutYxHFSwWi8ViKVZU9UNVfT1Fst9v+GXAClX9o5qFWr4G/MIZsEvFBhFpBeZh3nocp6q3OHbEMDMgdsG8FVmDmbozPInNbcAvgX87+oXPAT/FLIe6ETOInOyNSr9wzs9szBuC9cAS4BQnLYKZ9XCKk/YVb90iMiFd+0HMwj/ngonirkabezZm6vmfMO3IokQy08sOPsSo+1cAJ6nqc4W2Z6ggJujJnao6Pl3ePNV/I2ZK1qeqOnmA6pyCeaVYBvyfqt42EPVaLBaLxWIpfkTkNkzn6oeFtiXfDDYRdSDELP31CkbQ/H3MXLiiX1PXEhxVnY0ZFRjIOhdjlrSzWCwWi8Vi2WQZbFOYgrIn8CHmNdiRmNWb2gtrksVisVgsFovFMvgZ8lOYLBaLxWKxWCwWS+4Yqm8gLBaLxWKxWCwWSx7IiwZi1KhRWl9fn4+iMyYSiVBWVlZoMwJhbc09g8VOMLbOmzdvjaqOLrQtA8GeoZC+W1lZaDMGFFWl95LhmwbW702LTP1ua9tWVV/fJAY0ve2jwfT7ZOk/g/06v/HGG0nbJXnpQEyYMIHXX0+1Mpihra2NqqqqrPIEKWPZsmX4dWYGyg5ra2FsTWdnsdk6adKkwFEsBzv/qayE1mTLjw9dPmlsZExDQ/qMQwzr96ZFpn6LvLfJaBTr6+u720dBfp8GmiC/ZQNZZqbHBs2fzW96pmmD/TqLSNJ2ScFWYYrF0scHS5cnSBnFYoe1NfM81tahiYgcCRwZLSujNBKBaNR8ACorIRaDSKRnOx6Hzk6zXVFh/nZ0mL/l5RAKQbvT/igrg5KSnu1w2Hza2npvt7eDKpSWmmPc7ZISU2ZHh6k3FDJ1dnYau0SMTZEIdHX1bHt9qKpK6ZM0NZmyhpBPga5TR4dJG0o+BblOkQhs2DC0fApwnaS52fgd1KdNAPe519DQQDQaJRaL0dLSQnNzMxUVFcRiMaLOdSsvL0dViTjXrby8HIBO57qVlZUhIt3b4XCYkpISOpzrmLhdWlpKOBymo6MDVaWkpIRwOExnZ2ev7UgkwsaNG1FVysrKuu0UEcrLy3ttV1RUEI1G6erqAujjg3e7ubmZ0tLSfvm0YcMGYrFYYJ82btxILBbr5VM8HicUCvXyqaWlhXA4nNKn5uZmwuFwUp9cm5Jdp/b29u7fetcn9zrn8jol8ymT6+T6EPTeS0ZOOxDuF6S+vr7bgWQ3E0A0GqWzs9P3ZnJvvGQnOhwOd19kSH3iW1tbaW5uTnni250Ho9+Jb2lpSeqDu+2m+33pW1pa0n7pOzs7iUajKW+m9vZ2KioqfG+mrq4u2traUt5MsViM9vZ23y+9a5+fT+4XIpVP7jnx+4K0tLT4fkFaWlrSfkFaW1uprKxM+SBraWnpcy8m+hSNRolEIr4PMu+9lswn9z7w+9K3OT+6qXxy78Whjqo+CjxKdfVsyspMIyWRxJGRxEaG2/BxSXzIJW4n1jHQ2wBVVeiwYT22DRGfepHKp3XrjP1DyScXP5/KymBEwsrPg92nAHVqbW1vv4PYOMRxn3szZ86cHQ6HCYfD1NTUUFtbC5jflYqE65DYeEucBpO4HQ6HfbdramrS5ldVqqurA5VXWlraJ93rg7stIt22ZurTsGHDetmdzqfa2lrf/O62iOBeh2Q+edMTfUq0yetTeXl5H5u819nPplQ+pcvfn+vk9SHIvZeMnHYg3C/IrrvuOjvViXe3o9Eo4XDY92YKhUK+J8ZtmHlJPPHV1dW+F26zzTbrtS/ZiXcbvYk+uNujRo3qdVwynyoqKrrzpPrSl5eXJ72hXZ/cMvzOSVlZmW+6e94T070+DR8+vE+eRJ8SvxCJPiWek2Q+uf4mSw+Hw718SeVTRUVF9zVK5lNNTU3Sh4DXJ/ec+D3Ikt1r3m3vPZLK55EjR/qek8pNZCTOYrFYLMVJkIbjQJaZ6bFB86fL55fe37RiIhd25kW05Ls07LqlcP0elP5qC7h+D7OdAnfEOJsyisWOlGV4ypl4+wzfcoKUkc6WXJThS7Gc14Dn1LeMDGzJ+3m1BKexEbbf3kyn2H57s22xWPKO+9Ubv80k+9XLgOVr2zj4qrlMvugJDr5qLsvXthXMFt/fsgKUmemxQfOny+eX3t+0YiIXduYlDsSMGTP0zTffTJ54/R6weiGggED15rDPeaAx0Lj5xM3/nZ0dlIfDfdPevAPa1/eUUTECdjrebKv2/NU4zc1N1NbU9E2b/yh0NvXYVT4Mph7ulOlBlWhXlHCpd7TYybPoyYQyamHK//TNB0SjXYTDpY4NCSyZA53NvcvZ+qA+2aLRKOHSFC+NljwDEU8ZZTUw+Qu9/VEl2tXVu4xuexSWvgCRFk8Z1VC/L8nOSVt7G1WVVT3Huiz7N0Q9wthwNUzcM0l90BXrorSkpG/ax69A1PMADVfB+N361qVKVyzWuwy3nJVvQNQzJai0Esbu0tdnVWe+ZChpGp99AF0dnnLKYfS0PnljsTgloVDPsd70tYsh5sxBlhCM2gbOeoVEHBH1G6o6s0/iUKS6WvMiot5uO5g/3/wfCsG0afD++7mvpx9YUe2mxabm9/bbm6+eamZfPZH32lR3qM6/hYVn5syZmiiiPviquSxZ3YKqkYVsPbqGOeftVxD7mpube80uKHSZmR4bNH+6fH7pmaYVo4g6k/MqIknbJQMvol6zmJ5GoELrp/DPC5Jm7fWCRUI9H7cx5pbRsR7evQcQ8+1DnLxCVSxuRiLdNAmZ/70NfzDby//j1NV7+bkS7bsPSVZGM6x8q28+oMQVkJmdfY9L3P6071PXlFHSZz/Qu/MApiOwZnEvGwBCcefJ3ssf6TmmVxmt0PTfPmUAlESiEPPU6aZHExqE0VZoW5e0PonHjD+JadGE0Zdom9OIlz62iMYhTp/9vToPAF3tECr15OkpS2NxI/ZLktar8wDQFYGaMX3yaizWc58l2vLZBz3/a7znuljyw6JFPf/H47BwYeFssVg2IRYu7Bk3sV+94DSubu0+b6pmu1DkY9nhbMrM9Nig+dPl80vvb1oxkQs78yKinjRpUkoRdXhkA6F1HyIaRyVEfEQ9bSf8nfKKSpASOqNRQCirqCQWV2Jx85YhXFbWLVytuu2A3mWMnEzkjH8lFVEvWrSICRMm9BEcV9/2BWTdEk8ZWxP/5ktJRdQdHR2Ulpb2ERzX3H4grF3cXYbWTSE6+19JRdRdXV1UVVUlFRyH/rRnH3/aZj3bR0StqlRWViYVHFfcsl+fMtpnPd1HRF1aWkpENangONl5jZ32TFIR9eJFi5g4cWIfwXHiedWRWxM79amkIupoNNqtg/D6VHnL/r3OK3VTaD/h4aQi6lgsRk1NTR8Rdcmf9upjR+uxf+32wetTSUkJ8dLSpCLqZOckeuwdfUTUIkJHV1dSEXX16sRzMplYNLrJiqjzvgrTNtvAggV0D+dts03RrIRjV2EaQj7ZVZj6+NRQP4zFH5pBvFBImTolDpGYXYUJ/1WYJo2qonFNK3GnEzFpVFXaRWbytQpTNBqltbU1p6swdXV1EYlE+rUKk7uYTlCf3AVP0q1Y5NqWyqeuri7fxYCam5uTLjITCoW6F1wp5lWYXB+yWYVp4KcwrVsKf/0qumYxMmoKnHAPjJyUNGt7e3tyYWkGZaR8dTTAdqQsI4NyiqUMGATndRBeGzuFKUc0NsKRR8IHH5gG0fvvw+TJua+nH2xqU1pcrN+bBt/9LvzudxAKKdOmCY8+CkHc39SnMC1f28bpt7/G4s9aqCor4Z/n7suEutzGYgiK729ZAcrM9Nig+dPl80vPNK0YpzBlcl6zmsIkIucCszHzM25U1d9lYmgvRk6Cs16hJcD8K7fHlE0ZxWJHyjI85XyU5iYLUkY6W3JRhi/Fcl4DnlPfMjKwJe/n1RKchgbTabjjDpg1CxYvLpoOhMUyVInH4aGH4KCD4C9/XprTjpMIWwF3AGMwk1ZvUOUaEUYC9wL1wDLgeFXWiyDANcDhQBtwiiopRjULz4S6Kuactx/fuectXl26rmCdB0jzW1aAMjM9Nmj+dPn80vubVkzkws60qzCJyA6YzsPuwM7AESIyJeuaLRaLJZ989aswdixceWWhLbFYhjxPPw0ffQRnnJGX4ruA81XZFvgccJYI2wEXAs+oMgV4xtkGOAyY4nzOBP6YF6tyzNQxw1i5sYONbYNjJR/Lpk2QZVy3BV5W1TZV7QLmAl/yOyCIOCNxTf7+5AlSRrHYYW3NPI+11ZIVZWVwzjmmZfPuu4W2xmIZ0tx0E9TVwdFH575sVVa5bxBUaQbmA+OAo4DbnWy3A27tRwF3ONLBl4ERImyZe8tyy7QtzRvqBZ80pcmZP/LxO5VNmZkeG+Zig8gAACAASURBVDR/Nr/p/U0rJnJhZ5ApTO8BvxSROqAd80rw9cRMInImpqfP2LFjWbZsmW+hyQKaZZonSBlr164tCjusrZnnyUUZ6ezMVT0DZaslQ77xDfjFL+Cqq+C22wptjcUyJFm9Gh5+GM4+u2/Q6mDESkXE2664QVVvSJZThHpgOvAKsIUqq8B0MkTY3Mk2DvjYc9gKZ9+q/lg3UGw7ZhgACz5pZo+GuoLYEIvF0v6WDWSZmR4bNH+6fH7p/U0rJnJhZ9oOhKrOF5FfA3OAFuAdzOvExHw3ADeAEVGnm3ceZA3abNbp9eJny0DZYW3NPE+ubC2WezEf62tb0rDZZnDaafCnP8GvfmWmNFkslpxyxx1m4af+T18q6QqyeIQINcCDwHdUafJbTTPJvtyvGJNjthhWzoiqMAs+aU6fOU9Eo9Gcj6JnU2amxwbNny6fX3p/04qJXNgZKBK1qt6sqjNUdV9gHWAXsrdYLIODc881y15ed12hLbFYhhyqcOONsNdeJoZjvhAhjOk83KXK35zdn7pTk5y/nzn7VwBbeQ4fD6zMn3W5QUSYNqa2oFOYLJagBF2FaXNV/UxEJgDHAHumyZ+2TKuByBxra//y5KKMYrF1KJD3OBCJ69ZvtRUccQT88Y/wne/AiBE2DoSNA2HjQOTo3vv3q2UsXBjm1uvboCUOZWVIc7PxO6hPaXBWVboZmK/KVZ6kR4BZwOXO37979p8twj3AHsBGd6pTofCLA+Fdi39yXQUPvfMp7e0ddHX1rM0PgzcORDQaLbo4EG6ch1Q+edNsHIjkBA0k96CjgYgCZ6nq+oDHpSTI/Kts5qgVmx3W1szzWFuHJqr6KPAo1dWzKSszjZREqhKWMUxsZCR2xhIfconbF14Ijz4K991nJmon1pnvbYCqKnTYsB7bsvVpoH3I5jqtW2fsH0o+ufj5VFZmOqy5tLHQPiXUceNtMGwYHDerCpxIDlpb29vvIDb6szfwdWCeCG87+y7GdBzuE+F0YDlwnJP2BEavuQSzjOupmVaYa9zn3syZM2eHw2HC4TA1NTXd01rD4TAVFRXsNKGOu15byWdtMSbW9Z7yWpZw3hK3E39fErdramrS5i8pKeke7EpXXmlpaZ9070CZu93R0dFta2KDNJ1PVVVVfcr086mystI3v7vd0dGBex2S+eRNT1aed5/Xp3g8TnV175Am3uvsZ1Mqn9Ll78918vqQ6CP4dxy6y02bA1DVfYLkc3vY9fX1vj03gEgkgoj49kZbW1u78yfrubW1tXWnp+q5tba2+vZGN27cSDQa9e25rV+/ntra2pQ97HXr1lFbW5uy59bZ2UlzczN1dXW+owadnZ3dka+T+dTW1kZJSYlvb7Szs5N4PJ6yNxqNRtEUkajd7ebm5rS9UbdHnWrUwD0nfj3spqYmRowYkbKHvXHjRoYPH+7bw25paWH06NEpe9gtLS197sVEnyKRSPe5c++9RJ+891oynzo6OuhKEYna3d6wYQPV1dUpRw02lUjUBWGvveBznzNRrr71LTMyarFYsmLDBrj/fhNupTqPYeBUeZHkugaAA5PkV+Cs/FmUP6Y5Qur5q5qZWDfwsfWsBiJ9utVAGIK+gQiE28OeMWPG7FQ9N3fbff3j1xuNRCJ9RKfenlU0Gu2Tnthzq66u9u35VVVV+aaXlpbS1dXVZ5TAm7+2trZXGal8cvenGjUoLy9P2iP2+pQs3bsdj8epShht8qY3Nzf3iT6Y6FNFRUWf85roU2KPOtGnxHOSzCdV7bY1mU/enryfz6WlpSlHQmpqapKOInh9cjtCfiMhye41rw1dXV1pRw3S3Yu5jv5pSeD88+G44+CRR+BLvitRWyyWANx9t5ndlKfYD5sk22xRi4hZyvXQHcYU2hyLJSWBRNSZEkQDEeT1SLo8QcooFjusrZnnsbZacsrRR0N9vQ0sZ7HkiJtugl12gRkzCm3J0KGyrIRJddUsWFWYlZjy8TuVTZmZHhs0fza/6f1NKyZyYWdeOhBBUE2/olq6PEHKKBY7rK2Z57G2WnJKaakRUf/73/DKK4W2xmIZ1LzxBrz1FsyebbTQltwxbcvCrcSUj9+pbMrM9Nig+bP5Te9vWjGRCzvz0oEIYpg7/zybPEHKKBY7rK2Z57G2WnLOaafB8OH2LYTFkiU33WS02CeeWGhLhh7Txgzjo3VttHb2CbmVd/LxO5VNmZkeGzR/Nr/p/U0rJnJhZ041EJmKqDs7O31F1JFIpHs5rGQiancZKui/iLrNWY7OT0Tt1pFKRO2mpxNRp1t6LYiIuqKiwldEHY1GaWtrSymi7urqor293VdE7dbv51M6EXWq6+b1yXtdkvnU3NycdpmylpYWKisrsxZRu/eje+8l+uS9D5L51NXVRUtLi6+IurW1FcCKqAtJba2JTn3FFbBsmZnSZLFYMqK1Fe66y0iKEheZsmTPtDG1qMKiT5uZPmGzQptjsSQlLyLqXXfdNa2IOhKJUFZW5iuiFhHfZcrchpiXTEXUm222Wa8ykomoS0pKuvMk82nUqFG9ykjmU3l5eXee/oqoU6V7t8PhsO85c897YrrXJ3flIy+ZiqgTz0kyn7zC5WQ+eX1J5XN5eXnWImr3nPiJqJPda16bvPdIKp9Hjhzpe06siHqAOOccuOoquOYauPrqQltjsQw67r8fmputeDpfbLulWYlpwScD34GwGoj06VYDYSiYBsJisVgKwvjx8JWvmDkYbpAvi8USmBtvhKlT4fOfL7QlQ5NxIyqpKS9lwSobkdpSvBRMA+FOD8kmT5AyisUOa2vmeaytlrxx/vnQ0mJaQhaLJTAffAAvvWTePljxdH4IhYSpY2qZ/8nAr8SUj9+pbMrM9Nig+bP5Te9vWjGRCztzOoXJYrFY0uFqpaJlZZRGIhCNmg8YVWYsBq7Aq7IS4nFwH3buNDRXp1NeDqGQWYweTITbkpKe7XDYfBytU/d2QwPss4+ZxvR//wddXaBqji0vN+XH46bsigpTfyxmWkyVlca+rq6eba8PVVUpfZKmJlNWPnxqbzc+lJaaY9ztPPsU6Dp1dJi0oeRTkOsUifS85RoiPt305wrC4RAnH9UEraVJfZLmZuN3UJ82AdznXkNDQy8NX3Nzcx8tYnl5OVNGV/H4e5/S1NTUPf3WT6eXqBFNpT300+kF1R56NaJ+ekp32437lUoj6udTED2l14eWlhaAlLpX77l3fUjmU7LrEkT36tXuuj651zmX1ymZT5lcp1Ta3lTXKRl5EVFPmjQprYgaSCuiBnxF1CKStYjavVH8TrxrU6qbyU33+4J0dnZmLaLu6urqtifVzSQiviLqUCiUVkQdj8fTCsPTiahT+ej1qbOz0/cL0tnZmfYLEolEus9Lf0XUQFoRtfdeS+ZTKBRKK6J2H6Sbuoja1UpRXT2bsjLTSEkkIRhin0ZGYgTNxIdc4nZiHWVlcMEFcMQR8PDDcNJJ6fNnsw1QVYUOG9ZjWz58GshtCH6d1q0z9g8ln1z8fCor66syHsQ+dXbCHXfBUUfB5lOGp6xTa2t7+x3ExiGO+9ybOXNmt0bUqyVM1OltP34z7nn9v7TEwwxLoZ9Mp7vzC0qbKn9paWn373y68lJpDxO3vRpDP91rsu26urpex6TzaeTIkb753e2ysrKkmlLXJ296ok+JNnn/r6mpSasZzcV1ymQ72XXy+uCnEfVjUIuokwmCMxVRV1dXZy2iThTY5ktEHYlE0oqoU4mk06V7faqsrMxaRD1s2LABEVFHIpEBEVH397wm8zlVuhVRDzCHHQbTphlB9Ykn2vkYFksa/v53WLvWxH6w5JdpY8zv64JPmhg7YuB+G4IEAh7IMjM9Nmj+dPn80vubVkzkwk6rgRhE89+trf3Lk4syisVWSw4JheC734U334S5cwttjcVS9Nx4I0ycCAcdVGhLhj5TnQ7E/AGOSG01EOnTrQbCYFdhslgsmy5f/zqMHm0Dy1ksaVi6FJ5+Gk4/3fS9LfllWEWYcSMqWVgAIbXFEoS8PAaCvBpJnL7RnzxByigWO6ytmeextlryTmWlEVE/9hgsWFBoayyWouXmm03H4dRTC23JpsO2W9ay4JOBXco1H79T2ZSZ6bFB82fzm97ftGIiF3YWTEQdCoXSiqi9SvFkImqv2Le/ImpXuOwnom5vbycajaYUUbe1tRGNRn1F1G4d2Yio4/F4t0C5vyLqkpKStCLqaDSaVpGfTkTtnhM/EbVrt5+IOhaL+Yqou7q6KC0tzUpEHQqF0oqovfdaMp/cc+Inou7o6CAajW7yIuqi4/DD4Wc/g+22g223hUcfNas0WSwWwCz6dOutRjY0fnyhrdk0WL62jTc+Ws/6tigHXfU8t8zanQl1VekPzJKSkpKiKjPTY4PmT5fPL72/acVELuzMi4h6xowZaUXU7rJefiLqSCTSS6jrluHS3NzcJz1TEXUoFPJNLy0tpaurK+VKCe7qOt4ykvnk+pvoo7fOdCJqdxkwPxF1snMSJN3rU0dHR588mYqow+Gw73mtqamhubmZKmd1kGQ+xeNxqqurU6a7/mQronY7Qn4i6v6eVy8lJSW+58SKqAvEqaeaJSjBvIU48kh4//3C2mSxFBH/+AesXAnXX19oSzYdTr/9NTa0mUGvDz9r5fTbX2POefvlvd6Ojo6cj6JnU2amxwbNny6fX3p/04qJXNgZaAqTiHxXRN4XkfdE5K8iUpH+KIvFYhkELFzY83883nvbYrFw002wxRbwxS8W2pJNh8bVrbjL0aizbbEUE2k7ECIyDvg2MFNVdwBKgK+mOSZtxVYDkTnW1v7lyUUZxWKrJQ9MndpbFTpyZOFssViKjJUr4fHHzYs6+wgbOBpGVxPyNKW2HDEw47ZWA5E+3WogDEFF1KVApYiUAlXAymwrDjL/Kps5asVmh7U18zzWVsuA8OijJh5ESQkMH24Wun/11UJbZbEUnMZGmD7dBI1+4AGzbRkYbp61G5NH11AiQkhgy+EDM8XVaiDSp1sNhCGtBkJV/ysiVwDLgXbgKVV9KjGfiJwJnAkwduxYli1b5ltuR0dHnznpmeYJUsbatWuLwg5ra+Z5clFGOjtzVc9A2WrJAw0NPZqHDRtgxx3N8q5vvdU3gq/FsglxyCHw2Wfm/8ZGKw8aSCbUVXVrHv74/If8+p8LeGv5eqZP2Cyv9VoNRPp0q4EwpO1AiMhmwFHAJGADcL+IfE1V7/TmU9UbgBsAZsyYofX19b7lJhOdZponSBkAfrYMlB3W1szz5MrWYrkXg55XSwEZMQJuvx0OPBB+8AO47rpCW2SxFIT334cPP+zZtvKgwnHynhO54YUPueaZxdx26u6FNsdiAYJNYToIWKqqq1U1CvwN2CvbihNXzelPniBlFIsd1tbM81hbLQXhC18wEaqvvx7++c9CW2OxDDgffGC+BqWlPfKgUMjIhSwDT3V5KWfs08DzC1fz9scb8lpXPn6nsikz02OD5s/mN72/acVELuwM0oFYDnxORKrEqKMPBOb7HWBF1P3Lk4syrK2ZM5hstQwQv/qViQtx2mlGE2GxbCLMn286D6GQWb7VlQdNm2bkQpbCMGuvekZUhbn2mcV5rceKqNOnWxG1IW0HQlVfAR4A3gTmOcfckOaYtBW7QbiyyROkjGKxw9qaeR5rq6VgVFTAnXfCmjXwrW/1xImwZEZjI2y/PVtssw1sv71V4RY5CxbAAQeY/597Dg46yExl6uoyf218xcJRU17KGZ+fxLMLPuPdFfl7C5GP36mgZS5f28bBV81l8kVPcPBVc1m+ti1je4Lmz+Y3vb9pxUQu7Az0DkNVLwUuTZfPjURdX1+fNhJ1JBLpjjgMySNRu5F8IXkk6kgkknUk6tbWVlTVNxJ1U1MTqpoyErWb7heJ2g0Cl00k6ra2NsrLy30jUUciEd9I1G5kbb9I1O3t7d3nvb+RqN1z4heJuqmpiVAolDISdVNTEyLiG4m6paWl+zr1NxJ1JBLpvt/cey/RJ++9lswnty6/SNRu+pCMRC1SDfwBiADPo3pXgS3KnunT4ac/hYsvhqOOgpNOKrRFg48jj4QFC5B43AbpyzWNjXDEEbBokZlblGUE9YULTedBFZ5/3rxxKBZEuAU4AvhMlR2cfT8BZgOrnWwXq/KEk3YRcDoQA76typP5sav3c0/z/NybtVc9N/5rKdc+s5ibZu2WlzqCDADnq8zTb3+ND1e3EFf4cHULp9/+Gn+bPSMvdaXL55fe37RkLF/bxum3v0bj6lYaRldz86zdBiTieC6uc8EiUbuNYb9I1LFYrDtSsbcMl3g83ic900jUtbW1vcpIFolaVXtFTE70acSIEb3KSOZTSUlJ1pGo3YaoXyRqEfE9Z21tbX0iHif6VF1d3aeMTCNRJ56TZD6FQiHfSNReX1L5XFJSknUk6ra2trSRqJPda16bvPdIKp+HDRvme06KLhK1SPePNiYGjLv/UOAaTEyYm1C9HDgGeADVRxG5Fxj8HQgwQurHHoOzzoJ99oEJEwpt0eBi4UKjvgWrws0V69aZwAxnnQXOoEa2nbNFi0znIRYznYdtt82duTniNuA64I6E/VercoV3hwjbYWJVbQ+MBZ4WYRtVYkEqEs9zTz3PPUl47qnnuaeqj8oAPPdqK8Kc/vlJXDVnEe/9dyM7jBue8zoKuYxr4+pW4k67Nq5me6gv43rqba/yoRMkcInTaRqIiOO5uM5B40BkhNVA9C9PLsqwtmbOYLJ1gLkNOLTXHpES4HrgMGA74AREtgPGAx87uQL9UA8KSkrgL38xLatTTulpDFuCsfXWvbcnTiyMHYOdpUvhd78zrfzNN4eTT+7pPEBWnbPFi02xXV3w7LNG+lNsqPICsC5g9qOAe1TpVGUpsATIZOmi20h47kmS554U6Ll3yt71DKso5Zo8aSEKqYGYNLr3IFz9qKohrYFYuqa1u/MA5u3fQEUcz8V1zotcPMirkc7OzrQOpMsTpIxiscPamnkea2uBUX0BkfqEvbsDS1A1k9lF7sH8YK/A/Ji+jc/AhDdeTCQcZu0gmRNfecklDL/oIpp+/GPaTjut3+U0rVmTQ6uKn6ojjmDYwoVoKAQixJuaWPvKK8RHj85ZHSXLlzPijDMoXbaMrkmT2HDjjcSK5E1RJte7ZPlyRsyeTenSpXRNmkTz+edTNm8e5c88Q9jpHESnTKHzzDPpPPhghv3gB5Q2NiLxOArExo9nTYbfp6XLSjn2pLF0RoQH7lzJqKoon+TgK5n5fR4rFZHXPTtucJaGT8fZIpwMvA6cr8p6YBzwsifPCmdfIFT1BUnx3FPnuSdZPPfGjRvXHServ7F/jtlhM257/VPmvD6fKaNy++a6s7Ozz4yDgSrzmO2G85vPWhFAgZ22KKexsTEje5ataeHHT69ixcYIW40o57LDJjB2WFmffOls8kvPNM17nVc2RbjoH8tZvqETFNzhdrfVvOWwcNo4arkgF9e5YOtNBelkZDNHrdjssLZmnsfaWpSMo2fEDcwP6B7AtcB1iHwRSLlWizdeDNXVOmawqDIvuABeeolhV1zBsBNOMILgfjJofM4Fr78OU6fy6eOPM6apiZLPf57Nzz3XzJNJE3gxMIce2h2wINzYyOizzioqnUXg633kkcYPVcJLljDyW98ySyHtsw+ceSYcdRThyZMJAzUAO+9sjlm4EInHKZ04kTGTJkGAGQBgqjp+FkSiRjC9005b9dfFpGR2n7/XpaozM6zij8DPMW2vnwNXAqfR0ybzku3D1/e5Jxk892bOnNkrTla6OEXJ+O4W43jwvWd5YH4rf/56bueb5SNeUdAy5/9nPcMrw7x6yYH84IF3efy9T5i972TqJ2weuK4T7n6aVU1G3/jxhk4ufXpV0ilB2cR26k+ae51nXzWX5es7u2/IcSMqqCor7dZ+7Dt1TL/uiUzJxXXOaQciExF1LBajs7PTV0Qdi8VSClfD4TDxeDxrEbVbn5+Iuq2tLakP7rab7ieidufZZyOi7uzspKKiwldEHY/HfUXUqppWRB2NRrvPa39F1O458RNRt7W1pRQcu2LwVIJjd7u9vZ3KysqsRNSxWIxIJOIrovbea8l8UtW0Imo3/yAXUSf/cVZtBU4daGMGDBG48UYTpfprX4NXXoGyvqNaFg8ffwwvvAA/+Yk5f9Onm+lgxx4LZ5xh/g/Y2E3JokVmDo7LYNZZLFjQe7WvUMiEga6rS57fG0H96qvhvPPgoYfgmGPSVtXYaKYttbWZaUs77ZQD+wcYVT51/xfhRuAxZ3MF4O0NjQdWZlld0ueeFui5N7wyzKl7T+LaZxYzf1UT2245LGdlF0oD0dQR5an3P+H4mVtRXlrCxYdvy9MffMpvn27k1tOCdSDaIzFWNXV2b7s6iv7YlC8NxJLVLb16s59s7OTDyw4E4Oy73+SRd1ZywWHTGF6Z39kKubjOeRFR77rrrmlF1NFolHA47Cuidlfo8eLddhtmXjIVUQ8fPrzXvmQiarfRm+iDu51oRzKfKioquvP0V0TtluF3Ttzzmmm616fa2to+eTIVUY8cOdL3vNbU1HT7myw9HA5TVlbmmw7mnGQronbPiZ+IOtm95t323iOpfB4xYoTvOSk6EXVysv5xdgcaomVllEYiEI2aD0BlpdEbOJ07KitNo9DpzHWPWrtL0JWXm4aW2/kqKzO6BXc7HDYfp0Pbvd3ebhprpaXmGHe7pMSU2dFh6g2FTJ2dnWb/735nVmO6+GL44Q9NA7iysrcPVVUpfZKmJlNWsfgUi/X4EImYifAZ+pTyOv3lL6b+I44wdXd2mgADl1wCv/ylWTnonHP679P8+fDFL5r/4/Gexnc4DCtXwqhRufcp0+sUicCGDel9uuOO3vqaUAi22QZqa43WId11+vrX4eab4Tvfgb33NrYl+NS4LMSRJ9awcJEgAtVVyvNPRdl5qsKG3N570txs/A567/UDEbZUZZWz+SXgPef/R4C7RbgKI6KeArzar0p6yNlzr6Ghodcqgs3NzX0G8vwGI8H8Nn1993Hc8mIjVz05n99/dec+A6ypBu78BrncgTRV9R248w6w+g1GutvRaLR7oC2VT4/MW01nV5xDp21Gc3Mzw8vCnH3AZH795CIef3MZB243Jq1P1zy3zJxrel45ja4to7m5uc/AXVdXF+FwOKVPbrsgmU8dHR3EYrGk10lVuwcb3cFI9zp/8Glb7zECgUmjqrrzn77neB57dxU3zV3EmXtP8L1O3oHw/lwn14eg915SVDXnn+nTp2s6Wlpass4TpIylS5cWhR3W1szz5KKMdHbmqp5c2Qq8rnn4Tvb7A/UK73m2SxUaFSYplCm8o7B9v8quqkp7zoqS009XFVGtr1ctKVHdbjvVDz8MdOiqgPmGBLvsorr77qqa4Hc8rnrCCaqg+vDD/Sv7xRdVhw9X3Wor1TlzzDUoKVEdP95cm4MPVu3oyIET2RHoet95p7F5331Vt90243uqmxdfNOf0Bz9ImrzddqqhkMkCqg0NmRWfCZne5zCvVX2eFaB/BV0FGgVdAXo66F9A54G+C/oI6Jae/JeAfgi6EPQwv7KT10c9nuceZrC1EZgElAHv0M/n3q677trtd5DfJz9+9PA8nXjBYzrpwsf0oCuf14/WtGZVnmqw37J8lPnlP/5bD7jiOY3H4937OqMxPeA3z+q+v3lW2yNdvsd/sHKjNlz0uM6+9WU96MrnteHCx3WbS57QrS9+XF9asiZjm/zSM01bunSpbmyP6N6XP6O7/2KOHvDb57ThwseTXrNTb31Vp//sKW3r9Pc3WzK5zqnaJXlZhSkI8QCrmaTLE6SMYrHD2pp5HmtrgRH5K/AfYCoiKxA5HdUu4GzgSUxE+vtQLZ4J5wPB1VebkdZly8woqruEpqWHDz6At99OHjtDxIyW77abSX/nnczK/uc/4eCDYYst4MUXe0c7+/hjuOUWmDMHTjjB7CtmHnwQZs2C/fc3fn3wQf+jtu29N5x6Klx1VVINiHdFXYCPPsrO9IFElRNU2VKVsCrjVblZla+rsqMqO6nyv9rzNgJVfqnKZFWmqvKPTOoSz3NPRFaIyOma5LmnRfDce3GxEat74yZkSz5+p9KV+dHaVl5btp5jZ4zvtYpnWWmICw6ZzEdr27jpX6nV/bG4cuHf5jGiMsyPDpvCnPP248PLDufliw6kvq6a2Xe8zrwVGzOyyS890zRV5aK/zWPVxg7+8LVdefZ7+/PhZYcz57z9+sR8+L/9J7OuNcI9ry33tS9bcnGdCyaiDoXS913S5QlSRrHYYW3NPI+1tcConpBi/xNgAjb1h0E7hck75SLmWbHRnXfvTlWxU5jg9ttNPYceas6LO4XJ69N995lG7xFHwNy5MH58ep/++leYPdsEKnj8cRg2zJTv9enoo+HTT+HCC81yp9ddZ85BsU1hevpp08mZOdNMYerqMvmzuU4//zk8/LARXT/2mPExFkM7I9TWDGPDRvMMCoWUqVvHoSOal3tvIKYw5QtN8dzTHD33cjWFSUT4aG3P/H4z37+lO2htf6cwtba2ZjU1JtkUptbWVl+f7nl5GSJwxA6b99Ea7rxFGQdPG8V1zy7hyB23YGSF9PHprldX8M7HG7jyyztSRrRb91pTFubPJ+zI1257k5NveYV7ztidrUaUdWtXXR+S+dTa2prSp9bW1pTXqaurq88Upr+9/QmPv7uO7x44mZ3G1vjqKXcZP4yZE4bzp+eX8KWdRlNd4R88uL/XyfUhmylMeRFRT5o0Ka2IOhQKpRVRA74iahHJWkTtXmy/E9/Z2Uk8Hk95M7npfl8Qdw5gNiLqeDzebU+qm0lEfEXUJSUlaUXUXsFwf0XU7jnxe5BFIhFExHcupnsvpPqCdHV1dW/3V0TtRvD2E1F777VkPoVCobQialecCsHmFwAAIABJREFUPshF1FmjjlaK6urZlJUlFyMnBOXr08hIXMEn8SGXuJ1YR7bb06aZ0WIwjaKpU2HEiNT5Aaqq0GHDes9P91JonzLdhuTXSdV0Dg46yMzjBxP8rLy8tw/19SZ68uc/b+bwP/ts73OYWOdNN8E3vgF77mkax4l5vfkvuMA0qn/0Ixg5En7/e9NY7a9PXjK5TmVlyX16+mkjxt9lF3jySRg+vHe6n41+2yNGwOWXm/P0+OOmDuAPt1axYaORhaxfD1OnCo8+WgIVJZn7FMAmra31/z5sggsQuM+9mTNndmtEvVpCP52eS6JOr2F0jRHlOsuBNoyu8dV8JmpE+6s9dEmlPUzc9mpAE30qLQ3z2Pur2WtyHRNH9xWD19XV8ZOjh3Pglc9z+ZOL+ePXdu2V3tRVwrXPL2PfbUZzzK5bdWsbXBq2HMmdZ3yO4/70H067403u/+aejB1Rm1JT6vrkTU/0qa6uLqXu1W3TuCz+tJkb3ljP57cexTkHTiUUkrTn9ZyDpjLrlld5auF6jp+5Vdr8/blOXh+C3HtJSTavKdtPEA1Ea2v6uXrp8gQpI90cw4Gyw9qaeZ5clBFkjmkx2UqxaSDy+RmsGghVMz998mRVUB01ymogvPz73+a83H579y5fv++/3+Q/+WSjj0jGb39r8hx6qGqA75qqmrK+/31z3EUXZeBA7kjq99y5qpWVqjvtpLp2be4rjcWM9mTzzVXXr9fnn1ctLVU94giTNBDkWgMxlD651EB8tKZVD7ryeZ14wWO67Y/+kRMNRJDfslyW+UrjWp14wWP64Bsf+x77+2cW6cQLHtMXFn3WnRaPx/X0217TqT98QpevbfWta96KDbrDj/+pX7jiOV3b0pnVb3rQtPZIlx5y1Vzd5Sf/0E+b2n3r8xKPx/Xwa17QA377nHbFUjwTsyST65yqXVKwORUx7xSAfuYJUkax2GFtzTyPtdVStDQ0wJIl8OUvmyklm21WaIuKh7vvNiPaRx8dLP+Xvww//amZxvPb3/ZOUzWrXX3/+3DccfD3v/d9Q5AKEfj1r81o/GWXmZH5QvPKK2blqPp6o9MYOTL3dYRC8Mc/wpo1LD/3So47DiZPhjvvNEmWocOEuirmnLcfp39+El0xpa4m+zc7+fid8ivzwTdWUFVWwqE7jPE99ox9GphYV8VPHnmfSJeZv//k+5/w9PxP+e5B27DVyCrfunYYN5wbZ81kxfp2Tr31VZraOpPmC2Jz0LSfP/YBCz9t5qIvjGfz2uBxb0SEsw7YmsY1rfzzvU8CH5cJubjOBdNAeIUy/c0TpIxiscPamnkea+vQZEhoINx56OeeCw88AFdcYRq5sGlrIFpa4J57TCO5rKxn/n8yDYTXp/POg3nzjG5h/Hg46iiT/u1vm/gbJ58Mf/qTyd/WlplPv/ylmUJ10UXmmFNPzcynXGkgPvjAnJfRo414uqrKpOfjOjU00H7KtzjmlqPoqOri4cfaGK5x6ByY79Ng1kDki3xoINztfSdvxs0vLmXOux9z4LRRWWkgWlpacq6BaGlpSepTeyTGY++u5KBpoyjRGJFIvM906JaWlu7tSw6bypl3vsWfnl3AV3fbikv//j7Ttqjh+F1G09bW1j1tHUg6xXvXrYZx5bHbc+598zj73nncdtoehDSW1KeWlpaUPrk2JfPJnao8Z8Fq7nplOWfsPZHtNiOpNsXvOu01oZr6ukquf24x+06qSelTf6+T60M2Gggxbydyg0cDMXvhwoVpNRClpaW+X5Curq5upXiyEx+Px7t7Uam+IIsWLWLChAkpT7wbrMzvxLe3t3fPEUsVSM6NaZHqSx+NRqmurvbVQKxcuZKGhgZfDURVVVVaDQTgq4EAfL/0bv2uD8l8WrRoERMnTkypgXC/LOk0EO48w1QaiPLy8rQaiNra2pQPso8++oipU6em1UC4Nrj3XrJAcu69lswn95z7PZxbW1spKSlJ+aVfsmQJ22233RuaeUTWwUl1tdKaPMjPoOJLXzKRlZct6z2XPQmfNDYO7UjUTzxhGskPP2w6AQ6B/G5rg/32Mw3tMWNg6VLTKD3jDLjhhuyCzkWj5k3HI4+Y+BSONiDfdPv93ntmpaXqahNcb+LEvNarCrNOiPCXe8t4ZMp5HDn/t6YxP0Bkep+LvNemukN1Hk0qGmbOnKmvv/46AMuWLctJ1OFoLM6uP5/DIduP4Yrjds6urCSxorIlVZl/f/u/nHvP29w9ew/2mjwq0LEn3vAy/2lcC5hYD3/+2q78j+ftRRD7H3xjBeff/w6H7TCG606cQUmo77PFr5x0aZ80Rzn82n/RMLqG+7+xJytXLO/Xdb7v9Y/5wQPvctupu7H/1ODRuIOQyXUWkaTtkrwEkpsxY0a3SChVILm2tjbKy8t9RUKxWKxPqG2vw21tbX3SMw0kFw6HqfK8Ek8mPlHV7jzJfHIb9i7JfHL9TfTRW2e6QHJuR8VPMNPW1tbLlqDpXp+i0WifPJkGkquoqPA9rzU1Nb1sSeaTiPimu/5kG0jOjRLuF0gu2b2WeF7TCdTKysp8z8kgCSRnSeRHPzIN5uuuM0HSNmXuvttM5zrssMyPraoy57G+3oRKBtNpeOml7CNWh8Nw772mc3PKKVBTE3yKVbYsWmQE5eXlRiie584DwLXXwl/uLeOnx77DkQ9eDTdNNVO5LEOScEmIA6ZtzrMLPiMW16QN4qAMZAfiwTf/y7gRlXxuUopo60mOXbmxvVck5yueWphxB+LYXcezemMrlz+1hEsemsdlx+zYZ4ZAfzsQbR2dnHvPO6Dw+69Op6y0//MGj95lHL+bs4g/PPdhQTsQqUjrmYhMFZG3PZ8mEflOVrViNRD9wdravzy5KKNYbLUUITNmmKVIr7rKRA3eVGltNR2A447r/yo748b1DlagapbIzQUVFUZDseOO5q1RSQlsv31PZyWXNDbC9tuzxZQppo5oFJ55xogR8syzz8L555v+0Q/v3cm8+bjoIvjss7zXbSkcB227BetaI7y1fH1W5QyUBuLTpg5eXLyaL00fR8inw5N47Mfreq9W2Li691vsoPaftNtYzj5ga+557WNm/HwOky96goOvmsvytW1py0mWtnxtGwdfNZddfjmXN5dv4LxDtukT4yFTykpDzN63gVeXreO1ZeuyKiuRAdFAqOpCYBcAESkB/gs8lG3FVgOROdbW/uXJRRnFYutQYEhpINzt733PNNSuvBIuvnjT1EA88IDpRPzv//b44v3rp4Hw+jRlCixe3GPr1lsbbUUufBKhe9pcPA7z58Phh8PLL+f23jv8cFi8GInHTRnDhsFWW/XWPOThOi37rIrjjwszdUqcO65tJtRVae7JPfaA737XBNmzGoiCkE8NRDgcZu+GEZSGhCfeWcEOY6qKXgPx4OuriCv8z9QRvkvCezUQJSUl1NdVsnRtG3GFkEB9XWWvpfr9NBBen1pbW/n2AZO44z/LWN9mbP5wdQun3vYKD585M2MNxKm3vk7jmrbutyN3/mcZJ84ci4h0X+dMNBDudTl2ly259pnFXDtnIX8+aeei0kBkOoXpQOBDVfWNYRmkoZQ4naQ/eYKUUSx2WFszz2NtHZroUIgDkbi9334maNr11xsxdXWS6dxDPQ7E/fcbAfRhh/Us9+P6lCwOBCT36YknTGTvhQtNfI1HHzVTjnLlg/eNg6pZTcsbsyAX996SJb3fpHz8cc89kafr0tYGX/oKdMXg4UdKqN3K8WnGDNPBvfxy+OY3YZ998n7v2TgQfdE8xIHwbldUwB4NI3nhw/X82CmnP3EgysvLu6cE5yoORGVlZXdet4H68DurmDFhBDvWb+Hr4+jRo3vVc+upe3D67a/RuLqVhtHV3DxrN2pre76zo0aN6pU/lQ+uTa2dPSPxcYVla9qpra3tTk/mU6JN5eXlLF3b1mtq1bK17d3XL3HKd7rzmnidztingd8+uZClG6JsP7Yq7fFBrpPXh/7Ggci0A/FV4K/JEkTkTOBMgLFjx7Ls/9k78/i4yur/v5+ZyWRv0izdt6SUthQKdGHfEUWggAKCIlYpoCgI+lVEWZVF/P4UBVm0UmQRUValXwQEpKCobVlaSvc2TRe6pUmbzJLZn98fT+5kMnPn3jtLkklyP6/XvCZ3nnPPPefem3uf5XzOaW42VGQl/spMxoqO1tbWgrDDtjVzmXzoMLMzX8fpK1ttFDBuuUVVVv7Nb1QMyVDCvn2qINp3vpN7rtDGRli9Oj926WHqVFi3rruD3xspeGtqoKVF/a0VGuxFaFzzlStVjb0pU5IEbr5Z8VO++U344AO1amBj0OFT00fy48VraN7nY1Jddpx0rehtPpGs8+NPOtiwx8ud5x+a8b5a6lqr8mZyjfXlbNyrZuMdAhrry031JLdFY5Jil4POcCxFTz7w5WMm8vCSzTy8ZDMPfGlWXnTm4zpb3lsI4QbOBX6o1y6lXAgsBJg1a5Y0Y5x7PJ4UUmqmMlZ0AIbs976yw7a1Gx0dHXi93pSKjckwa7ciU1lZmTKj0RvHyUVHRUUFw4alVuC0McBw3HFw+umqlsHVV1uvVzAY8OyzKqzm0kv72xJzLF7cvcJRUaFWR5YuVWE++cB//wutrVBVhfR6EdoqSi/iF7+Ap59WGWvPOktHoLxchdZ94xtqqnraNGVTvjOCNTXBvHmMTFw9GsxZxwoM2gDijbV7uOLE7M67FuqSTyTrfP6DHbhdDubNHJN3e6zKa3KL5s/l4oX/YVd7gNFVJSyaP9dUT3LbfW9upDMcY2RlMfu8QRrrK+J68oGq0iLOO2IMTy3dxt9WvczkLv25cCwikQjbWv0pqzmZ6Mxk+PFZ4AMp5Z6MLbVhIwFer5dRo0YRDocNl8m0NK5GMJM5cOAA1YlL6b10nGx1xGIxdu/ebQ8gBgtuvVWFM/3ud6pGxFDBU0/BIYfAzJn9bYk5Elc42tsVqforX4EPP8x90OfxqBSx48fDypXsaW3t9bS9r78OP/iBylL7Q93pvS7cf7/61rgfJ50Eb7yhlivykeK1sxPOOAO2bEFIqVZ55s3r3dUkGz0wvqaMaaMqcxpA9DZCkRgvrdzJGdNHUlXW/ythE2rLeOO7J3PET/7O2TPHZNwp/9fGffz6Hxu5cPY4fn7R4ZYnYDPFvzerCIWYhE17vSx4fLnhSowRQpEYm1t8XP3n1exuDyBR/I9MdWYygPgiacKXkmFzILKTyYeOgWKrVgfECFaW1/Kx1JqP42SrwzEES8MOShK15sPMmapjds89cPHFat/BTqLetg3efRduv111yPV8yoRE3Zc+uVzw61+rlEU33AD/+7+53XvXXKPqVyxerHQnFpLLs09NWx185oIKNm0WFLvh1us9iHBJ+uuUmM1KSvjkE5g+XZ2HQw9V9+7s2XDkkdDQoGz75BOVVWvDBkVmf+459b1mjfqsW6cGI6tWwebNPXkfsZg6Zihkk6jpfRK1RsY9sbGaR/+zg5Z2PyUO4zpZeiTqcDiMz+fLK4k6HA4TCoWQUvLqqp20+UKcO3NkvN6TkU9aYTYrhOOioiIikUgPUnU6wnEkEonbFotGmT2+in+s28N3Tp3Uoy2dTx6Ph/YQXPenD2msLeP7p00kFArhcDjwdGXj03zKhUStXadtrd3ZpiSwca+XO176iM/PGodLSL759Ec0t/ppqCvnwUsOY8wwNx2BCJ94ImzY3cHmvV62tPppbguwva2TaFINuJiEphZv/DrljUQthCgDzgDylkg6Go2ahnyYyVjRUSh22LamIrmIYfJy2m+/fCSNI4xn5o0KId51111cffXVnHvuucyfP58LLriA4447jrvvvptTTjlFV8f111/Pr371qxRdd999NzfddBPPPfccDz30EEuWLAHgjjvu4JZbbuH3v/89Z555JuPGjQPgscceo7W1lf9JiIWXUhIMBvnWt77FNddcwxFHHGHo22DFoCRRJ27fdpsKZXr+efjWt7p/H6wk6ocfVn9/5Ss9ibOQHYm6r7fPO09VvL7/fpXe9fTTe8pYvU7PPw9PPKFqgWhxRG536jnJg81+Pxz76e7MrOEIXHLVMDXZn+46JXI/HA5Vb+PWWxUn4sMPVQXxRx5R8kVFalCxZYsaFGrpdOfOVftqAz0hVGraww6DL35RZXnaubP7GFOnovs/bpOo806i1nScdcR4fvfv7bzbtJ/zjxzbo90KidrpdMbtyBeJOhAIxG3929pW6ircnHbIaIqcDlOfysrKUnQa+VRaWmoor20HAgES62qdfsho7vrbWtqCMLa6ske7nj5XkZsr/rgUfyjKn646hpG16jrGYjHKk5Jo5EqiLioqorG+gs0tXmISBFDqdvLYf3ew6N/bcTsdhKMxJLCpxcd5v1lGudtFqy8U1+F2OWisK2fGmCrOPXwM46uLuX9JE5/s74xntGqsr4if/7yRqKWUfiB9pY8uaCPsSZMmGY7cAEKhEEIIwxG2z+eLy+uN3Px+f7w93cjN5/MZjkbb29sJh8OGI+z9+/dTWVmZdoTd1tZGZWWl4ayBx+OhtrbWsBK1VrE63WjU7/fjdDoNK1EHg8F41WQ9n8LhMFJKw1kDj8djOhOijajTzRpo50RvhB2LxbjtL6tYvbO9q3q2ej999Ek7gS4S0sa9Xs66/11mjquKt08bVcHNZ03D6XQSi8WIxWLcfvvtjBgxgpNPPpk///nP1NTUEIvFWLBgAQ6Hg0gkQkNDAx988AHDhg3j8MMPJxqN8vjjj7Nr1y527tzJbbfdxr333svo0aP54IMPiEQi3HLLLVRXVxOJRLjyyivjMyHnnXce//rXv+IzGMOHD2fTpk2cf/75PPDAA/zwhz/E4XAQi8U46aSTCAQC3HDDDRxyyCEsXryYF154gWOOOSZ+zmOxWHymo1ObybQxsHHqqYpMfc89itlq4UE8oPHHPyr+R0NDf1uSPX76U3j1VVVkbtWq1E6/GXbsgCuvVB3s227rFRNBLaY89pg6RGJZB22y3xCJ3I9EfsL8+d1KNm/uHlBon0QEAnD99WrAcNhhKmwtsbM0fz7Mm4dcv75PuB82UnH4uGrqKop5Y+2elAGEFYTD4bxnDNR07veF+Me6vXzl2EkUOa2tvmdqj1X5ZLlTp9Vz19/WsmT9Xi49eqKhnnA4zIPvbGXpljZ+cdHhTBlZ2aOtNzIuLpo/N4WvUFbs5OWPdnHbSz3DBAPhGJ87ciST6yvin7HDS3sUGPR4PBw9eUSKzkzQb5WoPR6PaSXqUChkWP03HA7nXIm6rKzMsN3lchGJRNLOEhQVFVFZWdlDRzqfcq1ErckajV6Tq2Int3s8npSKx8k+lZSUpJzXTCtRJ5+TRBscDgcOpwOHQ8TD3YQgPnjQEIjEerQ7nc74cZxdMbtOp5MvfelLOJ1O1qxZwwUXXMCGDRvYtk2VjtcGOIceeihPPvkkn/vc53A6nbz77rssXLiQRx55hHXr1tHa2spdd93F0qVLOXDgAB9//HEPXePGjYufA4fDgdPpxOl0MnnyZJqbmznxxBPp6OjoIVNUVER7ezvV1dV84xvf4J///Gc8RZ42+NPOTVFRkV2JerBACDWz+5nPqN7eYK4A/NFH8PHHKn3tQEZZGTz5pBoIXXcdPP649X1jMTXwCAbhD3/olQxHUsJLLymOw9q1cOyxahJ/27aek/2GMMtu5XAoPsSUKSr8DlQRvMRVi2nT4Je/ND3GnqamXud+2NCHwyH41PQRvPzRLkKRWE6VkPONxR/tJByVXDBrXH+bkoLJ9RWMrS7lrXUtXHq0cbX4fze18cBbm/jCnHFcMLtvfEmXfWr+cZP4w3+3sqnFi+xaSZhcX8FPP2/ORzPLaGWGXrmzbA5EdjL50DFQbL1t3gyevvIY/vz1Y+OfKSMq0AbIDgEHjSjv0X7bvBkpehwOB8XFxdTV1TFjxgwOHDjAzJkzaWxsZPv27XG5Cy+8kNtuuy1+b5588sn84he/4OOPP+bwww9n9OjRPPvss2zfvl1X186dOwH4xz/+wYcffsjvfvc7ALZu3cqUKVMIBoMM10kHOWLECNrb21m4cGG8wI2NIYAzzlBZfe6+uzumfjDiqadU7P5FF/W3JbnjqKNUpqInnoAXM6iV+stfqirT990HBx+cd7PefRdOOEHRNGIxeOEF9dubb6r+vNPZnVQp71i8uA8OYiPfOH36SDzBCMu2ZF69uDdmzzWdz7+/g2mjKjlkjPWkIZnaY1U+WU4IwSlT6/n35n0EI9G0evZ0BPjhS+s5eEQlPz43NQ1tf9R7WjR/LgfVV+AUIp6hyQz5sDO/yX4zgM2ByByDzdZk/kLyEt1vv3yk6XFuvfXWeBzmPffc06NNCxHSOA2TJ09m8uTJKToikQi3dYUdXNTVEUqn67TTTuO0006L/97S0sLEiRN55JFH+NrXvhb/fcKECbz33nscccQRfOYzn2HLli2cdtppigy1bRsnn5z9qN/GAIC2CnH22Wpme8GC/rYo/4jFVO7Qz3wG6uv725r84Oab4eWX1arR8cfDiBHG8itXqkHH+efn/RqvXatWHP76Vxg9Gn77W7j8csW9ht4vl9F3B7GRb5xwUB3FLgdvrN3DCVPqMto3H+9/PZ1b9wdYuaOdm8+e3qv2WJXXkzt16gieWrqN95r3M3tcRUp7JBrj2qc/JBCK8eClsyh1p2Yv643zZ4ZsVhLyYWevDCCMiK0arMSJmcnkI9asr+ywbU1FNBo1LBCj8Soy0ZGIm266iQNaBpQsdWi44YYbdDMm3XLLLQBcdtllPcK8EgcaZ3URKrU0rrfeequpTYMZgzoLE3RnLDr2WDjiCLjzTpVjU8rBlYXp7bdVheWbbzb3qVCzMOndew88oHgsV1yhiMXavZjsUyymiMM1NfDznyvbkn3KIAtT0zYX8y4pY/0GQWWFpL1DUFEBd94S4vqvd1JeISBWCt4ss2X14f+T8HiU31av0xBAX2Vh0raPn1zL66t38d1TxsfDZK1kYWpvb6eqqiqvWZg8Hg/PfLgfp0Nw+kFV8RB2Kz4Z8Sn1fNq/fz8VFRWmWZi8Xi91dXU9fDpqUhVFTsFrq3bQUDaC+vr6Hj499M/tLNvSxq2fnsjIUqmbsSiRu5vPLEzpeK/ZXiftvFq99/SQ1wFEpiTqYDBo+A8SCoXi6bD0Trx2Y0L2JGp/14PQ6MRrx0j3D6K1m5Gozf7prZCoS0pKDG+mcDiM3+9PezNFIhE6OzsN/+m14xv5ZEaiTnfdNBJ1KBSKpzxzOBxEo1GklD22NaJ9Imk6eTsUClFUVEQ0GiXWlULQ5XIhpYzr0HRHo9GUdlADCI0UDYpXoZ0rbTsWi8V9TG7XSNPawySdT9o5TvZhqJGoB30WJm27vBx+/GOV5ecvf4H58wdXFqYXXlDX6UtfMvepkLMwJePYY1VFtu99D555RvEbEqH5dO21apngtddUJiINiT5lkIXpnOMU3UBKONAuqKlRfOe6umKg2HR/Q5/6+P9JVlb29NvOwtRnWZg0fHrGKP6xvoWdfpg2Sl1vK1mYErMI5SsLUzQmeWnlWk6aUkfD6J75eMx8MuJT6vmUScYjPU7pMY21/HtLO9//1OQeWZje3tDCw+9s4ZK547lo7sS0vFe3263Lzc01C1Mu23rXKfG8Wrn39NArJOrZs2ebkqhDoRBut9vwH0QIYfgPog0CEpEpiXr48OE9dOid+ETyrp5PdXV1PXTo+VRcXByXyZZEna49+R/C6Jxp5z25PdEnbfYhEZmSqJPPid5104jI0E2K1qC1GbWDuj5aBz0ZLpcrfhxtO7kd1AAi8VjJx4DuTn+6do1Yna4dVHq5xN+0v20S9SDGvHlw+OGqQzoQqjRbRSikqk+ff76q5jzYcP31irV83XVqNWJiEqnylVfUSsX118OnP53z4QKB7sGDhvZ2qMss+sSGjThOm6bC795Ys4dpo6xzDqx0HDPFhzt97O4IcPM5mYUvZWOPVfl0cicfXM+dL6+lxR9D6+Lsau/kO39ewbRRldx+7gwcMpo3e/sL+bCz3+j5VsKczGSs6CgUO2xbLci0bYEHj4Yf18CDRyPbtuTlOPnQUSjn1cYAg8aF2LgR/vzn/rYmf3j1VbWq8KUv9bclvQOnU2XQisXga1/rWSBt717122GHqfSvOaKjAz77WTV40PKPWMqqZMOGAUYMK+Hw8dW8sXavuXACeuM99eKHO6kscfGp6SMz3jdTe6zKp5M7ZaoaeL29YR+geA/ffvpDAuEoD3xpFiVFTsNjDJT3fD7s7JUBhBXDQhYyk5jJWNFRKHbYtibhlRtxPDEPfn929+fhY6FlHcgotKzD+dsTera/cmOKGi0MSQ933XUXsViMc889l+effx6A4447jiVdReD0dFx//fWGuh566CHuuOOOOOn6jjvuAODRRx9lx44dcfnHHnuMX/ziFynHCQaDXHHFFaxYscLg5NgYVDj/fFWU6847Vdz3YMAf/wi1tXmZfS9YNDSoDEtvvaWqVYPq5S9YoOL7n3oqNfQnQ7S0wGmnwb/+pQ41fbqd8EgPQvCoEOwVgo8TfqsRgteFYGPX9/Cu34UQ3C8Em4TgIyGY1X+W9y/OmD6CFdsPsNcTMBfuQj7e/4nwBiO8vnYv58wcQ0lRKuk43/ZYlU8nN7m+nPE1pby9QQ28fvH6BpY37+fuzx3GQSMqTI+R7/PXW8iHnf2WhcmGjRSEkzgAEXNOwB133EFdXR2nn346f/rTn6ipqYkXf3O73TgcDhobG1mxYgV1dXXMmqXeJX/4wx/YvXs3n3zyCbfddht33nknY8eOjXfsb7rpJl1d3/zmNwmFQlx77bUA1NfXs3XrVi688EIefPDBOKka4PTTT0dKyXe+8514IbnFixdzwgkn5OmE2RgQcDjgqqvg299m5LTz4hutAAAgAElEQVRp3b3DgZon3+NR4T1f/Wqv1DwoKCxYoPgrN96osk0tWQL/93/wq1+pFYgcsG2bGn9t3aoOcfbZKiLKhi4eAx4Ankj47UbgTSm5Rwhu7Nr+AfBZYErX52jg4a7vIYcZY1Xo0tF3v8lBXek9J9SWmeyVX7yyahed4RgXzs68qF1/QAjBKQeP4Ln3t/Pa6t08vGQzXzxqfFZF+QY7eoVE3dDQYEqi1oi3ZlkGjEjUDocjZxK1RoI2IlFrZO50JGqt3YhErZHGcyFRa8c2IlE7HA5DErXT6TQlUUspTYnhZiRq7ZykJVGf/hMikUj8OkajUVwLj0e0bkLIGFI4kDUHEb70xZ4k6lCox7aUkou7ih6tWLGC888/n82bN7NhwwZGjhwZJzZPnTqVhQsXct555xGNRlmyZAkPPvggjz76KGvWrGHv3r3cfPPNvPPOO+zevZuVK1dy3nnnsXHjRjZs2MC4ceMIBoMEAgFuv/12fvCDHxAMBhkzZgxr167l9NNPZ9++fQSDwTipOhwOs337dsrLy1mwYAFvv/02wWCQSCRiV6Ieanj4YQBELKaC3efNG5jpMZua4MQTVdadV15R2wN1IGQFQsAjj6ilgSOOUNmDystVbz8HrFunSoV4PPD666rOg430kJJ3hGBS0s/nAad0/f04sAQ1gDgPeEJKJPBfIagWgtFSsquPzC0Y3P3yOkAtnG1u8bLg8eWm6T7zFcO/rdXPgseXs3GvlyKHoK4iO719zYEAmDFmGE/+N8bXn3wft9PB5cc3WN53KHEgBjSJWo8QnE0l6lxJ1Mlkbj2fEm3NlkStZRwyIlGnI0mbtSf6JKXMmURdWVmZOYn6S8/A05fAvo2IuinELv5jSuVpDYn7lZaWUl1dzWGHHYbP5+OII47g4IMP5p133okPcC6++GKOPfZYVq1ahdPp5NRTT+WBBx5g+/btXHbZZYwZM4aXXnqJnTt3MmrUKA499FB8Ph+zZs3i4IMPZsmSJRQXF/OFL3yBGTNm8Oabb3LllVeyc+dOPvvZz9LZ2UldXV1KJerx48fj9XpZtGgRPp/PrkQ9VLFhQ/ffsZhKrzMQMW8edBVVZNu2gTsQygSjRimiuJaOtbNTZdbK0u/33lOcB4dDLWgccUT+TB1iGKkNCqRklxBoRTvGAtsT5HZ0/TbkBhBNLd3FS2Oy53ZvY8Hjy9nU4gUgEpNc8fh7OVU97kv87p9N8b/DsRjffOqDAWN7X6Lf6kAEg8GUTmemMlZ0FIodtq2piEQiPQcGNQ3wraXd7cEgZhGTN910U7zTnm0huWAwaFpILhgMEovF+Otf/9rjd62Q3MKFC00LyZ166ql2ITmGUB2IRJ+mTFGDCI2M63Sq7REjBpZPiQMfbSB04ID5dRpIdSD07r1dCX1Pze/OTvPrlFQH4q133Zx7gYva4ZLXX+pkyiFF0N5PPuldp36rAxF1CSHe6z7JLJRSLiQ7CJ3f+p3Z2td1IJxOJ5NqS2lq9ceze02sLe0RkdGbdSCaWnzx40qgqcVrKeok2ad9+/ZlVAeitbU16zoQmk/N+7oHWlIq2xOvk1ZDQe86aZEZiT7ZdSBs2MgjYolZTXoJVgvJWcGNN95oWEhu/vz5WRWS64vzUGgYMnUgEvHyyzBvHnL9esS4cbBnD3z+8/CPf3Tnyx8IPo0fD83NaltLFZSY738w1IHQu/emTlVxR1oneOrUbl+NfEqoA/GXv8All6iSEX//u2Ds2PL+9SkR/V4HwhmRUs4hM+zRQpOEYDSgpRvaAYxPkBsH7MxQd97R13UgAH7/taNZ8PhyNrd4iUm49JiJpvUI8lUHorG+nI171QqEQ0BjfQXFxcUZ+9TXdSBcLheN9RXxc6bZnnidkm2y60DkEULoTQD0hJUZbjOZXGfJ+9IO29ZuVFRUsHv37nj9hXQwa7ci4/V648UCe/M4uehIfljYGIRobITVq9nT1MSoxkaV2efss+FTn4I334T6+v620BouuQTuuUfNLE+dOnRSBS1erMK11q/Pyu/HHlN87Llz1ViyttZ0FxvmeAmYD9zT9f3XhN+vEYI/ocjT7UOR/wAwobaM1797MrGYZN4D/+LRfzXz5WMmUuxK/67Kx/sf4JH5czj150uQEhrrylk0f25WejK1x6q8kdyi+XO5/LFlbNnnp7E+1XajffN1/nob+bCzX0nUgOFylkZgBv0lOo3ADNmTqDs7O+MVj9Mt/fj9fkKhUFoStc/nIxQKmZKoNb9zIVE7HA7D5Szorqys55PD4TAlUScvM2ZDovZ6vXHORvISXVlZWdyP0tLStEt0gUCAkpISwyW6cDjMsGHD0i6ltrW1pZSjT/ZJ49oYLaUmVrrW80lKGSd1p1t29Hq98cJ3yT7ZJOohhlNPVZ3Qc87pHkQMhKphmzap9KZNTeaygwldA8BscO+98D//oy7ziy8Ozrp7vQ0heBpFmK4Tgh3AbaiBwzNCsADYBlzUJf434CxgE+AHvpaicIjB4RD86KzpXPrIUp78z1auODF94gMrE8BWEIrEiEn43wtmcv7hI7PusGZqj1V5I7kJtWX87drj0tpstG++zl9vIx929gqJetasWaYkai1rkdFyVigUSlkKSlya8Xg8uktFiTAjUTudTsN2l8tFJBJJu9RTVFREOBxOu5yl+aT5m+xj4jHNSNRaDJ3RcpXeObHSnuhTIBBIkcmURJ28jKfnk5SSsq6l9WyXUj0eDy6XK+1SakVFBXr3YqJP2kDIaCk12/OaCJfLZXhObBL1EMPpp6t0qPPmqZQ8b74JNTX9bZUxli6F44/vbysKHk1N6rKuXduAlHDmmSqEaYAkaCk4SMkX0zSdriMrgW/1rkUDD8cfVMfJB9fz639s4qLZ46kq00/BnA8OJMCy5jYA5jbU5KQz032tyufCF822rZCQDzstFZITQlQLIZ4TQqwTQqwVQhyb01Ft2LBhw4YaOPz1r7B2rZqi3r+/vy1Kj127YPt2OOqo/rak4KEGDyClmuXbutUePNjof9z42Wl0BMI8tGRTrx9r+ZY26iqKmdTHdSds9B2sVqK+D3hVSjkNOBxYayRsZWkkefY1GxkrOgrFDtvWzGVsW20MCXzmMyq2ZfVqNaDIE/E/71i2TH0fPSRrcmUENXjo3k7M4mvDRn9h+uhhXDBrHL//dzM79utzA/P1nlrevJ+jGoYjhMhJZ6b7WpXP5Z2ebVshIR92mg4ghBDDgJOARQBSypCUMuc3nBnh1IqMFR2FYodta+Yytq02hgw++1l44QX46CNVnrgQBxFLl6o0nUce2d+WFDR++tOegwctaZMNG4WA755xMAK49+/6o9p8vKd27PfzyYFO5k6qyVlnpvtalc/lnZ5tWyEhH3Za4UA0Ai3A74UQhwPvA9dJKXtUJBFCXAVcBTBmzBiatVR/aaARZHORsaKjtbW1IOywbc1cJh86zOzM13H6ylYbgxhnnw3PPw8XXKCC5v/+dxg2rL+t6sbSpTBzZmoKUBtx3H8//OhHcO65sHEjbNggmTpVDJlkVTYKH2OqS7n8hAZ+8/ZmLj+hgUPHVvVoDwQCOc9OL+/iPxzVUJOzzkz3tSpvJmfUnm1bISEfdloZQLiAWcC1UsqlQoj7gBuBWxKFuoq+LASYNWuWnDRpkqFSPdJppjJWdAAY2dJXdti2Zi6TL1sL5V60el5tDGHMmwfPPgsXXginnKIKdG3c2J06tDF99pReRTQKy5fDl7/cP8cfAFi0CK67Ds4/H555RtVX2920RaXttWGjgHD1KZN56r9bueg3/yYUkfFUpRPyxFdYtmU/lcUupo0qoAkQG3mHFQ7EDmCHlFIrEfwcakCRFjYHIjuZfOiwbc0cA8lWG0MA552neqAffqiKl0Wj6nvevP6zaf168Hhs/kMaPP00XHmlWjj605/U4MGGjULFsJIi3C4HneEYUSnZ3OJlwePLgfy8p5Y3tzF70nCcDpGzTpsD0TvIh52mKxBSyt1CiO1CiKlSyvWotGlrcj2wzYHIHLat2cnkQ0eh2DoYoNWLCbvduEIhCIfVB1R4TDQKXTU6KC1V1X+7anLEK+Z21eCguFgFmWs1NNxuVeRM2y4qUh+tmKC23dmpAtVdLrWPtu10Kp2BQHfV4ZISdfxoFIRQNoVCEIl0byf6UFaW1ifR0aF0mfl01lnq2Fql8lhMdeIPHOgfn959V31Pn65syPQ6BQKqbYBcp0zuvb88F+Gy+WWcdEKM5/8YobizEzq7fAiFuvksA8inXK+T8HiU31Z9GgLQnnuNjY3xGkBaPaXkekxGNaVAv1ZRcp2s5FpFyXWy9vtCcdtiEppafPh8PoLBINFo1LD+UmKdrOT6S3vb/Wza6+W8maMIBAKEw2HC4TAOhyMrn/x+P+Fw2JJPRUVFdHZ2Eg6H09b+0nyIRCI4nc60Pmk69Gp/aTbpXadoNIrH4+nhk3ads7lO6epk6fmUyXXSfLB67+nBah2Ia4GnhBBuoAmTwiwykUGWBlbir3KJUbOKvrLDtjVzGdvWwQmtXgzl5VfidqtOSjLKkpbSkzsZyVyS5Idc8nbyMfp6G6CsDDlsWLdtZj5Nm9adzkcIFcZUXd0/Prz/PlRVwZw5qhOY4FMPpPOprU35PUCuUw8YXKfXXoOLLy9mzhxY/LKTskonkOCT293zmvWFDwXw/yQrKzO7V4cAtOfenDlz4nWyEuspGdUq0mBUq0jTYbSdWCersb6CjXu9ADgENNaXU15ebqn+kga9+ktrN6sB8zEH1VNSUkJJSUm81lK2PpnVlEq2yUg+sYaUXl0tzafE9kxqfwWDQd36ZFZsSueTmXw21ynRByv3nh4sDSCklCuAOWZy2gh70qRJppWoQ6FQSsVj6DkaDYVC8ZGc3sgtHA7H27OtRO3vmkkxGrlpx0hXiVprN5o1MKrabLUStd/vj1eKTjca1SpnpxuNRiIR00rU2vGNfDKrRJ3uuiX6lHhd9HzyeDymI2yv10tpaWnaStRerzflXkz2KRQKxe9H7d5L9inxPtDzKRKJ4PV6DWcNfD6VdyCdT3Ylahs9oFWqXrtWddqfeKL/bFm6FObO7Tl4GOJ4+23Fd5g+HV55BWx6k42BhEXz5/K5h96l1RdiUq3iQOQDy7a04XY5mDmuylzYxoBGv1Wi7uzsNK1EHYvFUqrzJo6spJQp7ZlWoh42bFgPHelGbpqMnk/Dhw/voUPPJ5fLlXMlaq3zbTTadDgchuess7NTtz3Rp4qKihSZTCtRJ58TPZ+cTmeP85psU6Iv6XzWqlDnUom6s7PTtBK13r2WbJNZe1VVleE5sStR2+iBxkZYswZWrlSd93vuUdwICxyzvMLvh1Wr4MYb+/a4BYylS9XYbtIklSxr+PD+tsiGjcwwobaM+y45ki8vWsqPz5sRJ1Anv0szxfLmNo4YX02xqztkNxedme5rVd5Mzqg927ZCQj7s7JXpJJtEnZ1MPnTYtmaOgWSrjSGIww+Hn/wEnnsOnnqq74//wQcqXt0mUANqPHfmmTBiBLzxhvq2YWMg4rCuFK4f7WiP/5bLe8oXjLB6ZwdHddV/yIfOTPe1Kp/LOz3btkJCPuzslQGEVQ5ErjJWdBSKHbatmcvYttqw0YXvfx+OPx6uuQa2b+/bYy/tSsB31FF9e9wCxNq1qlh4RQW8+SaMHdvfFtmwkT2qyoqYVFvGqoQBRC7vqQ+27Scak8xt6DmAyEVnpvtalc/lnZ5tWyEhH3b2W0CrlUGGmYwVHYVih21r5jK2rTZsdMHphMcfV9l3vvrV7uxMfYGlS2HiRBg5su+OWWBoaoIpU+CQQxQf/LHHVPiSDRsDHYeNq+ajHQfi27m8p5ZtacMhYNaEnokDctGZ6b5W5XN5p2fbVkjIh515DdbKhEQdjUZNSdSJBGU9EnUsFsuZRK0dz4hErRGt05GotXYjErXf78+ZRB0MBk1J1LFYzJBELaU0JVEnEoazJVFr58SIRO33+w1J1H6/35RErXE6ciFRR6NRUxJ14r2m55OU0pRErcnbJGobWWHyZPjlL+Gqq+DXv1ZVy/oCy5YN2fClaFSRpS+4oDsbq5Tw7W/D6tX9a5sNG/nAzLFVLF65kxZPkPrK4pzSjS/b0saMMVVUlvQMj8lFZ6b7WpXPJTV7tm2FhHzY2Ssk6tmzZ5uSqLW8vkYkaofDYUgY1jpmiciURF1VVdXjNz0StdbpTfZB2062Q8+nkpKSuEy2JGpNh9E50c5rpu2JPlVWVqbIZEqirqmpMTyvFRUVcX/12ouKinC73YbtoM5JriRq7ZwYkaj17rXE7cR7JJ3P1dXVhufEJlHbMMUVV8BLLylC8xlnqCnx3sSePbB1K1x7be8ep4AgJfz3v6og3DPPwO7dPdu1khw2bAwGaNmSPv6knVOnjcg6Nj4YibJi+wEuPXpiSpvNgSg8DGgOhDa7m4uMFR2FYodta+Yytq02bCRBCHjkERWEf9ll3QXCegsa/2GQr0BICStWqHFZQwMcdxz89rfq+5lnVEkOLYOtw6FKctiwMRgwY2wVQsDKrjCmbN9TH3/STjAS46iG1JRkubz7Mt3Xqnwu7/Rs2woJ+bDT5kAMoPh329bsZPKho1BstWGDkSNh4UKVHemOO3r3WEuXKv7FrFm9e5w+RFMTzJihiiYfdJCKBDvkEDjySPj5z9Xfjz+uFl+efx4uugheflkNIpxO9b14cX97YcNGflBR7GJyfUWcSJ3te2rZlv0AzEnKwJSLzmz2tTkQ1lBwHIhMYCX+KpcYtUKzw7Y1cxnbVhs20uBzn4P58+Huu+Hss+GYY3rnOMuWwcyZqZWMBzDmzesu8L15M9x/P5x8Mlx/veI61NWl7tPYaHMebAxezBxXxT837otz9rLBsi2tTK4vp64itYKxzYEoPBQcB0IjUTc0NJiSqB0OhymJWkppSKIGciZRa4RhIxJ1IBAgGo2mJVFr7UYk6nA4jNPpzIlEHYvFCIfDhiRqIYQhidrpdJqSqK1U1zYjUWvnxIhErelMR6LWfjMiUUcikXh7tiRqh8NhSqJOvNf0fBJCmJKoQ6EQ0WjUJlHbyA/uuw/eekuFMq1YAeXl+dUfi6kBxBe/mF+9/Qi/v3vwoMHphCVL+s0kGzb6HTPHVvHCB5+wpyNIbVnmsfHRmOS9rfs5Z+Zo3XabA1F4yIed/VaJ2ufzmVaijkQiPYi6mg4NPp8vpT1TErXL5aI84cWr1x6LxeIyej5Fo9EeOvR80vxN9jHxmGYkap/PZ0qi9vl8PWyx2p7oUygUSpHJlERdXFxseF4rKirw+XyUdc1spvMp8bzrtft8vpxJ1D6fz7QStd69lnxek+89PZuNzolNoraREaqqVKzNaaepOhEPPZRf/evXQ0fHoOE/LF+uxlpSKiqJlDafwYYNUKlcQfEgTpiUmkTFDOt3e/AEIszVCV8C1afItsOa6b5W5c3kjNqzbSsk5MPOfuNAxCzkMTeTsaKjUOywbc1cxrbVhg0TnHIKfOc78PDD8Mor+dU9SAjU4TDcfjsceyz4fPDEEzB9us1nsGFDw4wxw3A6BKt2tGf1nlre3AaQdgCRy7sv032tyufyTs+2rZCQDzv7bQDhcJgf2kzGio5CscO2NXMZ21YbNizgrrsUK/jyy6G1NX96ly2DykrVyx6gWLdOZVL68Y9VJNaqVWoVYvVqVZNv9WrFb7BhYyijpMjJwSMr+eiT9qzeU8u2tDG6qoRxw/VX0XN592W6r1X5XN7p2bYVEvLSd8qDHSkQQpjKJIeLZCNjRUeh2GHbmrmMbesAghCNCLEIIZ7rb1OGHEpK4A9/gH37VG/Y5VIDiqam3PQuXQpz53bnLx1AiMUUOfrII2HLFnj2WXjySaiuNt/Xhg2rEEI0CiEWiUHw3GusK+dfG1uYeecSzrj3bba1+i3tJ6VkWXMbRzXUpO375fLuy3Rfq/K5vNOzbSsk5MPOfq1EXVpaakii7uzsjDPF9UjUwWAwPorKlkTd3t5OcXGxIYm6o6ODsrKytITj9vZ2ysrKTCtRV1dX51yJuqqqypBEHYlE4rwMPZ9isRiRSMSQRO3xeHrwMrIhUbe1tVFWVmZIotZ4BekIx16vl4qKCtNK1DU1NTlXoi4rKzMkUSfea3o+6ZHbk7cPHDhASUlJ4ZGohXgUOAfYi5SHJvx+JnAf4AQeQcp70uqQsglYYA8g+glHHAE1NbB3r9pet06lG8o2dVBnJ3z0keJWDDBs3w5f+xq8+SacdZYqmzFan9tpYwhDJDz3ZMJzTyQ996TBc092PfcGwwBieXMbsa7kAptbvCx4fDmvf/dk0/22tvpp8QTThi+BfgFbq8h0X6vyZnJG7dm2FRLyYWe/kag9Ho8piToUChkSV8PhcM4k6uLiYlOSdSKZW8+nZDvS+ZQriVqTNSJRezyeODE5XXsyWVfPp+TzmimJuqyszPC8VlRUIKU0JFEnk9fT+ZQriVobCBmRqPXutUQbIpGIKYm6pKTE8Jz0I4n6MeAB4In4L0I4gQeBM4AdwHKEeAn1Uv1p0v6XI+XePrHURnokhi/lWi75gw9UjM8A4j9ICU89Bddco0xfuFAV7rawIG5jAEAImgEPEAUiUjJHCGqAPwOTgGbgC1Ky36LKx0h67gmd554weO7JQfTc2+ftLiwWk9DU4rO037Iu/sNRDekHENFoNGu7Mt3XqryZnFF7tm2FhHzYaWkAIYRopsc/rpyT64GthDmZyVjRUSh22LZmLmPb2keQ8h2EmJT061HApq6VBRDiT8B5SPlT1KxdVhBCXAVcBRAqKqI11zCbAYaOfft6TXdtQwOuzZsRUiKFINLQkPX5LXvlFYYBe0eOJJaHa9Sbfm/d5uLSy0fR1FyElILDDg2y8P49TJoYYc+WXjusJfSm34WMXvT7VClJVH4j8KaU3CMEN3Zt/8CKIinlOyLNc69rZQHR9dyTeXzujR07lubmZgBa88lZyhHjq9xsPaBW5YWAcdXuuJ1GeGvVJwwrduLy76O5Wd+fYDCYMglpFZnua1XeTM6oPdO2QrrOGnK5JhoyWYE4VUpp6algpaNkxXAzmVyd70s7bFszl7Ft7VeMBbYnbO8A0k9HC1EL3AUciRA/7BpopEBKuRBYCEB5uRw1BBmsvebza6/BOefA2rWIsjKKXnst+2Nt2gTjxzMijwXq8u23z6cyKF15JXi96jchIBIt5phTJ+T1WLlgKN7jkKnfH2d7mPOAU7r+fhxYgsUBRBpk9NwTCc89IcQPpYXn3pw5c+SkSZPibYl/9yeevHIEZ973Dv5QlIPqK1g0fy4Tas0LSK7Zt4WjGutobGhIK2OHMBXOddaQjxCmXmHHWSmRrcWj5yJjRUeh2GHbmrmMbWu/Qm8WIP0/tpStSPkNpJycbvBgo5fR2Ahr1sBPfqJ617ncb0uXFmT4UjAIL72kMiqNGKG+tcEDqDCmDRv6zz4b2SLqEkK8l/C5SkdIAn8XgveFQGsfKSW7ALq+R+RoSEbPPSllq5TyG1LKyekGDwMFE2rL+OYpkwF49hvHWho87O0IsLXVz9EG4UuQ27sv032tyufyTs+2rZCQDzutrkB0/eMKCfy2azTdA4lLdGPGjDFd+goEAikx6ZnKWNFhtnTUV3bYtmYukw8dVpYOB5KtfYgdwPiE7XHAznwo1pIthN1uXKGQ6uhqD7PSUohGoYvgTmmpiufvIrSjnb8uAjvFxSpLkEZAd7tVgn9tu6hIffz+ntudnaq36XKpfbRtp1PpDATUcR0OdcxgUNklhLIpFFKB9tp2og9lZWl9Eh0dSldv+nTppXDHHfDLX8I992Tu07590NyspvYPHDD1ydJ1CgRUWxY+RcKSJf8p5ukX3LzwooMD7YLamhhfuVRyyfkBvvndEtZtdBCLCRwOydSDYtDuzek65e3eC4W6z2E/33t588nC/5PweJTfVn3CaSU0+ngp2SkEI4DXhWCdiXw26PXnXmNjYzyBhpaMJDmZiVFCFtBP9JGcZCZd8hKjRB+HjlKDhn+t38VnDh2TNnmJlmTmXxt2AzBjRHebXuIcj8eDy+XKyqcDBw4QjUYt+9Te3k40Gk2bOCfx3Gsc1uTEOZFIJJ5MRs8nzSa969TZ2RnnF2g+adc5X9cpnU9GyYCSk8xoPli993QhpTT9AGO6vkcAK4GTjOSPPPJIaQaPx5OzjBUdW7ZsKQg7bFszl8mHDjM783WcfNkKvCct/E/m/QOTJHycsO2S0CShQYJbwkoJM/J6zLIy03M22LBr8+a+OdCll0pZWSllR0fm+y5eLCVI+fbbeTMnE783b5bykEOkdDikHD5cytpaZU5lpZRf+YqUf/ublKFQqrzTqb776hRbQZ9d7wJDpn7DKp/M4NkB8naQ3wO5HuTort9Gg1yfmR4mkfDcQ02qNgENgLurv5PX597s2bPjflt5P/UlOkMROfmHL8u7X15jSf7Wv6yS025+RYYiUUM5K+/HfO1rVT6Xd3qmbYV2naXM7Lym65dYCmGSUu7s+t4LvIgiGqWFFQ6E2WytFRkrOgrFDtvWzGVsW/sIQjwN/AeYihA7EGIBUkaAa4DXgLXAM0iZZU5QG32Oa68Fj0eVXc4US5eqmePZs/NvlwnWrFGRU2vWqAnr/fvV5Plzz8GePfD44/DZz6pJbw2NjXZhuMEOISgXgkrtb+DTKOLES8D8LrH5wF+t6+x+7gkhdgghFkid554cQs+9kiInM8dVxTMrmWHpljZmTaymyGnclczl3Zfpvlblc3mnZ9tWSMiHnaYDCCFEuRCiUvub7n/ctFADFmPYHIjMYduanUw+dBSKrb0CKb+IlKORsggpxyHloq7f/4aUB6N4DXf1s5U2MsFRR8GcOfDAAyq0JBMsXQqHHgpdKZR7G7t2qWir2bNV/bvkBD5+P9XiKdYAACAASURBVFxwQVeki42hipHAv4RgJbAMeFlKXgXuAc4Qgo2o1Kvpa9UkQUr5RSnlaCllkZRynOx67kkp/yalPFgqXsOQe+7NGj+MVTva6QwZp/ls7wyzfo/HsP6DBpsDUXjoKw7ESODFrlUFF/BHKeWruR5Yi8fKRcaKjkKxw7Y1cxnb1sEJmwPRyxwIzacFC+Dqq1WqojPPtOZTKKQGEBdd1JOInWcOhDfk5sX/K+IPT8EbS1zEYoI5s2Pcd0+ABx8pZlNTEqfB2zkw+QI2B8IiB8IYUtIEHK7zeytwuqmCAsBA4ECEQiGm1riIxCTvN+9j1rjKtLH1y7Z6kBIOG1mq60MyB8LpdGbl0/79++MFcq34dODAASKRiCUOhFYENh0HwuVy6fqk2aR3nfx+f/xdX8gcCM2HXDgQpgMIqfIhp/zj6iGTStShUIhgMGh4M4VCITwej+6J1sgvWnu2laj9XQ9CoxOvHSPdP4jWbvRPb1S12Wolar/fT0lJieHNFA6H8fv9aW+mSCRCZ2enYSVq7fhGPplVok533RJ9Srwuej55PB7TfxCv10tpaanuP4jVStShUCh+P2r3XrJPifeBnk+RSASv12v4T+/zqcI8BVeJuo8huwpOUl5+JW636qQkI6kYYkonI3n5Nfkhl7ydfIy+3gYoK0MOG9ZtW2/79NWvwi23wKOPwrnnWrN5/Xro6IBjj01dgcjiOjXtLGHePFi/voqDDxZ8//vFvPkmvPii6oNOmgQ/+pHifU+b5gDKOOciuvaBqVMFixc7QSvS2EfXycinjK6T2w3V1fm1sb99snBMWVnZ028rNg5yaM+9OXPmxAvtJhZkNSp2qsGo2Kmmw2jbrNhpUVERxx08CiGa+GB7ByccPDKt/PvbdlDkFBw3dQylbqeuD4nbyQV1rfpUWVlpWpQ2edtIPnFbrzBvYlHadAWRk21K9MntdusWOLZqk55PZvJm23qFdhN9sHLv6aFXKlHPnj3btBK1loPW6GZyOByGJ0brmCUi00rUNTU1KTdUIrQqx9rvej7V19f32E/Pp5KSkrhMtpWoNR1G58Ttdhu26+X+Tfapuro6RSbTStTJ50TPJ81fvfaioqIevqTzqaSkJOdK1No5MXqQ6d1riduJ90g6n2traw3PST9WorYxGFFSAlddpTIxNTer3roZli5V33lK4TpvHqxbB7GYYO1auPxyGD4cLrtMfY47LrVStMZpsGHDRt9jRHUFU0dWstyEB7G8uY1Dx1bFBw9GsDkQhYd82JnXAUQm0NJy5SJjRUeh2GHbmrmMbevghB3C1EchTJ2dqlDCz36muBC33mru0zvvqNn+yZNzCmGKFpXw0v85WLOmiMTU+g6HZNfadooru3xqL8zrlLd7zw5hyksI02DAQAlh8vv9HDmukpc+2kOH14uQMmXlPxiJ8dGOA1x29HjTCA0tqqKqqiornw4cOBCfPLWaxrW4uNg03CcYDFJVVZU23CcQCFBdXZ026qSkpET3OgUCgbiNhRzCpPnQqyFM2cAqidpsBGQmY0VHodhh25q5jG3r4IQdwtRHIUxuN1RVwfnnw+9/r2pDJB5TT37FCpg7V79TZ+E6tXWWsmgRPPggbN2q+pPRqOpbOhwwbZqgeGRCaEuBXqcesEOY7BCmPGCghDDFYjGOnTKSp9/bSfOBCDPHVafIf7y5lXBUctxBI1IiPPIdwlRSUpJRCFNxcXGvhzCFw+G0IUzBYHBAhDAl+pBtCFOvVKK2YcOGDRsFgmuvhbY2+OMfjeUCAVi5UmVwyhAffwxf/zqMGwc33AANDfDCCyoUafp0cDol06YpPrcNGzYKG0d1ZVZatkU/jGl5cxtCYCkDk43Bi7yuQGhLdA0NDaYkaofDYUqidjgchiTqRLJutiTqWCyGx+MxXPrRCLRmWQaMlh3D4TDBYDAnEnUsFovrSbec5XQ6DUnULpfLlEQNmBLDzUjU2jkxWqLTCN/plujC4TA+n89wiS4SiRCJRHIiUTscDlMStRkx3OVymZKotWXDoU6ittHHOOkklZb1gQcUCSFdnZ4PP1ShMRb5D9GoGhDcfz+89ZaaxP7yl9V4ZebMbrnVq2F30xZG2QUabNgoeJSUlFBZVMT4mlKWN7dxxYmp/7fLm9uYOrKSqjJrIbk2B6LwUHAciExI1IFAgOLiYsPlLCkl5UmZQBKXZgKBAGVJS7OZkqiLi4tTbEyEy+WKDyaSfUiUT/xNzyfN32QfE3WYkagDgYBue/I5Sb4xrLQn/qaVN09EpiTqsrIyw/NaUVHRwxY9n5xOp2G75k+uJOpAIIDb7TZcStW715LPq9myoxZvmK59qJCobQ5EH3IgNJ+uvhq+9S149VU4/nh9n955R+mbPl35p+NT08YoZ32hnI2bHDhdEA4Lxo+Ncc+dMa6YH6a2osungHEa10K/Tnm792wOhM2B6MJA4kCUlZUxZ0I1b2/YR0dHBw6HIz5xFwxHeK+5jc/PGms6GWlzIGwORMawORDZyZjBtjU7GTMMJFsHA2wORB9yIDTMnw833dRdyllPfuVKFYM0bVpKeygEr7zh5rLLVIFrgFhYiTdtceByOVCvkzQ+tbUp+wfIdeoBmwNhcyDygIHEgSgvL+foxjpeXLGLlqCDyfUV8faN2w/QGY5xdGNdyqSXzYGwORA2bNjII7a1+jnj3rc54u53OOPet9nW6u9vk2wMNZSXq/Cl55+HnTv1ZZYu7cF/iMXgn/+Eb3wDRo1SXGxt8KBh1y416WzDho3BhbkNit+wPIkHoaV3PcrmPwx59MoAQqSLsU2AldGNmYwVHYVih21r5jKFYKte519KSSAcZb8vxK72TppavGxuC/H+1v28u2kfb6zZw+KVO3n2ve08+Z9mPv/wu2za6yUqYXOLlwWPL8/ZZhs2MsY3v6nCRn7zm9S2lhZoaoKjj2b1alXcrbFR0SeefFItWrz8sopucnS9NRwOmDq1b12wYcNG70J7HzbWlVNX4WZZUj2IZVvamFBTxqgq66vpubynM93Xqnwu/Y9s2woJ+bCz30jUgGk8XCKxVS92LBKJxNuzJVH7fL44uTld7JjP54tzJfR88nq9cT5HOp+0Za1cSNSRSCS+f7p4OE0uXTycECJOPta7LiUlJXR2dsZtzJZE3dHREed0pIvx03gFyTF+uzxhrnryA7a0+mmoLWPhZbMYXVmk61MoFIrHMSb61Nzi5Rt/XMGWVj+Tardw17mHUOEGXyhKWDrwBiN0+IP4glH8oSjBGHT4Q/hCEQIRiT8UxRMIs3JHB6GoCsnbuNfLyf/vLcwD9NIjJqGpxRsn59skaht9hsmT4ayz4Le/hZtuoumT4njF58aRpVzAXbz6u6tZ8QM1OPj0p+HOO9XKg7aiPm1aYpVoO6uSDRuDDVoIuhCCORNrehSUk1KyvLmN06aNTLe7oc5c7Mm3vJmcUXu2bYWEfNjZKyTqWbNmmZKotaxFRvFwoVAoJZYsMbbL4/HoxpolwoxE7XK5TNsjkUjaWDGzeLhEn3IlUWskHL14t22tfhY8vpymFi+N9RUsmj+XCbVlKT55PB5dYnpJSYnS8bt/99RRqR+3mBzT53C6CEZirN/r4bo/rWBbm59xw8u4+ezpDC93E4rECEaiXd8xguEYHT4/ODsIRdW29v38+9tpD6jBwOZ9fs598D8c1VBDJCYJRWJEYpJINEYoKgmFI8QQXb/HiEQloWgMbyAS7+g37fPzxUffwwxlbidlbhcVxU7Ki12UF7vig4dEfPv0KZQWOSkpclBa5KTU7USGQwyvqujxe0nX55KF/2HLPh8xCQ4BjfUVutfRJlFjk6h72Sf51a/R+vJ/2XDHm1z06Jns2i2QUrBxZzn38CPmloe4755OLv6Sk5E1XT5EgJDyqbEmzOp3k67TAQvXySZRDx6fbBJ1VhgoJOr29naqqqpwu90cPraCV1fvZvPOVibUD2PtzgPs94eZObrMMPth8rbH46Guri4rn1pbW6msrLTsU1tbGxUVFaaEY6/XS11dXVrCscfjob6+XtentrY2Kisrda+T1+uNbxcyiVrzoeBI1EMFRp12PcRiqnMbisYIRdQnHI2xdZ+Pm//6MTv2dzK2uokfnjWdkcOKicYgJqX6xMDn91Fc0omUEI11/S7VSPLHi9ewpyOABDbt9XLhb/7N9Z86mKiUxGKSSEx9+zoDFLndRKKSqJREY7H4cZ5Zvp0Dneom2rjXy1n3/5NjGmsJRqIEw2oAEIzECISj+AIhomwkEFa/RWOpHe1tbX6uevJ9y+fT7XTgdqnVgUT4QlH2eUMUOQUup+qcu0pcuBwOhIxSWuzG5RS4nQ5cToHL4eDxfzf30CEE/OriIyh3uygrdlLRNUCoKHYRC3UyoqYapyM19O6Me99mc4s33vmfXF/Bd884OEVObzCr4fdfPSrlPhnKsEnUvUOibtrhjq8OTJkCP/uZG78fNmyAjRthwwY3Gzd+nv1cAHclGydwEmXZikSdeSTn2iTq/NnY3z5ZOKZNok7FQCNRAxx/8Ej4+yZWtwSZPKaIVbvVwPHEaaMNsx/qbWdLoq6srMyIRJ0JYTlbEnWyTYk+ud3uAUGiTvQhWxJ1rwwgBgIHIrnz//CXZ1FbXowvFMEfiuILdn93+IMEY234gpGukJcIvlCUxSt24unq7G7c6+WMX75NQ115ygAhFFGDhrDObHYytu/v5JtPfZC1XwAS2OsJ8qMXV5nKOh0CpxA4HYLOcLRHmzcYYeeBToqLHBS7HAwvd1PsclDschIJ+qmtrqLY5aCkyKl+L3Lw01fWkbgy5hDwxOVH43YpHYnfDhmlvLREbTsdOLo68Hqd9sXXnqBrfygUSnngALy7aV+KjvOOGJtGh0N38ACwaP5cS51/o3txQm0Zr3/35LS22rChh6am7nChyZNVdefyctUXT/7s3w9/+Qv4fGrfdevgvPO6dU2YoAYVl1wiOHj3O0x58WdcP+55mnaWqIlhokyt3gOM6RdfbdiwURhIfJcdMnoY5W4n7zW3ce7hY1i+pY26imIa6soNNBjrzMWefMrbHIgC40D0FazM/AfCUVo8Qdbs8bPRv4d93iAtniD7vOrzj3V7CYRjgOr8f+redywfv7TISXmxMz540BCMxBhfU4a7q0Oszai7XQ6cQlLqLurRgS7qkvn+cyuJJXW6H/vaUTiEwOFAfQtBNBKhuLioa7v7d4cDvv7E+2zb70dKNds+saaMP111LA4HuBwONUhwCqLhMKUlxTgdSkfiYE+v4/63607UPQfNzc1MmjQp5fdn39uRouOEKXW6OlSHOrUQjdVOuxE0HZtbvEzOYdbf7vzbyAe6BwMNce5AY6OKRNm1C7ZuhW3bur+3bYM33+yONNmwAc44I1WvEGqit6ame/CgweGAFSvgoIOSJpw7joDX32H63JuZt/7nrF8vmRpdx+IbPgYu7q1TYMOGjQEGl9PBrInD4xWplzfv56iG4ZYmiW0MfvQKiXrSpEmmJOpQKERFRYVhPJzX64132hJjxb766HK2tHbGw3XO+fU/ObZxOG2+CHs9AVp9IbzBxNn0LfG/hpW4qC0vig8e4rYDN501lWKnoMQlKC92MryiDLdDEgl4qa8exvBh5bgdEIuqgcPnFr5PU2Jse105D1x8mK5PHo+H2tpa3Ri/h94qY0urP66nobaMI0eXpMTD+f1hamoqdOPhHr5kBtc+szpOPP71F2ZQ4Yz0iIcjJoiFw0SdENSJW7zvwul8+9k1NO3z0VBbxn0XTicUCmVEon7okplc/fTKuI6HLpmZlhje0dFBdXV1Sozf6GFF/OXrcxJiMYvSVtf2er3U19enxPiNKHfwwpWz2Lp1K1OnTo0XTtF8SPRJ49oYxWJqFbOT70XNp0AgED9X6eIW29raKC8vtytRDyG0t8P27XD22epbSsGaNXDYYVBXB598okLBE1FbCxMndoeta3A4VCakmpruT1WVCjMHmDFDrTxooebTpqnjpGDYMPjKV2h85Nes3vEDeO01uOwyOHtlr5wDGzZsDBwEg8Eek2VzJ9Xwyzc2sHZXB58c6OSKExty1tmb+1qVN5Mzas+2rZCQDzstDyCEEE7gPeATKeU5ejK9RaL2BiMs27qf5c1tLNvSRlNrd0dLAh2BCJta/NRVFHPYuGrqKoqpryymrsJNzN/OjMnjqasoprbCTbFLvW31ZtuvOOkgXd89nlLd+PZHdWLbjXxKR6L+/deOTpktr6zsXlFJjIdLR6I+pLycN783QjcWP5lEna74yyGVlbzxP/o6rFairqyEN/7nlBQdejF+Usp4dWezWEyjGL9cK1FrAyGjWMxkonyyDZFIxDRu0YzQP1RI1IMBTU1wzjlqZWDcOLj2WrVaoK0ebN+uvjs69Pf3++Hkk1Vo0cSJ3d/jx6swJdAfEJx5ZnqbFi/OIEPSNdfAQw/B736nlkDKy9UBbdiwYSMBcyfVICU8vGRzfNuGDchsBeI6YC0wzEzQyvKW0chnnzfI8i1t/HdzCx9s72D1zvZ4R3/GmCqqS4to7wwj6e78v/7dk3V1NTc3M2lcdcrvmYTJpLNVC28JBoOm8WRG/mp60oUFWdFhVSYfOqzAtjU7GRt9j0SugdbxnjQJ9uzpHghs397z7/ff71452LoVvvc99XddnRoMHHQQnHqq+nvCBPjBD9S+iYOBJ54wtiujAQEqJGr1aotOT58On/oUPPww1NfDnDndSxk2bNgYskh+Tx05oZoip+D/PtpJZbGL6aNNu4CmOntzX6vyubzTs20rJOTDTksDCCHEOOBsVO6O7+ZywGT+wiPz5yAQLGtuY/mWNpY3t9G0TwXzFrscHDmhmmtOPYi5DTUcOWE4FcUuXQ5Epsgktt1sQGRlwJSPmMF8HMe2tXeO01e2DgZkksa1aVcp8y4oYv0GwdSDYix+MUJjgzRMO9m01cm8z7lYv8nB1CmSPz4Zoaakkw6PoN1fRIffRfu+MB0d0O518rNfuWlt1cKLJNOmKVXhcM/rVV4uGT82xvjxglhMoAIfFZwOScf2dpXsRieV5pwZMeZd4Gb9RgdTp0oWPxuCA10+pEml2VjTqVKmJqbSPED+Up6ecw688Qbs2KHiplatUksgvZEe1E7jOnh8stO4ZoWBksY1EAgQiUTiYbbN+7w4hCDclbVx/Y4WJtSUpU0Pqhe2HgqFEEJk5ZOWFtWqTz6fj1AoZJryNBwO43A40qY8DYVCOBwOXZ+0Y+hdJ83fRJ8KMY2r5kMuaVyFlWISQojngJ8ClcD30oUwaZg1a5b84AP9TEJn3Ps2m1q88Uw9ToeIpwCtKi1izsThzG2oYe6kGhqqHNRUV6U9jlHqTA1ms/pWdJjJ5EOHbWvv6DCzs9BsbWhoeF9KOcdQ0WBBeblMYf4mITGMRwgYNQpuuAE8HvB61XfyZ+XKVP5AJhBCrRiMH69WD7Tv6mrVlmyXtqJgZfZ/d1MToxobszcunzjkEFi7Vv0thFqVsLyEkRkKyu8+hO23NQjxsV/KQzNL7TNAMWfOHPnee6oukZX3U18j+T11xr1vs2mvF4maMjloRPqID6s6e3Nfq/K5vNMzbRsI19kIQgjdfonpCoQQ4hxgr5TyfSHEKQZyVwFXAYwZM4bm5mZduc0JgwdQ9Qy+c+JoDhtdxqThxTi0N7RsZ+/uAB0H9qe1LRAIpMS1J6O1tdWw3YoOM5l86ADb1t7QYWZnvo7TV7YONaxfrzrpoCY4d+2C73xHbZeUqArJlZXdn9ra7slXDQ6HKr5cVaX4w8nfxxyTOhj46U+N7co0vKggsWFD999SKmds2LBhIwFNLb54UVbZtW3DBlgLYToeOFcIcRZQAgwTQvxBSvnlRCEp5UJgIcDs2bNlutHW5Pqt8RUIjb9w3dmzdGXz0WkDDEd+hdJ51GDbml8dZnbm6zj5PK82ujF1as/O/eTJsHSpGjgUpWYABvRXB664Iv0xshkMZMQ3KFQkn9ypU/vbIhs2bPQzkpN7NNaX90g401if+UJRss7e3NeqvJmcUXu2bYWEfNjpMBOQUv5QSjlOSjkJuAT4R/LgIRMsmj+Xg+orcAphmp/faULqM2u3Ais68mGHbWvmMratNhYvVgMAp1N9v/oqDB+efvCgt49V8nEkor6HTMRJpifKhg0bgx7J76lF8+cy2WKfzarO3tzXqnwu7/Rs2woJ+bCzVwrJGfEqNPKylfirQCBgOEoya7cCKzryYYdta+Yytq02spnpHxSrA30B+0TZsGEjCcnvKa3Plk+dvbmvVflc3unZthUS8mFnRgMIKeUSYEm69kwLyQWDQUNGfigUihf/0mOvh8PheLsRI9/j8aRlr/u7skkYsde1Y6TLMpCuQFlyITmzzAnBYDBt0TVVSM5PSUmJISM/HA6nLbomhCASidDZ2WmYOUE7vpFP6QrJaT6lu26JPiVeFz2fPB6PaZYBr9dLaWlp2mwQXq835V7UKySn3Y/avZfsU+J9oOdTJBLB6/UaZk7wdRGG7UJyNmzYsGHDho2BjLyuQGiF5GbPnm1aSC4QCJgWkpNSpsSMJ4+YktuTi3mZFe+qqqpKsTERLpcrPphI9kHbrqmp6fGbnk9FRUVpC8lpxywuLtYtFKf5pLUZFVVzOp2G50wvDj/Zp8rKyrRF1xJt0iskpyH5nOj55HK5epzXZJsSfUnnc1FRUc6F5AKBgGkhOUi91xJtSrxH0vlcXV1teE6GSiG5TNK49lXayb5KpSk6OpSuQeSTpetkp3EdPD7ZaVyzwkBK45pLelC9SWOtv9dXPgGmKU+DwWDcBz2ftL6Snk+aDXo+SSnjk42FnMZVO2YuaVx7JYTJCvoq7rxQ7LBtzVzGtnVwQptooLz8Stxu1UlJRllZz+3kTkYyGT35IZe8nXyMvt4GKCtDDhvWbdsg8akH0vnU1qbsH0w+aTDyye1W+X/zaWN/+2ThmLKysqffVmwc5NCee3PmzIlPsCZOxBlNcmkwm+QymlyE1AlWPXltwtOKvnQTd8nbJSUlPSZKM/Gpurq6x3HNfKqqqjKU17Y1m9L5lNie7JPWkdeQ6JMQQtdGo4nsbK5TJtt61ynRByv3nh4s1YHIFEKIdmCjiVgV0J6jjBUddcC+ArDDtjVzmXzoMLMzX8fJl63lUsp6Ez2DAscIEVsKQy1uywVE+tuIfoDt99BChn7PLpXyPdOkLoMBQogWYGvXppX3U1/DyrusL3Vmuq9V+Vze6Zm2DfTrPFG3XyKlzPsHWNgXMhZ1vFcgdti29oOtZnYONFvtz8D+DNVrbPs9tD5D1e/BcJ6svMv6Umem+1qVz+WdnmnbYL3OvTXit5IPMB8y+cg72Fd22LZmLmPbasOGDRs2bPQdeuM9lYvOTPe1Kp/LOz3btkJCznb2SghTIUEI8Z7UKcFdiLBtzT8Gip0wsGy1kR2G6jW2/R5aGKp+Zwr7PA0NDNbrPBRiDhf2twEZwLY1/xgodsLAstVGdhiq19j2e2hhqPqdKezzNDQwKK/zoF+BsGHDhg0bNmzYsGHDRv4wFFYgbNiwYcOGDRs2bNiwkSfYAwgbNmzYsGHDhg0bNmxYxoAfQAghxgsh3hJCrBVCrBZCXKcjc4oQol0IsaLrc2t/2JpgT7MQYlWXLe/ptAshxP1CiE1CiI+EELP6wcapCedrhRCiQwhxfZJMv51XIcSjQoi9QoiPE36rEUK8LoTY2PU9PM2+87tkNgoh5veTrf9PCLGu6/q+KISoTrOv4b1iY+BgqFzLXP43BzLS+H27EOKThGfkWf1pY28g3Tt4KFxzGzaGMgY8B0IIMRoYLaX8QAhRCbwPnC+lXJMgcwrwPSnlOf1kZg8IIZqBOVJK3cIiXS+Za4GzgKOB+6SUR/edhSn2OIFPgKOllFsTfj+FfjqvQoiTAC/whJTy/7d350FylHUYx78PNwZLQAQjIgFEOTwCIoccBrmVAkEQCIqcCkWkQCkJ4sFRloACJUEOESqo3HIIFEcil7E0EAgh4RBRVIykgkrkEIRK8vhHv1sMk53NbLI7szP7fP7Z3nfe7v51vzPd/fb7vt0fKmlnAy/YPlPSeGA12yfVzbc68BCwBWCq78vHbM9rcay7AvfYni/pLID6WEu+v9LHdyU6x3ApyyX9bXa6Btt9KvCK7R+2M7bB1OgcDBxKl5d5xHDW8S0QtufYnl6mXwaeBNZub1RLbW+qk5BtTwVWLQfpdtkJ+HNt5aHdbP8GeKEueW/gijJ9BdVJrN5uwGTbL5RKw2Rg90ELlN5jtT3Jds+bWqcC7x3MGCJaZSl+mx2twXZ3vT7OwV1f5gNN0ghJD0saEjc7Y3CU3htTJF1cbsR2pI6vQNSSNArYDHigl4+3kfSopDskbdrSwBZlYFI5UHy5l8/XBv5e8/9s2lspOhC4usFnQ2m/rmV7DlQnNWDNXvIMtX0LcDhwR4PPFvddic4xnMuymd9mtxpXuipe3u3deOrOwcO5zIHeu7WV9N0lPVW6KY+v+egk4LrWRhkDoZ9lbarWypWorkE6UtdUICStAtwAHG/7pbqPpwPr2v4oMAG4udXx1dnW9ubAHsCxpem7lnqZpy19zSStAOwFXN/Lx0NtvzZjyOxbAEmnAPOBKxtkWdx3JTpHynL4uQjYABgNzAHOaW84g2cx5+DhaiJ1LdylS/CPqY4DmwAHSdpE0s7AE8DcVgcZA2IiTZY1MMX2HlQVxtNaHOeA6YoKhKTlqQ5cV9q+sf5z2y/ZfqVM3w4sL2mNFodZG89z5e/zwE3AlnVZZgPr1Pz/XuC51kS3iD2A6bYXOagNtf0KzO3p6lX+Pt9LniGzb8sA7j2Bg91gMFIT35XoEMO8LJv5bXYd23NtL7C9ELiULi3zBufgYVnmtRp0a9sS+JPtZ2y/AVxD1d1rR2BrYCxw9pR/zQAACABJREFUlKSuuD4bLvpT1uV4ADAPWLGFYQ6ojv+CShJwGfCk7XMb5Hl3yYekLam2+9+ti/ItsYwoA82QNALYFXisLtstwCGqbA282NMU3AYH0aD70lDar8UtQM9Tlb4E/KqXPHcBu0parXQn2LWktZSk3anuPuxl+9UGeZr5rkQHSFk29dvsOnVj1/ahC8u8j3PwsCzzJvTajdb2KbaPB64CLq25yIzO1WtZS9pX0iXAz4EL2hLZAFiu3QEMgG2BLwKzJM0oad8E3gdg+2JgP+AYSfOB14ADG93xbYG1gJvKdfdywFW275R0dE28t1M9gelPwKvAYe0IVNLbgF2Ar9Sk1cbZtv0q6WpgDLCGpNnAd4EzgeskHQE8C+xf8m4BHG37SNsvSDoDmFYWdbrtQR342CDWk6nuPEwu34Wpto+W9B7gp7Y/TYPvymDGGoNm2JRlf36b3aTBdo+RNJqqm+RfqTmWdpFG5+CuL/Ml1Gc3WtsTWxdKDLJey7q00i3SW6bTdPxjXCMiIiKGojKw/LaaR/tuA5xqe7fy/8kAtr/frhhjYAy3su74LkwRERERHWIasKGk9cpDSg6k6u4V3aeryzoViIiIiIgBVrq1/R74oKTZko4o7/8ZRzX27kngOtuPtzPOWHrDsazThSkiIiIiIpqWFoiIiIiIiGhaKhAREREREdG0VCDaQNICSTMkPSbp+vK41I4h6ZV2xxARAyPHo4iI6K9UINrjNdujy6O+3gCObndArSKpG949EtFNcjyKiIh+SQWi/aYA7weQdLOkhyU9LunLJW1ZSRPL3cFZkk4o6cdJekLSTEnX1C9U0qGSbpR0p6SnJZ1d89krNdP7SZpYpidKukjSvZKekfRJSZdLerInT81850iaLuluSe8qaRuU9T0saYqkjWqWe66ke4GzBnb3RcQAyvEoIiIWK3df2qjc/doD6Hkj7eHlTckrA9Mk3QCMonrNfc+LSVYteccD69l+vSat3mhgM+B14ClJE2z/vUHeHqsBnwL2Am6lesvokSWe0bZnACOA6ba/Luk7VG9cHQf8hOqNz09L2gq4sCwL4APAzrYXNLd3IqKVcjyKiIhmpQWiPVaWNAN4CHgWuKykHyfpUWAqsA6wIfAMsL6kCZJ2B14qeWcCV0r6AjC/wXrutv2i7f8BTwDrNhHbra6e7TsLmGt7lu2FwONUFw8AC4Fry/QvgO0krQJ8Ari+bNslwMia5V6fk3XEkJTjUUT0SdIppTVyZhkztdVi8t8naYsBWO+hki7oR/4xkm5b2vUuCUmjJI1tx7rbIS0Q7fGa7dG1CZLGADsD29h+VdJ9wEq250n6KLAbcCzweeBw4DPADlR35r4tadPy0pJar9dML+DN8q59+cdKDeZZWDf/Qhp/X0xVGf1P/XbV+G+D9IhorxyPIqIhSdsAewKbl1bGNYAV2hzWUDQKGAtc1eY4WiItEEPHO4B55WS9EbA1QPmhLmP7BuDbwOaSlgHWsX0v8A1gVWCVfqxrrqSNy3L2WYJYlwH2K9Njgd/afgn4i6T9S9wqFxoR0XlyPIqIHiOBf9l+HcD2v2w/ByBpJ0mPlDFRl0tasXZGScfUjXk6VNKEMv0FSQ+WFo1LJC1b0g+T9EdJ91N1W1yEpBFlfdPK+vduNk+J4WZJt0r6i6Rxkr5W8kyVtHrJ19c4qvMl/U7V+Kye48+ZwPZle06QtGnN9s2UtOHSFMJQkwrE0HEnsJykmcAZVN0GANYG7ivN8BOBk4FlgV9ImgU8Apxn+z/9WNd44DbgHmDOEsT6X2BTSQ9T9Sk+vaQfDBxRuj08Dizyg46IjpDjUUT0mASsUy7qL5T0SQBJK1EdBw6w/WGqVsFj6ub9JbBvzf8HANdK2rhMb1taChcAB0saCZxGVXHYBdikQUynAPfY/jiwI/ADSSP6kedDVDcctgS+B7xqezPg98AhJc9PgK/a/hhwItU4qh4jge2oWmbOLGnjgSnlqXbnUT3R7kdl+7YAZjfYlo6kqntpRERERMSiSuvA9lQX4l+hulh+BJhge4eSZyfgWNv7lm6PJ9p+SNIk4DvA08A0YAOqLpDfBJ4vq1gZuBqYAexr+5CyzOOAD9geVxfPQ1RdHnu6Sq5O1bVyrbLePfvIsxVVxeWosqxnqbpr/kPS4cBHgG8B/wSeqlntirY3VvUUuMm2ryzzv2z77aXr54m29yzpY6kqMT8DbrT9dH/2+VCXMRARERER0VB56MB9VC2Qs4AvUV3sN+NaqvFSfwBusm1JAq6wfXJtRkmf5a3johoR8DnbT70lUVqriTxbseiYqtrxVsux+HFUtfOrtwy2r5L0ANUYsbskHWn7nr43q3OkC1NERERE9ErSB+v6748G/kZVIRgl6f0l/YvA/b0s4kbgs8BBvPnEtLuB/SStWdaxuqR1gQeAMZLeKWl5YP8GYd0FfLVURJC02RLm6dUSjqN6GXh7zz+S1geesX0+cAtVy0bXSAUiIiIiIhpZBbhC5WWRVOMSTi2PZD6M6nHJs6ju3l9cP7PteZRHN9t+sKQ9QdVNaFJZ5mRgpO05wKlUYxF+DUxvENMZwPLATEmPlf+XJE9f+juOaiYwX9Kjql6yeQDwWBkzthFVV6aukTEQERERERHRtLRARERERERE01KBiIiIiIiIpqUCERERERERTUsFIiIiIiIimpYKRERERERENC0ViIiIiIiIaFoqEBERERER0bT/A+raazjjQA7UAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -614,7 +614,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAADaCAYAAAA2YqSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydeZgcVfW/39M9+5I9rCEkk0CAsIQQVr8QZAfZkU1UQGRRUJFFQcQgCCKyCYIKIosoiyA/iYAISoKAIluQBJJAFkiAQJJhklm7e7rP749b1dPT00vNdPdU9eS+z9PPzK26de/ndHV31a17zzmiqlgsFovFYrFYLBaLF0J+C7BYLBaLxWKxWCzlgx1AWCwWi8VisVgsFs/YAYTFYrFYLBaLxWLxjB1AWCwWi8VisVgsFs/YAYTFYrFYLBaLxWLxjB1AWCwWi8VisVgsFs/YAYTFYhk6iCgiv08pVyCyGpG/FqHtfRFZh8gbiCxC5HlEDvdw3GmI/NL5/2hEtutnv6c5NsxzXvcNSH//+vwZIv/r1ZfIVxD5Tsn7tlgsFkvgsQMIi8UylGgHtkek1ikfCHxYxPb/herOqE4Bvg38EpH9+3H80UD/BhCGh1Cd5ry+2mevSMUA2syMyHBgL1R3BMKI7OC8n6cBtw+sSS4TYYEI/xNhngi756k/R4QZA+krrZ3TRPhlP+rvK0Lhg80BIMIEEb7Uz2NEhH+KMMwp/06ET0WYn1ZvlAjPiPCu83dkyvG3iPCec26mpxxzqlP/XRFOzaPjehH26492i8VS3tgBhMViGWo8BXzB+f9k4IHkHpHdEHnJmUV4CZEpzvYLEPmd8/8OiMxHpC5nL6rzgCuB85zjxiLyKCKvOK/P9aovshdwJPBzZyZhEiJnOnXfdI7N3Wfv9uYgcg0ic4HvZO1fZDQif3ds/g0i7yMyJkfLCaAKEQFqgRhwMXALqjHP+pIy2RM4HJiuyo7AAcCK/razATAB+jeAAA4D3lRlvVO+BzgkQ71LgH+oshXwD6cMcCiwlfM6C/gVmAEHMAvYHdgNmOUOOrJwa0qbFotlA8AOICwWy1DjQeAkRGqAHYGXU/YtBPZBdWfgR8A1zvabgcmIHAPcDZyNaoeHvl4HtnH+/wVwE6q7AscBv+1VU/Ul4HHgYmcmYQnwZ1R3RXUn4B3gjCz9nJiyhOn0lO0jUJ2J6g05+p8FvODY/DgwPqdFqq3Ao8AbwDJgHbArqn/J815kY1NgjSoR0zxrVPkIQIT9RXhDhLecp+fVqQeK8A0RrkspnybCrc7/Xxbhv86Mxm9ECDvbTxdhsQhzgd6DuJ526p3+XnH6P8prHUfD/xNhtgjLRDhPhAucOv9xbr4RYZIIfxPhNRH+JWI+JyLc4zz1f0mEpSJ80enyWmBvx57vijA1xb7/ibBVBlNOAZLnRZXngeYM9Y4C7nX+vxczE+Zuv08VVeU/wAgRNgUOBp5RpVmVz4BngENECDv65zvn7LtOv+8Do0XYJNP7bbFYhh7Fm/a2WCyWIKD6P0QmYGYfnkzbOxy4F5GtAAUqnWMSiJwG/A/4DaoveuxNUv4/ANgOSW4ahkhjnuO3R+QnwAigAXg6S72HUD0v4/b8/e8DHAuA6hOIfJZHE6heB86Nu8hvgR8h8nXgIOB/qP4kbxs9/B34kQiLgWeBh1SZK0IN5on5/qosFuE+4BuYwZzLI8C/ge855ROBq0XY1vn/c6rERLgdOEWEZ4AfA7tgBj7PYQZC6VwG/FOVr4kwAvivCM/2o872wM5ADfAe8H1VdhbhJuCrjg13AOeo8q6zZOt2SC7z2RT4P8zg83HHzkuAi1Q5HMAZKP1ClT+IUAVmgJTG54CzM2xPZ2NVPgZQ5WMRNnK2b07v2aCVzrZs26cBm6uyvaNxREqd1x09j3rQY7FYyhw7gLBYLEORx4HrgX2B0SnbrwKeQ/UYZ5AxJ2XfVkAbsFk/+tkZM3MAZkZ3T1Q7e9UQST8mlXuAo1F90xnA7NuPvsH4fLjk6l/72a577M7Of4uBX6C6DyIPIrIVqu96aUKVNhF2AfYGPg88JMIlODMcqix2qt4LnEvKAEKV1c5T+j2Ad4EpwItOvV2AV5y3txb4FLPkZo4qqx3THwK2ziDrIOBIES5yyjX0nZnJVec5VVqBVhHWAbOd7W8BO4rQAOwF/Cnl9KfOrvw/VRLA2yJsnPGNMwOny0QYB/xZlUzv9yhHx0DJ9OHUHNuXAk3O4OYJzODQ5VP6992xWCxljF3CZLFYhiK/A65E9a207cPpcao+LbnVOA7/AvO0fjQiXyQfIjsClwO3OVv+jusPYfZPy3BUK5A6K9EIfIxIJWY5SiFk6//5ZNsih0LOtezpXIVZ6lVJzxPwBODdVwNQJa7KHFVmORqPI/NNaiYeAk5wjnlMNXmDe68q05zXFFWucLvz0KYAx6UcP141ORD0UieSUi+RUk5gHsyFgJaUY6epsm3KManHZ3wfVPkjxmemE3g6i5Nyt4in6/gnztIknL+fOttXAluk1BsHfJRtu7OcaSfMwPtcei/Tq3G0WiyWDQA7gLBYLEMP1ZWo/iLDnuuAnyLyIr2XhNwE3I7qYowfwrWIbJTh+L1xw7iagcO3Uf2Hs+/bwAwn/OnbwDkZjn8QuNhpYxJmAPIyZo35wgFYmkq2/n8M7IPI65in6h8kjxB5EpHMT41FjgZeQfUjVFuAfyPyFqCovulVlAhT0tbvTwPex9g7QYTJzvavAHMzNPFnzJr9k+lZsvUP4IvuUhwnytCWmPdyXxFGi1AJHJ9F1tPAt0TMzbsIOw+wTkYcp+ZlIqZ/J9rRTnkO6zW4FKEJWKrKLZgZtR0zHLMIaPIg6XFIRlI6lR6/iceBrzr69gDWOUudngYOEmGk4zx9EGYQMwYIqfIo5rM7PaWPraF39CeLxTJ0EdWBzWxbLBaLpQwRWQ7MQHXN4HTHLpgoPSOAbozPwFmqrBFhf8xSswrgFeAbqkREmIPxB3jVaeOvwHaqPTfLIpwIXIp5EBYDzlXlPyKc7mz/GJgHhFXp5T8iQi1mqdRemBmA5aocLsK+Tr+H56hzGjDDbVOE5U55Teo+ESZiohptipnBeVCVK0W4B/irKo84x7ep0uAMeP4GjMEsbasBvuzYtgr4kmpvB2kRLgc+VjUzASI8gFkGNwb4BJilyl0ijAYexizB+gA4XpVmZ3D0S0zkpg7g9JT3/GvAD5yurlblbmcQdDc9Dx8vVeUpR/v/gB1U6cZisQx57ADCYrFYNiQGeQBhKR3OcqT7VDnQZx3HYML0Xu6nDovFMngMySVMInKKiPw9f83gIiITRERlAAmixHC3iHwmIv8thb6gISL3iEhUzM3RYPW5tYi0iUhcTIQaiyX4qE6wg4ehgbPc6E5xEsn5SAVwg88aLEMIEblCRO73W4clO2U7gBCR5SLS6dzAua9fAqjqH1T1IL81+sj/YTLwjlPV3fwWM4hcp6oTUjeIyIEi8pyItIrIWhGZJyLfF5MjIOuPlDN4m5y+PRVVXayqDcC/immExWKxeEWVh1MSyfml4U+qtPipwZId534pKmkJJJ3roYqJSFfM/twHoO692Sci8lcRGdBMWSEPVFPaUBFpT9Hky+dVRL4kIh+LyDIR2Tdl+yQReUlEMoVrDiRlO4BwOEJVG1JemeKkb4hsCSxX1fa8NYcwInI8Jr76H4EtVXU0Jnb8OHpHGLFYLBaLZSizDBOIAAAR2QET/riUjHAesu2ECRTxmJhw1X6xU8r94ohMFQoZpOTDaftaTPCBb2H8j1xuAS5Q1Xip+i825T6AyIiInCYiL6SUDxKRRSKyTkRuF5G5qUtORORrIvKOs+TnaRHZMmWfisg5IvKus/82EZGUfl4UkZtEpEVElorIXs72FSLyqYicmtLWcBG5T0RWi8j7IvJDEQk5+8Iicr2IrBGRpcAX0mwaLiJ3OSPXD0XkJ5lGqiJyBia03p7OKPvHafurHa3bp2wb68zmbCQiY5wnBS0i0iwi/3I15nnP9xWRlSLyA8eG5SJySsr+L4jIGyKy3nlvrkjZVyMi9zszBC0i8oqIbJzyHi91ZhCWpbaZR48ANwJXquqdqtoMoKqLVPVb6jGGvdNWS8pTi/ZSPLGxWCwWi6WE/B6T5NDlVOC+1Ap5rtMnOtfiYU75UBFZJSJj83WsqqvURMW7AvhZyn3PZiLyqHNPtExEvp2lieedv+61eE/nif0/nfuGNSLyBxHJOCjIRcq9y/dFZBUmSAAicriYGZoWMTMDO6Ycs7OIvO7clzwkIg+KSQiaj9HAh6r6MSapZpPT3hed7f/pr34/GZIDiFTETNk9gonKMRoT9m6vlP1HYyJNHAuMxSxHeSCtmcOBXTGj6BOAg1P27Y6JPjEa86T7QafuZEwEjV+KSINT91ZMHPomYCbmy3y6s+9Mp5+dgRlAehz6ezERTCY7dQ4C+qy7V9W7MOEb/+2Msmel7Y9gwiKenLL5BGCuqn4KXIiJAT4W2Nh5b7x62m+Cif6xOebH6Q4RmeLsa3fsHYEZHH3Dee9x6g7HzAqMdvR3ikg9ZlR+qKo2Ys7bPI9apmBmGgrOiqqqI9ynFphcAf+iJ5eAxWKxWCxB5z/AMBHZ1nn4eCKQvnw363VaVR/CJDe8RURGA3cBX1fV1f3Q8GdgI2CKM4iYDbyJuWfYHzhfRA7OcNw+zl/3WvxvTGS0n2KSF26LuX+4oh9aUtkEGIVZvXGWiEzH5BI6G3NP8hvgcecBbBXw/zADslHAnzA5arywGhgtIuMwy8wXOPeHP8Tco5YV5T6A+H/O6NB9nZmhzmHAAlX9s6p2Y25IV6XsPxv4qaq+4+y/BpgmKbMQwLWq2qKqHwDPYeKYuyxT1budaaeHMB/iK1U1oqp/B6LA5JQv7KWq2qqqyzFOZ19x2jkBuFlVVzhPy3/qduA8jT8UOF9V250b/ZuAkwbwnoEZ6KQOIL7kbAMTMnBTzJKfmKr+S/sXqutyx/a5mEylJwCo6hxVfUtVE6r6P8wgbWZKn6OByaoaV9XXVNVd05sAtheRWlX9WFUXeNThrvVMnmvnKUGLiHSIyFdS6p6Q9jnKuDZSRE7EvFfHqWrMow6LxWKxWIKAOwtxICYPS68HYXmu02CSB+6HSSQ4W1X/2s/+P3L+jsI8aB2rqleqalRVlwJ34vG+RlXfU9VnnPuN1ZgVBzPzHPZ6ynX+lpTtCWCW01Yn5oHub1T1Zeee5F5M8sc9nFcl5n4tpqqPYEJQe9GcAL6Beah9kdPPlZiHyzuI8dd8OnWFSJAp2VqvQeJoVX02T53NgBVuQVVVRFam7N8S+IWIpEaQEMyI+H2nnDrg6AAaUsqfpPzf6fSRvq0Bc0NbldImzv+bZ9KZVm9LzAf2Y7MyBzCDv9T6/eGfQK2I7I6xbRrwmLPv55hR/N+dvu5Q1Ws9tvtZmt/F+xi7cPq6Ftge8z5UY0buYH7UtgAedKYg7wcuU9V256b9IuAuMcm/LlRVLwm31jp/N8Ws/URVT3K0vEDvJGIPq+qXUw8WEU0r74xZr3hQP5+4WCwWi8USBH6PWQ40kbTlS5D3Oo2qtojIn4AL8P7UPRX3fqcZ2AHYLO2BXRiPQUnEJPq8Bdgbk4AxBHyW57Dpqvpehu2rVbUrpbwlcKqIfCtlWxXmfkYxy41S7xFS79dyoibx6D8cG3bErDi5GFiOCYCzBWYZ+h5e2/SLcp+B8MLHmKUsQHJt/LiU/SuAs51lKu6rVlVfKrKONZgn7akzG+PpeQLwMb0de8enaYwAY1I0DlPVqQMR4oyCH8bMQnwJ+Kuqtjr7WlX1QlVtAo4ALhCR/T02PdJZdpRqg/vE4Y+YrKdbqOpw4NeYgRrOKP7HqrodZpnS4ThrNVX1aVU9EDMQWIh5QuEF9+nKsR7rZ8VZ4/kYcJ6qvlFoexaLxWKxDDaq+j7mgdphmOVE6WS9TgOIyDTga5iZiVsyHJ+PY4BPMUvJV2BWcKTeezWq6mGZpGfY9lNn+46qOgyzZFwy1PNCevsrgKvTtNWp6gOYe7XNJeVpLr3v1zzhHP9L4NuYB8xh5/y8Quas84FjQxhAPIGZGjpajAf8uZj1bi6/Bi4VkamQdFY+vtginCVODwNXi0ijs0TqAnrWID4MfFtExonISOCSlGM/Bv4O3CAiw0Qk5DgQ5Zuuy8UfMUuqTqFn+ZLrODTZ+XCvB+LOyys/FpEqEdkbMxBwn140As2q2iUiu2EGLm6fnxeRHZxlXusxA624iGwsIkc6g5II0OZVi/N04EJgloicKSIjxbAVxrfDE85n5lHgD84aUIvFYrFYypUzgP00c5TGXNfpGsz9yg8wvpubi8g3vXToXMvPA2ZhlnEngP8C6x3n5VoxgWS2F5FdMzSxGrPMqCllWyPmnqBFRDbHPMUvFncC54jI7s59Q70YB/NGjB9IN+Z+rUJEjgUGEi7/68AbqjoPs2KiVkS2Az4PLC2SHSWl3AcQs6V3HojH0iuoSZh0PHAd5iRtB7yKuSFFVR8DfoZZPrMemI/xNygF38I4KS0FXsDcuP/O2Xcn8DTGoeh1+j4d+CpmCu1tzDTdI5in8gNCVV92tGwGPJWyaytMdIA2zBfldlWdAyAiT4nID3I0u8rR9hHwB+CclOVG3wSuFJFW4EeYAZPLJo4964F3gLmYH6oQZhDwEWbKc6bTjlcbH8L4YHwZ80RhjdPvHaRMy+ZhHGaK9Py0z1q/nzhYLBaLxeInqrpEVV/NsjvXdfqnwEpV/ZWaYCxfBn7iPJTLRouItANvYWY9jlfV3zk64phVDtMwsyJrMEt3hmfQ3AFcDbzo+C/sAfwYEw51HeZBcaYZlQHhvD9nYmYIPgPeA05z9kUxKxtOc/admNq3iIzPd48gJrjPd8Bkblfjf3seZnn5rzH3ioFH+ucfW/6I8fxfCZyiqs/5rWeoICYhyv2qOi5f3RL1fydmSdYnqjppkPrcCjPdWAV8U1XvGYx+LRaLxWKxBAMRuQczuPqh31oGk3J3ovaEmLBgL2Mcmi/GrJMrq3i7ltyo6pmYJwaD2ee7mHB3FovFYrFYLBsM5b6EySt7AkswU2RHYKI3dforyWKxWCwWi8ViKT82uCVMFovFYrFYLBaLZeBsKDMQFovFYrFYLBaLpQiUxAdizJgxOmHChJx1VJXeYXQHVicajVJVVTUofQVJT5C0DFU9QdLitvP666+vUdWxOSsGkD1DIf1fba3fMgBv5yNIlJteKD/N5aYXyk/zQPR2dGyrqq+W3YNOL/dAGzLl9Nn1U2up+/ba/muvvZbxvqMkA4jx48fz6qvZooQZ2tvbqa+vL7jO8uXLyfdFLVZfQdITJC1DVU+QtLjtNDQ0eM54GST+XVsL7ZnCjg8+q5YuZZOmpvwVA0K56YXy01xueqH8NA9Er8j8svRVnDBhQt57oA0ZL9fEoOCn1lL37bV9Ecl431GSkb2XEU2+p61e63ihWH0FSU+QtHit44Ug6QmSlmK2Y7FYLBaLX5TTtcxPraXuu9D2izoDISJHAEdMmDCBWCxGPB4nFosBUFNT06scj8epra0lGo0CUF1dDUAkEgGMYZ2dnYTDYQAqKysJh8N0dXX1Kre1tdHa2kpFRQWVlZV0dXWhqoTDYSorK4lEIrS3t9PY2EhlZSXRaJREIkEoFKKqqiqps7Ozk5EjRybLIkJNTQ2xWIzu7m4AEokE1dXVWW2qrq4mEonQ2tqa1SYR4bPPPqOuri6rTV1dXXR0dDBs2LCCbGpvbycWixVsU3t7OxUVFQXb1NbWRmdnZ1abVJVIJMLw4cOz2iQixONxKisrs9pUU1NDa2srlZWVWW1S1eRnp5TnyatNsViMjo6OvDZZLBaLxVLOxGKx5PU56PiptdR9F9p+UQcQqjobmD19+vQzKysrqayspKamJrk/tezeuLk3by6pI6JoNEpjY2Ov/enGNjQ09KrT0NDQp76qUldXl/H41LKrORX3xtnVXFNTk9UmMDen6ZrTR3l1dXW96mTTVOusGR+oTfX19UWxKRKJFMWmhoaGnDal/p/rPLW2tiZtz2STWz9dT6pNrobUOqU4T15tqqys9GSTxWKxWCzlTDwe91uCZ/zUWuq+C21/8J2TmpfBbbvTcOOWcNvuppyFnEuhnHa2vHd6UdopNz1B0jJU9QRJS952LPlZuhSmTmXjrbeGqVNN2WKxlBTna8e4rSdu8F+7D9Z2cOCNc5l06ZMceONcPljb4bckXyina5mfWkvdd6HtlyQPxC677KKvvfZa5p237Q5rFoMmAIHGTeGgq0w5ETd/nVd3d4yKEKDas92t89It0L4GUNNO3WjY7SxTVnX+JkCVeDxOOCQ9+zTR8/+8P0BnS087tSNgp5OdNlJR4okE4VDKmCu1zv8ehq7Peso1I2DHE/rWg5R2Mrz3bz0CXS2929n+uD7V4ol4by2pzP9zWhvDYeoxaVrM30QiQajXh0h7/rwzGyLrenZVD4dtD8/w3kAiESeU7b1Z9CRE1qe0Mwy2PiSthWx6UjS9+3eIpCzjqW6EyQf07Q8lkVBCIemzHYAlz0G0rWdzVQNMnJliuzptuFpStzvtvP9viKU4BVfWwRa79+7HqZ9Qp51M37UPX4WY4ycoIRizNZz7ct96mOnGqqqq11R1RsYKQaa+Xn13ot5uO3jnHfN/KATbbAMLFviryQPl5iwL5ae53PRC+WieOtV87VT7/7UTmd+hun15eNumMGPGDM3kRH3gjXN579M2FAgJTBrbwDMXzBx8gT5jlzAFo2+v7YtIxvuOkkRhyjkoWfOucwMPoND6ETx6Rsaq3sUpdKyBOdekbRcQIeT8NeVQyv8CsY7e7XR+Bm/c33N8CqG+m3o2pN6wu+W3/tS3HhByByvgaEk7Lr389l/SOzVtSJYBRJ821sGipzJq6dEgaWV6Dx7c8rLnM7ehCXN16N2wc9z63psj62HlfzPrSKj5Zc20L5LmAxBphU9Sr0Qpx7hXq/TtIr0HD2DKLR+kVZWUNiTlPDl/Y2k3w7EOiKZsS6mviTiEK5LlXnViKUFGNGG+H1lwfTgsA2Tx4p7/EwlYtMg/LRbLBsLChT3PTor9tRNhOdAKxIFuVWaIMAp4CJgALAdOUOWzbG0MJktXtycfGybUlDdE7AAiGH0HygfCixN15agmQs1LEE2gCDp8PB3H3kdVdTVIiGgsDhKisqqa9o5OZ3uYiqoqwuFKuqJRkBD1fzwCcduREDpqEomzX6QrEkEVwo5TdSQSYf369YwYMSKjw3H413shze+ltDOZ9tP+mdGRNRaLUVdXl9HhuO6ez/fYJSESoyYRP/sloK9z7po1a5IO0OnOudW/3QfWvptsh9Fb0X7aP/s45w5Fm1SVjo4ORo0aldHhuPquffro6TztuYwOxy0tLUmfh3Qn6vp790dS9Ojoreg+45+F23TSY0WwqYnOtraMNrX7/QS/3JkyxdzNJBI9ZYvFUjKeeqr3xGsoVJKv3edVWZNSvgT4hyrXinCJU/5+0XsdAE1j63lvdRuq5lFS09iym1wpCtYHIhh9F9p+SZYwTZ8+XV9//fXMO5uXwQMnoWveRcZsBSc/CKMmZqza1tbWxzHVthM8LUO1nSBpcdtpbGwsqyVM7kOFWFXVmRWtrRCLmRdAbS3E4+BEYqO21tzcOwM5XKd3Z+BGdbW5A+l0Zm2qqiAc7ilXVppXR0fvcmenuYtZsQJOPBFduBBRhQcfhGOOMe0nnBm0mhrTfzxuZohqa42+7u6ecqoNdXUlt2nVihVssskmmW2qqDDHuOVw2LTps02rli9nk003Hdh58sGmVR9/zCYTJpTus1cCm1atWsUmW2zh3/cpj01//msVJ32tlq0nJ+iOwXvLQkzZWpn9UCdNW8Q8nScZuTLnEiZnBmJG6gBChEXAvqp8LMKmwBxVBuVpgft719TUdObChQv7PERdvrqNc/44jyVrOqipDPHXb+7Bxg25Ixy65VzRAIGckShTy7miAboRGXNFA8wVXdONcJgruqaIsHbtWhoaGsrCplgsRn19fV6bSnGe1q9fz7Bhw0p2npqbm2loaMhr07BhwzLed3gaQIjId4AzMYPmO1X15lz1c/pAOHR3d/eJMjOQOl4ScBWrryDpCZKWoaonSFrcdiorK8tqAJEkCD4QDp+8+SYbH3CA8YmYM6fvMsKAUS5r3VMpN83lpheCrfn+++G002D33eHJJ2H48IEmknszCtPeStl0h6re0bOfZcBnGMez36hyhwgtqoxIqfOZKiMLs6h/ZPOBcPnZ3xZy5/NLeeNHB9JYUx5LeYqJl2tiUPBTa6n79tp+Nh+IvFGYRGR7zOBhN2An4HAR2SrXMV4GJV7WcxdrzXex+gqSniBp8VrHC0HSEyQtxWxnQ0cbG+HKK+H55+Gxx/yWY7EMKX7zG/jqV2HmTHj6aTN4GDjhblWdkfK6I63C51SZDhwKnCvCPoX0Nljsu/VYuhPKS0vW+i3FF8rpWuan1lL3XWj7XsK4bgv8R1U7VLUbmAscU1CvkJxqKbTOYPYVJD1B0uK1jheCpCdIWorZjgU480wTHubii3uWeFgsloK48UY45xw47DB44gnItnKzWKjykfP3U+AxzIPOT5ylSzh/Py2tiv4zfcuRNFRXMHfxar+l+EI5Xcv81Frqvgtt38vcyHzgahEZDXQChwF95uZE5CzgLIDNNtuM5cuX52y0q6urT2KvgdRZuzb/CL5YfQVJT5C0DFU9QdLitmMpEhUVcMMNcMghcOutcNFFfiuyWMoWVbjqKpg1C44/3ixhSsvLWXREqAdCqrQ6/x8EXAk8DpwKXOv87RvG0GcqwyE+N3k0cxetRlXLKi+CxeKSdwChqu+IyM+AZ4A24E2gz7DFmVq8A4wPRL713F7CR3kNMTVYfQVJT5C0DFU9QdLitmMpIgcfDIceau58Tj0Vxo71W5HFUnaowiWXwHXXma/Rb39rxueDwMbAY869dwXwR1X+JsIrwMMinAF8ABw/KGr6ycytN+LpBZ+wZHUbkzdq9FvOoJLvgVqQ8FNrqfsutH1PmahV9S5Vna6q+6vGY6MAACAASURBVADNQPZg9R7xEj6qWCGsitVXkPQESYvXOl4Ikp4gaSlmO5YUbrgB2tvNo1OLxdIvEgk47zwzePjmN+F3vxu0wQOqLFVlJ+c1VZWrne1rVdlfla2cv82Do6h/7LP1GADmLNrwljGV07XMhnHNjqcBhIhs5PwdDxwLPJCrvnWiLn1fQdLitY4XgqQnSFqK2Y4lhW23NYu2f/ObsshKbbEEhe5u+NrX4Pbb4Xvfg1/+MkMuUUtWxo2sY/JGDRukH0Q5XcusE3V2vH7dHxWRt4HZwLmqGoisjhaLxVIwV1wBjY3WD8Ji8Ug0Cl/6Etx7rwlodu21gY+GHEj23XosLy9rpjNaPk/kLRYXT5ONqrq3l3puEpWJEydmzUTtlkOhEJFIJGdyjlAoRGtrK5A9OUdbWxutra05k3PEYjE6OjpyJhzp7u4mFovlTM7h9p8rOUckEklqzpacIxaL0dramjPhSCwWo7OzsyCb2tvbi2KTiBTFpra2tpw2qSqJRIJYLJYzMUw4HKajoyNnEhUgqTlbEhX3s1PK8+TVJvd85rPJUgLGjIEf/QguvBD+9jfjWG2xWDLS1QVf/KKJsnTDDXDBBX4rKl9mThnLb19Yxn+WruXz22zkt5xBo5yuZ9YHIjtFXa2oqrOB2bvsssuZlZWVVFZW9hKYWu7q6qK6ujp58+ZSlRK6QVWpr++dhDLdGbWhoYHGxsZe5fT6qVFw0o93y11dXbiaU0lNsuG2k80mMDenqXrSbQKoq6vr00YuzQO1qb6+vig2JRKJPh+0gdjU0NBAbW1tVptcPZk0p5YzRTVKT4YSj8dz2uRqSD1XpThPXm2qrKykrq4ur02WEnHeefCrX5lBxAEHDN5CbouljGhrg6OOgueeM6v+zjrLb0Xlza4TRlFTGWLu4tUb1AAiHo97CiwSBPzUWuq+C22/JFdJrz4Q+UY/Xup4oVh9BUlPkLQMVT1B0uK2U264s5KxqioqolGIxcwLoLYW4nGzHsItJxI9eRnc98wNX1tdbRZZd3aaclUVhMM95cpK8+ro6F3u7DShYioqoKoKaW2FlhZzbHW1aT+RgB//GE45BW65xSzuFjGaolGz4Nstp9pQV1d6m6JRozeHTclyuk2hkOkzEjG6BsumSKRH8wDP06DaFImY/kr82SuqTdGoCQAwSN+nlvZKvnB8LS//V7jvVx18+UsKUe82SWur0daf81RmuL93TU1NeVdhVFdXI6rsOn4Ezy38hB8cYvLzps+Eu+VcM+FAzlUYqeVcM+HV1dU5Vyyk25Btdj/XyhIRobm5mcbGxrKwKRqNIiJ5bSrFeVq3bh3Dhw8v2Xlyz0M+m7JhH7NZLJaS4c5KUl9/JlVVmYPDp8284MxSJUkffKXNWvYpp/eRVtbGRhgxou/+k0+GO++Ea64xAwg3hW6e9kpuU1VVb70D0VRoGfpnU3V1X839PE+DalNzs7GnxJ+9opabmyFthh4oyfdpzRo46AiYPx8efhiOPba+134vmrW5mYy/ATnLKykn3N+7GTNm5F2F4bL/dptwxey3+bg1xpaj6/vMhKeXc81kQ/aZ8IGW02fCvdiUa2UJQGNjY6/Z/yDb5C5zzmdTKc5TIpFIrsIpxXlKPw/5bEqnJDETvCRFST8ZA63jhWL1FSQ9QdLitY4XgqQnSFqK2Y4lCyImlW5zM/zkJ36rsVgCwccfw8yZ8M478PjjcOyxfisaWsycYpYuPb8BRWMqp2uZn1pL3Xeh7Rd1BqI/TtRA3qmuaDSad1rIixN1V1cXdXV1Oae6otEow4YNyzktJCJJh9hMNnl1ol6/fj3V1dU5p7oikUjSh2GgNnlxovZik6unUJu8OFF3d3cnpwuzTUmCScGea/qus7MzqXGgTtTFOE9ebfLiRN3pLi2wlI6dd4bTTjPLmM45ByZP9luRxeIb778P++8Pn3xi4gvMnOm3oqHHxDH1bDm6jjmLVvOVPSf4LWdQ8LLMPSj4qbXUfRfafkmcqKdPn553+s7LtFA0Gu3jkDwQJ2pVTTqoZpsGcqPt5JoWam1tLYoTdXqdbJpyORx7scmLE7UXmyKRSFFs8uJEne08pJZbW1vzOhyHQqE+evrrRF2M8+TVJi9O1CEbZH1wuPpqs07je9+DP//ZbzUWiy+8+64ZPLS2wrPPwu67+61o6DJz67H86dWVRLrjVFeE/ZZTcqLRaNnMQviptdR9F9q+vSOxWCyWVDbdFC65BB57DObM8VuNxTLozJ8Pe+9t/KHnzLGDh1Izc+uxdMbivLrcptiylA/WB6LIdbxgfSBK35f1gbAUxIUXwhZbmCD3NnyuZQNg6VKYOtUEU9ppJxNYae5c87+ltOzRNJqqcGiDyUpdTtcy6wORHTsDYbFYLOnU1pr0um+8Affd57cai6XkHHGEcZROJMxr2DDYdlu/VW0Y1FdXsOvEkcxdtGEMICxDg5I4UU+YMCGvE3U0GqWhoSGnE3VbW1tyXXohTtTr169nxIgROR2O29raGDNmTE6H41gsRl1dXcFO1GvWrMkbA7m1tZWRI0cWZJMXJ2ovNrnOxIXa5MWJuqOjg1GjRuV0OI5Go0md2RyO161bl/R5GKgTdTHOk1ebvDhRr1u3rl/fR0uBnHyycab+wQ/g+OMhzcfFYhlKLFxoZh1cli3zT8uGyMytx3LNkwv5qKWTzUbU5j+gjIlEInlDhAYFP7WWuu9C27dO1GmOrIPhRO01BnJQnKiLZZMXJ2r3/0KdqGtqagp2oi7GefJqkxcn6lKntbekIQI33QR77QU/+xlcdZXfiiyWknDnnWbWwSUUgilT/NOzIbLvlI245smFPL94NSftNt5vORZLXnzzgfAy6inWyKtYfQVJT5C0eK3jhSDpCZKWYrZj6Qd77gknnQTXXw8ffOC3GoulqKjClVfCWWeZEK3bbGN8ILbZBmbP9lvdhsVWGzWw6fCaDcIPopyuZX5qLXXfhbbvmw+El0GGlzqD2VeQ9ARJi9c6XgiSniBpKWY7ln5y7bXm76WX+qvDYiki8Th885swaxaceio884zxgejuhgULoKnJb4UbFiLCzK3H8sK7a4jFE/kPKGPK6Vrmp9ZS911o+yUZQHhJTuGuNy+0jheK1VeQ9ARJi9c6XgiSniBpKWY7ln6y5ZYmGtMf/wgvv+y3GoulYLq6jFvPr39txsV33w1pKywtPjBz67G0RrqZt6LFbyklpZyuZX5qLXXfhbbvqxN1JBLJm4nadUguxIm6tbU1Wc7lRO06ruZyOO7q6irYiTqfTa5zbqE2eXWi9mKTSyE2eXWirqmpKdjh2O0/m01enKiLcZ5KZZNlkLnkEvjd7+C734UXXzT+ERZLGdLSAkceCS+8YGIEfOtbfiuyuOw1eQzhkDB30Wp2nTDKbzkWS05K4kS9yy675HWi7urqyutErap9HEcH4kRdUVGRbCebI6urN5fDcVdXV1EcjkeNGtWnjXRNqe0O1CYvTtRebGpoaOhzHgZikxcn6mznIbXsas5mExgH6Fw2uRpyOVEX4zx5tcmLE3X658oyiDQ2wk9+Al//Ojz0kPGLsFjKjA8/hEMOgcWL4cEH4YQT/FZkSWV4bSXTx49gzuJPuejgoevFnn4tDDJ+ai1134W272kJk4h8V0QWiMh8EXlARAoOBxMO50/X7qXOYPYVJD1B0uK1jheCpCdIWorZjmWAnHYaTJsG3/8+dHb6rcZi6RfvvGNiArz/Pjz1lB08BJV9p2zE/A/Xs7q1fJb59Jdyupb5qbXUfRfaft4ZCBHZHPg2sJ2qdorIw8BJwD3ZjvHiA9HV1ZV39OOljheK1VeQ9ARJy1DVEyQtbjvlhrusMVZVRUU0CrGYeYFJ1haPg7OMkdpaE0vSXTLnzv64dldXm/iS7s17VZUJGeOWKyvNq6Ojd7mz04SbqaiAqiqktdWs4wiHTZtdXabfUMj0GYkYXSJGUzRqPEtFTDjXgw+Ga64x2arr6kpvUzRq9OawKVkeiE21tb1tKIZNkUiP5gGep0G1KRIx/ZX4s1dUm6JRaG/3dJ5eejnM4Sc3UF0Nzz/RxrQd4tA5uDZJa6vR1p/zVGa4v3dNTU15l3G7S2nTl3HP2NzMRP/znY85ZtpmyaW1uZbSAnmX0uZb8iwiVFdX51zynG6DV5sGkmMpKDZ5yVkmIiU5T+vWrWP48OElO09r166lsbExr03Z8LqEqQKoFZEYUAd85PE4i8WyAeMua6S+/kyqqswNRzppS7eoTUuilJ7/Im3ZY59yeh9pZW1shBEjPNfvVT7oIDjqKLj5Zjj3XNNOqW2qquqtt7+ai1GG/tlUXd1Xcz/P06Da1Nxs7CnxZ6+o5eZmqK+nD2nnafaztZx4IowbB08/DRMnpi2FHCSbtLmZjL8BOcsrKSfc37sZM2bkXcbtkr6Me9fJmzKmYQEvLf2ME3eb0Gdpba6lsJA7H9FAyulLaQdi00BzLLn4aZOXnGWZysWwKZFIUO98x0txntLPQz6b0sk7gFDVD0XkeuADoBP4u6r+Pb2eiJwFnAWw2WabsXz58pztxmIx1q5dW3CdfPuL2VeQ9ARJy1DVEyQtbjuWAPDzn8PUqfDDH8Jvf+u3GoslK3fdBWefDdOnwxNPwNixfiuy5CMUEvbZaizPLfqUeEIJh4ZewAbrAxGMvgtt38sSppHAUcBEoAX4k4h8WVXvT62nqncAdwDssssuOmHChJztxmKxvOK91AEYrL6CpCdIWoaqniBpcduxBICttoLzzjOzEOedZ/wiLJYAoWpW2f3wh2bF3SOPQNoDzyGDCGHgVeBDVQ4XYSLwIDAKeB34iipRPzX2l5lTxvLnNz7krQ/XMW2LEfkPKDOsD0Qw+i60fS9O1AcAy1R1tarGgD8De+U6wKsPRDHqeKFYfQVJT5C0eK3jhSDpCZKWYrZjKQKXXw6jRpn8EB5+7yyWwSIeN6FZf/hD+MpXTEbpoTp4cPgO8E5K+WfATapsBXwGnOGLqgLYe6uxiMDcRUMzK3U5Xcv81Frqvgtt38sA4gNgDxGpE5O2bn96f1ktFotlw2LkSLjiCnjuOXOHZrEEgK4uE2H4ttvge9+De+4Z2gniRBgHfAH4rVMWYD/gEafKvcDR/qgbOKPqq9hx3AjmLv7UbykWS1byDiBU9WXMl/F14C3nmDsK7Tjd4WOgdQazryDpCZIWr3W8ECQ9QdJSzHYsReLss2GbbeCii3oi31gsPrFuncnx8MgjcNNNJmBYyFOg9iATrxCRV1NeZ6VVuBn4HpBwyqOBFlW6nfJKYPNBEltUZm49lnkrWmjpGHq/LeV0LfNTa6n7LrR9T0er6ixgVr56bgiziRMn5g1hFg6H82aiVtWiZKKOxWKoas5wX/F4nIqKipyhsSoqKoqSibqrq4vu7u6c4b7cPguxyUsmai82JRKJotjkJRO1qlJRUZEzLFs4HM6btTkejyc1DzQTdTHOk1ebvGSijsfj+b6ClsGkshKuvx4OPxxuvx3OP99vRZYNlI8+gkMPNbkeHnhgKOU5DHer6oxMe0Q4HPhUlddE2NfdnKFqWa4xnLn1WG75x7u88N4aDt9xM7/lFBXrRB2MvkvuRN0f3BBm06dPzxvCrK2tjbq6upyhsdra2vpk380UCitfJmq3r0zHu+W2tjYyZQtOHaG1tbX1ycqcbqOXTNRuXOFsNrmac2Vt9mKTl0zUXmyKRqNFsclLJups5yG17GrOZhOYwUB6mLj+ZqIuxnnyalNlZf5M1PliMlt84LDD4HOfMzkhLroIpkwxS5qamvxWZtlAWLTIOEqvXQtPPgkHHOC3okHjc8CRIhwG1ADDMDMSI0SocGYhxlGmYedH1lUSEvjWH9/gF8++y12n7sr40XX5DywDurq6+lwvg4qfWkvdd6Ht+zbB6cXR2kudwewrSHqCpMVrHS8ESU+QtBSzHUsREYFVq0zirHgcFi6EI47wW5VlA+Hll834tbMT5s7doAYPqHKpKuNUmYBJbvtPVU4BngO+6FQ7FfiLTxIL4uzfv0ZCzfTJktVtnHHvK35LKhrldC3zU2up+y60fd8GEF7CRxUrhFWx+gqSniBp8VrHC0HSEyQtxWzHUmRSc94kEuaRsMVSIpYuNWlINt9qInvuaXLJvfSSyfVgAeD7wAUivIfxibjLZz0DYunq9uT/Ce1dLnfK6Vpmw7hmpyQeGiZYU268rL0q1vqvYvUVJD1B0uK1jheCpCdIWorZjqXITJliZh4Sjh/n+PH+6rEMaY44wvg6qJrrbE0NTJrksyifUWUOMMf5fymwm596ikHT2HqWrG4j4TwkHjeyNvcBZUQ5XcusD0R2ijqAcJ2oJ0yYkNeJOhaLUV9fn9OJur29PWlgIU7Ura2tDB8+PK/D8ejRo3M6HHd3d1NbW1uwE/XatWtpaGjI63A8YsSIgm3K50TtxabW1takf0ChNuVzou7s7GTkyJF5HY6rq6tzOhyvW7cuqXWgTtTFOE/9sSmfE/W6dev69X20DBKzZ5u7ukWLTNib1lZYsQK22MJvZZYhyMKFvVOPLFninxZL6bjr1F05495XWLK6DYCKcIhId5zqivJ5ep+NSCRSNoMIP7WWuu9C2/fNidq9ccvlRJ3JeXcgTtSqmtfh2P0/l8Nxa2srNTU1BTtRp2vOpimXc64Xm7w4UXuxqaqqqig2eXGidv/PdZ5aW1vzOhynn4eBOFEX4zx5tcmLE3X6d8USEJqaYMEC8//8+bDXXmZA8cILQz6Dl2VweeWV3oOHUMhMgFmGHuNH1/HMBTMB+Mc7n3DGva9y/dOLuOwL2/msrHCsD0Qw+rY+EIPYV5D0BEmL1zpeCJKeIGkpZjuWErL99vDww/DWW3Dyycax2mIpAsuWmYjBm28OW28N4bCyzTY2j+GGwP7bbsxX9tiSO/+1jBfeXeO3nIIpp2uZ9YHITkkGENYHovR9BUmL1zpeCJKeIGkpZjuWEnPIIXDLLfDXv8LFF/utxjIEaG42eR66u+HZZ81quZWLl7FggY0YvKHwg8O2ZfJGDVzw8Dw+ay/v5HLldC2zPhDZKckAwsu0SNRD5lYvdbxQrL6CpCdIWrzW8UKQ9ARJSzHbsQwC554L3/qWSQn8m9/4rcZSxnR1wVFHmWBff/mLXbK0oVJbFeYXJ02jpSPG9x/9X1ktA0qnnK5lfmotdd+Ftu+bE3U0Gk1m54XMzrldXV0knMgmhThRr1+/HhHJ6XDc1tbWx7E13ZE1FosRDocLdqJet24diUQip3Nua2sroVCoIJu8OFF7samzszN5HgqxyYsTdUdHB1VVVTkdjqPRKCKS0+G4o6MjqXmgTtTFOE9ebfLiRN3R0dGv76PFZ268Ed57zwwmmprgwAP9VmQpMxIJOO00407z0EPwf//ntyKLn0zdbDjfO2QKP3niHR58ZQUn71aeEd/ca3M54KfWUvddaPu+OVG3t7fndaLu7u6mvr6+1/6BOFGLSF6HY/cmMJfDcXt7e1GcqIcPH97LrkyaQqFQTudcLzZ5caL2YlNtbW2f8zAQm7w4UWc7D6nl9vb2vA7HdXV1ffT014m6GOfJq01enKjT91sCTkUFPPigyfR1/PHw73/Dttv6rcpSRlx6qRk4/PzncMIJfquxBIGvfW4icxat5srZb7PbxFFMGlt+gRpCId/cb/uNn1pL3Xeh7fvmA5F+szbQOl4oVl9B0hMkLV7reCFIeoKkpZjtWAaRYcOML0RNDXzhC7B6td+KLGXC7bfDddeZCawLL/RbjSUohELCDSfsRHVliPMfnEe0u3ye5ruU07XMT62l7rvQ9n3zgXCXkxRaxwvF6itIeoKkxWsdLwRJT5C0FLMdyyCz5ZZm8frHH8Mxx4CzVM5iycbs2caF5ogj4Be/AA/P5CwbEBsPq+Fnx+3IWx+u46ZnF/stp9+U07XMT62l7rvQ9n2bm4l7CG/opc5g9hUkPUHS4rWOF4KkJ0haitmOxQd23x3uvRdefBG+/vXewfwtlhReeQVOOgl22QUeeADKKOKlZRA5eOomnLzbeH49dwkvLSmv0K7ldC3zU2up+y60fV8zUUcikZxO1LFYLOmQXIgTdVtbW7Kcy+HYdVzNlbW5q6urYCfqtra2nDa5DsfFsMlLJup8NkWj0aLZ5CUTdU1NTU6H4+7u7rwOx5GUp7wDdaIuxnnyapMXJ+qIfXJd3pxwAixeDJdfbgL5X36534osAcPN9bDxxmYWIs31zGLpxeWHb8vLS9dywUNv8rfz92ZEXXksDfKyzD0o+Km11H0X2n5JnKh32WWXvE7UsViMysrKnE7UrvNpKgNxoq6urk4el629mpqavA7HruZCnajHjBnTx4E2XZOrpxCbvDhRe7Fp2LBhfdoYiE1enKiznYfUsqs5m01gHKDT9fTXiboY58mrTV6cqIcPH46lzLnsMjOI+NGPzCDixBP9VmQJCKm5Hp56ygwiLJZc1FVV8IuTduaY21/kB4+9xW1fml4WN+fp931Bxk+tpe670PbzLmESkSkiMi/ltV5Ezs91jPWBKH1fQdLitY4XgqQnSFqK2U5REKlH5F5E7kTkFL/llA0icOedJh7nqafCf/7jtyJLALC5HoKLiNSLyL0icqcE8Lduh3HDufCgKTz51ioeeW2l33I8EahrWR6sD0R28g4gVHWRqk5T1WnALkAH8FhBvRK8tePlpidIWrzW8UKQ9ARJSzHbyYrI7xD5FJH5adsPQWQRIu8hcomz9VjgEVTPBI4srbAhRnU1PPYYbL55z12jZYMlNdfDfffZXA+DgYj8TkQ+lbTfOhE5REQWich7kvZbpwH+rTtrnyb2aBrFrMcXsHxNu99y8mJ9IILRd6Ht99eJen9giaq+X1CveFt7VaypuGL1FSQ9QdLitY4XgqQnSFqK2U4O7gEOSes0DNwGHApsB5yMyHbAOGCFU6t8rgZBYcwYeOIJE5HpiCNg/Xq/FVl8wuZ68IV7SPutkwy/dVImv3XhkHDjCdOoDIf4zkPziMWDHdq1HJZZuVgfiOz01wfiJOCBLELOAs4C2GyzzVie56laIpFgzZrckQO81Fm7dm3O/cXsK0h6gqRlqOoJkha3nZKi+jwiE9K27ga8h+pSAEQeBI4CVmIurPPI8iAi9TchWlnJ2qVLSyK7v6zPcy4Gjaoqqm69lZGnn070iCP47M47TfK5NAKjtx+Um2a/9N5z/zCuu24Mp39lHaccs5ZV/fiK2Pd44Kjq85Llt06d3zrpx2+dUz/5e7f55pvnvQcqBd/9v4254pmVXPnoK5yxW3CdaLxcE4OCn1pL3Xeh7XseQIhIFWb67tJM+1X1DuAOgOnTp+uECRNyttfZ2Zl0qC2kDsBg9RUkPUHSUmw9sVgsGQEpE93d3X0cjAdSp7GxMWcilWL1U6iWhoYGhg0bRmdnZ842SsTm9Dx9A3Mx3R24BfglIl8AZmc6MPU3gfp63aSpqbRK+0FgtDQ1QWcn1WefzSa33gq33pqxWmD09oNy0zzYemfPhst+bCag7rx7OOFw/4MkDP33eH7+KsUj52+d5Pitg96/dzNmzMh7D1QKTpsAC5rhD6+v5Ihdt2K3iaMGXYMXvN4vBAE/tZa670Lb788MxKHA66r6yYB7S8ENT1loncHsK0h6gqTFax0vdHd309bWxiabbJI1zXokEskbPcBLnZaWFkaMGFFQG6XWkkgkWLVqFcOGDSvae9xPMs1xKqrtwOmDLWZIctZZsGgR3Hij8Z497zy/FVlKjM31EEgy/tZpmf3WzTpyKv9d3sx3H5rHk9/Zm+G1lfkPGmR8upYNCD+1lrrvQtvvjw/EyWRZvmSxFJtMg4cP1nZw4I1z2e6KZznwxrl8sLbDB2WDS7ZB1CCyEtgipTwO+MgnLUOX666DI4+E73zHxPC0DFlsrofAMiR+6xqqK7j5xGmsWt/F5f9vvqeomBbLQPA0AyEidcCBwNke6+etkx6Tf6B1vFCsvoKkJ0havNbxQk1NDa2trfx49gLe/qi3Y+mbK1voihk/gHc/beOgm+ey07iep/bbbTaMWUdMBfrmUMjE9ddfz5VXXsnRRx/NqaeeynHHHcdee+3FNddcw7777puxjfPPP5+bb745WXbrXH311Vx66aU88sgj3H777cyZMweAq666ih/84AfcfffdHHjggYwbNw6Ae+65h7Vr13LhhRf2aj8SiXDuuedy3nnnMW3atD7vjQ+8AmyFyETgQ4wf1Je8Huwml4xVVVERjUIsZl4AtbUQj4OTTJLaWhOSxk2Y59rrJPCjuhpCIXCXclVVmUe3brmy0rw6OnqXOztN5ueKCqiqQlpboaXFHFtdbdpPJEzbNTWm/3jchFytrTX6urt7yqk21NUVz6bbbjMRmU48Ef72N9huO6M/GjV6c9iULAfFpkikR/MAz9Og2hSJmP5K/Nlr/qiLQw+upzsW4qm/xNi4tgtaBmhTNArt7b5/n7yeJ2ltNdr6c54Gl1eArWSAv3XQ83vX1NSUN5mum9A0VzJdEUmWcyU0BXolNJ08soJzZ07glueWseeEYRy+/UZ5E8+KCNXV1TkTz6bbMFCb3CTB/bEpU5LWwbApFArlTXo80POUz6ZYLEZ7e3vJzpN7HvLZlA1PAwhV7QBG56vnfnkmTpyY98ujqiQSiZwnJRKJZD0J/clE3dnZmUyqlu2DFolEGD58eM6TAuQ9KV4yUa9bty6ZXCzbB62rq4uGhoaCbPKSidqLTZ2dnckn4YXY5CUTdSwWI5FIkIgnUE0Agoi5PrmDB5euWAJVRURQ1eR5rKioSNp41VVXMWrUKD7/+c/z4IMPMmrUKBKJBGeccQbhcJhYLMbEiRN5/fXXGTZsGDvttBPxeJz77ruPlStXsmrVKq6++mquvfZaNt10U15//XW6u7u5/PLLGTFiBNFoSUsFtgAAIABJREFUlLPOOovKykq6u7s56qijeOGFF4jH48TjcUaOHMl7773Hcccdx4033sill16KiJBIJNhnn33o6uri4osvZuLEiTz33HM88sgj7LHHHiQSieR74Z7D9vYSh+cTeQDYFxiDyEpgFqp3IXIe8DQQBn6H6gKvTbrJJamvP5OqKnPDkU5aAj3S12SmD5zSl4Oll9P7SCtrYyOkLhfLU7/fZRiYTSNGmMhMu+0GX/oSvPyy2VZV1VtvKTQX2ybXnlT6eZ4G1abmZmNPCT97XV1w1FeqWP4BPPssTNmhCijAhubmzNMXg/x98lrW5mYy/gbkLJcmr4Gk/NaJ81unqndJ2m+d9uO3Dnp+72bMmJE3ma5LrmS6mcpekusCfOfAbfn3shau/tt7/N+UTdmisc7T8fmStBbDprq6uj5teLHJa/1i2tTV1UV1dXXJzlOu+uFwOKmjFOdJVXtty2dTOiXJRD19+vS8Xx531JPrpESj0T5ZnQeSiVpVk1l+82UDznVSWltbqampKTgTdU1NTa862TTlytrsxSYvmai92BSJRIpik5dM1K2trYRCIX589A6kc+CNc1myuo2EQkhg0tgGHj5nrz71wKztq66upqKiglNOOYVwOMzbb7/Ncccdx+LFi/nggw/YYostqK6uRkTYfvvt+f3vf88xxxxDOBzmhRde4NZbb+X3v/89CxcuZO3atVx99dW8/PLLtLS0MH/+fI477jjefvttVqxYwRZbbJF8T0KhEOFwmHA4zKRJk1iyZAlTpkxh/fr1yc+7m2V93bp1jBw5kq997WvMmzcvqTk1C7t7DsOlXiitenKW7U8CT5a2cwsA48aZdS17721yRDz3nN+KLEUgNdfDQw/ZXA9+o1l+63QI/daFQ8JNJ07j0Jv/xfkPzeOhs/agIuz7kljAJDDzaUa93/iptdR9F9p+MD5NFosH7jp1VyaNbSDsDB7uOnVXT8dVV1czZswYpk6dSktLCzvuuCNNTU18+OGHyTpf/OIXmTVrVnL53cyZM7n55puZP38+22+/PZtuuil/+tOfWLFiRa+2dthhB5qamlixwgTv+Oc//8kbb7zBnXfeCcD777/P5MmTaW9vZ+TIkX20bbTRRqxbt4577rknOXNl2cDZZRe4/34zA3H66ebu01LWlEWuh6VLYepUszRp6lRTtpQ140bW8ZNjtue19z/jtueW+C3HUgJc39BJlz456L6hRZ2BcLE+EKXvK0havNbxgusDkYnxo+t45oKZxOPxvE/j3VmWK664Irnt2muv7VXHXSLk+jRMmjSJSZMmJfen9jNr1iwAjj/++F5tuXXctvbbbz/222+/ZBurV69m0qRJ3HfffZx+ek8gj/Hjx/Pqq68ybdo0Dj74YBYsWMABBxxAJBLhgw8+YObMmRnfG8sGwrHHwsUXw89/zsYPPwzbbmtmJsosbKcFbr/d+Mifey6kuTwFiyOOgHfeMetF33nHeHq//bbfqrIiQg3wPFCNuZd5RJVZIkwEHgRGAa8DX1El6p9Sfzlq2uY88b+PuenZxfziH4uTD9/Gj67Lf3CJKKdrmZ9avfR9+j3/Zclqs7x5yeo2zrj3FZ65oO/9w0Dbz0VJBhBeiMfjfZa5DKTOYPYVJD1B0lJsPfnwElXCS52LLrooZ5Sj/vRz2WWXZdx/+eWX093d3WvwAPQaaBx22GHstddeyTCuP/rRjzK2Veq09qXAOlEXYNPjj5v30L2hO+wweO01/x2OrRO1J5uWrqhk3wMqWPGh0FAP3/lmDFlfRJuK7US9cKHRBj2DiFtvNVMm1dVBdKKOAPup0iZCJfCCCE8BFwA3qfKgCL8GzgB+5aXBQgmKE3W6f+G7n5iAJAmF91a3cfrdL/P4N3bzzYnai89kPpsGy4nai79uqZyoOzo6qKury2nT0jU9vpEJNYMIr5+99evXU1NTU1onaq+4X54JEybkNSAajSIiOU+K6wQMhTlRr1+/nhEjRuT8oLW1tTFmzJicHzRXS6FO1M3NzTQ2Nub8oLW2tjJy5MiCbPLiRO3Fpra2tl7lgdrkxYm6o6Mj+WV1HaTD4bBxrE4kEBHi8XjySy0iVFRUEI/Hk5maKyoqiEQiyRvuioqKpJN1ajkajSadrqEnJnI4HEZE6OzspKqqKllO39/d3U00GqWmpoZwOEx3dzeqSigUIhQKJXXGYjFqa2uT5Ww2ubMY6Ta557AclzhZJ2qHgdj03nspohXefbfHadY6UQfWibqlBf7yiEnn4ebD7OiEo4+vYsGCItpQTCfqSMQMCNzBh4ip9+1vw7XXwve/D2eeOQAn6J5ysZ2oVVHAzTha6bwU2I+e6En3AlcwSAOIIDlRp+7/oLkrRSMsX2uCsHhprxRO1F79QHPZVEi5PzZ58dfNVC6GTYlEIud5inYnCIvQnfKwUxWemP8pR++8ed7zlH4erBN1GThRNzY2lpUTdbFs8uJE7WpKbz91yVIkEunTfvpMQkVFRZ/PVvqPRlVVVa866cui8u13y66WXJpdp+pc+7PZlOm7ZNkAmDLFPBV2fSASCRPq9dxz/dVl6cO6dWbC6OGH4emn+z5ATyRMrsDActVVZvAwfjx8+KH57D3+OLz/Plx5pclPcs01ZlndOecMYvKKeIWIvJqy4Q4n4zMAIoSB14DJwG3AEqBFFTfM4EpMhukNmqax9ckAJAAbDcud2NRSHvxl3od0J5TNhtfwyfoI40fX0lBdwfkPzeOlJWv48ZHbU1tVuuArJXGi9uIDkS8zr9c6XihWX0HSEyQtXut4IWc7zcvgtt2punZTuG13U86ClzwQ+fDSRrHqeKFY77GlTJg9G7bZBg2HjQ/EAQeYx9q33+63Mguwfj384Q8mWNZGG8FXvwpvvgnf+hb85z8mjYf7XCMUMvfkgeSNN8wsw6mnmgFDdzcsWACTJsF++8GcOea1/fZw0UUwYYKpPygzouFuVZ2R8rojda8qcVWmYZK+7QZsm6GRDT6TWk8AEqG6IkRbVzcfr+v0TU85Xcv81Jqr70RC+fXcJWy76TBevGQ/lvz0MJ676PM89s3Pcd7nJ/On11Zy5C9fYPEn2b+nhdrmWxSmYq1jH8y+gqQnSFq81vFCsp2nLoG7v9D79as9YfVCROOweqEpp+5/6pJ+6bn++utJJBIceeSRPProowDstddezHGSwGVq4/zzz8+o9+qrryaRSHD77bdz1VVXJZ2ur7rqKlSVu+++m5Ure6bj77nnHm644YY+7UciEb7+9a8zb9687O+NZcOgqQkWLOCTxYuNM+sTT5hs1eeeC78alBUZljRaW+GBB+CYY8yg4ctfNm4p554LL71k8gDecAPsvnty/Ec4bP7Onu23+gzEYibS19ixcOON2evNnGmSV7z4IsyYYcJKTZhgZi5cXxcfUaUFmAPsAYwQSa6uKMts0sXGDUCy5KeH8bfz9yGuykV/epNEwp9rSjldy/zUmqvvv7+9iiWr2/nGvpN6PbSvCIe46OAp3Pe13fisI8qRv3yBh19ZkbGtQm0riRO1F1HRaDTv6MdLHS8Uq68g6QmSlmLryUqsM3c5hXg8TkVFBVdccQUjRoxg//33TyaS6+7u5swzz6SqqopQKERTUxPz5s1jzJgxTJ8+HYD777+flStX8sknn3DNNddwzTXXsPnmmydv7C+77P+3d97hbVXnH/+8kmw5HtmDDIjjkIQVMpywIWHTQoAyAmU0gRTKKJu28IMwOmihjEJJw2ggYZQVRgil7CRAGU0C2TvOIIEQJ04c27ItSzq/P45ky7IlXVvr2jmf59FjXfnonO+rK91733vO+7530LVrV2pqarj66qvr+7rmmmvwer1cd911APTo0YOSkhLGjx/Pww8/zOTJk+s1nnjiiSiluOmmmygsLGTOnDnMmjWLY6IkiY/52dgUE0SdBJvCK1FPnw6XXgrXXKPbXHmlCaJOcRB1ZSW880kur77h5D/vO6ipEfr0DnDVFQHGn1HNEaP9ONxBG/Y02FTUL4vlX0TYVG6zIOr779fTJi++2DB+rP00ahS8/LL2mh58EO66S/+96ir9nezdO21B1CL0AOqUYrcIHYCTgPuBOcB56ExME4BZcTtLEnYNog7f7tkBfntSEfe8u5anP13HpYf1TXsQ9c6dO+PGTLbEplQGUYdsyUQQdXl5OZ06dWpiU3Z2No9/spb9uuQwZkABPp+viU2j9y3gtUkjuW3WKn77+hI+W7OdO04tIs/tqt9Pof3QJoOo45UH93q99YGjiQRRV1RUxP2iVVZW1n+xYgUc19TUJBxEHc+mUBB1ojZZDaK2YlOIRGxqURD1ib9vEnDsfPIoZOc6RAVQ4kB12x/fxW82DqIOBkWH7PP5fIwfPx6Hw8GiRYs4++yzWb9+PWvWrKFnz5714x5wwAE89dRTnHXWWfj9fubNm8fDDz/MCy+8wKJFi9i+fTt33nknn376Kdu2bWPx4sWcddZZ/PDDD6xZs4Y+ffrg9Xqprq7mnnvu4Xe/+x1er5c+ffqwevVqBg4cyI4dO6itra2vRF1XV8d3331HXl4el156KQsWLKC2thafz9dsJerQZ9qWMEHUQRKxKTuiEvXrr8N55+mcoHl58KtfmSDq1mxD1CDqqio94fPqqzm8+66+pu7dW8cRjx8PRx3lCMYoNY4NS6sNiQZRL1um4xouvFBXPg9hZT+dcIJ+fPst/PGPOk/t1Kl6id3NN0P37k3en4JK1L2BGcE4CAfwqlK8I8IK4GUR/gh8C0yL1UkysWsQdeT2hGMH8d+Ne3jow3UcO7gXB/XpGLV9KoKorcaBtsSmlmy39SDqz9aWsuz7Cv58zlA6dSyIatOA3jn868qj+MecdTzy0RqW/VDB4xeN4OACrTVyP9giiLq4uDjuj8fr9TYJUo0UHPK0wmlNEHV2dnZ9P9F2qtvtjhtwHNKcaMBx9+7dG73WnCa3213fprU2WQmitmJTx44d436xrNhkJYja7XazY8eO5gOSL3oVXroQtWMt0n0Q8vOX69tFBlGHMiO5XC5yc3Pp3LkzQ4cOpaqqiuHDhzN48GDef//9+krU48eP54gjjmDp0qU4nU7Gjh3LE088wdatW5k4cSJ9+vTh7bff5vvvv2efffbhkEMOoaqqipEjRzJ48GA+/fRTsrOzOf/88zn44IP5+OOPueKKK/j+++855ZRTqKmpoXv37k0qUe+7775UVlby/PPPU1VVFbMSdadOnTAYcLth5kw491x951dEz0QYWkxJiS5/sHr1AAYN0te/8+bBO+9op6FXL7j8cu00HH20njhoF/h82rBOneCxx1rfz4gR2qFduhT+9CcdG/Hoo3o24tZb9QeYIpRiCTCimddL0PEQhiiICPefeyin/u1TbnzlW97+9THkZKXvy21iIBIb+x9z1tOro5tzRsbPD+B0CNedOIjRA7pyw8vf8rN/fMHk0w/kkiP6J2xbxupAGAwtpusAuPZrAhYKyYVIdyG5WbMaz5aXlpbSv3//pBSSMxjqcbv1hds55+gZCBF9e9zQIs44I1T+QFi1SjsQPXvCxInaaTj22HbkNITzyCMwf75ejtSjR+L9DR2q+7r7bu1IPPwwPP64/m7+9rfQp0/iYxiSSte8bB48fxgTnvkf97+3irvHHZxpSQYLfLN5F1+W7OTO0w/E7bJ+cDqiqBvvXn8st7y2mMmzlvNlyU5+f8YBdI8zyxATpVTSHyNGjFDx2LNnT1LabNiwIW1j2UmPnbQkW8/WrVuV3++P2qampiZuP1ba7Nq1K+E+Uq3F7/errVu3KqX0ZwMsUCn4zab8kZsb9zNIFz+sX59pCS0ipt7qaqV+8hOlQKmnn06fqDjY+TMOBJT64gulrr5af2zhD4dDKZ8v0wqt0erPeNUqpdxupc4+W38YqWDNGqUmTlTK6VQqO1upLl1UwOFQ6qCDlGqBblhapTJ97GrFo7i4uGWfVwa5e9Yy1f9376h5q7enbUwr1wt2IZNamxt70vT5ati976vKmrpW9en3B9QTc9epotveUQNv/7cacNs76qSH5qpNO6qivifadYeZgTDYjvz8fLZt2xb1/3V1dU2WSLWmTWVlJZ5QgGAKx0lUS+RyL4OhnpwceOMNnRboiiv0TMSkSZlWZUvWroUXXtCPkhL90XXsqDMrKaVjfkNZk9otgYD+fuTm6nTAFlKut4pBg+DZZ2HyZCguhl27ENDVrceN02liDbbgtp8cwBfrd3DLa4t5/8bj6JqXwB1pQ0pZva2Cj1b+yA0nDiLP3brLd4dD+NWYgTz/1Sa27NIJE9aVVjJpxnw+vLllKx9SEkQ9YMCAuEHUQNwgaiApQdS1tbVxA469Xm/cgGMRSUoQtZVo/VCF5ERsshJEbcUmpVRSbLISRO3z+cjPz68PnG4uqwJQX+k5WlYFj8dTHxcRzaZQ9exoNu3Zs6c+NibWfgrFmsSzKVamiKqqKjp37hwz2D1UabstYbIwJTkLU7SMRdOnw8UXayfC79eBsSYLE6U/+HjljSxeeD2Hr//nQERxwnE+Jt8G54yrY8c2H+MuzGP1OgdDBitmv1gJuwMp++4lNbNUa7IwTZmiU7FOnaqD3MOzLqXCpn79GteLUEpX1Nu9O+EsTHakLWRhijwPBerquG/cYH7+7Lf8buZi/nbeQQQCgZRmYUq1TcnMwhTSnwmbamtrG9n0949W0SHLwcWH9cXj8SSULeuH3Q1ZLJWCktLK+us8q1mYRM9OJJfi4mK1cOHCmG1CwbuJttm4cSOFhYVpGctOeuykpb3qsZOWUD9ut3uhUmpUzIZ2JC9PUVWVaRUAbCspYZ+iokzLsIxlvTU1cPbZ8MEHMG2azu+fITL5GVdX6yLKL7wA772nr78PPVRnv/35z6FvM3GHbe07Aa3QXFKiYxXGjNEpplI1+xDJwQc3VFQPTfNYnIEQWeZR6pB0lb1OGqNGjVILFiyI39BGPPXpeu57dxX3nzuUC0bvl9KxrJwT7UImtYaP/V2Zh7EPzuWyowq584yDEu775Ifn1VcndwgM7JEfdQZCRJq97rBUSE5EOovITBFZJSIrReTIWO2tOCXxPBurbayQrLHspMdOWqy2sYKd9NhJSzL7MbRTcnLgrbfg5JP1MpXp0zOtKG34/fDJJzqxUK9eegLm2291RtHFi/Xj1lubdx72CpSCX/5SzxI8+WT6nAdoXFHdthX1DL88poijBnbj3tkr2LAjtTd72tK5LJNaw8d+8tP1OAR+eWxybnRMmzCaAd1ycYowsEc+0yaMbnEfVpcwPQq8p5Q6T0Sygdx4bzAYDAZDmgk5EWedpa+mRWDChEyrShlLl+qZhhdfhK1boaBAZ7e99FJ9oz1uPEMwj2uv1athyBB9cdvGZiIs8fTTMGeOdh723Te9Y4cqqrfBWZ69CYdDeGj8ME595FNufGURM686kiynpXvMbZbNOz1MmjGfktIqinrkMW3CaPbrZr/L2+0VNby6YAvnjuzHPp1y4r/BAvt1y+WtX41qUnagJcT9dohIR+A4gsVYlFJepVTM2vVi4e5GvKBSq22skKyx7KTHTlqstrGCnfTYSUsy+zG0czp0gFmz4MQT9TKmGTMyrShhSkr0ShiXCwYPhttvh2HD9NKkhx6C4cN1FtFt23Ts7gknWAyGHjcOVq5E/H5YsQKOOEKvf9q4Ud+1bw9s3qynX0480aT6NcSkd6cO3HfOUBZ/t5u/f7IuZePY5Vx22fT/sXZ7JX6l6gOJI8mk1tDYz3y+EZ8/wK/GDIzzjtb131qszEAUAaXAsyIyDFgI3KCUajTHJSJXAlcC9OnTh40bN8bsNDzHfiJtdu7cGUd+8saykx47aWmveuykJdSPwWCJkBNx5pnaiRCBX/wi06paTfA6H6V0NqW//AUOPxz+/ne44IIEShmsXt3YUSgt1bM3oFM0HXpow2PYMDjkEGhLWdGU0rUY/H49C5HOpUuGNskZh/bhk1XbefyTtYwZ3J3i/l2TPobVOk6pJnypllKwbnsl3++upk/nhurtmdTqdDopr67jha828dOhvRnQPbnhQInaZsWBcAEjgeuUUl+LyKPAbcDk8EZKqaeApwBGjhyp4gWEVlRUxJ06sdIGiBt8mqyx7KTHTlraqx47aQn1YzBYJjdX300fN05XRnM44JJLMq2qVehibw3bTid89VUSOu7SBXbs0M8dDj298eyzsGSJDpxYskSvkdqzp+E9Awc2OBQh52LAAP1+u/HcczqS/LHHtEaDwQL3nnkw8zeWceMri/jPDceR38qUodGoqamxxSxEvtvFnhpf/bYCxvx1Dj8b0ZerxgykqEd+RrXW1NTw/PxtVNb6uHpscmcfQv0nYpuVb8UWYItS6uvg9ky0A2EwGAwxMWlc05TGNVYqzddf19mZJkzQ7SZMaDNpXH11ilvvyiUQyEaf3gWHQzFksIKKqsT206JFsGsXdOyIqqpCBg+Gf/0LCgv1mqiQTUrB9u2wbJmOzF6+XD9/660GryY/X89OHHCAXms1bJh2Ti69VE+ZDBoEb74JvXunL43r7t1w4416qubiixv2T4ZS00pFhdbWkv3UxmiLaVybS3mK38+fxg1h4nOLmPzmYn5/+uBmbWitTfHS86cjjau3rg6nCPluJ9VeP4Xdcpn8k/35eM0uXl24ldcWbOHogV3YsMPDtj21FHbL5e/jD2b/fTqnbT+VlpUz7fMNjBncnQGds6ioqEhKatqWpuePRlwHQim1TUS+E5EhSqnVwInAiljvMTEQqR/LTlqstrGCnfTYSUsy+0knSqnZwGzy8q4gO1tfcESSGxG01qFD4+2ciKCx4MEu6nbkGBHbqqBA58C32L7F25Bcm7KzG+ttjaZ334UzzoCrroK8PLjoopa9H1pmk9vdVHML91N5dTY//wX85z96FdZXXwlr1sCQIcLs2aIjpltiQ/i23w/XXQddu8LKlfxYXt58gG/Ipi5ddJD1uec2/M/j0c5EaKZiyRLtVDSX/WrNGv3eyPSliXzXysr0vowkN1df0E+cqC/mn3tO2xlpU4gEf09Wt1VZGc0eA2Jub6EtETrejRo16oqsrCyysrLICfu8I7eh4eItRGTK0MjtyPNA5HZk4dF47aNtH3dgAdceX8nfP1nHyQf34adDezdrQ2ts6tq1a5M+0mFTCJfLxfoyL7uq63jw/GGcV9yv/n9jD96X608awrP/3cAT89YTCN4jKNnp4YaZK+tTnaZjP300/3t2eer49QmDyI04/jZnU+T/4+2nyP0Qz6ZIrM5LXQe8GMzAVAIknGDcytqrZK09S9ZYdtJjJy1W21jBTnrspCWZ/Rj2QnJzdYahM87Qd8WhqRNhI4LJkVizBp54Qi/jTyqPPgrz58NLL0G3blBe3vI+cnNh9Gj9CKEUbNminYlx4xpmKAIBvQ4rXbzyio6B+etf9bIsg6EVXH/iID5du4Pb31jKiP0607tTh/hvsoAdzmVzV28HYMzgpgFUPQrc/Pa0A3hyXgl65rMhRqK0opYeBe4m70k2Xl+AZ7/czOjCLowuTH4cCqQnBgKl1CIgbvGq0PRdYWFh3Ok7r9dbX50Xmp9CqaysrPeAEqlEvWfPHjp37hxzqquyspLu3bvHnBaqq6sjNzc34UrUO3bsoKCgIOZUV0VFBV26dEnIJiuVqK3YFKrInKhNVipRezweunbtGnNK0uv11uuMNn23e/fues862vRd6LuTyv1k1aa6urq4lSV3746Z/MxgiE1eHrzzDpx+uo6F+O1vddoim6Uv/ewzOOccPUnw/vs6o1JSKSmBO+/UztQFFyS3bxGdJnXffeHAAxsKqIH+e9VV8Oc/6xmNVFFaqmdXDjsMbropdeMY2j1ZTgd/u2A4p/3tU054cC5en0pKulM7xEDMXV3K0L6dYjoDRT3y6outgXYlxv51Dtccvz+TjhlATlbqHKFZi7byQ3kN950zNGVjpCMGwjKh6buRI0fGnb4LXbjFmuryer1Ngk+bmwYKb9PctJBSqn76J9a0UkhzOOHTQhUVFeTk5MScFnK73U00R04DFRQUNGoTTVOH4BKB1tqUl5dnK5vy8/Nj2hT+PNZ+qqioaDKdFzl9l5OT00RP5PRd5HcnFfvJqk1ZWVmWbDIYEiIvT1ch7tlTF04AfZE7bpzl6sCpZPp0uPJKHe87e3YKbp6HshK5XDB1amqzEs2erT/X1at1DMSRR+pMSG++qXPPXnxxasa/7jo9o/LMMxZz2RoM0RnQPY9OHbLYXqFvsK0PpjuNVrW4LbDb4+Wbzbu49vj9Y7abNmE0lz37NRt3VlPUI4+7xx3EjC838df3V/PiV5v4zWlDOGtYXxyO5P6O/QHF1HnrOaBXHmObmSGxC8kNrW/JwK74Q1tpk86x7KTHTlqstrGCnfTYSUsy+zHs5eTlNQRAg74zvnp15vSgZxv+7//ggQd0uYLXXkvRTfrp0+Gjj+Af/4B+/eI2T4hgAbVGXHednoW49FJ9gT91qp4BShZvvqmXL/3hDzqY22BIAjsrvfXPAwpKShOrVJ3pc9lna3cQUDB2SM+Y7fbrlss71x5Rf5MQ4JhBPfhy/U7+9O4KbnplMc98vpE7Tj+QI4q6JU3fB8u3UVJaxUPnHmwppri1JLofUpJ3zgRRp34sO2mx2sYKdtJjJy3J7MdgYMiQhrvfIsm9iG0hlZV6ydIDD8DVV+ug6ZQ4D9u2wc03w7HHpiCowiIjRsAXX2jH4ZtvdArYu+9uyIyUCGVl+gMcPhx+97vE+zMYghT1yCN0VSeitxMh0+eyOau30zk3i+H7do7btjmtRw7sxtvXHsMjFwxjR2UtFz71FVc8t4CS0sqEtSml+Mfc9RR2y+WMYX0T7i8Wie6HlDgQykIFzxoLB0wrbayQrLHspMdOWqy2sYKd9NhJSzKsl53jAAAgAElEQVT7MRiYPVunHAWd9ebttzMiY/NmOOYYHZrx97/DlCk6U2hKuO46nXL06aczW7PB6dSzEKtXw/nnw+9/r1PAfvBBYv3edBPs3KnrWJibDYYkMm3CaAYEnYaOOVlMmzA6zjtik8lzWSCg+HRNKccN6oHTwtKjaFodDuFnI/ox59ax/ObUIXyxbgenPPIp97y9nLIqb7PvscLn63awdGs5V40ZSJ03dhrVREl0PyR1HqmlQdShAGBoPjg3FIQKiQdROxyOuEHU8XLr1tXVUVNTk3AQ9Z49e1BKxQ3ODdnQWpusBlHHsyk0fqI2WQ2idrvdcYOo4wUcV1dX12tubRB1MvaTVZusBFFXh/KzGwyJUlQEK1boFJ8TJuisQQOTX6goFl99pctTVFfrLLOnnprCwd56C2bOhD/9KaOzLY3o1UsXqbvsMj1zcOqpOqj7kUd0vYiW8O67el/eeWdDDQuDIUns1y2XT24Zyw0vf8vc1aX07JhYFiIrN5lTxbLvy9lR6WXsEGuxBfG05mQ5ufb4/Rk/al8e+WgNz325kde/2cJ1J+zPhKMKcbtaFoc0Zc46enV087ORffFWe1r03paS6H7IWBB16KIqVhC13++Pm/vWShC1w+GIG3AcuuiLFXDs8XiSEnDcuXPnRnY1p8npdMYMzrVik5Ugais25ebmNtkPrbHJShB1tP0Qvu3xeOIGHOfl5TXR09Ig6mTsJ6s2WQmizmsu57vNMYXkbFBILpZNp58O++8PkyfDmDG6fRoKyf3rdTeXX+mkb+8Ac/5dy4FDXVCeJJsi91N5OVxzjb7Lf8UVWlekTbW1erwUf/eatam4GD7/XE+/3H+/dgYmT4Zf/1prjFdIbscObdeBB+olWqHPPU2/J1NIroH2Ukgu2o2ss4f1Ztai73nn202cemCPVtvkCX6nMmHTJyt/RICRfTrUJ5GJtZ/8fj+1tbVxbcrBy+0nFXJRcW/++tF67nt3FTP+u4FbTh7EGYf2rm8fy6ZFW8r5qqSM208bjK+2Bo/H06r9ZLWQXGg/tLaQHEqppD9Gjhyp4uH1epPSZsOGDWkby0567KSlveqxk5ZQP8AClYLfbMofublx7UsXP6xfn2kJLSLlel94QSlQ6rXXktZlNM1+v1KTJ+vhjjtOqdLSpA0ZnSuvVMrhUGr+/KhNbPOdWLNGqZNP1h9QcbFSCxZEbVqvOWTfV1+lSWTraM1nDEurVKaPXa14FBcXt9jWtoDPH1BH3PeRmvjM1wn1Y+WcmCrOnvK5OvPvn1lu31qtn60pVac+Mk/1/9076uwpn6sFG3fGfc+k6fPVsHvfV5U1dQmNbRWr/Ue77shYDERcz8ZiGyskayw76bGTFqttrGAnPXbSksx+DIZGXHihjoe4996GmgUpwOPRK3T+8Ae4/HL48EPo3j1lw2nmzYOnntLxAaPiljLKPIMG6eIXL72k0+wedhhcf330Qncff6ztu/lmOPzw9Go17HU4HcI5I/syb00p2/e0fv18ps5lu6q8LPpud9zsS+G0Vusxg7rz7+uP5YFzD2XrrmrOnfol1774DZt3Nr8safW2Cj5a+SMTjyokz+1KaGyrJNp/xiLJrDgZVtqkcyw76bGTFqttrGAnPXbSksx+DIZGOJ1w112wbJmOE0gBW7fCccfB66/Dgw/CP/+pV7+klOpq+OUvdbzH73+f4sGSiIh26lau1LERjz+ulye9+mpDZWtAqqq0fYMGtS37DG2ac0f2I6DgzW+3trqPTJ3LPl1bilJYjn+AxLQ6HcL40fsy59ax3HDiID5ZtZ2THp7Hn/69gnJP42V6U+euIzfbycSjCpMythUS7T9jQdRW1pX5/f76gOREgqg9Hk/ctXLV1dX168iirSsLBAJJCaK2sv7P4/EkbJOVIGorNvl8vqTYZCWIura2lpycnJhrGgOBQNyA47q6unrNrQ2iTsZ+smqTlSDqkH6DIemMH6+nBu69F849N6kFyBYuhDPPhD17dLKnM85IWtexufdeWLdO133IbX3V3IzRubN2HiZM0GlnL7hA146YMgUGDiT/wQdh0yb49FMdS7CXIcK+wHPAPkAAeEopHhWhK/AKUAhsBMYrxa5M6WxvFPXIp7h/F2Yu3MKVxxW1qk6BM0MFDueuLqVLbhaH9oufvjVEMrTmuV3cdPJgLjp8Px76YDX//HwDry3cwvUnDOKSI/qzrbyG2Ut+4PKjC+mc23BnJdWfU6L9pySIuri4OG4QdV1dHVlZWTGDqENZhsJpTRC12+2uf1+0/nJycuIGHIc0JxpE3bVr1yYBtJGaQnoSsclKELUVm0SkSR+tsclKEHW0/RC+HdIczSbQVaQj9bQ0iDoZ+8mqTVaCqCO/VwZD0nA6dT2CCy/UVdwuvDAp3c6cCb/4hS58/cUXMHRoUrqNzzff6KmOyy/XlenaMqNHw//+p4vf3XknHHQQ5OWRt2sXdO0KffpkWmGm8AG3KMU3IhQAC0X4EJgIfKwUfxHhNuA2wBTGSCLnFffj9jeWsmRLOcMs1FKIJPJcmA4CAcW8NaWMGWwtfWuIZGrt1TGHB84bxsSjBnDfuyv5/TsrmPZ5CWVVdfgDio9WbufSIwrZr1vzCXKSTaL9ZywGIjTzkGgbKyRrLDvpsZMWq22sYCc9dtKSzH4MhmY5/3xdvfjee3X2nwRQCv74R93l8OH6+jdtzoPPp5f29OihnYj2gMulYyFWrtSZjXYFb6jv3g3jxmVWW4ZQih+U4pvg8wpgJdAXOAuYEWw2Azg7MwrbL6cf2hu3y8HMhVta9f5MnMuWbC2nrMrbovgHSI3Wg/p05PlJh/HsxNFsr6iluk4fbzftrGLSjPkpHTucRPvPWAxEwEKwnpU26RzLTnrspMVqGyvYSY+dtCSzH4OhWRwOuOceWLUKXn65VV2UlOgb5H0HDWDyZDjrLPjkEz0DkTYeegi+/VYv/0lJSesM0revTt0aIhDQBen2ckQoBEYAXwO9lOIH0E4GkM5v315Bx5wsTjtkH95e/D01dS2/2ZCJc9nc1dsRgeMGW49/gNRpFRGOP6Bno7wVAQUlpQ2/71R/Ton2nzEHwmGhEqiVNukcy0567KTFahsr2EmPnbQksx+DISrnnAOHHqqDcoPxN1apqNBVpVeuBKUEEVi7tqH8QFpYu1Y7QT/7mY7laI8MGdJQSdvhsE9hvJTgd4nIgrDHlZEtRMgHXgduVIo96de4d3JecT/Kq+v4eOX2Fr83E+eyOatLGdavM13zWpa9IdVai3rkEVpR5RC9na6xE+0/JUHUAwYMiBtE7XA44gZRA0kJovZ6vfXr+KMFsvp8viaBrZGBrKHxEw2irq2tJRAIxAzOraurq48Baa1NVoKordiklEqKTVaCqAOBAHV1dTEDjh0OR9yA40AgkHAQdTL2k1WbrARRmxkIQ8pxOHQsxLnn6lSil14a9y1Ll8LUqbqocvAnB+hlTGm9OR4I6IJqbreefWivzJ4N48ahVq9GhgzR2+0Wp08pFTX/rghZaOfhRaV4I/jyjyL0VoofROgNtPwK1xCXowZ2p3enHGYu/I7TD21Z5fTIeMNUs7OyliVbdnPjiYNb/N5Ua502YTSTZsynpLSKoh55TJswOm1jJ9q/7StRRwaOtiaIOrxycbRAVo/HEzfgOFmVqHNycuJWOPZ4PDEDjq3YlKxK1D6fL24lais2WQmijrYfwretVKKuq6tLuBJ1MvaTVZusBFGbLEyGtHD22TBsmJ6F+PnP9fr7CGprdUrWqVN1EWW3W8ddf/qpTgwUKkSc1pvj//xnQ92H9hxYXFQEy5fzY0kJ+xQVZVpNxhBBgGnASqV4OOxfbwMTgL8E/87KgLx2T6gmxNS569m+p4aeHa1PNTaXCCWVtCZ9a4hUa92vWy4f3jwmI2Mn2r8lB0JENgIVgB+IeUfAKn4LQXpW2qRzLDvpsZMWq22sYCc9dtKSzH7SSWhWsi47G5fXC3V1+gE69aTfD6FArg4d9JVnqLhNyOELzvzgduur0upqvZ2drbMHhbazsvQjmH63fru6Wt8Od7kgOxupqNDBp06n7rOmpuGKNydHj+/363z8HTpofT5fw3a4Dbm5qbfJ69V6Y9hUv50sm267TTsPTz+tMxkFbdqwycGTL+TyzAwnpaXC/kV+HvxzgImX+umWV0PJRgfjLspn9RphyP4BZr9YBR5Xq/ZTi2zatQt+8xu9hur887V9LdlPtbV6vBR/95K6n7xeHQ+R4d+TVZukokJra8nvKTZHA5cCS0VYFHzt/9COw6siTAI2A+db6SwZhI53RUVFcVdhhGbCY63CCM2EQ+x04kDcdOLx0r6LCG63O+aKhUgbzjp0H6bMWc/LX5Vw2ZH7WrZp9+7d+P3+tNn00fJtdM3NYkBnJz6fL6ZNkdterxen05mR/VReXo5SKuH9FO27F9oP8WyKRktmII5XSu1oQfuYWMkd3Jr8wqkcy0567KTFahsr2EmPnbQks590EpqVJC/vCrKzm68eFpmjPzKnfeQi+ohZyybbkWNEbKuCAp1j32L7Fm9Dcm3Kzm6sNxka421fcAE88AA89BD+y37Jfz5xM3VqLv/5j77uO/NMuOYaOPFEJw6HE8gCcigaDstXwLb6u+Mdo9uYLM1KwcSJ+sL02WcbPquW7KeyMr2PUvzdS+p2WRnk5dGENP+erG6rsjKaPQbE3I6e5UcpPgeiHRQzkrs3dLwbNWpU3FUYIWKtwmhu20pq+5a0j7cdORMebkNBART378LsZaVcd/KB9eeoeDZ17Nixkc5U2uQPKP5bUsYJQ3rSMbjiIJZNkduVlZVxV8s0t50Mm5RS5AV/44nsp+a2oel+iGdTJCmJ0LByoRO5M1rbxgrJGstOeuykxWobK9hJj520JLMfgyEuIvx445+5b/14BvarYdw4ndho8mS9ROnNN+HkkxtieTPK66/DW2/p9LP7759pNQbDXsV5xf1Yu72SJVvKLb8nneeyxVt2s9tTx9gDWpeMK5Pn3VSPnWj/VmcgFPCBiCjgSaXUU5ENgtkRrgTo06cPGzdujNmh1+uN691YabNz586Y/0/mWHbSYyct7VWPnbSE+jEYUolS8NlnOrbh9ddPoY5TOaHqCx56+TDOPMdFBuo/xaasDH79axg5Em6+OdNqDIa9jtMP7c09by9n5sItlovKpTMGYu6q7TgEjhvUvVXvT3e8RjrHTksMBHC0Uup7EekJfCgiq5RSn4Y3CDoVTwGMHDlSFRYWxuywoqIibmVdK20A0jWWnfTYSUt71WMnLaF+DIZUUF4Ozz8PTzwBy5frVUDXXitcdcBchlx1PFQ8DVm/zLTMptx6K+zYAe+912ywt8FgSC3hNSHuOP1AcrKccd+Tzni+uWtKGb5vZzrnti7jUCZjD1M9dqL9W5qAVkp9H/y7HXgTOCyhUbHf2vG2psdOWqy2sYKd9NhJSzL7MRhKSnTRaadT11rr3Ruuu04vl582DbZuhUcegSFXjoHDDtNlpe02A/bRRzrm4Te/0eWuDQZDRmhpTYh0nctKK2pZsqWc41tYfTqcTJ53Uz12ov3HdSBEJE9ECkLPgVOAZYmKigzmaG0bKyRrLDvpsZMWq22sYCc9dtKSzH4Mezfffw9HHw0rVuhkObt367jZ+fP14/LLw+JwRXRhtk2bYPr0DKqOoKoKrrwSBg2Cu+7KtBqDYa8mvCaEFdJ1Lvt0TSkAYxNwIDJ53k312In2b2UGohfwuYgsBv4H/Fsp9V6sNyil4nZqJad9svLeJ2ssO+mxkxarbaxgJz120pLMfgx7H9u26dpqxx0H/frp7XAqKmBUtOTcp50GRxwBf/pTQ0rQTHPXXbBhg679EJllyGAwpJVQTYh5a0rZvqcmbvt0ncvmrN5O93w3B/fpGL9xFDJ53k312In2H3fRqFKqBBhmpbNQDuTCwsK4OZC9Xi8OhyNmbl2Px1Of1zaRStR79uxBKRUzX3BlZSUulytmbt26urr6qsHN2WS1EvWuXbvw+Xwx8wWHV+BurU1WKlFbsamqqqq+fSI2WalE7fF4cLlcMfM6e71elFIxcyBXVlZGzYlstRJ1MvaTVZusVKKurKy08jM0GAD48Ud44w149VVdX00pOOQQnaxoxgx9/W2p4JuIftOpp8Izz8DVV6fNhmaZPx/+9jf41a+0R2QwGDLOuSP7MWXOet78diu/GjMwZtvQeS2V+PwBPlu7g5MO7IXD0fqlOunQmqmxE+0/Y5WoQxdusXLrer3epFSiVkrFrdoceh4rt25FRUVSKlEXFBQ0ahNNU6wKx1ZsslKJOp02WalEHXoeaz9VVFTErdqck5PTRE9LK1EnYz9ZtclKJWqzhMkQj9LSBqdh7lztIBxwgL5hf/75Ou4B4OKLYdw4WL1aOw+zZ8fp+OST4aij4L779BqnTKU29Hph0iTYZx+4//7MaDAYDE0o6pFPcf8uzFy4hSuPK8p4zN6i73ZTXl3H8Qe0vPq0wRoZqwNht7XjbU2PnbRYbWMFO+mxk5Zk9mNoX+zcqVfynHKKDoa+6irYsgXuuAOWLtWxDvfc0+A8ABQV6WxLPp/+W1QUZ5DQLMSWLXqwdBOK+s7J0UbddRd06pR+HQaDISpWa0Kk+ly2eaeHK55bAMDDH6xh805Pq/syMRDRyVgZICvpo5KVwipZY9lJj520WG1jBTvpsZOWZPZjaPvs2qUTEJ12mr4Zf8UV+hr7d7+DRYtg1Sr4/e/1kqWk3Qg88UQ45hg9C1ETf51zUhk3ThsViq977LH0jm8wGOJy+qG9cbsczFwYvYI4pP5cNmnGfHZ59JLljTurmDRjfqv7Mmlco5MSB8IEUad+LDtpsdrGCnbSYyctyezH0DYI3XTvN3gABx+sHYMZM+D006FXL72SaM0auOUW+OYbWLtWxzkPG5ZEpyGc0CzE99/D00+nYIAYrFql12OFWL06veMbDIa4hNeEqKmLfnGaynPZ5p0e1m5viBcMKCgprWp1fyaIOjpJjYFoaRB1bW1tzCBqr9fbKEi1tUHUFRUV9duxgqhDgauxAo5ramoSDqKOZ1MoODdRm6wGUVuxKUQiNlkNos7JyUk44Lgm7A5pa4Ook7GfUmWTof0TuukeCAgrVsCIEfr1/v3hxhth/HgoLk6RsxCN44+HMWPgz3+GX/4y9RmQfD69/irceYgb9W0wGDLFecX9mLXoez5euZ3TD+2dtnFr6vw8MW89/5i7ntAhUQEOgaIeeWnTsTeRkiDq4uLiuEHUoRLasYKoHQ5HzMBTsBZE7Xa7698Xrb+cnJy4AcchzYkGHPfo0aNJAG2kppCeRGyyEkRtxaZOnTo16aM1NlkJoo62H8K3myu/Hhlw3Llz5yZ6WhpEnYz9ZNUmK0HUnTt3xtD+qayEd96BlSsbVuyAdhS+/FLXdctYfGJoFmLsWHjqKbjhhtSNtWULXHQRfPYZnHceLFump1ksRX0bDIZMEF4TIpoDkey1/XNWbefut5ezuczDGYf25rKjBnDbG0soKa2iqEce0yaMbnXfJgYiOkl1IFqC3+9vckHVmjbpHMtOeuykpb3qsZOWUD+G9onHA+++C6+8Av/+N1RXg8sFfr92IhwOnU3p8MMzrRQ9A3H88XoW4oorwqrOJZF334Vf/ELHWjz/PFxySfLHMBgMSSdUE2Lq3PVs31NDz45NL1KTdU7cssvD72ev4IMVP1LUI48XJh3OMYO6A/DhzWMS7h+Sp9WOYyfaf0ocCKsxEPG8HyttrJCsseykx05a2qseO2kJ9dPWCC1rrMvOxuX1Ql2dfoBe/uL369Scoe1AoKFYWegzCy3dcrv1lXR1td7Ozgans2E7K0s/PJ7G29XV+irc5YLsbKSiQpdfdjp1nzU1DQURcnL0+H6/vtveoYPW5/M1bIfbkJvbaptqauC9eR149Q0Xb//bQVWV0LOH4rJfBLhgnIc+vRVnXZzP6rXCkP0DzH6xCqqczdpUv50um/7v/3Rq10ce0UEYkfuptlZ/xi3dT16vDuR49FEdAPLss9pz8npTa1NtrR4/xd+9pO4nr1dX5M7w78mqTVJRobW1ZD+1MULHu6KiorjLuENLaWMt4xaR+u1YS2mBuEtp4y15FhHcbnfMJc+RNkSz6bQhXZgyB2Yu2MwvjylsYlNZWRkFBQWttqnSU830L7/jyc83g8CNxw/g0sP6kpPtarJsO1GbvF5vff2pdO+n8vJyOnXqlLL9FNoP8WyKRsZmIAyGtszmnR4mzZhPSWklRT3ymTZhNPt1a9mdWKUU60uruPL5BWzcUcXAHpta1Y+dCS1rJC/vCrKz9QVHJJF3sCPX1Uc6X5E1CCK3I8eI2FYFBRC+HCxO+xZvQ1SbvF748EN45ZUcZs2CPXugWze9UueCC2DMGMHlcgJ6ad3ylbCtpIR9ioqAsGqqydbcUptOOklnZXrsMR2QkZfXeD+53Y0/49Brscb44Qe48EL46iudi/bhhxt/F1JpU1mZHivF372kbpeV6c89kjT/nqxuq7Iymj0GxNyOnc3HboSOd6NGjYq7jDtErGXczW1bWdbdkvbxtiOX0lqxaWhhL4r7d+HNRT9w9fGDWl1jqTmb/rtuB5NnLaOktIpTDurFXeMOol+X3JjvT8QmKzXLmttOxn4KBALkBX/jqdhPkfshnk2RpCSIesCAAXG975B3E8+rS0YQtdfrxePxxPS+Q15rLK/O4XAkJYg6FBwey1P1er0xA46t2GQliNqKTUBSbLISRB0aO9ZdEofDETfgWClVrznSph+r/Fz14iJKdlQxoNsGnrxkBPt1zW1iU01tLR6vn9qAUOtXlO3x4Knz4/ULd72ziu17alHAuu2VnDXlc848tBc1dX68foXXBx5vHdV1/uBrUOP1U+PzU1MXoNYXoLrOTyBssm59aSWXPfs1s64aHdUmQ9ujrg4++UQvT3rzTX1jvnNnOPdc7TSccIK+sdvmuPdendZ16lS49dbE+po1Cy67TN+ZfuUVHSFuMBjaLOcV9+P2N5ayZEs5w/ZtfDMh8mLcCtvKa/jjv1fwzpIf2K9rLs9OHM3xB/RMltyotEZrWxk70f4zFkRdW1tryauL/H9rgqjDg7WjeXG1tbVxA45DmhMNos7Pz29kV3OaQmMlYpOVIGorNiml4u6nWDbpu/XfsL60koE9dgbvsjfvfdfW1uJyuQiIA68vQLUvQHmVD69PsXFnNZPfWsbW3dX06dSBW04ZTJe8bHz+Kur8AeoCCp8/gM+/m+paLzic+nW/fj30/3/9bzO7gzmi1+/wcOY/vuKQPp2o8vqoqvVR5fVTVevD47UWc6CAXZ46Xln4PR2ynORkOemQ5cSd5aRDloOcLBdd87OCrzkatZkyd119oGxAwcad1fX7O/KOQiA8E43B1vj9MG+evhZ+/XVd7K2gAM4+W18bn3JK8zfE2xRHH60NeeABuPrq5u+Gx8Prhd/+Vi9ZKi7WH9jAgcnXajAY0srph/bmnreXM3PhliYOREtuhtX5A8z4YiOPfLiGuoDixpMGcdWYgeRkOZMtuVkyeeMu1WMn2n/GYiC8Xm9c78dKGyska6xYbUJLWvRFcuylKFb6ibc0JhlalFJUVdegHC4CSuELKAKBhr+byzzc+tpiNpd56Ncllzt+eiBd8rKp9fmpDd5Fr/X5qfUFKK/0IM6s+tf0XXbd7j/LtlFZq++kr91eySl/m8egngV4g229vgBefyDYp76DH48tu6u56dXFcdtF4nIIvkDj/j1ePw4H7NMxhzy3izy3k9xsF1n46VKQ2+i1fLeL3GwnN768iM27PDrAVWBgj/yoQVsVFRVNnMoQ7y/fxvrSSgIqfrq50GydwV6UlOiUq6tXw7776pvyH34IP/6or6nHjdMzDaed1nT1SJvn3nvhyCNhyhTtCLSEkhL9wSxYANdfrx2RDN7tMxgMySO8JsQdpx/Y6ILf6rXd/zaUMfmtZaz+sYLjh/TgnjMPpn+39KZkTdZ1qB3HTrT/vT4GojVr2ZVS1PoC9XepPV4/k2bMZ+uu6volLeOf/IJbThmC1x/A6wtQF/zr9QWoqK5BHFl4/f761+r8us8v1u+ov/Mdutg+sHdHAgoCAUVAKQIKfMHlO/6AQinqXw8oxbbymvqL5LXbKxn74Bzy3C78AdXwUIqWOJ+byzz86oWFltq6HILb5cCd5cTtctQ7DyFq6gJ0z88m2+XA7XKS7XIEnzsg4KMgtwNul4NspwN3lv6b7XJw62uLGy35cQi8dtVRZDmFLKeDLKfgcjhwOYXaag+dOxaQ5XKQFXzN5RBEhJMfntfoon1gj3xevvLIJnbEuvB/ftLhTb43rWHahNFhzl7r+zGkF6Xgu+90ZtHLLoPt2/XrGzfCpk16edL48broWyqSFNmGI47QnlFoFiLK76UJM2fCpEk60PbNN/XUjMFgaFe0tiZEaUUtf/7PSt74Zit9O3fgyUuLOeWgXkjG8lcbmiMlDoSVnWzF60mW5xXej1KKPdU+dlTVsqOilhteXsSPe2rqL/zHPf45Jx/UC4836BzU+qkKPq+qraPaG6DK6yMQ4+JbAdv21PKbmUua/X+2y4Hb6SDL1XBxnO1ykOV0NFk2U1MXIN/twiGCQ3SKNBEBpXA5Hfp1h/6fQwQRePObrY31KP1DdorgdDR+EAiQleXCFdx2iOBy6r+TZy1r5GQ4BJ67/HDcWfpiPyfoILhdTkT5KMjNIdvpwOVsXOC8uQv2Zy87rNnPxuv1Rg3cmTp3fZN+ivt3ab6fPFfUfqxetMf6/u3XLZcPbx4TU29L+tm4cSOFhYWt7seQOnbtgqVLtbOwdGnD8/Ly5ts7HPDaa+nVmFHuvVfnl338cbj99thta2rg5pt13MThh8PLL0Oc773BYGibRKsJEe1c5g8oXvx6EziMiPYAABeSSURBVH99fzU1dX6uGTuQX5+wP7nZmbvXbWIgopPRIOp4Kczq6uqipsbaVuHj6pcWU7KjisJuG/jzWQeR1yGb78sq2FnlZZfHx65qP6V7qimtqGVXjY+yqjp2Vnmpi7JERgHl1XX8d20pHbKd5LtddMhy0CPPRYcu7volLG6nkJvtpFOemxyXg2yH4sGPStgWDKoVoF+XHGZMHEW2y4Hy1ZHtcpDXwU2W00FlZSXZ2dnNBhyfMeUrSnZUNVrSMvWCg5sEHNfW1tKhQ4dmg6gXb97Fhp2e+j4GdMvl5rH7RQ2idjqdze6nZz/PbdLPYf07RuwnVzDYvRZ/rcKXlYUKNLbpiYuHc+Xz37Bhp4cB3XN58pIRVFZWRg2iDgVlRwZRP3regVz36nI27vRQ2C2XR887sFEF8ciqzSGNkd+9fQrcvHPtEaxdu5b+/fvjdrvqK6OHvnuhAP5o+yk82D03NzduYHg0m0wlavtQU6OLt0U6C1vD/PFOnWDoUJ01aehQOOQQXQph7dqGzJV7XZHkww6Dn/4UHnwQrr0WOnZsvt2aNXpaZvFiHXR9331tNHrcYDBYwUpNiBDfbN7F5LeWsfz7PRy9fzfuPfMQ9u+ZH7W9IfOkJIh65MiRcYOoraTG8nq9TZaQeHz6i3bzK4vYFQyELdnh4YJpC5royXY66JafTZcOLnp16sBBfTrTPd9N9/zs4F83//fmUr5Lwlr20QP3aXJXO1YMRLQUZs9MPKzJ0pj8/KYpypRS9ZWLI4Okn73s8CZaCgoa+ggPzq2oqCAvL6/Z/RTqJ1xLaP80l+4rmk0Dc3L4+NbjY95lD7UPZXJqLjD8oP1y+fjWns3uh8iA45qamiZ6WlqJOpZNoe2Kioq41bVj2RT+PF4lauNAJEZDrMIABgzQN8JLSxschrVrdfAz6ADngw7SGZIOOUQ7C0OHQt++TatAv/tuQwzEXlsk+Z57tCPx97/DHXc0/f+//gW/+pWOcXjnHb22y2AwtHvOHdmPKXPW8+a3W/nVGJ0goba2tv58W1bl5YH3VvHy/O/o1dHN4xeN4PShvW2zXClca3sbO9H+LTsQIuIEFgBblVJntHrEFqCU4rsyDws37WL+xjIWbtrF6h8rml27LwJTLhpJ93w33YIOQsccV/2d5GgX/i8kaS17S5aiWOknlua2pMVgyASBgC4rsG4drF+v/06ZousugLBuHVxzjT5uFBVp5+D88xtmFQYN0nWyrFBUBMuXp9KaNsDo0dqLeugh+PWvG173eHSA9LRpOrr8pZegX7/M6TQYDGmlKLjUeObCLVx5XFG9YxAIKF5Z8B33v7eKihofVxw7gBtOGky+e68PzW0ztGRP3QCspFE1o+ax4jk25/X4/AFWbatg/sYyFmzaxfwNZWyv0MtK8t0uRuzXmZ8c0pvRhV2YPGsZG8KW+gzskc9PhzYfpBPLwwpdJIenTW2J5tZgpZ94beykxWobK9hJj520JLOf9oLPB5s3NzgI4c5CSUlDQV3QzoCvcSw/Dod2KFqTfdTQDPfco1OxPvYYXHwxrFihlyytWKErV997r3WvzGCwgAjPAGcA25XikOBrXYFXgEJgIzBeKXZlSqOhaU2INTtq+P1zS1j83W4OG9CVP5x1CEP2sedNykyed1M9dqL9Wzqai0g/4HTgT8DNiQwYnvWosHse14wdyOayahZsKuPbzbvrg4j7du7AYYVdOKyoG8X9u3DAPh110G+QZ4NLfaxkr7Hi0CSrjRWSMZadtFhtYwU76bGTlmT2Y3fC06IOHqxLBNTWNnYQ1q+HDRsaOwU5ObqEwP7768RAoef776/Tqw4bBqtWNcQqHHCAcR6SysiRukL1PffQ6+679WtdusB77+l6EQZD8pkOPA48F/babcDHSvEXEW4Lbv8uA9oMQYb164QAZ0/5Lx07ZFFeXUf3/GweHj+Mn43oa+tzWya1pXrsRPu3ejvob8BvgaguoohcCVwJ0KdPHzZu3NhsuwmvrGPzLh1svL60ilteW6KDhbvmcMqgTgztncvQfXLpma+DUnNyAO8uvtvc9AbC0+f0Z+fOnXTr1o1AxXY2VjSvTfcTOwF7strs3Lkz5v+TNZadtLRXPXbSEupnb+AnP9HxtqCDmsOvPQsK9PKi4cPhvPO0cxByFHr31o5BNGbPDjkmiiFDZO+MVUg1JSUQCFB/Wure3TgPhpShFJ+KUBjx8lnA2ODzGcBcjAORUW54eRGhlefl1XV0zHHx8S1j6dTB/kkUTAxEdOI6ECISnB5UC0VkbLR2SqmngKcARo4cqaKtvd+yewXhIQwOgcV3n0JBTtMvktW19/HW+VvpJ1lt0qXHTlraqx47aQn1szewfn3jbYcDPv9cOwnduzcNYrZKKFZhW8kG9ikqSlyooSmbNjXejtyZBkOL8LtEJDxDylPBa41Y9FKKHwCU4gcReqZOn8EKJaVVjbaran1twnkwxCbG/bp6jgbOFJGNwMvACSLyQqw3xJoWKeqRR2glUih2oTnnAZpmvWktVvpJVhsrJGMsO2mx2sYKdtJjJy3J7MfuDBnSMJMQWmp05JHQo0frnQdDmojceXtdTltDcnH6lFKjwh7xnAeDDYm87ivs3nbWjmbyvJvqsRPtP64DoZS6XSnVTylVCFwIfKKUuqS1A06bMJqBPfJxisSNXXA6nVH/1xKs9JOsNlZIxlh20mK1jRXspMdOWpLZj92ZPVs7DU6n/muWGrUhgjtPmZ1nyBw/itAbIPh3e4b17PVEXvc9dcmITEuyTCbPu6keO9H+U5ISQzWXZzVIS1KD1tTUJMUDs9JPstqkS4+dtLRXPXbSEupnb8CkRW3DBHfejyUlZpmYIVO8DUwA/hL8Oyuzcgyh674QbWk5brLO33YcO9H+W+RAKKXmogOSmiVUibqwsDBuJepQ5d9Ylai9Xm/9Fy1aNeDKykoqKipwuVxRqwFXVFTUb0erBhxe0ThaNeC6ujpqamqi2uR2u+vHi2ZTqC5FLJtqamqSYlNVVRV1dXVJsSlEIjZVVlZSXV0ds2qzx+MhJycnqVWbm7NJKVX/3UnlfkqVTQaDwbC3I8JL6IDp7iJsAe5GOw6vijAJ2AycnzmFBkP7JSWVqIuLi+NWoq6pqYlbiVop1SR7TaS3FFlNuLlqwC6Xq76faNWAQ3oj/x9eDTiUTSeaTaAvTiNnViKj3Lt27dqkj0hN4f221qa8vLyk2JSfn99kP7TGpvz8/LhVm6Pth/Dt5rIaRVZtLigoiGlTSEOsStTJ2E9WbbJSidoU8zMYDIYGlOLnUf51YlqFGFpEW4rnMzEQ0bESRJ0S7LZ2vK3psZMWq22sYCc9dtKSzH4MBoPBYMgUbelcZmIgoiOx4hVa3alIObA2TrNOQHkS2nQHdqRpLDvpsZOW9qrHTlpC/XRWSvWI0852HCES+Bqq47dMCy7AF7eVfWhreqHtaW5reqHtaW6F3uIOSi3I2I3O1iIipcCmuA33XqycE+1CJrWmemyr/fdv9rpDKZX0BzpXc7raLNgb9dhJS3vVYyctVvsxj/gPK5+1nR5tTW9b1NzW9LZFzW1Nr3mk7tGWzmWZ1JrqsRPtP1WevZXcfclqY4X2qMdOWqy2sYKd9NhJSzL7MRgMBoMhU7Slc1kmtaZ67IT6T8kSpnQiIguUUqMyrSOEnfTYSQsYPbGwk5b2Tlv7rNuaXmh7mtuaXmh7mtuaXoPBEJs2t7awGexWmdJOeuykBYyeWNhJS3unrX3WbU0vtD3NbU0vtD3NbU2vwWCIQZufgTAYDAaDwWAwGAzpoz3MQBgMBoPBYDAYDIY00WYdCBHZV0TmiMhKEVkuIjfYQJNTRL4VkXdsoKWziMwUkVXBz+jIDGq5KbiPlonISyKSE/9dSR3/GRHZLiLLwl7rKiIfisja4N8uGdbz1+C+WiIib4pI53Tp2Ruw4/HCKnY6rsTDTscdq2T6+GQFux3D4mGOcQZD+6fNOhDofNK3KKUOBI4ArhWRgzKs6QZgZYY1hHgUeE8pdQAwjAzpEpG+wPXAKKXUIYATuDDNMqYDp0W8dhvwsVJqEPBxcDuTej4EDlFKHQqsAW5Po569ATseL6xip+NKPGxx3LGKTY5PVpiOvY5h8ZiOOcYZkoyI5InIQhE5I9NarJApvekat806EEqpH5RS3wSfV6BPVH0zpUdE+gGnA//MlIYwLR2B44BpAEopr1JqdwYluYAOIuICcoHv0zm4UupToCzi5bOAGcHnM4CzM6lHKfWBUipUZOkroF+69OwN2O14YRU7HVfiYcPjjlUyenyygt2OYfEwx7j2STJncpubpQr732kislpE1olIuGP8O+DVFoyRIyL/E5HFQb33plnvbcA+wF1pHrdFn1NrabMORDgiUgiMAL7OoIy/Ab8FAhnUEKIIKAWeDS59+KeI5GVCiFJqK/AgsBn4AShXSn2QCS0R9FJK/QD64hLomWE94VwO/CfTItorNjleWMVOx5V42Oa4YxUbH5+sYOdjWDzMMa5tEncmV0R6ikhBxGv7N9PXdJrOUiEiTmAK8BPgIODnInKQiJwErAB+bIHeWuAEpdQwYDhwmogckUa9/dHHlibY7HNqFW3egRCRfOB14Eal1J4MaTgD2K6UWpiJ8ZvBBYwEpiqlRgBVZGh6O7gu9yxgANAHyBORSzKhpS0gInegD9IvZlpLe8QOxwur2PC4Eg/bHHesYo5P6ccc49ouFmdyxwCzQrFEInIF8FgzfTU3qwZwGLBOKVWilPICL6N/o8ejnZaLgCtEJO71q9JUBjezgo/I1KOp0nsmMDY45n7N6LXN59RaXKnqOB2ISBb6YuBFpdQbGZRyNHCmiPwUyAE6isgLSqlMnYi2AFuUUqE7rDPJ3In8JGCDUqoUQETeAI4CXsiQnhA/ikhvpdQPItIb2J5hPYjIBOAM4ERl8isnHRsdL6xit+NKPOx03LGKXY9PVrDdMSwe5hjXfog2k6uUek1EBgAvi8hr6Nmmk1vQdV/gu7DtLcDhSqlfB8edCOxQSlmalQ3eqV8I7A9MCTs+pVQveunSz4Dz0bMgjfTa7XNqDW12BkJEBL3WdqVS6uFMalFK3a6U6qeUKkQH4H2SyZO8Umob8J2IDAm+dCJ6SisTbAaOEJHc4D47EXsEVr4NTAg+nwDMyqAWROQ09LrFM5VSnkxqaY/Y6XhhFbsdV+Jhs+OOVex6fLKCrY5h8TDHuPZDvJlcpdQDQA0wFb2/KyPbxOq+mdfqnU2l1HSllOWMdEopv1JqODrm5jAROSQNevvTMHv8HlGcezt9Tq2hzToQ6LtzlwIniMii4OOnmRZlI64DXhSRJei1f/dlQkTQ258JfAMsRX/n0lqRVEReAr4EhojIFhGZBPwFOFlE1qK9/r9kWM/jQAHwYfC7/ES69OwlmONFerDFcccqdjg+WcFux7B4mGNc+8XKTK6IHAscArwJ3N3CIbYA+4Zt9yMJiQ2CCR3m0nw8QbL1FqBnjzeilxadICJNZjXt+Dm1BFOJ2mAwGAwGg8EQk+As3QygTCl1Y5Q2I4CX0NnjNqCXA5Yope5spm0h8E4whXLoNRc6ze+JwFZgPnCRUmp5K/T2AOqUUrtFpAPwAXB/+J35VOsVkbHArUqpMyL6ss3n1Fra8gyEwWAwGAwGgyE9WJnJzQXOV0qtD66/nwBsiuwoyiwVwVS/vwbeRy8nfDWBi+LewJzgjOh84MNmlvVkSq+dPqdWYWYgDAaDwWAwGAwGg2XMDITBYDAYDAaDwWCwjHEgDAaDwWAwGAwGg2WMA2EwGAwGg8FgMBgsYxwIGyAi/mAw0jIReU1EcjOtqSWISEtyFxsMBpthjkEGg8FgaAnGgbAH1Uqp4cEUXV7gqkwLShfBVGQGgyGzmGOQwWAwGCxjHAj78Rm65Doi8paILBSR5SJyZfA1p4hMD94pXCoiNwVfv15EVojIEhF5ObJTEZkoIm+IyHsislZEHgj7X2XY8/NEZHrw+XQRmSoic0SkRETGiMgzIrIy1CbsfQ+JyDci8nEw9zIiMjA43kIR+UxEDgjr92ERmQPcn9yPz2AwJIg5BhkMBoMhJsaBsBHBO2E/QVdEBbhcKVUMjAKuF5Fu6OqufZVShyilhgLPBtveBoxQSh1K9LuHw4ELgKHABSKyb5R24XQBTgBuAmYDjwAHA0NFZHiwTR7wjVJqJDCPhoqKTwHXBW24FfhHWL+DgZOUUrdY0GAwGNKAOQYZDAYAEbkjeONgSXB54+Fx2s8VkVFJGHeiiDzegvZjRSSytkNaEJFCEbkoE2PbATN1aw86iMii4PPPgGnB59eLyM+Cz/cFBgGrgSIR+Tvwb3RlRYAlwIsi8hbwVpRxPlZKlQOIyAqgP/BdHG2zlVJKRJYCPyqllgbfvxwoBBYBAeCVYPsXgDdEJB84CnhNREJ9ucP6fU0p5Y8ztsFgSA/mGGQwGAAQkSOBM4CRSqlaEekOZGdYlh0pBC4C/pVhHRnBzEDYg9D64+FKqeuUUl7R5c9PAo5USg0DvgVylFK7gGHAXOBa4J/BPk4HpgDFwMIo63prw577aXAgw6sJ5kR5TyDi/QGiO6AK/d3aHWbXcKXUgWFtqqK812AwpB9zDDIYDCF6AzuUUrUASqkdSqnvAUTkRBH5Nrh88RkRCXfKEZGrI5YnTgzebEBELhGR/wVnNJ4UEWfw9ctEZI2IzENXu26CiOQFx5sfHP8sq22CGt4SkdkiskFEfi0iNwfbfCUiXYPtYi15fExEvggupTwvOORfgGOD9twkIgeH2bdERAYlshPsjnEg7EsnYJdSyhP8Eh8BELwT4FBKvQ5MBkaKiAPYVyk1B/gt0BnIb8FYP4rIgcF+fha3dVMcQOgHdRHwuVJqD7BBRM4P6hYRGdaKvg0GQ2YwxyCDYe/kA2Df4EX9P0RkDICI5ADTgQuCyxddwNUR750JnBO2fQHwiogcGHx+tFJqOPoGwsUi0hu4F+04nAwcFEXTHcAnSqnRwPHAX0UkrwVtDkEfGw4D/gR4lFIjgC+BXwTbxFry2Bs4Bj0z85fga7cBnwVvTjyCXrr5aNC+UcCWKLa0C8wSJvvyHnCViCxBLxn4Kvh6X+DZ4IkW4HbACbwgIp0AAR5RSu1uwVi3Ae+glxIso2UnftB38g4WkYVAOfogAXAxMFVE7gSygJeBxS3s22AwZAZzDDIY9kKUUpUiUgwci74Qf0VEbkPPQm5QSq0JNp2BnoX8W9h7S4N36Y8A1gJDgP8G2xUD84NLCjsA24HDgblKqVIAEXkFHZ8UySnAmSJya3A7B9ivBW3mKKUqgAoRKUfHU4GO9zrUwpLHt5RSAWCFiPSK8tF9CdwhIv2AN5RSa6O0axcYB8IGKKWanCyDU4c/ifKWkc28dkycMaaj7xyEts8Iez4Tfdcg8j0Tw55vRHvwzf0vpH9yxPs3AKfF6tdgMGQecwwyGAzhBOOD5gJzg/FHE9DxRlZ4BRgPrALeDMYwCTBDKXV7eEMROZvGSxijIcC5SqnVEe/vZaHN4TRd/hi+NNJF2JLHKOOHv1+aa6CU+peIfI1ezvm+iPxSKfVJbLPaLmYJk8FgMBgMBoMBABEZErF+fziwCe0QFIrI/sHXL0VnPYvkDeBs4Oc0JDf4GDhPRHoGx+gqIv2Br4GxItJNRLKA86PIeh+4LuiIICIjWtmmWVq55LECKAhtiEgRUKKUegx4GzjU6vhtEeNAGAwGg8FgMBhC5AMzJFjXBR2XcI9Sqga4DL3MZyn67v0TkW8OJlpYAfRXSv0v+NoK4E7gg2CfHwK9lVI/APegl/98BHwTRdMf0MsQl4jIsuB2a9rE4mJgkogsBpYDTQK1I1gC+ERkseh6OBcAy0RntDsAeK6F47cpRCkrM0cGg8FgMBgMBoPBYGYgDAaDwWAwGAwGQwswDoTBYDAYDAaDwWCwjHEgDAaDwWAwGAwGg2WMA2EwGAwGg8FgMBgsYxwIg8FgMBgMBoPBYBnjQBgMBoPBYDAYDAbLGAfCYDAYDAaDwWAwWOb/AUsjSJlpDPYqAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -632,7 +632,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -650,7 +650,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -668,7 +668,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -686,7 +686,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAADaCAYAAAA2YqSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd3gc1dWH37OrYlUbF8AGjCwbDMYYXKgJYDohFNNLIFTTIYRACoRQEgIhCQFCiwlgIPT64YTQAjY1gMF0gsEVGwO2saxVW6205/vjzsir1WpnpF3tjqT7Ps8+2pm5c+/vzKxmbjnnXlFVLBaLxWKxWCwWi8UPoXwLsFgsFovFYrFYLL0H24CwWCwWi8VisVgsvrENCIvFYrFYLBaLxeIb24CwWCwWi8VisVgsvrENCIvFYrFYLBaLxeIb24CwWCwWi8VisVgsvrENCIvF0ncQUUTuTdguQGQlIv/MQt5TEVmLyDxEPkPkZUQO8HHeiYjc5Hyfhsi4LpZ7omPDe87nnm7p71qZf0Dkg3ZliRyPyE96vGyLxWKxBB7bgLBYLH2JemA8IiXO9t7A8izm/wqqE1EdC5wH3ITInl04fxrQtQaE4SFUt3U+P+5wVKSgG3mmRmQgsDOqE4AwIls71/NE4JbuZcklInwswgcivCfCDh7pZ4swpTtlJeVzogg3dSH9VBEyb2x2AxGqRDi2i+eICC+KUOls3ynCtyJ8lJRusAjPi/C583e9hPNvFOEL595MSjjnBCf95yKc4KHjTyLs0RXtFould2MbEBaLpa/xb+CHzvdjgAfajohsj8jrzijC64iMdfZfgMidzvetEfkIkdK0pai+B1wJnOOcNwyRxxB52/l8r116kZ2Bg4A/OiMJoxGZ7qR93zk3fZnt85uNyO8RmQP8pNPyRYYg8pxj898QWYLI0DQ5x4EiRAQoAWLARcCNqMZ862uTyU7AAcAkVSYAewFfdjWffkAVdK0BAewPvK9KrbM9E9gvRbpfAv9RZTPgP842wA+AzZzPacCtYBocwGXADsD2wGVuo6MT/pqQp8Vi6QfYBoTFYulrPAgcjcgAYALwZsKx/wG7ojoR+A3we2f/9cAYRA4B7gJOR7XBR1nvAls4328A/oLqdsBhwN/bpVR9HXgKuMgZSVgAPI7qdqhuA3wKnNJJOUcluDCdlLB/EKq7ofrnNOVfBrzq2PwUMDKtRaoR4DFgHrAIWAtsh+r/eVyLzhgOrFIlarJnlSpfAYiwpwjzRPjQ6T0vTjxRhDNFuDZh+0QR/up8P06Et5wRjb+JEHb2nyTCfBHmAO0bcevyKXPKe9sp/2C/aRwNT4owS4RFIpwjwgVOmv86lW9EGC3CMyK8I8IrIuZ3IsJMp9f/dREWinC4U+Q1wC6OPT8VYasE+z4QYbMUpvwIaLsvqrwMfJci3cHA3c73uzEjYe7+e1RRVf4LDBJhOLAv8Lwq36myBnge2E+EsKP/I+ee/dQpdwkwRIQNU11vi8XS98jesLfFYrEEAdUPEKnCjD48nXR0IHA3IpsBChQ658QRORH4APgbqq/5LE0Svu8FjEPadlUiUuFx/nhEfgcMAsqBZztJ9xCq56Tc713+rsChAKj+C5E1HppA9VpwKu4ifwd+g8ipwD7AB6j+zjOPdTwH/EaE+cALwEOqzBFhAKbHfE9V5otwD3AmpjHn8ijwBvBzZ/so4CoRtnS+f0+VmAi3AD8S4XngCmAypuHzEqYhlMwlwIuqnCzCIOAtEV7oQprxwERgAPAF8AtVJorwF+DHjg0zgDNU+dxx2boF2tx8hgPfxzQ+n3Ls/CVwoSoHADgNpRtUuU+EIjANpCS+B5yeYn8yG6iyAkCVFSKs7+zfiPajQcucfZ3t3xbYSJXxjsZBCWnedfQ85kOPxWLp5dgGhMVi6Ys8BfwJmAoMSdj/W+AlVA9xGhmzE45tBtQBI7pQzkTMyAGYEd2dUG1sl0Ik+ZxEZgLTUH3facBM7ULZYGI+XNKVr13M1z13ovNtPnADqrsi8iAim6H6uZ8sVKkTYTKwC7A78JAIv8QZ4VBlvpP0buBsEhoQqqx0eul3BD4HxgKvOekmA287l7cE+BbjcjNblZWO6Q8Bm6eQtQ9wkAgXOtsD6Dgyky7NS6pEgIgIa4FZzv4PgQkilAM7A48k3P7E0ZUnVYkDn4iwQcoLZxpOl4iwMfC4Kqmu92BHR3dJ9ePUNPsXAtVO4+ZfmMahy7d07X/HYrH0YqwLk8Vi6YvcCVyJ6odJ+weyLqj6xLa9JnD4Bkxv/RBEDscLkQnApcDNzp7ncOMhzPFtU5wVARJHJSqAFYgUYtxRMqGz8l9uy1vkB5DWlz2Z32JcvQpZ1wMeB/zHagCqtKoyW5XLHI2HkbqSmoqHgCOdc55Qbavg3q3Kts5nrCqXu8X5yFOAwxLOH6na1hD0kyaakC6esB3HdMyFgJqEc7dVZcuEcxLPT3kdVLkfEzPTCDzbSZByi4iv9/g3jmsSzt9vnf3LgE0S0m0MfNXZfsedaRtMw/ts2rvpDXC0WiyWfoBtQFgslr6H6jJUb0hx5FrgakReo71LyF+AW1Cdj4lDuAaR9VOcvwvuNK6m4XAeqv9xjp0HTHGmP/0EOCPF+Q8CFzl5jMY0QN7E+Jj/rxuWJtJZ+VcAuyLyLqZXfWnbGSJPI5K611hkGvA2ql+hWgO8gciHgKL6vl9RIoxN8t/fFliCsbdKhDHO/uOBOSmyeBzjs38M61y2/gMc7rriOLMMbYq5llNFGCJCIXBEJ7KeBc4VMZV3ESZ2M01KnKDmRSKmfGe2o208TmvXuBShGlioyo2YEbUJKc75DKj2IekpaJtJ6QTWxU08BfzY0bcjsNZxdXoW2EeE9Zzg6X0wjZihQEiVxzC/3UkJZWwO7Wd/slgsfRdR7d7ItsVisVh6ISKLgSmorspNcUzGzNIzCGjBxAycpsoqEfbEuJoVAG8DZ6oSFWE2Jh5grpPHP4FxqusqyyIcBfwK0xEWA85W5b8inOTsXwG8B4RVaRc/IkIJxlVqZ8wIwGJVDhBhqlPuAWnSnAhMcfMUYbGzvSrxmAijMLMaDceM4DyoypUizAT+qcqjzvl1qpQ7DZ5ngKEY17YBwHGObV8Dx6q2D5AW4VJghaoZCRDhAYwb3FDgG+AyVe4QYQjwMMYFaylwhCrfOY2jmzAzNzUAJyVc85OBi52irlLlLqcRdBfrOh9/pcq/He0fAFur0oLFYunz2AaExWKx9Cdy3ICw9ByOO9I9quydZx2HYKbpvTSfOiwWS+7ocy5MIvIjEXnOO2VwEZEqEVHpxuJQYrhLRNaIyFs9oS9oiMhMEWkWUzHKVZmbi0idiLSKmZ3GYukdqFbZxkPfwHE3ul2cheTySAHw5zxrsPQSRORyEflHvnVYMqNXNiBEZLGINDoVOPdzE4Cq3qeq++RbYx75Pmb13Y1Vdft8i8kh16pqVeIOEdlbRF4SkYiIrBaR90TkF2LWB+j0IeY03sYk709EVeerajnwSjaNsFgslq6gysMJC8nlS8MjqtTkU0N/xakPNUvS4pDO+07FzDaXzfLcDk637vWNiPxTRLo1CpZJh2lCHioi9Qma8vJbFJFjRWSFiCwSkakJ+0eLyOsikmoq5l5Lr2xAOByoquUJn1RzpPdHNgUWq2q9Z8o+jIgcgZlb/X5gU1Udgpk3fmPazy5isVgsFktvZhFmkgEARGRrzNTGPckgpxNtG8wkEE+ImYo6X2yTUB8clCpBJo0UL5y8r8FMLHAuJrbI5UbgAlVt7any80FvbkCkREROFJFXE7b3EZHPRGStiNwiInMSXU5E5GQR+dRx+XlWRDZNOKYicoaIfO4cv1lEJKGc10TkLyJSIyILRWRnZ/+XIvKtiJyQkNdAEblHRFaKyBIR+bWIhJxjYRH5k4isEpGFwA+TbBooInc4LdvlIvK7VC1ZETkFM63eTk4r/Iqk48WO1vEJ+4Y5oznri8hQpyehRkS+E5FXXI0e13yqiCwTkYsdGxaLyI8Sjv9QROaJSK1zbS5PODZARP7hjBDUiMjbIrJBwjVe6IwgLErM00OPANcBV6rq7ar6HYCqfqaq56rP+eudvGoSejXqe6JHx2KxWCyWDLgXs4ChywnAPYkJPN7DRznv2kpn+wci8rWIDPMqWFW/VjPj3eXAHxLqNSNE5DGnzrNIRM7rJIuXnb/uu3Ynp8f+RadesEpE7hORlI2CdCTUTX4hIl9jJgBARA4QM0JTI2ZkYELCORNF5F2n3vGQiDwoZrFPL4YAy1V1BWbBzGonv8Od/f/tqv6g0+caEImIGdJ7FDMjxxDMlHc7Jxyfhpll4lBgGMYd5YGkbA4AtsO0so8E9k04tgNm5okhmJ7uB520YzCzZ9wkIuVO2r9i5qCvBnbD/LOf5Byb7pQzEZgCJM9Bfzdm9pIxTpp9gA5+96p6B2bqxjecVvhlScejmCkRj0nYfSQwR1W/BX6Gmf97GLCBc238RtlviJn5YyPMw2uGiIx1jtU79g7CNI7OdK49TtqBmFGBIY7+RhEpw7Taf6CqFZj79p5PLWMxIw0Zr4iqqoPcXg3MOgGvsG4dAYvFYrFY8s1/gUoR2dLpXDwKSHbP7fQ9rKoPYRYuvFFEhgB3AKeq6souaHgcWB8Y6zQiZgHvY+oEewLni8i+Kc7b1fnrvmvfwMx6djVmYcItMfWDy7ugJZENgcEY74zTRGQSZp2g0zF1jr8BTzkdrEXAk5gG2WDgEcz6M35YCQwRkY0xbuQfO/W/X2PqoH2O3tyAeNJpPbqf6SnS7A98rKqPq2oLpkL6dcLx04GrVfVT5/jvgW0lYRQCuEZVa1R1KfASZg5zl0WqepczLPUQ5kd+papGVfU5oBkYk/AP/StVjajqYkzA2fFOPkcC16vql05v+dVuAU5v/A+A81W13qno/wU4uhvXDExDJ7EBcayzD8x0gcMxLj8xVX1FuzZN16WO7XMwq5QeCaCqs1X1Q1WNq+oHmEbabgllDgHGqGqrqr6jqq4/bxwYLyIlqrpCVT/2qcP1BW27104vQo2INIjI8Qlpj0z6HaX0nRSRozDX6jBVjfnUYbFYLBZLLnBHIfbGrLHSrqPL4z0MZmHAPTCLBM5S1X92sfyvnL+DMR2pw1T1SlVtVtWFwO34rLeo6heq+rxTn1iJ8SjYzeO0dxPe4zcm7I8Dlzl5NWI6bP+mqm86dY67MQs77uh8CjH1sZiqPoqZXtqP5jhwJqbT+kKnnCsxncdbi4nHfDbRA6S302P+YDlgmqq+4JFmBPClu6GqKiLLEo5vCtwgIomzRwimxbzE2U5scDQA5Qnb3yR8b3TKSN5XjqnQFiXkifN9o1Q6k9JtivlBrzCeOYBp+CWm7wovAiUisgPGtm2BJ5xjf8S08p9zypqhqtf4zHdNUtzFEoxdOGVdA4zHXIdiTMsezENvE+BBZ4jyH8AlqlrvVNovBO4Qs/DXz1TVz2Jbq52/wzG+oajq0Y6WV2m/gNjDqnpc4skioknbEzH+jPt0sUfGYrFYLJZccC/GHWgUSe5L4PkeRlVrROQR4AL897on4tZnvgO2BkYkdciF8TnpiJhFPG8EdsEsrhgC1nicNklVv0ixf6WqNiVsbwqcICLnJuwrwtRXFONulFgHSKyPpUXNoqL/cWyYgPEouQhYjJngZhOMm/mOfvMMMr15BMIPKzCuLECbb/zGCce/BE533FTcT4mqvp5lHaswPe2JIxsjWddDsIL2gb0jkzRGgaEJGitVdavuCHFayQ9jRiGOBf6pqhHnWERVf6aq1cCBwAUisqfPrNdz3I4SbXB7JO7HrHi6iaoOBG7DNNRwWvlXqOo4jJvSATi+nKr6rKrujWkI/A/Tg+EHt/flUJ/pO8XxAX0COEdV52Wan8VisVgs2UZVl2A6zPbHuBMl0+l7GEBEtgVOxoxM3JjifC8OAb7FuIp/ifHQSKxbVajq/qmkp9h3tbN/gqpWYlzCJUU6PyTn/yVwVZK2UlV9AFMX20gSemtpXx/zhXP+TcB5mA7ksHN/3ib1ivK9kr7egPgXZuhompgI+bMx/nAutwG/EpGtoC1Y+Yhsi3BcnB4GrhKRCsdF6gLW+Sg+DJwnIhuLyHrALxPOXQE8B/xZRCpFJOQEGHkN56XjfoxL1Y9Y577kBhaNcX78tUCr8/HLFSJSJCK7YBoCbu9GBfCdqjaJyPaYhotb5u4isrXj5lWLaWi1isgGInKQ0yiJAnV+tTi9Bz8DLhOR6SKynhg2w8R2+ML5zTwG3Of4iFosFovFElROAfbQ1LMwpnsPD8DURy7GxGZuJCJn+SnQeVefA1yGcdOOA28BtU7wcomYiWLGi8h2KbJYiXEzqk7YV4F559eIyEaYXvxscTtwhojs4NQLysQEmFdg4kBaMPWxAhE5FOjOdPinAvNU9T2MR0SJiIwDdgcWZsmOvNObGxCzpP06EE8kJ1CzWNIRwLWYmzgOmIupkKKqTwB/wLjP1AIfYeINeoJzMUFMC4FXMRX3O51jtwPPYgKO3qVj78GPMUNsn2CG8R7F9Mp3C1V909EyAvh3wqHNMLMH1GH+kW5R1dkAIvJvEbk4TbZfO9q+Au4DzkhwNzoLuFJEIsBvMA0mlw0de2qBT4E5mAdZCNMI+AozJLqbk49fGx/CxGAch+lxWOWUO4OEYVsPNsYMoZ6f9Fvrco+ExWKxWCw9iaouUNW5nRxO9x6+GlimqreqmWzlOOB3TqdbZ9SISD3wIWbU4whVvdPR0YrxYtgWMyqyCuO6MzCF5gbgKuA1J35hR+AKzHSoazEdwalGVLqFc32mY0YI1gBfACc6x5oxngsnOseOSixbREZ61QHETN7zEzCrsquJrz0H4z5+G6Yu2CeQrsXI9m7EzAywDPiRqr6Ubz19BTELpvxDVTf2SttD5d+Occn6RlVH56jMzTDDkUXAWao6MxflWiwWi8ViyQ0iMhPTuPp1vrUEjd4cRO0LMdOGvYkJaL4I40fX5+bj7c+o6nRMj0Iuy/wcMx2exWKxWCwWS7+iN7sw+WUnYAFmCO1AzOxNjfmVZLFYLBaLxWKx9E76lQuTxWKxWCwWi8ViyYz+MAJhsVgsFovFYrFYskSPxEAMHTpUq6qq0qZRVdpPtdu9NM3NzRQVFWWUj9USfC1+0vRVLe++++4qVR2WNmEA2SkU0g9KSnyn93PN8k3QNVp9mRF0fRB8jV3V19CwparO7XWdmX7qOT1NX/st9DT51JPrsv2U984772RUt+iRBsTIkSOZO7ezmcQM9fX1lJWVZZxm8eLFeP0Te+VjtQRfi580fVVLeXm575Uwg8QbJSVQn2o68tR8vXAhG1ZXeyfMI0HXaPVlRtD1QfA1dlWfyEe9MiaxqqrKs57T0/h5z+SToOnLp55cl+2nPBHJqG7RI61+P60sr55Xv2n84JWP1RJ8LX7S9FctFovFYul/BP39EDR9+dST67JzUV5WRyBE5EDgwKqqKmKxGK2trcRiMQAGDBjQbru1tZWSkhKam5sBKC4uBiAajQLG+MbGRsLhMACFhYWEw2GamprabdfV1RGJRCgoKKCwsJCmpiZUlXA4TGFhIdFolPr6eioqKigsLKS5uZl4PE4oFKKoqIhYLEYkEqGsrIzi4uI23SLCgAEDiMVitLS00NDQwODBg9Pa1NzcTDQazcimaDRKKGTadZ3ZtGbNGkpKStq2U9lUV1dHXV1dRjY1NDSw3nrroaqd2tTS0kJZWVnbdiqb3GucqU1e96m2tpbS0tIO9yVxu66ujubm5oxt8rpPsViMcDjczsZkmxobG4lEIohIWpssFovFYkkmFotRWFiYbxmdEjR9+dST67JzUV5WGxCqOguYNWnSpOmFhYUUFhYyYMCAtuOJ25FIhOLi4rbKm0tiq6m5uZmKiop2x5MvSHl5ebs05eXlHdKrKqWlpSnPLywspLW1te285OMFBQXt0qazafXq1RnbFIvFOhxPtqmkpKRdmlQ2lZeXZ8UmV3tnNkUiEYqKijq0dhPLLCwszIpNXveppaWlLY/ObCovL8+KTV73KRKJpPwtdsVm1yaLxWKxWJJpbW3Nt4S0BE1fPvXkuuxclJf7wKXvFsHNO1B+3aZw8w5muxPSukI5+Wx696Tu52O1BF9LF/T0Oy2W3LFwIWy1FRtsvjlstZXZtlgsbTj/Imy8+ais/ouIsIkIL4nwqQgfi/ATZ/9gEZ4X4XPn73rZKTEzlq5uYO/r5jD6V0+z93VzWLq6ocfKCvr7IWj68qkn12XnorweWQdi8uTJ+s4776Q+ePMOsOozUAUEKobD3leCxhM+raBxWltihEMC8XjH46//FepXAU4+pUNg+9PMcdTJ3/xtbW0x+STsQ+Pw3n3QWLMuj5JBsM0xTrr2tMZbCYcS2luJaT54GJrWrNseMAgmHNkxHdAajzv5JJXx4aPQVNM+j/GHpbyEHbQk8tHjSfkMhK0OSdJhvsdbW9vccNr0KPDpLIiuXZe8eCBseUDK6xKPJ+ZB+zSfPQ3RBBec4krYfL+kHBwt8TihDj94J6/Pn4NoJCGfChizV4fyjHuQdNSBwoKXoLlu3a6ichi1W4Ld2pY2Hm8lJM49StgPwJI3IJYQGFxYCpvs0CGfuCbYk3zdls+FmBM3KCEYujmc/SapiMViFBUVvaOqU1ImCDJlZdpngqi32AI++8x8D4XM9scf51dTCgJ9DbH6skFQNY4ZAwsWmO9d+RcR+ahBdXyn0Z4iDAeGq/KuCBXAO8A04ETgO1WuEeGXwHqq/CJjQ3wyZcoUTRVEvfd1c/ji2zoUCAmMHlbO8xfs1iMaguYilEzQ9FkXpvaISEZ1ix6ZhSlto2TV5+0rZZGv4PFTUyYN+y8RGlbB7N8n7ReQkFORE2j7GzLfYw3t82hcA/P+se7cBEKoc35S/tC+wu5uf/hIx3RuPu52Yn6p8vjk/1IZ62jppAHRIZ+18Nm/O+hAnAZVYj6unsTGg7u96OWOeYBpiHVozLj5JPnvR2th2Vsd8xCBuJqnbapjiY0HMNvfJL6Z3Ip6HELhjvuhfePB3a5ZYtJIUnrVdflIgiaR9o0HML+h5vqEtCYfjbdCOPHfKzGPhElHNG7+JzrBjeOw5Jkvvlj3PR5f15iwWPo5jY3wu9+tazxAdv9FVFkBrHC+R0T4FNgIOBiY6iS7G5gNuWtAdMbClfVt3YNxNds9RdAq6MkETZ9tQGSXnAdRFw6uJvTdF4gqiqADR9Jw6D0UFQ8ACdEcawEJUVg8gPqGRoqKikFCFBQVEy4opCnaDBKi7P4DkO8WIBpHJYQOHk38jNdpaoqi0C6Iura2lkGDBnUIZC29a3dY/XlbHgzZjMaTZ6cMOI5EIgwbNiy1Tbd/n1CClvjg0TSc+FLKIOq6uro23/rE4NzSmbt3yKP51FdSBhyvWrWK8vLylMG52bApfNvOyHdfrLu2Q8ZQf8KLQMeA42g0SmVlZcqA41zbtGbNGioqKlIGUae06bhnsmPTj57qYFNTUxNFRUUpg6jLZu7RTkt88GjisVjKIOqamqQGYS/AfQ7EioooaG6GWMx8AEpKoLUVnAB2SkpMbSMaRWprwQlUb/tbXGwaqI1Oo6uoCMLhdduFhebT0NB+u7HRNAQLCsw57nY4bPJsajLlhkIwYABEo0aXiNHU3AwtLeu2N9sM5s835wBssgm49yaNTYDJP0c2SW0t1Nb6synxvpSW+r5PmdgktbXmuvXUfcrUpmh03X0Nym8vySZpaAjMb++ZF4s4+8ISFi4SBlbGidQJ8bgQCiljx8RhbZ23TbQWiEhiV/4MVZ1BCkSoAiYCbwIbOI0LVFkhwvqpzsk27vOturo6ZT2namgpi1bVE3daEVVDStomEUlVJxCRtBN2JG4nT9jR0NDQVs9JN2FH4rsl3cQqrg3pJospLi5OOwlJok2RSIRQKNQlmxLrbtm2qaamhtbW1oxs6s59co/F4/Gc3afm5mbC4XBamzKlR1yYJk2apO+++27qg98tggeORld9jgzdDI55EAaPSpm0rq6uQyBqVvOxWoKvpQv59FUtFRUV1oUp3yxcCAceiP7vf0g4bCqnr74K48fnW1k7An0NsfqyQRA0rlgB558PDz8MY8fCrbfCppvCgQfCZ58pY8cKs2aBH5leLkzr0lEOzAGuUuVxEWpUGZRwfI1q7uIgOnNhWrq6gVPufpsFK+uIK1y4z1jO2WNMj2hI+54JAEHTl089uS7bT3mZujD5akCIyE+A6Rg/jNtV9fp06dPGQDi0tLS0mw2ou2n8LMbllY/VEnwtftL0VS2FhYW2AREQvl64kA0BdtnF9Py+8ooZnQgIQb+GVl/m5FNjayvcdhtcfLEZ5LjkEvj5z83ARnf1+WlAiFAI/BN4VpXrnH2fAVOd0YfhwGxVxnbDrG7RWQPCRVWZdvNr1DTG+M8Fu1EQzv6cNX7eM/kkaPryqSfXZfspL9MGhOcvWkTGYxoP2wPbAAeISNo3pp9GiR/f7mz5f3vlY7UEX4ufNP1ViyXHVFfDCy+Y2tRee8HSpflWZLH0OPPmwU47wTnnwPbbw4cfwqWXtm889AQiCHAH8KnbeHB4CjjB+X4CkDpoME+ICGdOHcOS1Q3868MVPVJG0N8PQdOXTz25LjsX5flpEm8J/FdVG1S1BTOEeEimBfuZ3z5bc+B75WO1BF+LnzT9VYslD2y5JTz3HKxdaxoRX3+db0UWS48QicBPfwpTpsCSJXDffeann8OBt+8BxwN7iPCe89kfuAbYW4TPgb2d7UCxz7gNGLN+ObfOXuCrY7WrBP39EDR9+dST67JzUZ6f8ZSPgKtEZAjQCOwPdBi3E5HTgNMARowYweLFi9Nm2tTU1G6hr+6mWb16ddrjfvKxWoKvxU+avqrFElAmToSnn4Z99jGf2bNh8OB8q7JYsoIqPPkknHceLF8Op58Ov/89rJfj1RZUeZUO0/O1sWcutXSVUEg4Y7fRXPjI+7z02bfsscUG+ZZksWQNzwaEqn4qIn8AngfqgPeBDk0bZ9aEGWBiILx8u/1MMeV3GqpMy7Jagq/FT5q+qsUSYPu/l3QAACAASURBVHbeGf7v/+CHP4T99jOuTZWV+VZlsWTEkiVw7rkwaxZMmACPPAI77phvVb2Tg7cdwV+en8/NLy1g97HrZ3WBL69OqnwTNH351JPrsnNRnq+oHlW9Q1UnqequwHdA5xPX+8TPMtvZWorbKx+rJfha/KTpr1oseWbPPU0Na948Mw1NQ8+tPGux9CSxGPzxjzBuHPznP+b73Lm28ZAJheEQp+1azTtL1vDWou+ymnfQ3w9B05dPPbkuOxfl+WpAiMj6zt+RwKHAA+nS2yBqqyXbWvyk6a9aLAHgwAPh3nvNrEyHHbZuHn6LpZfwxhswebKZVWmvveDTT+HCC80yEJbMOHLKJgwpK+KW2Qu8E3eBoL8fgqbPBlFnF7/zij0mIp8As4CzVXVND2qyWCyW3sfRR8Ptt8Mzz8Cxx5qFsyyWgLNmjYlv2Hln8/2JJ4xX3siR+VbWdygpCnPy90cxZ/5KPlq+Nt9yLJas4GtSWlXdxU86d4XGUaNGdboStbsdCoWIRqNpV8kLhUJtKzh2tvJfXV0dkUgk7WqGsVisbcXGVCv/xWIx6urq0q7856bzWvkvGza5xzuzyV1FOt1qhnV1dRnbFIvFaG5uTrtCo4i02d2ZTbFYLCs2ed0nt5x0KzTW1dVlxSav++T+NtOtOtnY2EgkEkm76mTQenAsHpxyCtTVmVW2Tj4ZZs40K/9aLAFDFe6/Hy64AFatMjMtXXEFVFTkW1nf5LgdN+XW2Qu4dc4Cbj52UlbyDFqMQTJB02djILJLVle1UNVZwKzJkydPLywspLCwsJ0RidtNTU0UFxe3Vd5cEpfXVlXKytqvL5McmFpeXk5FwhMveeU9t2Lnlpt8fmFhIaFQqNPj7kIcTU1NeNm0evXqjG1qamqitLS0g42JlJSUdNCQnF95eXnbed21qampqU17Zza5aZKXRU8ss7CwsN096q5NXvfJrXgn2pBsU3l5eVZs8rpPqY4nay4pKWl3XTqzqbfhdiTEioooaG42jtVuQ6ikxKyf4DTeKCmBeByiUaS2FtxZp9y/xcWmAt7YaLaLiiAcXrddWGg+btyBu93YaGpIBQXmHHc7HDZ5NjWZckMhGDDAuBy1toKI0dTcbEYQ3G3HBqmtNcfS2XTGGfDdd3DllUbLTTeZfHJgk9TWQm1tl2wCzMraPu8TYPLvxn2S2lqoqenx+9Rtm6JRoy+Avz3XJmloWKexm/fp84+inHVhCS/MLmT77eI880g9Eye0QqgQmjO0qbnZzP3q16Zehvt8q66u9uxULC4ubuusCgHHbr8xt7+6mA8Xf0PVkFKKiooQEc/OKnc7ubMqFotRUVFBNBpN21mV2DmVrlMxlQ3pbHK3oX1HqWtTU1MTFRUVXbIpsfM32za5HYyZ2NSd+xQOh2ltbc3pfVJV4vF4WpsypUeWxfMbA+HVQvKTxg9e+VgtwdfiJ01f1dLbcDsSKCubTlGRqXAkk9S4oqQEraxcV+FJvnbJq1UlbyeX0UPbWlmJL5suv9xUsv74Rxg6FK65Jic2aWXlulmgMrUZUt6ndnTRJq2shEGDuq+pp20qLm6vz92XbjvHNmhpaUeNPu9TNGp+ildfXUxxMdx8M5x+eohwOGnYIRONq1Z1HMZIe/4yehPu823KlCmeHaUubuVt+q5juPuNpfxj7jf84fAJbcfTdVal2nY7qyKRSFsnYFfO76xTsTMb0tnUmQ3udrrO21Q2ddcGPza5DS53u7s2+dWQaFMkEsnYxq7cp0gk4tmhnSl2bN1isViyjQj84Q9w5plw7bVw1VX5VmTp57z4opmS9fLLYdo0+N//4KyzzCCCJTcMqyjmyCmb8Pi8ZaxY25hvORZLRvRIA8KP20Vyq6i7afzglY/VEnwtftL0Vy2WgCJi3JeOPx4uvRSuvz7fiiz9kG+/NT/BPfc0XkTPPAMPPgjDh+dbWf/ktF2riSv8/ZVFGecV9PdD0PTlU0+uy85FeVl1YepKEDXg6XPmJ5DVTxC164/emc9ZQ0MDAwYMSOtzFo1GGThwYMZB1F42uWVB576BkUiEoqKijIOovWyKRqNUVlamvU+uu1qmQdR+bPK6T/X19RQXF2ccRO3HJq/75PoeZhpEXV9f7/VvZwkyoRDceSfU15so1YoKE2htsfQw8TjccQf84hcmrv+SS8wn2bvJkls2GVzKwduM4P43l3L27mMYXNZ9lxI/7uL5JGj68qkn12XnorweCaKeNGmSp2+gH/+s5ubmDsG33QmiVtW2gNZUPmatra2eAcfusUyDqL1sikQingHHRUVFaYNv/QZR+7HJK+DYrfhnGkTtxyav+9TS0tKpf2NXgqj92OR1n1Id704QdYudCrT3U1BgpruZNg2mT4eyMjPlq8XSQ3z0kYnlf+012HVXuO022HLLfKuyuJwxdTSPz1vOzNcXc8Hem3c7n+bm5sD18icSNH351JPrsnNRno2BsFgslp6muBgeewx22cX4k8yalW9Flj5Ifb0ZcZg40cQ43HknzJ5tGw9BY/MNKth73Abc/fpi6qK2k8jSO7ExEFZLr9DiJ01/1WLpJZSWmobDxIlwxBHwn//kW5GlD/Gvf8FWW5mY/eOPNw2Ik04yoTiW4HHW1NGsbYzxwJtLu51H0N8PQdNnYyCyix2BsFgsllxRWQn//jdsthkcdBC8/nq+FVl6OcuXw+GHwwEHmDbqnDlm5GHo0Hwrs6Rj4sj12Hn0EG5/ZSHRltZ8y7FYukyPBFFXVVX5WrW5vLw8bcBxXV1dmx96JkHUtbW1DBo0qNPg3JqaGiorK9MGHEciEYYNG5ZxELWXTe6K2dB5wPHq1aspLy/POIjay6ZIJMLQoUPTBhy7gdaZBlH7scnrPq1Zs4aKioqMg6j92OR1n9zF6DINol6zZo3/f0BL72DIEHj+eeOcvv/+Zn7NSdlZmdbSf2htNZN8/frXZnalq66CCy9MvfSFJZicNXUMx93xJo+9s5xjdxjZ5fOj0WhW5/XPNkHTl089uS47F+XZIOqABVEnLnTSmU3JNucziNr9nmkQtR+bchVE7ccmr/uUqLUzm2wQdT9mww3hhRdMTMS++5pu43Hj8q3K0kuYOxdOPx3efdf8fG6+GUaPzrcqS1f53pghTNh4IH97eQFHTtmYgrB1CrH0HvIWA+GnZZSt1pNXPlZL8LX4SdNftVh6KSNHmkZEOAx77w0LF+ZbkSXALFxogqFHjBnFdtvB0qVmPYd//9s2HnorIsJZU0ezZHUDT3/0dZfPD/r7IWj68qkn12Xnory8NXf9NDL8pMlGWVZL8LX4SdNftVh6MZttZhoRTU1mpa/ly/OtyBIwVOHtt2H77U1gtKogYmIcjjqq7wdJi3CnCN+K8FHCvm1F+K8I74kwV4Tt86kxE/YZtyGjh5Vxy0tfdHnu/qC/H4KmL596cl12LsrrkQaEn38C17880zR+8MrHagm+Fj9p+qsWSy9n/Hh49llYvRr22sssHWzp18TjZg2HCy6AqirTeFi9et1xVfj887zJyzUzgf2S9l0LXKHKtsBvnO1eSSgknDl1DP/7OsJLn3Xtfz/o74eg6cunnlyXnYvy8hpE7WfVZjf4NpMg6kgkkjY41yuQ1Q04Thecmy2b/AQc19XVAWQliDqdTW6cilfAcXFxccZB1H5s8rpPbhnZCKLO1Ca3rEyDqN0yLH2cKVPMPJz77ms+L70EgwblW5Ulh7S0wCuvwKOPwhNPwIoVJiB6n33giivgmmtMoyEeNwucjx2bb8W5QZWXRahK3g1UOt8HAl/lVFSWOXjbEfzl+fnc8tIC9thig3zLsVh80SNB1JMnT/YMom5qavIMOFbVdue7eSTiJ4i6oKCgLZ9UwbmhUKjT427AsWtPpkHUfmxKPp5s03rrrddBQ3J+foKovWwqLCz0DDh202QaRO3HJq/75Fa8E21ItslPELUfmyD9fUrU2tn5foKogzYE7Ae3IyFWVERBczPEYuYDUFJipo9xGm+UlJgaUTSK1NYaVx5Y97e42NSWGhvNdlGRiRlwtwsLzaehof12Y6Pppi0oMOe42+GwybOpaV1NbMAAiEaNLhGjqbnZ1OjcbccGqa01x3zaBJj8/di09dZw771w7LGw336mJlle3iWbpLYWamu7ZBNg5v/sCZuS7pPU1kJNTY/fp27bFI0afTn67TWHBvDS8y08+mSYJ/9VyKrVIUpKlB/sFeOwg1o4YFoBlSXGhl23DXHgseV8Nl8YOybOrPvqIVrcI/cpo/+n5maIRPzfJ1oLRGQu65ihqjNIz/nAsyL8CeNJsbNH+qzhPt+qq6s9O0q9OuCKiooQEaLRKMdvP4JrnlvA659/y9YblgAdO6+SO6tisRixWIxoNJq2syqxcypdp2IqGzKxqampqdPO385sSuz8zbZNbpmZ2JTKBj82AZ6ditm8T6rq2aGdKb4aECLyU+BUTKv/Q+AkVW3KpGD3gmaaJhtlWS3B1+InTX/VEjTcjgTKyqZTVJR6XklnVrQ2SkrQysp1FZ6kxhfJi+IkbyeX0UPbWllJV2xqhx+bDjvMRMYecQSccIIZlXDP86FRKyvNWhNdsKnTbciOTQloZWX7kZUc3bd2pLOpuLjjyE+Wf3tN8SKee84sTP7UU1BTU0R5uVnH4fDDYb/9hLKyIsA9z/yt3hY+/gS+XriQDaurWdcB72ET5Pb/adUqSOowSn/+shZVnULXOBP4qSqPiXAkcAewVxfz6Bbu823KlCmeHaUu6ToV3e0Tvr8Zt7/2JX97ZTF3n9w+pCNVxymY2QBdDenSe20nzsyYyobu2uR2RPrR4DVrYTZscivumdjUFQ2JNrn3qivnZ3Kf3PK8bMoEzxgIEdkIOA+YoqrjgTBwdLpz/MRAuC21TNP4wSsfqyX4Wvyk6a9aLH2MQw6BmTONG9MRRyT01Fp6K/X1ZkDpmGNg2DA4+GDTeDjoIPN35Up44AHTfiwry7faXsEJwOPO90eg9wZRu5QUhTn5+6OYM38lHy1f6+ucoL8fgqYvn3pyXXYuyvMbRF0AlIhIAVBKL/c3tFgslkBz3HFw663wz3/C8ccbdxBLr6K2Fu6/Hw491DQajjjCTLh1zDHwzDPwzTdw991w4IEdBwgsnnwF7OZ83wPoEyHlx+24KeXFBdw6Z0G+pVgsnni6MKnqchH5E7AUaASeU9XnktOJyGnAaQAjRoxg8eLFafONxWKsTpxWoptpvI77ycdqCb4WP2n6qhZLP+X0040v+UUXmW7p2283PuaWwPLdd2ZE4dFHzWLjzc0wfDicfLIZXdhlFxNGYPGPCA8AU4GhIiwDLgOmAzeIUAA04dQ9ejsDSwo5bsdN+dvLC1i4so7qYeVp0ye7uASNoOnLp55cl52L8jwfZSKyHnAwMAqoAR4RkeNU9R+J6ZygpxkAkydP1qqqqrT5pvIH604agEzLslqCr8VPmr6qxdKPufBC05X929+agOrrr+/7E//3Mr75Bp580sQ0vPSSiRUeORLOPts0Gnbaybb7MkGVYzo5NDmnQnLEyd+v4s7XFvG3OQv5w+ET0qYNeoxc0PTlU0+uy85FeX4ea3sBi1R1parGMH6HaWc8sDEQVku2tfhJ01+1WPo4V1wB558PN94Il16abzUWzHp/f/0rTJ0KI0bAGWfAokXws5/BW2/B4sVw3XXwve/ZxoOla6xfMYCjpmzC4/OWsWJtY9q0QX8/BE2fjYHILn4GU5cCO4pIKcaFaU9gbvpTLBaLxZIVRExttK4OrrrKzHDzi1/kW1W/Y/FiM8rw2GPwxhtm37hxcMklZvakrbe2g0OW7HDartXc/9ZS/v7KIi49YFy+5VgsKfETA/GmiDwKvAu0APNwXJUyKtiHI6ifNNkoy2oJvhY/afqrFks/QARuu81M5/PLXxp3prPPzreqPs/8+abB8Oij8O67Zt+228Lvfmfck7bYIr/6LH2TTQaXctA2I7j/zaWcs/sY1itLPfVm0N8PQdOXTz25LjsX5fkqQVUvwwQupcVdYGXUqFGeC6yEw2HPRS5UNSsrUcdiMVS104U7otEora2taRfuaGlpoaCgIOOVqL1sAjxXbY5Go7S0tGS8ErWXTW4Z6RZYCYVCbXZ3ZpOflaj92OR1n5qammhpacl4JWo/NnndJxHJykrUQRsCtuSRcNhM21NfD+ecYxoRJ5yQb1V9goULzWxIn302iqoq+OEP4cUX4aOPzPHtt4drrzUzKo0enVepln7CmVNH88S85dz1+mIu2HvzlGmCFqScTND02SDq7NIjK1FPmjTJc4GVuro6SktL0y5yUVdX12EF4+6sRO2Wler8wsJC4vG456rNdXV1aVdtBn8rUXvZlOp4sk0FBQXt9nV3JWovm+rq6jxXbXbTZLoStR+bvO5Ta2tru+PdXYnaj01e96murs5zYRw/K1G32uk7LYkUFsJDD5lVx04+2czOdPjh+VbV6znwQPj0U1AVFiww4Sa77GJi1g89FDbZJN8KLf2NzTeoYO9xG3D364s5bddqyos7Vteampo6vGeCRND05VNPrsvORXl5C+/yE2jtJ002yrJagq/FT5r+qsXSzxgwAP7v/2DHHeHYY+Hpp/OtqNfz2WeQ+K8WDsPLL8NPfmIbD5b8cdbU0axtjPHAm0tTHg/6+yFo+vKpJ9dl56K8vDUg/Ewxla1pqLzysVqCr8VPmv6qxdIPKSuDf/0Lxo83zvizZ+dbUa+lvr792gyhEIwdmz89FovLxJHrsVP1EG5/ZSHRlo6j0UF/PwRNn53GNbv0SANCfExF4cc/K1s+XF75WC3B1+InTX/VYumnDBoEzz4Lo0YZp/3qajbYfHPYaivj1G/xRBWmT4do1KzdEA4rW2wBs2blW5nFYjh88kZ8G4myxaXPsPd1c1i6uqHtWNDfD0HTZ2MgsktWYyDcIOqqqirPIOpYLEZZWVnagOP6+vq2i5BJEHUkEmHgwIGdBueuXbuWioqKtAHHdXV1DB06NOMgai+bGhsb22IUOrNp9erVlJWVZRxE7WVTXV0dQ4YMSRtw3NzcTEVFRcZB1H5s8rpPNTU1lJeXZxxE7ccmr/sUjUYpLCzMOIi6pqbG/z+gpf8xbBi88AJUVcGiRQjA//5nnPo//jjP4oLPX/4CDzxgZse9+GL4euEiNqyuzrcsi6WN2+aYzgBVWLCyjlPufpvnL9gNoO09E1SCpi+fenJddi7Ky1sQdSQS8Qw4dityiXQniFpV0wZRJwffJpI4FVY2gqi9bIrFYp4Bx2VlZWmDb/0GUfuxySvgOBKJZCWI2o9NXveppaWlLY9Mgqj92OR1nxK1dmaTnyDqlpYWLJa0jBgB8fi67XjcOPVb0vLii3DRRSZI+le/yrcaiyU1C1fWt32PO40Il6DFGCQTNH02BiK72BgIq6VXaPGTpr9qsVgYO7b9ksfhMLz2Wv70BJwlS+DII81lmznTLgBnCS7Vw8oIJfw+QyJ88lUtEPz3Q9D02RiI7GJjIKyWXqHFT5r+qsViYdYs2GILNBw2zvxDh8L3v28Wm6utzbe6QNHYaEYdYjF44gmzsLfFElTuOGE7Rg8rJyzCJuuVMLC0kENvfY1Z738V+PdD0PTZGIjs0iMNCD9DJ67/eaZp/OCVj9USfC1+0vRXLRYL1dXw8cd8M3++6V7/7DM4/3y49VYYNw6eeirfCgOBKpxxhllV+h//sLMtWYLPyCGlPH/Bbiy4en9e+cUe/Pu8XRg3vJJzH5jHH/79P1rjwXITSiRo76986sl12bkoL29B1M3NzRQUFKQNOG5qaiLu+PZmEkRdW1uLiKQNzlXVtAHHrl98pkHUXjZFo9G2453ZVFtbSzwezziI2sumSCTSFkPSmU1uoE6mQdR+bPK6T2vXriUej2ccRO3HJq/7FI1GUdWMg6jXrl3r/x/QYnEpLzcRwkcfbaYZOvhgOOIIs0LahhvmW13euOkmuOceuPxyE2dusfQ21q8cwAOn7cjlT33M319fyherG7nh6IkMLAlWbz/Q9o4MCvnUk+uyc1Fe3oKo6+vrPQOOW1paKCsra3e8O0HUIpI2iFpV28rpLOA4FAplJYjay6bW1tYOx5NtqqysbJemu0HUXja5FV3oPODYTZNpELUfm7zuUzweb3e8u0HUfmzyuk+JWjuzyU8QddAewJZexg47wDvvwB//CFdeCc8/D3/6k1nFup85/s+ZAz/9qWk4XHppvtVYLN2nuCDM1YdOYLOhA/j9M18w7ebXmHH8ZDbbIFj+eKFQ3sJsU5JPPbkuOxfl5S0GIrly1t00fvDKx2oJvhY/afqrFoslLYWFZo7S99+HCRPg1FNhzz3h88/zrSxnfPmlGYAZPRruvbd9vLnF0ls5fqdR3D99RyJNMQ655XWe+/jrfEtqR9DeX/nUk+uyc1Fe3mIgXPeSTNP4wSsfqyX4Wvyk6a9aLBZfjB0LL70EM2aYIIAJE+Caa0w0cR+mqcks1t3YCE8+CQMH5luRxZIdYrEY248azFPnfJ9RQ8s47d53uOGFz4kHJC4iaO+vfOrJddm5KC9v/TCtrR2XZe9OmmyUZbUEX4ufNP1VS84QKUPkbkRuR+RH+ZZj6QahkImJ+OQT2H9/swDCdtvB3Ln5VtYjqMJZZ8Hbb5vYhy23zLciS1cR4U4RvhXho6T954rwmQgfi3Bt5uVImYjcLSK3Sy95vrnvhxGDSnjkjJ04dOJG/OWF+Zx53zvURfO/hlCg3l/kV0+uy85FeXldidor4Dgx+DaTIOq6ujrPgON0gazuqs3pgnPBXxC1l01ucDN0HnBcX28WlsnGStReAcfFxcWeqza7dndmk58gar82pbtPdXV1Ke9Ld1ai9rLJ6z651zfTIGrXph5D5E7gAOBbVMcn7N8PuAEIA39H9RrgUOBRVGch8hBwX8+Ks/QYI0bAY4+ZeUzPPtvESpx/vomTSIrd6c3cdhvcdRf8+tdwyCH5VmPpJjOBm4B73B0i7A4cDExQJSrC+qlOlITnmyY83yTp+aYJzzdVnSW95PmW6C4+oDDMn4/chnEjKvn9059y6C2vMeP4KVQNzd//sx939lySTz25LjsX5fVIEPXkyZM9g6hjsRiFhYVpA47dIN9EuhNEXVxc3HZeqvzcT6rjbsDxgAEDshJE7WWTW9lMtjGRIUOGdAhSTs7PTxC1l03uceg84Ni9j5kGUfuxyes+uRX5RBuSbfITRO3HJq/7lKi1M5v8BFEnrhreQ8wk6eWMSBi4GdgbWAa8jchTwMbAh06qYHUtWbrHIYfA7rvDL38J110Hjz9uat377ptvZRnz6qtw3nlmoOXyy/OtxtJdVHlZhKqk3WcC16gSddJ828npM+nQ+Oj4fJNe+nxLfoeJCKfuUs0WG1ZyzgPvctBNr/LXYyex2+bDAqEv3+RTT67LzkV5nrUTERkLPJSwqxr4jape39k5fmMgvBa68JPGD175WC3B1+InTV/V0qOovoxIVdLe7YEvUF0IgMiDmN6+ZZiX7Ht04v4oIqcBpwE0FxayeuFC31JqV63qmvY8EHSN3db3859TuPvuDLz4Ygr224/GadOoveQSdPDgYOjrIiu+DnPoIRuxyUbKdVctZ+USf7OZBf3+QvA1dl1fa4GIJPrQzVDVGR4nbQ7sIsJVQBNwoSpvJydS1Zelk+ebOs836ebzbaONNmLx4sUeMnuW5ubmlMGyGxfCLdOq+PUzSznprrc4bYcNOGqbITnvBe9MX77Ip55cl52L8jwbEKr6GbAttLXclwNPZFpwkPzrrZbga/GTpr9q6QE2Ar5M2F4G7ADcCNyEyA+BWalOdF785uVfVqYbVld3qeCups8HQdfYbX3V1Wa9iKuuouSaayh59VW4/no49tisTvna09cvGoVDfgQNjfDiSzB2fFWXzg/6/YXga+yavo9aVHVKF4soANYDdgS2Ax4WoVoVP9HDaZ9v4vP5NmXKFK2qquqi7OwSiUQ6jO67VAH/3KKaix75gNv+u4KvGsP84bAJlBSFA6EvH+RTT67LzkV5XQ2i3hNYoKpLMi3YT0s4W61lr3ysluBr8ZOmv2rpAVIVqqjWo3oSqmeiGnj/YEs3GDAAfvtbM0vT6NFw3HHGB2hJxo/8nHHeefDf/8LMmTB+vGdyS+9kGfC4s1bnW0AcGOrz3JTPN1WtV9WTVPVM7SXPN6/3Q2lRATcdO5GL9h3LrA++4vDbXmfZmoYcqbMxEPksO4gxEEcDD6Q6kDi0N2LECM+hvXg8ziqPoU4/aVavXp32uJ98rJbga/GTpq9qyQPLgE0StjcGvsqHEEue2HpreO01uPlms4bEVlvB734H554L4dz1YHaVGTPM55e/hMMPz7caSw/yJLAHMFuEzYEiwK/vVJ95viXG+XWGiHD27mPYcngFP3ngPQ666TVuPnYSO40eEgh9uSSfenJddi7K892AEJEi4CDgV6mOJw7tTZo0yXNor7GxkZKSkozTAGRaltWSXy0NDQ189913nisntrS0pA0q9joOUFFR4ekX6CefntZSXl5OZWUljY2NafPoId4GNkNkFMZl8WjgWL8nu7OxxYqKKGhuNusMuLEcJSXQ2grODFiUlEA8DtEoUltrJu2HdX+Li83Uo+51KCoyFVh3u7DQfBoa2m83Npo5PAsKzDnudjhs8mxqMuWGQqbXPRo1ukSMpuZmaGlZt+3YILW15phPmwCTf45sktpaqK3tkk0AlJamtunHPzZB1j//uVnG+d574YYbYMqUbtkktbVQU9Mj9+mN90s555wC9tmjhd9dWA/NndiU7j5Fo0ZfAH977n2ShoZ1GgP022uzqbkZIhH/NnkgwgPAVGCoCMuAy4A7gTudqV2bgRN8ui+B83yTDJ9v1dXVnrNNes1iWFRUhIiknfEvcTt5xr/m5mYqKyuJRqNpZ/yL7HbyXAAAIABJREFUxWJst1Ep9588kZ888gnH/f1Nfr53NcdutxElJSVtMzOmsiETmxobG6msrOySTYkzaHrZ5M5amG62yUQbamtrKSkpyfl9cmdmDIVCWbeps/sUj8cZMGBAWpsyRp0xQK8PJsjoOT9pJ06cqF7U1tZmJc2iRYsyzsdqya+W5cuXayQS0Xg8njZdU1NTRsdVVdesWeOZxk8+PamltbVVly9frqrm+gJz1ef/aZc/8IDCCoWYwjKFU5z9+yvMV1igcEm38i4t9bwGiaxYsKBL6fNB0DX2mL54XPX++1WHDVMtKFC9+GLVxsYuZ9NT+r76SnX4cNVRo1RXr+5+PkG/v6rB19hVffBhvfbQ8w3jMbECiGFGHk5x9u8PzAcW0M3n2+TJk7txdbKLn/dvMmsbm/WUmW/ppr/4p170yHvaFGvpAWWG7ujrSfKpJ9dl+ykv07pFV1yYjqET9yWLJVMKCwtT+uwtXd3AKXe/zcKV9YwaWsqdJ27PyCGleVCYO7xGYrKK6jGd7H8aeDp3QiyBRgSOOQb22Qd+9jP4/e/h0UeNv9Buu+VVWnMzHHEErF0LzzwDWZ44ytKL0U6eb9qPn2+VAwqZcfwU/vLCfP764hfM/6aOvx0/mQ0qg+VuZAk+vmoqIlKKmTP5cZ/pPdP48c/Klg+XVz5WS/61FBQUcMWsjznqb2+0++xz/Rw+/7aOVlW+WFnPPtfPaXf8ilkft8vDiz/96U/E43EOOuggHnvsMQB23nlnZs+e3Wk+559/fkq9V111FfF4nIcffpipU6e2Hfvtb39LQUEBd911F8uWLWvbP3PmTP785z93yCsajXLqqafy3nvvdTgWNB9SSz9nyBATnfzcc8YFZepUOO20dS41eeCnPzXhGnfcARMm5E2GxZJzuvt+CIWEn+0zllt/NIn530Q44K+v8s6SNVlWF7z3l42ByC6+RiBUtQHwjLhxfQNHjRrl6RuoqsTj8bT+WdFotFMfs66sRN3Y2EhZWVmnPmf19fWUlJSk9Tlrampi0KBBGa9E7WVTLBbz9A1cu3YtxcXFGa9E7WVTU1MTAwcOTOsbGI/HKSkpyWgl6ng8TjQapbW1FVVFxLjXgtIUax9EbLa17Xi8NU48Hqe1tZXm5ua2iv3AgQOZOnUqDz/8MEOHDqWlpYUTTzyRcDiMqlJVVcXcuXOprKxk4sSJtLa2ctddd/HNN9+wfPlyrr76aq666io22mgj5s2bR2trKxdffDGDBw8mHo9z4oknEgqFiMViHHLIIbz22mttK1wPHjyYBQsWMG3aNK6//np+/etfEwqFaGlpYZdddiEajfLzn/+ckSNHMnv2bB577DF23nnnttXZRYR4PE5jY2PPr0TdA9gYiD4UA9GZTbvsAnPnmhXabr4ZnnrKxEa4C9DlKAbizr/HueWWAVx4bpSjDxWoz8AmGwMRyBiIoBGkGIhYLEZFRUW34wV2GVXBo6fvwOn/eJejZ7zBr/cbw7E7jsqaTU1NTVRUVAQmBiISibQtkpvrGIjW1tYesSmTOnam9MhK1JMmTfJciToSiXiu2tzc3NxhHtvurEStqpSWlqY8v7CwkNbWVs9Vm91jma5E7WWT+0BItjGR4uLitCsY+12J2o9NXqs2RyIRioqKMlqJuqamhnA4zJXTOnYf7n3dHBasrCOuEBIYPaych07fuUO6UChEPB6nuLiYUCjEcccdRzgc5pNPPuGwww5j/vz5LF++nE022YRwOEwoFGLChAnce++9HHLIIYTDYd544w1mzJjBrbfeyqeffsqaNWu4+uqrefPNN1mzZk27vBYtWkRVVVXbdRGRtmtQXV3NF198weabb04kEmm7FgUFBRQVFVFTU8PAgQM55ZRTeP/99ykqKmpbodzNLxQKUVJS0vaw6E24zwHKyqZTVGQqHMmUJrmhlZSglZXrKjzJvSfJq2ombyeX0UPbWllJV2xqRw5s0spKqKzs9vkdSGfTgAFw441wwgkwfTocfTRMmwY33QSDBqW0SSsr1x3LUONbb8GZP4U994Srrys2b7NMbSoubq8vhQ35+u25aGlpR40B+O21sWoVJM8/n/b8ZfQm3OfblClTPOs5LunqBKm2U73TE3Hfn+77xSt9uu1x5TDr3F0494F5XPavz/lidTO/OXBc1mxyz/NrU3dsSLWd7Engdma6dZFc3icw9ypTG1PZlEkdO1Ny6GxtsXSdO07YjtHDygmLUD20jDtO2M7XecXFxQwdOpStttqKmpoaJkyYQHV1NcuXL29Lc/jhh3PZZZe1udzttttu/PnPf+aTTz5h/PjxDB8+nEceeYQvv/yyQ16jRo3iyy/NWkQvvvgi8+bN4/bbbwdgyZIljBkzhvr6etZbb70O2tZff33Wrl3LzJkz20ZmLJZey+TJpjZ/7bUmCGHcOLjtNtMj3UN88w0ceigMHw4PPmg6xi0WS/cYVFrEXSdux2m7VnPvf5fwo7+/yaq6aL5lWQJOjzx2bQyE1dJVOotfGDmklOcvMEGara2thNPMQe/mcfnll7ftu+aaa9qlicVixONxrr/+egBGjx7N6NGj26Vxy7nssssAOOKIIzrk5Q4VxuNx9thjD/bYY4+2YytXrmT06NHcc889nHTSSetsGTmSuXPnsu2227Lvvvvy8ccfs9deexGNRlm6dCm7pQhGDZoPqcWSkoICuOgiOOQQOP10OPNMuO8+uP122GKLrBYVi8GRR8Lq1fD66zDU7/JhFksfI5vvh4JwiIv335Jxwyv5xWMfcNBfX2XGj6cwfqOBgdCXDWwMRHbJ2whEa2trVtJkoyyrJf9azIximaXxk8eFF17oOcuRXy2XXHJJyrwuvfRSVJWTTjqJjTfeuG3/HnvswamnngrA/vvvz/Tp0zn77LMpLi7mN7/5DaNGjeqQV7aur8WSE8aMgRdegLvugo8/hm22MStbuz76WeDCC+Hll03bZOLErGVrsfQ6euL9MG3iRjx2pnETPuzW13ly3nKPMzonaO+vfOrJddm5KC+rIxBucFFVVZVncFFzczMikjbAo76+vi19JkHUtbW1DBo0qNOglZqaGiorK9MGrUQiEYYNG5ZxELWXTQ0NDW3HO7NpzZo1lJeXZxxE7WVTJBJh6NChaYOLotEooVAo4yDqxsbGtliAUCiUEFAtbWmampragsfjcRM8LSL/396Zh0lRXf/7Pd09+8KwiSLKMKKgEkVAVDTuezRR44Yb4haNGhM1fl1i4pIYjYmJ/kzcgoJx3xWTuETFXVRQERBEhxFxYYfpYWa6p7vv74/bNfT0dHfVTC9VM3Pf5+lnuqpu3fs5VT1Vdeuecw+BQIBoNEprayvFxcUEAgGUUu3/QInL4XC4ve7E7UB7vIF1rKxlv9/fadk6pqDjFaxEMUqp9oDpWCzWvpzKpsSg6WSbrGOybl3uZ8bINyaIug8EUdvZdPzxsP/+8Mtfwm9/CzNmQDTKkGXLYNtt9RSw223X5fN0/31RbrutjF+eF+KU4wWacmyTCaI2QdQ2eCmIurm5Gb/fn/Pg3JEDS3ho6lgueeozfvnox1zz3HwaWyOMGFjOXafswlb9yxzZFAwGGTBggGeCqNeuXUtVVZUrQdStra3tzwiFCKJ28oydLeLkbWtXGTdunJo7d27GMsFgsFNgbXfKNDQ02GZctqvHaHFXy7fffsvAgQM7BfskEwqFMpax2w46YLsmOQCxG/XkW8u3337L0KFDCQaDVFdXz1FKTchYmRepqFBs3Oi4+Pf19WxeV5dHQdnjdY2e1Pf889q1KXFCgH794IQT9MNncbH+m/xJWj9n2WD2umQiu+/YxEt3fEFRRfqyZHB1TEl9PRx5JGrxYmTUKJg5E7x2HON48hwn0FV9IvOblRpTkUdJeWHChAnqww8/dFWDk/tvNrRFY0z4/f/Y0LKps1dZEuCgHYbg9wkBnxDwCwGfr315018f0UiY8rLSTuvbl5P39SfWkVSn38H+CX9TudLn4ngl5qaqG6zjMp3kpsr3uepOeyKS1bOFazEQdg9XTss4wa4eo8V9LWlzOKxdCg+fCKuXUDxwJJz0KAzo7OqTsY4E/vznP3Pddddx1FFHMWXKFH76058yadIkbrjhhvZcDqnyQFgxE4lt/eEPf+CKK67gzjvvZM2aNUQiEa699lquv/56rrzySu677z4OOuigdjem6dOns2bNGi655JIOdYVCIc4//3wuuOACxo4d22Fbro6vweAaRxxhzcu8iQ0b4Nln9Zty65Mh6HoVgziGDxnMNzz60QSKdl+VuU3rzbjDzgmzZkFjIwKwaBEceaR2wTIYPEy+7w9Ffh9NrR1nAmwKRfjwq7VEo4pITBGNJf6NEY0p2qK5fzHdVfxJHYpOHZN4hyRxOWW5hM6K3+dj1uKVBOPH5MtVTZw544P2OM1MFPpeXoj2XJu7Ihc+77lqy2jxiJb/Xg7ff9pxw7dzoC0+vL56MdyxBwwdv2n75j+Aw27soOWaa66hpqaGAw44gEceeYQBAwYQiUQ4++yz292k6urq+Pjjjxk0aBDjxo0D4IEHHuD777/n66+/5sYbb+SGG25gyy23bE/wdtVVV7XXNXXq1Pa6fv7znxMOh7nwwgsBGDx4MA0NDRx//PHccsstXH311e1yDzjgAJRS/OpXv6K2tpbXXnuNZ599lr322iv9cTEYejqjRukHc8vNZfTozg/okYh2dUnsVIRCRDaGOPFnw1nxSSVv3TqXzWrv37Q9Rfkurd+4Uf9tbNykIxaDxYsLe3wMhm5QiPtD3eCKTlOpO3lgjsUUG1taCRQVt3csrI5GW7TjciTasQPSsWMSS9ieZn+rXEyl6NjE2pdDbRHARyTeVsfysQ77tbRFO7Ufjan2zgNATEH9Kmej7IW+lxeivbx0IJwID4fDtj0kJ2WcYFeP0eK+lmg0mvrHaHUeQL8dTFhOWUd89GDy5Mn4/X7mzZvXnruhvr6eoUOHtpffYYcdmDZtGkcffTQAb7zxRnseiPnz57Nq1Squv/563n77bVavXt2hri+++KJ9ZKG1tZWrrrqKK6+8EoDhw4ezePFittlmm5QxDCtXrqS6uprTTz8dO1c/y3/RYOjRzJzZ2UUomUBAf5JyGlx+Kbz6gY7LnnB6njz5dtxxUwcH9N/HHtOxHAaDR8nV/TcT06bs2sllxwk+n0AsQllxGdBFl8I8kQs3ooNueZ0vVjah0CE9dYOded8V4lwVuj1Xg6jtAo7D4XB78G02QdTBYDBj0EowGLQNWrEyGObbJruA46KiovbsxLkIos5kk5WIxC6IuqSkJOsg6nA4DPtf2ynguOieHyJrliAqhhIfauC2qNOe6xhEnZCJGnR2bL/fT1VVFaNHj2bt2rXsuOOObLnlljz33HNEo1FisRhHHnkkO++8M4sWLSIajTJp0iRuuukmli1bxqmnnsrgwYN56KGHWLZsGf3792f06NGsXr2aHXfckeHDhzNr1ixCoRDHHXccY8aM4T//+Q9nnHEG9fX1HHjggaxbt46qqira2traA6vD4TA1NTWsX7+eadOmsWHDhvbzlyoTtckTYegV1NXBggWs6KJ//MMPw1/+AhdcAKefnj95HTo4dXVQWaljNN59V+e3SEroZDD0FRKnUjfoDtXU6e/z5aqNlBX5HXeoeiN5yUQ9fvx42wyN4XCY4uLijFnyErP7JtaRiJNM1ImZklNl+rMyBKfabr3RLikpyUkmajubrE5Aso2JDBw4sEOZ7maitrOppKTENhO1VSbbTNTl5eUdcjy0fz/p0fYYCAaOxHfSo+D3d8oHYXU8/H4/1113Xfv6m2++uUM5q9Nx2223AXoUYocdduhQxsoDcf3112sJJ53Uqa72Dk5RETOT3qauXbuWkSNHcv/993P22We3H4u6ujrmzZvHxIkTOfTQQ1mwYAEHH3wwSimWL1/Ofvvt1ykTdaa8F17FzMJkZmFKZ5M0NuoZhBzY9MniUs48M8AP94hyyzXNEM6jTQMGwJw5rFi6lM232EKXufZa+NvfdCfivvtg+HAzC5OZhclTszBZbeVjxqJc2BQOh2ltbfXMLEzWC9xsbOpfDP8+f3f++fYy/vzyEr5ZvZ7NKny2Nvl8Ppqamgp2nhLPQTqbsiUvszCNHz9ezZkzJ2MZqwORbRknsw3Z1WO0uKvl22+/ZciQIbYPy3aJ5Oy2g7NZmJzUk28t1ixM8WFIMwuTR/C6xt6ib80a2HVX/dw5Zw4MGVIAcaTQ98gjcNZZUFEBjz4K8YkW3KS3nGMLMwtT93Fy/3UTr+nLpZ5gaxt73vgqu9cN5O7T7G/PhT4WTtrLdhamvCSSc9IpsXpB2ZZxgl09Rov7WlpaWmx/N5FIJKvtTnFSTz61xBJmo8nV8TUYegrRKEyeDN98A08+WbjOQ0pOPBHef1+PUBx4INx8c+cZpQwGl/D6/cFr+nKpp6q0iNP3HMFLC1ew+Ht7V+NCH4tCtOfaLEwGg0VNTQ1r1qyhMXEmlBS0tbV1csfqynaApqYmmq3h+SzqybeWZLc1g6GvcNVV8PLL8M9/wm67ua0G2GEH3Yk480y47DJ45x2YPl3nsjAYDH2WqZNqmfZmPX9/7Qtum7yL23IKTl6CqEeMGGHrGwg48s/KRRB1KBTK6HNmBa5m8jkLhUI5CaK2s0lEbIOoLT++bIOo7WyyAqQz+QZa27IJoi4qKqK8vJzi4uKMNjU3N7fHZqSyaePGjZSUlGS0ae3atbbZtZVSlJaWZrTJyiiZziZrFCGTv+OGDRuoqqpK+9traWnx3BscgyGfPPYY3HQTnHuufl73DFVV2oVp0iT49a9hwgQ9PLLTTm4r6/WIcC9wBLBSKcYkbbsUuBkYrBSr3dDnJl5yD0qF1/TlWk//imJO2X0497xZz8UHbUftoPSeeIU+FoVozwRReyyIOpXfWrJNVVVVngmitvRmE0Tt1Ca78+T3+ztsT2VTZWVlTmyyO0+ptidrLisr63Bc0tlkMPQFPv0Upk7Vz+i33uq2mhSIwC9/qTsPxx8Pu+8Od90Fp57qtrLeznTgduD+xJUibAUcBCxzQZMncJK01028pi8fes784Qjue6eBO2Z9yU3Hpn+hUOhjUYj2HMVAiEiNiDwhIotE5DMR2SNTeRMDYbTkWouTMn1Vi8HQ01m3Do4+WnsFPfGEnujHs+y1F8ydq/2rTjsNzjtv06xHhpyjFG8Aa1Ns+itwGdBng1K8fn/wmr586NmsqpTJu27Fk3OX88369Hmq+nIMxK3AC0qpY0WkGCi328FgMPRNzDSuZhrXrkzjGo0oTj6pkmXL/Mx6fiNblEUgmJspT7tsUyi0aYrUTDaVlsIzz8D11+tEFe+/Dw8+qPNdmGlc8z6Nqwg/Br5Rik8K/ZLbS9O4Wm69Xp3GNRgMdtmmfE7jarlS5/o8TdltGA/OXsbt/1vElYeMTGmT5VpeqPPk1KU+G2w7ECJSDewNnA6glAoDGdPjOhk6sQswdVrGCXb1GC3e1+KkTF/V4jUsV0YqKs6muDj16+SkbMOUlaGqqzc98CS4nwH6ISXTcnIbeVpW1dV0xaYOFMAmVV0N1dXd3r8TObZJVVdD4tTFxcX87jfw35fgjjtg0sFJkwfk+jxCZptKSjrqS2FDh+U//xn23FNnudtrL92JOOSQ3GpOWlbl5Z01euC3187q1TpmxPH+XwVEJHE+1LuVUneTBhHKgauAg9OVySfW9W3ChAm2rtoWmdyaUy2ncuFNJNE92fp0Zf90bs3pbOiuTYn7ObWpuzY4sWnAgAEd9OTqPFVVwTHjtuSpj7/lV4dsz2ZlpZ1s8vl8ndrL53lqbW21danPFicjEHXAKuA+EdkZmANcpJTqMMG7iJwDnAMwdOhQGhoaMlaai7n2Qccd2JGLOfuNFne1OCnTW7UYDL2Vp56CP/xBB0z/7Gduq+kmRx8NY8bAT38Khx0G11wDv/mNfhtvcIA/0sW56LcBRkD76MMwYK4IE5Xi+zwI9Cxej5Hzmr586jlv35E8MWc5095cyhWHb1/QtlNRiPacdCACwDjgQqXUbBG5FbgcuDqxUPyNwd0A48aNU3ZJzILBYKfA2u6UAWwTptnVY7R4X4uTMr1Vi8HQG1m4EKZMgYkT4fbbtXdLj2XbbeG99/T0Ub/7nf7+wAM6f4QhpyjFp8Bm1rIIDcCEvjgLU2trq6dHqb2mL596Rgyq4IidhvLAe19x3r7bUFPe8U1/oY9FIdpz8opkObBcKTU7vvwEukNhMBgMBkOX2bABjjpKe948+WRnD5seSXk5zJihfbFeeQXGjdNptA1ZIcLDwLvAKBGWi+ClCX4NhnbO328kG8NR7nu7wW0pBcG2A6GU+h74WkRGxVcdACzMtI+JgTBacq3FSZm+qsVg6EnEYnDKKbB0KTz+OAwb5raiHCKiRyHefFMbOmkS3HOPyV6dBUoxWSm2UIoipRimFNOSttf2xdEH8P79wWv68q1n1OZVHLzDEO57eynB1o6TBBT6WBSiPadOmhcCD4rIPGAscEO2DTvxz8qVD5ddPUaL97U4KdNXtRgMPYnrroPnn4e//hX23tttNXli4kQ91eu++8I55+ggj5b0UzwaDN3B6/cHr+krhJ4L9h9JY2uEB97rmJ6kr8ZAoJT6GLANcrKmN6utrbWd3iwcDlNZWZlxiqmmpqYOicG6m4m6sbGRmpqatNNmrV+/nurq6ozTZgWDQQYPHpz1tFl2NjU3N7f3HNPZtHr1aiorK7PORG1nUzAYtM3aHAqFqK6uzjoTtROb7M7TunXrqKqqyji9WVNTE+FwOGub7M5Ta2srxcXFGadsa2lpIRgMZpyybd26dXb/dgaD56mvhyOPhEWLRhCLwTHHwPnnu60qzwwaBP/5j+4xXXed7lA8+SRss43bygy9BK/FGCTjNX2F0LPTsBp+uO0g/vlmPadPqqWs2F+wthMpRHt5yUQ9btw42+nNgsGg7RRT4XC4UyBqqqm/7DJRK6Uoj09zl2qarGg0apu12dqWySYnmajtbGpra7PN2pxscyqbnGSidmKTXdZm63u2maid2GR3niKRSHsd6WxykonaiU125ylRazqbnGSituZ87kmYPBAmD0SyTUf+qJpFnwuxmACKzxYopC2S95wJecsD0ZXzdPHFsOOOepqp8eN19upDDjF5ILLIA+EmJg+EyQNhZ9M5e27NqdPnMuOtJZy865Z9Nw9Evkiez7a7ZXLRltHifS1OyvRVLV7D5IGIY/JAQEkJX30Fny1ODAMQPv9CUh/HfC9DbvNAONVw/PHarenYY+HEE+GKK/SohPVA3oX6el8eiOX0JLyUB8J6+PZqHohAIOCpPBD9+/enLP6/kc/z9MPRVUysHcCM2d8wde9tKQn4EZH2tnNpU7rz1NLSkvc8EHmZqNoEURstudbipExf1WIweJH6ejj7bBg5smMMsc8Ho0al36/XUlsLb72lD8of/6hHIVaudFuVoQfj9fuD1/QVUs/5+4/kuw2tPDX3m4K3Xaj28tKBUA5mnLCGerIt4wS7eowW72txUqavajEYvMTnn+vEzNttB//6l/bcefNN2GEH8PsVo0fDzJluq3SJ0lK4+26491545x091eu777qtytBD8fr9wWv6Cqln720HsdOwftwx60si0VjBj0Uh2supf0RXg6gtHzBI7Z9l+cBB9kHUPp8vrc9ZY2Nju4ZMAcclJSVZ+5zZ2RQOh20DjoPBoK0fndMg6kw2BYPB9kDgTAHHll3pbHISRO3EJrvz1NjYiFIqJ0HUdjbZnadwOGzr7+gkiNqy2WDwOgsX6qzSjzyiPWIuvBB+/WsYOlRvX7AAvq9fyuZ1de4K9QJTp8Iuu+js1XvvDbfcAhdc0MOz6RkKjZOXtW7iNX2F1CMinL/fSH72rzk8P+87DhhZXbC2oTC2uhZE3dzcbOufFY1G24OfE+tIxEkQtc/nyxhEDaTdbvmcWQ+B2QZR29kUi8U6bU+2qV+/fh3KdDeI2s4mv99vG3Bslck2iNqJTZD5PCUHy3c3iNqJTU7Ok91v10kQtdcuwAZDMvPmwe9/D088oV3xL7lEf4YMcVuZxxk7VieaO+00+MUv9EjE3XdD0nXEYEiH16ZJTcZr+gqt56Dth7DdkEr+/toXHLid7USmOaUQtpoYCKOlR2hxUqavajEY3OCjj/R0rDvvDC+8oOOCGxrgT38ynQfH1NTAM8/ADTfAo4/CbrvBokVuqzL0ELx+f/CavkLr8fn0KMSSlU3MWlLYKdl7dQyE5R6SbRkn2NVjtHhfi5MyfVWLwVBI3n9f53QYNw5efRV+9zvdcfjDH3TqA0MX8fl07+ull2DVKth1Vz2cYzDY4PX7g9f0uaHniJ2GUjuwnH/M+rKgHgWFsDUvHQgnODmQuTrYdvUYLd7X4qRMX9ViMBSCt9+GQw/VL8nfeUe7LX31FVxzDQwY4La6XsABB+hkc2PGwHHHaT+wHporwVAYvH5/8Jo+N/T4fcJ5+27Dwu+bmPX5qoK12+NiILoSRB2NRm0DjhMTf2QTRN3c3JwxOLe5udk2cYeVsCXbIGo7m2KxmG3AcUs86U+2QdR2NllxKpkCjiORSLvd6WxyEkTtxCa789QcT36UbRC1E5vszlMsFstJELVlk8HgFq+/rlMWvPoqDB4MN90E553Xeap/Qw4YNkwf8Esv1YHV77+vXZusSHSDIQGvxRgk4zV9buk5epdh/PXlz/n7q1+w73aDHbn5Z0shbM1LEPX48eNtg6jb2tooKirKGHBszZyUSHeCqEtKStr3S1Wf9Um13Qo4Li0tzUkQtZ1N1sNmso2J9O/fv1OQcnJ9ToKo7WyytkP6gGPrPGYbRO3EJrvzZD3IJ9qQbJOTIGonNtmdJ6uOdPuDsyDqnphIztDzUQpeeUV3HN58EzaJBNIGAAAgAElEQVTfHP7yFz0la0WF2+p6OcXFcNttsMcecNZZ2lfs0Udhn33cVmbwGF6LMUjGa/rc0lMc8HHOD0dw3b8XMXvpWnavG5j3Ngtha16eTpwMnVjTuGZbxgl29Rgt3tfipExv1dLTsEYi24qLCYTD2g3DcsUoK4NoFCy7ysogFoNQCGlsBGvuautvSYn2EY+PUFFcDH7/puWiIv2xRmqs5ZYW/RRsZfq1lv1+XWdrq27X59Nz84dCWpeI1hQOQySyaTlugzQ26m0ObQI2ZQEugE3S2AiNjV2yCdDTJ7W1ocJtvPC/ANf9pZz3ZvvYcmiM225q5axz/JSVxG1a332bpLER1q/P+3lKtKlL5ykU0vq88Ns7/HDtN3b88dq96YIL4KWXGPL55zo73yOPwPbbe+a3125TOAzBoPPz1MOwrm91dXW2nhZ2I/jFxcWISMbR7sTl5NHu1tZWampq2qeHzzSCb41uZ/JKSGVDNjZt3LiRmpqaLtmU6D2Sa5vWr19PRUVFwc+T3+/n8NE13PZqgKn3vU84EmPEoApuP2EMW/Yryct5ikQilJeXZ7QpW1x7vRmLxXJSJhdtGS3e1+KkTF/V4jWskUgqKs6muFg/cCSTNMUtZWWo6upNDzwJo0eAfkjJtJzcRp6WVXU1XbGpAwWwSVVXQ3V1l/dXCma+UMz118OHH8LWW8Mdd8DUqT5KShLsytImVV2tZx7qgk05XYbM56mkpKM+a12m5XxqHjtWn5Azz4RbbwVAAJYsgZNP1sk1wBO/vXZWr+7s35Zx/+X0JKzr24QJE2w9LSwyeSWkWnbieZG4za683XLySHcubbL264pN3bHBiU0VFRXto/6FPk/RaJSAz8f6Nt0BqF+9kQsfW8DLF++Tdv9szpOV58vOpmxwLYja57Nv2kmZXLRltHhfi5MyfVWLwdBdYjF48kmd0+wnP4G1a+Gf/9TPp+ee2/m50uAC1dXw2GP67b5FLAaLF7unyeAJvH5/8Jo+N/X4fD7WNW8adYspqF+1Ma/t5Zu8BFGPGDHCdmjP5/PZBhwDOQmiDofDiEjaYTBrqCzTkFFbW1t7EHQ2QdR2NomIbcCxZUO2QdR2Nll+/JmG9kQkJ0HUTmyyO0+hUIhYLJZ1ELUTm+zOk4jkJIjaa9PgGXoH0Sg8/rieSWnBAth2W5gxA046SXupGDyGCIweDZ99poeLALbYwl1NBtfJ5dvkfOA1fW7qKS4upm5wBV+samr/Fx4xuDzzTlm2l288n4k6Ofi2O0HUzc3NWWeibm5uzhhwDM4zUWeyqbm52TbguKSkJCeZqO1sam5utg04tspkG0TtxCYoTCZqJzbZnafm5mbbIVmTidpQaCIRePhhnbNh8WLtQv/QQ9rN3mMTphiSmTkTjjwStWgRUlICy5fD5Zfrk2lOXp8k1WQdXsJr+tzU09bWxrQpu3LmjA/4clUTMQVjhvbLa3v5ttXRGIeINIjIpyLysYh8mIuGo9FoTsrkoi2jxftanJTpq1oMBjva2uDee/VL7NNO065Jjz0G8+fD5Mnm+bNHUFcHCxawYskSWLdO+5jddJNOzrF6tdvq8oYI94qwUoT5CetuFmGRCPNEeFqEmkx19Fa8fn/wmj439USjUbYeWM7LF+9D/R9/xM/2qeOZj7/l/ncb8tZevumKk9R+SqmxSqkJuWjYyTy4uZor164eo8X7WpyU6ataDIZ0hEJw112w3XY6DrdfP3j6afjoI52rzGMuyganlJToKPdp0/Q8u+PHw5w5bqvKF9OBQ5PWvQyMUYqdgM+BKwotygt4/f7gNX1u6klu+7JDRnPg9ptx7cyFvLkk9wnmCmFrXm4fToQnu490t4wT7OoxWryvxUmZvqrFYEimtRVuv13P9nnuuTBkCDz/vJ7Q56ijTMeh13DGGfDWWzouYs89Yfp0txXlHKV4A1ibtO4lpYjEF98DhhVcmAfw+v3Ba/rc1JPctt8n/O3EXdh2s0p+/uBcvlzVlNf28oHTGAgFvCQiCrhLKXV3cgEROQc4B2Do0KE0NDRkrDAcDtsGeTgps2bNmozbndRjtHhfi5MyvVWLwWBHfT0ceSQsXjyCQYP0JD2rVulnymnT4KCDdByuoRcyYYIefTjxRJg6VWev/tvfUk9h60migSTX6LtTPWNk4Azg0RyL6hF4LcYgGa/pczsGolMMb0mAf06ZwE9uf5uzZnzI0z+fRE15bv5vC2Gr0w7Enkqpb0VkM+BlEVmklHojsUD8H/5ugHHjxqna2tqMFQaDwU5BqN0pA5BtW0aL97U4KdNbtRgMmYjF4MADoaEBlBJWrNBpAV59Ffbd13Qc+gSDB8OLL8KVV8LNN8PHH8MTT8DQoW4rc4A/0l3XaBGuAiLAg7nV1DPwWoxBMl7T53YMRCqG9S/nrlPHc9I9szn/oblMnzqRIn/2Q8SeiYFQSn0b/7sSeBqYmG3DXvKvN1q8r8VJmb6qxdD3WLEC/vUvnUtsyBBYunTT7J6gYx/22890HvoUgQD86U86On7ePBg3TsdH9FJEmAIcAZysFH1yujqv3x+8ps9LMRCJTKgdwA3H/IC3v1jDtTMX5L29XGHbgRCRChGpsr4DB8Om2RDS7GPbcHIGwO6WcYJdPUaL97U4KdNXtRh6P21t8MYb+gXzuHGw+eZ6NqX//Q8OOwy23HJTXIPPB6NGuavX4CLHHQezZ+sEdPvvD7fd1rF32QsQ4VDg/4AfK0Wz23rcwuv3B6/pc1OPXdvHjh/Gz/ap44H3luVkZqZC2OpkBGII8JaIfAK8D/xbKfVCph2czF1vJfnKtowT7OoxWryvxUmZvqrF0DtpaNAzKB19NAwcCPvso18wV1Xpqf/nzIHvvoP779edi9Gjwe9XjB6tUwYY+jA77ggffACHHw4XXaR7m8098zlbhIeBd4FRIiwX4UzgdqAKeFmEj0W401WRLuH1+4PX9Lmpx0nbemamITmZmakQttrGQCil6oGdnVRmZaKura21zUQdDofx+XwZszY3NzcTieiJFrLJRN3Y2IhSKm2G4/Xr1xONRjNmbbbayDYTtZ1NLS0t7dvT2bR+/XoikUjWmajtbAoGg/j9/oxZm0OhEH6/P+tM1E5ssjtP69atIxKJZJ2J2olNdueptbWVaDSadSbqdevWOfnXM/QCmpvh9de1K/sLL+hEbwBbb61zNRx6qH6h3C9F7qF4igC+r1/K5nV1hRVu8CbWnL033AC//S18+ik89ZT+sfQglGJyitXTCi7Eg1j3IK/iNX1u6nHStp6ZaSzH3vEOP39wLs+cvyfbDK603a+77WWLa5mog8GgbdbmcDick0zUyRmKk7dHo1HbrM3WtmwzUdvZ1NbWZpu1Odnm7maidmKTXdZm63u2maid2GR3niKRSHsd2WSidmKT3XlK1JrOJieZqL12ATbkDqXgs890Z+HFF3XnIRSC0lId/HzuubrTMGqUiWUwdBOfD37zG50n4qST9IxNDz2kf1gGg8Fz5HNmplyT0w6EhYmBMFpyrcVJmb6qxWtYI5FtxcUEwmHtwG8Np5aVQTQK1vS0ZWV6GqFQCGls1AkMYNPfkhL9ENTSopeLi3XaZGu5qEh/LPcMa7mlRT+hBwJ6H2vZ79d1trbqdn0+/cQeCmldIlpTOAyRyKbluA3S2Ki3ObQJ0PXHbVq/QXjlnVJe/F+AF14Uvv5Ge5FuPzrGz88Mc8j+EfbeRyirTrCp2blN0tgIjY1dsgnQ0zZ106aunCdpbIT16/N+nrptUyik9Xnwt2fZJM3NmzQ6PU977KGn5ZoyRbs1XXklXHyx3p5rm8JhCAad29TDsK5vdXV1tp4WJSUlGUe7i4uLEZGMo92Jy8mj3bFYjLa2NkKhUMbR7sTR7UxeCalsyMamtrY2Wltbu2RTovdIPmwKBoMFP09+v7/dY8aJTTVFwt8n78yp933Iz+7/gDsn/4CqivIunSefz2frEZMteelAOCEajdrOUeukTC7aMlq8r8VJmd6qpadhjURSUXE2xcWp56OPjwi2U1aGqq7e9MCT3HFKToqTvJzcRp6WVXU1XbEpFoO5c61RhlLefVc/V1VX66lXf3MIHHIIDB/uA5Js7oZGVV2tK8/CRjubOtDF86Sqq6Gmpvua8m1TSUlHfda6TMsFtkGVl3fW6OQ87bILvPcenH22DqKZPx9mzND75tKm1at1oI7j/ZfTk7CubxMmTLD1tLDINNqdajnVCHwi1uh2a2truxdBV/bP5JWQyobu2tTa2tq+n1ObumuDE5uADnoKdZ5An6vypP/TTPvvPrKSG4/ZiUse/4S/vPYVvz/qB106T62trbYeMdmSl3ykJojaaMm1Fidl+qoWg7dInmJ1113h6qv1S9vLL9cBz6tXw5NPwjnnwPDhbis29BnKy+GBB+DWW3Vq8okTYeFCt1UZuonX7w9e0+f1IOpkfprFzEyeCKLuCl0NorYbXgmHw+3Bt9kEUVvBwOmGjOwCWa2AY7sho1zY5CTguKlJpzzPRRB1JpusOBW7gOOSkpKsg6id2GR3nqw2chFEna1NVlvZBlGbRHLep60N3n1XjzK88AJ89JFeP3iwdjU/5BA4+GDYbDN3dRoMgHYj+sUvYOxYPeXrxIkwfToce6zbygwGQxKXHTKaL1du5NqZCxkxqIIfbjvYbUnt5CWIevz48bZDe1aa7UzDKz6fz9EQkV0QdUlJSft+qepLHAJMNwxWWlqakyBqO5ush81kGxMZNGhQpyDl5PqcBFHb2WRth/RDe1aZbIOondhkd56sB/lEG5JtchJE7cQmu/PkZFjZSRB18pClwR3q6+HII2Hx4hGMGgV33LEpAPqVV7TLt98PkyZp75BDDtEeI768jPEaDDlg7721f92xx+qOxGWX6R+vueb0GLweI+c1fV7OA5GO7s7M5JU8EHnBiW93rvy/7eoxWryvxUmZvqrFkH+OOEJ3GKJRYeFCnZPh3HP189fkyXp2zDVrNiV7Gz/edB4MPYAtt4RZs/SP+U9/0kNmq1e7rcrgEK/fH7ymz0092bRtzcxUEvBx1owPWd8czmt7TjExEEZLj9DipExf1WLIDxs2wMMPwwkn6M5D4mXN59Ou44nJ3lLlZzAYPE9JiR5SmzYN3npL934//NBtVQYHeP3+4DV9PS0GIpFh/cu569TxfLOuhZ8/OJe2aCyv7TnBvCMzGLrAsjXNHHTL64y94Q0OuuV1lq3pWnZXpRRfrGxi/7/MYv+7FnSrDkP++OYb/Sx1yCE6huGkk/QL2pqaTbkYfD6d9Xn77U1+BkMv4owzdAcCYK+94L773NVjMBg6MH74AP54zA9458s1XPPcAkcv6/NJXoKoR4wYYRtEbc2pazfvbi6CqMPhMM3NzWmDc8PhsG3AcTgczklguJ1NPp/PNuA4MUt0NkHUdjaFw2HbgONEu9PZ5CSI2olNdufJClBPFUTdsKqJcx/6mKVrmqkb1MCdJ+3MkMpAJ5uUUrRFFd+va2JdUzPN4RihKLRGFeubWvjji1+yuimMAr5Y2cSPb3+TH43ZnHBMsbG1jZa2KOFIjJa2GK2RKK1tMUKRGK1tMVrborS2RYkm/M9/uaqJqffN5oWL9kprkyF/KAWLFsEzz+jP++/r9SNHwi9/CUcdBbvtBl99ZcVAKEaNEmbOdFe3wZAXJkzQow+TJ+sOxfvv6xmbcjj1oyF3JMfxeQ2v6XNTT67a/un4YSxZ2cSdr3/JdkOqmDKpNq/tZcK1IGprphu7OWqTt3cniDoxWDtVYKvf70+73QpiDYVCOQmitrPJOi7JNiZSUVHRoUx3g6jtbAqFQrYBx1aZVAHHy9Y0c+Y9s/lyVRPbDP6WaVN2ZeuqsrQ2FRcXE4rEaInGCMUChKMxwqEYDcvX8ttnFvDN+haG9ivj4oO2o39FMW3RGJGYoi26gbaoojUURvn8RKIx2qIx2qKKSFQRicV44L2vWNesOxRfrNrIT+6Yzdita9gYirIxFNGfsP4eiTnr1StgfUuE5+evoLTIR1mRn9L4pyTgo195MWXFfkoDfkrjf8uKffxj1pft7jAxBQ1rWlIGXQcCAXzGkT7nxGIwe/amTsPnn+v1u+6qY0iPOqrz6EJdHSxYAN/XL2Xzujp3hBsMhWDwYD07wFVX6biIjz+GJ57Q8RIGT+H2G2g7vKbPTT25bPuyQ0bxxcomrnt+IXWDU8/MVAhb8zLdghPh4XDYtofkpIwT7OrJVsuyNc2cOeOD+IPyV/pBeWB5yrLp6rHqqF/VRN3gym7V4VSLUoqYgqbmVmISIKoU0agiqvTD9tdrWrjk8Y9ZtraZrfqXc/URO1BTXkQoEiMUiRJqf6seZUNTMxIo6rDOKvffT78nGNJZE5esbOLgv73OtptVEY5v139jhCP6bX1b1P53s3x9Cxc//oltuUREOvqvA2wM69GBqtIAW/Qrpbw4QGWJn4qSAAGiDKgqp7wkQGVJgPJiP5UlASpKApz3wBy+WtuMUuAT2GZwJS9fvE+nNoPBYKeZpyxeWrCCL1c1EYvXUTe4Iq12MwKRG1pbdSLeZ56B557TuRoCAdhvP7joIvjxj2HYMLdVGgweIRCAm27SIxJTp8K4cfD443rmJoNnyNUzUr7wmj439eSybV/SzExP/3xPRm7W8aVsIWzt0/O1deWh3UIpRTgaozkUZWM4QnM4ypnTP2D5upZ2t5bj73qHSw4epd+gxx+Q2+Lfgy2t4At0XB+N8friVWwM66h562F7hy2qialND/wxpYjGFJFoFBEfsYT1MaWIxWBFY2v7G/QlK5vY98+vUV4cIBrT+0bjdTjlq7XNnHW/s4A6n9D+9r20yN/eebBobYsxqLKY4oCPkoA//tdHccCHxKJUlpdSEl9nrS8J+Ln4sY9JlOwTePK8SRT5fQT8QsDno9jvI9S6kX7VVRT5fBQFfAR8QpHfh98nHHTL6x0e2rcZXMmT501KaUemh//7z9it02+mq0ybsmtCJ697dRjsWb8e/vMf3Wn473+hqQkqK+Hww/Uow2GHdU7oazAYEjjuONhxRz1LwAEHwF/+AhdeaIJ/DAaXsWZmOurvb3PWjA945vw9qSkvrKthXjoQ4uDi4qRnlKveU2I9rW1RVjeFWN0U5rwH5vD9htYEf/a3OHTM5mwMR2kORdo7CJaLS3M4SnM4mtHFRQHfN4b49RPzUm4vDvgo8euHY+tT5Pe1dx426YxRXhxARM8D7BPBJ/rYCoqA34fIpvV+EUSEp+Yu76hHwfETtiLg12UDPsHnE/wioGIUFwXa11l/f/vs/I4zzghMnzpRP9gX+Skt0g/2JQEfPqUf/EsDPgL+ju42qR7a75s6MeVxCYfDaVOs//21LzrVs8vW/VPUEUhbR1ce2jP97rYeWM7LF++TUa/TOhoaGqitrU1bzq4eQ2e++QaefVZ3Gl57DSIRnQ36pJN0p2H//fWkMwaDwSE77KBjIU47TQ/XzZ4N99yjs1obXMXr9wev6esNMRCJWDMzTb57Nj9/cC4zzphIUfw5rMfFQHQ1iDpTcK4VOJsuOPf7YITzHv6E+tUbGT6gnmuPGE1ZaTHfrW1idVOYdS0R1rVEWLGhhVXBEOtaIqzZ2EZT0ltxC+3P3sari1ZQXuSnvNhPRWkRNWUBtqgqpjQA/SrKKCvyUeqHsmI//SpKKQsIN724hO8bQyj0i5lhNaXcc9JOVJSVUBzwQTRCkV8oLy0hEom0u3gl2nTUXR+ydE1z+0PyiIHl3HPyTikDjoPBYPu+yQHHn3y9jvrVGze5xwyq4NIDatMGUVdXV3c6T9PfXsrSpDp22aI0xXnS04gVS4yW5pZONt167PZc+PhClq7eyIiB5dx67Pa0tLSkzUSdzqY7Tx7LOf+ay9I1zYwYWM6dJ4+lubm5k03Nzc0UFxenDKLerMLHU2eP46uvvmK77bZDKdUe2J0qMDxxOVVgeDQabd+eKjDc+m1nm4m6udnM0JQJpfQUq1Y8wwcf6PXbbgsXX7wpCNqEkhgMWdCvHzz9NPzxj3D11TB/vl428UAGg6tYMzNd8vgnXPPcAn5/1BhHL/FzQV6CqMeNG2cbRB0MBm0DjsPhcAdXEqUU68J+PmhYy7UzF9DYqjsDS9e0cNqMjzrp6VdWxKDKYvqXBRgzrIbBlSUMqixmUGUJgypLuHbmApavb7H1Z7f0pnNrGVc7uNPb7XSuUKnqKSoq4r6pnV1jyspSBxwnZ3ZODL699/SJnbSUJ70psoKorY5I8nm6L15Hopaqqo51WOcpGAymDaLeoaqKVy7ZLOWb9q7YtE0FvHLpfhnPQSAQIBKJtG/PJhN1JpssUmlJtCndeU7ESSbqSCR1h7cvE412DIJeskSvnzgRbrhBdxpGjzZeFgZDTvH5dGD1+PF6SG/8eHjoIe0LaHCFxIlOvIjX9LmpJ59tp5qZqRC2Ou5AiIgf+BD4Ril1RP4kbSIaU8z/ZgMfNKzlw4Z1fNCwlpXBUMqyInDvlF1156CqmIEV8bf/pH/4325IVdb+7NA1lxS7OjI9JPc0LQZDrmhthVde0e5JVhB0UZF2Sbr4Yh0EPXSo2yoNhj7AoYfqqV6POQZ+9CO49lrdsTDDfAaDayTOzDQiwWskn3RlBOIi4DOg2q6gk+GTVD2jlnCUj79ezwcNa/mgYS1zl61jY0jHBmxZU8Ye2wxkQu0AJtYO4IKH5nbyi99v9GaO24JND8qppk51WkdXsavHSTt9UYuTMn1VS29l/Xr49783BUFv3AhVVR2DoE32Z4PBBerq4J134Jxz4Le/1R2K++/Pyz+kCPcCRwArlWJMfN0A4FGgFmgAjleKdTlv3ON4/f7gNX1u6sl324kzM53/0FwePWtXdsjz+19HHQgRGQb8CPgDcHE2DSbOfDR8YAVn7jWChjUb+aBhHfO/2UAkphCBUUOq+MlOW7DbNoOYUDuALWvKOtTTlaBYuw6Nkw5PrnzKjJb8tdVXtfR06uutJG0jqKuDk0/WCXFnzdJB0JtvDqecojsN++1ngqANBk9QXg7/+pcOMrr4Yhg7Fvx+hjQ0wKhRMHNmrmIkpgO3A/cnrLsceEUpbhTh8vjy/+WisZ6E1+8PXtPnpp5CtG3NzHTk/3uLn/zjPWJKOZ5htDs4HYH4G3AZkLY/IyLnAOcADB06lIaGhpTlpjz6BcvW6YDj+tUbueqZ+RT5hNGblXH8zgP5webljNm8nKoSHbRaWtpG2/oVNKzvXNc9xwxnzZo1DBw4kFhwJQ3B1Np0PemHc+y2Oy2zZs2ajNuNluzqyYXenqilt3PkkToQWilhyRK45hodw3DppbrTsOuuxjvCYPAkInpa15131v6E0SgCOr37kUfq7ItZohRviFCbtPonwL7x7zOAWfTBDoTXYgyS8Zq+3hoDkciw/uVUlATaE+d+uaqJM2d8kDa+NxtsOxAiEh86VHNEZN905ZRSdwN3A4wbN06l879fvn4hiZOg+gQ+vfYQSov8nco69cG38/W3q8dJO0aLu1qclOmtWno7ixd3TPTn9+sOhcFg6CEkJ5iLxfQ/tiOiARFJTDZ0d/x5IhNDlOI7AKX4ToTU/ssGQx/ku/WbXjzGFNSv2piXdpy819sT+LGINACPAPuLyAOZdsg0VFM3uAJffLMVu5Cq8wCdZ6XpLnb1OGnHaHFXi5MyfVVLT2fUqE0jDD6fXjYYDD2Mbv8j+yNKqQkJH7vOgyGO1+8PXtPnpp5Ctp38nF03uCIv7dh2IJRSVyilhimlaoETgVeVUqd0t8FpU3Zlm8GV+EVsYxf8/tQdi65iV4+TdowWd7U4KdNXtfR0Zs7ULkt+v2L0aL1sMBh6GPF/ZOX3U4B/5BUibAEQ/7syn415Fa/fH7ymz009hWx72pRdqRtU4eg5OxvykolaqfSZmrsyRWhra2tOem129Thpx2hxV4uTMr1VS2+nrk67Sn9fv5TNTWIqg6FnEv9HXlFfX4j/4+eAKcCN8b/P5rtBL5Kr+0y+8Jo+N/UUsu2tB5bz9Dnj8z4Nf5c6EEqpWehgpZRYmahra2ttM1GHw2FCoVDGTNThcLjdBzxVNmC/309TUxPBYDBlNuCioiJCoRDBYDBtNuC2tjbbbMCRSIRgMJgyw3GubbL0QOoMx1bWZiCjTU1NTTQ1NWVlk5XsL1PGcGsK3ExZm3Nlk915strIZFNTU1N7huh82mS1lW0m6r4QA2EwGAzpEOFhdMD0IBGWA79DdxweE+FMYBlwnHsKDYa+SV4yUY8fP942E3Vra6ttJmqlVKeZbJJ7cJWVlR16WakyHAcCgfZ6kvcvKirC5/Ol3W5lbbbsyWTTmjVrcmJT8vZkm/r3799JQ3J9lZWV7ft116aioiLbrM1WmUxZm5OzTHfXJrvzZD14J9qQbJOTTNRObILM5ylRa7r9nWSi9to0eAaDwVBIlGJymk0HFFSIB/HS2/1UeE1fX4mBKFR7rk2O6CX/eqPF+1qclOmrWgwGg8HQ9/D6/cFr+vpKDESh2pNM8QrdrlRkA7DEplg/YEMOygwCVmdZj9HifS1OyvRWLTVKqcE25TzH7iKx2dDShV0CQCRfenKE1zUafdnhdX3gfY1d1De+TKkPe1ymFxFZBXzlsgwn9xk38Zo+N/UUum0n7Q3P6tlCKZXzD3oe50KV+TDbeowW72vJod5ep6W3fJwcD7c/Xtdo9PVufT1Bo9f19aaP1+8PXtPnpp5Ct12I9vLV63cyf1uuyjjBrh6jpfvtFEqLkzJ9VYvBYDAY+h5evz94TZ+begrddt7by4sLUyERkQ+VUhPc1gFGSzqMltR4SYsX6AnHw+sajb7s8Lo+8L5Gr+szGAy5ocf5HabAS1krjZbUGC2p8ZIWL9ATjofXNRp92eF1feB9jV7XZzAYckCPH4EwGAwGg8FgMBgMhaM3jEAYDEDUvm8AAA6SSURBVAaDwWAwGAyGAtEjOxAispWIvCYin4nIAhG5yAOa/CLykYg87wEtNSLyhIgsih+jPVzU8qv4OZovIg+LSKn9Xjlr+14RWSki8xPWDRCRl0VkSfxvfxe13Bw/R/NE5GkRqSmEFq+R6th4CS9eb5IRkVIReV9EPolrvNZtTcl46RqZChFpEJFPReRjEfnQbT3JeOm6noyIjIofN+vTKCK/dFuXwWDIHz2yA4GeY/oSpdT2wO7A+SKyg8uaLgI+c1mDxa3AC0qp0cDOuKRLRLYEfgFMUEqNAfzAiQWUMB04NGnd5cArSqltgVfiy25peRkYo5TaCfgcuKJAWrzGdDofGy/hxetNMiFgf6XUzsBY4FAR2d1lTcl46RqZjv2UUmM9GgTsiet6KpRSi+PHbSwwHmgGnnZZlqELiEiFiMwRkSPc1pIOL2l0W0u+2u9KvT2yA6GU+k4pNTf+PYi+kG7plh4RGQb8CPinWxoStFQDewPTAJRSYaXUehclBYAyEQkA5cC3hWpYKfUGsDZp9U+AGfHvM4Cj3NKilHpJKWUlXHoPGFYILV4jzXnyDF673qRCaZrii0Xxj2cC3Lx0jeyJePC6nokDgC+VUm4nWfMMdqOsuR7lTNeeiBwqIotF5AsRSX559hYwCvi7Td05GUm00bhBRCIi8n3Sbv8HPBYvl7MRuWQtCcsNGY7XU+j7wN+z9a5IbD/pt/CViKxI0377sXBqV9K2dL8F23otemQHIhERqQV2AWa7KONvwGVAzEUNFnXAKuC++D/5P0Wkwg0hSqlvgD8Dy4DvgA1KqZfc0JLAEKXUd6AfDIHNXNZjcQbwX7dFGDLjketNSuI39o+BlcDLSikvafTSNTIdCngp/vbtHLfFJOGZ67oDTgQedluEx5hO5lFW21FOEdlMRKqS1o102p6I+NGdg8OAHYDJVhsiciDwLHCzA1vSjiTmUOM5wCSgX5LGhcCKePGMI3JZapkOHA5sQerjdQL6PnA1cD4pvCuyaD8CXAKMAaLo0byfJrV/LFDPpmORru5ku6yyKX8LKY5xRnp0B0JEKoEngV8qpRpd0nAEsFIpNceN9lMQAMYBdyildgE2Ujg3nQ6Iji/4CTACGApUiMgpbmjxMiJyFfqi8aDbWgzp8cL1JhNKqWjchWQYMFFExritCTx5jUzHnkqpceib6vkisrfbghLwzHU9EyJSDPwYeNxtLV7CbpTV4SjnPsCz1ptuETkbuK0L7U0EvlBK1SulwsAj6PszwH5ADXr0qL+IpHw2dDCSmCuNj6JfhGxI0rg7cBJwHvYjct3WEl/eCginOV6TgDJgMrqzk8q7olvtJ/wWJgJLgHnoF52J7U8GrgdOAc6Ov/DoVHeG312630LiMT473e/AIpBpo5cRkSL0zfxBpdRTLkrZE/ixiBwOlALVIvKAUsqtB+XlwPKEt49P4N6N5kBgqVJqFYCIPIX+x3vAJT0AK0RkC6XUdyKyBfoi5RoiMgU4AjhAmTmVPYuHrje2KKXWi8gs9JsnLwSme+0amRKl1LfxvytF5Gn0TfYNd1W146XreiYOA+YqpRy9wTR0Jt0op1LqcREZATwiIo+jR60P6kLVWwJfJywvB3aL131VvO1LgeFKqXQjhdZIYlWqjXnQ2BZfl6jx9Hj7g9EjcjsDc4CLlFIbc6hl83j7FonH6yIRqQduQsefPZ/sXZGjY7EO/cw0O67Hav+nInIZetTjI2BqN+ru9FtQSl0A7cd4dYbfAdBDRyBERNA9z8+UUre4qUUpdYVSaphSqhZ9Ml9188aolPoe+FpERsVXHYAeknKDZcDuIlIeP2cH4H7g33PAlPj3KehhW1cQkUPR/oY/Vko1u6XDkBkvXW/SISKDJT6Ll4iUoTvvi9xVpfHaNTIVogMHq6zvwMF4o/MFeO66nonJGPelbmM3yqmU+hPQCtyBvm80JZfJVH2KdckvrZ4Agmm0ORpJzLdGpdR04F0cjMjlS0uCd8VWwCDSeFdk2b51HU/8LbQfi3jdnwM/60bdGY+zUmq6Uso2xqVHdiDQb7ROBfaXTdPGHe62KA9xIfCgiMxDz8hygxsi4m/LngDmAp+if28Fy1IqIg+jLzSjRGS5iJwJ3AgcJCJL0D32G13Ucjv6TcrL8d/wnYXQ4jXSHBsv0ROuN1sAr8X/5z9Ax0B4crpUjzIEeEtEPgHeB/6tlHrBZU3JeOK6ng4RKUdfUz09QudVnIxyisgP0b7xTwO/62ITy9EPvBbD6NqkJtZIYgPa5WV/EenkTZBjjUVpNKYakRuXYy3fxdu3SDxe7d4VSqk29G9+Uq7aj/8WzkePAli/hQ7ny+XfgkYpZT7mYz7mYz7mYz7mYz55+gC1wPw02wS4H/hbhv13QY8qboN+GfcQ8Hun7aFd1uvRMYnFwCfAjk41JpXbF+22k0+N2wItyRoTyr4JjIp/vwa4OcdatgHCqY4X2pVoATr2QdAzOl6Yi/YTfgu3pjtfXak71Tl18ltw8umpIxAGg8FgMBgMnifdKKuI/EdEhuJslLMcOE4p9aXSvulTgJRT5aZqT+kpwy8AXkS7Ej+mlFrQBY1OyJXGT+Iai4EX02ixG5HLRsts9LS2PrSb0HL01KY3i8hQ5cy7olvto2dAOhUd0Kzix+ErOp4vR3WnO6d2vwWnSLw3YjAYDAaDwWAwGAy2mBEIg8FgMBgMBoPB4BjTgTAYDAaDwWAwGAyOMR0Ig8FgMBgMBoPB4BjTgXAREYnGg6Xmi8jj8Wnwegwi0pV5hw0Ggwcw1x2DwWAwZIvpQLhLi1JqrFJqDHq6sHPdFlQoRKTHZkE3GHo45rpjMBgMhqwwHQjv8CYwEkBEnhGROSKyQETOia/zi8j0+FvDT0XkV/H1vxCRhSIyT0QeSa5URE4XkadE5AURWSIif0rY1pTw/VgRmR7/Pl1E7hCR10SkXkT2EZF7ReQzq0zCfn8Rkbki8oqIDI6v2ybe3hwReVNERifUe4uIvIZOAW8wGNzFXHcMBoPB0GVMB8IDxN+KHYaeTxjgDKXUeGAC8AsRGYie53hLpdQYpdQPgPviZS8HdlFK7UT6N4ljgROAHwAniMhWacol0h/YH/gVMBP4K7Aj8AMRGRsvUwHMVUqNA15nUzbEu9FJVcYDlwL/SKh3O+BApdQlDjQYDIY8Ya47BkPfRUSuir8smBd3adzNpvwsEZmQg3ZPF5Hbu1B+XxF5Ptt2u4OI1IrISW603RMww7nuUiYiH8e/vwlMi3//hYgcHf++FToj42KgTkT+H/Bv4KX49nnoZCrPAM+kaecVpdQGABFZCAwHvrbRNlMppUTkU2CFUurT+P4L0JkNPwZiwKPx8g8AT4lIJTql++MiYtVVklDv40qpqE3bBoMhf5jrjsHQhxGRPYAjgHFKqZCIDEInbTN0pBY4CZ3p2ZCEGYFwF8sXeaxS6kKlVFhE9gUOBPZQSu0MfASUKqXWATsDs4DzgX/G6/gR8HdgPDAnjY9vKOF7lE0dx8QsgqVp9okl7R8jfcdToX9T6xPsGquU2j6hzMY0+xoMhsJgrjsGQ99mC2C1UioEoJRarZT6FkBEDhCRj+Iui/eKSGJHHBE5L8kl8fT4CwZE5BQReT8+onGXiPjj66eKyOci8jo663YnRKQi3t4H8fZ/4rRMXMMzIjJTRJaKyAUicnG8zHsiMiBeLpOb420i8k7cffLYeJM3Aj+M2/MrEdkxwb55IrJtNiehp2M6EN6jH7BOKdUc/3HvDhB/Q+BTSj0JXA2MExEfsJVS6jXgMqAGqOxCWytEZPt4PUfblu6MD7D+0U4C3lJKNQJLReS4uG4RkZ27UbfBYCgc5rpjMPQdXgK2ij/U/0NE9gEQkVJgOnBC3GUxAJyXtO8TwDEJyycAj4rI9vHveyqlxqJfGpwsIlsA16I7DgcBO6TRdBXwqlJqV2A/4GYRqehCmTHo68FE4A9As1JqF+Bd4LR4mUxujlsAe6FHZm6Mr7sceDP+QuKvaHfNW+P2TQCWp7GlT2BcmLzHC8C5IjIP7T7wXnz9lsB98ZsuwBWAH3hARPoBAvxVKbW+C21dDjyPdiuYT9ceAkC/1dtRROYAG9AXD4CTgTtE5DdAEfAI8EkX6zYYDIXDXHcMhj6CUqpJRMYDP0Q/iD8qIpejRx6XKqU+jxedgR55/FvCvqvib+l3B5YAo4C34+XGAx/E3QjLgJXAbsAspdQqABF5FB2TlMzBwI9F5NL4cimwdRfKvKaUCgJBEdmAjqECHeO1kwM3x2eUUjFgoYgMSXPo3gWuEpFhwFNKqSVpyvUJTAfCRZRSnW6c8SHFw9LsMi7Fur1s2piOfqNgLR+R8P0J9NuE5H1OT/jegO7Zp9pm6b86af+lwKGZ6jUYDO5grjsGgyEeEzQLmBWPOZqCjjFywqPA8cAi4Ol43JIAM5RSVyQWFJGj6Oi2mA4BfqqUWpy0/xAHZXajs8tjojtkgAQ3xzTtJ+4vqQoopR4SkdloF84XReQspdSrmc3qvRgXJoPBYDAYDIY+goiMSvLfHwt8he4Q1IrIyPj6U9EznSXzFHAUMJlNExq8AhwrIpvF2xggIsOB2cC+IjJQRIqA49LIehG4MN4RQUR26WaZlHTTzTEIVFkLIlIH1CulbgOeA3Zy2n5vxHQgDAaDwWAwGPoOlcAMiedyQcclXKOUagWmot18PkW/vb8zeef45AoLgeFKqffj6xYCvwFeitf5MrCFUuo74Bq0+8//gLlpNF2Pdj2cJyLz48vdKZOJk4EzReQTYAHQKVA7iXlAREQ+EZ0D5wRgvuhZ7EYD93ex/V6FKOVkZMlgMBgMBoPBYDAYzAiEwWAwGAwGg8Fg6AKmA2EwGAwGg8FgMBgcYzoQBoPBYDAYDAaDwTGmA2EwGAwGg8FgMBgcYzoQBoPBYDAYDAaDwTGmA2EwGAwGg8FgMBgcYzoQBoPBYDAYDAaDwTH/H+0vhTJ7xAbwAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -704,7 +704,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -714,7 +714,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1133,7 +1133,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1263,7 +1263,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1281,7 +1281,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAADaCAYAAAA2YqSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydeXwbxdnHv49s+XYS4gRCEhLHISTcJAQoUK5yvEDhhUKhBVrCFdq+QGmhLVdbekNbjkKhBzcUKGehXC2EK5RS7itATpwQ0gTI7duSpef9Y3bttSytVpZkyc58Px997N2ZnXme3dVqZnZ+84iqYrFYLBaLxWKxWCxBCBXaAIvFYrFYLBaLxTJ4sB0Ii8VisVgsFovFEhjbgbBYLBaLxWKxWCyBsR0Ii8VisVgsFovFEhjbgbBYLBaLxWKxWCyBsR0Ii8VisVgsFovFEhjbgbBYLEMHEUXkL57tUkRWI/JYDsreH5GNiLyFyEJEXkDkiADHnYLIdc7/RyOyXYb1nuL48LbzuaNf9mdW568RebdXXSJfR+TcvNdtsVgslqLHdiAsFstQohXYAZFKZ/tg4L85LP9fqE5HdSrwbeA6RA7M4Pijgcw6EIZ7Ud3F+ZzcJ1WktB9lJkdkOLAXqjsBJYjs6JzPU4A/9K9ILhHhfRHeFeFtEfZIk/95EWb2p66Eck4R4boM8u8vQvadzX4gQr0IJ2Z4jIjwrAjDnO1bRPhMhPcS8o0UYY4Ii52/m3mOv1aEJc61meE5ZpaTf7EIs9LYcYUIX8jEdovFMrixHQiLxTLU+AfwRef/E4C/dqeI7I7IS85bhJcQmersPw+RW5z/d0TkPUSqfGtRfRv4GXC2c9xoRB5E5DXns3ev/CJ7Af8L/NZ5kzAZkdlO3necY/3r7F3e84j8CpG5wLkp6xepQ+Qpx+c/I/IRIqN8So4DZYgIUAlEge8D16IaDWxft5nsCRwBzFBlJ+Ag4ONMy9kEqIfMOhDA4cA7qjQ527cBhybJdyHwjCpTgGecbYDDgCnO50zgj2A6HMClwB7A7sClbqcjBb/3lGmxWDYBbAfCYrEMNe4BvopIBbAT8IonbQGwL6rTgR8Dv3L2/w7YGpEvAbcC30C1LUBdbwLTnP+vAa5GdTfgWOCmXjlVXwIeAb7vvEn4EPgbqruhujMwHzg9RT1f8UxhOtWzfwSq+6F6pU/9lwIvOj4/Akzw9Ui1GXgQeAtYCmwEdkP172nORSq2BNao0mmKZ40qKwFEOFCEt0SY54yel3sPFOFbIvzGs32KCL93/v+aCK86bzT+LEKJs/9UERaJMBfo3YnrKafaqe81p/6jguZxbHhYhEdFWCrC2SKc5+R52Wl8I8JkEf4pwhsi/EvE3Cci3OaM+r8kQqMIX3aqvBzYx/HnuyJs7/HvXRGmJHHlJKD7uqjyArAuSb6jgNud/2/HvAlz99+hiqryMjBChC2B/wHmqLJOlfXAHOBQEUoc+99zrtl3nXo/AupEGJPsfFsslqFH7l57WywWSzGg+i4i9Zi3D08kpA4HbkdkCqBA2DkmjsgpwLvAn1H9d8DaxPP/QcB2SPeuYYjUpjl+B0R+AYwAaoAnU+S7F9Wzk+5PX/++wDEAqD6OyPo0NoHqb8BpuIvcBPwYkTOAQ4B3Uf1F2jJ6eAr4sQiLgKeBe1WZK0IFZsT8QFUWiXAH8C1MZ87lAeA/wA+c7a8AvxRhW+f/vVWJivAH4CQR5gA/BXbFdHyew3SEErkEeFaV00QYAbwqwtMZ5NkBmA5UAEuAC1SZLsLVwMmODzcA31RlsTNl6w/QPc1nS+DzmM7nI46fFwLfU+UIAKejdI0qd4lQBqaDlMDewDeS7E9kC1VWAaiySoTNnf3j6P02aIWzL9X+XYBxquzg2DjCk+dNx54HA9hjsVgGObYDYbFYhiKPAFcA+wN1nv0/B55D9UtOJ+N5T9oUoAUYm0E90zFvDsC80d0T1fZeOUQSj/FyG3A0qu84HZj9M6gbjObDxa9+zbBc99jpzn+LgGtQ3ReRexCZguriIEWo0iLCrsA+wAHAvSJciPOGQ5VFTtbbgbPwdCBUWe2M0n8OWAxMBf7t5NsVeM05vZXAZ5gpN8+rstpx/V5gmyRmHQL8rwjfc7Yr6Ptmxi/Pc6o0A80ibAQedfbPA3YSoQbYC7jfc/m9b1ceViUOfCDCFklPnOk4XSLCeOBvqiQ73yMdO/pLsptTffY3Ag1O5+ZxTOfQ5TMy++5YLJZBjJ3CZLFYhiK3AD9DdV7C/uH0iKpP6d5rhMPXYEbr6xD5MukQ2Qn4EXC9s+cpXD2ESd8lyVHNgPetRC2wCpEwZjpKNqSq/4XuskUOA9+57In8HDPVK0zPCHgcCK7VAFSJqfK8Kpc6Nh5L8kZqMu4FjneOeUi1u4F7uyq7OJ+pqvzErS5AmQIc6zl+gmp3RzBInk5PvrhnO44ZmAsBGzzH7qLKtp5jvMcnPQ+q3I3RzLQDT6YQKXeJBPod/9SZmoTz9zNn/wpgK0++8cDKVPud6Uw7YzreZ9F7ml6FY6vFYtkEsB0Ii8Uy9FBdgeo1SVJ+A1yGyL/pPSXkauAPqC7C6BAuR2TzJMfvg7uMq+k4fBvVZ5y0bwMzneVPPwC+meT4e4DvO2VMxnRAXsHMMV/QD0+9pKr/p8C+iLyJGVVf3n2EyBOIJB81FjkaeA3VlahuAP6DyDxAUX0nqFEiTE2Yv78L8BHG33oRtnb2fx2Ym6SIv2Hm7J9Az5StZ4Avu1NxnFWGJmLO5f4i1IkQBo5LYdaTwDkipvEuwvR+5kmKI2peKmLqd1Y72jnNYb06lyI0AI2qXIt5o7ZTkmMWAg0BTHoEuldSmkWPbuIR4GTHvs8BG52pTk8Ch4iwmSOePgTTiRkFhFR5EHPvzvDUsQ30Xv3JYrEMXUS1f2+2LRaLxTIIEVkGzER1zcBUx66YVXpGAF0YzcCZqqwR4UDMVLNS4DXgW6p0ivA8Rg/wulPGY8B2qj2NZRG+AlyEGQiLAmep8rIIpzr7VwFvAyWq9NKPiFCJmSq1F+YNwDJVjhBhf6feI3zynALMdMsUYZmzvcabJsIkzKpGW2Le4Nyjys9EuA14TJUHnONbVKlxOjz/BEZhprZVAF9zfPsEOFG1t0BahB8Bq1TNmwAR/oqZBjcK+BS4VJWbRagD7sNMwVoOHKfKOqdzdB1m5aY24FTPOT8NuNip6peq3Op0gm6lZ/DxIlX+4dj+LrCjKl1YLJYhj+1AWCwWy6bEAHcgLPnDmY50hyoHF9iOL2GW6f1RIe2wWCwDx5CYwiQiJ4nIU+lzFi8iUi8iKv0ICCWGW0VkvYi8mg/7ig0RuU1EImIaQwNV5zYi0iIiMTEr0lgsgw/Vett5GBo4041uFCeQXAEpBa4ssA2WIYqI/ERE7iy0HZbeDJoOhIgsE5F2pwHnfq4DUNW7VPWQQttYQD6Pibg7XlV3L7QxA8hvVLXeu0NEDhaR50SkWUTWisjbInKBmJgAKR9ETudt68T9XlR1karWAP/KpRMWi8XSX1S5zxNIrlA23K/KhkLaYAmG05aKSEIwSee3UsWsTpfL+tzBUbfd9qmIPCYi/Xprls1gq6cMFZFWj00FuXdF5EQRWSUiS0Vkf8/+ySLykogkW7q5aBg0HQiHI1W1xvNJti76pshEYJmqtqbNOYQRkeMw66nfDUxU1TrMWvHj6b2iiMVisVgsmypLMYsSACAiO2KWQs4nI5wBuJ0xi0Y8JGbp6kKxs6ctOSJZhmw6Kelwyr4csxDBORgtksu1wHmqGstX/blgsHUgkiIip4jIi57tQ0RkoYhsFJE/iMhc75QTETlNROY7U36eFJGJnjQVkW+KyGIn/XoREU89/xaRq0Vkg4g0ishezv6PReQzEZnlKWu4iNwhIqtF5CMR+aGIhJy0EhG5QkTWiEgj8MUEn4aLyM1O7/S/IvKLZL1RETkds5Tenk5P+qcJ6eWOrTt49o123uZsLiKjnNGADSKyTkT+5dqY5pzvLyIrRORix4dlInKSJ/2LIvKWiDQ55+YnnrQKEbnTeUOwQUReE5EtPOe40XmDsNRbZhp7BLgK+Jmq3qiq6wBUdaGqnqMB16x3ytrgGZlozceojMVisVgsBeIvmICHLrOAO7wZ0vyGf8X5nR7mbB8mIp+IyOh0FavqJ2pWyPsJ8GtPm2isiDzotJeWisi3UxTxgvPX/Z3e0xmxf9ZpU6wRkbtEJGmnwA9Pu+YCEfkEs2AAInKEmDc0G8S8GdjJc8x0EXnTabPcKyL3iAkOmo464L+qugoTYLPBKe/Lzv6XM7V/oBkSHQgvYl7LPYBZhaMOs8zdXp70ozErSxwDjMZMR/lrQjFHALthesrHA//jSdsDs9pEHWak+x4n79aYFTOuE5EaJ+/vMevONwD7Yb6wpzpps516pgMzgcR152/HrFiytZPnEKDPvHtVvRmzXON/nJ70pQnpnZhlEE/w7D4emKuqnwHnY9b8Hg1s4ZyboMr6MZjVPsZhHkA3iMhUJ63V8XcEpnP0Lefc4+QdjnkrUOfY3y4i1Zie92GqWou5bm8HtGUq5k1D1lFQVXWEOzKBiQ3wL3piB1gsFovFMph5GRgmIts6A5NfARKn9qb8DVfVezGBDq8VkTrgZuAMVV2dgQ1/AzYHpjqdiEeBdzDtiQOB74jI/yQ5bl/nr/s7/R/MKmmXYQIZbotpW/wkA1u8jAFGYmZ2nCkiMzBxhb6Baa/8GXjEGZwtAx7GdMhGAvdj4tUEYTVQJyLjMVPQ33fajj/EtF+LnsHWgXjY6QG6n9lJ8hwOvK+qf1PVLkyD9BNP+jeAy1R1vpP+K2AX8byFAC5X1Q2quhx4DrNuuctSVb3VebV0L+ZG/ZmqdqrqU0AE2NrzpbxIVZtVdRlGZPZ1p5zjgd+p6sfOaPllbgXOaPxhwHdUtdVp6F8NfLUf5wxMR8fbgTjR2QdmicAtMVN+oqr6L81saa4fOb7PxUQmPR5AVZ9X1XmqGlfVdzGdtP08ddYBW6tqTFXfUFV3Dm8c2EFEKlV1laq+H9AOdz5n97V2RgI2iEibiHzdk/f4hPso6fxHEfkK5lwdq6rRgHZYLBaLxVLsuG8hDsbEZOk1SJbmNxxMIMEvYIIKPqqqj2VY/0rn70jMIOxoVf2ZqkZUtRG4kYBtHlVdoqpznLbIasxshP3SHPampw1wrWd/HLjUKasdM9j7Z1V9xWmv3I4JBPk55xPGtOWiqvoAZjnqIDbHgW9hBry/59TzM8zA845itJxPemePFBt5m9+VJ45W1afT5BkLfOxuqKqKyApP+kTgGhHxrhghmF7vR862t8PRBtR4tj/1/N/u1JG4rwbToC3zlInz/7hkdibkm4i5KVeZmTmA6ex582fCs0CliOyB8W0X4CEn7beYnvpTTl03qOrlActdn6C7+AjjF05dlwM7YM5DOaZ3DubBtRVwj/Oa8U7gElVtdRrt3wNuFhPs63xVDRJga63zd0vM/E5U9auOLS/SO2jYfar6Ne/BIqIJ29MxcxIPyXBUxWKxWCyWYucvmOlAk0iYvgRpf8NR1Q0icj9wHsFH3b24baF1wI7A2ITBvBICLlgiJujntcA+mGCMIWB9msNmqOqSJPtXq2qHZ3siMEtEzvHsK8O0dRQz3cjbfvC25XxRE4T0GceHnTCzUb4PLMMsjrMVZor654KWOZAMtjcQQViFmcoCdM+NH+9J/xj4hjNNxf1UqupLObZjDWak3ftmYwI9vfxV9Bb2TkiwsRMY5bFxmKpu3x9DnJ7ufZi3ECcCj6lqs5PWrKrnq2oDcCRwnogcGLDozZxpR14f3FGFuzFRTrdS1eHAnzAdNZye+k9VdTvMNKUjcOZjquqTqnowpiOwADMKEQR3BOWYgPlT4szjfAg4W1XfyrY8i8VisViKCVX9CDPYdjhmOlEiKX/DAURkF+A0zJuJa5Mcn44vAZ9hppl/jJnd4W2X1arq4clMT7LvMmf/Tqo6DDOdXJLkC0Ji+R8Dv0ywrUpV/4ppx40Tz0gvvdtygXCOvw74NmbwucS5Pq+RPAJ9UTAUOxCPY17/HC1G5X4WZk6by5+Ai0Rke+gWKx+XayOcKU73Ab8UkVpnitR59MwzvA/4toiMF5HNgAs9x64CngKuFJFhIhJyRELpXsn5cTdmStVJ9ExfcsVBWzs3cBMQcz5B+amIlInIPpiOgDtCUQusU9UOEdkd03Fx6zxARHZ0pnk1YTpaMRHZQkT+1+mUdAItQW1xRgDOBy4VkdkispkYpmC0HYFw7pkHgbuceZ4Wi8VisQxFTge+oMlXcPT7Da/AtGUuxug6x4nI/wWp0PmdPxu4FDPFOw68CjQ54uVKMYvM7CAiuyUpYjVmmlGDZ18tpr2wQUTGYUbxc8WNwDdFZA+nTVEtRmBei9GBdGHacqUicgzQn6X0zwDeUtW3MbMpKkVkO+AAoDFHfuScwdaBeFR6x4F4KDGDmgBJxwG/wVyI7YDXMQ1SVPUh4NeY6TNNwHsYvUE+OAcjRGoEXsQ03G9x0m4EnsSIht6k7wjAyZjXZB9gXsU9gBmV7xeq+opjy1jgH56kKZgVAFowX4Y/qOrzACLyDxG52KfYTxzbVgJ3Ad/0TDf6P+BnItIM/BjTYXIZ4/jTBMwH5mIeRiFMJ2Al5rXmfk45QX28F6PB+Bpm1GCNU+8NeF69pmE85jXodxLutYxHFSwWi8ViKVZU9UNVfT1Fst9v+GXAClX9o5qFWr4G/MIZsEvFBhFpBeZh3nocp6q3OHbEMDMgdsG8FVmDmbozPInNbcAvgX87+oXPAT/FLIe6ETOInOyNSr9wzs9szBuC9cAS4BQnLYKZ9XCKk/YVb90iMiFd+0HMwj/ngonirkabezZm6vmfMO3IokQy08sOPsSo+1cAJ6nqc4W2Z6ggJujJnao6Pl3ePNV/I2ZK1qeqOnmA6pyCeaVYBvyfqt42EPVaLBaLxWIpfkTkNkzn6oeFtiXfDDYRdSDELP31CkbQ/H3MXLiiX1PXEhxVnY0ZFRjIOhdjlrSzWCwWi8Vi2WQZbFOYgrIn8CHmNdiRmNWb2gtrksVisVgsFovFMvgZ8lOYLBaLxWKxWCwWS+4Yqm8gLBaLxWKxWCwWSx7IiwZi1KhRWl9fn4+iMyYSiVBWVlZoMwJhbc09g8VOMLbOmzdvjaqOLrQtA8GeoZC+W1lZaDMGFFWl95LhmwbW702LTP1ua9tWVV/fJAY0ve2jwfT7ZOk/g/06v/HGG0nbJXnpQEyYMIHXX0+1Mpihra2NqqqqrPIEKWPZsmX4dWYGyg5ra2FsTWdnsdk6adKkwFEsBzv/qayE1mTLjw9dPmlsZExDQ/qMQwzr96ZFpn6LvLfJaBTr6+u720dBfp8GmiC/ZQNZZqbHBs2fzW96pmmD/TqLSNJ2ScFWYYrF0scHS5cnSBnFYoe1NfM81tahiYgcCRwZLSujNBKBaNR8ACorIRaDSKRnOx6Hzk6zXVFh/nZ0mL/l5RAKQbvT/igrg5KSnu1w2Hza2npvt7eDKpSWmmPc7ZISU2ZHh6k3FDJ1dnYau0SMTZEIdHX1bHt9qKpK6ZM0NZmyhpBPga5TR4dJG0o+BblOkQhs2DC0fApwnaS52fgd1KdNAPe519DQQDQaJRaL0dLSQnNzMxUVFcRiMaLOdSsvL0dViTjXrby8HIBO57qVlZUhIt3b4XCYkpISOpzrmLhdWlpKOBymo6MDVaWkpIRwOExnZ2ev7UgkwsaNG1FVysrKuu0UEcrLy3ttV1RUEI1G6erqAujjg3e7ubmZ0tLSfvm0YcMGYrFYYJ82btxILBbr5VM8HicUCvXyqaWlhXA4nNKn5uZmwuFwUp9cm5Jdp/b29u7fetcn9zrn8jol8ymT6+T6EPTeS0ZOOxDuF6S+vr7bgWQ3E0A0GqWzs9P3ZnJvvGQnOhwOd19kSH3iW1tbaW5uTnni250Ho9+Jb2lpSeqDu+2m+33pW1pa0n7pOzs7iUajKW+m9vZ2KioqfG+mrq4u2traUt5MsViM9vZ23y+9a5+fT+4XIpVP7jnx+4K0tLT4fkFaWlrSfkFaW1uprKxM+SBraWnpcy8m+hSNRolEIr4PMu+9lswn9z7w+9K3OT+6qXxy78Whjqo+CjxKdfVsyspMIyWRxJGRxEaG2/BxSXzIJW4n1jHQ2wBVVeiwYT22DRGfepHKp3XrjP1DyScXP5/KymBEwsrPg92nAHVqbW1vv4PYOMRxn3szZ86cHQ6HCYfD1NTUUFtbC5jflYqE65DYeEucBpO4HQ6HfbdramrS5ldVqqurA5VXWlraJ93rg7stIt22ZurTsGHDetmdzqfa2lrf/O62iOBeh2Q+edMTfUq0yetTeXl5H5u819nPplQ+pcvfn+vk9SHIvZeMnHYg3C/IrrvuOjvViXe3o9Eo4XDY92YKhUK+J8ZtmHlJPPHV1dW+F26zzTbrtS/ZiXcbvYk+uNujRo3qdVwynyoqKrrzpPrSl5eXJ72hXZ/cMvzOSVlZmW+6e94T070+DR8+vE+eRJ8SvxCJPiWek2Q+uf4mSw+Hw718SeVTRUVF9zVK5lNNTU3Sh4DXJ/ec+D3Ikt1r3m3vPZLK55EjR/qek8pNZCTOYrFYLMVJkIbjQJaZ6bFB86fL55fe37RiIhd25kW05Ls07LqlcP0elP5qC7h+D7OdAnfEOJsyisWOlGV4ypl4+wzfcoKUkc6WXJThS7Gc14Dn1LeMDGzJ+3m1BKexEbbf3kyn2H57s22xWPKO+9Ubv80k+9XLgOVr2zj4qrlMvugJDr5qLsvXthXMFt/fsgKUmemxQfOny+eX3t+0YiIXduYlDsSMGTP0zTffTJ54/R6weiGggED15rDPeaAx0Lj5xM3/nZ0dlIfDfdPevAPa1/eUUTECdjrebKv2/NU4zc1N1NbU9E2b/yh0NvXYVT4Mph7ulOlBlWhXlHCpd7TYybPoyYQyamHK//TNB0SjXYTDpY4NCSyZA53NvcvZ+qA+2aLRKOHSFC+NljwDEU8ZZTUw+Qu9/VEl2tXVu4xuexSWvgCRFk8Z1VC/L8nOSVt7G1WVVT3Huiz7N0Q9wthwNUzcM0l90BXrorSkpG/ax69A1PMADVfB+N361qVKVyzWuwy3nJVvQNQzJai0Esbu0tdnVWe+ZChpGp99AF0dnnLKYfS0PnljsTgloVDPsd70tYsh5sxBlhCM2gbOeoVEHBH1G6o6s0/iUKS6WvMiot5uO5g/3/wfCsG0afD++7mvpx9YUe2mxabm9/bbm6+eamZfPZH32lR3qM6/hYVn5syZmiiiPviquSxZ3YKqkYVsPbqGOeftVxD7mpube80uKHSZmR4bNH+6fH7pmaYVo4g6k/MqIknbJQMvol6zmJ5GoELrp/DPC5Jm7fWCRUI9H7cx5pbRsR7evQcQ8+1DnLxCVSxuRiLdNAmZ/70NfzDby//j1NV7+bkS7bsPSVZGM6x8q28+oMQVkJmdfY9L3P6071PXlFHSZz/Qu/MApiOwZnEvGwBCcefJ3ssf6TmmVxmt0PTfPmUAlESiEPPU6aZHExqE0VZoW5e0PonHjD+JadGE0Zdom9OIlz62iMYhTp/9vToPAF3tECr15OkpS2NxI/ZLktar8wDQFYGaMX3yaizWc58l2vLZBz3/a7znuljyw6JFPf/H47BwYeFssVg2IRYu7Bk3sV+94DSubu0+b6pmu1DkY9nhbMrM9Nig+dPl80vvb1oxkQs78yKinjRpUkoRdXhkA6F1HyIaRyVEfEQ9bSf8nfKKSpASOqNRQCirqCQWV2Jx85YhXFbWLVytuu2A3mWMnEzkjH8lFVEvWrSICRMm9BEcV9/2BWTdEk8ZWxP/5ktJRdQdHR2Ulpb2ERzX3H4grF3cXYbWTSE6+19JRdRdXV1UVVUlFRyH/rRnH3/aZj3bR0StqlRWViYVHFfcsl+fMtpnPd1HRF1aWkpENangONl5jZ32TFIR9eJFi5g4cWIfwXHiedWRWxM79amkIupoNNqtg/D6VHnL/r3OK3VTaD/h4aQi6lgsRk1NTR8Rdcmf9upjR+uxf+32wetTSUkJ8dLSpCLqZOckeuwdfUTUIkJHV1dSEXX16sRzMplYNLrJiqjzvgrTNtvAggV0D+dts03RrIRjV2EaQj7ZVZj6+NRQP4zFH5pBvFBImTolDpGYXYUJ/1WYJo2qonFNK3GnEzFpVFXaRWbytQpTNBqltbU1p6swdXV1EYlE+rUKk7uYTlCf3AVP0q1Y5NqWyqeuri7fxYCam5uTLjITCoW6F1wp5lWYXB+yWYVp4KcwrVsKf/0qumYxMmoKnHAPjJyUNGt7e3tyYWkGZaR8dTTAdqQsI4NyiqUMGATndRBeGzuFKUc0NsKRR8IHH5gG0fvvw+TJua+nH2xqU1pcrN+bBt/9LvzudxAKKdOmCY8+CkHc39SnMC1f28bpt7/G4s9aqCor4Z/n7suEutzGYgiK729ZAcrM9Nig+dPl80vPNK0YpzBlcl6zmsIkIucCszHzM25U1d9lYmgvRk6Cs16hJcD8K7fHlE0ZxWJHyjI85XyU5iYLUkY6W3JRhi/Fcl4DnlPfMjKwJe/n1RKchgbTabjjDpg1CxYvLpoOhMUyVInH4aGH4KCD4C9/XprTjpMIWwF3AGMwk1ZvUOUaEUYC9wL1wDLgeFXWiyDANcDhQBtwiiopRjULz4S6Kuactx/fuectXl26rmCdB0jzW1aAMjM9Nmj+dPn80vubVkzkws60qzCJyA6YzsPuwM7AESIyJeuaLRaLJZ989aswdixceWWhLbFYhjxPPw0ffQRnnJGX4ruA81XZFvgccJYI2wEXAs+oMgV4xtkGOAyY4nzOBP6YF6tyzNQxw1i5sYONbYNjJR/Lpk2QZVy3BV5W1TZV7QLmAl/yOyCIOCNxTf7+5AlSRrHYYW3NPI+11ZIVZWVwzjmmZfPuu4W2xmIZ0tx0E9TVwdFH575sVVa5bxBUaQbmA+OAo4DbnWy3A27tRwF3ONLBl4ERImyZe8tyy7QtzRvqBZ80pcmZP/LxO5VNmZkeG+Zig8gAACAASURBVDR/Nr/p/U0rJnJhZ5ApTO8BvxSROqAd80rw9cRMInImpqfP2LFjWbZsmW+hyQKaZZonSBlr164tCjusrZnnyUUZ6ezMVT0DZaslQ77xDfjFL+Cqq+C22wptjcUyJFm9Gh5+GM4+u2/Q6mDESkXE2664QVVvSJZThHpgOvAKsIUqq8B0MkTY3Mk2DvjYc9gKZ9+q/lg3UGw7ZhgACz5pZo+GuoLYEIvF0v6WDWSZmR4bNH+6fH7p/U0rJnJhZ9oOhKrOF5FfA3OAFuAdzOvExHw3ADeAEVGnm3ceZA3abNbp9eJny0DZYW3NPE+ubC2WezEf62tb0rDZZnDaafCnP8GvfmWmNFkslpxyxx1m4af+T18q6QqyeIQINcCDwHdUafJbTTPJvtyvGJNjthhWzoiqMAs+aU6fOU9Eo9Gcj6JnU2amxwbNny6fX3p/04qJXNgZKBK1qt6sqjNUdV9gHWAXsrdYLIODc881y15ed12hLbFYhhyqcOONsNdeJoZjvhAhjOk83KXK35zdn7pTk5y/nzn7VwBbeQ4fD6zMn3W5QUSYNqa2oFOYLJagBF2FaXNV/UxEJgDHAHumyZ+2TKuByBxra//y5KKMYrF1KJD3OBCJ69ZvtRUccQT88Y/wne/AiBE2DoSNA2HjQOTo3vv3q2UsXBjm1uvboCUOZWVIc7PxO6hPaXBWVboZmK/KVZ6kR4BZwOXO37979p8twj3AHsBGd6pTofCLA+Fdi39yXQUPvfMp7e0ddHX1rM0PgzcORDQaLbo4EG6ch1Q+edNsHIjkBA0k96CjgYgCZ6nq+oDHpSTI/Kts5qgVmx3W1szzWFuHJqr6KPAo1dWzKSszjZREqhKWMUxsZCR2xhIfconbF14Ijz4K991nJmon1pnvbYCqKnTYsB7bsvVpoH3I5jqtW2fsH0o+ufj5VFZmOqy5tLHQPiXUceNtMGwYHDerCpxIDlpb29vvIDb6szfwdWCeCG87+y7GdBzuE+F0YDlwnJP2BEavuQSzjOupmVaYa9zn3syZM2eHw2HC4TA1NTXd01rD4TAVFRXsNKGOu15byWdtMSbW9Z7yWpZw3hK3E39fErdramrS5i8pKeke7EpXXmlpaZ9070CZu93R0dFta2KDNJ1PVVVVfcr086mystI3v7vd0dGBex2S+eRNT1aed5/Xp3g8TnV175Am3uvsZ1Mqn9Ll78918vqQ6CP4dxy6y02bA1DVfYLkc3vY9fX1vj03gEgkgoj49kZbW1u78yfrubW1tXWnp+q5tba2+vZGN27cSDQa9e25rV+/ntra2pQ97HXr1lFbW5uy59bZ2UlzczN1dXW+owadnZ3dka+T+dTW1kZJSYlvb7Szs5N4PJ6yNxqNRtEUkajd7ebm5rS9UbdHnWrUwD0nfj3spqYmRowYkbKHvXHjRoYPH+7bw25paWH06NEpe9gtLS197sVEnyKRSPe5c++9RJ+891oynzo6OuhKEYna3d6wYQPV1dUpRw02lUjUBWGvveBznzNRrr71LTMyarFYsmLDBrj/fhNupTqPYeBUeZHkugaAA5PkV+Cs/FmUP6Y5Qur5q5qZWDfwsfWsBiJ9utVAGIK+gQiE28OeMWPG7FQ9N3fbff3j1xuNRCJ9RKfenlU0Gu2Tnthzq66u9u35VVVV+aaXlpbS1dXVZ5TAm7+2trZXGal8cvenGjUoLy9P2iP2+pQs3bsdj8epShht8qY3Nzf3iT6Y6FNFRUWf85roU2KPOtGnxHOSzCdV7bY1mU/enryfz6WlpSlHQmpqapKOInh9cjtCfiMhye41rw1dXV1pRw3S3Yu5jv5pSeD88+G44+CRR+BLvitRWyyWANx9t5ndlKfYD5sk22xRi4hZyvXQHcYU2hyLJSWBRNSZEkQDEeT1SLo8QcooFjusrZnnsbZacsrRR0N9vQ0sZ7HkiJtugl12gRkzCm3J0KGyrIRJddUsWFWYlZjy8TuVTZmZHhs0fza/6f1NKyZyYWdeOhBBUE2/olq6PEHKKBY7rK2Z57G2WnJKaakRUf/73/DKK4W2xmIZ1LzxBrz1FsyebbTQltwxbcvCrcSUj9+pbMrM9Nig+bP5Te9vWjGRCzvz0oEIYpg7/zybPEHKKBY7rK2Z57G2WnLOaafB8OH2LYTFkiU33WS02CeeWGhLhh7Txgzjo3VttHb2CbmVd/LxO5VNmZkeGzR/Nr/p/U0rJnJhZ041EJmKqDs7O31F1JFIpHs5rGQiancZKui/iLrNWY7OT0Tt1pFKRO2mpxNRp1t6LYiIuqKiwldEHY1GaWtrSymi7urqor293VdE7dbv51M6EXWq6+b1yXtdkvnU3NycdpmylpYWKisrsxZRu/eje+8l+uS9D5L51NXVRUtLi6+IurW1FcCKqAtJba2JTn3FFbBsmZnSZLFYMqK1Fe66y0iKEheZsmTPtDG1qMKiT5uZPmGzQptjsSQlLyLqXXfdNa2IOhKJUFZW5iuiFhHfZcrchpiXTEXUm222Wa8ykomoS0pKuvMk82nUqFG9ykjmU3l5eXee/oqoU6V7t8PhsO85c897YrrXJ3flIy+ZiqgTz0kyn7zC5WQ+eX1J5XN5eXnWImr3nPiJqJPda16bvPdIKp9Hjhzpe06siHqAOOccuOoquOYauPrqQltjsQw67r8fmputeDpfbLulWYlpwScD34GwGoj06VYDYSiYBsJisVgKwvjx8JWvmDkYbpAvi8USmBtvhKlT4fOfL7QlQ5NxIyqpKS9lwSobkdpSvBRMA+FOD8kmT5AyisUOa2vmeaytlrxx/vnQ0mJaQhaLJTAffAAvvWTePljxdH4IhYSpY2qZ/8nAr8SUj9+pbMrM9Nig+bP5Te9vWjGRCztzOoXJYrFY0uFqpaJlZZRGIhCNmg8YVWYsBq7Aq7IS4nFwH3buNDRXp1NeDqGQWYweTITbkpKe7XDYfBytU/d2QwPss4+ZxvR//wddXaBqji0vN+XH46bsigpTfyxmWkyVlca+rq6eba8PVVUpfZKmJlNWPnxqbzc+lJaaY9ztPPsU6Dp1dJi0oeRTkOsUifS85RoiPt305wrC4RAnH9UEraVJfZLmZuN3UJ82AdznXkNDQy8NX3Nzcx8tYnl5OVNGV/H4e5/S1NTUPf3WT6eXqBFNpT300+kF1R56NaJ+ekp32437lUoj6udTED2l14eWlhaAlLpX77l3fUjmU7LrEkT36tXuuj651zmX1ymZT5lcp1Ta3lTXKRl5EVFPmjQprYgaSCuiBnxF1CKStYjavVH8TrxrU6qbyU33+4J0dnZmLaLu6urqtifVzSQiviLqUCiUVkQdj8fTCsPTiahT+ej1qbOz0/cL0tnZmfYLEolEus9Lf0XUQFoRtfdeS+ZTKBRKK6J2H6Sbuoja1UpRXT2bsjLTSEkkIRhin0ZGYgTNxIdc4nZiHWVlcMEFcMQR8PDDcNJJ6fNnsw1QVYUOG9ZjWz58GshtCH6d1q0z9g8ln1z8fCor66syHsQ+dXbCHXfBUUfB5lOGp6xTa2t7+x3ExiGO+9ybOXNmt0bUqyVM1OltP34z7nn9v7TEwwxLoZ9Mp7vzC0qbKn9paWn373y68lJpDxO3vRpDP91rsu26urpex6TzaeTIkb753e2ysrKkmlLXJ296ok+JNnn/r6mpSasZzcV1ymQ72XXy+uCnEfVjUIuokwmCMxVRV1dXZy2iThTY5ktEHYlE0oqoU4mk06V7faqsrMxaRD1s2LABEVFHIpEBEVH397wm8zlVuhVRDzCHHQbTphlB9Ykn2vkYFksa/v53WLvWxH6w5JdpY8zv64JPmhg7YuB+G4IEAh7IMjM9Nmj+dPn80vubVkzkwk6rgRhE89+trf3Lk4syisVWSw4JheC734U334S5cwttjcVS9Nx4I0ycCAcdVGhLhj5TnQ7E/AGOSG01EOnTrQbCYFdhslgsmy5f/zqMHm0Dy1ksaVi6FJ5+Gk4/3fS9LfllWEWYcSMqWVgAIbXFEoS8PAaCvBpJnL7RnzxByigWO6ytmeextlryTmWlEVE/9hgsWFBoayyWouXmm03H4dRTC23JpsO2W9ay4JOBXco1H79T2ZSZ6bFB82fzm97ftGIiF3YWTEQdCoXSiqi9SvFkImqv2Le/ImpXuOwnom5vbycajaYUUbe1tRGNRn1F1G4d2Yio4/F4t0C5vyLqkpKStCLqaDSaVpGfTkTtnhM/EbVrt5+IOhaL+Yqou7q6KC0tzUpEHQqF0oqovfdaMp/cc+Inou7o6CAajW7yIuqi4/DD4Wc/g+22g223hUcfNas0WSwWwCz6dOutRjY0fnyhrdk0WL62jTc+Ws/6tigHXfU8t8zanQl1VekPzJKSkpKiKjPTY4PmT5fPL72/acVELuzMi4h6xowZaUXU7rJefiLqSCTSS6jrluHS3NzcJz1TEXUoFPJNLy0tpaurK+VKCe7qOt4ykvnk+pvoo7fOdCJqdxkwPxF1snMSJN3rU0dHR588mYqow+Gw73mtqamhubmZKmd1kGQ+xeNxqqurU6a7/mQronY7Qn4i6v6eVy8lJSW+58SKqAvEqaeaJSjBvIU48kh4//3C2mSxFBH/+AesXAnXX19oSzYdTr/9NTa0mUGvDz9r5fTbX2POefvlvd6Ojo6cj6JnU2amxwbNny6fX3p/04qJXNgZaAqTiHxXRN4XkfdE5K8iUpH+KIvFYhkELFzY83883nvbYrFw002wxRbwxS8W2pJNh8bVrbjL0aizbbEUE2k7ECIyDvg2MFNVdwBKgK+mOSZtxVYDkTnW1v7lyUUZxWKrJQ9MndpbFTpyZOFssViKjJUr4fHHzYs6+wgbOBpGVxPyNKW2HDEw47ZWA5E+3WogDEFF1KVApYiUAlXAymwrDjL/Kps5asVmh7U18zzWVsuA8OijJh5ESQkMH24Wun/11UJbZbEUnMZGmD7dBI1+4AGzbRkYbp61G5NH11AiQkhgy+EDM8XVaiDSp1sNhCGtBkJV/ysiVwDLgXbgKVV9KjGfiJwJnAkwduxYli1b5ltuR0dHnznpmeYJUsbatWuLwg5ra+Z5clFGOjtzVc9A2WrJAw0NPZqHDRtgxx3N8q5vvdU3gq/FsglxyCHw2Wfm/8ZGKw8aSCbUVXVrHv74/If8+p8LeGv5eqZP2Cyv9VoNRPp0q4EwpO1AiMhmwFHAJGADcL+IfE1V7/TmU9UbgBsAZsyYofX19b7lJhOdZponSBkAfrYMlB3W1szz5MrWYrkXg55XSwEZMQJuvx0OPBB+8AO47rpCW2SxFIT334cPP+zZtvKgwnHynhO54YUPueaZxdx26u6FNsdiAYJNYToIWKqqq1U1CvwN2CvbihNXzelPniBlFIsd1tbM81hbLQXhC18wEaqvvx7++c9CW2OxDDgffGC+BqWlPfKgUMjIhSwDT3V5KWfs08DzC1fz9scb8lpXPn6nsikz02OD5s/mN72/acVELuwM0oFYDnxORKrEqKMPBOb7HWBF1P3Lk4syrK2ZM5hstQwQv/qViQtx2mlGE2GxbCLMn286D6GQWb7VlQdNm2bkQpbCMGuvekZUhbn2mcV5rceKqNOnWxG1IW0HQlVfAR4A3gTmOcfckOaYtBW7QbiyyROkjGKxw9qaeR5rq6VgVFTAnXfCmjXwrW/1xImwZEZjI2y/PVtssw1sv71V4RY5CxbAAQeY/597Dg46yExl6uoyf218xcJRU17KGZ+fxLMLPuPdFfl7C5GP36mgZS5f28bBV81l8kVPcPBVc1m+ti1je4Lmz+Y3vb9pxUQu7Az0DkNVLwUuTZfPjURdX1+fNhJ1JBLpjjgMySNRu5F8IXkk6kgkknUk6tbWVlTVNxJ1U1MTqpoyErWb7heJ2g0Cl00k6ra2NsrLy30jUUciEd9I1G5kbb9I1O3t7d3nvb+RqN1z4heJuqmpiVAolDISdVNTEyLiG4m6paWl+zr1NxJ1JBLpvt/cey/RJ++9lswnty6/SNRu+pCMRC1SDfwBiADPo3pXgS3KnunT4ac/hYsvhqOOgpNOKrRFg48jj4QFC5B43AbpyzWNjXDEEbBokZlblGUE9YULTedBFZ5/3rxxKBZEuAU4AvhMlR2cfT8BZgOrnWwXq/KEk3YRcDoQA76typP5sav3c0/z/NybtVc9N/5rKdc+s5ibZu2WlzqCDADnq8zTb3+ND1e3EFf4cHULp9/+Gn+bPSMvdaXL55fe37RkLF/bxum3v0bj6lYaRldz86zdBiTieC6uc8EiUbuNYb9I1LFYrDtSsbcMl3g83ic900jUtbW1vcpIFolaVXtFTE70acSIEb3KSOZTSUlJ1pGo3YaoXyRqEfE9Z21tbX0iHif6VF1d3aeMTCNRJ56TZD6FQiHfSNReX1L5XFJSknUk6ra2trSRqJPda16bvPdIKp+HDRvme06KLhK1SPePNiYGjLv/UOAaTEyYm1C9HDgGeADVRxG5Fxj8HQgwQurHHoOzzoJ99oEJEwpt0eBi4UKjvgWrws0V69aZwAxnnQXOoEa2nbNFi0znIRYznYdtt82duTniNuA64I6E/VercoV3hwjbYWJVbQ+MBZ4WYRtVYkEqEs9zTz3PPUl47qnnuaeqj8oAPPdqK8Kc/vlJXDVnEe/9dyM7jBue8zoKuYxr4+pW4k67Nq5me6gv43rqba/yoRMkcInTaRqIiOO5uM5B40BkhNVA9C9PLsqwtmbOYLJ1gLkNOLTXHpES4HrgMGA74AREtgPGAx87uQL9UA8KSkrgL38xLatTTulpDFuCsfXWvbcnTiyMHYOdpUvhd78zrfzNN4eTT+7pPEBWnbPFi02xXV3w7LNG+lNsqPICsC5g9qOAe1TpVGUpsATIZOmi20h47kmS554U6Ll3yt71DKso5Zo8aSEKqYGYNLr3IFz9qKohrYFYuqa1u/MA5u3fQEUcz8V1zotcPMirkc7OzrQOpMsTpIxiscPamnkea2uBUX0BkfqEvbsDS1A1k9lF7sH8YK/A/Ji+jc/AhDdeTCQcZu0gmRNfecklDL/oIpp+/GPaTjut3+U0rVmTQ6uKn6ojjmDYwoVoKAQixJuaWPvKK8RHj85ZHSXLlzPijDMoXbaMrkmT2HDjjcSK5E1RJte7ZPlyRsyeTenSpXRNmkTz+edTNm8e5c88Q9jpHESnTKHzzDPpPPhghv3gB5Q2NiLxOArExo9nTYbfp6XLSjn2pLF0RoQH7lzJqKoon+TgK5n5fR4rFZHXPTtucJaGT8fZIpwMvA6cr8p6YBzwsifPCmdfIFT1BUnx3FPnuSdZPPfGjRvXHServ7F/jtlhM257/VPmvD6fKaNy++a6s7Ozz4yDgSrzmO2G85vPWhFAgZ22KKexsTEje5ataeHHT69ixcYIW40o57LDJjB2WFmffOls8kvPNM17nVc2RbjoH8tZvqETFNzhdrfVvOWwcNo4arkgF9e5YOtNBelkZDNHrdjssLZmnsfaWpSMo2fEDcwP6B7AtcB1iHwRSLlWizdeDNXVOmawqDIvuABeeolhV1zBsBNOMILgfjJofM4Fr78OU6fy6eOPM6apiZLPf57Nzz3XzJNJE3gxMIce2h2wINzYyOizzioqnUXg633kkcYPVcJLljDyW98ySyHtsw+ceSYcdRThyZMJAzUAO+9sjlm4EInHKZ04kTGTJkGAGQBgqjp+FkSiRjC9005b9dfFpGR2n7/XpaozM6zij8DPMW2vnwNXAqfR0ybzku3D1/e5Jxk892bOnNkrTla6OEXJ+O4W43jwvWd5YH4rf/56bueb5SNeUdAy5/9nPcMrw7x6yYH84IF3efy9T5i972TqJ2weuK4T7n6aVU1G3/jxhk4ufXpV0ilB2cR26k+ae51nXzWX5es7u2/IcSMqqCor7dZ+7Dt1TL/uiUzJxXXOaQciExF1LBajs7PTV0Qdi8VSClfD4TDxeDxrEbVbn5+Iuq2tLakP7rab7ieidufZZyOi7uzspKKiwldEHY/HfUXUqppWRB2NRrvPa39F1O458RNRt7W1pRQcu2LwVIJjd7u9vZ3KysqsRNSxWIxIJOIrovbea8l8UtW0Imo3/yAXUSf/cVZtBU4daGMGDBG48UYTpfprX4NXXoGyvqNaFg8ffwwvvAA/+Yk5f9Onm+lgxx4LZ5xh/g/Y2E3JokVmDo7LYNZZLFjQe7WvUMiEga6rS57fG0H96qvhvPPgoYfgmGPSVtXYaKYttbWZaUs77ZQD+wcYVT51/xfhRuAxZ3MF4O0NjQdWZlld0ueeFui5N7wyzKl7T+LaZxYzf1UT2245LGdlF0oD0dQR5an3P+H4mVtRXlrCxYdvy9MffMpvn27k1tOCdSDaIzFWNXV2b7s6iv7YlC8NxJLVLb16s59s7OTDyw4E4Oy73+SRd1ZywWHTGF6Z39kKubjOeRFR77rrrmlF1NFolHA47Cuidlfo8eLddhtmXjIVUQ8fPrzXvmQiarfRm+iDu51oRzKfKioquvP0V0TtluF3Ttzzmmm616fa2to+eTIVUY8cOdL3vNbU1HT7myw9HA5TVlbmmw7mnGQronbPiZ+IOtm95t323iOpfB4xYoTvOSk6EXVysv5xdgcaomVllEYiEI2aD0BlpdEbOJ07KitNo9DpzHWPWrtL0JWXm4aW2/kqKzO6BXc7HDYfp0Pbvd3ebhprpaXmGHe7pMSU2dFh6g2FTJ2dnWb/735nVmO6+GL44Q9NA7iysrcPVVUpfZKmJlNWsfgUi/X4EImYifAZ+pTyOv3lL6b+I44wdXd2mgADl1wCv/ylWTnonHP679P8+fDFL5r/4/Gexnc4DCtXwqhRufcp0+sUicCGDel9uuOO3vqaUAi22QZqa43WId11+vrX4eab4Tvfgb33NrYl+NS4LMSRJ9awcJEgAtVVyvNPRdl5qsKG3N570txs/A567/UDEbZUZZWz+SXgPef/R4C7RbgKI6KeArzar0p6yNlzr6Ghodcqgs3NzX0G8vwGI8H8Nn1993Hc8mIjVz05n99/dec+A6ypBu78BrncgTRV9R248w6w+g1GutvRaLR7oC2VT4/MW01nV5xDp21Gc3Mzw8vCnH3AZH795CIef3MZB243Jq1P1zy3zJxrel45ja4to7m5uc/AXVdXF+FwOKVPbrsgmU8dHR3EYrGk10lVuwcb3cFI9zp/8Glb7zECgUmjqrrzn77neB57dxU3zV3EmXtP8L1O3oHw/lwn14eg915SVDXnn+nTp2s6Wlpass4TpIylS5cWhR3W1szz5KKMdHbmqp5c2Qq8rnn4Tvb7A/UK73m2SxUaFSYplCm8o7B9v8quqkp7zoqS009XFVGtr1ctKVHdbjvVDz8MdOiqgPmGBLvsorr77qqa4Hc8rnrCCaqg+vDD/Sv7xRdVhw9X3Wor1TlzzDUoKVEdP95cm4MPVu3oyIET2RHoet95p7F5331Vt90243uqmxdfNOf0Bz9ImrzddqqhkMkCqg0NmRWfCZne5zCvVX2eFaB/BV0FGgVdAXo66F9A54G+C/oI6Jae/JeAfgi6EPQwv7KT10c9nuceZrC1EZgElAHv0M/n3q677trtd5DfJz9+9PA8nXjBYzrpwsf0oCuf14/WtGZVnmqw37J8lPnlP/5bD7jiOY3H4937OqMxPeA3z+q+v3lW2yNdvsd/sHKjNlz0uM6+9WU96MrnteHCx3WbS57QrS9+XF9asiZjm/zSM01bunSpbmyP6N6XP6O7/2KOHvDb57ThwseTXrNTb31Vp//sKW3r9Pc3WzK5zqnaJXlZhSkI8QCrmaTLE6SMYrHD2pp5HmtrgRH5K/AfYCoiKxA5HdUu4GzgSUxE+vtQLZ4J5wPB1VebkdZly8woqruEpqWHDz6At99OHjtDxIyW77abSX/nnczK/uc/4eCDYYst4MUXe0c7+/hjuOUWmDMHTjjB7CtmHnwQZs2C/fc3fn3wQf+jtu29N5x6Klx1VVINiHdFXYCPPsrO9IFElRNU2VKVsCrjVblZla+rsqMqO6nyv9rzNgJVfqnKZFWmqvKPTOoSz3NPRFaIyOma5LmnRfDce3GxEat74yZkSz5+p9KV+dHaVl5btp5jZ4zvtYpnWWmICw6ZzEdr27jpX6nV/bG4cuHf5jGiMsyPDpvCnPP248PLDufliw6kvq6a2Xe8zrwVGzOyyS890zRV5aK/zWPVxg7+8LVdefZ7+/PhZYcz57z9+sR8+L/9J7OuNcI9ry33tS9bcnGdCyaiDoXS913S5QlSRrHYYW3NPI+1tcConpBi/xNgAjb1h0E7hck75SLmWbHRnXfvTlWxU5jg9ttNPYceas6LO4XJ69N995lG7xFHwNy5MH58ep/++leYPdsEKnj8cRg2zJTv9enoo+HTT+HCC81yp9ddZ85BsU1hevpp08mZOdNMYerqMvmzuU4//zk8/LARXT/2mPExFkM7I9TWDGPDRvMMCoWUqVvHoSOal3tvIKYw5QtN8dzTHD33cjWFSUT4aG3P/H4z37+lO2htf6cwtba2ZjU1JtkUptbWVl+f7nl5GSJwxA6b99Ea7rxFGQdPG8V1zy7hyB23YGSF9PHprldX8M7HG7jyyztSRrRb91pTFubPJ+zI1257k5NveYV7ztidrUaUdWtXXR+S+dTa2prSp9bW1pTXqaurq88Upr+9/QmPv7uO7x44mZ3G1vjqKXcZP4yZE4bzp+eX8KWdRlNd4R88uL/XyfUhmylMeRFRT5o0Ka2IOhQKpRVRA74iahHJWkTtXmy/E9/Z2Uk8Hk95M7npfl8Qdw5gNiLqeDzebU+qm0lEfEXUJSUlaUXUXsFwf0XU7jnxe5BFIhFExHcupnsvpPqCdHV1dW/3V0TtRvD2E1F777VkPoVCobQialecCsHmFwAAIABJREFUPshF1FmjjlaK6urZlJUlFyMnBOXr08hIXMEn8SGXuJ1YR7bb06aZ0WIwjaKpU2HEiNT5Aaqq0GHDes9P91JonzLdhuTXSdV0Dg46yMzjBxP8rLy8tw/19SZ68uc/b+bwP/ts73OYWOdNN8E3vgF77mkax4l5vfkvuMA0qn/0Ixg5En7/e9NY7a9PXjK5TmVlyX16+mkjxt9lF3jySRg+vHe6n41+2yNGwOWXm/P0+OOmDuAPt1axYaORhaxfD1OnCo8+WgIVJZn7FMAmra31/z5sggsQuM+9mTNndmtEvVpCP52eS6JOr2F0jRHlOsuBNoyu8dV8JmpE+6s9dEmlPUzc9mpAE30qLQ3z2Pur2WtyHRNH9xWD19XV8ZOjh3Pglc9z+ZOL+ePXdu2V3tRVwrXPL2PfbUZzzK5bdWsbXBq2HMmdZ3yO4/70H067403u/+aejB1Rm1JT6vrkTU/0qa6uLqXu1W3TuCz+tJkb3ljP57cexTkHTiUUkrTn9ZyDpjLrlld5auF6jp+5Vdr8/blOXh+C3HtJSTavKdtPEA1Ea2v6uXrp8gQpI90cw4Gyw9qaeZ5clBFkjmkx2UqxaSDy+RmsGghVMz998mRVUB01ymogvPz73+a83H579y5fv++/3+Q/+WSjj0jGb39r8hx6qGqA75qqmrK+/31z3EUXZeBA7kjq99y5qpWVqjvtpLp2be4rjcWM9mTzzVXXr9fnn1ctLVU94giTNBDkWgMxlD651EB8tKZVD7ryeZ14wWO67Y/+kRMNRJDfslyW+UrjWp14wWP64Bsf+x77+2cW6cQLHtMXFn3WnRaPx/X0217TqT98QpevbfWta96KDbrDj/+pX7jiOV3b0pnVb3rQtPZIlx5y1Vzd5Sf/0E+b2n3r8xKPx/Xwa17QA377nHbFUjwTsyST65yqXVKwORUx7xSAfuYJUkax2GFtzTyPtdVStDQ0wJIl8OUvmyklm21WaIuKh7vvNiPaRx8dLP+Xvww//amZxvPb3/ZOUzWrXX3/+3DccfD3v/d9Q5AKEfj1r81o/GWXmZH5QvPKK2blqPp6o9MYOTL3dYRC8Mc/wpo1LD/3So47DiZPhjvvNEmWocOEuirmnLcfp39+El0xpa4m+zc7+fid8ivzwTdWUFVWwqE7jPE99ox9GphYV8VPHnmfSJeZv//k+5/w9PxP+e5B27DVyCrfunYYN5wbZ81kxfp2Tr31VZraOpPmC2Jz0LSfP/YBCz9t5qIvjGfz2uBxb0SEsw7YmsY1rfzzvU8CH5cJubjOBdNAeIUy/c0TpIxiscPamnkea+vQZEhoINx56OeeCw88AFdcYRq5sGlrIFpa4J57TCO5rKxn/n8yDYTXp/POg3nzjG5h/Hg46iiT/u1vm/gbJ58Mf/qTyd/WlplPv/ylmUJ10UXmmFNPzcynXGkgPvjAnJfRo414uqrKpOfjOjU00H7KtzjmlqPoqOri4cfaGK5x6ByY79Ng1kDki3xoINztfSdvxs0vLmXOux9z4LRRWWkgWlpacq6BaGlpSepTeyTGY++u5KBpoyjRGJFIvM906JaWlu7tSw6bypl3vsWfnl3AV3fbikv//j7Ttqjh+F1G09bW1j1tHUg6xXvXrYZx5bHbc+598zj73nncdtoehDSW1KeWlpaUPrk2JfPJnao8Z8Fq7nplOWfsPZHtNiOpNsXvOu01oZr6ukquf24x+06qSelTf6+T60M2Gggxbydyg0cDMXvhwoVpNRClpaW+X5Curq5upXiyEx+Px7t7Uam+IIsWLWLChAkpT7wbrMzvxLe3t3fPEUsVSM6NaZHqSx+NRqmurvbVQKxcuZKGhgZfDURVVVVaDQTgq4EAfL/0bv2uD8l8WrRoERMnTkypgXC/LOk0EO48w1QaiPLy8rQaiNra2pQPso8++oipU6em1UC4Nrj3XrJAcu69lswn95z7PZxbW1spKSlJ+aVfsmQJ22233RuaeUTWwUl1tdKaPMjPoOJLXzKRlZct6z2XPQmfNDYO7UjUTzxhGskPP2w6AQ6B/G5rg/32Mw3tMWNg6VLTKD3jDLjhhuyCzkWj5k3HI4+Y+BSONiDfdPv93ntmpaXqahNcb+LEvNarCrNOiPCXe8t4ZMp5HDn/t6YxP0Bkep+LvNemukN1Hk0qGmbOnKmvv/46AMuWLctJ1OFoLM6uP5/DIduP4Yrjds6urCSxorIlVZl/f/u/nHvP29w9ew/2mjwq0LEn3vAy/2lcC5hYD3/+2q78j+ftRRD7H3xjBeff/w6H7TCG606cQUmo77PFr5x0aZ80Rzn82n/RMLqG+7+xJytXLO/Xdb7v9Y/5wQPvctupu7H/1ODRuIOQyXUWkaTtkrwEkpsxY0a3SChVILm2tjbKy8t9RUKxWKxPqG2vw21tbX3SMw0kFw6HqfK8Ek8mPlHV7jzJfHIb9i7JfHL9TfTRW2e6QHJuR8VPMNPW1tbLlqDpXp+i0WifPJkGkquoqPA9rzU1Nb1sSeaTiPimu/5kG0jOjRLuF0gu2b2WeF7TCdTKysp8z8kgCSRnSeRHPzIN5uuuM0HSNmXuvttM5zrssMyPraoy57G+3oRKBtNpeOml7CNWh8Nw772mc3PKKVBTE3yKVbYsWmQE5eXlRiie584DwLXXwl/uLeOnx77DkQ9eDTdNNVO5LEOScEmIA6ZtzrMLPiMW16QN4qAMZAfiwTf/y7gRlXxuUopo60mOXbmxvVck5yueWphxB+LYXcezemMrlz+1hEsemsdlx+zYZ4ZAfzsQbR2dnHvPO6Dw+69Op6y0//MGj95lHL+bs4g/PPdhQTsQqUjrmYhMFZG3PZ8mEflOVrViNRD9wdravzy5KKNYbLUUITNmmKVIr7rKRA3eVGltNR2A447r/yo748b1DlagapbIzQUVFUZDseOO5q1RSQlsv31PZyWXNDbC9tuzxZQppo5oFJ55xogR8syzz8L555v+0Q/v3cm8+bjoIvjss7zXbSkcB227BetaI7y1fH1W5QyUBuLTpg5eXLyaL00fR8inw5N47Mfreq9W2Li691vsoPaftNtYzj5ga+557WNm/HwOky96goOvmsvytW1py0mWtnxtGwdfNZddfjmXN5dv4LxDtukT4yFTykpDzN63gVeXreO1ZeuyKiuRAdFAqOpCYBcAESkB/gs8lG3FVgOROdbW/uXJRRnFYutQYEhpINzt733PNNSuvBIuvnjT1EA88IDpRPzv//b44v3rp4Hw+jRlCixe3GPr1lsbbUUufBKhe9pcPA7z58Phh8PLL+f23jv8cFi8GInHTRnDhsFWW/XWPOThOi37rIrjjwszdUqcO65tJtRVae7JPfaA737XBNmzGoiCkE8NRDgcZu+GEZSGhCfeWcEOY6qKXgPx4OuriCv8z9QRvkvCezUQJSUl1NdVsnRtG3GFkEB9XWWvpfr9NBBen1pbW/n2AZO44z/LWN9mbP5wdQun3vYKD585M2MNxKm3vk7jmrbutyN3/mcZJ84ci4h0X+dMNBDudTl2ly259pnFXDtnIX8+aeei0kBkOoXpQOBDVfWNYRmkoZQ4naQ/eYKUUSx2WFszz2NtHZroUIgDkbi9334maNr11xsxdXWS6dxDPQ7E/fcbAfRhh/Us9+P6lCwOBCT36YknTGTvhQtNfI1HHzVTjnLlg/eNg6pZTcsbsyAX996SJb3fpHz8cc89kafr0tYGX/oKdMXg4UdKqN3K8WnGDNPBvfxy+OY3YZ998n7v2TgQfdE8xIHwbldUwB4NI3nhw/X82CmnP3EgysvLu6cE5yoORGVlZXdet4H68DurmDFhBDvWb+Hr4+jRo3vVc+upe3D67a/RuLqVhtHV3DxrN2pre76zo0aN6pU/lQ+uTa2dPSPxcYVla9qpra3tTk/mU6JN5eXlLF3b1mtq1bK17d3XL3HKd7rzmnidztingd8+uZClG6JsP7Yq7fFBrpPXh/7Ggci0A/FV4K/JEkTkTOBMgLFjx7Ls/9k78/i4yur/v5+ZyWRv0izdt6SUthQKdGHfEUWggAKCIlYpoCgI+lVEWZVF/P4UBVm0UmQRUValXwQEpKCobVlaSvc2TRe6pUmbzJLZn98fT+5kMnPn3jtLkklyP6/XvCZ3nnPPPefem3uf5XzOaW42VGQl/spMxoqO1tbWgrDDtjVzmXzoMLMzX8fpK1ttFDBuuUVVVv7Nb1QMyVDCvn2qINp3vpN7rtDGRli9Oj926WHqVFi3rruD3xspeGtqoKVF/a0VGuxFaFzzlStVjb0pU5IEbr5Z8VO++U344AO1amBj0OFT00fy48VraN7nY1Jddpx0rehtPpGs8+NPOtiwx8ud5x+a8b5a6lqr8mZyjfXlbNyrZuMdAhrry031JLdFY5Jil4POcCxFTz7w5WMm8vCSzTy8ZDMPfGlWXnTm4zpb3lsI4QbOBX6o1y6lXAgsBJg1a5Y0Y5x7PJ4UUmqmMlZ0AIbs976yw7a1Gx0dHXi93pSKjckwa7ciU1lZmTKj0RvHyUVHRUUFw4alVuC0McBw3HFw+umqlsHVV1uvVzAY8OyzKqzm0kv72xJzLF7cvcJRUaFWR5YuVWE++cB//wutrVBVhfR6EdoqSi/iF7+Ap59WGWvPOktHoLxchdZ94xtqqnraNGVTvjOCNTXBvHmMTFw9GsxZxwoM2gDijbV7uOLE7M67FuqSTyTrfP6DHbhdDubNHJN3e6zKa3KL5s/l4oX/YVd7gNFVJSyaP9dUT3LbfW9upDMcY2RlMfu8QRrrK+J68oGq0iLOO2IMTy3dxt9WvczkLv25cCwikQjbWv0pqzmZ6Mxk+PFZ4AMp5Z6MLbVhIwFer5dRo0YRDocNl8m0NK5GMJM5cOAA1YlL6b10nGx1xGIxdu/ebQ8gBgtuvVWFM/3ud6pGxFDBU0/BIYfAzJn9bYk5Elc42tsVqforX4EPP8x90OfxqBSx48fDypXsaW3t9bS9r78OP/iBylL7Q93pvS7cf7/61rgfJ50Eb7yhlivykeK1sxPOOAO2bEFIqVZ55s3r3dUkGz0wvqaMaaMqcxpA9DZCkRgvrdzJGdNHUlXW/ythE2rLeOO7J3PET/7O2TPHZNwp/9fGffz6Hxu5cPY4fn7R4ZYnYDPFvzerCIWYhE17vSx4fLnhSowRQpEYm1t8XP3n1exuDyBR/I9MdWYygPgiacKXkmFzILKTyYeOgWKrVgfECFaW1/Kx1JqP42SrwzEES8MOShK15sPMmapjds89cPHFat/BTqLetg3efRduv111yPV8yoRE3Zc+uVzw61+rlEU33AD/+7+53XvXXKPqVyxerHQnFpLLs09NWx185oIKNm0WFLvh1us9iHBJ+uuUmM1KSvjkE5g+XZ2HQw9V9+7s2XDkkdDQoGz75BOVVWvDBkVmf+459b1mjfqsW6cGI6tWwebNPXkfsZg6Zihkk6jpfRK1RsY9sbGaR/+zg5Z2PyUO4zpZeiTqcDiMz+fLK4k6HA4TCoWQUvLqqp20+UKcO3NkvN6TkU9aYTYrhOOioiIikUgPUnU6wnEkEonbFotGmT2+in+s28N3Tp3Uoy2dTx6Ph/YQXPenD2msLeP7p00kFArhcDjwdGXj03zKhUStXadtrd3ZpiSwca+XO176iM/PGodLSL759Ec0t/ppqCvnwUsOY8wwNx2BCJ94ImzY3cHmvV62tPppbguwva2TaFINuJiEphZv/DrljUQthCgDzgDylkg6Go2ahnyYyVjRUSh22LamIrmIYfJy2m+/fCSNI4xn5o0KId51111cffXVnHvuucyfP58LLriA4447jrvvvptTTjlFV8f111/Pr371qxRdd999NzfddBPPPfccDz30EEuWLAHgjjvu4JZbbuH3v/89Z555JuPGjQPgscceo7W1lf9JiIWXUhIMBvnWt77FNddcwxFHHGHo22DFoCRRJ27fdpsKZXr+efjWt7p/H6wk6ocfVn9/5Ss9ibOQHYm6r7fPO09VvL7/fpXe9fTTe8pYvU7PPw9PPKFqgWhxRG536jnJg81+Pxz76e7MrOEIXHLVMDXZn+46JXI/HA5Vb+PWWxUn4sMPVQXxRx5R8kVFalCxZYsaFGrpdOfOVftqAz0hVGraww6DL35RZXnaubP7GFOnovs/bpOo806i1nScdcR4fvfv7bzbtJ/zjxzbo90KidrpdMbtyBeJOhAIxG3929pW6ircnHbIaIqcDlOfysrKUnQa+VRaWmoor20HAgES62qdfsho7vrbWtqCMLa6ske7nj5XkZsr/rgUfyjKn646hpG16jrGYjHKk5Jo5EqiLioqorG+gs0tXmISBFDqdvLYf3ew6N/bcTsdhKMxJLCpxcd5v1lGudtFqy8U1+F2OWisK2fGmCrOPXwM46uLuX9JE5/s74xntGqsr4if/7yRqKWUfiB9pY8uaCPsSZMmGY7cAEKhEEIIwxG2z+eLy+uN3Px+f7w93cjN5/MZjkbb29sJh8OGI+z9+/dTWVmZdoTd1tZGZWWl4ayBx+OhtrbWsBK1VrE63WjU7/fjdDoNK1EHg8F41WQ9n8LhMFJKw1kDj8djOhOijajTzRpo50RvhB2LxbjtL6tYvbO9q3q2ej999Ek7gS4S0sa9Xs66/11mjquKt08bVcHNZ03D6XQSi8WIxWLcfvvtjBgxgpNPPpk///nP1NTUEIvFWLBgAQ6Hg0gkQkNDAx988AHDhg3j8MMPJxqN8vjjj7Nr1y527tzJbbfdxr333svo0aP54IMPiEQi3HLLLVRXVxOJRLjyyivjMyHnnXce//rXv+IzGMOHD2fTpk2cf/75PPDAA/zwhz/E4XAQi8U46aSTCAQC3HDDDRxyyCEsXryYF154gWOOOSZ+zmOxWHymo1ObybQxsHHqqYpMfc89itlq4UE8oPHHPyr+R0NDf1uSPX76U3j1VVVkbtWq1E6/GXbsgCuvVB3s227rFRNBLaY89pg6RGJZB22y3xCJ3I9EfsL8+d1KNm/uHlBon0QEAnD99WrAcNhhKmwtsbM0fz7Mm4dcv75PuB82UnH4uGrqKop5Y+2elAGEFYTD4bxnDNR07veF+Me6vXzl2EkUOa2tvmdqj1X5ZLlTp9Vz19/WsmT9Xi49eqKhnnA4zIPvbGXpljZ+cdHhTBlZ2aOtNzIuLpo/N4WvUFbs5OWPdnHbSz3DBAPhGJ87ciST6yvin7HDS3sUGPR4PBw9eUSKzkzQb5WoPR6PaSXqUChkWP03HA7nXIm6rKzMsN3lchGJRNLOEhQVFVFZWdlDRzqfcq1ErckajV6Tq2Int3s8npSKx8k+lZSUpJzXTCtRJ5+TRBscDgcOpwOHQ8TD3YQgPnjQEIjEerQ7nc74cZxdMbtOp5MvfelLOJ1O1qxZwwUXXMCGDRvYtk2VjtcGOIceeihPPvkkn/vc53A6nbz77rssXLiQRx55hHXr1tHa2spdd93F0qVLOXDgAB9//HEPXePGjYufA4fDgdPpxOl0MnnyZJqbmznxxBPp6OjoIVNUVER7ezvV1dV84xvf4J///Gc8RZ42+NPOTVFRkV2JerBACDWz+5nPqN7eYK4A/NFH8PHHKn3tQEZZGTz5pBoIXXcdPP649X1jMTXwCAbhD3/olQxHUsJLLymOw9q1cOyxahJ/27aek/2GMMtu5XAoPsSUKSr8DlQRvMRVi2nT4Je/ND3GnqamXud+2NCHwyH41PQRvPzRLkKRWE6VkPONxR/tJByVXDBrXH+bkoLJ9RWMrS7lrXUtXHq0cbX4fze18cBbm/jCnHFcMLtvfEmXfWr+cZP4w3+3sqnFi+xaSZhcX8FPP2/ORzPLaGWGXrmzbA5EdjL50DFQbL1t3gyevvIY/vz1Y+OfKSMq0AbIDgEHjSjv0X7bvBkpehwOB8XFxdTV1TFjxgwOHDjAzJkzaWxsZPv27XG5Cy+8kNtuuy1+b5588sn84he/4OOPP+bwww9n9OjRPPvss2zfvl1X186dOwH4xz/+wYcffsjvfvc7ALZu3cqUKVMIBoMM10kHOWLECNrb21m4cGG8wI2NIYAzzlBZfe6+uzumfjDiqadU7P5FF/W3JbnjqKNUpqInnoAXM6iV+stfqirT990HBx+cd7PefRdOOEHRNGIxeOEF9dubb6r+vNPZnVQp71i8uA8OYiPfOH36SDzBCMu2ZF69uDdmzzWdz7+/g2mjKjlkjPWkIZnaY1U+WU4IwSlT6/n35n0EI9G0evZ0BPjhS+s5eEQlPz43NQ1tf9R7WjR/LgfVV+AUIp6hyQz5sDO/yX4zgM2ByByDzdZk/kLyEt1vv3yk6XFuvfXWeBzmPffc06NNCxHSOA2TJ09m8uTJKToikQi3dYUdXNTVEUqn67TTTuO0006L/97S0sLEiRN55JFH+NrXvhb/fcKECbz33nscccQRfOYzn2HLli2cdtppigy1bRsnn5z9qN/GAIC2CnH22Wpme8GC/rYo/4jFVO7Qz3wG6uv725r84Oab4eWX1arR8cfDiBHG8itXqkHH+efn/RqvXatWHP76Vxg9Gn77W7j8csW9ht4vl9F3B7GRb5xwUB3FLgdvrN3DCVPqMto3H+9/PZ1b9wdYuaOdm8+e3qv2WJXXkzt16gieWrqN95r3M3tcRUp7JBrj2qc/JBCK8eClsyh1p2Yv643zZ4ZsVhLyYWevDCCMiK0arMSJmcnkI9asr+ywbU1FNBo1LBCj8Soy0ZGIm266iQNaBpQsdWi44YYbdDMm3XLLLQBcdtllPcK8EgcaZ3URKrU0rrfeequpTYMZgzoLE3RnLDr2WDjiCLjzTpVjU8rBlYXp7bdVheWbbzb3qVCzMOndew88oHgsV1yhiMXavZjsUyymiMM1NfDznyvbkn3KIAtT0zYX8y4pY/0GQWWFpL1DUFEBd94S4vqvd1JeISBWCt4ss2X14f+T8HiU31av0xBAX2Vh0raPn1zL66t38d1TxsfDZK1kYWpvb6eqqiqvWZg8Hg/PfLgfp0Nw+kFV8RB2Kz4Z8Sn1fNq/fz8VFRWmWZi8Xi91dXU9fDpqUhVFTsFrq3bQUDaC+vr6Hj499M/tLNvSxq2fnsjIUqmbsSiRu5vPLEzpeK/ZXiftvFq99/SQ1wFEpiTqYDBo+A8SCoXi6bD0Trx2Y0L2JGp/14PQ6MRrx0j3D6K1m5Gozf7prZCoS0pKDG+mcDiM3+9PezNFIhE6OzsN/+m14xv5ZEaiTnfdNBJ1KBSKpzxzOBxEo1GklD22NaJ9Imk6eTsUClFUVEQ0GiXWlULQ5XIhpYzr0HRHo9GUdlADCI0UDYpXoZ0rbTsWi8V9TG7XSNPawySdT9o5TvZhqJGoB30WJm27vBx+/GOV5ecvf4H58wdXFqYXXlDX6UtfMvepkLMwJePYY1VFtu99D555RvEbEqH5dO21apngtddUJiINiT5lkIXpnOMU3UBKONAuqKlRfOe6umKg2HR/Q5/6+P9JVlb29NvOwtRnWZg0fHrGKP6xvoWdfpg2Sl1vK1mYErMI5SsLUzQmeWnlWk6aUkfD6J75eMx8MuJT6vmUScYjPU7pMY21/HtLO9//1OQeWZje3tDCw+9s4ZK547lo7sS0vFe3263Lzc01C1Mu23rXKfG8Wrn39NArJOrZs2ebkqhDoRBut9vwH0QIYfgPog0CEpEpiXr48OE9dOid+ETyrp5PdXV1PXTo+VRcXByXyZZEna49+R/C6Jxp5z25PdEnbfYhEZmSqJPPid5104jI0E2K1qC1GbWDuj5aBz0ZLpcrfhxtO7kd1AAi8VjJx4DuTn+6do1Yna4dVHq5xN+0v20S9SDGvHlw+OGqQzoQqjRbRSikqk+ff76q5jzYcP31irV83XVqNWJiEqnylVfUSsX118OnP53z4QKB7sGDhvZ2qMss+sSGjThOm6bC795Ys4dpo6xzDqx0HDPFhzt97O4IcPM5mYUvZWOPVfl0cicfXM+dL6+lxR9D6+Lsau/kO39ewbRRldx+7gwcMpo3e/sL+bCz3+j5VsKczGSs6CgUO2xbLci0bYEHj4Yf18CDRyPbtuTlOPnQUSjn1cYAg8aF2LgR/vzn/rYmf3j1VbWq8KUv9bclvQOnU2XQisXga1/rWSBt717122GHqfSvOaKjAz77WTV40PKPWMqqZMOGAUYMK+Hw8dW8sXavuXACeuM99eKHO6kscfGp6SMz3jdTe6zKp5M7ZaoaeL29YR+geA/ffvpDAuEoD3xpFiVFTsNjDJT3fD7s7JUBhBXDQhYyk5jJWNFRKHbYtibhlRtxPDEPfn929+fhY6FlHcgotKzD+dsTera/cmOKGi0MSQ933XUXsViMc889l+effx6A4447jiVdReD0dFx//fWGuh566CHuuOOOOOn6jjvuAODRRx9lx44dcfnHHnuMX/ziFynHCQaDXHHFFaxYscLg5NgYVDj/fFWU6847Vdz3YMAf/wi1tXmZfS9YNDSoDEtvvaWqVYPq5S9YoOL7n3oqNfQnQ7S0wGmnwb/+pQ41fbqd8EgPQvCoEOwVgo8TfqsRgteFYGPX9/Cu34UQ3C8Em4TgIyGY1X+W9y/OmD6CFdsPsNcTMBfuQj7e/4nwBiO8vnYv58wcQ0lRKuk43/ZYlU8nN7m+nPE1pby9QQ28fvH6BpY37+fuzx3GQSMqTI+R7/PXW8iHnf2WhcmGjRSEkzgAEXNOwB133EFdXR2nn346f/rTn6ipqYkXf3O73TgcDhobG1mxYgV1dXXMmqXeJX/4wx/YvXs3n3zyCbfddht33nknY8eOjXfsb7rpJl1d3/zmNwmFQlx77bUA1NfXs3XrVi688EIefPDBOKka4PTTT0dKyXe+8514IbnFixdzwgkn5OmE2RgQcDjgqqvg299m5LTz4hutAAAgAElEQVRp3b3DgZon3+NR4T1f/Wqv1DwoKCxYoPgrN96osk0tWQL/93/wq1+pFYgcsG2bGn9t3aoOcfbZKiLKhi4eAx4Ankj47UbgTSm5Rwhu7Nr+AfBZYErX52jg4a7vIYcZY1Xo0tF3v8lBXek9J9SWmeyVX7yyahed4RgXzs68qF1/QAjBKQeP4Ln3t/Pa6t08vGQzXzxqfFZF+QY7eoVE3dDQYEqi1oi3ZlkGjEjUDocjZxK1RoI2IlFrZO50JGqt3YhErZHGcyFRa8c2IlE7HA5DErXT6TQlUUspTYnhZiRq7ZykJVGf/hMikUj8OkajUVwLj0e0bkLIGFI4kDUHEb70xZ4k6lCox7aUkou7ih6tWLGC888/n82bN7NhwwZGjhwZJzZPnTqVhQsXct555xGNRlmyZAkPPvggjz76KGvWrGHv3r3cfPPNvPPOO+zevZuVK1dy3nnnsXHjRjZs2MC4ceMIBoMEAgFuv/12fvCDHxAMBhkzZgxr167l9NNPZ9++fQSDwTipOhwOs337dsrLy1mwYAFvv/02wWCQSCRiV6Ieanj4YQBELKaC3efNG5jpMZua4MQTVdadV15R2wN1IGQFQsAjj6ilgSOOUNmDystVbz8HrFunSoV4PPD666rOg430kJJ3hGBS0s/nAad0/f04sAQ1gDgPeEJKJPBfIagWgtFSsquPzC0Y3P3yOkAtnG1u8bLg8eWm6T7zFcO/rdXPgseXs3GvlyKHoK4iO719zYEAmDFmGE/+N8bXn3wft9PB5cc3WN53KHEgBjSJWo8QnE0l6lxJ1Mlkbj2fEm3NlkStZRwyIlGnI0mbtSf6JKXMmURdWVmZOYn6S8/A05fAvo2IuinELv5jSuVpDYn7lZaWUl1dzWGHHYbP5+OII47g4IMP5p133okPcC6++GKOPfZYVq1ahdPp5NRTT+WBBx5g+/btXHbZZYwZM4aXXnqJnTt3MmrUKA499FB8Ph+zZs3i4IMPZsmSJRQXF/OFL3yBGTNm8Oabb3LllVeyc+dOPvvZz9LZ2UldXV1KJerx48fj9XpZtGgRPp/PrkQ9VLFhQ/ffsZhKrzMQMW8edBVVZNu2gTsQygSjRimiuJaOtbNTZdbK0u/33lOcB4dDLWgccUT+TB1iGKkNCqRklxBoRTvGAtsT5HZ0/TbkBhBNLd3FS2Oy53ZvY8Hjy9nU4gUgEpNc8fh7OVU97kv87p9N8b/DsRjffOqDAWN7X6Lf6kAEg8GUTmemMlZ0FIodtq2piEQiPQcGNQ3wraXd7cEgZhGTN910U7zTnm0huWAwaFpILhgMEovF+Otf/9rjd62Q3MKFC00LyZ166ql2ITmGUB2IRJ+mTFGDCI2M63Sq7REjBpZPiQMfbSB04ID5dRpIdSD07r1dCX1Pze/OTvPrlFQH4q133Zx7gYva4ZLXX+pkyiFF0N5PPuldp36rAxF1CSHe6z7JLJRSLiQ7CJ3f+p3Z2td1IJxOJ5NqS2lq9ceze02sLe0RkdGbdSCaWnzx40qgqcVrKeok2ad9+/ZlVAeitbU16zoQmk/N+7oHWlIq2xOvk1ZDQe86aZEZiT7ZdSBs2MgjYolZTXoJVgvJWcGNN95oWEhu/vz5WRWS64vzUGgYMnUgEvHyyzBvHnL9esS4cbBnD3z+8/CPf3Tnyx8IPo0fD83NaltLFZSY738w1IHQu/emTlVxR1oneOrUbl+NfEqoA/GXv8All6iSEX//u2Ds2PL+9SkR/V4HwhmRUs4hM+zRQpOEYDSgpRvaAYxPkBsH7MxQd97R13UgAH7/taNZ8PhyNrd4iUm49JiJpvUI8lUHorG+nI171QqEQ0BjfQXFxcUZ+9TXdSBcLheN9RXxc6bZnnidkm2y60DkEULoTQD0hJUZbjOZXGfJ+9IO29ZuVFRUsHv37nj9hXQwa7ci4/V648UCe/M4uehIfljYGIRobITVq9nT1MSoxkaV2efss+FTn4I334T6+v620BouuQTuuUfNLE+dOnRSBS1erMK11q/Pyu/HHlN87Llz1ViyttZ0FxvmeAmYD9zT9f3XhN+vEYI/ocjT7UOR/wAwobaM1797MrGYZN4D/+LRfzXz5WMmUuxK/67Kx/sf4JH5czj150uQEhrrylk0f25WejK1x6q8kdyi+XO5/LFlbNnnp7E+1XajffN1/nob+bCzX0nUgOFylkZgBv0lOo3ADNmTqDs7O+MVj9Mt/fj9fkKhUFoStc/nIxQKmZKoNb9zIVE7HA7D5Szorqys55PD4TAlUScvM2ZDovZ6vXHORvISXVlZWdyP0tLStEt0gUCAkpISwyW6cDjMsGHD0i6ltrW1pZSjT/ZJ49oYLaUmVrrW80lKGSd1p1t29Hq98cJ3yT7ZJOohhlNPVZ3Qc87pHkQMhKphmzap9KZNTeaygwldA8BscO+98D//oy7ziy8Ozrp7vQ0heBpFmK4Tgh3AbaiBwzNCsADYBlzUJf434CxgE+AHvpaicIjB4RD86KzpXPrIUp78z1auODF94gMrE8BWEIrEiEn43wtmcv7hI7PusGZqj1V5I7kJtWX87drj0tpstG++zl9vIx929gqJetasWaYkai1rkdFyVigUSlkKSlya8Xg8uktFiTAjUTudTsN2l8tFJBJJu9RTVFREOBxOu5yl+aT5m+xj4jHNSNRaDJ3RcpXeObHSnuhTIBBIkcmURJ28jKfnk5SSsq6l9WyXUj0eDy6XK+1SakVFBXr3YqJP2kDIaCk12/OaCJfLZXhObBL1EMPpp6t0qPPmqZQ8b74JNTX9bZUxli6F44/vbysKHk1N6rKuXduAlHDmmSqEaYAkaCk4SMkX0zSdriMrgW/1rkUDD8cfVMfJB9fz639s4qLZ46kq00/BnA8OJMCy5jYA5jbU5KQz032tyufCF822rZCQDzstFZITQlQLIZ4TQqwTQqwVQhyb01Ft2LBhw4YaOPz1r7B2rZqi3r+/vy1Kj127YPt2OOqo/rak4KEGDyClmuXbutUePNjof9z42Wl0BMI8tGRTrx9r+ZY26iqKmdTHdSds9B2sVqK+D3hVSjkNOBxYayRsZWkkefY1GxkrOgrFDtvWzGVsW20MCXzmMyq2ZfVqNaDIE/E/71i2TH0fPSRrcmUENXjo3k7M4mvDRn9h+uhhXDBrHL//dzM79utzA/P1nlrevJ+jGoYjhMhJZ6b7WpXP5Z2ebVshIR92mg4ghBDDgJOARQBSypCUMuc3nBnh1IqMFR2FYodta+Yytq02hgw++1l44QX46CNVnrgQBxFLl6o0nUce2d+WFDR++tOegwctaZMNG4WA755xMAK49+/6o9p8vKd27PfzyYFO5k6qyVlnpvtalc/lnZ5tWyEhH3Za4UA0Ai3A74UQhwPvA9dJKXtUJBFCXAVcBTBmzBiatVR/aaARZHORsaKjtbW1IOywbc1cJh86zOzM13H6ylYbgxhnnw3PPw8XXKCC5v/+dxg2rL+t6sbSpTBzZmoKUBtx3H8//OhHcO65sHEjbNggmTpVDJlkVTYKH2OqS7n8hAZ+8/ZmLj+hgUPHVvVoDwQCOc9OL+/iPxzVUJOzzkz3tSpvJmfUnm1bISEfdloZQLiAWcC1UsqlQoj7gBuBWxKFuoq+LASYNWuWnDRpkqFSPdJppjJWdAAY2dJXdti2Zi6TL1sL5V60el5tDGHMmwfPPgsXXginnKIKdG3c2J06tDF99pReRTQKy5fDl7/cP8cfAFi0CK67Ds4/H555RtVX2920RaXttWGjgHD1KZN56r9bueg3/yYUkfFUpRPyxFdYtmU/lcUupo0qoAkQG3mHFQ7EDmCHlFIrEfwcakCRFjYHIjuZfOiwbc0cA8lWG0MA552neqAffqiKl0Wj6nvevP6zaf168Hhs/kMaPP00XHmlWjj605/U4MGGjULFsJIi3C4HneEYUSnZ3OJlwePLgfy8p5Y3tzF70nCcDpGzTpsD0TvIh52mKxBSyt1CiO1CiKlSyvWotGlrcj2wzYHIHLat2cnkQ0eh2DoYoNWLCbvduEIhCIfVB1R4TDQKXTU6KC1V1X+7anLEK+Z21eCguFgFmWs1NNxuVeRM2y4qUh+tmKC23dmpAtVdLrWPtu10Kp2BQHfV4ZISdfxoFIRQNoVCEIl0byf6UFaW1ifR0aF0mfl01lnq2Fql8lhMdeIPHOgfn959V31Pn65syPQ6BQKqbYBcp0zuvb88F+Gy+WWcdEKM5/8YobizEzq7fAiFuvksA8inXK+T8HiU31Z9GgLQnnuNjY3xGkBaPaXkekxGNaVAv1ZRcp2s5FpFyXWy9vtCcdtiEppafPh8PoLBINFo1LD+UmKdrOT6S3vb/Wza6+W8maMIBAKEw2HC4TAOhyMrn/x+P+Fw2JJPRUVFdHZ2Eg6H09b+0nyIRCI4nc60Pmk69Gp/aTbpXadoNIrH4+nhk3ads7lO6epk6fmUyXXSfLB67+nBah2Ia4GnhBBuoAmTwiwykUGWBlbir3KJUbOKvrLDtjVzGdvWwQmtXgzl5VfidqtOSjLKkpbSkzsZyVyS5Idc8nbyMfp6G6CsDDlsWLdtZj5Nm9adzkcIFcZUXd0/Prz/PlRVwZw5qhOY4FMPpPOprU35PUCuUw8YXKfXXoOLLy9mzhxY/LKTskonkOCT293zmvWFDwXw/yQrKzO7V4cAtOfenDlz4nWyEuspGdUq0mBUq0jTYbSdWCersb6CjXu9ADgENNaXU15ebqn+kga9+ktrN6sB8zEH1VNSUkJJSUm81lK2PpnVlEq2yUg+sYaUXl0tzafE9kxqfwWDQd36ZFZsSueTmXw21ynRByv3nh4sDSCklCuAOWZy2gh70qRJppWoQ6FQSsVj6DkaDYVC8ZGc3sgtHA7H27OtRO3vmkkxGrlpx0hXiVprN5o1MKrabLUStd/vj1eKTjca1SpnpxuNRiIR00rU2vGNfDKrRJ3uuiX6lHhd9HzyeDymI2yv10tpaWnaStRerzflXkz2KRQKxe9H7d5L9inxPtDzKRKJ4PV6DWcNfD6VdyCdT3Ylahs9oFWqXrtWddqfeKL/bFm6FObO7Tl4GOJ4+23Fd5g+HV55BWx6k42BhEXz5/K5h96l1RdiUq3iQOQDy7a04XY5mDmuylzYxoBGv1Wi7uzsNK1EHYvFUqrzJo6spJQp7ZlWoh42bFgPHelGbpqMnk/Dhw/voUPPJ5fLlXMlaq3zbTTadDgchuess7NTtz3Rp4qKihSZTCtRJ58TPZ+cTmeP85psU6Iv6XzWqlDnUom6s7PTtBK13r2WbJNZe1VVleE5sStR2+iBxkZYswZWrlSd93vuUdwICxyzvMLvh1Wr4MYb+/a4BYylS9XYbtIklSxr+PD+tsiGjcwwobaM+y45ki8vWsqPz5sRJ1Anv0szxfLmNo4YX02xqztkNxedme5rVd5Mzqg927ZCQj7s7JXpJJtEnZ1MPnTYtmaOgWSrjSGIww+Hn/wEnnsOnnqq74//wQcqXt0mUANqPHfmmTBiBLzxhvq2YWMg4rCuFK4f7WiP/5bLe8oXjLB6ZwdHddV/yIfOTPe1Kp/LOz3btkJCPuzslQGEVQ5ErjJWdBSKHbatmcvYttqw0YXvfx+OPx6uuQa2b+/bYy/tSsB31FF9e9wCxNq1qlh4RQW8+SaMHdvfFtmwkT2qyoqYVFvGqoQBRC7vqQ+27Scak8xt6DmAyEVnpvtalc/lnZ5tWyEhH3b2W0CrlUGGmYwVHYVih21r5jK2rTZsdMHphMcfV9l3vvrV7uxMfYGlS2HiRBg5su+OWWBoaoIpU+CQQxQf/LHHVPiSDRsDHYeNq+ajHQfi27m8p5ZtacMhYNaEnokDctGZ6b5W5XN5p2fbVkjIh515DdbKhEQdjUZNSdSJBGU9EnUsFsuZRK0dz4hErRGt05GotXYjErXf78+ZRB0MBk1J1LFYzJBELaU0JVEnEoazJVFr58SIRO33+w1J1H6/35RErXE6ciFRR6NRUxJ14r2m55OU0pRErcnbJGobWWHyZPjlL+Gqq+DXv1ZVy/oCy5YN2fClaFSRpS+4oDsbq5Tw7W/D6tX9a5sNG/nAzLFVLF65kxZPkPrK4pzSjS/b0saMMVVUlvQMj8lFZ6b7WpXPJTV7tm2FhHzY2Ssk6tmzZ5uSqLW8vkYkaofDYUgY1jpmiciURF1VVdXjNz0StdbpTfZB2062Q8+nkpKSuEy2JGpNh9E50c5rpu2JPlVWVqbIZEqirqmpMTyvFRUVcX/12ouKinC73YbtoM5JriRq7ZwYkaj17rXE7cR7JJ3P1dXVhufEJlHbMMUVV8BLLylC8xlnqCnx3sSePbB1K1x7be8ep4AgJfz3v6og3DPPwO7dPdu1khw2bAwGaNmSPv6knVOnjcg6Nj4YibJi+wEuPXpiSpvNgSg8DGgOhDa7m4uMFR2FYodta+Yytq02bCRBCHjkERWEf9ll3QXCegsa/2GQr0BICStWqHFZQwMcdxz89rfq+5lnVEkOLYOtw6FKctiwMRgwY2wVQsDKrjCmbN9TH3/STjAS46iG1JRkubz7Mt3Xqnwu7/Rs2woJ+bDT5kAMoPh329bsZPKho1BstWGDkSNh4UKVHemOO3r3WEuXKv7FrFm9e5w+RFMTzJihiiYfdJCKBDvkEDjySPj5z9Xfjz+uFl+efx4uugheflkNIpxO9b14cX97YcNGflBR7GJyfUWcSJ3te2rZlv0AzEnKwJSLzmz2tTkQ1lBwHIhMYCX+KpcYtUKzw7Y1cxnbVhs20uBzn4P58+Huu+Hss+GYY3rnOMuWwcyZqZWMBzDmzesu8L15M9x/P5x8Mlx/veI61NWl7tPYaHMebAxezBxXxT837otz9rLBsi2tTK4vp64itYKxzYEoPBQcB0IjUTc0NJiSqB0OhymJWkppSKIGciZRa4RhIxJ1IBAgGo2mJVFr7UYk6nA4jNPpzIlEHYvFCIfDhiRqIYQhidrpdJqSqK1U1zYjUWvnxIhErelMR6LWfjMiUUcikXh7tiRqh8NhSqJOvNf0fBJCmJKoQ6EQ0WjUJlHbyA/uuw/eekuFMq1YAeXl+dUfi6kBxBe/mF+9/Qi/v3vwoMHphCVL+s0kGzb6HTPHVvHCB5+wpyNIbVnmsfHRmOS9rfs5Z+Zo3XabA1F4yIed/VaJ2ufzmVaijkQiPYi6mg4NPp8vpT1TErXL5aI84cWr1x6LxeIyej5Fo9EeOvR80vxN9jHxmGYkap/PZ0qi9vl8PWyx2p7oUygUSpHJlERdXFxseF4rKirw+XyUdc1spvMp8bzrtft8vpxJ1D6fz7QStd69lnxek+89PZuNzolNoraREaqqVKzNaaepOhEPPZRf/evXQ0fHoOE/LF+uxlpSKiqJlDafwYYNUKlcQfEgTpiUmkTFDOt3e/AEIszVCV8C1afItsOa6b5W5c3kjNqzbSsk5MPOfuNAxCzkMTeTsaKjUOywbc1cxrbVhg0TnHIKfOc78PDD8Mor+dU9SAjU4TDcfjsceyz4fPDEEzB9us1nsGFDw4wxw3A6BKt2tGf1nlre3AaQdgCRy7sv032tyufyTs+2rZCQDzv7bQDhcJgf2kzGio5CscO2NXMZ21YbNizgrrsUK/jyy6G1NX96ly2DykrVyx6gWLdOZVL68Y9VJNaqVWoVYvVqVZNv9WrFb7BhYyijpMjJwSMr+eiT9qzeU8u2tDG6qoRxw/VX0XN592W6r1X5XN7p2bYVEvLSd8qDHSkQQpjKJIeLZCNjRUeh2GHbmrmMbesAghCNCLEIIZ7rb1OGHEpK4A9/gH37VG/Y5VIDiqam3PQuXQpz53bnLx1AiMUUOfrII2HLFnj2WXjySaiuNt/Xhg2rEEI0CiEWiUHw3GusK+dfG1uYeecSzrj3bba1+i3tJ6VkWXMbRzXUpO375fLuy3Rfq/K5vNOzbSsk5MPOfq1EXVpaakii7uzsjDPF9UjUwWAwPorKlkTd3t5OcXGxIYm6o6ODsrKytITj9vZ2ysrKTCtRV1dX51yJuqqqypBEHYlE4rwMPZ9isRiRSMSQRO3xeHrwMrIhUbe1tVFWVmZIotZ4BekIx16vl4qKCtNK1DU1NTlXoi4rKzMkUSfea3o+6ZHbk7cPHDhASUlJ4ZGohXgUOAfYi5SHJvx+JnAf4AQeQcp70uqQsglYYA8g+glHHAE1NbB3r9pet06lG8o2dVBnJ3z0keJWDDBs3w5f+xq8+SacdZYqmzFan9tpYwhDJDz3ZMJzTyQ996TBc092PfcGwwBieXMbsa7kAptbvCx4fDmvf/dk0/22tvpp8QTThi+BfgFbq8h0X6vyZnJG7dm2FRLyYWe/kag9Ho8piToUChkSV8PhcM4k6uLiYlOSdSKZW8+nZDvS+ZQriVqTNSJRezyeODE5XXsyWVfPp+TzmimJuqyszPC8VlRUIKU0JFEnk9fT+ZQriVobCBmRqPXutUQbIpGIKYm6pKTE8Jz0I4n6MeAB4In4L0I4gQeBM4AdwHKEeAn1Uv1p0v6XI+XePrHURnokhi/lWi75gw9UjM8A4j9ICU89Bddco0xfuFAV7rawIG5jAEAImgEPEAUiUjJHCGqAPwOTgGbgC1Ky36LKx0h67gmd554weO7JQfTc2+ftLiwWk9DU4rO037Iu/sNRDekHENFoNGu7Mt3XqryZnFF7tm2FhHzYaWkAIYRopsc/rpyT64GthDmZyVjRUSh22LZmLmPb2keQ8h2EmJT061HApq6VBRDiT8B5SPlT1KxdVhBCXAVcBRAqKqI11zCbAYaOfft6TXdtQwOuzZsRUiKFINLQkPX5LXvlFYYBe0eOJJaHa9Sbfm/d5uLSy0fR1FyElILDDg2y8P49TJoYYc+WXjusJfSm34WMXvT7VClJVH4j8KaU3CMEN3Zt/8CKIinlOyLNc69rZQHR9dyTeXzujR07lubmZgBa88lZyhHjq9xsPaBW5YWAcdXuuJ1GeGvVJwwrduLy76O5Wd+fYDCYMglpFZnua1XeTM6oPdO2QrrOGnK5JhoyWYE4VUpp6algpaNkxXAzmVyd70s7bFszl7Ft7VeMBbYnbO8A0k9HC1EL3AUciRA/7BpopEBKuRBYCEB5uRw1BBmsvebza6/BOefA2rWIsjKKXnst+2Nt2gTjxzMijwXq8u23z6cyKF15JXi96jchIBIt5phTJ+T1WLlgKN7jkKnfH2d7mPOAU7r+fhxYgsUBRBpk9NwTCc89IcQPpYXn3pw5c+SkSZPibYl/9yeevHIEZ973Dv5QlIPqK1g0fy4Tas0LSK7Zt4WjGutobGhIK2OHMBXOddaQjxCmXmHHWSmRrcWj5yJjRUeh2GHbmrmMbWu/Qm8WIP0/tpStSPkNpJycbvBgo5fR2Ahr1sBPfqJ617ncb0uXFmT4UjAIL72kMiqNGKG+tcEDqDCmDRv6zz4b2SLqEkK8l/C5SkdIAn8XgveFQGsfKSW7ALq+R+RoSEbPPSllq5TyG1LKyekGDwMFE2rL+OYpkwF49hvHWho87O0IsLXVz9EG4UuQ27sv032tyufyTs+2rZCQDzutrkB0/eMKCfy2azTdA4lLdGPGjDFd+goEAikx6ZnKWNFhtnTUV3bYtmYukw8dVpYOB5KtfYgdwPiE7XHAznwo1pIthN1uXKGQ6uhqD7PSUohGoYvgTmmpiufvIrSjnb8uAjvFxSpLkEZAd7tVgn9tu6hIffz+ntudnaq36XKpfbRtp1PpDATUcR0OdcxgUNklhLIpFFKB9tp2og9lZWl9Eh0dSldv+nTppXDHHfDLX8I992Tu07590NyspvYPHDD1ydJ1CgRUWxY+RcKSJf8p5ukX3LzwooMD7YLamhhfuVRyyfkBvvndEtZtdBCLCRwOydSDYtDuzek65e3eC4W6z2E/33t588nC/5PweJTfVn3CaSU0+ngp2SkEI4DXhWCdiXw26PXnXmNjYzyBhpaMJDmZiVFCFtBP9JGcZCZd8hKjRB+HjlKDhn+t38VnDh2TNnmJlmTmXxt2AzBjRHebXuIcj8eDy+XKyqcDBw4QjUYt+9Te3k40Gk2bOCfx3Gsc1uTEOZFIJJ5MRs8nzSa969TZ2RnnF2g+adc5X9cpnU9GyYCSk8xoPli993QhpTT9AGO6vkcAK4GTjOSPPPJIaQaPx5OzjBUdW7ZsKQg7bFszl8mHDjM783WcfNkKvCct/E/m/QOTJHycsO2S0CShQYJbwkoJM/J6zLIy03M22LBr8+a+OdCll0pZWSllR0fm+y5eLCVI+fbbeTMnE783b5bykEOkdDikHD5cytpaZU5lpZRf+YqUf/ublKFQqrzTqb776hRbQZ9d7wJDpn7DKp/M4NkB8naQ3wO5HuTort9Gg1yfmR4mkfDcQ02qNgENgLurv5PX597s2bPjflt5P/UlOkMROfmHL8u7X15jSf7Wv6yS025+RYYiUUM5K+/HfO1rVT6Xd3qmbYV2naXM7Lym65dYCmGSUu7s+t4LvIgiGqWFFQ6E2WytFRkrOgrFDtvWzGVsW/sIQjwN/AeYihA7EGIBUkaAa4DXgLXAM0iZZU5QG32Oa68Fj0eVXc4US5eqmePZs/NvlwnWrFGRU2vWqAnr/fvV5Plzz8GePfD44/DZz6pJbw2NjXZhuMEOISgXgkrtb+DTKOLES8D8LrH5wF+t6+x+7gkhdgghFkid554cQs+9kiInM8dVxTMrmWHpljZmTaymyGnclczl3Zfpvlblc3mnZ9tWSMiHnaYDCCFEuRCiUvub7n/ctFADFmPYHIjMYduanUw+dBSKrb0CKb+IlKORsggpxyHloq7f/4aUB6N4DXf1s5U2MsFRR8GcOfDAAyq0JBMsXQqHHgpdKZR7G7t2qWir2bNV/bvkBD5+P9XiKdYAACAASURBVFxwQVeki42hipHAv4RgJbAMeFlKXgXuAc4Qgo2o1Kvpa9UkQUr5RSnlaCllkZRynOx67kkp/yalPFgqXsOQe+7NGj+MVTva6QwZp/ls7wyzfo/HsP6DBpsDUXjoKw7ESODFrlUFF/BHKeWruR5Yi8fKRcaKjkKxw7Y1cxnb1sEJmwPRyxwIzacFC+Dqq1WqojPPtOZTKKQGEBdd1JOInWcOhDfk5sX/K+IPT8EbS1zEYoI5s2Pcd0+ABx8pZlNTEqfB2zkw+QI2B8IiB8IYUtIEHK7zeytwuqmCAsBA4ECEQiGm1riIxCTvN+9j1rjKtLH1y7Z6kBIOG1mq60MyB8LpdGbl0/79++MFcq34dODAASKRiCUOhFYENh0HwuVy6fqk2aR3nfx+f/xdX8gcCM2HXDgQpgMIqfIhp/zj6iGTStShUIhgMGh4M4VCITwej+6J1sgvWnu2laj9XQ9CoxOvHSPdP4jWbvRPb1S12Wolar/fT0lJieHNFA6H8fv9aW+mSCRCZ2enYSVq7fhGPplVok533RJ9Srwuej55PB7TfxCv10tpaanuP4jVStShUCh+P2r3XrJPifeBnk+RSASv12v4T+/zqcI8BVeJuo8huwpOUl5+JW636qQkI6kYYkonI3n5Nfkhl7ydfIy+3gYoK0MOG9ZtW2/79NWvwi23wKOPwrnnWrN5/Xro6IBjj01dgcjiOjXtLGHePFi/voqDDxZ8//vFvPkmvPii6oNOmgQ/+pHifU+b5gDKOOciuvaBqVMFixc7QSvS2EfXycinjK6T2w3V1fm1sb99snBMWVnZ028rNg5yaM+9OXPmxAvtJhZkNSp2qsGo2Kmmw2jbrNhpUVERxx08CiGa+GB7ByccPDKt/PvbdlDkFBw3dQylbqeuD4nbyQV1rfpUWVlpWpQ2edtIPnFbrzBvYlHadAWRk21K9MntdusWOLZqk55PZvJm23qFdhN9sHLv6aFXKlHPnj3btBK1loPW6GZyOByGJ0brmCUi00rUNTU1KTdUIrQqx9rvej7V19f32E/Pp5KSkrhMtpWoNR1G58Ttdhu26+X+Tfapuro6RSbTStTJ50TPJ81fvfaioqIevqTzqaSkJOdK1No5MXqQ6d1riduJ90g6n2traw3PST9WorYxGFFSAlddpTIxNTer3roZli5V33lK4TpvHqxbB7GYYO1auPxyGD4cLrtMfY47LrVStMZpsGHDRt9jRHUFU0dWstyEB7G8uY1Dx1bFBw9GsDkQhYd82JnXAUQm0NJy5SJjRUeh2GHbmrmMbevghB3C1EchTJ2dqlDCz36muBC33mru0zvvqNn+yZNzCmGKFpXw0v85WLOmiMTU+g6HZNfadooru3xqL8zrlLd7zw5hyksI02DAQAlh8vv9HDmukpc+2kOH14uQMmXlPxiJ8dGOA1x29HjTCA0tqqKqqiornw4cOBCfPLWaxrW4uNg03CcYDFJVVZU23CcQCFBdXZ026qSkpET3OgUCgbiNhRzCpPnQqyFM2cAqidpsBGQmY0VHodhh25q5jG3r4IQdwtRHIUxuN1RVwfnnw+9/r2pDJB5TT37FCpg7V79TZ+E6tXWWsmgRPPggbN2q+pPRqOpbOhwwbZqgeGRCaEuBXqcesEOY7BCmPGCghDDFYjGOnTKSp9/bSfOBCDPHVafIf7y5lXBUctxBI1IiPPIdwlRSUpJRCFNxcXGvhzCFw+G0IUzBYHBAhDAl+pBtCFOvVKK2YcOGDRsFgmuvhbY2+OMfjeUCAVi5UmVwyhAffwxf/zqMGwc33AANDfDCCyoUafp0cDol06YpPrcNGzYKG0d1ZVZatkU/jGl5cxtCYCkDk43Bi7yuQGhLdA0NDaYkaofDYUqidjgchiTqRLJutiTqWCyGx+MxXPrRCLRmWQaMlh3D4TDBYDAnEnUsFovrSbec5XQ6DUnULpfLlEQNmBLDzUjU2jkxWqLTCN/plujC4TA+n89wiS4SiRCJRHIiUTscDlMStRkx3OVymZKotWXDoU6ittHHOOkklZb1gQcUCSFdnZ4PP1ShMRb5D9GoGhDcfz+89ZaaxP7yl9V4ZebMbrnVq2F30xZG2QUabNgoeJSUlFBZVMT4mlKWN7dxxYmp/7fLm9uYOrKSqjJrIbk2B6LwUHAciExI1IFAgOLiYsPlLCkl5UmZQBKXZgKBAGVJS7OZkqiLi4tTbEyEy+WKDyaSfUiUT/xNzyfN32QfE3WYkagDgYBue/I5Sb4xrLQn/qaVN09EpiTqsrIyw/NaUVHRwxY9n5xOp2G75k+uJOpAIIDb7TZcStW715LPq9myoxZvmK59qJCobQ5EH3IgNJ+uvhq+9S149VU4/nh9n955R+mbPl35p+NT08YoZ32hnI2bHDhdEA4Lxo+Ncc+dMa6YH6a2osungHEa10K/Tnm792wOhM2B6MJA4kCUlZUxZ0I1b2/YR0dHBw6HIz5xFwxHeK+5jc/PGms6GWlzIGwORMawORDZyZjBtjU7GTMMJFsHA2wORB9yIDTMnw833dRdyllPfuVKFYM0bVpKeygEr7zh5rLLVIFrgFhYiTdtceByOVCvkzQ+tbUp+wfIdeoBmwNhcyDygIHEgSgvL+foxjpeXLGLlqCDyfUV8faN2w/QGY5xdGNdyqSXzYGwORA2bNjII7a1+jnj3rc54u53OOPet9nW6u9vk2wMNZSXq/Cl55+HnTv1ZZYu7cF/iMXgn/+Eb3wDRo1SXGxt8KBh1y416WzDho3BhbkNit+wPIkHoaV3PcrmPwx59MoAQqSLsU2AldGNmYwVHYVih21r5jKFYKte519KSSAcZb8vxK72TppavGxuC/H+1v28u2kfb6zZw+KVO3n2ve08+Z9mPv/wu2za6yUqYXOLlwWPL8/ZZhs2MsY3v6nCRn7zm9S2lhZoaoKjj2b1alXcrbFR0SeefFItWrz8sopucnS9NRwOmDq1b12wYcNG70J7HzbWlVNX4WZZUj2IZVvamFBTxqgq66vpubynM93Xqnwu/Y9s2woJ+bCz30jUgGk8XCKxVS92LBKJxNuzJVH7fL44uTld7JjP54tzJfR88nq9cT5HOp+0Za1cSNSRSCS+f7p4OE0uXTycECJOPta7LiUlJXR2dsZtzJZE3dHREed0pIvx03gFyTF+uzxhrnryA7a0+mmoLWPhZbMYXVmk61MoFIrHMSb61Nzi5Rt/XMGWVj+Tardw17mHUOEGXyhKWDrwBiN0+IP4glH8oSjBGHT4Q/hCEQIRiT8UxRMIs3JHB6GoCsnbuNfLyf/vLcwD9NIjJqGpxRsn59skaht9hsmT4ayz4Le/hZtuoumT4njF58aRpVzAXbz6u6tZ8QM1OPj0p+HOO9XKg7aiPm1aYpVoO6uSDRuDDVoIuhCCORNrehSUk1KyvLmN06aNTLe7oc5c7Mm3vJmcUXu2bYWEfNjZKyTqWbNmmZKotaxFRvFwoVAoJZYsMbbL4/HoxpolwoxE7XK5TNsjkUjaWDGzeLhEn3IlUWskHL14t22tfhY8vpymFi+N9RUsmj+XCbVlKT55PB5dYnpJSYnS8bt/99RRqR+3mBzT53C6CEZirN/r4bo/rWBbm59xw8u4+ezpDC93E4rECEaiXd8xguEYHT4/ODsIRdW29v38+9tpD6jBwOZ9fs598D8c1VBDJCYJRWJEYpJINEYoKgmFI8QQXb/HiEQloWgMbyAS7+g37fPzxUffwwxlbidlbhcVxU7Ki12UF7vig4dEfPv0KZQWOSkpclBa5KTU7USGQwyvqujxe0nX55KF/2HLPh8xCQ4BjfUVutfRJlFjk6h72Sf51a/R+vJ/2XDHm1z06Jns2i2QUrBxZzn38CPmloe4755OLv6Sk5E1XT5EgJDyqbEmzOp3k67TAQvXySZRDx6fbBJ1VhgoJOr29naqqqpwu90cPraCV1fvZvPOVibUD2PtzgPs94eZObrMMPth8rbH46Guri4rn1pbW6msrLTsU1tbGxUVFaaEY6/XS11dXVrCscfjob6+XtentrY2Kisrda+T1+uNbxcyiVrzoeBI1EMFRp12PcRiqnMbisYIRdQnHI2xdZ+Pm//6MTv2dzK2uokfnjWdkcOKicYgJqX6xMDn91Fc0omUEI11/S7VSPLHi9ewpyOABDbt9XLhb/7N9Z86mKiUxGKSSEx9+zoDFLndRKKSqJREY7H4cZ5Zvp0Dneom2rjXy1n3/5NjGmsJRqIEw2oAEIzECISj+AIhomwkEFa/RWOpHe1tbX6uevJ9y+fT7XTgdqnVgUT4QlH2eUMUOQUup+qcu0pcuBwOhIxSWuzG5RS4nQ5cToHL4eDxfzf30CEE/OriIyh3uygrdlLRNUCoKHYRC3UyoqYapyM19O6Me99mc4s33vmfXF/Bd884OEVObzCr4fdfPSrlPhnKsEnUvUOibtrhjq8OTJkCP/uZG78fNmyAjRthwwY3Gzd+nv1cAHclGydwEmXZikSdeSTn2iTq/NnY3z5ZOKZNok7FQCNRAxx/8Ej4+yZWtwSZPKaIVbvVwPHEaaMNsx/qbWdLoq6srMyIRJ0JYTlbEnWyTYk+ud3uAUGiTvQhWxJ1rwwgBgIHIrnz//CXZ1FbXowvFMEfiuILdn93+IMEY234gpGukJcIvlCUxSt24unq7G7c6+WMX75NQ115ygAhFFGDhrDObHYytu/v5JtPfZC1XwAS2OsJ8qMXV5nKOh0CpxA4HYLOcLRHmzcYYeeBToqLHBS7HAwvd1PsclDschIJ+qmtrqLY5aCkyKl+L3Lw01fWkbgy5hDwxOVH43YpHYnfDhmlvLREbTsdOLo68Hqd9sXXnqBrfygUSnngALy7aV+KjvOOGJtGh0N38ACwaP5cS51/o3txQm0Zr3/35LS22rChh6am7nChyZNVdefyctUXT/7s3w9/+Qv4fGrfdevgvPO6dU2YoAYVl1wiOHj3O0x58WdcP+55mnaWqIlhokyt3gOM6RdfbdiwURhIfJcdMnoY5W4n7zW3ce7hY1i+pY26imIa6soNNBjrzMWefMrbHIgC40D0FazM/AfCUVo8Qdbs8bPRv4d93iAtniD7vOrzj3V7CYRjgOr8f+redywfv7TISXmxMz540BCMxBhfU4a7q0Oszai7XQ6cQlLqLurRgS7qkvn+cyuJJXW6H/vaUTiEwOFAfQtBNBKhuLioa7v7d4cDvv7E+2zb70dKNds+saaMP111LA4HuBwONUhwCqLhMKUlxTgdSkfiYE+v4/63607UPQfNzc1MmjQp5fdn39uRouOEKXW6OlSHOrUQjdVOuxE0HZtbvEzOYdbf7vzbyAe6BwMNce5AY6OKRNm1C7ZuhW3bur+3bYM33+yONNmwAc44I1WvEGqit6ame/CgweGAFSvgoIOSJpw7joDX32H63JuZt/7nrF8vmRpdx+IbPgYu7q1TYMOGjQEGl9PBrInD4xWplzfv56iG4ZYmiW0MfvQKiXrSpEmmJOpQKERFRYVhPJzX64132hJjxb766HK2tHbGw3XO+fU/ObZxOG2+CHs9AVp9IbzBxNn0LfG/hpW4qC0vig8e4rYDN501lWKnoMQlKC92MryiDLdDEgl4qa8exvBh5bgdEIuqgcPnFr5PU2Jse105D1x8mK5PHo+H2tpa3Ri/h94qY0urP66nobaMI0eXpMTD+f1hamoqdOPhHr5kBtc+szpOPP71F2ZQ4Yz0iIcjJoiFw0SdENSJW7zvwul8+9k1NO3z0VBbxn0XTicUCmVEon7okplc/fTKuI6HLpmZlhje0dFBdXV1Sozf6GFF/OXrcxJiMYvSVtf2er3U19enxPiNKHfwwpWz2Lp1K1OnTo0XTtF8SPRJ49oYxWJqFbOT70XNp0AgED9X6eIW29raKC8vtytRDyG0t8P27XD22epbSsGaNXDYYVBXB598okLBE1FbCxMndoeta3A4VCakmpruT1WVCjMHmDFDrTxooebTpqnjpGDYMPjKV2h85Nes3vEDeO01uOwyOHtlr5wDGzZsDBwEg8Eek2VzJ9Xwyzc2sHZXB58c6OSKExty1tmb+1qVN5Mzas+2rZCQDzstDyCEEE7gPeATKeU5ejK9RaL2BiMs27qf5c1tLNvSRlNrd0dLAh2BCJta/NRVFHPYuGrqKoqpryymrsJNzN/OjMnjqasoprbCTbFLvW31ZtuvOOkgXd89nlLd+PZHdWLbjXxKR6L+/deOTpktr6zsXlFJjIdLR6I+pLycN783QjcWP5lEna74yyGVlbzxP/o6rFairqyEN/7nlBQdejF+Usp4dWezWEyjGL9cK1FrAyGjWMxkonyyDZFIxDRu0YzQP1RI1IMBTU1wzjlqZWDcOLj2WrVaoK0ebN+uvjs69Pf3++Hkk1Vo0cSJ3d/jx6swJdAfEJx5ZnqbFi/OIEPSNdfAQw/B736nlkDKy9UBbdiwYSMBcyfVICU8vGRzfNuGDchsBeI6YC0wzEzQyvKW0chnnzfI8i1t/HdzCx9s72D1zvZ4R3/GmCqqS4to7wwj6e78v/7dk3V1NTc3M2lcdcrvmYTJpLNVC28JBoOm8WRG/mp60oUFWdFhVSYfOqzAtjU7GRt9j0SugdbxnjQJ9uzpHghs397z7/ff71452LoVvvc99XddnRoMHHQQnHqq+nvCBPjBD9S+iYOBJ54wtiujAQEqJGr1aotOT58On/oUPPww1NfDnDndSxk2bNgYskh+Tx05oZoip+D/PtpJZbGL6aNNu4CmOntzX6vyubzTs20rJOTDTksDCCHEOOBsVO6O7+ZywGT+wiPz5yAQLGtuY/mWNpY3t9G0TwXzFrscHDmhmmtOPYi5DTUcOWE4FcUuXQ5Epsgktt1sQGRlwJSPmMF8HMe2tXeO01e2DgZkksa1aVcp8y4oYv0GwdSDYix+MUJjgzRMO9m01cm8z7lYv8nB1CmSPz4Zoaakkw6PoN1fRIffRfu+MB0d0O518rNfuWlt1cKLJNOmKVXhcM/rVV4uGT82xvjxglhMoAIfFZwOScf2dpXsRieV5pwZMeZd4Gb9RgdTp0oWPxuCA10+pEml2VjTqVKmJqbSPED+Up6ecw688Qbs2KHiplatUksgvZEe1E7jOnh8stO4ZoWBksY1EAgQiUTiYbbN+7w4hCDclbVx/Y4WJtSUpU0Pqhe2HgqFEEJk5ZOWFtWqTz6fj1AoZJryNBwO43A40qY8DYVCOBwOXZ+0Y+hdJ83fRJ8KMY2r5kMuaVyFlWISQojngJ8ClcD30oUwaZg1a5b84AP9TEJn3Ps2m1q88Uw9ToeIpwCtKi1izsThzG2oYe6kGhqqHNRUV6U9jlHqTA1ms/pWdJjJ5EOHbWvv6DCzs9BsbWhoeF9KOcdQ0WBBeblMYf4mITGMRwgYNQpuuAE8HvB61XfyZ+XKVP5AJhBCrRiMH69WD7Tv6mrVlmyXtqJgZfZ/d1MToxobszcunzjkEFi7Vv0thFqVsLyEkRkKyu8+hO23NQjxsV/KQzNL7TNAMWfOHPnee6oukZX3U18j+T11xr1vs2mvF4maMjloRPqID6s6e3Nfq/K5vNMzbRsI19kIQgjdfonpCoQQ4hxgr5TyfSHEKQZyVwFXAYwZM4bm5mZduc0JgwdQ9Qy+c+JoDhtdxqThxTi0N7RsZ+/uAB0H9qe1LRAIpMS1J6O1tdWw3YoOM5l86ADb1t7QYWZnvo7TV7YONaxfrzrpoCY4d+2C73xHbZeUqArJlZXdn9ra7slXDQ6HKr5cVaX4w8nfxxyTOhj46U+N7co0vKggsWFD999SKmds2LBhIwFNLb54UVbZtW3DBlgLYToeOFcIcRZQAgwTQvxBSvnlRCEp5UJgIcDs2bNlutHW5Pqt8RUIjb9w3dmzdGXz0WkDDEd+hdJ51GDbml8dZnbm6zj5PK82ujF1as/O/eTJsHSpGjgUpWYABvRXB664Iv0xshkMZMQ3KFQkn9ypU/vbIhs2bPQzkpN7NNaX90g401if+UJRss7e3NeqvJmcUXu2bYWEfNjpMBOQUv5QSjlOSjkJuAT4R/LgIRMsmj+Xg+orcAphmp/faULqM2u3Ais68mGHbWvmMratNhYvVgMAp1N9v/oqDB+efvCgt49V8nEkor6HTMRJpifKhg0bgx7J76lF8+cy2WKfzarO3tzXqnwu7/Rs2woJ+bCzVwrJGfEqNPKylfirQCBgOEoya7cCKzryYYdta+Yytq02spnpHxSrA30B+0TZsGEjCcnvKa3Plk+dvbmvVflc3unZthUS8mFnRgMIKeUSYEm69kwLyQWDQUNGfigUihf/0mOvh8PheLsRI9/j8aRlr/u7skkYsde1Y6TLMpCuQFlyITmzzAnBYDBt0TVVSM5PSUmJISM/HA6nLbomhCASidDZ2WmYOUE7vpFP6QrJaT6lu26JPiVeFz2fPB6PaZYBr9dLaWlp2mwQXq835V7UKySn3Y/avZfsU+J9oOdTJBLB6/UaZk7wdRGG7UJyNmzYsGHDho2BjLyuQGiF5GbPnm1aSC4QCJgWkpNSpsSMJ4+YktuTi3mZFe+qqqpKsTERLpcrPphI9kHbrqmp6fGbnk9FRUVpC8lpxywuLtYtFKf5pLUZFVVzOp2G50wvDj/Zp8rKyrRF1xJt0iskpyH5nOj55HK5epzXZJsSfUnnc1FRUc6F5AKBgGkhOUi91xJtSrxH0vlcXV1teE6GSiG5TNK49lXayb5KpSk6OpSuQeSTpetkp3EdPD7ZaVyzwkBK45pLelC9SWOtv9dXPgGmKU+DwWDcBz2ftL6Snk+aDXo+SSnjk42FnMZVO2YuaVx7JYTJCvoq7rxQ7LBtzVzGtnVwQptooLz8Stxu1UlJRllZz+3kTkYyGT35IZe8nXyMvt4GKCtDDhvWbdsg8akH0vnU1qbsH0w+aTDyye1W+X/zaWN/+2ThmLKysqffVmwc5NCee3PmzIlPsCZOxBlNcmkwm+QymlyE1AlWPXltwtOKvnQTd8nbJSUlPSZKM/Gpurq6x3HNfKqqqjKU17Y1m9L5lNie7JPWkdeQ6JMQQtdGo4nsbK5TJtt61ynRByv3nh4s1YHIFEKIdmCjiVgV0J6jjBUddcC+ArDDtjVzmXzoMLMzX8fJl63lUsp6Ez2DAscIEVsKQy1uywVE+tuIfoDt99BChn7PLpXyPdOkLoMBQogWYGvXppX3U1/DyrusL3Vmuq9V+Vze6Zm2DfTrPFG3XyKlzPsHWNgXMhZ1vFcgdti29oOtZnYONFvtz8D+DNVrbPs9tD5D1e/BcJ6svMv6Umem+1qVz+WdnmnbYL3OvTXit5IPMB8y+cg72Fd22LZmLmPbasOGDRs2bPQdeuM9lYvOTPe1Kp/LOz3btkJCznb2SghTIUEI8Z7UKcFdiLBtzT8Gip0wsGy1kR2G6jW2/R5aGKp+Zwr7PA0NDNbrPBRiDhf2twEZwLY1/xgodsLAstVGdhiq19j2e2hhqPqdKezzNDQwKK/zoF+BsGHDhg0bNmzYsGHDRv4wFFYgbNiwYcOGDRs2bNiwkSfYAwgbNmzYsGHDhg0bNmxYxoAfQAghxgsh3hJCrBVCrBZCXKcjc4oQol0IsaLrc2t/2JpgT7MQYlWXLe/ptAshxP1CiE1CiI+EELP6wcapCedrhRCiQwhxfZJMv51XIcSjQoi9QoiPE36rEUK8LoTY2PU9PM2+87tkNgoh5veTrf9PCLGu6/q+KISoTrOv4b1iY+BgqFzLXP43BzLS+H27EOKThGfkWf1pY28g3Tt4KFxzGzaGMgY8B0IIMRoYLaX8QAhRCbwPnC+lXJMgcwrwPSnlOf1kZg8IIZqBOVJK3cIiXS+Za4GzgKOB+6SUR/edhSn2OIFPgKOllFsTfj+FfjqvQoiTAC/whJTy/7d350FylHUYx78PNwZLQAQjIgFEOTwCIoccBrmVAkEQCIqcCkWkQCkJ4sFRloACJUEOESqo3HIIFEcil7E0EAgh4RBRVIykgkrkEIRK8vhHv1sMk53NbLI7szP7fP7Z3nfe7v51vzPd/fb7vt0fKmlnAy/YPlPSeGA12yfVzbc68BCwBWCq78vHbM9rcay7AvfYni/pLID6WEu+v9LHdyU6x3ApyyX9bXa6Btt9KvCK7R+2M7bB1OgcDBxKl5d5xHDW8S0QtufYnl6mXwaeBNZub1RLbW+qk5BtTwVWLQfpdtkJ+HNt5aHdbP8GeKEueW/gijJ9BdVJrN5uwGTbL5RKw2Rg90ELlN5jtT3Jds+bWqcC7x3MGCJaZSl+mx2twXZ3vT7OwV1f5gNN0ghJD0saEjc7Y3CU3htTJF1cbsR2pI6vQNSSNArYDHigl4+3kfSopDskbdrSwBZlYFI5UHy5l8/XBv5e8/9s2lspOhC4usFnQ2m/rmV7DlQnNWDNXvIMtX0LcDhwR4PPFvddic4xnMuymd9mtxpXuipe3u3deOrOwcO5zIHeu7WV9N0lPVW6KY+v+egk4LrWRhkDoZ9lbarWypWorkE6UtdUICStAtwAHG/7pbqPpwPr2v4oMAG4udXx1dnW9ubAHsCxpem7lnqZpy19zSStAOwFXN/Lx0NtvzZjyOxbAEmnAPOBKxtkWdx3JTpHynL4uQjYABgNzAHOaW84g2cx5+DhaiJ1LdylS/CPqY4DmwAHSdpE0s7AE8DcVgcZA2IiTZY1MMX2HlQVxtNaHOeA6YoKhKTlqQ5cV9q+sf5z2y/ZfqVM3w4sL2mNFodZG89z5e/zwE3AlnVZZgPr1Pz/XuC51kS3iD2A6bYXOagNtf0KzO3p6lX+Pt9LniGzb8sA7j2Bg91gMFIT35XoEMO8LJv5bXYd23NtL7C9ELiULi3zBufgYVnmtRp0a9sS+JPtZ2y/AVxD1d1rR2BrYCxw9pR/zQAACABJREFUlKSuuD4bLvpT1uV4ADAPWLGFYQ6ojv+CShJwGfCk7XMb5Hl3yYekLam2+9+ti/ItsYwoA82QNALYFXisLtstwCGqbA282NMU3AYH0aD70lDar8UtQM9Tlb4E/KqXPHcBu0parXQn2LWktZSk3anuPuxl+9UGeZr5rkQHSFk29dvsOnVj1/ahC8u8j3PwsCzzJvTajdb2KbaPB64CLq25yIzO1WtZS9pX0iXAz4EL2hLZAFiu3QEMgG2BLwKzJM0oad8E3gdg+2JgP+AYSfOB14ADG93xbYG1gJvKdfdywFW275R0dE28t1M9gelPwKvAYe0IVNLbgF2Ar9Sk1cbZtv0q6WpgDLCGpNnAd4EzgeskHQE8C+xf8m4BHG37SNsvSDoDmFYWdbrtQR342CDWk6nuPEwu34Wpto+W9B7gp7Y/TYPvymDGGoNm2JRlf36b3aTBdo+RNJqqm+RfqTmWdpFG5+CuL/Ml1Gc3WtsTWxdKDLJey7q00i3SW6bTdPxjXCMiIiKGojKw/LaaR/tuA5xqe7fy/8kAtr/frhhjYAy3su74LkwRERERHWIasKGk9cpDSg6k6u4V3aeryzoViIiIiIgBVrq1/R74oKTZko4o7/8ZRzX27kngOtuPtzPOWHrDsazThSkiIiIiIpqWFoiIiIiIiGhaKhAREREREdG0VCDaQNICSTMkPSbp+vK41I4h6ZV2xxARAyPHo4iI6K9UINrjNdujy6O+3gCObndArSKpG949EtFNcjyKiIh+SQWi/aYA7weQdLOkhyU9LunLJW1ZSRPL3cFZkk4o6cdJekLSTEnX1C9U0qGSbpR0p6SnJZ1d89krNdP7SZpYpidKukjSvZKekfRJSZdLerInT81850iaLuluSe8qaRuU9T0saYqkjWqWe66ke4GzBnb3RcQAyvEoIiIWK3df2qjc/doD6Hkj7eHlTckrA9Mk3QCMonrNfc+LSVYteccD69l+vSat3mhgM+B14ClJE2z/vUHeHqsBnwL2Am6lesvokSWe0bZnACOA6ba/Luk7VG9cHQf8hOqNz09L2gq4sCwL4APAzrYXNLd3IqKVcjyKiIhmpQWiPVaWNAN4CHgWuKykHyfpUWAqsA6wIfAMsL6kCZJ2B14qeWcCV0r6AjC/wXrutv2i7f8BTwDrNhHbra6e7TsLmGt7lu2FwONUFw8AC4Fry/QvgO0krQJ8Ari+bNslwMia5V6fk3XEkJTjUUT0SdIppTVyZhkztdVi8t8naYsBWO+hki7oR/4xkm5b2vUuCUmjJI1tx7rbIS0Q7fGa7dG1CZLGADsD29h+VdJ9wEq250n6KLAbcCzweeBw4DPADlR35r4tadPy0pJar9dML+DN8q59+cdKDeZZWDf/Qhp/X0xVGf1P/XbV+G+D9IhorxyPIqIhSdsAewKbl1bGNYAV2hzWUDQKGAtc1eY4WiItEEPHO4B55WS9EbA1QPmhLmP7BuDbwOaSlgHWsX0v8A1gVWCVfqxrrqSNy3L2WYJYlwH2K9Njgd/afgn4i6T9S9wqFxoR0XlyPIqIHiOBf9l+HcD2v2w/ByBpJ0mPlDFRl0tasXZGScfUjXk6VNKEMv0FSQ+WFo1LJC1b0g+T9EdJ91N1W1yEpBFlfdPK+vduNk+J4WZJt0r6i6Rxkr5W8kyVtHrJ19c4qvMl/U7V+Kye48+ZwPZle06QtGnN9s2UtOHSFMJQkwrE0HEnsJykmcAZVN0GANYG7ivN8BOBk4FlgV9ImgU8Apxn+z/9WNd44DbgHmDOEsT6X2BTSQ9T9Sk+vaQfDBxRuj08Dizyg46IjpDjUUT0mASsUy7qL5T0SQBJK1EdBw6w/WGqVsFj6ub9JbBvzf8HANdK2rhMb1taChcAB0saCZxGVXHYBdikQUynAPfY/jiwI/ADSSP6kedDVDcctgS+B7xqezPg98AhJc9PgK/a/hhwItU4qh4jge2oWmbOLGnjgSnlqXbnUT3R7kdl+7YAZjfYlo6kqntpRERERMSiSuvA9lQX4l+hulh+BJhge4eSZyfgWNv7lm6PJ9p+SNIk4DvA08A0YAOqLpDfBJ4vq1gZuBqYAexr+5CyzOOAD9geVxfPQ1RdHnu6Sq5O1bVyrbLePfvIsxVVxeWosqxnqbpr/kPS4cBHgG8B/wSeqlntirY3VvUUuMm2ryzzv2z77aXr54m29yzpY6kqMT8DbrT9dH/2+VCXMRARERER0VB56MB9VC2Qs4AvUV3sN+NaqvFSfwBusm1JAq6wfXJtRkmf5a3johoR8DnbT70lUVqriTxbseiYqtrxVsux+HFUtfOrtwy2r5L0ANUYsbskHWn7nr43q3OkC1NERERE9ErSB+v6748G/kZVIRgl6f0l/YvA/b0s4kbgs8BBvPnEtLuB/SStWdaxuqR1gQeAMZLeKWl5YP8GYd0FfLVURJC02RLm6dUSjqN6GXh7zz+S1geesX0+cAtVy0bXSAUiIiIiIhpZBbhC5WWRVOMSTi2PZD6M6nHJs6ju3l9cP7PteZRHN9t+sKQ9QdVNaFJZ5mRgpO05wKlUYxF+DUxvENMZwPLATEmPlf+XJE9f+juOaiYwX9Kjql6yeQDwWBkzthFVV6aukTEQERERERHRtLRARERERERE01KBiIiIiIiIpqUCERERERERTUsFIiIiIiIimpYKRERERERENC0ViIiIiIiIaFoqEBERERER0bT/A+raazjjQA7UAAAAAElFTkSuQmCC\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1299,7 +1299,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxAAAADaCAYAAAA2YqSyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydeZgcVfW/39M9+5I9rCEkk0CAsIQQVr8QZAfZkU1UQGRRUJFFQcQgCCKyCYIKIosoiyA/iYAISoKAIluQBJJAFkiAQJJhklm7e7rP749b1dPT00vNdPdU9eS+z9PPzK26de/ndHV31a17zzmiqlgsFovFYrFYLBaLF0J+C7BYLBaLxWKxWCzlgx1AWCwWi8VisVgsFs/YAYTFYrFYLBaLxWLxjB1AWCwWi8VisVgsFs/YAYTFYrFYLBaLxWLxjB1AWCwWi8VisVgsFs/YAYTFYhk6iCgiv08pVyCyGpG/FqHtfRFZh8gbiCxC5HlEDvdw3GmI/NL5/2hEtutnv6c5NsxzXvcNSH//+vwZIv/r1ZfIVxD5Tsn7tlgsFkvgsQMIi8UylGgHtkek1ikfCHxYxPb/herOqE4Bvg38EpH9+3H80UD/BhCGh1Cd5ry+2mevSMUA2syMyHBgL1R3BMKI7OC8n6cBtw+sSS4TYYEI/xNhngi756k/R4QZA+krrZ3TRPhlP+rvK0Lhg80BIMIEEb7Uz2NEhH+KMMwp/06ET0WYn1ZvlAjPiPCu83dkyvG3iPCec26mpxxzqlP/XRFOzaPjehH26492i8VS3tgBhMViGWo8BXzB+f9k4IHkHpHdEHnJmUV4CZEpzvYLEPmd8/8OiMxHpC5nL6rzgCuB85zjxiLyKCKvOK/P9aovshdwJPBzZyZhEiJnOnXfdI7N3Wfv9uYgcg0ic4HvZO1fZDQif3ds/g0i7yMyJkfLCaAKEQFqgRhwMXALqjHP+pIy2RM4HJiuyo7AAcCK/razATAB+jeAAA4D3lRlvVO+BzgkQ71LgH+oshXwD6cMcCiwlfM6C/gVmAEHMAvYHdgNmOUOOrJwa0qbFotlA8AOICwWy1DjQeAkRGqAHYGXU/YtBPZBdWfgR8A1zvabgcmIHAPcDZyNaoeHvl4HtnH+/wVwE6q7AscBv+1VU/Ul4HHgYmcmYQnwZ1R3RXUn4B3gjCz9nJiyhOn0lO0jUJ2J6g05+p8FvODY/DgwPqdFqq3Ao8AbwDJgHbArqn/J815kY1NgjSoR0zxrVPkIQIT9RXhDhLecp+fVqQeK8A0RrkspnybCrc7/Xxbhv86Mxm9ECDvbTxdhsQhzgd6DuJ526p3+XnH6P8prHUfD/xNhtgjLRDhPhAucOv9xbr4RYZIIfxPhNRH+JWI+JyLc4zz1f0mEpSJ80enyWmBvx57vijA1xb7/ibBVBlNOAZLnRZXngeYM9Y4C7nX+vxczE+Zuv08VVeU/wAgRNgUOBp5RpVmVz4BngENECDv65zvn7LtOv+8Do0XYJNP7bbFYhh7Fm/a2WCyWIKD6P0QmYGYfnkzbOxy4F5GtAAUqnWMSiJwG/A/4DaoveuxNUv4/ANgOSW4ahkhjnuO3R+QnwAigAXg6S72HUD0v4/b8/e8DHAuA6hOIfJZHE6heB86Nu8hvgR8h8nXgIOB/qP4kbxs9/B34kQiLgWeBh1SZK0IN5on5/qosFuE+4BuYwZzLI8C/ge855ROBq0XY1vn/c6rERLgdOEWEZ4AfA7tgBj7PYQZC6VwG/FOVr4kwAvivCM/2o872wM5ADfAe8H1VdhbhJuCrjg13AOeo8q6zZOt2SC7z2RT4P8zg83HHzkuAi1Q5HMAZKP1ClT+IUAVmgJTG54CzM2xPZ2NVPgZQ5WMRNnK2b07v2aCVzrZs26cBm6uyvaNxREqd1x09j3rQY7FYyhw7gLBYLEORx4HrgX2B0SnbrwKeQ/UYZ5AxJ2XfVkAbsFk/+tkZM3MAZkZ3T1Q7e9UQST8mlXuAo1F90xnA7NuPvsH4fLjk6l/72a577M7Of4uBX6C6DyIPIrIVqu96aUKVNhF2AfYGPg88JMIlODMcqix2qt4LnEvKAEKV1c5T+j2Ad4EpwItOvV2AV5y3txb4FLPkZo4qqx3THwK2ziDrIOBIES5yyjX0nZnJVec5VVqBVhHWAbOd7W8BO4rQAOwF/Cnl9KfOrvw/VRLA2yJsnPGNMwOny0QYB/xZlUzv9yhHx0DJ9OHUHNuXAk3O4OYJzODQ5VP6992xWCxljF3CZLFYhiK/A65E9a207cPpcao+LbnVOA7/AvO0fjQiXyQfIjsClwO3OVv+jusPYfZPy3BUK5A6K9EIfIxIJWY5SiFk6//5ZNsih0LOtezpXIVZ6lVJzxPwBODdVwNQJa7KHFVmORqPI/NNaiYeAk5wjnlMNXmDe68q05zXFFWucLvz0KYAx6UcP141ORD0UieSUi+RUk5gHsyFgJaUY6epsm3KManHZ3wfVPkjxmemE3g6i5Nyt4in6/gnztIknL+fOttXAluk1BsHfJRtu7OcaSfMwPtcei/Tq3G0WiyWDQA7gLBYLEMP1ZWo/iLDnuuAnyLyIr2XhNwE3I7qYowfwrWIbJTh+L1xw7iagcO3Uf2Hs+/bwAwn/OnbwDkZjn8QuNhpYxJmAPIyZo35wgFYmkq2/n8M7IPI65in6h8kjxB5EpHMT41FjgZeQfUjVFuAfyPyFqCovulVlAhT0tbvTwPex9g7QYTJzvavAHMzNPFnzJr9k+lZsvUP4IvuUhwnytCWmPdyXxFGi1AJHJ9F1tPAt0TMzbsIOw+wTkYcp+ZlIqZ/J9rRTnkO6zW4FKEJWKrKLZgZtR0zHLMIaPIg6XFIRlI6lR6/iceBrzr69gDWOUudngYOEmGk4zx9EGYQMwYIqfIo5rM7PaWPraF39CeLxTJ0EdWBzWxbLBaLpQwRWQ7MQHXN4HTHLpgoPSOAbozPwFmqrBFhf8xSswrgFeAbqkREmIPxB3jVaeOvwHaqPTfLIpwIXIp5EBYDzlXlPyKc7mz/GJgHhFXp5T8iQi1mqdRemBmA5aocLsK+Tr+H56hzGjDDbVOE5U55Teo+ESZiohptipnBeVCVK0W4B/irKo84x7ep0uAMeP4GjMEsbasBvuzYtgr4kmpvB2kRLgc+VjUzASI8gFkGNwb4BJilyl0ijAYexizB+gA4XpVmZ3D0S0zkpg7g9JT3/GvAD5yurlblbmcQdDc9Dx8vVeUpR/v/gB1U6cZisQx57ADCYrFYNiQGeQBhKR3OcqT7VDnQZx3HYML0Xu6nDovFMngMySVMInKKiPw9f83gIiITRERlAAmixHC3iHwmIv8thb6gISL3iEhUzM3RYPW5tYi0iUhcTIQaiyX4qE6wg4ehgbPc6E5xEsn5SAVwg88aLEMIEblCRO73W4clO2U7gBCR5SLS6dzAua9fAqjqH1T1IL81+sj/YTLwjlPV3fwWM4hcp6oTUjeIyIEi8pyItIrIWhGZJyLfF5MjIOuPlDN4m5y+PRVVXayqDcC/immExWKxeEWVh1MSyfml4U+qtPipwZId534pKmkJJJ3roYqJSFfM/twHoO692Sci8lcRGdBMWSEPVFPaUBFpT9Hky+dVRL4kIh+LyDIR2Tdl+yQReUlEMoVrDiRlO4BwOEJVG1JemeKkb4hsCSxX1fa8NYcwInI8Jr76H4EtVXU0Jnb8OHpHGLFYLBaLZSizDBOIAAAR2QET/riUjHAesu2ECRTxmJhw1X6xU8r94ohMFQoZpOTDaftaTPCBb2H8j1xuAS5Q1Xip+i825T6AyIiInCYiL6SUDxKRRSKyTkRuF5G5qUtORORrIvKOs+TnaRHZMmWfisg5IvKus/82EZGUfl4UkZtEpEVElorIXs72FSLyqYicmtLWcBG5T0RWi8j7IvJDEQk5+8Iicr2IrBGRpcAX0mwaLiJ3OSPXD0XkJ5lGqiJyBia03p7OKPvHafurHa3bp2wb68zmbCQiY5wnBS0i0iwi/3I15nnP9xWRlSLyA8eG5SJySsr+L4jIGyKy3nlvrkjZVyMi9zszBC0i8oqIbJzyHi91ZhCWpbaZR48ANwJXquqdqtoMoKqLVPVb6jGGvdNWS8pTi/ZSPLGxWCwWi6WE/B6T5NDlVOC+1Ap5rtMnOtfiYU75UBFZJSJj83WsqqvURMW7AvhZyn3PZiLyqHNPtExEvp2lieedv+61eE/nif0/nfuGNSLyBxHJOCjIRcq9y/dFZBUmSAAicriYGZoWMTMDO6Ycs7OIvO7clzwkIg+KSQiaj9HAh6r6MSapZpPT3hed7f/pr34/GZIDiFTETNk9gonKMRoT9m6vlP1HYyJNHAuMxSxHeSCtmcOBXTGj6BOAg1P27Y6JPjEa86T7QafuZEwEjV+KSINT91ZMHPomYCbmy3y6s+9Mp5+dgRlAehz6ezERTCY7dQ4C+qy7V9W7MOEb/+2Msmel7Y9gwiKenLL5BGCuqn4KXIiJAT4W2Nh5b7x62m+Cif6xOebH6Q4RmeLsa3fsHYEZHH3Dee9x6g7HzAqMdvR3ikg9ZlR+qKo2Ys7bPI9apmBmGgrOiqqqI9ynFphcAf+iJ5eAxWKxWCxB5z/AMBHZ1nn4eCKQvnw363VaVR/CJDe8RURGA3cBX1fV1f3Q8GdgI2CKM4iYDbyJuWfYHzhfRA7OcNw+zl/3WvxvTGS0n2KSF26LuX+4oh9aUtkEGIVZvXGWiEzH5BI6G3NP8hvgcecBbBXw/zADslHAnzA5arywGhgtIuMwy8wXOPeHP8Tco5YV5T6A+H/O6NB9nZmhzmHAAlX9s6p2Y25IV6XsPxv4qaq+4+y/BpgmKbMQwLWq2qKqHwDPYeKYuyxT1budaaeHMB/iK1U1oqp/B6LA5JQv7KWq2qqqyzFOZ19x2jkBuFlVVzhPy3/qduA8jT8UOF9V250b/ZuAkwbwnoEZ6KQOIL7kbAMTMnBTzJKfmKr+S/sXqutyx/a5mEylJwCo6hxVfUtVE6r6P8wgbWZKn6OByaoaV9XXVNVd05sAtheRWlX9WFUXeNThrvVMnmvnKUGLiHSIyFdS6p6Q9jnKuDZSRE7EvFfHqWrMow6LxWKxWIKAOwtxICYPS68HYXmu02CSB+6HSSQ4W1X/2s/+P3L+jsI8aB2rqleqalRVlwJ34vG+RlXfU9VnnPuN1ZgVBzPzHPZ6ynX+lpTtCWCW01Yn5oHub1T1Zeee5F5M8sc9nFcl5n4tpqqPYEJQe9GcAL6Beah9kdPPlZiHyzuI8dd8OnWFSJAp2VqvQeJoVX02T53NgBVuQVVVRFam7N8S+IWIpEaQEMyI+H2nnDrg6AAaUsqfpPzf6fSRvq0Bc0NbldImzv+bZ9KZVm9LzAf2Y7MyBzCDv9T6/eGfQK2I7I6xbRrwmLPv55hR/N+dvu5Q1Ws9tvtZmt/F+xi7cPq6Ftge8z5UY0buYH7UtgAedKYg7wcuU9V256b9IuAuMcm/LlRVLwm31jp/N8Ws/URVT3K0vEDvJGIPq+qXUw8WEU0r74xZr3hQP5+4WCwWi8USBH6PWQ40kbTlS5D3Oo2qtojIn4AL8P7UPRX3fqcZ2AHYLO2BXRiPQUnEJPq8Bdgbk4AxBHyW57Dpqvpehu2rVbUrpbwlcKqIfCtlWxXmfkYxy41S7xFS79dyoibx6D8cG3bErDi5GFiOCYCzBWYZ+h5e2/SLcp+B8MLHmKUsQHJt/LiU/SuAs51lKu6rVlVfKrKONZgn7akzG+PpeQLwMb0de8enaYwAY1I0DlPVqQMR4oyCH8bMQnwJ+Kuqtjr7WlX1QlVtAo4ALhCR/T02PdJZdpRqg/vE4Y+YrKdbqOpw4NeYgRrOKP7HqrodZpnS4ThrNVX1aVU9EDMQWIh5QuEF9+nKsR7rZ8VZ4/kYcJ6qvlFoexaLxWKxDDaq+j7mgdphmOVE6WS9TgOIyDTga5iZiVsyHJ+PY4BPMUvJV2BWcKTeezWq6mGZpGfY9lNn+46qOgyzZFwy1PNCevsrgKvTtNWp6gOYe7XNJeVpLr3v1zzhHP9L4NuYB8xh5/y8Quas84FjQxhAPIGZGjpajAf8uZj1bi6/Bi4VkamQdFY+vtginCVODwNXi0ijs0TqAnrWID4MfFtExonISOCSlGM/Bv4O3CAiw0Qk5DgQ5Zuuy8UfMUuqTqFn+ZLrODTZ+XCvB+LOyys/FpEqEdkbMxBwn140As2q2iUiu2EGLm6fnxeRHZxlXusxA624iGwsIkc6g5II0OZVi/N04EJgloicKSIjxbAVxrfDE85n5lHgD84aUIvFYrFYypUzgP00c5TGXNfpGsz9yg8wvpubi8g3vXToXMvPA2ZhlnEngP8C6x3n5VoxgWS2F5FdMzSxGrPMqCllWyPmnqBFRDbHPMUvFncC54jI7s59Q70YB/NGjB9IN+Z+rUJEjgUGEi7/68AbqjoPs2KiVkS2Az4PLC2SHSWl3AcQs6V3HojH0iuoSZh0PHAd5iRtB7yKuSFFVR8DfoZZPrMemI/xNygF38I4KS0FXsDcuP/O2Xcn8DTGoeh1+j4d+CpmCu1tzDTdI5in8gNCVV92tGwGPJWyaytMdIA2zBfldlWdAyAiT4nID3I0u8rR9hHwB+CclOVG3wSuFJFW4EeYAZPLJo4964F3gLmYH6oQZhDwEWbKc6bTjlcbH8L4YHwZ80RhjdPvHaRMy+ZhHGaK9Py0z1q/nzhYLBaLxeInqrpEVV/NsjvXdfqnwEpV/ZWaYCxfBn7iPJTLRouItANvYWY9jlfV3zk64phVDtMwsyJrMEt3hmfQ3AFcDbzo+C/sAfwYEw51HeZBcaYZlQHhvD9nYmYIPgPeA05z9kUxKxtOc/admNq3iIzPd48gJrjPd8Bkblfjf3seZnn5rzH3ioFH+ucfW/6I8fxfCZyiqs/5rWeoICYhyv2qOi5f3RL1fydmSdYnqjppkPrcCjPdWAV8U1XvGYx+LRaLxWKxBAMRuQczuPqh31oGk3J3ovaEmLBgL2Mcmi/GrJMrq3i7ltyo6pmYJwaD2ee7mHB3FovFYrFYLBsM5b6EySt7AkswU2RHYKI3dforyWKxWCwWi8ViKT82uCVMFovFYrFYLBaLZeBsKDMQFovFYrFYLBaLpQiUxAdizJgxOmHChJx1VJXeYXQHVicajVJVVTUofQVJT5C0DFU9QdLitvP666+vUdWxOSsGkD1DIf1fba3fMgBv5yNIlJteKD/N5aYXyk/zQPR2dGyrqq+W3YNOL/dAGzLl9Nn1U2up+/ba/muvvZbxvqMkA4jx48fz6qvZooQZ2tvbqa+vL7jO8uXLyfdFLVZfQdITJC1DVU+QtLjtNDQ0eM54GST+XVsL7ZnCjg8+q5YuZZOmpvwVA0K56YXy01xueqH8NA9Er8j8svRVnDBhQt57oA0ZL9fEoOCn1lL37bV9Ecl431GSkb2XEU2+p61e63ihWH0FSU+QtHit44Ug6QmSlmK2Y7FYLBaLX5TTtcxPraXuu9D2izoDISJHAEdMmDCBWCxGPB4nFosBUFNT06scj8epra0lGo0CUF1dDUAkEgGMYZ2dnYTDYQAqKysJh8N0dXX1Kre1tdHa2kpFRQWVlZV0dXWhqoTDYSorK4lEIrS3t9PY2EhlZSXRaJREIkEoFKKqqiqps7Ozk5EjRybLIkJNTQ2xWIzu7m4AEokE1dXVWW2qrq4mEonQ2tqa1SYR4bPPPqOuri6rTV1dXXR0dDBs2LCCbGpvbycWixVsU3t7OxUVFQXb1NbWRmdnZ1abVJVIJMLw4cOz2iQixONxKisrs9pUU1NDa2srlZWVWW1S1eRnp5TnyatNsViMjo6OvDZZLBaLxVLOxGKx5PU56PiptdR9F9p+UQcQqjobmD19+vQzKysrqayspKamJrk/tezeuLk3by6pI6JoNEpjY2Ov/enGNjQ09KrT0NDQp76qUldXl/H41LKrORX3xtnVXFNTk9UmMDen6ZrTR3l1dXW96mTTVOusGR+oTfX19UWxKRKJFMWmhoaGnDal/p/rPLW2tiZtz2STWz9dT6pNrobUOqU4T15tqqys9GSTxWKxWCzlTDwe91uCZ/zUWuq+C21/8J2TmpfBbbvTcOOWcNvuppyFnEuhnHa2vHd6UdopNz1B0jJU9QRJS952LPlZuhSmTmXjrbeGqVNN2WKxlBTna8e4rSdu8F+7D9Z2cOCNc5l06ZMceONcPljb4bckXyina5mfWkvdd6HtlyQPxC677KKvvfZa5p237Q5rFoMmAIHGTeGgq0w5ETd/nVd3d4yKEKDas92t89It0L4GUNNO3WjY7SxTVnX+JkCVeDxOOCQ9+zTR8/+8P0BnS087tSNgp5OdNlJR4okE4VDKmCu1zv8ehq7Peso1I2DHE/rWg5R2Mrz3bz0CXS2929n+uD7V4ol4by2pzP9zWhvDYeoxaVrM30QiQajXh0h7/rwzGyLrenZVD4dtD8/w3kAiESeU7b1Z9CRE1qe0Mwy2PiSthWx6UjS9+3eIpCzjqW6EyQf07Q8lkVBCIemzHYAlz0G0rWdzVQNMnJliuzptuFpStzvtvP9viKU4BVfWwRa79+7HqZ9Qp51M37UPX4WY4ycoIRizNZz7ct96mOnGqqqq11R1RsYKQaa+Xn13ot5uO3jnHfN/KATbbAMLFviryQPl5iwL5ae53PRC+WieOtV87VT7/7UTmd+hun15eNumMGPGDM3kRH3gjXN579M2FAgJTBrbwDMXzBx8gT5jlzAFo2+v7YtIxvuOkkRhyjkoWfOucwMPoND6ETx6Rsaq3sUpdKyBOdekbRcQIeT8NeVQyv8CsY7e7XR+Bm/c33N8CqG+m3o2pN6wu+W3/tS3HhByByvgaEk7Lr389l/SOzVtSJYBRJ821sGipzJq6dEgaWV6Dx7c8rLnM7ehCXN16N2wc9z63psj62HlfzPrSKj5Zc20L5LmAxBphU9Sr0Qpx7hXq/TtIr0HD2DKLR+kVZWUNiTlPDl/Y2k3w7EOiKZsS6mviTiEK5LlXnViKUFGNGG+H1lwfTgsA2Tx4p7/EwlYtMg/LRbLBsLChT3PTor9tRNhOdAKxIFuVWaIMAp4CJgALAdOUOWzbG0MJktXtycfGybUlDdE7AAiGH0HygfCixN15agmQs1LEE2gCDp8PB3H3kdVdTVIiGgsDhKisqqa9o5OZ3uYiqoqwuFKuqJRkBD1fzwCcduREDpqEomzX6QrEkEVwo5TdSQSYf369YwYMSKjw3H413shze+ltDOZ9tP+mdGRNRaLUVdXl9HhuO6ez/fYJSESoyYRP/sloK9z7po1a5IO0OnOudW/3QfWvptsh9Fb0X7aP/s45w5Fm1SVjo4ORo0aldHhuPquffro6TztuYwOxy0tLUmfh3Qn6vp790dS9Ojoreg+45+F23TSY0WwqYnOtraMNrX7/QS/3JkyxdzNJBI9ZYvFUjKeeqr3xGsoVJKv3edVWZNSvgT4hyrXinCJU/5+0XsdAE1j63lvdRuq5lFS09iym1wpCtYHIhh9F9p+SZYwTZ8+XV9//fXMO5uXwQMnoWveRcZsBSc/CKMmZqza1tbWxzHVthM8LUO1nSBpcdtpbGwsqyVM7kOFWFXVmRWtrRCLmRdAbS3E4+BEYqO21tzcOwM5XKd3Z+BGdbW5A+l0Zm2qqiAc7ilXVppXR0fvcmenuYtZsQJOPBFduBBRhQcfhGOOMe0nnBm0mhrTfzxuZohqa42+7u6ecqoNdXUlt2nVihVssskmmW2qqDDHuOVw2LTps02rli9nk003Hdh58sGmVR9/zCYTJpTus1cCm1atWsUmW2zh3/cpj01//msVJ32tlq0nJ+iOwXvLQkzZWpn9UCdNW8Q8nScZuTLnEiZnBmJG6gBChEXAvqp8LMKmwBxVBuVpgft719TUdObChQv7PERdvrqNc/44jyVrOqipDPHXb+7Bxg25Ixy65VzRAIGckShTy7miAboRGXNFA8wVXdONcJgruqaIsHbtWhoaGsrCplgsRn19fV6bSnGe1q9fz7Bhw0p2npqbm2loaMhr07BhwzLed3gaQIjId4AzMYPmO1X15lz1c/pAOHR3d/eJMjOQOl4ScBWrryDpCZKWoaonSFrcdiorK8tqAJEkCD4QDp+8+SYbH3CA8YmYM6fvMsKAUS5r3VMpN83lpheCrfn+++G002D33eHJJ2H48IEmknszCtPeStl0h6re0bOfZcBnGMez36hyhwgtqoxIqfOZKiMLs6h/ZPOBcPnZ3xZy5/NLeeNHB9JYUx5LeYqJl2tiUPBTa6n79tp+Nh+IvFGYRGR7zOBhN2An4HAR2SrXMV4GJV7WcxdrzXex+gqSniBp8VrHC0HSEyQtxWxnQ0cbG+HKK+H55+Gxx/yWY7EMKX7zG/jqV2HmTHj6aTN4GDjhblWdkfK6I63C51SZDhwKnCvCPoX0Nljsu/VYuhPKS0vW+i3FF8rpWuan1lL3XWj7XsK4bgv8R1U7VLUbmAscU1CvkJxqKbTOYPYVJD1B0uK1jheCpCdIWorZjgU480wTHubii3uWeFgsloK48UY45xw47DB44gnItnKzWKjykfP3U+AxzIPOT5ylSzh/Py2tiv4zfcuRNFRXMHfxar+l+EI5Xcv81Frqvgtt38vcyHzgahEZDXQChwF95uZE5CzgLIDNNtuM5cuX52y0q6urT2KvgdRZuzb/CL5YfQVJT5C0DFU9QdLitmMpEhUVcMMNcMghcOutcNFFfiuyWMoWVbjqKpg1C44/3ixhSsvLWXREqAdCqrQ6/x8EXAk8DpwKXOv87RvG0GcqwyE+N3k0cxetRlXLKi+CxeKSdwChqu+IyM+AZ4A24E2gz7DFmVq8A4wPRL713F7CR3kNMTVYfQVJT5C0DFU9QdLitmMpIgcfDIceau58Tj0Vxo71W5HFUnaowiWXwHXXma/Rb39rxueDwMbAY869dwXwR1X+JsIrwMMinAF8ABw/KGr6ycytN+LpBZ+wZHUbkzdq9FvOoJLvgVqQ8FNrqfsutH1PmahV9S5Vna6q+6vGY6MAACAASURBVADNQPZg9R7xEj6qWCGsitVXkPQESYvXOl4Ikp4gaSlmO5YUbrgB2tvNo1OLxdIvEgk47zwzePjmN+F3vxu0wQOqLFVlJ+c1VZWrne1rVdlfla2cv82Do6h/7LP1GADmLNrwljGV07XMhnHNjqcBhIhs5PwdDxwLPJCrvnWiLn1fQdLitY4XgqQnSFqK2Y4lhW23NYu2f/ObsshKbbEEhe5u+NrX4Pbb4Xvfg1/+MkMuUUtWxo2sY/JGDRukH0Q5XcusE3V2vH7dHxWRt4HZwLmqGoisjhaLxVIwV1wBjY3WD8Ji8Ug0Cl/6Etx7rwlodu21gY+GHEj23XosLy9rpjNaPk/kLRYXT5ONqrq3l3puEpWJEydmzUTtlkOhEJFIJGdyjlAoRGtrK5A9OUdbWxutra05k3PEYjE6OjpyJhzp7u4mFovlTM7h9p8rOUckEklqzpacIxaL0dramjPhSCwWo7OzsyCb2tvbi2KTiBTFpra2tpw2qSqJRIJYLJYzMUw4HKajoyNnEhUgqTlbEhX3s1PK8+TVJvd85rPJUgLGjIEf/QguvBD+9jfjWG2xWDLS1QVf/KKJsnTDDXDBBX4rKl9mThnLb19Yxn+WruXz22zkt5xBo5yuZ9YHIjtFXa2oqrOB2bvsssuZlZWVVFZW9hKYWu7q6qK6ujp58+ZSlRK6QVWpr++dhDLdGbWhoYHGxsZe5fT6qVFw0o93y11dXbiaU0lNsuG2k80mMDenqXrSbQKoq6vr00YuzQO1qb6+vig2JRKJPh+0gdjU0NBAbW1tVptcPZk0p5YzRTVKT4YSj8dz2uRqSD1XpThPXm2qrKykrq4ur02WEnHeefCrX5lBxAEHDN5CbouljGhrg6OOgueeM6v+zjrLb0Xlza4TRlFTGWLu4tUb1AAiHo97CiwSBPzUWuq+C22/JFdJrz4Q+UY/Xup4oVh9BUlPkLQMVT1B0uK2U264s5KxqioqolGIxcwLoLYW4nGzHsItJxI9eRnc98wNX1tdbRZZd3aaclUVhMM95cpK8+ro6F3u7DShYioqoKoKaW2FlhZzbHW1aT+RgB//GE45BW65xSzuFjGaolGz4Nstp9pQV1d6m6JRozeHTclyuk2hkOkzEjG6BsumSKRH8wDP06DaFImY/kr82SuqTdGoCQAwSN+nlvZKvnB8LS//V7jvVx18+UsKUe82SWur0daf81RmuL93TU1NeVdhVFdXI6rsOn4Ezy38hB8cYvLzps+Eu+VcM+FAzlUYqeVcM+HV1dU5Vyyk25Btdj/XyhIRobm5mcbGxrKwKRqNIiJ5bSrFeVq3bh3Dhw8v2Xlyz0M+m7JhH7NZLJaS4c5KUl9/JlVVmYPDp8284MxSJUkffKXNWvYpp/eRVtbGRhgxou/+k0+GO++Ea64xAwg3hW6e9kpuU1VVb70D0VRoGfpnU3V1X839PE+DalNzs7GnxJ+9opabmyFthh4oyfdpzRo46AiYPx8efhiOPba+134vmrW5mYy/ATnLKykn3N+7GTNm5F2F4bL/dptwxey3+bg1xpaj6/vMhKeXc81kQ/aZ8IGW02fCvdiUa2UJQGNjY6/Z/yDb5C5zzmdTKc5TIpFIrsIpxXlKPw/5bEqnJDETvCRFST8ZA63jhWL1FSQ9QdLitY4XgqQnSFqK2Y4lCyImlW5zM/zkJ36rsVgCwccfw8yZ8M478PjjcOyxfisaWsycYpYuPb8BRWMqp2uZn1pL3Xeh7Rd1BqI/TtRA3qmuaDSad1rIixN1V1cXdXV1Oae6otEow4YNyzktJCJJh9hMNnl1ol6/fj3V1dU5p7oikUjSh2GgNnlxovZik6unUJu8OFF3d3cnpwuzTUmCScGea/qus7MzqXGgTtTFOE9ebfLiRN3pLi2wlI6dd4bTTjPLmM45ByZP9luRxeIb778P++8Pn3xi4gvMnOm3oqHHxDH1bDm6jjmLVvOVPSf4LWdQ8LLMPSj4qbXUfRfafkmcqKdPn553+s7LtFA0Gu3jkDwQJ2pVTTqoZpsGcqPt5JoWam1tLYoTdXqdbJpyORx7scmLE7UXmyKRSFFs8uJEne08pJZbW1vzOhyHQqE+evrrRF2M8+TVJi9O1CEbZH1wuPpqs07je9+DP//ZbzUWiy+8+64ZPLS2wrPPwu67+61o6DJz67H86dWVRLrjVFeE/ZZTcqLRaNnMQviptdR9F9q+vSOxWCyWVDbdFC65BB57DObM8VuNxTLozJ8Pe+9t/KHnzLGDh1Izc+uxdMbivLrcptiylA/WB6LIdbxgfSBK35f1gbAUxIUXwhZbmCD3NnyuZQNg6VKYOtUEU9ppJxNYae5c87+ltOzRNJqqcGiDyUpdTtcy6wORHTsDYbFYLOnU1pr0um+8Affd57cai6XkHHGEcZROJMxr2DDYdlu/VW0Y1FdXsOvEkcxdtGEMICxDg5I4UU+YMCGvE3U0GqWhoSGnE3VbW1tyXXohTtTr169nxIgROR2O29raGDNmTE6H41gsRl1dXcFO1GvWrMkbA7m1tZWRI0cWZJMXJ2ovNrnOxIXa5MWJuqOjg1GjRuV0OI5Go0md2RyO161bl/R5GKgTdTHOk1ebvDhRr1u3rl/fR0uBnHyycab+wQ/g+OMhzcfFYhlKLFxoZh1cli3zT8uGyMytx3LNkwv5qKWTzUbU5j+gjIlEInlDhAYFP7WWuu9C27dO1GmOrIPhRO01BnJQnKiLZZMXJ2r3/0KdqGtqagp2oi7GefJqkxcn6lKntbekIQI33QR77QU/+xlcdZXfiiyWknDnnWbWwSUUgilT/NOzIbLvlI245smFPL94NSftNt5vORZLXnzzgfAy6inWyKtYfQVJT5C0eK3jhSDpCZKWYrZj6Qd77gknnQTXXw8ffOC3GoulqKjClVfCWWeZEK3bbGN8ILbZBmbP9lvdhsVWGzWw6fCaDcIPopyuZX5qLXXfhbbvmw+El0GGlzqD2VeQ9ARJi9c6XgiSniBpKWY7ln5y7bXm76WX+qvDYiki8Th885swaxaceio884zxgejuhgULoKnJb4UbFiLCzK3H8sK7a4jFE/kPKGPK6Vrmp9ZS911o+yUZQHhJTuGuNy+0jheK1VeQ9ARJi9c6XgiSniBpKWY7ln6y5ZYmGtMf/wgvv+y3GoulYLq6jFvPr39txsV33w1pKywtPjBz67G0RrqZt6LFbyklpZyuZX5qLXXfhbbvqxN1JBLJm4nadUguxIm6tbU1Wc7lRO06ruZyOO7q6irYiTqfTa5zbqE2eXWi9mKTSyE2eXWirqmpKdjh2O0/m01enKiLcZ5KZZNlkLnkEvjd7+C734UXXzT+ERZLGdLSAkceCS+8YGIEfOtbfiuyuOw1eQzhkDB30Wp2nTDKbzkWS05K4kS9yy675HWi7urqyutErap9HEcH4kRdUVGRbCebI6urN5fDcVdXV1EcjkeNGtWnjXRNqe0O1CYvTtRebGpoaOhzHgZikxcn6mznIbXsas5mExgH6Fw2uRpyOVEX4zx5tcmLE3X658oyiDQ2wk9+Al//Ojz0kPGLsFjKjA8/hEMOgcWL4cEH4YQT/FZkSWV4bSXTx49gzuJPuejgoevFnn4tDDJ+ai1134W272kJk4h8V0QWiMh8EXlARAoOBxMO50/X7qXOYPYVJD1B0uK1jheCpCdIWorZjmWAnHYaTJsG3/8+dHb6rcZi6RfvvGNiArz/Pjz1lB08BJV9p2zE/A/Xs7q1fJb59Jdyupb5qbXUfRfaft4ZCBHZHPg2sJ2qdorIw8BJwD3ZjvHiA9HV1ZV39OOljheK1VeQ9ARJy1DVEyQtbjvlhrusMVZVRUU0CrGYeYFJ1haPg7OMkdpaE0vSXTLnzv64dldXm/iS7s17VZUJGeOWKyvNq6Ojd7mz04SbqaiAqiqktdWs4wiHTZtdXabfUMj0GYkYXSJGUzRqPEtFTDjXgw+Ga64x2arr6kpvUzRq9OawKVkeiE21tb1tKIZNkUiP5gGep0G1KRIx/ZX4s1dUm6JRaG/3dJ5eejnM4Sc3UF0Nzz/RxrQd4tA5uDZJa6vR1p/zVGa4v3dNTU15l3G7S2nTl3HP2NzMRP/znY85ZtpmyaW1uZbSAnmX0uZb8iwiVFdX51zynG6DV5sGkmMpKDZ5yVkmIiU5T+vWrWP48OElO09r166lsbExr03Z8LqEqQKoFZEYUAd85PE4i8WyAeMua6S+/kyqqswNRzppS7eoTUuilJ7/Im3ZY59yeh9pZW1shBEjPNfvVT7oIDjqKLj5Zjj3XNNOqW2qquqtt7+ai1GG/tlUXd1Xcz/P06Da1Nxs7CnxZ6+o5eZmqK+nD2nnafaztZx4IowbB08/DRMnpi2FHCSbtLmZjL8BOcsrKSfc37sZM2bkXcbtkr6Me9fJmzKmYQEvLf2ME3eb0Gdpba6lsJA7H9FAyulLaQdi00BzLLn4aZOXnGWZysWwKZFIUO98x0txntLPQz6b0sk7gFDVD0XkeuADoBP4u6r+Pb2eiJwFnAWw2WabsXz58pztxmIx1q5dW3CdfPuL2VeQ9ARJy1DVEyQtbjuWAPDzn8PUqfDDH8Jvf+u3GoslK3fdBWefDdOnwxNPwNixfiuy5CMUEvbZaizPLfqUeEIJh4ZewAbrAxGMvgtt38sSppHAUcBEoAX4k4h8WVXvT62nqncAdwDssssuOmHChJztxmKxvOK91AEYrL6CpCdIWoaqniBpcduxBICttoLzzjOzEOedZ/wiLJYAoWpW2f3wh2bF3SOPQNoDzyGDCGHgVeBDVQ4XYSLwIDAKeB34iipRPzX2l5lTxvLnNz7krQ/XMW2LEfkPKDOsD0Qw+i60fS9O1AcAy1R1tarGgD8De+U6wKsPRDHqeKFYfQVJT5C0eK3jhSDpCZKWYrZjKQKXXw6jRpn8EB5+7yyWwSIeN6FZf/hD+MpXTEbpoTp4cPgO8E5K+WfATapsBXwGnOGLqgLYe6uxiMDcRUMzK3U5Xcv81Frqvgtt38sA4gNgDxGpE5O2bn96f1ktFotlw2LkSLjiCnjuOXOHZrEEgK4uE2H4ttvge9+De+4Z2gniRBgHfAH4rVMWYD/gEafKvcDR/qgbOKPqq9hx3AjmLv7UbykWS1byDiBU9WXMl/F14C3nmDsK7Tjd4WOgdQazryDpCZIWr3W8ECQ9QdJSzHYsReLss2GbbeCii3oi31gsPrFuncnx8MgjcNNNJmBYyFOg9iATrxCRV1NeZ6VVuBn4HpBwyqOBFlW6nfJKYPNBEltUZm49lnkrWmjpGHq/LeV0LfNTa6n7LrR9T0er6ixgVr56bgiziRMn5g1hFg6H82aiVtWiZKKOxWKoas5wX/F4nIqKipyhsSoqKoqSibqrq4vu7u6c4b7cPguxyUsmai82JRKJotjkJRO1qlJRUZEzLFs4HM6btTkejyc1DzQTdTHOk1ebvGSijsfj+b6ClsGkshKuvx4OPxxuvx3OP99vRZYNlI8+gkMPNbkeHnhgKOU5DHer6oxMe0Q4HPhUlddE2NfdnKFqWa4xnLn1WG75x7u88N4aDt9xM7/lFBXrRB2MvkvuRN0f3BBm06dPzxvCrK2tjbq6upyhsdra2vpk380UCitfJmq3r0zHu+W2tjYyZQtOHaG1tbX1ycqcbqOXTNRuXOFsNrmac2Vt9mKTl0zUXmyKRqNFsclLJups5yG17GrOZhOYwUB6mLj+ZqIuxnnyalNlZf5M1PliMlt84LDD4HOfMzkhLroIpkwxS5qamvxWZtlAWLTIOEqvXQtPPgkHHOC3okHjc8CRIhwG1ADDMDMSI0SocGYhxlGmYedH1lUSEvjWH9/gF8++y12n7sr40XX5DywDurq6+lwvg4qfWkvdd6Ht+zbB6cXR2kudwewrSHqCpMVrHS8ESU+QtBSzHUsREYFVq0zirHgcFi6EI47wW5VlA+Hll834tbMT5s7doAYPqHKpKuNUmYBJbvtPVU4BngO+6FQ7FfiLTxIL4uzfv0ZCzfTJktVtnHHvK35LKhrldC3zU2up+y60fd8GEF7CRxUrhFWx+gqSniBp8VrHC0HSEyQtxWzHUmRSc94kEuaRsMVSIpYuNWlINt9qInvuaXLJvfSSyfVgAeD7wAUivIfxibjLZz0DYunq9uT/Ce1dLnfK6Vpmw7hmpyQeGiZYU268rL0q1vqvYvUVJD1B0uK1jheCpCdIWorZjqXITJliZh4Sjh/n+PH+6rEMaY44wvg6qJrrbE0NTJrksyifUWUOMMf5fymwm596ikHT2HqWrG4j4TwkHjeyNvcBZUQ5XcusD0R2ijqAcJ2oJ0yYkNeJOhaLUV9fn9OJur29PWlgIU7Ura2tDB8+PK/D8ejRo3M6HHd3d1NbW1uwE/XatWtpaGjI63A8YsSIgm3K50TtxabW1takf0ChNuVzou7s7GTkyJF5HY6rq6tzOhyvW7cuqXWgTtTFOE/9sSmfE/W6dev69X20DBKzZ5u7ukWLTNib1lZYsQK22MJvZZYhyMKFvVOPLFninxZL6bjr1F05495XWLK6DYCKcIhId5zqivJ5ep+NSCRSNoMIP7WWuu9C2/fNidq9ccvlRJ3JeXcgTtSqmtfh2P0/l8Nxa2srNTU1BTtRp2vOpimXc64Xm7w4UXuxqaqqqig2eXGidv/PdZ5aW1vzOhynn4eBOFEX4zx5tcmLE3X6d8USEJqaYMEC8//8+bDXXmZA8cILQz6Dl2VweeWV3oOHUMhMgFmGHuNH1/HMBTMB+Mc7n3DGva9y/dOLuOwL2/msrHCsD0Qw+rY+EIPYV5D0BEmL1zpeCJKeIGkpZjuWErL99vDww/DWW3Dyycax2mIpAsuWmYjBm28OW28N4bCyzTY2j+GGwP7bbsxX9tiSO/+1jBfeXeO3nIIpp2uZ9YHITkkGENYHovR9BUmL1zpeCJKeIGkpZjuWEnPIIXDLLfDXv8LFF/utxjIEaG42eR66u+HZZ81quZWLl7FggY0YvKHwg8O2ZfJGDVzw8Dw+ay/v5HLldC2zPhDZKckAwsu0SNRD5lYvdbxQrL6CpCdIWrzW8UKQ9ARJSzHbsQwC554L3/qWSQn8m9/4rcZSxnR1wVFHmWBff/mLXbK0oVJbFeYXJ02jpSPG9x/9X1ktA0qnnK5lfmotdd+Ftu+bE3U0Gk1m54XMzrldXV0knMgmhThRr1+/HhHJ6XDc1tbWx7E13ZE1FosRDocLdqJet24diUQip3Nua2sroVCoIJu8OFF7samzszN5HgqxyYsTdUdHB1VVVTkdjqPRKCKS0+G4o6MjqXmgTtTFOE9ebfLiRN3R0dGv76PFZ268Ed57zwwmmprgwAP9VmQpMxIJOO00407z0EPwf//ntyKLn0zdbDjfO2QKP3niHR58ZQUn71aeEd/ca3M54KfWUvddaPu+OVG3t7fndaLu7u6mvr6+1/6BOFGLSF6HY/cmMJfDcXt7e1GcqIcPH97LrkyaQqFQTudcLzZ5caL2YlNtbW2f8zAQm7w4UWc7D6nl9vb2vA7HdXV1ffT014m6GOfJq01enKjT91sCTkUFPPigyfR1/PHw73/Dttv6rcpSRlx6qRk4/PzncMIJfquxBIGvfW4icxat5srZb7PbxFFMGlt+gRpCId/cb/uNn1pL3Xeh7fvmA5F+szbQOl4oVl9B0hMkLV7reCFIeoKkpZjtWAaRYcOML0RNDXzhC7B6td+KLGXC7bfDddeZCawLL/RbjSUohELCDSfsRHVliPMfnEe0u3ye5ruU07XMT62l7rvQ9n3zgXCXkxRaxwvF6itIeoKkxWsdLwRJT5C0FLMdyyCz5ZZm8frHH8Mxx4CzVM5iycbs2caF5ogj4Be/AA/P5CwbEBsPq+Fnx+3IWx+u46ZnF/stp9+U07XMT62l7rvQ9n2bm4l7CG/opc5g9hUkPUHS4rWOF4KkJ0haitmOxQd23x3uvRdefBG+/vXewfwtlhReeQVOOgl22QUeeADKKOKlZRA5eOomnLzbeH49dwkvLSmv0K7ldC3zU2up+y60fV8zUUcikZxO1LFYLOmQXIgTdVtbW7Kcy+HYdVzNlbW5q6urYCfqtra2nDa5DsfFsMlLJup8NkWj0aLZ5CUTdU1NTU6H4+7u7rwOx5GUp7wDdaIuxnnyapMXJ+qIfXJd3pxwAixeDJdfbgL5X36534osAcPN9bDxxmYWIs31zGLpxeWHb8vLS9dywUNv8rfz92ZEXXksDfKyzD0o+Km11H0X2n5JnKh32WWXvE7UsViMysrKnE7UrvNpKgNxoq6urk4el629mpqavA7HruZCnajHjBnTx4E2XZOrpxCbvDhRe7Fp2LBhfdoYiE1enKiznYfUsqs5m01gHKDT9fTXiboY58mrTV6cqIcPH46lzLnsMjOI+NGPzCDixBP9VmQJCKm5Hp56ygwiLJZc1FVV8IuTduaY21/kB4+9xW1fml4WN+fp931Bxk+tpe670PbzLmESkSkiMi/ltV5Ezs91jPWBKH1fQdLitY4XgqQnSFqK2U5REKlH5F5E7kTkFL/llA0icOedJh7nqafCf/7jtyJLALC5HoKLiNSLyL0icqcE8Lduh3HDufCgKTz51ioeeW2l33I8EahrWR6sD0R28g4gVHWRqk5T1WnALkAH8FhBvRK8tePlpidIWrzW8UKQ9ARJSzHbyYrI7xD5FJH5adsPQWQRIu8hcomz9VjgEVTPBI4srbAhRnU1PPYYbL55z12jZYMlNdfDfffZXA+DgYj8TkQ+lbTfOhE5REQWich7kvZbpwH+rTtrnyb2aBrFrMcXsHxNu99y8mJ9IILRd6Ht99eJen9giaq+X1CveFt7VaypuGL1FSQ9QdLitY4XgqQnSFqK2U4O7gEOSes0DNwGHApsB5yMyHbAOGCFU6t8rgZBYcwYeOIJE5HpiCNg/Xq/FVl8wuZ68IV7SPutkwy/dVImv3XhkHDjCdOoDIf4zkPziMWDHdq1HJZZuVgfiOz01wfiJOCBLELOAs4C2GyzzVie56laIpFgzZrckQO81Fm7dm3O/cXsK0h6gqRlqOoJkha3nZKi+jwiE9K27ga8h+pSAEQeBI4CVmIurPPI8iAi9TchWlnJ2qVLSyK7v6zPcy4Gjaoqqm69lZGnn070iCP47M47TfK5NAKjtx+Um2a/9N5z/zCuu24Mp39lHaccs5ZV/fiK2Pd44Kjq85Llt06d3zrpx2+dUz/5e7f55pvnvQcqBd/9v4254pmVXPnoK5yxW3CdaLxcE4OCn1pL3Xeh7XseQIhIFWb67tJM+1X1DuAOgOnTp+uECRNyttfZ2Zl0qC2kDsBg9RUkPUHSUmw9sVgsGQEpE93d3X0cjAdSp7GxMWcilWL1U6iWhoYGhg0bRmdnZ842SsTm9Dx9A3Mx3R24BfglIl8AZmc6MPU3gfp63aSpqbRK+0FgtDQ1QWcn1WefzSa33gq33pqxWmD09oNy0zzYemfPhst+bCag7rx7OOFw/4MkDP33eH7+KsUj52+d5Pitg96/dzNmzMh7D1QKTpsAC5rhD6+v5Ihdt2K3iaMGXYMXvN4vBAE/tZa670Lb788MxKHA66r6yYB7S8ENT1loncHsK0h6gqTFax0vdHd309bWxiabbJI1zXokEskbPcBLnZaWFkaMGFFQG6XWkkgkWLVqFcOGDSvae9xPMs1xKqrtwOmDLWZIctZZsGgR3Hij8Z497zy/FVlKjM31EEgy/tZpmf3WzTpyKv9d3sx3H5rHk9/Zm+G1lfkPGmR8upYNCD+1lrrvQtvvjw/EyWRZvmSxFJtMg4cP1nZw4I1z2e6KZznwxrl8sLbDB2WDS7ZB1CCyEtgipTwO+MgnLUOX666DI4+E73zHxPC0DFlsrofAMiR+6xqqK7j5xGmsWt/F5f9vvqeomBbLQPA0AyEidcCBwNke6+etkx6Tf6B1vFCsvoKkJ0havNbxQk1NDa2trfx49gLe/qi3Y+mbK1voihk/gHc/beOgm+ey07iep/bbbTaMWUdMBfrmUMjE9ddfz5VXXsnRRx/NqaeeynHHHcdee+3FNddcw7777puxjfPPP5+bb745WXbrXH311Vx66aU88sgj3H777cyZMweAq666ih/84AfcfffdHHjggYwbNw6Ae+65h7Vr13LhhRf2aj8SiXDuuedy3nnnMW3atD7vjQ+8AmyFyETgQ4wf1Je8Huwml4xVVVERjUIsZl4AtbUQj4OTTJLaWhOSxk2Y59rrJPCjuhpCIXCXclVVmUe3brmy0rw6OnqXOztN5ueKCqiqQlpboaXFHFtdbdpPJEzbNTWm/3jchFytrTX6urt7yqk21NUVz6bbbjMRmU48Ef72N9huO6M/GjV6c9iULAfFpkikR/MAz9Og2hSJmP5K/Nlr/qiLQw+upzsW4qm/xNi4tgtaBmhTNArt7b5/n7yeJ2ltNdr6c54Gl1eArWSAv3XQ83vX1NSUN5mum9A0VzJdEUmWcyU0BXolNJ08soJzZ07glueWseeEYRy+/UZ5E8+KCNXV1TkTz6bbMFCb3CTB/bEpU5LWwbApFArlTXo80POUz6ZYLEZ7e3vJzpN7HvLZlA1PAwhV7QBG56vnfnkmTpyY98ujqiQSiZwnJRKJZD0J/clE3dnZmUyqlu2DFolEGD58eM6TAuQ9KV4yUa9bty6ZXCzbB62rq4uGhoaCbPKSidqLTZ2dnckn4YXY5CUTdSwWI5FIkIgnUE0Agoi5PrmDB5euWAJVRURQ1eR5rKioSNp41VVXMWrUKD7/+c/z4IMPMmrUKBKJBGeccQbhcJhYLMbEiRN5/fXXGTZsGDvttBPxeJz77ruPlStXsmrVKq6++mquvfZaNt10U15//XW6u7u5/PLLGTFiBNFoSUsFtgAAIABJREFUlLPOOovKykq6u7s56qijeOGFF4jH48TjcUaOHMl7773Hcccdx4033sill16KiJBIJNhnn33o6uri4osvZuLEiTz33HM88sgj7LHHHiQSieR74Z7D9vYSh+cTeQDYFxiDyEpgFqp3IXIe8DQQBn6H6gKvTbrJJamvP5OqKnPDkU5aAj3S12SmD5zSl4Oll9P7SCtrYyOkLhfLU7/fZRiYTSNGmMhMu+0GX/oSvPyy2VZV1VtvKTQX2ybXnlT6eZ4G1abmZmNPCT97XV1w1FeqWP4BPPssTNmhCijAhubmzNMXg/x98lrW5mYy/gbkLJcmr4Gk/NaJ81unqndJ2m+d9uO3Dnp+72bMmJE3ma5LrmS6mcpekusCfOfAbfn3shau/tt7/N+UTdmisc7T8fmStBbDprq6uj5teLHJa/1i2tTV1UV1dXXJzlOu+uFwOKmjFOdJVXtty2dTOiXJRD19+vS8Xx531JPrpESj0T5ZnQeSiVpVk1l+82UDznVSWltbqampKTgTdU1NTa862TTlytrsxSYvmai92BSJRIpik5dM1K2trYRCIX589A6kc+CNc1myuo2EQkhg0tgGHj5nrz71wKztq66upqKiglNOOYVwOMzbb7/Ncccdx+LFi/nggw/YYostqK6uRkTYfvvt+f3vf88xxxxDOBzmhRde4NZbb+X3v/89CxcuZO3atVx99dW8/PLLtLS0MH/+fI477jjefvttVqxYwRZbbJF8T0KhEOFwmHA4zKRJk1iyZAlTpkxh/fr1yc+7m2V93bp1jBw5kq997WvMmzcvqTk1C7t7DsOlXiitenKW7U8CT5a2cwsA48aZdS17721yRDz3nN+KLEUgNdfDQw/ZXA9+o1l+63QI/daFQ8JNJ07j0Jv/xfkPzeOhs/agIuz7kljAJDDzaUa93/iptdR9F9p+MD5NFosH7jp1VyaNbSDsDB7uOnVXT8dVV1czZswYpk6dSktLCzvuuCNNTU18+OGHyTpf/OIXmTVrVnL53cyZM7n55puZP38+22+/PZtuuil/+tOfWLFiRa+2dthhB5qamlixwgTv+Oc//8kbb7zBnXfeCcD777/P5MmTaW9vZ+TIkX20bbTRRqxbt4577rknOXNl2cDZZRe4/34zA3H66ebu01LWlEWuh6VLYepUszRp6lRTtpQ140bW8ZNjtue19z/jtueW+C3HUgJc39BJlz456L6hRZ2BcLE+EKXvK0havNbxgusDkYnxo+t45oKZxOPxvE/j3VmWK664Irnt2muv7VXHXSLk+jRMmjSJSZMmJfen9jNr1iwAjj/++F5tuXXctvbbbz/222+/ZBurV69m0qRJ3HfffZx+ek8gj/Hjx/Pqq68ybdo0Dj74YBYsWMABBxxAJBLhgw8+YObMmRnfG8sGwrHHwsUXw89/zsYPPwzbbmtmJsosbKcFbr/d+Mifey6kuTwFiyOOgHfeMetF33nHeHq//bbfqrIiQg3wPFCNuZd5RJVZIkwEHgRGAa8DX1El6p9Sfzlq2uY88b+PuenZxfziH4uTD9/Gj67Lf3CJKKdrmZ9avfR9+j3/Zclqs7x5yeo2zrj3FZ65oO/9w0Dbz0VJBhBeiMfjfZa5DKTOYPYVJD1B0lJsPfnwElXCS52LLrooZ5Sj/vRz2WWXZdx/+eWX093d3WvwAPQaaBx22GHstddeyTCuP/rRjzK2Veq09qXAOlEXYNPjj5v30L2hO+wweO01/x2OrRO1J5uWrqhk3wMqWPGh0FAP3/lmDFlfRJuK7US9cKHRBj2DiFtvNVMm1dVBdKKOAPup0iZCJfCCCE8BFwA3qfKgCL8GzgB+5aXBQgmKE3W6f+G7n5iAJAmF91a3cfrdL/P4N3bzzYnai89kPpsGy4nai79uqZyoOzo6qKury2nT0jU9vpEJNYMIr5+99evXU1NTU1onaq+4X54JEybkNSAajSIiOU+K6wQMhTlRr1+/nhEjRuT8oLW1tTFmzJicHzRXS6FO1M3NzTQ2Nub8oLW2tjJy5MiCbPLiRO3Fpra2tl7lgdrkxYm6o6Mj+WV1HaTD4bBxrE4kEBHi8XjySy0iVFRUEI/Hk5maKyoqiEQiyRvuioqKpJN1ajkajSadrqEnJnI4HEZE6OzspKqqKllO39/d3U00GqWmpoZwOEx3dzeqSigUIhQKJXXGYjFqa2uT5Ww2ubMY6Ta557AclzhZJ2qHgdj03nspohXefbfHadY6UQfWibqlBf7yiEnn4ebD7OiEo4+vYsGCItpQTCfqSMQMCNzBh4ip9+1vw7XXwve/D2eeOQAn6J5ysZ2oVVHAzTha6bwU2I+e6En3AlcwSAOIIDlRp+7/oLkrRSMsX2uCsHhprxRO1F79QHPZVEi5PzZ58dfNVC6GTYlEIud5inYnCIvQnfKwUxWemP8pR++8ed7zlH4erBN1GThRNzY2lpUTdbFs8uJE7WpKbz91yVIkEunTfvpMQkVFRZ/PVvqPRlVVVa866cui8u13y66WXJpdp+pc+7PZlOm7ZNkAmDLFPBV2fSASCRPq9dxz/dVl6cO6dWbC6OGH4emn+z5ATyRMrsDActVVZvAwfjx8+KH57D3+OLz/Plx5pclPcs01ZlndOecMYvKKeIWIvJqy4Q4n4zMAIoSB14DJwG3AEqBFFTfM4EpMhukNmqax9ckAJAAbDcud2NRSHvxl3od0J5TNhtfwyfoI40fX0lBdwfkPzeOlJWv48ZHbU1tVuuArJXGi9uIDkS8zr9c6XihWX0HSEyQtXut4IWc7zcvgtt2punZTuG13U86ClzwQ+fDSRrHqeKFY77GlTJg9G7bZBg2HjQ/EAQeYx9q33+63Mguwfj384Q8mWNZGG8FXvwpvvgnf+hb85z8mjYf7XCMUMvfkgeSNN8wsw6mnmgFDdzcsWACTJsF++8GcOea1/fZw0UUwYYKpPygzouFuVZ2R8rojda8qcVWmYZK+7QZsm6GRDT6TWk8AEqG6IkRbVzcfr+v0TU85Xcv81Jqr70RC+fXcJWy76TBevGQ/lvz0MJ676PM89s3Pcd7nJ/On11Zy5C9fYPEn2b+nhdrmWxSmYq1jH8y+gqQnSFq81vFCsp2nLoG7v9D79as9YfVCROOweqEpp+5/6pJ+6bn++utJJBIceeSRPProowDstddezHGSwGVq4/zzz8+o9+qrryaRSHD77bdz1VVXJZ2ur7rqKlSVu+++m5Ure6bj77nnHm644YY+7UciEb7+9a8zb9687O+NZcOgqQkWLOCTxYuNM+sTT5hs1eeeC78alBUZljRaW+GBB+CYY8yg4ctfNm4p554LL71k8gDecAPsvnty/Ec4bP7Onu23+gzEYibS19ixcOON2evNnGmSV7z4IsyYYcJKTZhgZi5cXxcfUaUFmAPsAYwQSa6uKMts0sXGDUCy5KeH8bfz9yGuykV/epNEwp9rSjldy/zUmqvvv7+9iiWr2/nGvpN6PbSvCIe46OAp3Pe13fisI8qRv3yBh19ZkbGtQm0riRO1F1HRaDTv6MdLHS8Uq68g6QmSlmLryUqsM3c5hXg8TkVFBVdccQUjRoxg//33TyaS6+7u5swzz6SqqopQKERTUxPz5s1jzJgxTJ8+HYD777+flStX8sknn3DNNddwzTXXsPnmmydv7C+77P+3d97hbVXnH/+8kmw5HtmDDIjjkIQVMpywIWHTQoAyAmU0gRTKKJu28IMwOmihjEJJw2ggYZQVRgil7CRAGU0C2TvOIIEQJ04c27ItSzq/P45ky7IlXVvr2jmf59FjXfnonO+rK91733vO+7530LVrV2pqarj66qvr+7rmmmvwer1cd911APTo0YOSkhLGjx/Pww8/zOTJk+s1nnjiiSiluOmmmygsLGTOnDnMmjWLY6IkiY/52dgUE0SdBJvCK1FPnw6XXgrXXKPbXHmlCaJOcRB1ZSW880kur77h5D/vO6ipEfr0DnDVFQHGn1HNEaP9ONxBG/Y02FTUL4vlX0TYVG6zIOr779fTJi++2DB+rP00ahS8/LL2mh58EO66S/+96ir9nezdO21B1CL0AOqUYrcIHYCTgPuBOcB56ExME4BZcTtLEnYNog7f7tkBfntSEfe8u5anP13HpYf1TXsQ9c6dO+PGTLbEplQGUYdsyUQQdXl5OZ06dWpiU3Z2No9/spb9uuQwZkABPp+viU2j9y3gtUkjuW3WKn77+hI+W7OdO04tIs/tqt9Pof3QJoOo45UH93q99YGjiQRRV1RUxP2iVVZW1n+xYgUc19TUJBxEHc+mUBB1ojZZDaK2YlOIRGxqURD1ib9vEnDsfPIoZOc6RAVQ4kB12x/fxW82DqIOBkWH7PP5fIwfPx6Hw8GiRYs4++yzWb9+PWvWrKFnz5714x5wwAE89dRTnHXWWfj9fubNm8fDDz/MCy+8wKJFi9i+fTt33nknn376Kdu2bWPx4sWcddZZ/PDDD6xZs4Y+ffrg9Xqprq7mnnvu4Xe/+x1er5c+ffqwevVqBg4cyI4dO6itra2vRF1XV8d3331HXl4el156KQsWLKC2thafz9dsJerQZ9qWMEHUQRKxKTuiEvXrr8N55+mcoHl58KtfmSDq1mxD1CDqqio94fPqqzm8+66+pu7dW8cRjx8PRx3lCMYoNY4NS6sNiQZRL1um4xouvFBXPg9hZT+dcIJ+fPst/PGPOk/t1Kl6id3NN0P37k3en4JK1L2BGcE4CAfwqlK8I8IK4GUR/gh8C0yL1UkysWsQdeT2hGMH8d+Ne3jow3UcO7gXB/XpGLV9KoKorcaBtsSmlmy39SDqz9aWsuz7Cv58zlA6dSyIatOA3jn868qj+MecdTzy0RqW/VDB4xeN4OACrTVyP9giiLq4uDjuj8fr9TYJUo0UHPK0wmlNEHV2dnZ9P9F2qtvtjhtwHNKcaMBx9+7dG73WnCa3213fprU2WQmitmJTx44d436xrNhkJYja7XazY8eO5gOSL3oVXroQtWMt0n0Q8vOX69tFBlGHMiO5XC5yc3Pp3LkzQ4cOpaqqiuHDhzN48GDef//9+krU48eP54gjjmDp0qU4nU7Gjh3LE088wdatW5k4cSJ9+vTh7bff5vvvv2efffbhkEMOoaqqipEjRzJ48GA+/fRTsrOzOf/88zn44IP5+OOPueKKK/j+++855ZRTqKmpoXv37k0qUe+7775UVlby/PPPU1VVFbMSdadOnTAYcLth5kw491x951dEz0QYWkxJiS5/sHr1AAYN0te/8+bBO+9op6FXL7j8cu00HH20njhoF/h82rBOneCxx1rfz4gR2qFduhT+9CcdG/Hoo3o24tZb9QeYIpRiCTCimddL0PEQhiiICPefeyin/u1TbnzlW97+9THkZKXvy21iIBIb+x9z1tOro5tzRsbPD+B0CNedOIjRA7pyw8vf8rN/fMHk0w/kkiP6J2xbxupAGAwtpusAuPZrAhYKyYVIdyG5WbMaz5aXlpbSv3//pBSSMxjqcbv1hds55+gZCBF9e9zQIs44I1T+QFi1SjsQPXvCxInaaTj22HbkNITzyCMwf75ejtSjR+L9DR2q+7r7bu1IPPwwPP64/m7+9rfQp0/iYxiSSte8bB48fxgTnvkf97+3irvHHZxpSQYLfLN5F1+W7OTO0w/E7bJ+cDqiqBvvXn8st7y2mMmzlvNlyU5+f8YBdI8zyxATpVTSHyNGjFDx2LNnT1LabNiwIW1j2UmPnbQkW8/WrVuV3++P2qampiZuP1ba7Nq1K+E+Uq3F7/errVu3KqX0ZwMsUCn4zab8kZsb9zNIFz+sX59pCS0ipt7qaqV+8hOlQKmnn06fqDjY+TMOBJT64gulrr5af2zhD4dDKZ8v0wqt0erPeNUqpdxupc4+W38YqWDNGqUmTlTK6VQqO1upLl1UwOFQ6qCDlGqBblhapTJ97GrFo7i4uGWfVwa5e9Yy1f9376h5q7enbUwr1wt2IZNamxt70vT5ati976vKmrpW9en3B9QTc9epotveUQNv/7cacNs76qSH5qpNO6qivifadYeZgTDYjvz8fLZt2xb1/3V1dU2WSLWmTWVlJZ5QgGAKx0lUS+RyL4OhnpwceOMNnRboiiv0TMSkSZlWZUvWroUXXtCPkhL90XXsqDMrKaVjfkNZk9otgYD+fuTm6nTAFlKut4pBg+DZZ2HyZCguhl27ENDVrceN02liDbbgtp8cwBfrd3DLa4t5/8bj6JqXwB1pQ0pZva2Cj1b+yA0nDiLP3brLd4dD+NWYgTz/1Sa27NIJE9aVVjJpxnw+vLllKx9SEkQ9YMCAuEHUQNwgaiApQdS1tbVxA469Xm/cgGMRSUoQtZVo/VCF5ERsshJEbcUmpVRSbLISRO3z+cjPz68PnG4uqwJQX+k5WlYFj8dTHxcRzaZQ9exoNu3Zs6c+NibWfgrFmsSzKVamiKqqKjp37hwz2D1UabstYbIwJTkLU7SMRdOnw8UXayfC79eBsSYLE6U/+HjljSxeeD2Hr//nQERxwnE+Jt8G54yrY8c2H+MuzGP1OgdDBitmv1gJuwMp++4lNbNUa7IwTZmiU7FOnaqD3MOzLqXCpn79GteLUEpX1Nu9O+EsTHakLWRhijwPBerquG/cYH7+7Lf8buZi/nbeQQQCgZRmYUq1TcnMwhTSnwmbamtrG9n0949W0SHLwcWH9cXj8SSULeuH3Q1ZLJWCktLK+us8q1mYRM9OJJfi4mK1cOHCmG1CwbuJttm4cSOFhYVpGctOeuykpb3qsZOWUD9ut3uhUmpUzIZ2JC9PUVWVaRUAbCspYZ+iokzLsIxlvTU1cPbZ8MEHMG2azu+fITL5GVdX6yLKL7wA772nr78PPVRnv/35z6FvM3GHbe07Aa3QXFKiYxXGjNEpplI1+xDJwQc3VFQPTfNYnIEQWeZR6pB0lb1OGqNGjVILFiyI39BGPPXpeu57dxX3nzuUC0bvl9KxrJwT7UImtYaP/V2Zh7EPzuWyowq584yDEu775Ifn1VcndwgM7JEfdQZCRJq97rBUSE5EOovITBFZJSIrReTIWO2tOCXxPBurbayQrLHspMdOWqy2sYKd9NhJSzL7MbRTcnLgrbfg5JP1MpXp0zOtKG34/fDJJzqxUK9eegLm2291RtHFi/Xj1lubdx72CpSCX/5SzxI8+WT6nAdoXFHdthX1DL88poijBnbj3tkr2LAjtTd72tK5LJNaw8d+8tP1OAR+eWxybnRMmzCaAd1ycYowsEc+0yaMbnEfVpcwPQq8p5Q6T0Sygdx4bzAYDAZDmgk5EWedpa+mRWDChEyrShlLl+qZhhdfhK1boaBAZ7e99FJ9oz1uPEMwj2uv1athyBB9cdvGZiIs8fTTMGeOdh723Te9Y4cqqrfBWZ69CYdDeGj8ME595FNufGURM686kiynpXvMbZbNOz1MmjGfktIqinrkMW3CaPbrZr/L2+0VNby6YAvnjuzHPp1y4r/BAvt1y+WtX41qUnagJcT9dohIR+A4gsVYlFJepVTM2vVi4e5GvKBSq22skKyx7KTHTlqstrGCnfTYSUsy+zG0czp0gFmz4MQT9TKmGTMyrShhSkr0ShiXCwYPhttvh2HD9NKkhx6C4cN1FtFt23Ts7gknWAyGHjcOVq5E/H5YsQKOOEKvf9q4Ud+1bw9s3qynX0480aT6NcSkd6cO3HfOUBZ/t5u/f7IuZePY5Vx22fT/sXZ7JX6l6gOJI8mk1tDYz3y+EZ8/wK/GDIzzjtb131qszEAUAaXAsyIyDFgI3KCUajTHJSJXAlcC9OnTh40bN8bsNDzHfiJtdu7cGUd+8saykx47aWmveuykJdSPwWCJkBNx5pnaiRCBX/wi06paTfA6H6V0NqW//AUOPxz+/ne44IIEShmsXt3YUSgt1bM3oFM0HXpow2PYMDjkEGhLWdGU0rUY/H49C5HOpUuGNskZh/bhk1XbefyTtYwZ3J3i/l2TPobVOk6pJnypllKwbnsl3++upk/nhurtmdTqdDopr67jha828dOhvRnQPbnhQInaZsWBcAEjgeuUUl+LyKPAbcDk8EZKqaeApwBGjhyp4gWEVlRUxJ06sdIGiBt8mqyx7KTHTlraqx47aQn1YzBYJjdX300fN05XRnM44JJLMq2qVehibw3bTid89VUSOu7SBXbs0M8dDj298eyzsGSJDpxYskSvkdqzp+E9Awc2OBQh52LAAP1+u/HcczqS/LHHtEaDwQL3nnkw8zeWceMri/jPDceR38qUodGoqamxxSxEvtvFnhpf/bYCxvx1Dj8b0ZerxgykqEd+RrXW1NTw/PxtVNb6uHpscmcfQv0nYpuVb8UWYItS6uvg9ky0A2EwGAwxMWlc05TGNVYqzddf19mZJkzQ7SZMaDNpXH11ilvvyiUQyEaf3gWHQzFksIKKqsT206JFsGsXdOyIqqpCBg+Gf/0LCgv1mqiQTUrB9u2wbJmOzF6+XD9/660GryY/X89OHHCAXms1bJh2Ti69VE+ZDBoEb74JvXunL43r7t1w4416qubiixv2T4ZS00pFhdbWkv3UxmiLaVybS3mK38+fxg1h4nOLmPzmYn5/+uBmbWitTfHS86cjjau3rg6nCPluJ9VeP4Xdcpn8k/35eM0uXl24ldcWbOHogV3YsMPDtj21FHbL5e/jD2b/fTqnbT+VlpUz7fMNjBncnQGds6ioqEhKatqWpuePRlwHQim1TUS+E5EhSqnVwInAiljvMTEQqR/LTlqstrGCnfTYSUsy+0knSqnZwGzy8q4gO1tfcESSGxG01qFD4+2ciKCx4MEu6nbkGBHbqqBA58C32L7F25Bcm7KzG+ttjaZ334UzzoCrroK8PLjoopa9H1pmk9vdVHML91N5dTY//wX85z96FdZXXwlr1sCQIcLs2aIjpltiQ/i23w/XXQddu8LKlfxYXt58gG/Ipi5ddJD1uec2/M/j0c5EaKZiyRLtVDSX/WrNGv3eyPSliXzXysr0vowkN1df0E+cqC/mn3tO2xlpU4gEf09Wt1VZGc0eA2Jub6EtETrejRo16oqsrCyysrLICfu8I7eh4eItRGTK0MjtyPNA5HZk4dF47aNtH3dgAdceX8nfP1nHyQf34adDezdrQ2ts6tq1a5M+0mFTCJfLxfoyL7uq63jw/GGcV9yv/n9jD96X608awrP/3cAT89YTCN4jKNnp4YaZK+tTnaZjP300/3t2eer49QmDyI04/jZnU+T/4+2nyP0Qz6ZIrM5LXQe8GMzAVAIknGDcytqrZK09S9ZYdtJjJy1W21jBTnrspCWZ/Rj2QnJzdYahM87Qd8WhqRNhI4LJkVizBp54Qi/jTyqPPgrz58NLL0G3blBe3vI+cnNh9Gj9CKEUbNminYlx4xpmKAIBvQ4rXbzyio6B+etf9bIsg6EVXH/iID5du4Pb31jKiP0607tTh/hvsoAdzmVzV28HYMzgpgFUPQrc/Pa0A3hyXgl65rMhRqK0opYeBe4m70k2Xl+AZ7/czOjCLowuTH4cCqQnBgKl1CIgbvGq0PRdYWFh3Ok7r9dbX50Xmp9CqaysrPeAEqlEvWfPHjp37hxzqquyspLu3bvHnBaqq6sjNzc34UrUO3bsoKCgIOZUV0VFBV26dEnIJiuVqK3YFKrInKhNVipRezweunbtGnNK0uv11uuMNn23e/fues862vRd6LuTyv1k1aa6urq4lSV3746Z/MxgiE1eHrzzDpx+uo6F+O1vddoim6Uv/ewzOOccPUnw/vs6o1JSKSmBO+/UztQFFyS3bxGdJnXffeHAAxsKqIH+e9VV8Oc/6xmNVFFaqmdXDjsMbropdeMY2j1ZTgd/u2A4p/3tU054cC5en0pKulM7xEDMXV3K0L6dYjoDRT3y6outgXYlxv51Dtccvz+TjhlATlbqHKFZi7byQ3kN950zNGVjpCMGwjKh6buRI0fGnb4LXbjFmuryer1Ngk+bmwYKb9PctJBSqn76J9a0UkhzOOHTQhUVFeTk5MScFnK73U00R04DFRQUNGoTTVOH4BKB1tqUl5dnK5vy8/Nj2hT+PNZ+qqioaDKdFzl9l5OT00RP5PRd5HcnFfvJqk1ZWVmWbDIYEiIvT1ch7tlTF04AfZE7bpzl6sCpZPp0uPJKHe87e3YKbp6HshK5XDB1amqzEs2erT/X1at1DMSRR+pMSG++qXPPXnxxasa/7jo9o/LMMxZz2RoM0RnQPY9OHbLYXqFvsK0PpjuNVrW4LbDb4+Wbzbu49vj9Y7abNmE0lz37NRt3VlPUI4+7xx3EjC838df3V/PiV5v4zWlDOGtYXxyO5P6O/QHF1HnrOaBXHmObmSGxC8kNrW/JwK74Q1tpk86x7KTHTlqstrGCnfTYSUsy+zHs5eTlNQRAg74zvnp15vSgZxv+7//ggQd0uYLXXkvRTfrp0+Gjj+Af/4B+/eI2T4hgAbVGXHednoW49FJ9gT91qp4BShZvvqmXL/3hDzqY22BIAjsrvfXPAwpKShOrVJ3pc9lna3cQUDB2SM+Y7fbrlss71x5Rf5MQ4JhBPfhy/U7+9O4KbnplMc98vpE7Tj+QI4q6JU3fB8u3UVJaxUPnHmwppri1JLofUpJ3zgRRp34sO2mx2sYKdtJjJy3J7MdgYMiQhrvfIsm9iG0hlZV6ydIDD8DVV+ug6ZQ4D9u2wc03w7HHpiCowiIjRsAXX2jH4ZtvdArYu+9uyIyUCGVl+gMcPhx+97vE+zMYghT1yCN0VSeitxMh0+eyOau30zk3i+H7do7btjmtRw7sxtvXHsMjFwxjR2UtFz71FVc8t4CS0sqEtSml+Mfc9RR2y+WMYX0T7i8Wie6HlDgQykIFzxoLB0wrbayQrLHspMdOWqy2sYKd9NhJSzKsl53jAAAgAElEQVT7MRiYPVunHAWd9ebttzMiY/NmOOYYHZrx97/DlCk6U2hKuO46nXL06aczW7PB6dSzEKtXw/nnw+9/r1PAfvBBYv3edBPs3KnrWJibDYYkMm3CaAYEnYaOOVlMmzA6zjtik8lzWSCg+HRNKccN6oHTwtKjaFodDuFnI/ox59ax/ObUIXyxbgenPPIp97y9nLIqb7PvscLn63awdGs5V40ZSJ03dhrVREl0PyR1HqmlQdShAGBoPjg3FIQKiQdROxyOuEHU8XLr1tXVUVNTk3AQ9Z49e1BKxQ3ODdnQWpusBlHHsyk0fqI2WQ2idrvdcYOo4wUcV1dX12tubRB1MvaTVZusBFFXh/KzGwyJUlQEK1boFJ8TJuisQQOTX6goFl99pctTVFfrLLOnnprCwd56C2bOhD/9KaOzLY3o1UsXqbvsMj1zcOqpOqj7kUd0vYiW8O67el/eeWdDDQuDIUns1y2XT24Zyw0vf8vc1aX07JhYFiIrN5lTxbLvy9lR6WXsEGuxBfG05mQ5ufb4/Rk/al8e+WgNz325kde/2cJ1J+zPhKMKcbtaFoc0Zc46enV087ORffFWe1r03paS6H7IWBB16KIqVhC13++Pm/vWShC1w+GIG3AcuuiLFXDs8XiSEnDcuXPnRnY1p8npdMYMzrVik5Ugais25ebmNtkPrbHJShB1tP0Qvu3xeOIGHOfl5TXR09Ig6mTsJ6s2WQmizmsu57vNMYXkbFBILpZNp58O++8PkyfDmDG6fRoKyf3rdTeXX+mkb+8Ac/5dy4FDXVCeJJsi91N5OVxzjb7Lf8UVWlekTbW1erwUf/eatam4GD7/XE+/3H+/dgYmT4Zf/1prjFdIbscObdeBB+olWqHPPU2/J1NIroH2Ukgu2o2ss4f1Ztai73nn202cemCPVtvkCX6nMmHTJyt/RICRfTrUJ5GJtZ/8fj+1tbVxbcrBy+0nFXJRcW/++tF67nt3FTP+u4FbTh7EGYf2rm8fy6ZFW8r5qqSM208bjK+2Bo/H06r9ZLWQXGg/tLaQHEqppD9Gjhyp4uH1epPSZsOGDWkby0567KSlveqxk5ZQP8AClYLfbMofublx7UsXP6xfn2kJLSLlel94QSlQ6rXXktZlNM1+v1KTJ+vhjjtOqdLSpA0ZnSuvVMrhUGr+/KhNbPOdWLNGqZNP1h9QcbFSCxZEbVqvOWTfV1+lSWTraM1nDEurVKaPXa14FBcXt9jWtoDPH1BH3PeRmvjM1wn1Y+WcmCrOnvK5OvPvn1lu31qtn60pVac+Mk/1/9076uwpn6sFG3fGfc+k6fPVsHvfV5U1dQmNbRWr/Ue77shYDERcz8ZiGyskayw76bGTFqttrGAnPXbSksx+DIZGXHihjoe4996GmgUpwOPRK3T+8Ae4/HL48EPo3j1lw2nmzYOnntLxAaPiljLKPIMG6eIXL72k0+wedhhcf330Qncff6ztu/lmOPzw9Go17HU4HcI5I/syb00p2/e0fv18ps5lu6q8LPpud9zsS+G0Vusxg7rz7+uP5YFzD2XrrmrOnfol1774DZt3Nr8safW2Cj5a+SMTjyokz+1KaGyrJNp/xiLJrDgZVtqkcyw76bGTFqttrGAnPXbSksx+DIZGOJ1w112wbJmOE0gBW7fCccfB66/Dgw/CP/+pV7+klOpq+OUvdbzH73+f4sGSiIh26lau1LERjz+ulye9+mpDZWtAqqq0fYMGtS37DG2ac0f2I6DgzW+3trqPTJ3LPl1bilJYjn+AxLQ6HcL40fsy59ax3HDiID5ZtZ2THp7Hn/69gnJP42V6U+euIzfbycSjCpMythUS7T9jQdRW1pX5/f76gOREgqg9Hk/ctXLV1dX168iirSsLBAJJCaK2sv7P4/EkbJOVIGorNvl8vqTYZCWIura2lpycnJhrGgOBQNyA47q6unrNrQ2iTsZ+smqTlSDqkH6DIemMH6+nBu69F849N6kFyBYuhDPPhD17dLKnM85IWtexufdeWLdO133IbX3V3IzRubN2HiZM0GlnL7hA146YMgUGDiT/wQdh0yb49FMdS7CXIcK+wHPAPkAAeEopHhWhK/AKUAhsBMYrxa5M6WxvFPXIp7h/F2Yu3MKVxxW1qk6BM0MFDueuLqVLbhaH9oufvjVEMrTmuV3cdPJgLjp8Px76YDX//HwDry3cwvUnDOKSI/qzrbyG2Ut+4PKjC+mc23BnJdWfU6L9pySIuri4OG4QdV1dHVlZWTGDqENZhsJpTRC12+2uf1+0/nJycuIGHIc0JxpE3bVr1yYBtJGaQnoSsclKELUVm0SkSR+tsclKEHW0/RC+HdIczSbQVaQj9bQ0iDoZ+8mqTVaCqCO/VwZD0nA6dT2CCy/UVdwuvDAp3c6cCb/4hS58/cUXMHRoUrqNzzff6KmOyy/XlenaMqNHw//+p4vf3XknHHQQ5OWRt2sXdO0KffpkWmGm8AG3KMU3IhQAC0X4EJgIfKwUfxHhNuA2wBTGSCLnFffj9jeWsmRLOcMs1FKIJPJcmA4CAcW8NaWMGWwtfWuIZGrt1TGHB84bxsSjBnDfuyv5/TsrmPZ5CWVVdfgDio9WbufSIwrZr1vzCXKSTaL9ZywGIjTzkGgbKyRrLDvpsZMWq22sYCc9dtKSzH4MhmY5/3xdvfjee3X2nwRQCv74R93l8OH6+jdtzoPPp5f29OihnYj2gMulYyFWrtSZjXYFb6jv3g3jxmVWW4ZQih+U4pvg8wpgJdAXOAuYEWw2Azg7MwrbL6cf2hu3y8HMhVta9f5MnMuWbC2nrMrbovgHSI3Wg/p05PlJh/HsxNFsr6iluk4fbzftrGLSjPkpHTucRPvPWAxEwEKwnpU26RzLTnrspMVqGyvYSY+dtCSzH4OhWRwOuOceWLUKXn65VV2UlOgb5H0HDWDyZDjrLPjkEz0DkTYeegi+/VYv/0lJSesM0revTt0aIhDQBen2ckQoBEYAXwO9lOIH0E4GkM5v315Bx5wsTjtkH95e/D01dS2/2ZCJc9nc1dsRgeMGW49/gNRpFRGOP6Bno7wVAQUlpQ2/71R/Ton2nzEHwmGhEqiVNukcy0567KTFahsr2EmPnbQksx+DISrnnAOHHqqDcoPxN1apqNBVpVeuBKUEEVi7tqH8QFpYu1Y7QT/7mY7laI8MGdJQSdvhsE9hvJTgd4nIgrDHlZEtRMgHXgduVIo96de4d3JecT/Kq+v4eOX2Fr83E+eyOatLGdavM13zWpa9IdVai3rkEVpR5RC9na6xE+0/JUHUAwYMiBtE7XA44gZRA0kJovZ6vfXr+KMFsvp8viaBrZGBrKHxEw2irq2tJRAIxAzOraurq48Baa1NVoKordiklEqKTVaCqAOBAHV1dTEDjh0OR9yA40AgkHAQdTL2k1WbrARRmxkIQ8pxOHQsxLnn6lSil14a9y1Ll8LUqbqocvAnB+hlTGm9OR4I6IJqbreefWivzJ4N48ahVq9GhgzR2+0Wp08pFTX/rghZaOfhRaV4I/jyjyL0VoofROgNtPwK1xCXowZ2p3enHGYu/I7TD21Z5fTIeMNUs7OyliVbdnPjiYNb/N5Ua502YTSTZsynpLSKoh55TJswOm1jJ9q/7StRRwaOtiaIOrxycbRAVo/HEzfgOFmVqHNycuJWOPZ4PDEDjq3YlKxK1D6fL24lais2WQmijrYfwretVKKuq6tLuBJ1MvaTVZusBFGbLEyGtHD22TBsmJ6F+PnP9fr7CGprdUrWqVN1EWW3W8ddf/qpTgwUKkSc1pvj//xnQ92H9hxYXFQEy5fzY0kJ+xQVZVpNxhBBgGnASqV4OOxfbwMTgL8E/87KgLx2T6gmxNS569m+p4aeHa1PNTaXCCWVtCZ9a4hUa92vWy4f3jwmI2Mn2r8lB0JENgIVgB+IeUfAKn4LQXpW2qRzLDvpsZMWq22sYCc9dtKSzH7SSWhWsi47G5fXC3V1+gE69aTfD6FArg4d9JVnqLhNyOELzvzgduur0upqvZ2drbMHhbazsvQjmH63fru6Wt8Od7kgOxupqNDBp06n7rOmpuGKNydHj+/363z8HTpofT5fw3a4Dbm5qbfJ69V6Y9hUv50sm267TTsPTz+tMxkFbdqwycGTL+TyzAwnpaXC/kV+HvxzgImX+umWV0PJRgfjLspn9RphyP4BZr9YBR5Xq/ZTi2zatQt+8xu9hur887V9LdlPtbV6vBR/95K6n7xeHQ+R4d+TVZukokJra8nvKTZHA5cCS0VYFHzt/9COw6siTAI2A+db6SwZhI53RUVFcVdhhGbCY63CCM2EQ+x04kDcdOLx0r6LCG63O+aKhUgbzjp0H6bMWc/LX5Vw2ZH7WrZp9+7d+P3+tNn00fJtdM3NYkBnJz6fL6ZNkdterxen05mR/VReXo5SKuH9FO27F9oP8WyKRktmII5XSu1oQfuYWMkd3Jr8wqkcy0567KTFahsr2EmPnbQks590EpqVJC/vCrKzm68eFpmjPzKnfeQi+ohZyybbkWNEbKuCAp1j32L7Fm9Dcm3Kzm6sNxka421fcAE88AA89BD+y37Jfz5xM3VqLv/5j77uO/NMuOYaOPFEJw6HE8gCcigaDstXwLb6u+Mdo9uYLM1KwcSJ+sL02WcbPquW7KeyMr2PUvzdS+p2WRnk5dGENP+erG6rsjKaPQbE3I6e5UcpPgeiHRQzkrs3dLwbNWpU3FUYIWKtwmhu20pq+5a0j7cdORMebkNBART378LsZaVcd/KB9eeoeDZ17Nixkc5U2uQPKP5bUsYJQ3rSMbjiIJZNkduVlZVxV8s0t50Mm5RS5AV/44nsp+a2oel+iGdTJCmJ0LByoRO5M1rbxgrJGstOeuykxWobK9hJj520JLMfgyEuIvx445+5b/14BvarYdw4ndho8mS9ROnNN+HkkxtieTPK66/DW2/p9LP7759pNQbDXsV5xf1Yu72SJVvKLb8nneeyxVt2s9tTx9gDWpeMK5Pn3VSPnWj/VmcgFPCBiCjgSaXUU5ENgtkRrgTo06cPGzdujNmh1+uN691YabNz586Y/0/mWHbSYyct7VWPnbSE+jEYUolS8NlnOrbh9ddPoY5TOaHqCx56+TDOPMdFBuo/xaasDH79axg5Em6+OdNqDIa9jtMP7c09by9n5sItlovKpTMGYu6q7TgEjhvUvVXvT3e8RjrHTksMBHC0Uup7EekJfCgiq5RSn4Y3CDoVTwGMHDlSFRYWxuywoqIibmVdK20A0jWWnfTYSUt71WMnLaF+DIZUUF4Ozz8PTzwBy5frVUDXXitcdcBchlx1PFQ8DVm/zLTMptx6K+zYAe+912ywt8FgSC3hNSHuOP1AcrKccd+Tzni+uWtKGb5vZzrnti7jUCZjD1M9dqL9W5qAVkp9H/y7HXgTOCyhUbHf2vG2psdOWqy2sYKd9NhJSzL7MRhKSnTRaadT11rr3Ruuu04vl582DbZuhUcegSFXjoHDDtNlpe02A/bRRzrm4Te/0eWuDQZDRmhpTYh0nctKK2pZsqWc41tYfTqcTJ53Uz12ov3HdSBEJE9ECkLPgVOAZYmKigzmaG0bKyRrLDvpsZMWq22sYCc9dtKSzH4Mezfffw9HHw0rVuhkObt367jZ+fP14/LLw+JwRXRhtk2bYPr0DKqOoKoKrrwSBg2Cu+7KtBqDYa8mvCaEFdJ1Lvt0TSkAYxNwIDJ53k312In2b2UGohfwuYgsBv4H/Fsp9V6sNyil4nZqJad9svLeJ2ssO+mxkxarbaxgJz120pLMfgx7H9u26dpqxx0H/frp7XAqKmBUtOTcp50GRxwBf/pTQ0rQTHPXXbBhg679EJllyGAwpJVQTYh5a0rZvqcmbvt0ncvmrN5O93w3B/fpGL9xFDJ53k312In2H3fRqFKqBBhmpbNQDuTCwsK4OZC9Xi8OhyNmbl2Px1Of1zaRStR79uxBKRUzX3BlZSUulytmbt26urr6qsHN2WS1EvWuXbvw+Xwx8wWHV+BurU1WKlFbsamqqqq+fSI2WalE7fF4cLlcMfM6e71elFIxcyBXVlZGzYlstRJ1MvaTVZusVKKurKy08jM0GAD48Ud44w149VVdX00pOOQQnaxoxgx9/W2p4JuIftOpp8Izz8DVV6fNhmaZPx/+9jf41a+0R2QwGDLOuSP7MWXOet78diu/GjMwZtvQeS2V+PwBPlu7g5MO7IXD0fqlOunQmqmxE+0/Y5WoQxdusXLrer3epFSiVkrFrdoceh4rt25FRUVSKlEXFBQ0ahNNU6wKx1ZsslKJOp02WalEHXoeaz9VVFTErdqck5PTRE9LK1EnYz9ZtclKJWqzhMkQj9LSBqdh7lztIBxwgL5hf/75Ou4B4OKLYdw4WL1aOw+zZ8fp+OST4aij4L779BqnTKU29Hph0iTYZx+4//7MaDAYDE0o6pFPcf8uzFy4hSuPK8p4zN6i73ZTXl3H8Qe0vPq0wRoZqwNht7XjbU2PnbRYbWMFO+mxk5Zk9mNoX+zcqVfynHKKDoa+6irYsgXuuAOWLtWxDvfc0+A8ABQV6WxLPp/+W1QUZ5DQLMSWLXqwdBOK+s7J0UbddRd06pR+HQaDISpWa0Kk+ly2eaeHK55bAMDDH6xh805Pq/syMRDRyVgZICvpo5KVwipZY9lJj520WG1jBTvpsZOWZPZjaPvs2qUTEJ12mr4Zf8UV+hr7d7+DRYtg1Sr4/e/1kqWk3Qg88UQ45hg9C1ETf51zUhk3ThsViq977LH0jm8wGOJy+qG9cbsczFwYvYI4pP5cNmnGfHZ59JLljTurmDRjfqv7Mmlco5MSB8IEUad+LDtpsdrGCnbSYyctyezH0DYI3XTvN3gABx+sHYMZM+D006FXL72SaM0auOUW+OYbWLtWxzkPG5ZEpyGc0CzE99/D00+nYIAYrFql12OFWL06veMbDIa4hNeEqKmLfnGaynPZ5p0e1m5viBcMKCgprWp1fyaIOjpJjYFoaRB1bW1tzCBqr9fbKEi1tUHUFRUV9duxgqhDgauxAo5ramoSDqKOZ1MoODdRm6wGUVuxKUQiNlkNos7JyUk44Lgm7A5pa4Ook7GfUmWTof0TuukeCAgrVsCIEfr1/v3hxhth/HgoLk6RsxCN44+HMWPgz3+GX/4y9RmQfD69/irceYgb9W0wGDLFecX9mLXoez5euZ3TD+2dtnFr6vw8MW89/5i7ntAhUQEOgaIeeWnTsTeRkiDq4uLiuEHUoRLasYKoHQ5HzMBTsBZE7Xa7698Xrb+cnJy4AcchzYkGHPfo0aNJAG2kppCeRGyyEkRtxaZOnTo16aM1NlkJoo62H8K3myu/Hhlw3Llz5yZ6WhpEnYz9ZNUmK0HUnTt3xtD+qayEd96BlSsbVuyAdhS+/FLXdctYfGJoFmLsWHjqKbjhhtSNtWULXHQRfPYZnHceLFump1ksRX0bDIZMEF4TIpoDkey1/XNWbefut5ezuczDGYf25rKjBnDbG0soKa2iqEce0yaMbnXfJgYiOkl1IFqC3+9vckHVmjbpHMtOeuykpb3qsZOWUD+G9onHA+++C6+8Av/+N1RXg8sFfr92IhwOnU3p8MMzrRQ9A3H88XoW4oorwqrOJZF334Vf/ELHWjz/PFxySfLHMBgMSSdUE2Lq3PVs31NDz45NL1KTdU7cssvD72ev4IMVP1LUI48XJh3OMYO6A/DhzWMS7h+Sp9WOYyfaf0ocCKsxEPG8HyttrJCsseykx05a2qseO2kJ9dPWCC1rrMvOxuX1Ql2dfoBe/uL369Scoe1AoKFYWegzCy3dcrv1lXR1td7Ozgans2E7K0s/PJ7G29XV+irc5YLsbKSiQpdfdjp1nzU1DQURcnL0+H6/vtveoYPW5/M1bIfbkJvbaptqauC9eR149Q0Xb//bQVWV0LOH4rJfBLhgnIc+vRVnXZzP6rXCkP0DzH6xCqqczdpUv50um/7v/3Rq10ce0UEYkfuptlZ/xi3dT16vDuR49FEdAPLss9pz8npTa1NtrR4/xd+9pO4nr1dX5M7w78mqTVJRobW1ZD+1MULHu6KiorjLuENLaWMt4xaR+u1YS2mBuEtp4y15FhHcbnfMJc+RNkSz6bQhXZgyB2Yu2MwvjylsYlNZWRkFBQWttqnSU830L7/jyc83g8CNxw/g0sP6kpPtarJsO1GbvF5vff2pdO+n8vJyOnXqlLL9FNoP8WyKRsZmIAyGtszmnR4mzZhPSWklRT3ymTZhNPt1a9mdWKUU60uruPL5BWzcUcXAHpta1Y+dCS1rJC/vCrKz9QVHJJF3sCPX1Uc6X5E1CCK3I8eI2FYFBRC+HCxO+xZvQ1SbvF748EN45ZUcZs2CPXugWze9UueCC2DMGMHlcgJ6ad3ylbCtpIR9ioqAsGqqydbcUptOOklnZXrsMR2QkZfXeD+53Y0/49Brscb44Qe48EL46iudi/bhhxt/F1JpU1mZHivF372kbpeV6c89kjT/nqxuq7Iymj0GxNyOnc3HboSOd6NGjYq7jDtErGXczW1bWdbdkvbxtiOX0lqxaWhhL4r7d+HNRT9w9fGDWl1jqTmb/rtuB5NnLaOktIpTDurFXeMOol+X3JjvT8QmKzXLmttOxn4KBALkBX/jqdhPkfshnk2RpCSIesCAAXG975B3E8+rS0YQtdfrxePxxPS+Q15rLK/O4XAkJYg6FBwey1P1er0xA46t2GQliNqKTUBSbLISRB0aO9ZdEofDETfgWClVrznSph+r/Fz14iJKdlQxoNsGnrxkBPt1zW1iU01tLR6vn9qAUOtXlO3x4Knz4/ULd72ziu17alHAuu2VnDXlc848tBc1dX68foXXBx5vHdV1/uBrUOP1U+PzU1MXoNYXoLrOTyBssm59aSWXPfs1s64aHdUmQ9ujrg4++UQvT3rzTX1jvnNnOPdc7TSccIK+sdvmuPdendZ16lS49dbE+po1Cy67TN+ZfuUVHSFuMBjaLOcV9+P2N5ayZEs5w/ZtfDMh8mLcCtvKa/jjv1fwzpIf2K9rLs9OHM3xB/RMltyotEZrWxk70f4zFkRdW1tryauL/H9rgqjDg7WjeXG1tbVxA45DmhMNos7Pz29kV3OaQmMlYpOVIGorNiml4u6nWDbpu/XfsL60koE9dgbvsjfvfdfW1uJyuQiIA68vQLUvQHmVD69PsXFnNZPfWsbW3dX06dSBW04ZTJe8bHz+Kur8AeoCCp8/gM+/m+paLzic+nW/fj30/3/9bzO7gzmi1+/wcOY/vuKQPp2o8vqoqvVR5fVTVevD47UWc6CAXZ46Xln4PR2ynORkOemQ5cSd5aRDloOcLBdd87OCrzkatZkyd119oGxAwcad1fX7O/KOQiA8E43B1vj9MG+evhZ+/XVd7K2gAM4+W18bn3JK8zfE2xRHH60NeeABuPrq5u+Gx8Prhd/+Vi9ZKi7WH9jAgcnXajAY0srph/bmnreXM3PhliYOREtuhtX5A8z4YiOPfLiGuoDixpMGcdWYgeRkOZMtuVkyeeMu1WMn2n/GYiC8Xm9c78dKGyska6xYbUJLWvRFcuylKFb6ibc0JhlalFJUVdegHC4CSuELKAKBhr+byzzc+tpiNpd56Ncllzt+eiBd8rKp9fmpDd5Fr/X5qfUFKK/0IM6s+tf0XXbd7j/LtlFZq++kr91eySl/m8egngV4g229vgBefyDYp76DH48tu6u56dXFcdtF4nIIvkDj/j1ePw4H7NMxhzy3izy3k9xsF1n46VKQ2+i1fLeL3GwnN768iM27PDrAVWBgj/yoQVsVFRVNnMoQ7y/fxvrSSgIqfrq50GydwV6UlOiUq6tXw7776pvyH34IP/6or6nHjdMzDaed1nT1SJvn3nvhyCNhyhTtCLSEkhL9wSxYANdfrx2RDN7tMxgMySO8JsQdpx/Y6ILf6rXd/zaUMfmtZaz+sYLjh/TgnjMPpn+39KZkTdZ1qB3HTrT/vT4GojVr2ZVS1PoC9XepPV4/k2bMZ+uu6volLeOf/IJbThmC1x/A6wtQF/zr9QWoqK5BHFl4/f761+r8us8v1u+ov/Mdutg+sHdHAgoCAUVAKQIKfMHlO/6AQinqXw8oxbbymvqL5LXbKxn74Bzy3C78AdXwUIqWOJ+byzz86oWFltq6HILb5cCd5cTtctQ7DyFq6gJ0z88m2+XA7XKS7XIEnzsg4KMgtwNul4NspwN3lv6b7XJw62uLGy35cQi8dtVRZDmFLKeDLKfgcjhwOYXaag+dOxaQ5XKQFXzN5RBEhJMfntfoon1gj3xevvLIJnbEuvB/ftLhTb43rWHahNFhzl7r+zGkF6Xgu+90ZtHLLoPt2/XrGzfCpk16edL48broWyqSFNmGI47QnlFoFiLK76UJM2fCpEk60PbNN/XUjMFgaFe0tiZEaUUtf/7PSt74Zit9O3fgyUuLOeWgXkjG8lcbmiMlDoSVnWzF60mW5xXej1KKPdU+dlTVsqOilhteXsSPe2rqL/zHPf45Jx/UC4836BzU+qkKPq+qraPaG6DK6yMQ4+JbAdv21PKbmUua/X+2y4Hb6SDL1XBxnO1ykOV0NFk2U1MXIN/twiGCQ3SKNBEBpXA5Hfp1h/6fQwQRePObrY31KP1DdorgdDR+EAiQleXCFdx2iOBy6r+TZy1r5GQ4BJ67/HDcWfpiPyfoILhdTkT5KMjNIdvpwOVsXOC8uQv2Zy87rNnPxuv1Rg3cmTp3fZN+ivt3ab6fPFfUfqxetMf6/u3XLZcPbx4TU29L+tm4cSOFhYWt7seQOnbtgqVLtbOwdGnD8/Ly5ts7HPDaa+nVmFHuvVfnl338cbj99thta2rg5pt13MThh8PLL0Oc773BYGibRKsJEe1c5g8oXvx6EziMiPYAABeSSURBVH99fzU1dX6uGTuQX5+wP7nZmbvXbWIgopPRIOp4Kczq6uqipsbaVuHj6pcWU7KjisJuG/jzWQeR1yGb78sq2FnlZZfHx65qP6V7qimtqGVXjY+yqjp2Vnmpi7JERgHl1XX8d20pHbKd5LtddMhy0CPPRYcu7volLG6nkJvtpFOemxyXg2yH4sGPStgWDKoVoF+XHGZMHEW2y4Hy1ZHtcpDXwU2W00FlZSXZ2dnNBhyfMeUrSnZUNVrSMvWCg5sEHNfW1tKhQ4dmg6gXb97Fhp2e+j4GdMvl5rH7RQ2idjqdze6nZz/PbdLPYf07RuwnVzDYvRZ/rcKXlYUKNLbpiYuHc+Xz37Bhp4cB3XN58pIRVFZWRg2iDgVlRwZRP3regVz36nI27vRQ2C2XR887sFEF8ciqzSGNkd+9fQrcvHPtEaxdu5b+/fvjdrvqK6OHvnuhAP5o+yk82D03NzduYHg0m0wlavtQU6OLt0U6C1vD/PFOnWDoUJ01aehQOOQQXQph7dqGzJV7XZHkww6Dn/4UHnwQrr0WOnZsvt2aNXpaZvFiHXR9331tNHrcYDBYwUpNiBDfbN7F5LeWsfz7PRy9fzfuPfMQ9u+ZH7W9IfOkJIh65MiRcYOoraTG8nq9TZaQeHz6i3bzK4vYFQyELdnh4YJpC5royXY66JafTZcOLnp16sBBfTrTPd9N9/zs4F83//fmUr5Lwlr20QP3aXJXO1YMRLQUZs9MPKzJ0pj8/KYpypRS9ZWLI4Okn73s8CZaCgoa+ggPzq2oqCAvL6/Z/RTqJ1xLaP80l+4rmk0Dc3L4+NbjY95lD7UPZXJqLjD8oP1y+fjWns3uh8iA45qamiZ6WlqJOpZNoe2Kioq41bVj2RT+PF4lauNAJEZDrMIABgzQN8JLSxschrVrdfAz6ADngw7SGZIOOUQ7C0OHQt++TatAv/tuQwzEXlsk+Z57tCPx97/DHXc0/f+//gW/+pWOcXjnHb22y2AwtHvOHdmPKXPW8+a3W/nVGJ0goba2tv58W1bl5YH3VvHy/O/o1dHN4xeN4PShvW2zXClca3sbO9H+LTsQIuIEFgBblVJntHrEFqCU4rsyDws37WL+xjIWbtrF6h8rml27LwJTLhpJ93w33YIOQsccV/2d5GgX/i8kaS17S5aiWOknlua2pMVgyASBgC4rsG4drF+v/06ZousugLBuHVxzjT5uFBVp5+D88xtmFQYN0nWyrFBUBMuXp9KaNsDo0dqLeugh+PWvG173eHSA9LRpOrr8pZegX7/M6TQYDGmlKLjUeObCLVx5XFG9YxAIKF5Z8B33v7eKihofVxw7gBtOGky+e68PzW0ztGRP3QCspFE1o+ax4jk25/X4/AFWbatg/sYyFmzaxfwNZWyv0MtK8t0uRuzXmZ8c0pvRhV2YPGsZG8KW+gzskc9PhzYfpBPLwwpdJIenTW2J5tZgpZ94beykxWobK9hJj520JLOf9oLPB5s3NzgI4c5CSUlDQV3QzoCvcSw/Dod2KFqTfdTQDPfco1OxPvYYXHwxrFihlyytWKErV997r3WvzGCwgAjPAGcA25XikOBrXYFXgEJgIzBeKXZlSqOhaU2INTtq+P1zS1j83W4OG9CVP5x1CEP2sedNykyed1M9dqL9Wzqai0g/4HTgT8DNiQwYnvWosHse14wdyOayahZsKuPbzbvrg4j7du7AYYVdOKyoG8X9u3DAPh110G+QZ4NLfaxkr7Hi0CSrjRWSMZadtFhtYwU76bGTlmT2Y3fC06IOHqxLBNTWNnYQ1q+HDRsaOwU5ObqEwP7768RAoef776/Tqw4bBqtWNcQqHHCAcR6SysiRukL1PffQ6+679WtdusB77+l6EQZD8pkOPA48F/babcDHSvEXEW4Lbv8uA9oMQYb164QAZ0/5Lx07ZFFeXUf3/GweHj+Mn43oa+tzWya1pXrsRPu3ejvob8BvgaguoohcCVwJ0KdPHzZu3NhsuwmvrGPzLh1svL60ilteW6KDhbvmcMqgTgztncvQfXLpma+DUnNyAO8uvtvc9AbC0+f0Z+fOnXTr1o1AxXY2VjSvTfcTOwF7strs3Lkz5v+TNZadtLRXPXbSEupnb+AnP9HxtqCDmsOvPQsK9PKi4cPhvPO0cxByFHr31o5BNGbPDjkmiiFDZO+MVUg1JSUQCFB/Wure3TgPhpShFJ+KUBjx8lnA2ODzGcBcjAORUW54eRGhlefl1XV0zHHx8S1j6dTB/kkUTAxEdOI6ECISnB5UC0VkbLR2SqmngKcARo4cqaKtvd+yewXhIQwOgcV3n0JBTtMvktW19/HW+VvpJ1lt0qXHTlraqx47aQn1szewfn3jbYcDPv9cOwnduzcNYrZKKFZhW8kG9ikqSlyooSmbNjXejtyZBkOL8LtEJDxDylPBa41Y9FKKHwCU4gcReqZOn8EKJaVVjbaran1twnkwxCbG/bp6jgbOFJGNwMvACSLyQqw3xJoWKeqRR2glUih2oTnnAZpmvWktVvpJVhsrJGMsO2mx2sYKdtJjJy3J7MfuDBnSMJMQWmp05JHQo0frnQdDmojceXtdTltDcnH6lFKjwh7xnAeDDYm87ivs3nbWjmbyvJvqsRPtP64DoZS6XSnVTylVCFwIfKKUuqS1A06bMJqBPfJxisSNXXA6nVH/1xKs9JOsNlZIxlh20mK1jRXspMdOWpLZj92ZPVs7DU6n/muWGrUhgjtPmZ1nyBw/itAbIPh3e4b17PVEXvc9dcmITEuyTCbPu6keO9H+U5ISQzWXZzVIS1KD1tTUJMUDs9JPstqkS4+dtLRXPXbSEupnb8CkRW3DBHfejyUlZpmYIVO8DUwA/hL8Oyuzcgyh674QbWk5brLO33YcO9H+W+RAKKXmogOSmiVUibqwsDBuJepQ5d9Ylai9Xm/9Fy1aNeDKykoqKipwuVxRqwFXVFTUb0erBhxe0ThaNeC6ujpqamqi2uR2u+vHi2ZTqC5FLJtqamqSYlNVVRV1dXVJsSlEIjZVVlZSXV0ds2qzx+MhJycnqVWbm7NJKVX/3UnlfkqVTQaDwbC3I8JL6IDp7iJsAe5GOw6vijAJ2AycnzmFBkP7JSWVqIuLi+NWoq6pqYlbiVop1SR7TaS3FFlNuLlqwC6Xq76faNWAQ3oj/x9eDTiUTSeaTaAvTiNnViKj3Lt27dqkj0hN4f221qa8vLyk2JSfn99kP7TGpvz8/LhVm6Pth/Dt5rIaRVZtLigoiGlTSEOsStTJ2E9WbbJSidoU8zMYDIYGlOLnUf51YlqFGFpEW4rnMzEQ0bESRJ0S7LZ2vK3psZMWq22sYCc9dtKSzH4MBoPBYMgUbelcZmIgoiOx4hVa3alIObA2TrNOQHkS2nQHdqRpLDvpsZOW9qrHTlpC/XRWSvWI0852HCES+Bqq47dMCy7AF7eVfWhreqHtaW5reqHtaW6F3uIOSi3I2I3O1iIipcCmuA33XqycE+1CJrWmemyr/fdv9rpDKZX0BzpXc7raLNgb9dhJS3vVYyctVvsxj/gPK5+1nR5tTW9b1NzW9LZFzW1Nr3mk7tGWzmWZ1JrqsRPtP1WevZXcfclqY4X2qMdOWqy2sYKd9NhJSzL7MRgMBoMhU7Slc1kmtaZ67IT6T8kSpnQiIguUUqMyrSOEnfTYSQsYPbGwk5b2Tlv7rNuaXmh7mtuaXmh7mtuaXoPBEJs2t7awGexWmdJOeuykBYyeWNhJS3unrX3WbU0vtD3NbU0vtD3NbU2vwWCIQZufgTAYDAaDwWAwGAzpoz3MQBgMBoPBYDAYDIY00WYdCBHZV0TmiMhKEVkuIjfYQJNTRL4VkXdsoKWziMwUkVXBz+jIDGq5KbiPlonISyKSE/9dSR3/GRHZLiLLwl7rKiIfisja4N8uGdbz1+C+WiIib4pI53Tp2Ruw4/HCKnY6rsTDTscdq2T6+GQFux3D4mGOcQZD+6fNOhDofNK3KKUOBI4ArhWRgzKs6QZgZYY1hHgUeE8pdQAwjAzpEpG+wPXAKKXUIYATuDDNMqYDp0W8dhvwsVJqEPBxcDuTej4EDlFKHQqsAW5Po569ATseL6xip+NKPGxx3LGKTY5PVpiOvY5h8ZiOOcYZkoyI5InIQhE5I9NarJApvekat806EEqpH5RS3wSfV6BPVH0zpUdE+gGnA//MlIYwLR2B44BpAEopr1JqdwYluYAOIuICcoHv0zm4UupToCzi5bOAGcHnM4CzM6lHKfWBUipUZOkroF+69OwN2O14YRU7HVfiYcPjjlUyenyygt2OYfEwx7j2STJncpubpQr732kislpE1olIuGP8O+DVFoyRIyL/E5HFQb33plnvbcA+wF1pHrdFn1NrabMORDgiUgiMAL7OoIy/Ab8FAhnUEKIIKAWeDS59+KeI5GVCiFJqK/AgsBn4AShXSn2QCS0R9FJK/QD64hLomWE94VwO/CfTItorNjleWMVOx5V42Oa4YxUbH5+sYOdjWDzMMa5tEncmV0R6ikhBxGv7N9PXdJrOUiEiTmAK8BPgIODnInKQiJwErAB+bIHeWuAEpdQwYDhwmogckUa9/dHHlibY7HNqFW3egRCRfOB14Eal1J4MaTgD2K6UWpiJ8ZvBBYwEpiqlRgBVZGh6O7gu9yxgANAHyBORSzKhpS0gInegD9IvZlpLe8QOxwur2PC4Eg/bHHesYo5P6ccc49ouFmdyxwCzQrFEInIF8FgzfTU3qwZwGLBOKVWilPICL6N/o8ejnZaLgCtEJO71q9JUBjezgo/I1KOp0nsmMDY45n7N6LXN59RaXKnqOB2ISBb6YuBFpdQbGZRyNHCmiPwUyAE6isgLSqlMnYi2AFuUUqE7rDPJ3In8JGCDUqoUQETeAI4CXsiQnhA/ikhvpdQPItIb2J5hPYjIBOAM4ERl8isnHRsdL6xit+NKPOx03LGKXY9PVrDdMSwe5hjXfog2k6uUek1EBgAvi8hr6Nmmk1vQdV/gu7DtLcDhSqlfB8edCOxQSlmalQ3eqV8I7A9MCTs+pVQveunSz4Dz0bMgjfTa7XNqDW12BkJEBL3WdqVS6uFMalFK3a6U6qeUKkQH4H2SyZO8Umob8J2IDAm+dCJ6SisTbAaOEJHc4D47EXsEVr4NTAg+nwDMyqAWROQ09LrFM5VSnkxqaY/Y6XhhFbsdV+Jhs+OOVex6fLKCrY5h8TDHuPZDvJlcpdQDQA0wFb2/KyPbxOq+mdfqnU2l1HSllOWMdEopv1JqODrm5jAROSQNevvTMHv8HlGcezt9Tq2hzToQ6LtzlwIniMii4OOnmRZlI64DXhSRJei1f/dlQkTQ258JfAMsRX/n0lqRVEReAr4EhojIFhGZBPwFOFlE1qK9/r9kWM/jQAHwYfC7/ES69OwlmONFerDFcccqdjg+WcFux7B4mGNc+8XKTK6IHAscArwJ3N3CIbYA+4Zt9yMJiQ2CCR3m0nw8QbL1FqBnjzeilxadICJNZjXt+Dm1BFOJ2mAwGAwGg8EQk+As3QygTCl1Y5Q2I4CX0NnjNqCXA5Yope5spm0h8E4whXLoNRc6ze+JwFZgPnCRUmp5K/T2AOqUUrtFpAPwAXB/+J35VOsVkbHArUqpMyL6ss3n1Fra8gyEwWAwGAwGgyE9WJnJzQXOV0qtD66/nwBsiuwoyiwVwVS/vwbeRy8nfDWBi+LewJzgjOh84MNmlvVkSq+dPqdWYWYgDAaDwWAwGAwGg2XMDITBYDAYDAaDwWCwjHEgDAaDwWAwGAwGg2WMA2EwGAwGg8FgMBgsYxwIGyAi/mAw0jIReU1EcjOtqSWISEtyFxsMBpthjkEGg8FgaAnGgbAH1Uqp4cEUXV7gqkwLShfBVGQGgyGzmGOQwWAwGCxjHAj78Rm65Doi8paILBSR5SJyZfA1p4hMD94pXCoiNwVfv15EVojIEhF5ObJTEZkoIm+IyHsislZEHgj7X2XY8/NEZHrw+XQRmSoic0SkRETGiMgzIrIy1CbsfQ+JyDci8nEw9zIiMjA43kIR+UxEDgjr92ERmQPcn9yPz2AwJIg5BhkMBoMhJsaBsBHBO2E/QVdEBbhcKVUMjAKuF5Fu6OqufZVShyilhgLPBtveBoxQSh1K9LuHw4ELgKHABSKyb5R24XQBTgBuAmYDjwAHA0NFZHiwTR7wjVJqJDCPhoqKTwHXBW24FfhHWL+DgZOUUrdY0GAwGNKAOQYZDAYAEbkjeONgSXB54+Fx2s8VkVFJGHeiiDzegvZjRSSytkNaEJFCEbkoE2PbATN1aw86iMii4PPPgGnB59eLyM+Cz/cFBgGrgSIR+Tvwb3RlRYAlwIsi8hbwVpRxPlZKlQOIyAqgP/BdHG2zlVJKRJYCPyqllgbfvxwoBBYBAeCVYPsXgDdEJB84CnhNREJ9ucP6fU0p5Y8ztsFgSA/mGGQwGAAQkSOBM4CRSqlaEekOZGdYlh0pBC4C/pVhHRnBzEDYg9D64+FKqeuUUl7R5c9PAo5USg0DvgVylFK7gGHAXOBa4J/BPk4HpgDFwMIo63prw577aXAgw6sJ5kR5TyDi/QGiO6AK/d3aHWbXcKXUgWFtqqK812AwpB9zDDIYDCF6AzuUUrUASqkdSqnvAUTkRBH5Nrh88RkRCXfKEZGrI5YnTgzebEBELhGR/wVnNJ4UEWfw9ctEZI2IzENXu26CiOQFx5sfHP8sq22CGt4SkdkiskFEfi0iNwfbfCUiXYPtYi15fExEvggupTwvOORfgGOD9twkIgeH2bdERAYlshPsjnEg7EsnYJdSyhP8Eh8BELwT4FBKvQ5MBkaKiAPYVyk1B/gt0BnIb8FYP4rIgcF+fha3dVMcQOgHdRHwuVJqD7BBRM4P6hYRGdaKvg0GQ2YwxyCDYe/kA2Df4EX9P0RkDICI5ADTgQuCyxddwNUR750JnBO2fQHwiogcGHx+tFJqOPoGwsUi0hu4F+04nAwcFEXTHcAnSqnRwPHAX0UkrwVtDkEfGw4D/gR4lFIjgC+BXwTbxFry2Bs4Bj0z85fga7cBnwVvTjyCXrr5aNC+UcCWKLa0C8wSJvvyHnCViCxBLxn4Kvh6X+DZ4IkW4HbACbwgIp0AAR5RSu1uwVi3Ae+glxIso2UnftB38g4WkYVAOfogAXAxMFVE7gSygJeBxS3s22AwZAZzDDIY9kKUUpUiUgwci74Qf0VEbkPPQm5QSq0JNp2BnoX8W9h7S4N36Y8A1gJDgP8G2xUD84NLCjsA24HDgblKqVIAEXkFHZ8UySnAmSJya3A7B9ivBW3mKKUqgAoRKUfHU4GO9zrUwpLHt5RSAWCFiPSK8tF9CdwhIv2AN5RSa6O0axcYB8IGKKWanCyDU4c/ifKWkc28dkycMaaj7xyEts8Iez4Tfdcg8j0Tw55vRHvwzf0vpH9yxPs3AKfF6tdgMGQecwwyGAzhBOOD5gJzg/FHE9DxRlZ4BRgPrALeDMYwCTBDKXV7eEMROZvGSxijIcC5SqnVEe/vZaHN4TRd/hi+NNJF2JLHKOOHv1+aa6CU+peIfI1ezvm+iPxSKfVJbLPaLmYJk8FgMBgMBoMBABEZErF+fziwCe0QFIrI/sHXL0VnPYvkDeBs4Oc0JDf4GDhPRHoGx+gqIv2Br4GxItJNRLKA86PIeh+4LuiIICIjWtmmWVq55LECKAhtiEgRUKKUegx4GzjU6vhtEeNAGAwGg8FgMBhC5AMzJFjXBR2XcI9Sqga4DL3MZyn67v0TkW8OJlpYAfRXSv0v+NoK4E7gg2CfHwK9lVI/APegl/98BHwTRdMf0MsQl4jIsuB2a9rE4mJgkogsBpYDTQK1I1gC+ERkseh6OBcAy0RntDsAeK6F47cpRCkrM0cGg8FgMBgMBoPBYGYgDAaDwWAwGAwGQwswDoTBYDAYDAaDwWCwjHEgDAaDwWAwGAwGg2WMA2EwGAwGg8FgMBgsYxwIg8FgMBgMBoPBYBnjQBgMBoPBYDAYDAbLGAfCYDAYDAaDwWAwWOb/AUsjSJlpDPYqAAAAAElFTkSuQmCC\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1317,7 +1317,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1335,7 +1335,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1353,7 +1353,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1371,7 +1371,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1389,7 +1389,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1399,7 +1399,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -1410,7 +1410,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -2138,7 +2138,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -2151,7 +2151,7 @@ ], "source": [ "epra.results.sort_index = True\n", - "epra.plot_hamiltonian_results(sweep_variable='Lj_1');" + "epra.plot_hamiltonian_results(swp_variable='Lj_1');" ] }, { From 8c6b0137c4b785c2f5f603a905603446d5c0b658 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Wed, 9 Feb 2022 14:55:35 -0500 Subject: [PATCH 087/125] Check for infinite values in the Quality Factors. --- pyEPR/core_quantum_analysis.