Showing preview only (356K chars total). Download the full file or copy to clipboard to get everything.
Repository: tomshaffner/PiThermalCam
Branch: master
Commit: cdee56f18ee7
Files: 25
Total size: 343.5 KB
Directory structure:
gitextract_801zmmcq/
├── .github/
│ └── workflows/
│ ├── publish_on_release_update.yml
│ └── publish_to_testpypi_on_setup_change.yml
├── .gitignore
├── LICENSE
├── MANIFEST.in
├── README.md
├── examples/
│ ├── cam_test.py
│ ├── live_video.py
│ └── web_server.py
├── icons/
│ ├── MatPlotLib Thermal Camera
│ ├── Run Flask Thermal Camera
│ └── Run OpenCV Thermal Camera
├── pithermalcam/
│ ├── __init__.py
│ ├── pi_therm_cam.py
│ ├── templates/
│ │ └── index.html
│ └── web_server.py
├── requirements.txt
├── requirements_without_opencv.txt
├── saved_snapshots/
│ └── note.txt
├── sequential_versions/
│ ├── Method Comparison.ipynb
│ ├── matplotlib_therm_cam.py
│ ├── opencv_therm_cam.py
│ └── sequential_config.ini
├── setup.cfg
└── setup.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/publish_on_release_update.yml
================================================
# This is a basic workflow to help you get started with Actions
name: Publish distribution 📦 to PyPI 🐍 on new release
# Controls when the action will run.
on:
# Triggers the workflow on push or pull request events but only for the master branch
release:
types: [created]
# 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-n-publish:
name: Build and publish Python 🐍 distribution 📦 to PyPI
# The type of runner that the job will run on
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.6] #, 3.7, 3.8, 3.9] # These are redundant and cause errors on upload since it's a version-independent build
# 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@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
# You can test your matrix by printing the current Python version
- name: Display Python version
run: python -c "import sys; print(sys.version)"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install flake8 pytest setuptools wheel twine
# Don't think I actually need to install dependencies, and opencv would be quite large anyway
# if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings.
flake8 . --count --exit-zero --statistics
# Included for the day when tests are added to the project
# - name: Test with pytest
# run: |
# pytest
- name: Install pypa/build
run: >-
python -m
pip install
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
with:
password: ${{ secrets.PROD_PYPI_API_TOKEN }}
================================================
FILE: .github/workflows/publish_to_testpypi_on_setup_change.yml
================================================
# This is a basic workflow to help you get started with Actions
name: Publish distribution 📦 to TestPyPI 🐍
# Controls when the action will run.
on:
push:
branches:
- master
paths:
- 'setup.py'
# 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-n-publish:
name: Build and publish Python 🐍 distribution 📦 to TestPyPI
# The type of runner that the job will run on
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.6] #, 3.7, 3.8, 3.9] # These are redundant and cause errors on upload since it's a version-independent build
# 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@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
# You can test your matrix by printing the current Python version
- name: Display Python version
run: python -c "import sys; print(sys.version)"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install flake8 pytest setuptools wheel twine
# Don't think I actually need to install dependencies, and opencv would be quite large anyway
# if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings.
flake8 . --count --exit-zero --statistics
# Included for the day when tests are added to the project
# - name: Test with pytest
# run: |
# pytest
- name: Install pypa/build
run: >-
python -m
pip install
build
--user
- name: Build a binary wheel and a source tarball
run: >-
python -m
build
--sdist
--wheel
--outdir dist/
- name: Publish distribution 📦 to Test PyPI
uses: pypa/gh-action-pypi-publish@master
with:
password: ${{ secrets.TEST_PYPI_API_TOKEN }}
repository_url: https://test.pypi.org/legacy/
================================================
FILE: .gitignore
================================================
# These are some examples of commonly ignored file patterns.
# You should customize this list as applicable to your project.
# Learn more about .gitignore:
# https://www.atlassian.com/git/tutorials/saving-changes/gitignore
# Project specific
/saved_snapshots/*
!/saved_snapshots/note.txt
*.png
# Pycache
*__pycache__
# Node artifact files
node_modules/
dist/
# Compiled Java class files
*.class
# Compiled Python bytecode
*.py[cod]
# Log files
*.log
# Package files
*.jar
# Maven
target/
dist/
# JetBrains IDE
.idea/
# Unit test reports
TEST*.xml
# Generated by MacOS
.DS_Store
# Generated by Windows
Thumbs.db
# Applications
*.app
*.exe
*.war
# Large media files
*.mp4
*.tiff
*.avi
*.flv
*.mov
*.wmv
.vscode/
build/
pithermalcam.egg-info
================================================
FILE: LICENSE
================================================
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
to the community. It requires the operator of a network server to
provide the source code of the modified version running there to the
users of that server. Therefore, public use of a modified version, on
a publicly accessible server, gives the public access to the source
code of the modified version.
An older license, called the Affero General Public License and
published by Affero, was designed to accomplish similar goals. This is
a different license, not a version of the Affero GPL, but Affero has
released a new version of the Affero GPL which permits relicensing under
this license.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU Affero General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
================================================
FILE: MANIFEST.in
================================================
recursive-include pithermalcam/templates *
recursive-exclude lib/pithermalcam/templates *
*.ini
================================================
FILE: README.md
================================================
# README #
Documentation of the pithermalcam project and accompanying [PyPI package](https://pypi.org/project/pithermalcam/), which connects an MLX90640 thermal camera up to a Raspberry Pi. (Built on a Pi 4)
Setup based primarily off the articles [by Joshua Hrisko at MakerPortal](https://makersportal.com/blog/2020/6/8/high-resolution-thermal-camera-with-raspberry-pi-and-mlx90640) and by [Валерий Курышев’s under the name Walker2000 at Habr](https://habr.com/en/post/441050/) and flask pieces based on the work of [Adrian Rosebrock at pyimagesearch](https://www.pyimagesearch.com/2019/09/02/opencv-stream-video-to-web-browser-html-page/). Many thanks these people for their great work.
Full details for this project are available at https://tomshaffner.github.io/PiThermalCam/, including comprehensive hardware/software setup, install, usage instructions, and examples of potential results. A cursory overview for development purposes only is included here.
### Manual Install/Setup ###
This section discusses software setup only, and assumes you have hardware set up, the MLX90640 correctly wired up, I2C turned on, and the I2C baudrate increased to 400k. Refer to the above full details link for detailed instructions on both the hardware and software installs.
The below install is for manual operation of the library. For the Pip install from PyPi skip the below and simply execute `pip3 install pithermalcam`.
1. Install, using apt-get, the following items:
libatlas-base-dev
python-smbus
i2c-tools
2. Install remaining requirements using either:
a.
pip3 install the requirements.txt
or
b.
pip3 install the requirements_without_opencv.txt
Download, build, and install OpenCV locally (painstaking process, but results in more optimized code.).
Install cmapy using --no-deps pip3 flag to avoid installing OpenCV via pip3.
### Usage ###
#### Pip Library Install ####
If you install the library via Pip you can follow the usage shown in the examples folder to see usage instructions.
#### Clone library locally ####
If you wish to clone the library, execute this clone command:
`git clone -b master --single-branch https://github.com/tomshaffner/PiThermalCam.git`
This clones the code without cloning the pictures for the accompanying article (which take up excessive space).
To operate from here:
1. Copy the icons to your desktop and make executable.
or
2. Run the files directly in python3:
Run pithermalcam/web_server.py to set up a flask server and stream live video over the local network.
Run pithermalcam/pi_therm_cam.py to display the video feed onscreen.
Check sequential_versions folder for sequential running approaches that are easier to track/follow (i.e. sequential running rather than object-oriented classes). These are less robust, but can be easier to understand/track/edit, particularly for those coming from a scientific background. Again, refer to the link at top for a detailed discussion.