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 558a09f..cba4187 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -894,7 +894,9 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=0, marker=markerf1, ms=4, legend=True, zorder=20, color=cmap) Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) - if not (len(Qs) == 0): + + Qs_inf = np.isinf(Qs).values.sum() + if not (len(Qs) == 0 or Qs_inf > 0): ax.set_yscale('log') ############################################################################ From fb6f384314b6af52224f091ac0b878dd526675a2 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Wed, 9 Feb 2022 16:23:17 -0500 Subject: [PATCH 088/125] Change syntax to pass pylint. In particular, separate numpy from pandas. --- pyEPR/core_quantum_analysis.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index cba4187..5eaeebd 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -895,7 +895,8 @@ def plot_hamiltonian_results(self, legend=True, zorder=20, color=cmap) Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) - Qs_inf = np.isinf(Qs).values.sum() + df_Qs = np.isinf(Qs) + Qs_inf = df_Qs.values.sum() if not (len(Qs) == 0 or Qs_inf > 0): ax.set_yscale('log') From 345ba655c949d595acb5abc04f7321d117afcf60 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Wed, 9 Feb 2022 17:03:25 -0500 Subject: [PATCH 089/125] Add int cast to pass pylint. --- pyEPR/core_quantum_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 5eaeebd..7ba46de 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -896,7 +896,7 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) df_Qs = np.isinf(Qs) - Qs_inf = df_Qs.values.sum() + Qs_inf = int(df_Qs.values.sum()) if not (len(Qs) == 0 or Qs_inf > 0): ax.set_yscale('log') From 1622b7309dfeaa1740cd9fc376148bc0f9512bef Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Wed, 9 Feb 2022 17:20:38 -0500 Subject: [PATCH 090/125] Change syntax to pass pylint. --- pyEPR/core_quantum_analysis.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 7ba46de..74f0b14 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -896,7 +896,8 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) df_Qs = np.isinf(Qs) - Qs_inf = int(df_Qs.values.sum()) + Qs_val = df_Qs.values + Qs_inf = Qs_val.sum() if not (len(Qs) == 0 or Qs_inf > 0): ax.set_yscale('log') From 427bc7d653f829726c83bb43be40706610d20d12 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Wed, 9 Feb 2022 17:28:18 -0500 Subject: [PATCH 091/125] Disable the pylint warning. --- pyEPR/core_quantum_analysis.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 74f0b14..b03780e 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -896,6 +896,8 @@ def plot_hamiltonian_results(self, Qs.plot(ax=ax, lw=1, alpha=0.2, color='grey', legend=False) df_Qs = np.isinf(Qs) + # pylint: disable=E1101 + # Instance of 'ndarray' has no 'values' member (no-member) Qs_val = df_Qs.values Qs_inf = Qs_val.sum() if not (len(Qs) == 0 or Qs_inf > 0): From 59456283d8927855aae3024c53ae61f3990aa306 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Thu, 10 Feb 2022 14:10:49 -0500 Subject: [PATCH 092/125] Use updated tag to include fix for issue #96. --- pyEPR/__init__.py | 4 ++-- setup.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index ad07f1f..091cac2 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.2 +@version: 0.8.5.3 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -92,7 +92,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.2" +__version__ = "0.8.5.3" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 8f20d1a..dd50639 100644 --- a/setup.py +++ b/setup.py @@ -6,8 +6,8 @@ pyEPR interfaces the classical distributed microwave analysis with that of quantum structures and Hamiltonians. It is chiefly based on the energy participation ratio approach; however, it has since v0.4 extended to cover a broad range of -design approaches. pyEPR stradels the analysis from Maxwell’s to Schrodinger’s -equations, and converts the solutions of distributed microwve (typically eignmode +design approaches. pyEPR straddles the analysis from Maxwell’s to Schrodinger’s +equations, and converts the solutions of distributed microwave (typically eigenmode simulations) to a fully diagonalized spectrum of the energy levels, couplings, and key parameters of a many-body quantum Hamiltonian. @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.2', + version='0.8.5.3', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 3030b31aabd30b5dfa829b9dee0e1c92e9885955 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Wed, 16 Feb 2022 15:01:36 +0200 Subject: [PATCH 093/125] Systematically fix typos in code and documentation (#99) --- README.md | 4 +- TODO.md | 14 ++-- docs/README.md | 2 +- docs/source/index.rst | 2 +- docs/source/installation.rst | 2 +- docs/source/key_classes_reference.rst | 6 +- pyEPR/README.md | 6 +- pyEPR/__config_user_old.py | 6 +- pyEPR/__init__.py | 8 +- pyEPR/_config_default.py | 18 ++--- pyEPR/_config_user.py | 6 +- pyEPR/ansys.py | 74 +++++++++---------- pyEPR/calcs/back_box_numeric.py | 14 ++-- pyEPR/calcs/basic.py | 6 +- pyEPR/calcs/constants.py | 2 +- pyEPR/calcs/convert.py | 2 +- pyEPR/calcs/hamiltonian.py | 10 +-- pyEPR/calcs/quantum.py | 4 +- pyEPR/calcs/transmon.py | 6 +- pyEPR/core.py | 6 +- pyEPR/core_distributed_analysis.py | 68 ++++++++--------- pyEPR/core_quantum_analysis.py | 42 +++++------ pyEPR/project_info.py | 20 ++--- pyEPR/toolbox/_logging.py | 2 +- pyEPR/toolbox/plotting.py | 10 +-- pyEPR/toolbox/pythonic.py | 10 +-- scripts/Alec/11ghz/EPR_test.py | 2 +- scripts/Alec/7ghz/7ghz_pyEPR.py | 6 +- scripts/Kaicheng/import_pyEPR.py | 6 +- scripts/hanhee/run_vs_pass.py | 2 +- .../minev/hfss-scripts/2017_10 R3C1 resim.py | 4 +- scripts/minev/hfss-scripts/import_pyEPR.py | 6 +- scripts/nick/import_pyEPR.py | 6 +- tests/README.md | 2 +- tests/test_project_info.py | 2 +- tests/test_quantum_analysis.py | 4 +- 36 files changed, 195 insertions(+), 195 deletions(-) diff --git a/README.md b/README.md index ebf4a0f..04200c5 100644 --- a/README.md +++ b/README.md @@ -208,7 +208,7 @@ Follow the same instructions above. You shouldn't have to install mingw or modif #### Legacy users -Warning: pyEPR organization was significnatly improved in v0.8-dev (starting 2020; current branch: master \[to be made stable soon\]). If you used a previous version, you will find that all key classes have been renamed. Please, see the tutorials and docs. In the meantime, if you cannot switch yet, revert to use the stable v0.7. +Warning: pyEPR organization was significantly improved in v0.8-dev (starting 2020; current branch: master \[to be made stable soon\]). If you used a previous version, you will find that all key classes have been renamed. Please, see the tutorials and docs. In the meantime, if you cannot switch yet, revert to use the stable v0.7. # HFSS Project Setup for `pyEPR` @@ -276,7 +276,7 @@ compiler = mingw32 [build_ext] compiler = mingw32 ``` -Next, let's install qutip. You can choose to use conda intall or pip install, or pull from the git directly as done here: +Next, let's install qutip. You can choose to use conda install or pip install, or pull from the git directly as done here: ```sh conda install git pip install git+https://github.com/qutip/qutip.git diff --git a/TODO.md b/TODO.md index 205c6df..eb0db8b 100644 --- a/TODO.md +++ b/TODO.md @@ -2,7 +2,7 @@ * ./pyEPR/ansys.py * LINE 46: : Replace `win32com` with Linux compatible package. * LINE 795: : check if variable does not exist and quit if it doesn't? - * LINE 1857: : make mesh tis own class with preperties + * LINE 1857: : make mesh tis own class with properties * LINE 1980: : create Wirebond class * LINE 2012: : Add option to modify these * LINE 2376: : Add a rotated rectangle object. @@ -14,7 +14,7 @@ * ./pyEPR/ansys.py * LINE 46: : Replace `win32com` with Linux compatible package. * LINE 795: : check if variable does not exist and quit if it doesn't? - * LINE 1857: : make mesh tis own class with preperties + * LINE 1857: : make mesh tis own class with properties * LINE 1980: : create Wirebond class * LINE 2012: : Add option to modify these * LINE 2376: : Add a rotated rectangle object. @@ -25,25 +25,25 @@ * ./pyEPR/core_distributed_analysis.py * LINE 149: : turn into base class shared with analysis! - * LINE 253: : replace this method with the one below, here because osme funcs use it still + * LINE 253: : replace this method with the one below, here because some funcs use it still * LINE 339: : maybe sort column and index? # todo: maybe generalize * LINE 488: : change to integer? * LINE 548: : These should be common function to the analysis and here! * LINE 849: : Update make p saved sep. and get Q for diff materials, indep. specify in pinfo * LINE 1046: : maybe load from data_file * LINE 1064: - * LINE 1139: : Move inside of loop to funciton calle self.analyze_variation + * LINE 1139: : Move inside of loop to function calle self.analyze_variation * LINE 1247: : this should really be passed as argument to the functions rather than a * LINE 1340: : THis need to be changed, wont work in the future with updating result etc. * LINE 1513: : Move to class for reporter ? * ./pyEPR/core_quantum_analysis.py * LINE 130: : remove all copies of same data - * LINE 574: : superseed by Convert.ZPF_from_EPR + * LINE 574: : supersede by Convert.ZPF_from_EPR * LINE 607: : avoide analyzing a previously analyzed variation - * LINE 741: : actually make into dataframe with mode labela and junction labels + * LINE 741: : actually make into dataframe with mode labels and junction labels * LINE 782: : ? - * LINE 825: : shouldmove these kwargs to the config + * LINE 825: : should move these kwargs to the config * ./pyEPR/project_info.py * LINE 134: : introduce modal labels diff --git a/docs/README.md b/docs/README.md index f27e7fc..2c6b60d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -44,7 +44,7 @@ Notes for developers. sphinx-apidoc -f -o source/ ../pyEPR -o source/api --no-toc -M -e make html ``` -You can alos use this to update the doc tree. +You can also use this to update the doc tree. # Updating `readthedocs.org` diff --git a/docs/source/index.rst b/docs/source/index.rst index b47fce5..3f88d12 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -21,7 +21,7 @@ Powerful, automated analysis and design of quantum microwave devices easy-to-use analysis functions and automation for the design of quantum chips based on superconducting quantum circuits, both distributed and lumped. pyEPR interfaces the classical distributed microwave analysis with that of quantum structures and Hamiltonians. It is chiefly based on the `energy participation ratio `_ approach; however, it has since v0.4 extended to cover a broad range of -design approaches. pyEPR stradels the analysis from Maxwell's to Schrodinger's equations, and converts the solutions of distributed microwve (typically eignmode simulations) +design approaches. pyEPR stradels the analysis from Maxwell's to Schrodinger's equations, and converts the solutions of distributed microwave (typically eigenmode simulations) to a fully diagonalized spectrum of the energy levels, couplings, and key parameters of a many-body quantum Hamiltonian. pyEPR contains both analytic and numeric solutions. diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 491bf78..178bf86 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -42,7 +42,7 @@ Installing locally via pip In the future, ``pyEPR`` can be installed using the Python package manager `pip `_. -However, for the moment, we recommend a local develper instalation, which allows for fast upgrades. We are still in active development. +However, for the moment, we recommend a local developer installation, which allows for fast upgrades. We are still in active development. Perform the steps in the :ref:`install-main` section. What you could do, once you have the local clone git, is to install pyEPR locally. Navigate to the local root folder of the repo. diff --git a/docs/source/key_classes_reference.rst b/docs/source/key_classes_reference.rst index cafefd0..925033e 100644 --- a/docs/source/key_classes_reference.rst +++ b/docs/source/key_classes_reference.rst @@ -1,12 +1,12 @@ Main classes ================================= -The first main class of pyEPR is :ref:`project-info`, which instansiates and stores the Ansys interfaces classes and user-defined parameters related to the design, such as junction names and properties. +The first main class of pyEPR is :ref:`project-info`, which instantiates and stores the Ansys interfaces classes and user-defined parameters related to the design, such as junction names and properties. -The second main class of pyEPR is :ref:`distributed-analysis`, which performs the EPR analysis on the ansys eigenfield solutions from the fields. It saves the calculated energy participation ratios (EPRs) and realted convergences, and other paramete results. It does not calculate the Hamiltonian. +The second main class of pyEPR is :ref:`distributed-analysis`, which performs the EPR analysis on the ansys eigenfield solutions from the fields. It saves the calculated energy participation ratios (EPRs) and related convergences, and other paramete results. It does not calculate the Hamiltonian. This is left for the third class. -The third main class of pyEPR is :ref:`quantum-analysis`, which uses the EPRs and other save quantities to create and diagonalizae the Hamiltonian. +The third main class of pyEPR is :ref:`quantum-analysis`, which uses the EPRs and other save quantities to create and diagonalize the Hamiltonian. .. _project-info: diff --git a/pyEPR/README.md b/pyEPR/README.md index 1256f25..e29dd21 100644 --- a/pyEPR/README.md +++ b/pyEPR/README.md @@ -12,8 +12,8 @@ A user should not edit `_config_default.py` directly. A user should overwrite va Contains the core analysis and run functions. ##### toolbox -Module that contains key and utility modues used in pyEPR. -- plotting: useful in visualizaiton and analysis. +Module that contains key and utility modules used in pyEPR. +- plotting: useful in visualization and analysis. - pythonic: useful pythonic functions - report: used to plot reports @@ -30,7 +30,7 @@ Contributed by Phil Rheinhold. Originally part of [pyHFSS](https://github.com/Ph Updated and modified by Zlatko Minev & Zaki Leghtas. ##### numeic_diag.py -Internal use only. For numerical diagonalizaiton. +Internal use only. For numerical diagonalization. Written by Phil Rheinhold. Updated by Zlatko Minev & Lysander Christakis. This file is tricky, use caution to modify. \ No newline at end of file diff --git a/pyEPR/__config_user_old.py b/pyEPR/__config_user_old.py index 76c683d..1e90320 100644 --- a/pyEPR/__config_user_old.py +++ b/pyEPR/__config_user_old.py @@ -36,7 +36,7 @@ th=3e-9, # Surface dielectric (dirt) constant - # units: relative permitivity + # units: relative permittivity eps_r=10, # Surface dielectric (dirt) loss tangent @@ -58,7 +58,7 @@ ansys=Dict( # method_calc_P_mj sets the method used to calculate the participation ratio in eigenmode. - # Valud values: + # Valid values: # 'line_voltage' : Uses the line voltage integral # 'J_surf_mag' : takes the avg. Jsurf over the rect. Make sure you have seeded # lots of tets here. I recommend starting with 4 across smallest dimension. @@ -71,7 +71,7 @@ ), plotting=Dict( - # Default color map for plottng. Better if made into a string name + # Default color map for plotting. Better if made into a string name # taken from matplotlib.cm default_color_map='viridis', # pylint: disable=no-member ), diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 091cac2..1c76675 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -147,7 +147,7 @@ logger.warning( """IMPORT WARNING: Python package 'pythoncom' could not be loaded - It is used in communicting with HFSS on PCs. If you wish to do this, please set it up. + It is used in communicating with HFSS on PCs. If you wish to do this, please set it up. For Linux, check the HFSS python linux files for the com module used. It is equivalent, and can be used just as well. %s""", config.internal.error_msg_missing_import) @@ -168,7 +168,7 @@ except (ImportError, ModuleNotFoundError): logger.error( """IMPORT ERROR: - Python package 'pint' could not be loaded. It is used in communicting with HFSS. Try: + Python package 'pint' could not be loaded. It is used in communicating with HFSS. Try: $ conda install -c conda-forge pint \n%s""", config.internal.error_msg_missing_import) @@ -185,7 +185,7 @@ from .ansys import parse_units, parse_units_user, parse_entry from .core import ProjectInfo, DistributedAnalysis, QuantumAnalysis,\ - Project_Info, pyEPR_HFSSAnalysis, pyEPR_Analysis # names to be depricated + Project_Info, pyEPR_HFSSAnalysis, pyEPR_Analysis # names to be deprecated __all__ = [ 'logger', @@ -199,7 +199,7 @@ 'QuantumAnalysis', 'Project_Info', 'pyEPR_HFSSAnalysis', - 'pyEPR_Analysis', # names to be depricated + 'pyEPR_Analysis', # names to be deprecated 'parse_units', 'parse_units_user', 'parse_entry' diff --git a/pyEPR/_config_default.py b/pyEPR/_config_default.py index a879ee2..9438672 100644 --- a/pyEPR/_config_default.py +++ b/pyEPR/_config_default.py @@ -26,7 +26,7 @@ ansys=Dict( # method_calc_P_mj sets the method used to calculate the participation ratio in eigenmode. - # Valud values: + # Valid values: # 'line_voltage' : Uses the line voltage integral # 'J_surf_mag' : takes the avg. Jsurf over the rect. Make sure you have seeded # lots of tets here. I recommend starting with 4 across smallest dimension. @@ -42,14 +42,14 @@ epr = Dict( - # Define the participation renomalizaiton method + # Define the participation renormalization method # False : no extra renormalization to enforce # can be more problematic for large pj, when sim isn't well converged # True or 1 : use enforcement of U_J_total to be U_mode-U_H # can be more problematic for small pj, when sim isn't well converged # 2 : use enforcement of U_J_total to be U_mode-U_H (i.e., 1) - # only when the total particiaption is above a certain threshold - # preffered method. + # only when the total participation is above a certain threshold + # preferred method. renorm_pj = 2, ), @@ -72,7 +72,7 @@ th=3e-9, # Surface dielectric (dirt) constant - # units: relative permitivity + # units: relative permittivity eps_r=10, # Surface dielectric (dirt) loss tangent @@ -93,7 +93,7 @@ ), plotting=Dict( - # Default color map for plottng. Better if made into a string name + # Default color map for plotting. Better if made into a string name # taken from matplotlib.cm default_color_map='viridis', # pylint: disable=no-member ), @@ -147,7 +147,7 @@ def update_recursive(d:collections.abc.Mapping, u:collections.abc.Mapping): Arguments: d {collections.abc.Mapping} -- dict to overwrite - u {collections.abc.Mapping} -- dcit used to update + u {collections.abc.Mapping} -- dict used to update Returns: same as d; Updated d @@ -162,11 +162,11 @@ def update_recursive(d:collections.abc.Mapping, u:collections.abc.Mapping): def get_config(): """Returns the config pointer. - If the config is not yet loaded, it will load the defualt config and then + If the config is not yet loaded, it will load the default config and then update it with the _config_user.config dictionary. Else, it will just return the pointer to the above-updated config, which the - user could have modified. The modificaitons will be kept. + user could have modified. The modifications will be kept. Returns: Dict : the config dictionary diff --git a/pyEPR/_config_user.py b/pyEPR/_config_user.py index c1334ce..0ed2961 100644 --- a/pyEPR/_config_user.py +++ b/pyEPR/_config_user.py @@ -42,7 +42,7 @@ th=3e-9, # Surface dielectric (dirt) constant - # units: relative permitivity + # units: relative permittivity eps_r=10, # Surface dielectric (dirt) loss tangent @@ -64,7 +64,7 @@ ansys=Dict( # method_calc_P_mj sets the method used to calculate the participation ratio in eigenmode. - # Valud values: + # Valid values: # 'line_voltage' : Uses the line voltage integral # 'J_surf_mag' : takes the avg. Jsurf over the rect. Make sure you have seeded # lots of tets here. I recommend starting with 4 across smallest dimension. @@ -77,7 +77,7 @@ ), plotting=Dict( - # Default color map for plottng. Better if made into a string name + # Default color map for plotting. Better if made into a string name # taken from matplotlib.cm default_color_map='viridis', # pylint: disable=no-member ), diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 595c960..bd25d63 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -140,14 +140,14 @@ def parse_entry(entry, convert_to_unit=LENGTH_UNIT): def fix_units(x, unit_assumed=None): ''' Convert all numbers to string and append the assumed units if needed. - For an itterable, returns a list + For an iterable, returns a list ''' unit_assumed = LENGTH_UNIT_ASSUMED if unit_assumed is None else unit_assumed if isinstance(x, str): # Check if there are already units defined, assume of form 2.46mm or 2.0 or 4. if x[-1].isdigit() or x[-1] == '.': # number return x + unit_assumed - else: # units are already appleid + else: # units are already applied return x elif isinstance(x, Number): @@ -187,7 +187,7 @@ def unparse_units(x): def parse_units_user(x): ''' - Convert from user assuemd units to user assumed units + Convert from user assumed units to user assumed units [USER UNITS] ----> [USER UNITS] ''' return parse_entry(fix_units(x, LENGTH_UNIT_ASSUMED), LENGTH_UNIT_ASSUMED) @@ -349,7 +349,7 @@ def set_property(prop_holder, value, prop_args=None): ''' - More general non obj oriented, functionatl verison + More general non obj oriented, functional version prop_args = [] by default ''' if not isinstance(prop_server, list): @@ -625,11 +625,11 @@ def __init__(self, project, design): self._ansys_version = self.parent._ansys_version try: - # This funciton does not exist if the desing is not HFSS + # This function does not exist if the design is not HFSS self.solution_type = design.GetSolutionType() except Exception as e: logger.debug( - f'Exception occured at design.GetSolutionType() {e}. Assuming Q3D design' + f'Exception occurred at design.GetSolutionType() {e}. Assuming Q3D design' ) self.solution_type = 'Q3D' @@ -662,7 +662,7 @@ def add_message(self, message: str, severity: int = 0): def save_screenshot(self, path: str = None, show: bool = True): if not path: - path = Path().absolute() / 'ansys.png' # TODOL find better + path = Path().absolute() / 'ansys.png' # TODO find better self._modeler.ExportModelImageToFile( str(path), 0, @@ -872,10 +872,10 @@ def _variation_string_to_variable_list(self, def set_variables(self, variation_string: str): """ - Set all variables to match a solved variaiton string. + Set all variables to match a solved variation string. Args: - variation_string (str) : Variaiton string such as + variation_string (str) : Variation string such as "Cj='2fF' Lj='13.5nH'" """ assert isinstance(variation_string, str) @@ -913,7 +913,7 @@ def set_variable(self, name: str, value: str, postprocessing=False): value {str} -- Value, such as '10nH' Keyword Arguments: - postprocessing {bool} -- Postprocessingh variable only or not. + postprocessing {bool} -- Postprocessing variable only or not. (default: {False}) Returns: @@ -1034,7 +1034,7 @@ def analyze(self, name=None): Return Value: None ----------------------------------------------------- - Will block the until the analysis is completly done. + Will block the until the analysis is completely done. Will raise a com_error if analysis is aborted in HFSS. ''' if name is None: @@ -1093,7 +1093,7 @@ def insert_sweep(self, "ExtrapToDC:=", False, ] - # not sure hwen extacyl this changed between 2016 and 2019 + # not sure when exactly this changed between 2016 and 2019 if self._ansys_version >= '2019': if count: params.extend([ @@ -1274,7 +1274,7 @@ def get_profile(self, variation=""): skipfooter=1, skip_blank_lines=True, engine='python') - # just borken down by new lines + # just broken down by new lines return df def get_fields(self): @@ -1353,7 +1353,7 @@ class AnsysQ3DSetup(HfssSetup): min_pass = make_int_prop("Min. Number of Passes") pct_error = make_int_prop("Percent Error") frequency = make_str_prop("Adaptive Freq", 'General') # e.g., '5GHz' - n_modes = 0 # for compatability with eigenmode + n_modes = 0 # for compatibility with eigenmode def get_frequency_Hz(self): return int(ureg(self.frequency).to('Hz').magnitude) @@ -1376,7 +1376,7 @@ def get_matrix( pass_number=0, frequency=None, MatrixType='Maxwell', - solution_kind='LastAdaptive', # AdpativePass + solution_kind='LastAdaptive', # AdaptivePass ACPlusDCResistance=False, soln_type="C"): ''' @@ -1464,7 +1464,7 @@ def _readin_Q3D_matrix(path: str): text = Path(path).read_text() s1 = text.split('Capacitance Matrix') - assert len(s1) == 2, "Copuld not split text to `Capacitance Matrix`" + assert len(s1) == 2, "Could not split text to `Capacitance Matrix`" s2 = s1[1].split('Conductance Matrix') @@ -1484,7 +1484,7 @@ def _readin_Q3D_matrix(path: str): df_cond = None var = re.findall(r'DesignVariation:(.*?)\n', - text) # this changed circe v2020 + text) # this changed circa v2020 if len(var) < 1: # didnt find var = re.findall(r'Design Variation:(.*?)\n', text) if len(var) < 1: # didnt find @@ -1499,7 +1499,7 @@ def _readin_Q3D_matrix(path: str): @staticmethod def load_q3d_matrix(path, user_units='fF'): - """Load Q3D capcitance file exported as Maxwell matrix. + """Load Q3D capacitance file exported as Maxwell matrix. Exports also conductance conductance. Units are read in automatically and converted to user units. @@ -1595,7 +1595,7 @@ def eigenmodes(self, lv=""): """ Export eigenmodes vs pass number - Did not figre out how to set pass number in a hurry. + Did not figure out how to set pass number in a hurry. import tempfile @@ -1621,7 +1621,7 @@ def eigenmodes(self, lv=""): soln_name = f'{setup.name} : AdaptivePas' available_solns = self._solutions.GetValidISolutionList() if not(soln_name in available_solns): - logger.error(f'ERROR Tried to export freq vs pass number, but solution `{soln_name}` was not in avaialbe `{available_solns}`. Returning []') + logger.error(f'ERROR Tried to export freq vs pass number, but solution `{soln_name}` was not in available `{available_solns}`. Returning []') #return [] self._solutions.ExportEigenmodes(soln_name, ['Pass:=5'], fn) # ['Pass:=5'] fails can do with '' """ @@ -1635,7 +1635,7 @@ def set_mode(self, n, phase=0, FieldType='EigenStoredEnergy'): Amplitude is set to 1 - No error is thorwn if a number exceeding number of modes is set + No error is thrown if a number exceeding number of modes is set FieldType -- EigenStoredEnergy or EigenPeakElecticField ''' @@ -1681,7 +1681,7 @@ def has_fields(self, variation_string=None): Determine if fields exist for a particular solution. variation_string : str | None - This must the string that describes the variaiton in hFSS, not 0 or 1, but + This must the string that describes the variation in hFSS, not 0 or 1, but the string of variables, such as "Cj='2fF' Lj='12.75nH'" If None, gets the nominal variation @@ -1704,7 +1704,7 @@ def create_report(self, Example ------------------------------------------------------ - Exammple plot for a single vareiation all pass converge of mode freq + Example plot for a single variation all pass converge of mode freq .. code-block python ycomp = [f"re(Mode({i}))" for i in range(1,1+epr_hfss.n_modes)] params = ["Pass:=", ["All"]]+variation @@ -2035,7 +2035,7 @@ def mesh_get_names(self, kind="Length Based"): return list(self._mesh.GetOperationNames(kind)) def mesh_get_all_props(self, mesh_name): - # TODO: make mesh tis own class with preperties + # TODO: make mesh tis own class with properties prop_tab = 'MeshSetupTab' prop_server = f'MeshSetup:{mesh_name}' prop_names = self.parent._design.GetProperties('MeshSetupTab', @@ -2185,12 +2185,12 @@ def draw_wirebond(self, **kwargs): ''' Args: - pos: 2D positon vector (specify center point) + pos: 2D position vector (specify center point) ori: should be normed - z: z postion + z: z position # TODO create Wirebond class - psoition is the origin of one point + position is the origin of one point ori is the orientation vector, which gets normalized ''' p = np.array(pos) @@ -2261,7 +2261,7 @@ def get_boundary_assignment(self, boundary_name: str): def append_PerfE_assignment(self, boundary_name: str, object_names: list): ''' This will create a new boundary if need, and will - otherwise append given names to an exisiting boundary + otherwise append given names to an existing boundary ''' # enforce boundary_name = str(boundary_name) @@ -2286,7 +2286,7 @@ def append_mesh(self, mesh_name: str, object_names: list, old_objs: list, **kwargs): ''' This will create a new boundary if need, and will - otherwise append given names to an exisiting boundary + otherwise append given names to an existing boundary old_obj = circ._mesh_assign ''' mesh_name = str(mesh_name) @@ -2326,7 +2326,7 @@ def _make_lumped_rlc(self, r, l, c, start, end, obj_arr, name="LumpRLC"): params += obj_arr params.append([ "NAME:CurrentLine", - # for some reason here it seems to swtich to use the model units, rather than meters + # for some reason here it seems to switch to use the model units, rather than meters "Start:=", fix_units(start, unit_assumed=LENGTH_UNIT), "End:=", @@ -2422,7 +2422,7 @@ def create_relative_coorinate_system_both(self, Modeler>Coordinate System>Create>Relative CS->Rotated Modeler>Coordinate System>Create>Relative CS->Both - Current cooridnate system is set right after this. + Current coordinate system is set right after this. cs_name : name of coord. sys If the name already exists, then a new coordinate system with _1 is created. @@ -2708,9 +2708,9 @@ def rename(self, new_name): ''' new_name = increment_name( new_name, self.modeler.get_objects_in_group( - "Sheets")) # this is for a clsoed polyline + "Sheets")) # this is for a closed polyline - # check to get the actual new name in case there was a suibtracted ibjet with that namae + # check to get the actual new name in case there was a substracted object with that name face_ids = self.modeler.get_face_ids(str(self)) self.modeler.rename_obj(self, new_name) # now rename if len(face_ids) > 0: @@ -2750,7 +2750,7 @@ def fillet(self, radius, vertex_index): def fillets(self, radius, do_not_fillet=[]): ''' - do_not_fillet : Index list of verteces to not fillete + do_not_fillet : Index list of vertices to not fillete ''' raw_list_vertices = self.modeler.get_vertex_ids(self) list_vertices = [] @@ -2816,7 +2816,7 @@ def clear_named_expressions(self): def declare_named_expression(self, name): """" - If a named epression has been created in the fields calculator, this + If a named expression has been created in the fields calculator, this function can be called to initialize the name to work with the fields object """ self.named_expression[name] = NamedCalcObject(name, self.setup) @@ -3066,7 +3066,7 @@ def get_active_project(): except AttributeError: is_admin = ctypes.windll.shell32.IsUserAnAdmin() != 0 if not is_admin: - print('\033[93m WARNING: you are not runnning as an admin! \ + print('\033[93m WARNING: you are not running as an admin! \ You need to run as an admin. You will probably get an error next.\ \033[0m') @@ -3103,7 +3103,7 @@ def load_ansys_project(proj_name: str, # Checks assert project_path.is_dir( ), "ERROR! project_path is not a valid directory \N{loudly crying face}.\ - Check the path, and especially \\ charecters." + Check the path, and especially \\ characters." project_path /= project_path / Path(proj_name + extension) diff --git a/pyEPR/calcs/back_box_numeric.py b/pyEPR/calcs/back_box_numeric.py index 57e904f..9371819 100644 --- a/pyEPR/calcs/back_box_numeric.py +++ b/pyEPR/calcs/back_box_numeric.py @@ -46,11 +46,11 @@ def epr_numerical_diagonalization(freqs, Ljs, ϕzpf, return_H=False, non_linear_potential=None): ''' - Numerical diagonalizaiton for pyEPR. Ask Zlatko for details. + Numerical diagonalization for pyEPR. Ask Zlatko for details. :param fs: (GHz, not radians) Linearized model, H_lin, normal mode frequencies in Hz, length M - :param ljs: (Henries) junction linerized inductances in Henries, length J - :param fzpfs: (reduced) Reduced Zero-point fluctutation of the junction fluxes for each mode + :param ljs: (Henries) junction linearized inductances in Henries, length J + :param fzpfs: (reduced) Reduced Zero-point fluctuation of the junction fluxes for each mode across each junction, shape MxJ :return: Hamiltonian mode freq and dispersive shifts. Shifts are in MHz. @@ -79,8 +79,8 @@ def black_box_hamiltonian(fs, ljs, fzpfs, cos_trunc=5, fock_trunc=8, individual= non_linear_potential = None): r""" :param fs: Linearized model, H_lin, normal mode frequencies in Hz, length N - :param ljs: junction linerized inductances in Henries, length M - :param fzpfs: Zero-point fluctutation of the junction fluxes for each mode across each junction, + :param ljs: junction linearized inductances in Henries, length M + :param fzpfs: Zero-point fluctuation of the junction fluxes for each mode across each junction, shape MxJ :return: Hamiltonian in units of Hz (i.e H / h) All in SI units. The ZPF fed in are the generalized, not reduced, flux. @@ -140,7 +140,7 @@ def make_dispersive(H, fock_trunc, fzpfs=None, f0s=None, chi_prime=False, use_1st_order=False): r""" Input: Hamiltonian Matrix. - Optional: phi_zpfs and normal mode frequncies, f0s. + Optional: phi_zpfs and normal mode frequencies, f0s. use_1st_order : deprecated Output: Return dressed mode frequencies, chis, chi prime, phi_zpf flux (not reduced), and linear frequencies @@ -275,7 +275,7 @@ def black_box_hamiltonian_nq(freqs, zmat, ljs, cos_trunc=6, fock_trunc=8, show_f slopes = np.zeros((nj, nz)) import matplotlib.pyplot as plt # Fit a second order polynomial in the region around the zero - # Extract the exact location of the zero and the assocated slope + # Extract the exact location of the zero and the associated slope # If you need better than second order fit, you're not sampling finely enough for i, z in enumerate(zeros): f0_guess = (freqs[z+1] + freqs[z]) / 2 diff --git a/pyEPR/calcs/basic.py b/pyEPR/calcs/basic.py index 2d35a59..2aac35b 100644 --- a/pyEPR/calcs/basic.py +++ b/pyEPR/calcs/basic.py @@ -13,7 +13,7 @@ def epr_to_zpf(Pmj, SJ, Ω, EJ): r''' INPUTS: All as matrices (numpy arrays) - :Pnj: MxJ energy-participatuion-ratio matrix, p_mj + :Pnj: MxJ energy-participation-ratio matrix, p_mj :SJ: MxJ sign matrix, s_mj :Ω: MxM diagonal matrix of frequencies (GHz, not radians, diagonal) :EJ: JxJ diagonal matrix matrix of Josephson energies (in same units as Om) @@ -32,7 +32,7 @@ def epr_to_zpf(Pmj, SJ, Ω, EJ): {Pmj}""") # Technically, there the equation is hbar omega / 2J, but here we assume - # that the hbar is absrobed in the units of omega, and omega and Ej have the same units. + # that the hbar is absorbed in the units of omega, and omega and Ej have the same units. # PHI=np.zeros((3,3)) # for m in range(3): # for j in range(3): @@ -43,7 +43,7 @@ def epr_to_zpf(Pmj, SJ, Ω, EJ): @staticmethod def epr_cap_to_nzpf(Pmj_cap, SJ, Ω, Ec): """ - Expeirmental. To be tested + Experimental. To be tested """ (Pmj, SJ, Ω, EJ) = map(np.array, (Pmj_cap, SJ, Ω, Ec)) return SJ * sqrt(Ω @ Pmj @ np.linalg.inv(Ec) /(4*4)) diff --git a/pyEPR/calcs/constants.py b/pyEPR/calcs/constants.py index ce43085..211efb7 100644 --- a/pyEPR/calcs/constants.py +++ b/pyEPR/calcs/constants.py @@ -1,5 +1,5 @@ """ -pyEPR constants and convinience definitions. +pyEPR constants and convenience definitions. @author: Zlatko Minev """ diff --git a/pyEPR/calcs/convert.py b/pyEPR/calcs/convert.py index 676a554..c78bd5e 100644 --- a/pyEPR/calcs/convert.py +++ b/pyEPR/calcs/convert.py @@ -204,7 +204,7 @@ def ZPF_from_EPR(hfss_freqs, hfss_epr_, hfss_signs, hfss_Ljs, Returns: M x J matrix of reduced ZPF; i.e., scaled by reduced flux quantum. type: np.array - and a tuple of matricies. + and a tuple of matrices. Example use: ϕzpf, (Ωm, Ej, Pmj, Smj) = Convert.ZPF_from_EPR(hfss_freqs, hfss_epr, hfss_signs, hfss_Ljs, to_df=True) diff --git a/pyEPR/calcs/hamiltonian.py b/pyEPR/calcs/hamiltonian.py index 578c3d1..bb7b205 100644 --- a/pyEPR/calcs/hamiltonian.py +++ b/pyEPR/calcs/hamiltonian.py @@ -1,7 +1,7 @@ """ Hamiltonian and Matrix Operations. Hamiltonian operations heavily draw on qutip package. -This package must be installded for them to work. +This package must be installed for them to work. """ try: import qutip @@ -18,9 +18,9 @@ class MatrixOps(object): @staticmethod def cos(op_cos_arg: Qobj): """ - Make cosine opertor matrix from arguemnt op_cos_arg + Make cosine operator matrix from argument op_cos_arg - op_cos_arg (qutip.Qobj) : argumetn of the cosine + op_cos_arg (qutip.Qobj) : argument of the cosine """ return 0.5*((1j*op_cos_arg).expm() + (-1j*op_cos_arg).expm()) @@ -53,7 +53,7 @@ def fock_state_on(d: dict, fock_trunc: int, N_modes: int): @staticmethod def closest_state_to(s: Qobj, energyMHz, evecs): """ - Returns the enery of the closest state to s + Returns the energy of the closest state to s """ def distance(s2): return (s.dag() * s2[1]).norm() @@ -75,7 +75,7 @@ def identify_Fock_levels(fock_trunc: int, evecs, """ Return quantum numbers in terms of the undiagonalized eigenbasis. """ - # to do: need to turn Fock_max into arb algo on each mdoe + # to do: need to turn Fock_max into arb algo on each mode def fock_state_on(d): return HamOps.fock_state_on(d, fock_trunc, N_modes) diff --git a/pyEPR/calcs/quantum.py b/pyEPR/calcs/quantum.py index ea866f1..dc36d2b 100644 --- a/pyEPR/calcs/quantum.py +++ b/pyEPR/calcs/quantum.py @@ -1,6 +1,6 @@ """ Implementation of basic quantum operation in numpy, -to effortleslly remove the need in the `qutip` package. +to effortlessly remove the need in the `qutip` package. """ import numpy as np @@ -13,7 +13,7 @@ def create(n: int): return mat def destroy(n: int): - """Returns matrix representation of an n-dimensional annhilation operator""" + """Returns matrix representation of an n-dimensional annihilation operator""" diag = np.sqrt(np.arange(1, n)) mat = np.zeros([n, n]) np.fill_diagonal(mat[:, 1:], diag) diff --git a/pyEPR/calcs/transmon.py b/pyEPR/calcs/transmon.py index 931a534..c2be374 100644 --- a/pyEPR/calcs/transmon.py +++ b/pyEPR/calcs/transmon.py @@ -1,5 +1,5 @@ """ -Transmon caluclations +Transmon calculations """ import math @@ -62,7 +62,7 @@ def dispersiveH_params_PT_O1(Pmj, Ωm, Ej): def transmon_get_all_params(Ej_MHz, Ec_MHz): """ Linear harmonic oscillator approximation of transmon. - Convinince func + Convenience func """ Ej, Ec = Ej_MHz, Ec_MHz Lj_H, Cs_F = Convert.Lj_from_Ej( @@ -87,7 +87,7 @@ def transmon_get_all_params(Ej_MHz, Ec_MHz): def transmon_print_all_params(Lj_nH, Cs_fF): """ Linear harmonic oscillator approximation of transmon. - Convinince func + Convenience func """ # Parameters - duplicates with transmon_get_all_params Ej, Ec = Convert.Ej_from_Lj(Lj_nH, 'nH', 'MHz'), Convert.Ec_from_Cs( diff --git a/pyEPR/core.py b/pyEPR/core.py index 02b85ec..5a54e4a 100644 --- a/pyEPR/core.py +++ b/pyEPR/core.py @@ -1,9 +1,9 @@ """ Main interface module to use pyEPR. -Contains code to conenct to Ansys and to analyze HFSS files using the EPR method. +Contains code to connect to Ansys and to analyze HFSS files using the EPR method. -This module handles the micowave part of the analysis and conenction to +This module handles the microwave part of the analysis and connection to Further contains code to be able to do autogenerated reports, @@ -18,7 +18,7 @@ from .core_quantum_analysis import QuantumAnalysis from .core_distributed_analysis import DistributedAnalysis -# Backwards compatability. To be depreciated. +# Backwards compatibility. To be depreciated. Project_Info = ProjectInfo pyEPR_HFSSAnalysis = DistributedAnalysis pyEPR_Analysis = QuantumAnalysis diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 7f71f01..a257dec 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -1,9 +1,9 @@ """ Main distributed analysis module to use pyEPR. -Contains code to conenct to Ansys and to analyze HFSS files using the EPR method. +Contains code to connect to Ansys and to analyze HFSS files using the EPR method. -This module handles the micowave part of the analysis and conenction to +This module handles the microwave part of the analysis and connection to Further contains code to be able to do autogenerated reports, @@ -50,12 +50,12 @@ class DistributedAnalysis(object): """ DISTRIBUTED ANALYSIS of layout and microwave results. - Main compuation class & interface with HFSS. + Main computation class & interface with HFSS. This class defines a DistributedAnalysis object which calculates and saves Hamiltonian parameters from an HFSS simulation. - Further, it allows one to calcualte dissipation, etc. + Further, it allows one to calculate dissipation, etc. """ def __init__(self, *args, **kwargs): @@ -66,7 +66,7 @@ def __init__(self, *args, **kwargs): Parameters: ------------------- project_info : ProjectInfo - Suplpy the project info or the parameters to create pinfo + Supply the project info or the parameters to create pinfo Use notes: ------------------- @@ -105,7 +105,7 @@ def __init__(self, *args, **kwargs): eprd.do_EPR_analysis(append_analysis=True); - Key internal paramters: + Key internal parameters: ------------------- n_modes (int) : Number of eignemodes; e.g., 2 variations (List[str]) : A list of string identifier of **solved** variation @@ -127,8 +127,8 @@ def __init__(self, *args, **kwargs): project_info = args[0] else: assert len(args) == 0, '''Since you did not pass a ProjectInfo object - as a arguemnt, we now assuem you are trying to create a project - info object here by apassing its arguments. See ProjectInfo. + as a argument, we now assume you are trying to create a project + info object here by passing its arguments. See ProjectInfo. It does not take any arguments, only kwargs. \N{face with medical mask}''' project_info = ProjectInfo(*args, **kwargs) @@ -144,7 +144,7 @@ def __init__(self, *args, **kwargs): self.fields = self.setup.get_fields() self.solutions = self.setup.get_solutions() - # Stores resutls from sims + # Stores results from sims self.results = Dict() # of variations. Saved results # TODO: turn into base class shared with analysis! @@ -251,11 +251,11 @@ def calc_p_junction_single(self, mode, variation, U_E=None, U_H=None): print(' p_j_' + str(mode) + ' = ' + str(pj_val)) return pj - # TODO: replace this method with the one below, here because osme funcs use it still + # TODO: replace this method with the one below, here because some funcs use it still def get_freqs_bare(self, variation: str): """ Warning: - Outdated. Do not use. To be depreicated + Outdated. Do not use. To be deprecated Args: variation (str): A string identifier of the variation, @@ -450,7 +450,7 @@ def get_nominal_variation_index(self): def get_ansys_variations(self): """ - Will update ansys inofrmation and result the list of variations. + Will update ansys information and result the list of variations. Returns: For example: @@ -506,7 +506,7 @@ def _update_ansys_variables(self, variations=None): def get_ansys_variables(self): """ - Get ansys variables for all variaitons + Get ansys variables for all variations Returns: Return a dataframe of variables as index and columns as the variations @@ -573,7 +573,7 @@ def calc_energy_electric(self, obj_dims (int | 3) : 1 - line, 2 - surface, 3 - volume. Default volume Example: - Example use to calcualte the energy participation ratio (EPR) of a substrate + Example use to calculate the energy participation ratio (EPR) of a substrate .. code-block:: python :linenos: @@ -699,7 +699,7 @@ def calc_current(self, fields, line: str): return I def calc_avg_current_J_surf_mag(self, variation: str, junc_rect: str, junc_line): - ''' Peak current I_max for mdoe J in junction J + ''' Peak current I_max for mode J in junction J The avg. is over the surface of the junction. I.e., spatial. Args: variation (str): A string identifier of the variation, @@ -727,7 +727,7 @@ def calc_current_using_line_voltage(self, variation: str, junc_line_name: str, ''' Peak current I_max for prespecified mode calculating line voltage across junction. - Make sure that oyu have set the correct variaitonin hFSS before running this + Make sure that oyu have set the correct variation in hFSS before running this Parameters: ------------------------------------------------ @@ -797,7 +797,7 @@ def get_junc_len_dir(self, variation: str, junc_line): def get_Qseam(self, seam, mode, variation, U_H=None): r''' - Caculate the contribution to Q of a seam, by integrating the current in + Calculate the contribution to Q of a seam, by integrating the current in the seam with finite conductance: set in the config file ref: http://arxiv.org/pdf/1509.01119.pdf ''' @@ -944,7 +944,7 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): For a single specific mode. Expected that you have specified the mode before calling this, `self.set_mode(num)` - Expected to precalc U_H and U_E for mode, will retunr pandas pd.Series object + Expected to precalc U_H and U_E for mode, will return pandas pd.Series object junc_rect = ['junc_rect1', 'junc_rect2'] name of junc rectangles to integrate H over junc_len = [0.0001] specify in SI units; i.e., meters LJs = [8e-09, 8e-09] SI units @@ -984,7 +984,7 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): _I_peak_1 = self.calc_avg_current_J_surf_mag( variation, j_props['rect'], line_name) - # could also use this to back out the V_peak using the impedences as in the line + # could also use this to back out the V_peak using the impedances as in the line # below for now, keep both methods _I_peak_2, _V_peak_2, _ = self.calc_current_using_line_voltage( @@ -1127,14 +1127,14 @@ def do_EPR_analysis(self, Modes to analyze for example modes = [0, 2, 3] - append_analysis (bool) : When we run the ansys anslysis, should we redo any variations + append_analysis (bool) : When we run the ansys analysis, should we redo any variations that we have already done? Ansys Notes: ------------------------ Assumptions: Low dissipation (high-Q). - It is easier to assume no lumped capcitors to simply calculations, but we have + It is easier to assume no lumped capacitors to simply calculations, but we have recently added Cj_variable as a new feature that is begin tested to handle capacitors. See the paper. @@ -1184,7 +1184,7 @@ def do_EPR_analysis(self, print_NoNewLine(' previously analyzed ...\n') continue - # QUESTION! should we set the current variaiton, can this save time, set the variables + # QUESTION! should we set the current variation, can this save time, set the variables # If not, clear the results self.results[variation] = Dict() @@ -1197,9 +1197,9 @@ def do_EPR_analysis(self, continue try: - # This should allow us to load the fields only once, and then do the calcualtions - # faster. The loading of the fields does not happen here, but a tthe firc ClcEval call. - # This could fail if more varialbes are added after the simulation is compelted. + # This should allow us to load the fields only once, and then do the calculations + # faster. The loading of the fields does not happen here, but a the first ClcEval call. + # This could fail if more variables are added after the simulation is completed. self.set_variation(variation) except Exception as e: print('\tERROR: Could not set the variation string.' @@ -1267,11 +1267,11 @@ def do_EPR_analysis(self, sol = pd.Series({'U_H': self.U_H, 'U_E': self.U_E}) # Fraction - report the peak energy, properly normalized - # the 2 is from the calcualtion methods + # the 2 is from the calculation methods print(f""" {'(ℰ_E-ℰ_H)/ℰ_E':>15s} {'ℰ_E':>9s} {'ℰ_H':>9s} {100*(self.U_E - self.U_H)/self.U_E:>15.1f}% {self.U_E/2:>9.4g} {self.U_H/2:>9.4g}\n""") - # Calcualte EPR for each of the junctions + # Calculate EPR for each of the junctions print( f' Calculating junction energy participation ration (EPR)\n\tmethod=`{self.pinfo.options.method_calc_P_mj}`. First estimates:') print( @@ -1394,8 +1394,8 @@ def results_variations_on_inside(results: dict): # Conver to pandas Dataframe if all are pd.Series if all(isinstance(new_res[key][variation], pd.Series) for variation in variations): - # print(key) # Conver these to datafrme - # Variations will vecome columns + # print(key) # Conver these to dataframe + # Variations will become columns new_res[key] = pd.DataFrame(new_res[key]) new_res[key].columns.name = 'variation' # sort_df_col : maybe sort @@ -1520,15 +1520,15 @@ def set_mode(self, mode_num, phase=0): def has_fields(self, variation: str = None): ''' Determine if fields exist for a particular solution. - Just calls `self.solutions.has_fields(variaiton_string)` + Just calls `self.solutions.has_fields(variation_string)` - variation (str | None) : String of variaiton label, such as '0' or '1' + variation (str | None) : String of variation label, such as '0' or '1' If None, gets the nominal variation ''' if self.solutions: #print('variation=', variation) - variaiton_string = self.get_variation_string(variation) - return self.solutions.has_fields(variaiton_string) + variation_string = self.get_variation_string(variation) + return self.solutions.has_fields(variation_string) else: return False @@ -1599,7 +1599,7 @@ def hfss_report_full_convergence(self, fig=None, _display=True): a given variation. Makes a plot inside hfss too. Keyword Arguments: - fig {matpllitb figure} -- Optional figure (default: {None}) + fig {matplotlib figure} -- Optional figure (default: {None}) _display {bool} -- Force display or not. (default: {True}) Returns: diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index b03780e..d4f0036 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -63,7 +63,7 @@ def __init__(self, dict_file=None, data_dir=None): upgraded to the HamiltonianResultsContainer class data_dir - the directory in which the file is to be saved or loaded - from, defults to the config.root_dir + from, defaults to the config.root_dir """ super().