================================================
FILE: examples/cam_test.py
================================================
import pithermalcam as ptc
# Run a camera test, getting the mean temperature back
ptc.test_camera()
================================================
FILE: examples/live_video.py
================================================
import pithermalcam as ptc
# Run this file locally to display the video feed live on screen
ptc.display_camera_live()
================================================
FILE: examples/web_server.py
================================================
import pithermalcam as ptc
# Run this file to start a flask server streaming the video to the local network
# IP Address and port of the video feed will be displayed as the server is started
ptc.stream_camera_online()
================================================
FILE: icons/MatPlotLib Thermal Camera
================================================
[Desktop Entry]
Version=1.0
Name=MatPlotLib Thermal Camera
Icon=applets-screenshooter
Path=/home/pi/PiThermalCam/sequential_versions/
Exec=/usr/bin/python3 /home/pi/PiThermalCam/sequential_versions/matplotlib_therm_cam.py
Terminal=true
Type=Application
StartUpNotify=true
Name[en_US]=Matplotlib Thermal Camera
================================================
FILE: icons/Run Flask Thermal Camera
================================================
[Desktop Entry]
Version=1.0
Name=Flask Thermal Camera
Icon=screensaver
Path=/home/pi/PiThermalCam/
Exec=/usr/bin/python3 /home/pi/PiThermalCam/pithermcam/web_server.py
Terminal=true
Type=Application
StartUpNotify=true
Name[en_US]=Flask Thermal Camera
================================================
FILE: icons/Run OpenCV Thermal Camera
================================================
[Desktop Entry]
Version=1.0
Name=OpenCV Thermal Camera
Icon=xfce4-ui
Path=/home/pi/PiThermalCam/sequential_versions/
Exec=/usr/bin/python3 /home/pi/PiThermalCam/sequential_versions/opencv_therm_cam.py
Terminal=true
Type=Application
StartUpNotify=true
Name[en_US]=OpenCV Thermal Camera
================================================
FILE: pithermalcam/__init__.py
================================================
# Imports here to enable access to these functions from the library import without having to import those files.
# Effectively using this init in the same manner as a C header file. If there's a more pythonic way to do this, it should change to that.
from pithermalcam.pi_therm_cam import pithermalcam
from pithermalcam import web_server
def test_camera():
"""Check for an average temperature value to ensure the camera is connected and working."""
try:
thermcam = pithermalcam() # Instantiate class
temp_c = None
temp_f = None
temp_c, temp_f = thermcam.get_mean_temp()
print("Camera seems to be connected and returning a value:")
print('Average MLX90640 Temperature: {0:2.1f}C ({1:2.1f}F)'.format(temp_c,temp_f))
print('To verify it\'s working, change the average temperature')
print('(e.g. by hold your hand over the camera) and run again to verify that the average temperature has changed.')
except ValueError as e:
if str(e) == 'No I2C device at address: 0x33':
print("ERROR: Camera not found. There seems to be no device connected to I2C address 0x33.")
except Exception as e:
print("Camera didn't seem to work properly. Returned the following error:")
raise(e)
def display_camera_live(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):
"""Display the camera live onscreen"""
thermcam = pithermalcam(output_folder=output_folder) # Instantiate class
thermcam.display_camera_onscreen()
def stream_camera_online(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):
"""Start a flask server streaming the camera live"""
# This is a clunky way to do this, the better approach would likely to be restructuring web_server.py with the Flask Blueprint approach
# If the code were restructure for this, the code would be much more complex and opaque for running directly though
web_server.start_server(output_folder=output_folder)
# Add attributes to existing pithermalcam object
setattr(pithermalcam, 'stream_camera_online', stream_camera_online)
================================================
FILE: pithermalcam/pi_therm_cam.py
================================================
# -*- coding: utf-8 -*-
#!/usr/bin/python3
##################################
# MLX90640 Thermal Camera w Raspberry Pi
##################################
import time,board,busio, traceback
import numpy as np
import adafruit_mlx90640
import datetime as dt
import cv2
import logging
import cmapy
from scipy import ndimage
# Set up logging
logging.basicConfig(filename='pithermcam.log',filemode='a',
format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',
level=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')
logger = logging.getLogger(__name__)
class pithermalcam:
# See https://gitlab.com/cvejarano-oss/cmapy/-/blob/master/docs/colorize_all_examples.md to for options that can be put in this list
_colormap_list=['jet','bwr','seismic','coolwarm','PiYG_r','tab10','tab20','gnuplot2','brg']
_interpolation_list =[cv2.INTER_NEAREST,cv2.INTER_LINEAR,cv2.INTER_AREA,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4,5,6]
_interpolation_list_name = ['Nearest','Inter Linear','Inter Area','Inter Cubic','Inter Lanczos4','Pure Scipy', 'Scipy/CV2 Mixed']
_current_frame_processed=False # Tracks if the current processed image matches the current raw image
i2c=None
mlx=None
_temp_min=None
_temp_max=None
_raw_image=None
_image=None
_file_saved_notification_start=None
_displaying_onscreen=False
_exit_requested=False
def __init__(self,use_f:bool = True, filter_image:bool = False, image_width:int=1200,
image_height:int=900, output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):
self.use_f=use_f
self.filter_image=filter_image
self.image_width=image_width
self.image_height=image_height
self.output_folder=output_folder
self._colormap_index = 0
self._interpolation_index = 3
self._setup_therm_cam()
self._t0 = time.time()
self.update_image_frame()
def __del__(self):
logger.debug("ThermalCam Object deleted.")
def _setup_therm_cam(self):
"""Initialize the thermal camera"""
# Setup camera
self.i2c = busio.I2C(board.SCL, board.SDA, frequency=800000) # setup I2C
self.mlx = adafruit_mlx90640.MLX90640(self.i2c) # begin MLX90640 with I2C comm
self.mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_8_HZ # set refresh rate
time.sleep(0.1)
def _c_to_f(self,temp:float):
""" Convert temperature from C to F """
return ((9.0/5.0)*temp+32.0)
def get_mean_temp(self):
"""
Get mean temp of entire field of view. Return both temp C and temp F.
"""
frame = np.zeros((24*32,)) # setup array for storing all 768 temperatures
while True:
try:
self.mlx.getFrame(frame) # read MLX temperatures into frame var
break
except ValueError:
continue # if error, just read again
temp_c = np.mean(frame)
temp_f=self._c_to_f(temp_c)
return temp_c, temp_f
def _pull_raw_image(self):
"""Get one pull of the raw image data, converting temp units if necessary"""
# Get image
self._raw_image = np.zeros((24*32,))
try:
self.mlx.getFrame(self._raw_image) # read mlx90640
self._temp_min = np.min(self._raw_image)
self._temp_max = np.max(self._raw_image)
self._raw_image=self._temps_to_rescaled_uints(self._raw_image,self._temp_min,self._temp_max)
self._current_frame_processed=False # Note that the newly updated raw frame has not been processed
except ValueError:
print("Math error; continuing...")
self._raw_image = np.zeros((24*32,)) # If something went wrong, make sure the raw image has numbers
logger.info(traceback.format_exc())
except OSError:
print("IO Error; continuing...")
self._raw_image = np.zeros((24*32,)) # If something went wrong, make sure the raw image has numbers
logger.info(traceback.format_exc())
def _process_raw_image(self):
"""Process the raw temp data to a colored image. Filter if necessary"""
# Image processing
# Can't apply colormap before ndimage, so reversed in first two options, even though it seems slower
if self._interpolation_index==5: # Scale via scipy only - slowest but seems higher quality
self._image = ndimage.zoom(self._raw_image,25) # interpolate with scipy
self._image = cv2.applyColorMap(self._image, cmapy.cmap(self._colormap_list[self._colormap_index]))
elif self._interpolation_index==6: # Scale partially via scipy and partially via cv2 - mix of speed and quality
self._image = ndimage.zoom(self._raw_image,10) # interpolate with scipy
self._image = cv2.applyColorMap(self._image, cmapy.cmap(self._colormap_list[self._colormap_index]))
self._image = cv2.resize(self._image, (800,600), interpolation=cv2.INTER_CUBIC)
else:
self._image = cv2.applyColorMap(self._raw_image, cmapy.cmap(self._colormap_list[self._colormap_index]))
self._image = cv2.resize(self._image, (800,600), interpolation=self._interpolation_list[self._interpolation_index])
self._image = cv2.flip(self._image, 1)
if self.filter_image:
self._image=cv2.bilateralFilter(self._image,15,80,80)
def _add_image_text(self):
"""Set image text content"""
if self.use_f:
temp_min=self._c_to_f(self._temp_min)
temp_max=self._c_to_f(self._temp_max)
text = f'Tmin={temp_min:+.1f}F - Tmax={temp_max:+.1f}F - FPS={1/(time.time() - self._t0):.1f} - Interpolation: {self._interpolation_list_name[self._interpolation_index]} - Colormap: {self._colormap_list[self._colormap_index]} - Filtered: {self.filter_image}'
else:
text = f'Tmin={self._temp_min:+.1f}C - Tmax={self._temp_max:+.1f}C - FPS={1/(time.time() - self._t0):.1f} - Interpolation: {self._interpolation_list_name[self._interpolation_index]} - Colormap: {self._colormap_list[self._colormap_index]} - Filtered: {self.filter_image}'
cv2.putText(self._image, text, (30, 18), cv2.FONT_HERSHEY_SIMPLEX, .4, (255, 255, 255), 1)
self._t0 = time.time() # Update time to this pull
# For a brief period after saving, display saved notification
if self._file_saved_notification_start is not None and (time.monotonic()-self._file_saved_notification_start)<1:
cv2.putText(self._image, 'Snapshot Saved!', (300,300),cv2.FONT_HERSHEY_SIMPLEX, .8, (255, 255, 255), 2)
def add_customized_text(self,text):
"""Add custom text to the center of the image, used mostly to notify user that server is off."""