__init__() @@ -129,7 +129,7 @@ def _inject_dic(self, add_dic): for key, val in add_dic.items(): # TODO remove all copies of same data # if key in self.keys(): - #raise ValueError('trying to overwrite an exsiting varation') + #raise ValueError('trying to overwrite an existing variation') self[str(int(key)+Init_number_of_keys)] = val return 1 @@ -141,7 +141,7 @@ def _do_sort_index(z: pd.DataFrame): z {pd.DataFrame} -- Input Returns: - Sorted DtaaFrame + Sorted DataFrame """ if isinstance(z, pd.DataFrame): return z.sort_index(axis=1) @@ -241,7 +241,7 @@ def __init__(self, data_filename, results = DistributedAnalysis.results_variations_on_inside( self.data.results) - # Convinience functions + # Convenience functions self.variations = variations or list(self.data.results.keys()) self._hfss_variables = results['hfss_variables'] self.freqs_hfss = results['freqs_hfss_GHz'] @@ -251,7 +251,7 @@ def __init__(self, data_filename, self.Cjs = results['Cjs'] # DataFrame self.OM = results['Om'] # dict of dataframes self.PM = results['Pm'] # participation matrices - raw, unnormed here - # participation matrices for capactive elements + # participation matrices for capacitive elements self.PM_cap = results['Pm_cap'] self.SM = results['Sm'] # sign matrices self.I_peak = results['I_peak'] @@ -294,7 +294,7 @@ def print_info(self): def get_vs_variable(self, swp_var, attr: str): """ - Convert the index of a dicitoanry that is stored here from + Convert the index of a dictionary that is stored here from variation number to variable value. Args: @@ -308,10 +308,10 @@ def get_vs_variable(self, swp_var, attr: str): def get_variable_vs(self, swpvar, lv=None): """ lv is list of variations (example ['0', '1']), if None it takes all variations - swpvar is the variable by which to orginize + swpvar is the variable by which to organize return: - ordered dicitonary of key which is the variation number and the magnitude + ordered dictionary of key which is the variation number and the magnitude of swaver as the item """ ret = OrderedDict() @@ -337,7 +337,7 @@ def get_variations_of_variable_value(self, swpvar, value, lv=None): has a specific value lv is list of variations (example ['0', '1']), if None it takes all variations swpvar is a string and the name of the variable we wish to filter - value is the value of swapvr in which we are intrested + value is the value of swapvr in which we are interested returns lv - a list of the variations for which swavr==value """ @@ -431,7 +431,7 @@ def get_Ejs(self, variation): def get_Ecs(self, variation): ''' ECs in GHz - Returns as padnas series + Returns as pandas series ''' Cs = self.Cjs[variation] return Convert.Ec_from_Cs(Cs, units_in='F', units_out='GHz') @@ -497,7 +497,7 @@ def _get_participation_normalized(self, variation, _renorm_pj=None, print_=False #s = self.sols[variation] # sum of participation energies as calculated by global UH and UE # U_mode = s['U_E'] # peak mode energy; or U bar as i denote it sometimes - # We need to add the capactiro here, and maybe take the mean of that + # We need to add the capacitor here, and maybe take the mean of that energies = self._get_ansys_total_energies(variation) @@ -525,7 +525,7 @@ def _get_participation_normalized(self, variation, _renorm_pj=None, print_=False idx_cap = Pm_cap > 0.15 else: raise NotImplementedError( - "Unkown _renorm_pj argument or config values!") + "Unknown _renorm_pj argument or config values!") if print_: # \nPm_cap_norm=\n{Pm_cap_norm}") @@ -560,10 +560,10 @@ def _get_participation_normalized(self, variation, _renorm_pj=None, print_=False def get_epr_base_matrices(self, variation, _renorm_pj=None, print_=False): r''' - Return the key matricies used in the EPR method for analytic calcualtions. + Return the key matrices used in the EPR method for analytic calculations. All as matrices - :PJ: Participatuion matrix, p_mj + :PJ: Participation matrix, p_mj :SJ: Sign matrix, s_mj :Om: Omega_mm matrix (in GHz) (\hbar = 1) Not radians. :EJ: E_jj matrix of Josephson energies (in same units as hbar omega matrix) @@ -573,7 +573,7 @@ def get_epr_base_matrices(self, variation, _renorm_pj=None, print_=False): Return all as *np.array* PM, SIGN, Om, EJ, Phi_ZPF ''' - # TODO: superseed by Convert.ZPF_from_EPR + # TODO: supersede by Convert.ZPF_from_EPR res = self._get_participation_normalized( variation, _renorm_pj=_renorm_pj, print_=print_) @@ -730,7 +730,7 @@ def analyze_variation(self, self.print_variation(variation) self.print_result(result) - self.n_modes = tmp_n_modes # TODO is this smart should consider defining the modes of intrest in the initilazaition of the quantum object + self.n_modes = tmp_n_modes # TODO is this smart should consider defining the modes of interest in the initialisation of the quantum object self.modes[variation]=tmp_modes return result @@ -812,7 +812,7 @@ def plotting_dic_x(self, Var_dic, var_name): dic['x_label'] = var_name dic['x'] = self.get_variable_value(var_name, lv=lv) else: - raise ValueError('more than one hfss variablae changes each time') + raise ValueError('more than one hfss variable changes each time') return lv, dic @@ -1023,11 +1023,11 @@ def get_participations(self, swp_variable='variation', _normed=True): """ - inductive (bool): EPR forjunciton inductance when True, else for capactiors + inductive (bool): EPR for junction inductance when True, else for capacitors Returns: ---------------- - Returns a multindex dataframe: + Returns a multiindex dataframe: index 0: sweep variable index 1: mode number column: junction number @@ -1177,7 +1177,7 @@ def quick_plot_mode(self, mode, junction, mode1=None, swp_variable='variation', def quick_plot_convergence(self, ax = None): """ - Plot a report of the Ansys converngece vs pass number ona twin axis + Plot a report of the Ansys convergence vs pass number ona twin axis for the number of tets and the max delta frequency of the eignemode. """ ax = ax or plt.gca() @@ -1193,7 +1193,7 @@ def quick_plot_convergence(self, ax = None): def extract_dic(name=None, file_name=None): - """#name is the name of the dictionry as saved in the npz file if it is None, + """#name is the name of the dictionary as saved in the npz file if it is None, the function will return a list of all dictionaries in the npz file file name is the name of the npz file""" with np.load(file_name, allow_pickle=True) as f: diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index c7be9ee..f916ec0 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -1,9 +1,9 @@ """ Main interface module to use pyEPR. -Contains code to conenct to Ansys and to analyze HFSS files using the EPR method. +Contains code to connect to Ansys and to analyze HFSS files using the EPR method. -This module handles the micowave part of the analysis and conenction to +This module handles the microwave part of the analysis and connection to Further contains code to be able to do autogenerated reports, @@ -36,7 +36,7 @@ class ProjectInfo(object): :py:class:`pyEPR.ansys.HfssDMSetup`, eigenmode :py:class:`pyEPR.ansys.HfssEMSetup`, or Q3D :py:class:`pyEPR.ansys.AnsysQ3DSetup`), the 3D modeler to design geometry :py:class:`pyEPR.ansys.HfssModeler`. * **Junctions:** The class stores params about the design that the user puts will use, such as the names and - properties of the junctions, such as whihc rectangle and line is associated with which junction. + properties of the junctions, such as which rectangle and line is associated with which junction. Note: @@ -55,7 +55,7 @@ class ProjectInfo(object): * ``rect`` (str): String of Ansys name of the rectangle on which the lumped boundary condition is defined. * ``line`` (str): - Name of HFSS polyline which spans the length of the recntalge. + Name of HFSS polyline which spans the length of the rectangle. Used to define the voltage across the junction. Used to define the current orientation for each junction. Used to define sign of ZPF. @@ -133,7 +133,7 @@ def __setitem__(self, key, value): def __getitem__(self, attr): if not (attr in diss_opt or attr == 'pinfo'): - raise AttributeError(f'dissipitive has no attribute "{attr}". '\ + raise AttributeError(f'dissipative has no attribute "{attr}". '\ f'The possible attributes are:\n {str(diss_opt)}') return super().__getattribute__(attr) @@ -144,7 +144,7 @@ def __setattr__(self, attr, value): self[attr] = value def __getattr__(self, attr): - raise AttributeError(f'dissipitive has no attribute "{attr}". '\ + raise AttributeError(f'dissipative has no attribute "{attr}". '\ f'The possible attributes are:\n {str(diss_opt)}') def __getattribute__(self, attr): @@ -157,7 +157,7 @@ def __repr__(self): return str(self.data()) def data(self): - """Return dissipatvie as dictionary""" + """Return dissipative as dictionary""" return {str(opt): self[opt] for opt in diss_opt} def __init__(self, @@ -191,7 +191,7 @@ def __init__(self, self.design_name = design_name self.setup_name = setup_name - # HFSS desgin: describe junction parameters + # HFSS design: describe junction parameters # TODO: introduce modal labels self.junctions = Dict() # See above for help self.ports = Dict() @@ -200,7 +200,7 @@ def __init__(self, self.dissipative = self._Dissipative() self.options = config.ansys - # Conected to HFSS variable + # Connected to HFSS variable self.app = None self.desktop = None self.project = None @@ -218,7 +218,7 @@ def __init__(self, def save(self): ''' - Return all the data in a dectionary form that can be used to be saved + Return all the data in a dictionary form that can be used to be saved ''' return dict( pinfo=pd.Series(get_instance_vars(self, self._Forbidden)), diff --git a/pyEPR/toolbox/_logging.py b/pyEPR/toolbox/_logging.py index e3c6582..4fee6ea 100644 --- a/pyEPR/toolbox/_logging.py +++ b/pyEPR/toolbox/_logging.py @@ -7,7 +7,7 @@ def set_up_logger(logger): logger.c_handler = logging.StreamHandler() # Jupyter notebooks already has a stream handler on the default log, - # Do not propage upstream to the root logger. + # Do not propagate upstream to the root logger. # https://stackoverflow.com/questions/31403679/python-logging-module-duplicated-console-output-ipython-notebook-qtconsole logger.propagate = False diff --git a/pyEPR/toolbox/plotting.py b/pyEPR/toolbox/plotting.py index c1402e0..5a5e08e 100644 --- a/pyEPR/toolbox/plotting.py +++ b/pyEPR/toolbox/plotting.py @@ -26,7 +26,7 @@ def mpl_dpi(dpi=200): ''' - Set the matpllib resolution for images dots per inch + Set the matplotlib resolution for images dots per inch ''' mpl.rcParams['figure.dpi'] = dpi mpl.rcParams['savefig.dpi'] = dpi @@ -36,7 +36,7 @@ def plt_cla(ax: Axes): ''' Clear all plotted objects on an axis - ax : mapltlib axis + ax : matplotlib axis ''' ax = ax if not ax is None else plt.gca() for artist in ax.lines + ax.collections + ax.patches + ax.images + ax.texts: @@ -67,7 +67,7 @@ def legend_translucent(ax: Axes, values=[], loc=0, alpha=0.5, leg_kw={}): def get_last_color(ax: Axes): ''' - gets the color fothe last plotted line + gets the color for the last plotted line use: datai.plot(label=name, marker='o') data.plot(label=name, marker='o', c=get_last_color(plt.gca())) @@ -141,7 +141,7 @@ def xarr_heatmap(fg, title=None, kwheat={}, fmt=('%.3f', '%.2f'), fig=None): ''' fig = plt.figure() if fig == None else fig df = fg.to_pandas() - # format indecies + # format indices df.index = [float(fmt[0] % x) for x in df.index] df.columns = [float(fmt[1] % x) for x in df.columns] import seaborn as sns @@ -161,7 +161,7 @@ def xarr_heatmap(fg, title=None, kwheat={}, fmt=('%.3f', '%.2f'), fig=None): Not seeing widgets: https://github.com/tqdm/tqdm/issues/451 conda update tqdm - # This might aleady work, will require a lot of updates, if not then do: + # This might already work, will require a lot of updates, if not then do: conda install nodejs jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter nbextension enable --py widgetsnbextension diff --git a/pyEPR/toolbox/pythonic.py b/pyEPR/toolbox/pythonic.py index 18803be..d0b015c 100644 --- a/pyEPR/toolbox/pythonic.py +++ b/pyEPR/toolbox/pythonic.py @@ -91,7 +91,7 @@ def df_find_index(s: pd.Series, find, degree=2, ax=False): def df_interpolate_value(s: pd.Series, find, ax=False, method='index'): """ Given a Pandas Series such as of freq with index Lj, - find the freq that would correspnd to Lj given a value not in the index + find the freq that would correspond to Lj given a value not in the index """ z = pd.Series(list(s) + [np.NaN], index=list(s.index.values)+[find]) z = z.sort_index() @@ -150,7 +150,7 @@ def sort_df_col(df): ''' sort by numerical int order ''' return df.sort_index(axis=1) - # Buggy code, deosnt handles ints as inputs or floats as inpts + # Buggy code, doesn't handles ints as inputs or floats as inputs col_names = df.columns if np.all(col_names.map(isint)): return df[col_names.astype(int).sort_values().astype(str)] @@ -184,7 +184,7 @@ def get_instance_vars(obj, Forbidden=[]): def deprecated(func): """This is a decorator which can be used to mark functions - as deprecated. It will result in a warning being emmitted + as deprecated. It will result in a warning being emitted when the function is used. See StackExchange""" def newFunc(*args, **kwargs): warnings.simplefilter('always', DeprecationWarning) # turn off filter @@ -268,7 +268,7 @@ class Print_colors: '''Colors class:reset all colors with colors.reset; two sub classes fg for foreground and bg for background; use as colors.subclass.colorname. - i.e. colors.fg.red or colors.bg.greenalso, the generic bold, disable, + i.e. colors.fg.red or colors.bg.green also, the generic bold, disable, underline, reverse, strike through, and invisible work with the main class i.e. colors.bold https://www.geeksforgeeks.org/print-colors-python-terminal/ @@ -323,7 +323,7 @@ class bg: def DataFrame_col_diff(PS, indx=0): ''' check weather the columns of a dataframe are equal, - returns a T/F series of the row index that specifies which rows are differnt + returns a T/F series of the row index that specifies which rows are different USE: PS[DataFrame_col_diff(PS)] ''' diff --git a/scripts/Alec/11ghz/EPR_test.py b/scripts/Alec/11ghz/EPR_test.py index 4701f32..5526848 100644 --- a/scripts/Alec/11ghz/EPR_test.py +++ b/scripts/Alec/11ghz/EPR_test.py @@ -11,7 +11,7 @@ # Specify the HFSS project to be analyzed project_info = ProjectInfo(r"C:\Users\awe4\Documents\Backed\hfss_simulations\11ghz\\") project_info.project_name = '11ghz_alec' # Name of the project file (string). "None" will get the current active one. - project_info.design_name = '11ghz_design1' # Name of the desgin file (string). "None" will get the current active one. + project_info.design_name = '11ghz_design1' # Name of the design file (string). "None" will get the current active one. project_info.setup_name = None # Name of the setup(string). "None" will get the current active one. project_info.junctions['bot_junc'] = {'rect':'bot_junction', 'line': 'bot_junc_line', 'Lj_variable':'bot_lj', 'length':0.0001} diff --git a/scripts/Alec/7ghz/7ghz_pyEPR.py b/scripts/Alec/7ghz/7ghz_pyEPR.py index ad6633f..79484c5 100644 --- a/scripts/Alec/7ghz/7ghz_pyEPR.py +++ b/scripts/Alec/7ghz/7ghz_pyEPR.py @@ -11,13 +11,13 @@ # Specify the HFSS project to be analyzed project_info = ProjectInfo(r"C:\Users\awe4\Documents\Simulations\HFSS\11ghz\\") project_info.project_name = '2017_08_Zlatko_Shyam_AutStab' # Name of the project file (string). "None" will get the current active one. - project_info.design_name = 'pyEPR_2_chips' # Name of the desgin file (string). "None" will get the current active one. + project_info.design_name = 'pyEPR_2_chips' # Name of the design file (string). "None" will get the current active one. project_info.setup_name = None # Name of the setup(string). "None" will get the current active one. - ## Describe the junctions in the HFSS desgin + ## Describe the junctions in the HFSS design project_info.junctions['jAlice'] = {'rect':'qubitAlice', 'line': 'alice_line', 'Lj_variable':'LJAlice', 'length':0.0001} project_info.junctions['jBob'] = {'rect':'qubitBob', 'line': 'bob_line', 'Lj_variable':'LJBob', 'length':0.0001} - # Dissipative elments EPR + # Dissipative elements EPR project_info.dissipative['dielectric_surfaces'] = None # supply names here, there are more options in project_info.dissipative. # Run analysis diff --git a/scripts/Kaicheng/import_pyEPR.py b/scripts/Kaicheng/import_pyEPR.py index 72bb9db..8746539 100644 --- a/scripts/Kaicheng/import_pyEPR.py +++ b/scripts/Kaicheng/import_pyEPR.py @@ -11,14 +11,14 @@ # Specify the HFSS project to be analyzed project_info = ProjectInfo(r"X:\Simulation\\hfss\\KC\\") project_info.project_name = '2013-12-03_9GHzCavity' # Name of the project file (string). "None" will get the current active one. - project_info.design_name = '9GHz_EM_center_SNAIL' # Name of the desgin file (string). "None" will get the current active one. + project_info.design_name = '9GHz_EM_center_SNAIL' # Name of the design file (string). "None" will get the current active one. project_info.setup_name = None # Name of the setup(string). "None" will get the current active one. - ## Describe the junctions in the HFSS desgin + ## Describe the junctions in the HFSS design project_info.junctions['snail'] = {'rect':'qubit', 'line': 'JunctionLine', 'Lj_variable':'LJ', 'length':0.0001} # project_info.junctions['jBob'] = {'rect':'qubitBob', 'line': 'bob_line', 'Lj_variable':'LJBob', 'length':0.0001} - # Dissipative elments EPR + # Dissipative elements EPR project_info.dissipative['dielectric_surfaces'] = None # supply names here, there are more options in project_info.dissipative. # Run analysis diff --git a/scripts/hanhee/run_vs_pass.py b/scripts/hanhee/run_vs_pass.py index 6b72cbf..12aee7a 100644 --- a/scripts/hanhee/run_vs_pass.py +++ b/scripts/hanhee/run_vs_pass.py @@ -161,7 +161,7 @@ def zkm_get_Hparams(PJ, SJ, Om, EJ, PHI): def do_plot(RES): ''' Make sure - %matplolib qt + %matplotlib qt TODO: in future just setup once, and then update lines only ''' # live plot https://stackoverflow.com/questions/11874767/how-do-i-plot-in-real-time-in-a-while-loop-using-matplotlib diff --git a/scripts/minev/hfss-scripts/2017_10 R3C1 resim.py b/scripts/minev/hfss-scripts/2017_10 R3C1 resim.py index a3eee27..f0eb107 100644 --- a/scripts/minev/hfss-scripts/2017_10 R3C1 resim.py +++ b/scripts/minev/hfss-scripts/2017_10 R3C1 resim.py @@ -10,11 +10,11 @@ project_info.design_name = '3. sweep both' project_info.setup_name = None - ## Describe the junctions in the HFSS desgin + ## Describe the junctions in the HFSS design project_info.junctions['jBright'] = {'rect':'juncV', 'line': 'juncH_line', 'Lj_variable':'LJ1', 'length':0.0001} project_info.junctions['jDark'] = {'rect':'juncH', 'line': 'juncV_line', 'Lj_variable':'LJ2', 'length':0.0001} - # Dissipative elments EPR + # Dissipative elements EPR project_info.dissipative['dielectric_surfaces'] = None # supply names here, there are more options in project_info.dissipative. # Run analysis diff --git a/scripts/minev/hfss-scripts/import_pyEPR.py b/scripts/minev/hfss-scripts/import_pyEPR.py index 182fbe9..6d6e1fb 100644 --- a/scripts/minev/hfss-scripts/import_pyEPR.py +++ b/scripts/minev/hfss-scripts/import_pyEPR.py @@ -11,14 +11,14 @@ # Specify the HFSS project to be analyzed project_info = ProjectInfo(r"C:\\Users\\rslqulab\Desktop\\Lysander\participation_ratio_project\\Shyam's autonomous stabilization simulations\\") project_info.project_name = '2017_08_Zlatko_Shyam_AutStab' # Name of the project file (string). "None" will get the current active one. - project_info.design_name = '2 pyEPR' # Name of the desgin file (string). "None" will get the current active one. + project_info.design_name = '2 pyEPR' # Name of the design file (string). "None" will get the current active one. project_info.setup_name = None # Name of the setup(string). "None" will get the current active one. - ## Describe the junctions in the HFSS desgin + ## Describe the junctions in the HFSS design project_info.junctions['jAlice'] = {'rect':'qubitAlice', 'line': 'alice_line', 'Lj_variable':'LJAlice', 'length':0.0001} project_info.junctions['jBob'] = {'rect':'qubitBob', 'line': 'bob_line', 'Lj_variable':'LJBob', 'length':0.0001} - # Dissipative elments EPR + # Dissipative elements EPR project_info.dissipative['dielectric_surfaces'] = None # supply names here, there are more options in project_info.dissipative. # Run analysis diff --git a/scripts/nick/import_pyEPR.py b/scripts/nick/import_pyEPR.py index 0ab53f6..0564998 100644 --- a/scripts/nick/import_pyEPR.py +++ b/scripts/nick/import_pyEPR.py @@ -11,14 +11,14 @@ # Specify the HFSS project to be analyzed project_info = ProjectInfo(r"X:\Simulation\\hfss\\KC\\") project_info.project_name = '2013-12-03_9GHzCavity' # Name of the project file (string). "None" will get the current active one. - project_info.design_name = '9GHz_EM_center_SNAIL' # Name of the desgin file (string). "None" will get the current active one. + project_info.design_name = '9GHz_EM_center_SNAIL' # Name of the design file (string). "None" will get the current active one. project_info.setup_name = None # Name of the setup(string). "None" will get the current active one. - ## Describe the junctions in the HFSS desgin + ## Describe the junctions in the HFSS design project_info.junctions['snail'] = {'rect':'qubit', 'line': 'JunctionLine', 'Lj_variable':'LJ', 'length':0.0001} # project_info.junctions['jBob'] = {'rect':'qubitBob', 'line': 'bob_line', 'Lj_variable':'LJBob', 'length':0.0001} - # Dissipative elments EPR + # Dissipative elements EPR project_info.dissipative['dielectric_surfaces'] = None # supply names here, there are more options in project_info.dissipative. # Run analysis diff --git a/tests/README.md b/tests/README.md index 8c12724..d175cba 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,5 +1,5 @@ # Unit Tests Module (work-in-progress) -This module is used for development unit testing, to be expandad in the future. +This module is used for development unit testing, to be expanded in the future. This is based on the python [built-in unittest module](https://docs.python.org/3/library/unittest.html). To execute a specific unit test you can run `python -m unittest test_name.py`, or to automatically run all unit tests you can run `python -m unittest` diff --git a/tests/test_project_info.py b/tests/test_project_info.py index 9cbb9dd..828462c 100644 --- a/tests/test_project_info.py +++ b/tests/test_project_info.py @@ -14,7 +14,7 @@ def setUp(self): assert ConnectionError('Failed to connect to HFSS. Opening it manually') def test_dissipative(self): - '''Test change of _Dissipative from a class to a dict with deprecation warninngs''' + '''Test change of _Dissipative from a class to a dict with deprecation warnings''' self.assertRaises(Exception, self.pinfo.dissipative.__getattr__, 'mot_exist', msg='Failed calling non-existing attr') self.assertRaises(Exception, self.pinfo.dissipative.__getitem__, 'not_exist', diff --git a/tests/test_quantum_analysis.py b/tests/test_quantum_analysis.py index eb4dbdf..27672de 100644 --- a/tests/test_quantum_analysis.py +++ b/tests/test_quantum_analysis.py @@ -27,7 +27,7 @@ def test_analyze_all_variations(self): ''' results = self.epra.analyze_all_variations( cos_trunc=8, fock_trunc=15, print_result=False)['0'] # Variation 0 - # TODO: Remove start/finish diagnolization messages (back_box_numeric L:153) + # TODO: Remove start/finish diagonalization messages (back_box_numeric L:153) for key, value in results.items(): if key == 'hfss_variables': # All numeric-only datatypes @@ -42,7 +42,7 @@ def test_analyze_variation(self): pass def test_hamiltonian(self): - pass # TODO: Need to pass **kwargs to epr_num_diag for return_H opption + pass # TODO: Need to pass **kwargs to epr_num_diag for return_H option def test_properties(self): pass From e13dae4c2751b829c337af9fd7dbe794517bdbbb Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Wed, 23 Mar 2022 15:26:41 +0200 Subject: [PATCH 094/125] Test compiling docs in CI (#104) --- .github/workflows/ci.yaml | 37 +++++++++++++++++++++++++++++++++++-- 1 file changed, 35 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 9fbce14..1b319e9 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -9,6 +9,7 @@ on: env: # Increment this to invalidate the cache without modifying requirements.txt PIPCACHEVERSION: 0 + PYTHONVERSION: '3.9.x' # qutip does not support 3.10 yet jobs: pylint: @@ -20,8 +21,7 @@ jobs: id: setup-python uses: actions/setup-python@v2 with: - # qutip does not support 3.10 yet - python-version: '3.9.x' + python-version: ${{ env.PYTHONVERSION }} - name: Set up cache id: cache uses: actions/cache@v2 @@ -35,3 +35,36 @@ jobs: run: python -m pip install . pylint - name: Run pylint run: pylint --errors-only --jobs=0 pyEPR + + test_docs: + runs-on: ubuntu-latest + steps: + - name: Check out repo + uses: actions/checkout@v2 + - name: Set up Python + id: setup-python + uses: actions/setup-python@v2 + with: + python-version: ${{ env.PYTHONVERSION }} + - name: Set up cache + id: cache + uses: actions/cache@v2 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('requirements.txt') }}-${{ env.PIPCACHEVERSION }} + restore-keys: | + ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('requirements.txt') }}- + ${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}- + - name: Install package and sphinx + run: python -m pip install . Sphinx + - name: Make docs + run: | + cd docs + make html + - name: Upload docs to artifact + uses: actions/upload-artifact@v2 + if: always() + with: + name: docs + path: docs/build/html/ + retention-days: 5 \ No newline at end of file From dfd1f63bc464b6a63bc478a9f2b0550586e22b14 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Thu, 24 Mar 2022 15:32:35 +0200 Subject: [PATCH 095/125] Add dissipative elements as arguments to `ProjectInfo` (#103) --- README.md | 6 ++++-- .../Tutorial 1. Startup example.ipynb | 16 +++++++++++++++- docs/source/examples_quick.rst | 6 ++++-- pyEPR/core_distributed_analysis.py | 2 +- pyEPR/project_info.py | 15 ++++++++++++++- 5 files changed, 38 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 04200c5..f3c55b0 100644 --- a/README.md +++ b/README.md @@ -104,14 +104,16 @@ pinfo.junctions['jBob'] = {'Lj_variable':'Lj_bob', 'rect':'rect_bob', 'lin pinfo.validate_junction_info() # Check that valid names of variables and objects have been supplied. # 2b. Dissipative elements: specify -pinfo.dissipative['dielectrics_bulk'] = ['si_substrate', 'dielectic_object2'] # supply names of hfss objects +pinfo.dissipative['dielectrics_bulk'] = ['si_substrate', 'dielectric_object2'] # supply names of hfss objects pinfo.dissipative['dielectric_surfaces'] = ['interface1', 'interface2'] +# Alternatively, these could be specified in ProjectInfo with +# pinfo = epr.ProjectInfo(..., dielectrics_bulk = ['si_substrate', 'dielectric_object2']) # 3. Perform microwave analysis on eigenmode solutions eprd = epr.DistributedAnalysis(pinfo) if 1: # automatic reports eprd.quick_plot_frequencies(swp_var) # plot the solved frequencies before the analysis - eprd.hfss_report_full_convergence() # report convergen + eprd.hfss_report_full_convergence() # report convergence eprd.do_EPR_analysis() # 4a. Perform Hamiltonian spectrum post-analysis, building on mw solutions using EPR diff --git a/_tutorial_notebooks/Tutorial 1. Startup example.ipynb b/_tutorial_notebooks/Tutorial 1. Startup example.ipynb index 13d7edb..9eb7129 100644 --- a/_tutorial_notebooks/Tutorial 1. Startup example.ipynb +++ b/_tutorial_notebooks/Tutorial 1. Startup example.ipynb @@ -867,7 +867,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Q is infinite, since we have not included dissipation yet in this example. The row index is the mode number" + "The Q is infinite, since we have not included dissipation yet in this example. The row index is the mode number. Dissipation can be modelled by selecting the corresponding HFSS objects for solids, sheets, and seams when initialising `ProjectInfo`. For example,\n", + "```python\n", + "pinfo = epr.ProjectInfo(project_path = path_to_project, \n", + " project_name = 'pyEPR_tutorial1',\n", + " design_name = '1. single_transmon',\n", + " dielectrics_bulk = ['si_substrate'],\n", + " dielectric_surfaces = ['interface'],\n", + " resistive_surfaces = None,\n", + " seams = None)\n", + "```\n", + "or after the fact with\n", + "```python\n", + "pinfo.dissipative['dielectrics_bulk'] = ['si_substrate']\n", + "pinfo.dissipative['dielectric_surfaces'] = ['interface']\n", + "```" ] }, { diff --git a/docs/source/examples_quick.rst b/docs/source/examples_quick.rst index 9a8a009..8ed75b5 100644 --- a/docs/source/examples_quick.rst +++ b/docs/source/examples_quick.rst @@ -29,14 +29,16 @@ succinctly plotted. pinfo.validate_junction_info() # Check that valid names of variables and objects have been supplied. # 2b. Dissipative elements: specify - pinfo.dissipative['dielectrics_bulk'] = ['si_substrate', 'dielectic_object2'] # supply names of hfss objects + pinfo.dissipative['dielectrics_bulk'] = ['si_substrate', 'dielectric_object2'] # supply names of hfss objects pinfo.dissipative['dielectric_surfaces'] = ['interface1', 'interface2'] + # Alternatively, these could be specified in ProjectInfo with + # pinfo = epr.ProjectInfo(..., dielectrics_bulk = ['si_substrate', 'dielectric_object2']) # 3. Perform microwave analysis on eigenmode solutions eprd = epr.DistributedAnalysis(pinfo) swp_var = 'Lj_alice' # Sweep variable from optimetric analysis that should be used on the x axis for the frequency plot eprd.quick_plot_frequencies(swp_var) # plot the solved frequencies before the analysis - eprd.hfss_report_full_convergence() # report convergen + eprd.hfss_report_full_convergence() # report convergence eprd.do_EPR_analysis() # 4a. Perform Hamiltonian spectrum post-analysis, building on mw solutions using EPR diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index a257dec..c6f44d3 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -70,7 +70,7 @@ def __init__(self, *args, **kwargs): Use notes: ------------------- - * If you change the setup or number of eignemodes in HFSS, etc. + * If you change the setup or number of eigenmodes in HFSS, etc. call `update_ansys_info()` diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index f916ec0..5246914 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -165,6 +165,10 @@ def __init__(self, project_name: str = None, design_name: str = None, setup_name: str = None, + dielectrics_bulk: list[str] = None, + dielectric_surfaces: list[str] = None, + resistive_surfaces: list[str] = None, + seams: list[str] = None, do_connect: bool = True): """ Keyword Arguments: @@ -179,7 +183,14 @@ def __init__(self, Defaults to ``None``, which will get the current active one. setup_name (str) : Name of the setup within the design. Defaults to ``None``, which will get the current active one. - + dielectrics_bulk (list(str)) : List of names of dielectric bulk objects. + Defaults to ``None``. + dielectric_surfaces (list(str)) : List of names of dielectric surfaces. + Defaults to ``None``. + resistive_surfaces (list(str)) : List of names of resistive surfaces. + Defaults to ``None``. + seams (list(str)) : List of names of seams. + Defaults to ``None``. do_connect (bool) [additional]: Do create connection to Ansys or not? Defaults to ``True``. """ @@ -198,6 +209,8 @@ def __init__(self, # Dissipative HFSS volumes and surfaces self.dissipative = self._Dissipative() + for opt in diss_opt: + self.dissipative[opt] = locals()[opt] self.options = config.ansys # Connected to HFSS variable From fb9eac3eae5522da80d9ea112bc39ae025000afb Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Thu, 28 Apr 2022 17:40:00 +0300 Subject: [PATCH 096/125] Fix docstrings formatting (#101) --- pyEPR/ansys.py | 4 ++-- pyEPR/calcs/back_box_numeric.py | 10 +++++----- pyEPR/calcs/basic.py | 5 ++--- pyEPR/calcs/convert.py | 26 +++++++++++++------------- pyEPR/core_distributed_analysis.py | 28 +++++++++++++--------------- 5 files changed, 35 insertions(+), 38 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index bd25d63..108cc9a 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -1382,9 +1382,9 @@ def get_matrix( ''' Arguments: ----------- - variation : an empty string returns nominal variation. + variation: an empty string returns nominal variation. Otherwise need the list - frequency : in Hz + frequency: in Hz soln_type = "C", "AC RL" and "DC RL" solution_kind = 'LastAdaptive' # AdaptivePass Internals: diff --git a/pyEPR/calcs/back_box_numeric.py b/pyEPR/calcs/back_box_numeric.py index 9371819..6d68f1e 100644 --- a/pyEPR/calcs/back_box_numeric.py +++ b/pyEPR/calcs/back_box_numeric.py @@ -86,8 +86,8 @@ def black_box_hamiltonian(fs, ljs, fzpfs, cos_trunc=5, fock_trunc=8, individual= All in SI units. The ZPF fed in are the generalized, not reduced, flux. Description: - Takes the linear mode frequencies, $\omega_m$, and the zero-point fluctuations, ZPFs, and - builds the Hamiltonian matrix of $H_full$, assuming cos potential. + Takes the linear mode frequencies, :math:`\omega_m`, and the zero-point fluctuations, ZPFs, and + builds the Hamiltonian matrix of :math:`H_{full}`, assuming cos potential. """ n_modes = len(fs) njuncs = len(ljs) @@ -145,8 +145,8 @@ def make_dispersive(H, fock_trunc, fzpfs=None, f0s=None, chi_prime=False, Output: Return dressed mode frequencies, chis, chi prime, phi_zpf flux (not reduced), and linear frequencies Description: - Takes the Hamiltonian matrix `H` from bbq_hmt. It them finds the eigenvalues/eigenvectors and assigns quantum numbers to them --- i.e., mode excitations, such as, for instance, for three mode, |0,0,0> or |0,0,1>, which correspond to no excitations in any of the modes or one excitation in the 3rd mode, resp. The assignment is performed based on the maximum overlap between the eigenvectors of H_full and H_lin. If this crude explanation is confusing, let me know, I will write a more detailed one :slightly_smiling_face: - Based on the assignment of the excitations, the function returns the dressed mode frequencies $\omega_m^\prime$, and the cross-Kerr matrix (including anharmonicities) extracted from the numerical diagonalization, as well as from 1st order perturbation theory. + Takes the Hamiltonian matrix `H` from bbq_hmt. It them finds the eigenvalues/eigenvectors and assigns quantum numbers to them --- i.e., mode excitations, such as, for instance, for three mode, :math:`|0,0,0\rangle` or :math:`|0,0,1\rangle`, which correspond to no excitations in any of the modes or one excitation in the 3rd mode, resp. The assignment is performed based on the maximum overlap between the eigenvectors of H_full and H_lin. If this crude explanation is confusing, let me know, I will write a more detailed one |:slightly_smiling_face:| + Based on the assignment of the excitations, the function returns the dressed mode frequencies :math:`\omega_m^\prime`, and the cross-Kerr matrix (including anharmonicities) extracted from the numerical diagonalization, as well as from 1st order perturbation theory. Note, the diagonal of the CHI matrix is directly the anharmonicity term. """ if hasattr(H, '__len__'): # is it an array / list? @@ -154,7 +154,7 @@ def make_dispersive(H, fock_trunc, fzpfs=None, f0s=None, chi_prime=False, H = H_lin + H_nl else: # make sure its a quanutm object assert type( - H) == qutip.qobj.Qobj, "Please pass in either a list of Qobjs or Qobj for the Hamiltonian" + H) == qutip.qobj.Qobj, "Please pass in either a list of Qobjs or Qobj for the Hamiltonian" print("Starting the diagonalization") evals, evecs = H.eigenstates() diff --git a/pyEPR/calcs/basic.py b/pyEPR/calcs/basic.py index 2aac35b..4734935 100644 --- a/pyEPR/calcs/basic.py +++ b/pyEPR/calcs/basic.py @@ -11,15 +11,14 @@ class CalcsBasic(): @staticmethod def epr_to_zpf(Pmj, SJ, Ω, EJ): r''' - INPUTS: - All as matrices (numpy arrays) + Arguments, All as matrices (numpy arrays): :Pnj: MxJ energy-participation-ratio matrix, p_mj :SJ: MxJ sign matrix, s_mj :Ω: MxM diagonal matrix of frequencies (GHz, not radians, diagonal) :EJ: JxJ diagonal matrix matrix of Josephson energies (in same units as Om) RETURNS: - reduced zpf (in units of $\phi_0$) + reduced zpf (in units of :math:`\phi_0`) ''' (Pmj, SJ, Ω, EJ) = map(np.array, (Pmj, SJ, Ω, EJ)) diff --git a/pyEPR/calcs/convert.py b/pyEPR/calcs/convert.py index c78bd5e..29f8b7c 100644 --- a/pyEPR/calcs/convert.py +++ b/pyEPR/calcs/convert.py @@ -25,13 +25,13 @@ class Convert(): Static container class for conversions of units and variables. TEST CONVERSION: - ```python - from pyEPR.toolbox.conversions import Convert - Lj_nH, Cs_fF = 11, 60 - Convert.transmon_print_all_params(Lj_nH, Cs_fF); + .. code-block:: python - ``` + from pyEPR.toolbox.conversions import Convert + + Lj_nH, Cs_fF = 11, 60 + Convert.transmon_print_all_params(Lj_nH, Cs_fF); ''' # Known SI prefixed _prefix = {'y': -24, # yocto @@ -109,7 +109,7 @@ def Ej_from_Lj(Lj, units_in='nH', units_out='MHz'): Josephson Junction energy from Josephson inductance. Returns in MHz - $E_j = \phi_0^2 / L_J$ + :math:`E_j = \phi_0^2 / L_J` ''' return Convert._convert_num( # Plank to go from Joules to Hz @@ -122,7 +122,7 @@ def Lj_from_Ej(Ej, units_in='MHz', units_out='nH'): Josephson Junction ind from Josephson energy in MHZ. Returns in units of nano Henries by default - $E_j = \phi_0^2 / L_J$ + :math:`E_j = \phi_0^2 / L_J` ''' return Convert._convert_num( lambda _x: (ϕ0**2.)/(_x*Planck), # Plank to go from Joules to Hz @@ -133,7 +133,7 @@ def Ic_from_Lj(Lj, units_in='nH', units_out='nA'): r''' Josephson Junction crit. curr from Josephson inductance. - $E_j = \phi_0^2 / L_J = \phi_0 I_C $ + :math:`E_j = \phi_0^2 / L_J = \phi_0 I_C` ''' return Convert._convert_num( lambda _x: ϕ0/_x, # Plank to go from Joules to Hz @@ -144,7 +144,7 @@ def Lj_from_Ic(Lj, units_in='nA', units_out='nH'): r''' Josephson Junction crit. curr from Josephson inductance. - $E_j = \phi_0^2 / L_J = \phi_0 I_C $ + :math:`E_j = \phi_0^2 / L_J = \phi_0 I_C` ''' return Convert._convert_num( lambda _x: ϕ0/_x, # Plank to go from Joules to Hz @@ -153,10 +153,10 @@ def Lj_from_Ic(Lj, units_in='nA', units_out='nH'): @staticmethod def Ec_from_Cs(Cs, units_in='fF', units_out='MHz'): r''' - Charging energy 4Ec n^2, where n=Q/2e + Charging energy :math:`4E_c n^2`, where :math:`n=Q/2e` Returns in MHz - $E_{C}=\frac{e^{2}}{2C}J$ + :math:`E_{C}=\frac{e^{2}}{2C}J` ''' return Convert._convert_num( # Plank to go from Joules to Hz @@ -166,11 +166,11 @@ def Ec_from_Cs(Cs, units_in='fF', units_out='MHz'): @staticmethod def Cs_from_Ec(Ec, units_in='MHz', units_out='fF'): r''' - Charging energy 4Ec n^2, where n=Q/2e + Charging energy :math:`4E_c n^2`, where :math:`n=Q/2e` Returns in SI units, in Farads. - $E_{C}=\frac{e^{2}}{2C}J$ + :math:`E_{C}=\frac{e^{2}}{2C}J` ''' return Convert._convert_num( # Plank to go from Joules to Hz diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index c6f44d3..e1c1bd3 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -537,7 +537,7 @@ def get_variable_vs_variations(self, variable: str, convert: bool = True): """ Get ansys variables - Return HFSS variable from self.get_ansys_variables() as a + Return HFSS variable from :py:func:`self.get_ansys_variables()` as a pandas series vs variations. Args: @@ -727,14 +727,13 @@ def calc_current_using_line_voltage(self, variation: str, junc_line_name: str, ''' Peak current I_max for prespecified mode calculating line voltage across junction. - Make sure that oyu have set the correct variation in hFSS before running this + Make sure that you have set the correct variation in HFSS before running this - Parameters: - ------------------------------------------------ + Args: variation: variation number junc_line_name: name of the HFSS line spanning the junction junc_L_Henries: junction inductance in henries - Cj_Farads : junction cap in Farads + Cj_Farads: junction cap in Farads TODO: Smooth? ''' lv = self._get_lv(variation) @@ -958,14 +957,13 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): variation (str): A string identifier of the variation, such as '0', '1', ... - Note: - -------------- - U_E and U_H are the total peak energy. (NOT twice as in U_ and U_H other places) - + .. note:: + U_E and U_H are the total peak energy. (NOT twice as in U_ and U_H other places) - Potential errors: If you dont have a line or rect by the right name you will prob - get an error of the type: - com_error: (-2147352567, 'Exception occurred.', (0, None, None, None, 0, -2147024365), None) + .. warning:: + Potential errors: If you dont have a line or rect by the right name you will prob + get an error of the type: com_error: (-2147352567, 'Exception occurred.', + (0, None, None, None, 0, -2147024365), None) ''' # ------------------------------------------------------------ @@ -1115,7 +1113,7 @@ def do_EPR_analysis(self, Args: variation (str): A string identifier of the variation, - such as '0', '1', ... + such as '0', '1', ... Optional Parameters: ------------------------ @@ -1127,8 +1125,8 @@ def do_EPR_analysis(self, Modes to analyze for example modes = [0, 2, 3] - append_analysis (bool) : When we run the ansys analysis, should we redo any variations - that we have already done? + append_analysis (bool) : + When we run the Ansys analysis, should we redo any variations that we have already done? Ansys Notes: ------------------------ From 3446d23733e4530c0b898f8ce173b5fbab9b17ab Mon Sep 17 00:00:00 2001 From: Priti Ashvin Shah <74020801+priti-ashvin-shah-ibm@users.noreply.github.com> Date: Wed, 11 May 2022 09:31:57 -0400 Subject: [PATCH 097/125] https://securitylab.github.com/research/github-actions-preventing-pwn-requests/ is the reason to update. This allows first time users that have forked the repository to make a pull request. (#110) --- .github/workflows/greetings.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 55a2b7c..7cb7730 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -1,6 +1,6 @@ name: Greetings -on: [pull_request, issues] +on: [pull_request_target, issues] jobs: greeting: From 523af53c56df9805e2f573f9cce9cb45efb59c0f Mon Sep 17 00:00:00 2001 From: GyeonghunKim <34947229+GyeonghunKim@users.noreply.github.com> Date: Thu, 26 May 2022 01:16:22 +0900 Subject: [PATCH 098/125] Replace attrdict to addict for python 3.10 compatibility (#108) * Replace Attrdict to addict.Dict for python>=3.10 compatibility * Add one line for filtering addict.Dict from obj's attributes. * replace attrdict to addict * add python 3.10 to classifiers * Add python 3.9 in setup.py --- pyEPR/__init__.py | 8 +------- pyEPR/toolbox/pythonic.py | 8 +++++--- requirements.txt | 2 +- setup.py | 2 ++ 4 files changed, 9 insertions(+), 11 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 1c76675..f15ea58 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -74,13 +74,7 @@ import warnings from pathlib import Path -try: - from attrdict import AttrDict as Dict -except (ImportError, ModuleNotFoundError): - raise ImportError("""Please install python package `AttrDict`. - AttrDict is in PyPI, so it can be installed directly - (https://github.com/bcj/AttrDict) using: - $ pip install attrdict""") +from addict import Dict ############################################################################## # Python header diff --git a/pyEPR/toolbox/pythonic.py b/pyEPR/toolbox/pythonic.py index d0b015c..d3a37ae 100644 --- a/pyEPR/toolbox/pythonic.py +++ b/pyEPR/toolbox/pythonic.py @@ -14,7 +14,7 @@ # Constants from collections import OrderedDict from ..calcs.constants import Planck, elementary_charge, epsilon_0, pi, π, ħ, ϕ0, e_el - +from .. import Dict # ============================================================================== # Utility functions @@ -177,8 +177,10 @@ def get_instance_vars(obj, Forbidden=[]): for v in dir(obj): if not (v.startswith('__') or v.startswith('_')): if not callable(getattr(obj, v)): - if not (v in Forbidden): - VARS[v] = getattr(obj, v) + # Added for using addict.Dict which is not callable. + if not isinstance(getattr(obj, v), Dict): + if not (v in Forbidden): + VARS[v] = getattr(obj, v) return VARS diff --git a/requirements.txt b/requirements.txt index 701a392..bc257ee 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -attrdict>=1.8.0 +addict numpy>=1.15.0 pandas>=1.0.1 matplotlib>=3.1.0 diff --git a/setup.py b/setup.py index dd50639..f770369 100644 --- a/setup.py +++ b/setup.py @@ -51,6 +51,8 @@ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering", "Environment :: Console", "License :: OSI Approved :: Apache Software License" ], From 1fefee74ed6cbab110d275d2e7f39442d6c90c17 Mon Sep 17 00:00:00 2001 From: Priti Ashvin Shah <74020801+priti-ashvin-shah-ibm@users.noreply.github.com> Date: Wed, 25 May 2022 12:56:31 -0400 Subject: [PATCH 099/125] Prepare for new tag for pyepr for pypi. (#112) --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index f15ea58..18d7f81 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.3 +@version: 0.8.5.4 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -86,7 +86,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.3" +__version__ = "0.8.5.4" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index f770369..10083e6 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.3', + version='0.8.5.4', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 48db6af7fcda6971c9652b84ebf1b900d9fca8ba Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Thu, 26 May 2022 10:10:59 -0400 Subject: [PATCH 100/125] Update the README to reflect addict vs attrdict in both files. --- README.md | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index f3c55b0..e92a1a8 100644 --- a/README.md +++ b/README.md @@ -184,11 +184,11 @@ pip install pyEPR-quantum $ echo %PATH% ` - 2. Install the required packages, including [pint](http://pint.readthedocs.io/en/latest/), [qutip](http://qutip.org/), and [attrdict](https://github.com/bcj/AttrDict). In a terminal window + 2. Install the required packages, including [pint](http://pint.readthedocs.io/en/latest/), [qutip](http://qutip.org/), and [addict](https://github.com/mewwts/addict). In a terminal window ```sh conda install -c conda-forge pint conda install -c conda-forge qutip - pip install attrdict + pip install addict ``` 3. Fork this pyEPR repository on GitHub with your GitHub account. You may clone the fork to your PC and manage it using the [SourceTree](https://www.sourcetreeapp.com/) git-gui manager. 4. Add the pyEPR repository folder to your python search path. Make sure to add the git remote to the master is set up, `git remote add MASTER_MINEV git://github.com/zlatko-minev/pyEPR.git`! [(Help?)](https://stackoverflow.com/questions/11266478/git-add-remote-branch) diff --git a/setup.py b/setup.py index 10083e6..6008400 100644 --- a/setup.py +++ b/setup.py @@ -57,5 +57,5 @@ "License :: OSI Approved :: Apache Software License" ], python_requires=">=3.5, <4", - # install_requires=['numpy','pandas','pint','matplotlib','attrdict','sympy','IPython'], + # install_requires=['numpy','pandas','pint','matplotlib','addict','sympy','IPython'], install_requires=requirements) From f30ab7e621c9ba4116812a595fdea873cb5e58d7 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Fri, 27 May 2022 11:40:16 -0400 Subject: [PATCH 101/125] Make the type hint more accurate. --- pyEPR/project_info.py | 30 +++++++++++++++++++++--------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 5246914..a2ab9e0 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -161,15 +161,15 @@ def data(self): return {str(opt): self[opt] for opt in diss_opt} def __init__(self, - project_path: str = None, - project_name: str = None, - design_name: str = None, - setup_name: str = None, - dielectrics_bulk: list[str] = None, - dielectric_surfaces: list[str] = None, - resistive_surfaces: list[str] = None, - seams: list[str] = None, - do_connect: bool = True): + project_path: str = None, + project_name: str = None, + design_name: str = None, + setup_name: str = None, + dielectrics_bulk: list =None, + dielectric_surfaces: list = None, + resistive_surfaces: list= None, + seams: list= None, + do_connect: bool = True): """ Keyword Arguments: @@ -194,6 +194,18 @@ def __init__(self, do_connect (bool) [additional]: Do create connection to Ansys or not? Defaults to ``True``. """ + if dielectrics_bulk == None: + dielectrics_bulk = [] + + if dielectric_surfaces == None: + dielectric_surfaces = [] + + if resistive_surfaces == None: + resistive_surfaces = [] + + if seams == None: + seams = [] + # Path: format path correctly to system convention self.project_path = str(Path(project_path)) \ From e652fc64e2bbf063ebe6b6cdd759412864025ee1 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Fri, 27 May 2022 12:23:48 -0400 Subject: [PATCH 102/125] Current code expects None values, as opposed to empty list. --- pyEPR/project_info.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index a2ab9e0..58cb5cb 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -194,18 +194,6 @@ def __init__(self, do_connect (bool) [additional]: Do create connection to Ansys or not? Defaults to ``True``. """ - if dielectrics_bulk == None: - dielectrics_bulk = [] - - if dielectric_surfaces == None: - dielectric_surfaces = [] - - if resistive_surfaces == None: - resistive_surfaces = [] - - if seams == None: - seams = [] - # Path: format path correctly to system convention self.project_path = str(Path(project_path)) \ From 1ff6a10a9abfa40b6d62453d9c0b98144b9fbc77 Mon Sep 17 00:00:00 2001 From: Priti A Shah Date: Fri, 27 May 2022 13:32:20 -0400 Subject: [PATCH 103/125] Prepare to use next tag for Metal. --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 18d7f81..b888e54 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.4 +@version: 0.8.5.5 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -86,7 +86,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.4" +__version__ = "0.8.5.5" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 6008400..890c9ab 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.4', + version='0.8.5.5', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 3660e76d80cfc044524c28ba2c5da3d9653f3ee1 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Tue, 5 Jul 2022 16:31:08 +0300 Subject: [PATCH 104/125] Add variation labels to plots in `hfss_report_full_convergence` (#119) * Improve assorted docstrings * Add variation labels to HFSS convergence report --- pyEPR/core_distributed_analysis.py | 24 +++++++++++++---------- pyEPR/core_quantum_analysis.py | 31 ++++++++++++++---------------- 2 files changed, 28 insertions(+), 27 deletions(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index e1c1bd3..c9a91ef 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -236,7 +236,7 @@ def setup_data(self): def calc_p_junction_single(self, mode, variation, U_E=None, U_H=None): ''' This function is used in the case of a single junction only. - For multiple junctions, see `calc_p_junction`. + For multiple junctions, see :func:`~pyEPR.DistributedAnalysis.calc_p_junction`. Assumes no lumped capacitive elements. ''' @@ -389,6 +389,7 @@ def get_variations(self): Returns: Returns a list of strings that give the variation labels for HFSS. + .. code-block:: python OrderedDict([ @@ -941,13 +942,14 @@ def calc_Q_external(self, variation, freq_GHz, U_E = None): def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): ''' For a single specific mode. - Expected that you have specified the mode before calling this, `self.set_mode(num)` + Expected that you have specified the mode before calling this, :func:`~pyEPR.DistributedAnalysis.set_mode`. + + Expected to precalc U_H and U_E for mode, will return pandas pd.Series object: - Expected to precalc U_H and U_E for mode, will return pandas pd.Series object - junc_rect = ['junc_rect1', 'junc_rect2'] name of junc rectangles to integrate H over - junc_len = [0.0001] specify in SI units; i.e., meters - LJs = [8e-09, 8e-09] SI units - calc_sign = ['junc_line1', 'junc_line2'] + * junc_rect = ['junc_rect1', 'junc_rect2'] name of junc rectangles to integrate H over + * junc_len = [0.0001] specify in SI units; i.e., meters + * LJs = [8e-09, 8e-09] SI units + * calc_sign = ['junc_line1', 'junc_line2'] WARNING: Cjs is experimental. @@ -1520,8 +1522,8 @@ def has_fields(self, variation: str = None): Determine if fields exist for a particular solution. Just calls `self.solutions.has_fields(variation_string)` - variation (str | None) : String of variation label, such as '0' or '1' - If None, gets the nominal variation + Args: + variation (str): String of variation label, such as '0' or '1'. If None, gets the nominal variation ''' if self.solutions: #print('variation=', variation) @@ -1607,7 +1609,7 @@ def hfss_report_full_convergence(self, fig=None, _display=True): if fig is None: fig = plt.figure(figsize=(11, 3.)) - for variation in self.variations: + for variation, variation_labels in self.get_variations().items(): fig.clf() # Grid spec and axes; height_ratios=[4, 1], wspace=0.5 @@ -1618,6 +1620,8 @@ def hfss_report_full_convergence(self, fig=None, _display=True): convergence_t = self.get_convergence(variation=variation) convergence_f = self.hfss_report_f_convergence(variation=variation) + axs[0].set_ylabel(variation_labels, fontsize='large') # add variation labels to y-axis of first plot + ax0t = axs[1].twinx() plot_convergence_f_vspass(axs[0], convergence_f) plot_convergence_max_df(axs[1], convergence_t.iloc[:, 1]) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index d4f0036..9adc18b 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -443,11 +443,10 @@ def analyze_all_variations(self, ''' See analyze_variation for full documentation - Specific params: - -------------------- - variations : None returns all_variations otherwise this is a list with number - as strings ['0', '1'] - analyze_previous :set to true if you wish to overwrite previous analysis + Args: + variations: None returns all_variations otherwise this is a list with number as strings ['0', '1'] + analyze_previous: set to true if you wish to overwrite previous analysis + **kwargs: Keyword arguments passed to :func:`~pyEPR.QuantumAnalysis.analyze_variation`. ''' result = OrderedDict() @@ -611,24 +610,22 @@ def analyze_variation(self, Core analysis function to call! Args: - --------------- junctions: list or slice of junctions to include in the analysis. None defaults to analysing all junctions modes: list or slice of modes to include in the analysis. None defaults to analysing all modes Returns: - ---------------- - f_0 [MHz] : Eigenmode frequencies computed by HFSS; i.e., linear freq returned in GHz - f_1 [MHz] : Dressed mode frequencies (by the non-linearity; e.g., Lamb shift, etc. ). - Result based on 1st order perturbation theory on the 4th order - expansion of the cosine. - f_ND [MHz] : Numerical diagonalization result of dressed mode frequencies. - only available if `cos_trunc` and `fock_trunc` are set (non None). - chi_O1 [MHz] : Analytic expression for the chis based on a cos trunc to 4th order, and using 1st - order perturbation theory. Diag is anharmonicity, off diag is full cross-Kerr. - chi_ND [MHz] : Numerically diagonalized chi matrix. Diag is anharmonicity, off diag is full - cross-Kerr. + dict: Dictionary containing at least the following: + * f_0 [MHz]: Eigenmode frequencies computed by HFSS; i.e., linear freq returned in GHz + * f_1 [MHz]: Dressed mode frequencies (by the non-linearity; e.g., Lamb shift, etc. ). + Result based on 1st order perturbation theory on the 4th order expansion of the cosine. + * f_ND [MHz]: Numerical diagonalization result of dressed mode frequencies. + only available if `cos_trunc` and `fock_trunc` are set (non None). + * chi_O1 [MHz]: Analytic expression for the chis based on a cos trunc to 4th order, and using 1st + order perturbation theory. Diag is anharmonicity, off diag is full cross-Kerr. + * chi_ND [MHz]: Numerically diagonalized chi matrix. Diag is anharmonicity, off diag is full + cross-Kerr. ''' # ensuring proper matrix dimensionality when slicing From f05295d475aa53cae1711ea14521b1ff0810fac4 Mon Sep 17 00:00:00 2001 From: Zach Parrott <51793790+zachparrott@users.noreply.github.com> Date: Wed, 20 Jul 2022 09:34:17 -0600 Subject: [PATCH 105/125] Add other parametric sweep options (#117) * fix keyboard mash? * Implemented all available options for parametric sweep definitions and parametric sweep from file. * Addition of demo tutorial and associated csv file --- _example_files/Lj_sweep_values.csv | 12 + ...Tutorial 4. Parametric sweep options.ipynb | 491 ++++++++++++++++++ pyEPR/ansys.py | 160 ++++-- 3 files changed, 628 insertions(+), 35 deletions(-) create mode 100644 _example_files/Lj_sweep_values.csv create mode 100644 _tutorial_notebooks/Tutorial 4. Parametric sweep options.ipynb diff --git a/_example_files/Lj_sweep_values.csv b/_example_files/Lj_sweep_values.csv new file mode 100644 index 0000000..9776950 --- /dev/null +++ b/_example_files/Lj_sweep_values.csv @@ -0,0 +1,12 @@ +*,Lj_1 +1,12.1891103111828nH +2,9.75128824894622nH +3,10.2388526613935nH +4,10.7264170738408nH +5,11.2139814862882nH +6,11.7015458987355nH +7,12.6766747236301nH +8,13.1642391360774nH +9,13.6518035485247nH +10,14.139367960972nH +11,14.6269323734193nH diff --git a/_tutorial_notebooks/Tutorial 4. Parametric sweep options.ipynb b/_tutorial_notebooks/Tutorial 4. Parametric sweep options.ipynb new file mode 100644 index 0000000..3d7ed89 --- /dev/null +++ b/_tutorial_notebooks/Tutorial 4. Parametric sweep options.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 1. Parametric sweep options\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Auhor:Zachary Parrott
Purpose:Demonstrate available options for creating parametric sweeps.\n", + "
File Status:In construction
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import pyEPR as epr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connect to example project" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO 02:31PM [connect_project]: Connecting to Ansys Desktop API...\n", + "INFO 02:31PM [load_ansys_project]: \tFile path to HFSS project found.\n", + "INFO 02:31PM [load_ansys_project]: \tOpened Ansys App\n", + "INFO 02:31PM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n", + "INFO 02:31PM [load_ansys_project]: \tOpened Ansys Project\n", + "\tFolder: C:/Users/zlp/github/pyEPR/_example_files/\n", + "\tProject: pyEPR_tutorial1\n", + "INFO 02:31PM [connect_design]: \tOpened active design\n", + "\tDesign: 1. single_transmon [Solution type: Eigenmode]\n", + "INFO 02:31PM [get_setup]: \tOpened setup `Setup1` ()\n", + "INFO 02:31PM [connect]: \tConnected to project \"pyEPR_tutorial1\" and design \"1. single_transmon\" 😀 \n", + "\n" + ] + } + ], + "source": [ + "project_path = '..\\\\_example_files'\n", + "project_name = 'pyEPR_tutorial1'\n", + "design_name = '1. single_transmon'\n", + "\n", + "pinfo = epr.ProjectInfo(project_path = project_path,\n", + " project_name = project_name,\n", + " design_name = design_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Qubit junction\n", + "pinfo.junctions['junction'] = {'Lj_variable' : 'Lj_1',\n", + " 'rect' : 'rect_jj1',\n", + " 'line' : 'line_jj1'}\n", + "pinfo.validate_junction_info() " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Already existing setup\n", + "setup_name = 'Setup1'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add parametric sweep of each type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single value\n", + "Specify a single variable value\n", + "\n", + "(\"12nH\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `single_value` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"single_value\"\n", + "swp_params = ('12nH')\n", + "swp_variable = 'Lj_1'\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'single_value',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = True, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "# setup_name=None will use the first setup\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear step\n", + "Specify a linear range of values with a constant step size.\n", + "\n", + "(start, stop, step)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `linear_step` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"linear_step\"\n", + "swp_variable = 'height'\n", + "swp_params = ('30mm','36mm','1mm')\n", + "\n", + "# 'height' is a geometric variable so cannot copy mesh between passes\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'linear_step',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = False, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "# setup_name=None will use the first setup\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear count\n", + "Specify a linear range of values and the number, or count of points within this range.\n", + "\n", + "(start, stop, count)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `linear_count` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"linear_count\"\n", + "swp_variable = 'pad_gap'\n", + "swp_params = ('80um', '120um', 5)\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'linear_count',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = False, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decade count\n", + "Specify a logarithmic (base 10) series of values, and the number of values to calculate in each decade.\n", + "\n", + "(start, stop, count)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `decade_count` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"decade_count\"\n", + "swp_variable = 'Lj_1'\n", + "swp_params = ('12nH', '100nH', 5)\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'decade_count',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = True, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Octave count\n", + "Specify a logarithmic (base 2) series of values, and the number of values to calculate in each octave.\n", + "\n", + "(start, stop, count)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `octave_count` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"octave_count\"\n", + "swp_variable = 'Lj_1'\n", + "swp_params = ('12nH', '100nH', 8)\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'octave_count',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = True, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "# setup_name=None will use the first setup\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exponential count\n", + "Specify an exponential (base e) series of values, and the number of values to calculate.\n", + "\n", + "(start, stop, count)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `exponential_count` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"exponential_count\"\n", + "swp_variable = 'Lj_1'\n", + "swp_params = ('12nH', '20nH', 4)\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'exponential_count',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = True, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric'\n", + ")\n", + "\n", + "# setup_name=None will use the first setup\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parametric from file\n", + "Only sweep parameter is the filename of either csv or txt file. \n", + "\n", + "Need to give full path location to the file, so for purpose of tutorial need to get where pyEPR is installed." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "cwd = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inserting optimetrics setup `param_file` for simulation setup: `Setup1`\n" + ] + } + ], + "source": [ + "opti_name = \"param_file\"\n", + "swp_variable = 'Lj_1'\n", + "filepath = cwd[:-len(\"_tutorial_notebooks\")] + \"_example_files\\\\\"\n", + "filename = \"Lj_sweep_values.csv\"\n", + "swp_params = filepath + filename\n", + "\n", + "sweep_settings = dict(\n", + " variable = swp_variable,\n", + " swp_type = 'file',\n", + " swp_params = swp_params,\n", + " name = opti_name,\n", + " setup_name = setup_name, \n", + " save_fields = True,\n", + " copy_mesh = True, \n", + " solve_with_copied_mesh_only = False, \n", + " setup_type = 'parametric_file'\n", + ")\n", + "\n", + "# setup_name=None will use the first setup\n", + "if opti_name not in pinfo.design.optimetrics.get_setup_names():\n", + " opti_setup = pinfo.design.optimetrics.create_setup(**sweep_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the setup and a given optimetrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis_setup = pinfo.design.get_setup(setup_name)\n", + "analysis_setup.solve(setup_name)\n", + "\n", + "pinfo.design.optimetrics.solve_setup(\"param_file\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pinfo.disconnect()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('epr_39')", + "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": "3.9.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "666176c80ac0b190efa7fb0fb35072d7c3fb2f81b4cb07356ffe3dd6a24ca381" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 108cc9a..c776d82 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -592,7 +592,7 @@ def new_dm_design(self, name: str): """Create a new driven model design Args: - name (str): Name of driven modal design + name (str): Name of driven modal design """ return self.new_design(name, "DrivenModal") @@ -666,7 +666,7 @@ def save_screenshot(self, path: str = None, show: bool = True): self._modeler.ExportModelImageToFile( str(path), 0, - 0, # can be 0 For the default, use 0, 0. For higher resolution, set desired and , for example for 8k export as: 7680, 4320. + 0, # can be 0 For the default, use 0, 0. For higher resolution, set desired and , for example for 8k export as: 7680, 4320. [ "NAME:SaveImageParams", "ShowAxis:=", "True", "ShowGrid:=", "True", "ShowRuler:=", "True", "ShowRegion:=", "Default", @@ -1881,50 +1881,140 @@ def create_setup(self, solve_with_copied_mesh_only=True, setup_type='parametric'): """ - Inserts a new parametric setup. - - - For type_='linear_step' swp_params is start, stop, step: - swp_params = ("12.8nH" "13.6nH", "0.2nH") + Inserts a new parametric setup of one variable. Either with sweep + definition or from file. Corresponds to ui access: - Right-click the Optimetrics folder in the project tree, - and then click Add> Parametric on the shortcut menu. + Right-click the Optimetrics folder in the project tree, and then click + Add> Parametric on the shortcut menu. + + Ansys provides six sweep definitions types specified using the swp_type + variable. + + Sweep type definitions: + - 'single_value' + Specify a single value for the sweep definition. + - 'linear_step' + Specify a linear range of values with a constant step size. + - 'linear_count' + Specify a linear range of values and the number, or count of points + within this range. + - 'decade_count' + Specify a logarithmic (base 10) series of values, and the number of + values to calculate in each decade. + - 'octave_count' + Specify a logarithmic (base 2) series of values, and the number of + values to calculate in each octave. + - 'exponential_count' + Specify an exponential (base e) series of values, and the number of + values to calculate. + + For swp_type='single_value' swp_params is the single value. + + For swp_type='linear_step' swp_params is start, stop, step: + swp_params = ("12.8nH", "13.6nH", "0.2nH") + + All other types swp_params is start, stop, count: + swp_params = ("12.8nH", "13.6nH", 4) + The definition of count varies amongst the available types. + + For Decade count and Octave count, the Count value specifies the number + of points to calculate in every decade or octave. For Exponential count, + the Count value is the total number of points. The total number of + points includes the start and stop values. + + For parametric from file, setup_type='parametric_file', pass in a file + name and path to swp_params like "C:\\test.csv" or "C:\\test.txt" for + example. + + Example csv formatting: + *,Lj_qubit + 1,12.2nH + 2,9.7nH + 3,10.2nH + + See Ansys documentation for additional formatting instructions. """ setup_name = setup_name or self.design.get_setup_names()[0] print( f"Inserting optimetrics setup `{name}` for simulation setup: `{setup_name}`" ) - if setup_type != 'parametric': - raise NotImplementedError() + if setup_type == 'parametric': + valid_swp_types = ['single_value', 'linear_step', 'linear_count', + 'decade_count', 'octave_count', 'exponential_count'] - if swp_type == 'linear_step': - assert len(swp_params) == 3 - # e.g., "LIN 12.8nH 13.6nH 0.2nH" - swp_str = f"LIN {swp_params[0]} {swp_params[1]} {swp_params[2]}" - else: - raise NotImplementedError() + if swp_type not in valid_swp_types: + raise NotImplementedError() + else: + if swp_type == 'single_value': + # Single takes string of single variable no swp_type_name + swp_str = f"{swp_params}" - self._optimetrics.InsertSetup("OptiParametric", [ - f"NAME:{name}", "IsEnabled:=", True, - [ - "NAME:ProdOptiSetupDataV2", - "SaveFields:=", - save_fields, - "CopyMesh:=", - copy_mesh, - "SolveWithCopiedMeshOnly:=", - solve_with_copied_mesh_only, - ], ["NAME:StartingPoint"], "Sim. Setups:=", [setup_name], - [ - "NAME:Sweeps", + else: + # correct number of inputs + assert len(swp_params) == 3, "Incorrect number of sweep parameters." + + # Not checking for compatible unit types + if swp_type == 'linear_step': + swp_type_name = "LIN" + else: + # counts needs to be an integer number + assert isinstance(swp_params[2], int), "Count must be integer." + + if swp_type == 'linear_count': + swp_type_name = "LINC" + elif swp_type == 'decade_count': + swp_type_name = "DEC" + elif swp_type == 'octave_count': + swp_type_name = "OCT" + elif swp_type == 'exponential_count': + swp_type_name = "ESTP" + + # prepare the string to pass to Ansys + swp_str = f"{swp_type_name} {swp_params[0]} {swp_params[1]} {swp_params[2]}" + + # talk with Ansys + self._optimetrics.InsertSetup("OptiParametric", [ + f"NAME:{name}", "IsEnabled:=", True, [ - "NAME:SweepDefinition", "Variable:=", variable, "Data:=", - swp_str, "OffsetF1:=", False, "Synchronize:=", 0 - ] - ], ["NAME:Sweep Operations"], ["NAME:Goals"] - ]) + "NAME:ProdOptiSetupDataV2", + "SaveFields:=", + save_fields, + "CopyMesh:=", + copy_mesh, + "SolveWithCopiedMeshOnly:=", + solve_with_copied_mesh_only, + ], ["NAME:StartingPoint"], "Sim. Setups:=", [setup_name], + [ + "NAME:Sweeps", + [ + "NAME:SweepDefinition", "Variable:=", variable, "Data:=", + swp_str, "OffsetF1:=", False, "Synchronize:=", 0 + ] + ], ["NAME:Sweep Operations"], ["NAME:Goals"] + ]) + elif setup_type == 'parametric_file': + # Uses the file name as the swp_params + filename = swp_params + + self._optimetrics.ImportSetup("OptiParametric", + [ + f"NAME:{name}", + filename, + ]) + self._optimetrics.EditSetup(f"{name}", + [ + f"NAME:{name}", + [ + "NAME:ProdOptiSetupDataV2", + "SaveFields:=" , save_fields, + "CopyMesh:=" , copy_mesh, + "SolveWithCopiedMeshOnly:=", solve_with_copied_mesh_only, + ], + ]) + else: + raise NotImplementedError() class HfssModeler(COMWrapper): From 87679c15c5da382820645812e95208f02abe1dc6 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Wed, 20 Jul 2022 18:34:35 +0300 Subject: [PATCH 106/125] Add DrivenTerminal support (#121) --- pyEPR/ansys.py | 29 +++++++++++++++++++++++++++++ pyEPR/project_info.py | 10 ++++++---- 2 files changed, 35 insertions(+), 4 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index c776d82..1993cbe 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -715,6 +715,8 @@ def get_setup(self, name=None): return HfssEMSetup(self, name) elif self.solution_type == "DrivenModal": return HfssDMSetup(self, name) + elif self.solution_type == "DrivenTerminal": + return HfssDTSetup(self, name) elif self.solution_type == "Q3D": return AnsysQ3DSetup(self, name) @@ -765,6 +767,26 @@ def create_dm_setup(self, ]) return HfssDMSetup(self, name) + def create_dt_setup(self, + freq_ghz=1, + name="Setup", + max_delta_s=0.1, + max_passes=10, + min_passes=1, + min_converged=1, + pct_refinement=30, + basis_order=-1): + + name = increment_name(name, self.get_setup_names()) + self._setup_module.InsertSetup("HfssDriven", [ + "NAME:" + name, "Frequency:=", + str(freq_ghz) + "GHz", "MaxDeltaS:=", max_delta_s, + "MaximumPasses:=", max_passes, "MinimumPasses:=", min_passes, + "MinimumConvergedPasses:=", min_converged, "PercentRefinement:=", + pct_refinement, "IsEnabled:=", True, "BasisOrder:=", basis_order + ]) + return HfssDTSetup(self, name) + def create_em_setup(self, name="Setup", min_freq_ghz=1, @@ -1331,6 +1353,11 @@ def _map_variables_by_name(self): def get_solutions(self): return HfssDMDesignSolutions(self, self.parent._solutions) +class HfssDTSetup(HfssDMSetup): + + def get_solutions(self): + return HfssDTDesignSolutions(self, self.parent._solutions) + class HfssEMSetup(HfssSetup): """ @@ -1724,6 +1751,8 @@ def create_report(self, class HfssDMDesignSolutions(HfssDesignSolutions): pass +class HfssDTDesignSolutions(HfssDesignSolutions): + pass class HfssQ3DDesignSolutions(HfssDesignSolutions): pass diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 58cb5cb..3d20395 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -319,18 +319,20 @@ def connect_setup(self): if len(setup_names) == 0: logger.warning('\tNo design setup detected.') + setup = None if self.design.solution_type == 'Eigenmode': logger.warning('\tCreating eigenmode default setup.') setup = self.design.create_em_setup() - self.setup_name = setup.name elif self.design.solution_type == 'DrivenModal': - logger.warning('\tCreating drivenmodal default setup.') + logger.warning('\tCreating driven modal default setup.') setup = self.design.create_dm_setup() - self.setup_name = setup.name + elif self.design.solution_type == 'DrivenTerminal': + logger.warning('\tCreating driven terminal default setup.') + setup = self.design.create_dt_setup() elif self.design.solution_type == 'Q3D': logger.warning('\tCreating Q3D default setup.') setup = self.design.create_q3d_setup() - self.setup_name = setup.name + self.setup_name = setup.name else: self.setup_name = setup_names[0] From e610dc4d45321c613cbba3391e8c188a67100982 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Wed, 20 Jul 2022 18:35:00 +0300 Subject: [PATCH 107/125] Add single surface `DistributedAnalysis.get_Qsurface` (#123) --- pyEPR/core_distributed_analysis.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index c9a91ef..8864abe 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -884,10 +884,10 @@ def get_Qdielectric(self, dielectric, mode, variation, U_E=None): str(mode)+' = ' + str(p_dielectric)) return pd.Series(Qdielectric) - def get_Qsurface_all(self, mode, variation, U_E=None): + def get_Qsurface(self, mode, variation, name, U_E=None): ''' - calculate the contribution to Q of a dielectric layer of dirt on all surfaces - set the dirt thickness and loss tangent in the config file + Calculate the contribution to Q of a dielectric layer of dirt on a given surface. + Set the dirt thickness and loss tangent in the config file ref: http://arxiv.org/pdf/1509.01854.pdf ''' if U_E is None: @@ -896,16 +896,13 @@ def get_Qsurface_all(self, mode, variation, U_E=None): Qsurf = OrderedDict() print('Calculating Qsurface for mode ' + str(mode) + ' (' + str(mode) + '/' + str(self.n_modes-1) + ')') -# A = self.fields.Mag_E**2 -# A = A.integrate_vol(name='AllObjects') -# U_surf = A.evaluate(lv=lv) calcobject = CalcObject([], self.setup) vecE = calcobject.getQty("E") A = vecE B = vecE.conj() A = A.dot(B) A = A.real() - A = A.integrate_surf(name='AllObjects') + A = A.integrate_surf(name=name) U_surf = A.evaluate(lv=lv) U_surf *= config.dissipation.th*epsilon_0*config.dissipation.eps_r p_surf = U_surf/U_E @@ -914,6 +911,14 @@ def get_Qsurface_all(self, mode, variation, U_E=None): print('p_surf'+'_'+str(mode)+' = ' + str(p_surf)) return pd.Series(Qsurf) + def get_Qsurface_all(self, mode, variation, U_E=None): + ''' + Calculate the contribution to Q of a dielectric layer of dirt on all surfaces. + Set the dirt thickness and loss tangent in the config file + ref: http://arxiv.org/pdf/1509.01854.pdf + ''' + return self.get_Qsurface(mode, variation, name='AllObjects', U_E=U_E) + def calc_Q_external(self, variation, freq_GHz, U_E = None): ''' Calculate the coupling Q of mode m with each port p From 955e7298444ca59f4db6ad8eddda96aee85b318c Mon Sep 17 00:00:00 2001 From: "Patrick J. O'Brien" Date: Wed, 20 Jul 2022 09:35:40 -0600 Subject: [PATCH 108/125] Fix EPR sign bug that caused EPR to always be positive (#125) --- pyEPR/core_distributed_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 8864abe..25526c7 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -742,7 +742,7 @@ def calc_current_using_line_voltage(self, variation: str, junc_line_name: str, "E").real().integrate_line_tangent(name=junc_line_name) v_calc_imag = CalcObject([], self.setup).getQty( "E").imag().integrate_line_tangent(name=junc_line_name) - V = np.sqrt(v_calc_real.evaluate(lv=lv)**2 + + V = np.sign(v_calc_real) * np.sqrt(v_calc_real.evaluate(lv=lv)**2 + v_calc_imag.evaluate(lv=lv)**2) # Get frequency From c66fa0b7deadb0bec22368caf5d6caf994167dd3 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 20 Jul 2022 11:36:34 -0400 Subject: [PATCH 109/125] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index e92a1a8..14e0e30 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( ## Who uses pyEPR? * Yale University, Michel Devoret lab [QLab](https://qulab.eng.yale.edu/), CT, USA * Yale University, Rob Schoelkopf lab [RSL](https://rsl.yale.edu/), CT, USA -* [IBM Quantum](https://www.ibm.com/quantum-computing/) +* [IBM Quantum](https://www.ibm.com/quantum-computing/) and IBM's Qiskit Metal * [QUANTIC](https://team.inria.fr/quantic/people.html#) (QUANTUM INFORMATION CIRCUITS), PARISINRIA, ENS, MINES PARISTECH, UPMC, CNRS. Groups of Zaki Leghtas and team. France * [Quantum Circuit Group](http://www.physinfo.fr/) Benjamin Huard, Ecole Normale Supérieure de Lyon, France * Emanuel Flurin, CEA Saclay, France @@ -55,6 +55,7 @@ Welcome to pyEPR :beers:!      (see [arXiv:2010.00620]( * Alice&Bob, France * Centre for Quantum Technologies / Qcrew * Quantum Device Lab ETHZ; Andreas Wallraff +* Bleximo * ... and many more! (Please e-mail `zlatko.minev@aya.yale.edu` with updates.) From 027c355981bda1d189b9342c1b9c61b617ce54ae Mon Sep 17 00:00:00 2001 From: "Patrick J. O'Brien" Date: Wed, 20 Jul 2022 10:20:54 -0600 Subject: [PATCH 110/125] Update version to 0.8.5.6 for EPR sign bug fix (#126) --- pyEPR/__init__.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index b888e54..e161546 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.5 +@version: 0.8.5.6 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -86,7 +86,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.5" +__version__ = "0.8.5.6" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/setup.py b/setup.py index 890c9ab..b4131b2 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.5', + version='0.8.5.6', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 570b8d813cb469c53c6f51970b1064807bafbad8 Mon Sep 17 00:00:00 2001 From: Patrick O'Brien Date: Fri, 22 Jul 2022 12:17:10 -0400 Subject: [PATCH 111/125] Fix EPR bug and bump to version 0.8.5.7 --- pyEPR/__init__.py | 2 +- pyEPR/core_distributed_analysis.py | 2 +- setup.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index e161546..aff8626 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -86,7 +86,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.6" +__version__ = "0.8.5.7" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 25526c7..7af2a25 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -742,7 +742,7 @@ def calc_current_using_line_voltage(self, variation: str, junc_line_name: str, "E").real().integrate_line_tangent(name=junc_line_name) v_calc_imag = CalcObject([], self.setup).getQty( "E").imag().integrate_line_tangent(name=junc_line_name) - V = np.sign(v_calc_real) * np.sqrt(v_calc_real.evaluate(lv=lv)**2 + + V = np.sign(v_calc_real.evaluate()) * np.sqrt(v_calc_real.evaluate(lv=lv)**2 + v_calc_imag.evaluate(lv=lv)**2) # Get frequency diff --git a/setup.py b/setup.py index b4131b2..1e83412 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.6', + version='0.8.5.7', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From ca1509486f626c3137632cc1e9bd146e74b0dcf5 Mon Sep 17 00:00:00 2001 From: Patrick O'Brien Date: Fri, 22 Jul 2022 15:13:15 -0400 Subject: [PATCH 112/125] Pass lv=lv to evaluate --- pyEPR/core_distributed_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 7af2a25..12154f5 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -742,7 +742,7 @@ def calc_current_using_line_voltage(self, variation: str, junc_line_name: str, "E").real().integrate_line_tangent(name=junc_line_name) v_calc_imag = CalcObject([], self.setup).getQty( "E").imag().integrate_line_tangent(name=junc_line_name) - V = np.sign(v_calc_real.evaluate()) * np.sqrt(v_calc_real.evaluate(lv=lv)**2 + + V = np.sign(v_calc_real.evaluate(lv=lv)) * np.sqrt(v_calc_real.evaluate(lv=lv)**2 + v_calc_imag.evaluate(lv=lv)**2) # Get frequency From 6b6dcdd3fb1060b79e8064f3dcd6e64925d26ded Mon Sep 17 00:00:00 2001 From: Patrick O'Brien Date: Fri, 22 Jul 2022 15:57:30 -0400 Subject: [PATCH 113/125] Update version to 0.8.5.7 in __init__.py --- pyEPR/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index aff8626..5355abc 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.6 +@version: 0.8.5.7 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR From 416c86837df05c140004ee66b2586fcaf72fbf3d Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Thu, 4 Aug 2022 16:55:45 +0300 Subject: [PATCH 114/125] Variation support to `calc_p_electric_volume` (#132) --- pyEPR/core_distributed_analysis.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 12154f5..5df07a9 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -657,12 +657,13 @@ def calc_energy_magnetic(self, def calc_p_electric_volume(self, name_dielectric3D, relative_to='AllObjects', + variation=None, E_total=None ): r''' - Calculate the dielectric energy-participatio ratio + Calculate the dielectric energy-participation ratio of a 3D object (one that has volume) relative to the dielectric energy of - a list of object objects. + a list of objects. This is as a function relative to another object or all objects. @@ -670,18 +671,17 @@ def calc_p_electric_volume(self, that might be stored in any lumped elements or lumped capacitors. Returns: - --------- ℰ_object/ℰ_total, (ℰ_object, _total) ''' if E_total is None: logger.debug('Calculating ℰ_total') - ℰ_total = self.calc_energy_electric(obj=relative_to) + ℰ_total = self.calc_energy_electric(obj=relative_to, variation=variation) else: ℰ_total = E_total logger.debug('Calculating ℰ_object') - ℰ_object = self.calc_energy_electric(obj=name_dielectric3D) + ℰ_object = self.calc_energy_electric(obj=name_dielectric3D, variation=variation) return ℰ_object/ℰ_total, (ℰ_object, ℰ_total) From 490886a5d89eaa12979b04fc3955749a0e5814dd Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Thu, 4 Aug 2022 16:56:19 +0300 Subject: [PATCH 115/125] Support synchronised variables in `Optimetrics.create_setup` (#130) * Fix code block formattings * Implement synchronised sweep support in Optimetrics --- pyEPR/ansys.py | 123 +++++++++++++++++++++++++++++-------------------- 1 file changed, 72 insertions(+), 51 deletions(-) diff --git a/pyEPR/ansys.py b/pyEPR/ansys.py index 1993cbe..911dcba 100644 --- a/pyEPR/ansys.py +++ b/pyEPR/ansys.py @@ -1730,9 +1730,11 @@ def create_report(self, pass_name: AdaptivePass, LastAdaptive Example - ------------------------------------------------------ + ------- Example plot for a single variation all pass converge of mode freq - .. code-block python + + .. code-block:: python + ycomp = [f"re(Mode({i}))" for i in range(1,1+epr_hfss.n_modes)] params = ["Pass:=", ["All"]]+variation setup.create_report("Freq. vs. pass", "Pass", ycomp, params, pass_name='AdaptivePass') @@ -1863,11 +1865,13 @@ class Optimetrics(COMWrapper): Optimetrics script commands executed by the "Optimetrics" module. Example use: - .. code-block python - opti = Optimetrics(pinfo.design) - names = opti.get_setup_names() - print('Names of optimetrics: ', names) - opti.solve_setup(names[0]) + + .. code-block:: python + + opti = Optimetrics(pinfo.design) + names = opti.get_setup_names() + print('Names of optimetrics: ', names) + opti.solve_setup(names[0]) Note that running optimetrics requires the license for Optimetrics by Ansys. """ @@ -1910,18 +1914,24 @@ def create_setup(self, solve_with_copied_mesh_only=True, setup_type='parametric'): """ - Inserts a new parametric setup of one variable. Either with sweep + Inserts a new parametric setup of one variable. Either with sweep definition or from file. + + *Synchronized* sweeps (more than one variable changing at once) + can be implemented by giving a list of variables to ``variable`` + and corresponding lists to ``swp_params`` and ``swp_type``. + The lengths of the sweep types should match (excluding single value). Corresponds to ui access: - Right-click the Optimetrics folder in the project tree, and then click + Right-click the Optimetrics folder in the project tree, and then click Add> Parametric on the shortcut menu. Ansys provides six sweep definitions types specified using the swp_type variable. Sweep type definitions: - - 'single_value' + + - 'single_value' Specify a single value for the sweep definition. - 'linear_step' Specify a linear range of values with a constant step size. @@ -1932,28 +1942,28 @@ def create_setup(self, Specify a logarithmic (base 10) series of values, and the number of values to calculate in each decade. - 'octave_count' - Specify a logarithmic (base 2) series of values, and the number of + Specify a logarithmic (base 2) series of values, and the number of values to calculate in each octave. - 'exponential_count' - Specify an exponential (base e) series of values, and the number of + Specify an exponential (base e) series of values, and the number of values to calculate. For swp_type='single_value' swp_params is the single value. - For swp_type='linear_step' swp_params is start, stop, step: + For swp_type='linear_step' swp_params is start, stop, step: swp_params = ("12.8nH", "13.6nH", "0.2nH") All other types swp_params is start, stop, count: swp_params = ("12.8nH", "13.6nH", 4) - The definition of count varies amongst the available types. + The definition of count varies amongst the available types. For Decade count and Octave count, the Count value specifies the number of points to calculate in every decade or octave. For Exponential count, - the Count value is the total number of points. The total number of + the Count value is the total number of points. The total number of points includes the start and stop values. For parametric from file, setup_type='parametric_file', pass in a file - name and path to swp_params like "C:\\test.csv" or "C:\\test.txt" for + name and path to swp_params like "C:\\test.csv" or "C:\\test.txt" for example. Example csv formatting: @@ -1962,7 +1972,7 @@ def create_setup(self, 2,9.7nH 3,10.2nH - See Ansys documentation for additional formatting instructions. + See Ansys documentation for additional formatting instructions. """ setup_name = setup_name or self.design.get_setup_names()[0] print( @@ -1970,40 +1980,51 @@ def create_setup(self, ) if setup_type == 'parametric': - valid_swp_types = ['single_value', 'linear_step', 'linear_count', - 'decade_count', 'octave_count', 'exponential_count'] - if swp_type not in valid_swp_types: + type_map = { + 'linear_count': 'LINC', + 'decade_count': 'DEC', + 'octave_count': 'OCT', + 'exponential_count': 'ESTP', + } + valid_swp_types = {'single_value', 'linear_step'} | set(type_map.keys()) + + if isinstance(variable, Iterable) and not isinstance(variable, str): + # synchronized sweep, check that data is in correct format + assert len(swp_params) == len(swp_type) == len(variable), \ + 'Incorrect swp_params or swp_type format for synchronised sweep.' + synchronize = True + else: + # convert all to lists as we can reuse same code for synchronized + swp_type = [swp_type] + swp_params = [swp_params] + variable = [variable] + synchronize = False + + if any(e not in valid_swp_types for e in swp_type): raise NotImplementedError() else: - if swp_type == 'single_value': - # Single takes string of single variable no swp_type_name - swp_str = f"{swp_params}" + swp_str = list() + for i, e in enumerate(swp_type): + if e == 'single_value': + # Single takes string of single variable no swp_type_name + swp_str.append(f"{swp_params[i]}") + else: + # correct number of inputs + assert len(swp_params[i]) == 3, "Incorrect number of sweep parameters." - else: - # correct number of inputs - assert len(swp_params) == 3, "Incorrect number of sweep parameters." + # Not checking for compatible unit types + if e == 'linear_step': + swp_type_name = "LIN" + else: + # counts needs to be an integer number + assert isinstance(swp_params[i][2], int), "Count must be integer." + + swp_type_name = type_map[e] + + # prepare the string to pass to Ansys + swp_str.append(f"{swp_type_name} {swp_params[i][0]} {swp_params[i][1]} {swp_params[i][2]}") - # Not checking for compatible unit types - if swp_type == 'linear_step': - swp_type_name = "LIN" - else: - # counts needs to be an integer number - assert isinstance(swp_params[2], int), "Count must be integer." - - if swp_type == 'linear_count': - swp_type_name = "LINC" - elif swp_type == 'decade_count': - swp_type_name = "DEC" - elif swp_type == 'octave_count': - swp_type_name = "OCT" - elif swp_type == 'exponential_count': - swp_type_name = "ESTP" - - # prepare the string to pass to Ansys - swp_str = f"{swp_type_name} {swp_params[0]} {swp_params[1]} {swp_params[2]}" - - # talk with Ansys self._optimetrics.InsertSetup("OptiParametric", [ f"NAME:{name}", "IsEnabled:=", True, [ @@ -2017,14 +2038,14 @@ def create_setup(self, ], ["NAME:StartingPoint"], "Sim. Setups:=", [setup_name], [ "NAME:Sweeps", - [ - "NAME:SweepDefinition", "Variable:=", variable, "Data:=", - swp_str, "OffsetF1:=", False, "Synchronize:=", 0 - ] + *[[ + "NAME:SweepDefinition", "Variable:=", var_name, "Data:=", + swp, "OffsetF1:=", False, "Synchronize:=", int(synchronize) + ] for var_name, swp in zip(variable, swp_str)] ], ["NAME:Sweep Operations"], ["NAME:Goals"] ]) elif setup_type == 'parametric_file': - # Uses the file name as the swp_params + # Uses the file name as the swp_params filename = swp_params self._optimetrics.ImportSetup("OptiParametric", From 5e5e0e161477206e69e06f3175c7c08d06a86110 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Wed, 10 Aug 2022 21:16:36 +0300 Subject: [PATCH 116/125] Don't extend `hfss_report_full_convergence` vertically (#135) --- pyEPR/core_distributed_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 5df07a9..ab3f7b1 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -1625,7 +1625,7 @@ def hfss_report_full_convergence(self, fig=None, _display=True): convergence_t = self.get_convergence(variation=variation) convergence_f = self.hfss_report_f_convergence(variation=variation) - axs[0].set_ylabel(variation_labels, fontsize='large') # add variation labels to y-axis of first plot + axs[0].set_ylabel(variation_labels.replace(' ', '\n')) # add variation labels to y-axis of first plot ax0t = axs[1].twinx() plot_convergence_f_vspass(axs[0], convergence_f) From f2ff1c950a5fbbf2f54eaba985ddbae5dc3831e2 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Tue, 8 Nov 2022 04:14:45 +0200 Subject: [PATCH 117/125] Support loss tangents for different surfaces in `do_EPR_analysis` (#144) --- pyEPR/_config_user.py | 16 +++++++++++++ pyEPR/core_distributed_analysis.py | 38 +++++++++++++++--------------- pyEPR/core_quantum_analysis.py | 12 ++++++---- pyEPR/project_info.py | 6 +++-- 4 files changed, 47 insertions(+), 25 deletions(-) diff --git a/pyEPR/_config_user.py b/pyEPR/_config_user.py index 0ed2961..932bcd9 100644 --- a/pyEPR/_config_user.py +++ b/pyEPR/_config_user.py @@ -49,6 +49,22 @@ # units: unitless, since this is tan(delta) tan_delta_surf=1e-3, + ################################################## + # Surface object specific dielectric properties. + # These will override ones above when applicable + dielectric_surfaces=Dict( + trace=Dict( + tan_delta_surf=0.001, + th=5e-9, + eps_r=10 + ), + gap=Dict( + tan_delta_surf=0.001, + th=2e-9, + eps_r=10 + ) + ), + ################################################## # Thin-film surface loss # units: Ohms diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index ab3f7b1..2013210 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -878,13 +878,11 @@ def get_Qdielectric(self, dielectric, mode, variation, U_E=None): U_dielectric = self.calc_energy_electric(variation, obj=dielectric) p_dielectric = U_dielectric/U_E # TODO: Update make p saved sep. and get Q for diff materials, indep. specify in pinfo - Qdielectric['Qdielectric_'+dielectric+'_' + - str(mode)] = 1/(p_dielectric*config.dissipation.tan_delta_sapp) - print('p_dielectric'+'_'+dielectric+'_' + - str(mode)+' = ' + str(p_dielectric)) + Qdielectric['Qdielectric_' + dielectric] = 1/(p_dielectric*config.dissipation.tan_delta_sapp) + print('p_dielectric'+'_'+dielectric+'_' + str(mode) + ' = ' + str(p_dielectric)) return pd.Series(Qdielectric) - def get_Qsurface(self, mode, variation, name, U_E=None): + def get_Qsurface(self, mode, variation, name, U_E=None, material_properties=None): ''' Calculate the contribution to Q of a dielectric layer of dirt on a given surface. Set the dirt thickness and loss tangent in the config file @@ -892,10 +890,15 @@ def get_Qsurface(self, mode, variation, name, U_E=None): ''' if U_E is None: U_E = self.calc_energy_electric(variation) + if material_properties is None: + material_properties = {} + th = material_properties.get('th', config.dissipation.th) + eps_r = material_properties.get('eps_r', config.dissipation.eps_r) + tan_delta_surf = material_properties.get('tan_delta_surf', config.dissipation.tan_delta_surf) + lv = self._get_lv(variation) Qsurf = OrderedDict() - print('Calculating Qsurface for mode ' + str(mode) + - ' (' + str(mode) + '/' + str(self.n_modes-1) + ')') + print(f'Calculating Qsurface {name} for mode ({mode}/{self.n_modes-1})') calcobject = CalcObject([], self.setup) vecE = calcobject.getQty("E") A = vecE @@ -904,11 +907,10 @@ def get_Qsurface(self, mode, variation, name, U_E=None): A = A.real() A = A.integrate_surf(name=name) U_surf = A.evaluate(lv=lv) - U_surf *= config.dissipation.th*epsilon_0*config.dissipation.eps_r + U_surf *= th * epsilon_0 * eps_r p_surf = U_surf/U_E - Qsurf['Qsurf_'+str(mode)] = 1 / \ - (p_surf*config.dissipation.tan_delta_surf) - print('p_surf'+'_'+str(mode)+' = ' + str(p_surf)) + Qsurf[f'Qsurf_{name}'] = 1 / (p_surf * tan_delta_surf) + print(f'p_surf_{name}_{mode} = {p_surf}') return pd.Series(Qsurf) def get_Qsurface_all(self, mode, variation, U_E=None): @@ -1308,17 +1310,15 @@ def do_EPR_analysis(self, dielectric, mode, variation, self.U_E)) # get Q surface - if self.pinfo.dissipative['resistive_surfaces']: - if self.pinfo.dissipative['resistive_surfaces'] == 'all': + if self.pinfo.dissipative['dielectric_surfaces']: + if self.pinfo.dissipative['dielectric_surfaces'] == 'all': sol = sol.append( self.get_Qsurface_all(mode, variation, self.U_E)) else: - raise NotImplementedError( - "Join the team, by helping contribute this piece of code.") - - if self.pinfo.dissipative['resistive_surfaces'] is not None: - raise NotImplementedError( - "Join the team, by helping contribute this piece of code.") + for surface, properties in self.pinfo.dissipative['dielectric_surfaces'].items(): + sol = sol.append( + self.get_Qsurface(mode, variation, surface, self.U_E, properties) + ) SOL[mode] = sol diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 9adc18b..3b9b5af 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -52,7 +52,7 @@ class HamiltonianResultsContainer(OrderedDict): def __init__(self, dict_file=None, data_dir=None): """ input: - dict file - 1. ethier None to create an empty results hamiltonian as + dict file - 1. either None to create an empty results hamiltonian as as was done in the original code 2. or a string with the name of the file where the file of the @@ -711,12 +711,15 @@ def analyze_variation(self, try: result['Q_coupling'] = self.Qm_coupling[variation][self.Qm_coupling[variation].columns[junctions]][modes]#TODO change the columns to junctions except: - result['Q_coupling'] = self.Qm_coupling[variation] + result['Q_coupling'] = self.Qm_coupling[variation] try: result['Qs'] = self.Qs[variation][self.PM[variation].columns[junctions]][modes] #TODO change the columns to junctions except: - result['Qs'] = self.Qs[variation][modes] + result['Qs'] = self.Qs[variation][modes] + + result['sol'] = self.sols[variation] + result['fock_trunc'] = fock_trunc result['cos_trunc'] = cos_trunc @@ -733,7 +736,8 @@ def analyze_variation(self, def full_report_variations(self, var_list: list=None): """see full_variation_report""" - if var_list is None: var_list =self.variations + if var_list is None: + var_list = self.variations for variation in var_list: self.full_variation_report(variation) diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index 3d20395..f6757dd 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -120,10 +120,12 @@ def __setitem__(self, key, value): # --- check valid inputs --- if not (key in diss_opt or key == 'pinfo'): raise ValueError(f"No such parameter {key}") - if key != 'pinfo' and (not isinstance(value, list) or \ + if key != 'pinfo' and (not isinstance(value, (list, dict)) or \ not all(isinstance(x, str) for x in value)) and (value != None): raise ValueError(f'dissipative[\'{key}\'] must be a list of strings ' \ - 'containing names of models in the project!') + 'containing names of models in the project or dictionary of strings of models containing ' \ + 'material loss properties!' + ) if key != 'pinfo' and hasattr(self['pinfo'], 'design'): for x in value: if x not in self['pinfo'].get_all_object_names(): From aa90e8be2ea4a4134104419451041ffc4f79cdc0 Mon Sep 17 00:00:00 2001 From: Zach Parrott <51793790+zachparrott@users.noreply.github.com> Date: Thu, 17 Nov 2022 15:47:01 -0700 Subject: [PATCH 118/125] Fix #145 .gitignore (#146) --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 08f0b6c..53ea855 100644 --- a/.gitignore +++ b/.gitignore @@ -102,6 +102,7 @@ ENV/ # exclude config pyEPR/config.py +pyEPR/_user_config.py __src__not_incl/ pyEPR/.vscode/ pyEPR/.pylintrc From 233cefe41268c003023960c96206f7f4149835af Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Fri, 16 Dec 2022 23:06:44 +0200 Subject: [PATCH 119/125] Add dtype to empty Pandas Series (#139) --- pyEPR/core_distributed_analysis.py | 10 +++++----- pyEPR/project_info.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 2013210..66fb774 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -932,7 +932,7 @@ def calc_Q_external(self, variation, freq_GHz, U_E = None): ''' if U_E is None: U_E = self.calc_energy_electric(variation) - Qp = pd.Series({}) + Qp = pd.Series({}, dtype='float64') freq = freq_GHz * 1e9 # freq in Hz for port_nm, port in self.pinfo.ports.items(): @@ -980,7 +980,7 @@ def calc_p_junction(self, variation, U_H, U_E, Ljs, Cjs): method = self.pinfo.options.method_calc_P_mj I_peak_ = {} V_peak_ = {} - Sj = pd.Series({}) + Sj = pd.Series({}, dtype='float64') for j_name, j_props in self.pinfo.junctions.items(): logger.debug(f'Calculating participations for {(j_name, j_props)}') Lj = Ljs[j_name] @@ -1100,8 +1100,8 @@ def get_junctions_L_and_C(self, variation: str): # for all variations and concat raise NotImplementedError() # TODO else: - Ljs = pd.Series({}) - Cjs = pd.Series({}) + Ljs = pd.Series({}, dtype='float64') + Cjs = pd.Series({}, dtype='float64') for junc_name, val in self.pinfo.junctions.items(): # junction nickname _variables = self._hfss_variables[variation] @@ -1244,7 +1244,7 @@ def do_EPR_analysis(self, self.set_mode(mode) # Get HFSS solved frequencies - _Om = pd.Series({}) + _Om = pd.Series({}, dtype='float64') temp_freq = freqs_bare_GHz[mode] _Om['freq_GHz'] = temp_freq # freq Om[mode] = _Om diff --git a/pyEPR/project_info.py b/pyEPR/project_info.py index f6757dd..412e2ff 100644 --- a/pyEPR/project_info.py +++ b/pyEPR/project_info.py @@ -238,7 +238,7 @@ def save(self): return dict( pinfo=pd.Series(get_instance_vars(self, self._Forbidden)), dissip=pd.Series(self.dissipative.data()), - options=pd.Series(get_instance_vars(self.options)), + options=pd.Series(get_instance_vars(self.options), dtype='object'), junctions=pd.DataFrame(self.junctions), ports=pd.DataFrame(self.ports), ) From 017ea780ca3d4afdd848c4625d4b73625abe25d8 Mon Sep 17 00:00:00 2001 From: Niko Savola Date: Tue, 20 Dec 2022 02:27:55 +0200 Subject: [PATCH 120/125] Refactor `DataFrame.append` to `pd.concat` (#138) --- pyEPR/core_distributed_analysis.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 66fb774..43419f4 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -1301,24 +1301,20 @@ def do_EPR_analysis(self, # get seam Q if self.pinfo.dissipative['seams']: for seam in self.pinfo.dissipative['seams']: - sol = sol.append(self.get_Qseam(seam, mode, variation, self.U_H)) + sol = pd.concat([sol, self.get_Qseam(seam, mode, variation, self.U_H)]) # get Q dielectric if self.pinfo.dissipative['dielectrics_bulk']: for dielectric in self.pinfo.dissipative['dielectrics_bulk']: - sol = sol.append(self.get_Qdielectric( - dielectric, mode, variation, self.U_E)) + sol = pd.concat([sol, self.get_Qdielectric(dielectric, mode, variation, self.U_E)]) # get Q surface if self.pinfo.dissipative['dielectric_surfaces']: if self.pinfo.dissipative['dielectric_surfaces'] == 'all': - sol = sol.append( - self.get_Qsurface_all(mode, variation, self.U_E)) + sol = pd.concat([sol, self.get_Qsurface_all(mode, variation, self.U_E)]) else: for surface, properties in self.pinfo.dissipative['dielectric_surfaces'].items(): - sol = sol.append( - self.get_Qsurface(mode, variation, surface, self.U_E, properties) - ) + sol = pd.concat([sol, self.get_Qsurface(mode, variation, surface, self.U_E, properties)]) SOL[mode] = sol From e3bbf532c7cae7228f0f97bea29dfd0792582837 Mon Sep 17 00:00:00 2001 From: Jagatheesan Jack Date: Mon, 17 Apr 2023 02:52:50 +0200 Subject: [PATCH 121/125] Replace usage of np.float with float (#147) (#150) The epr_numerical_diagonalization function uses np.float which has been deprecated in Numpy. Refer https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations The continued usage of np.float yields the following error in the epr_numerical_diaganolization function when later versions of Numpy is used. "AttributeError: module 'numpy' has no attribute 'float'." This commit replace np.float with float to solve this error. --- pyEPR/calcs/back_box_numeric.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/calcs/back_box_numeric.py b/pyEPR/calcs/back_box_numeric.py index 6d68f1e..8dda6ad 100644 --- a/pyEPR/calcs/back_box_numeric.py +++ b/pyEPR/calcs/back_box_numeric.py @@ -63,7 +63,7 @@ def epr_numerical_diagonalization(freqs, Ljs, ϕzpf, assert(all(Ljs < 1E-3) ), "Please input the inductances in Henries. \N{nauseated face}" - Hs = black_box_hamiltonian(freqs * 1E9, Ljs.astype(np.float), fluxQ*ϕzpf, + Hs = black_box_hamiltonian(freqs * 1E9, Ljs.astype(float), fluxQ*ϕzpf, cos_trunc, fock_trunc, individual=use_1st_order, non_linear_potential = non_linear_potential) f_ND, χ_ND, _, _ = make_dispersive( From 148edc0a9b6fc151eeac7d3d20d5ba1139afccf3 Mon Sep 17 00:00:00 2001 From: Clara Fontaine <42681983+clarayfontaine@users.noreply.github.com> Date: Wed, 26 Apr 2023 04:20:00 +0800 Subject: [PATCH 122/125] Correctly select a few modes in analyze_variation (#149) * Fix mode selection in analyze_variation Addressing issue #148: analyze_variation incorrectly chooses Pj, Sj, Om, PHI_zpf when passed a subset of modes * Remove redundant frequency selection The frequencies are already correctly selected outside of this if-statement, so this is not necessary --- pyEPR/core_quantum_analysis.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/pyEPR/core_quantum_analysis.py b/pyEPR/core_quantum_analysis.py index 3b9b5af..1b936c5 100644 --- a/pyEPR/core_quantum_analysis.py +++ b/pyEPR/core_quantum_analysis.py @@ -664,11 +664,10 @@ def analyze_variation(self, PJ_cap = PJ_cap[:, junctions] if modes is not None: - freqs_hfss = freqs_hfss[range(len(self.modes[variation])), ] - PJ = PJ[range(len(modes)), :] - SJ = SJ[range(len(modes)), :] - Om = Om[range(len(modes)), :][:, range(len(modes))] - PHI_zpf = PHI_zpf[range(len(modes)), :] + PJ = PJ[modes, :] + SJ = SJ[modes, :] + Om = Om[modes, :][:, modes] + PHI_zpf = PHI_zpf[modes, :] PJ_cap = PJ_cap[:, junctions] # Analytic 4-th order From 0abdc881ea8aa403427a8c454f0c72a25d83bd73 Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 14 Jun 2023 18:01:58 -0400 Subject: [PATCH 123/125] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 1e83412..52361c4 100644 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ setup( name='pyEPR-quantum', - version='0.8.5.7', + version='0.9.0', description=doclines[0], long_description=long_description, long_description_content_type="text/markdown", From 69ce057d005e4bf4095c85daf2c313801c26749e Mon Sep 17 00:00:00 2001 From: Zlatko Minev Date: Wed, 14 Jun 2023 18:02:23 -0400 Subject: [PATCH 124/125] Update __init__.py --- pyEPR/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyEPR/__init__.py b/pyEPR/__init__.py index 5355abc..34253b7 100644 --- a/pyEPR/__init__.py +++ b/pyEPR/__init__.py @@ -59,7 +59,7 @@ @author: Zlatko Minev, Zaki Leghas, ... and the pyEPR team @site: https://github.com/zlatko-minev/pyEPR @license: "BSD-3-Clause" -@version: 0.8.5.7 +@version: 0.9.0 @maintainer: Zlatko K. Minev and Asaf Diringer @email: zlatko.minev@aya.yale.edu @url: https://github.com/zlatko-minev/pyEPR @@ -86,7 +86,7 @@ "Will Livingston", "Steven Touzard" ] __license__ = "BSD-3-Clause" -__version__ = "0.8.5.7" +__version__ = "0.9.0" __maintainer__ = "Zlatko K. Minev and Asaf Diringer" __email__ = "zlatko.minev@aya.yale.edu" __url__ = r'https://github.com/zlatko-minev/pyEPR' From 4086a8ddec5ff5637fe42e5758eea1d2559a1571 Mon Sep 17 00:00:00 2001 From: Christian Kraglund Andersen <80969364+AndersenQubitLab@users.noreply.github.com> Date: Thu, 9 Nov 2023 16:32:16 +0100 Subject: [PATCH 125/125] Update core_distributed_analysis.py (#152) out-commenting a property not supported in latest version. Only visual impact --- pyEPR/core_distributed_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyEPR/core_distributed_analysis.py b/pyEPR/core_distributed_analysis.py index 43419f4..5af0181 100644 --- a/pyEPR/core_distributed_analysis.py +++ b/pyEPR/core_distributed_analysis.py @@ -1575,7 +1575,7 @@ def hfss_report_f_convergence(self, variation='0', save_csv=True): # Properties of lines curves = [f"{report_name}:re(Mode({i})):Curve1" for i in range( 1, 1+self.n_modes)] - set_property(report, 'Attributes', curves, 'Line Width', 3) + # set_property(report, 'Attributes', curves, 'Line Width', 3) set_property(report, 'Scaling', f"{report_name}:AxisY1", 'Auto Units', False) set_property(report, 'Scaling', f"{report_name}:AxisY1", 'Units', 'g')