cv2.putText(self._image, text, (300,300),cv2.FONT_HERSHEY_SIMPLEX, .8, (255, 255, 255), 2)
time.sleep(0.1)
def _show_processed_image(self):
"""Resize image window and display it"""
cv2.namedWindow('Thermal Image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Thermal Image', self.image_width,self.image_height)
cv2.imshow('Thermal Image', self._image)
def _set_click_keyboard_events(self):
"""Add click and keyboard actions to image"""
# Set mouse click events
cv2.setMouseCallback('Thermal Image',self._mouse_click)
# Set keyboard events
# if 's' is pressed - saving of picture
key = cv2.waitKey(1) & 0xFF
if key == ord("s"): # If s is chosen, save an image to filec
self.save_image()
elif key == ord("c"): # If c is chosen cycle the colormap used
self.change_colormap()
elif key == ord("x"): # If c is chosen cycle the colormap used
self.change_colormap(forward=False)
elif key == ord("f"): # If f is chosen cycle the image filtering
self.filter_image = not self.filter_image
elif key == ord("t"): # If t is chosen cycle the units used for Temperature
self.use_f = not self.use_f
elif key == ord("u"): # If t is chosen cycle the units used for temperature
self.change_interpolation()
elif key == ord("i"): # If i is chosen cycle interpolation algorithm
self.change_interpolation(forward=False)
elif key==27: # Exit nicely if escape key is used
cv2.destroyAllWindows()
self._displaying_onscreen = False
print("Code Stopped by User")
self._exit_requested=True
def _mouse_click(self,event,x,y,flags,param):
"""Used to save an image on double-click"""
global mouseX,mouseY
if event == cv2.EVENT_LBUTTONDBLCLK:
self.save_image()
def _print_shortcuts_keys(self):
"""Print out a summary of the shortcut keys available during video runtime."""
print("The following keys are shortcuts for controlling the video during a run:")
print("Esc - Exit and Close.")
print("S - Save a Snapshot of the Current Frame")
print("X - Cycle the Colormap Backwards")
print("C - Cycle the Colormap forward")
print("F - Toggle Filtering On/Off")
print("T - Toggle Temperature Units between C/F")
print("U - Go back to the previous Interpolation Algorithm")
print("I - Change the Interpolation Algorithm Used")
print("Double-click with Mouse - Save a Snapshot of the Current Frame")
def display_next_frame_onscreen(self):
"""Display the camera live to the display"""
# Display shortcuts reminder to user on first run
if not self._displaying_onscreen:
self._print_shortcuts_keys()
self._displaying_onscreen = True
self.update_image_frame()
self._show_processed_image()
self._set_click_keyboard_events()
def change_colormap(self, forward:bool = True):
"""Cycle colormap. Forward by default, backwards if param set to false."""
if forward:
self._colormap_index+=1
if self._colormap_index==len(self._colormap_list):
self._colormap_index=0
else:
self._colormap_index-=1
if self._colormap_index<0:
self._colormap_index=len(self._colormap_list)-1
def change_interpolation(self, forward:bool = True):
"""Cycle interpolation. Forward by default, backwards if param set to false."""
if forward:
self._interpolation_index+=1
if self._interpolation_index==len(self._interpolation_list):
self._interpolation_index=0
else:
self._interpolation_index-=1
if self._interpolation_index<0:
self._interpolation_index=len(self._interpolation_list)-1
def update_image_frame(self):
"""Pull raw temperature data, process it to an image, and update image text"""
self._pull_raw_image()
self._process_raw_image()
self._add_image_text()
self._current_frame_processed=True
return self._image
def update_raw_image_only(self):
"""Update only raw data without any further image processing or text updating"""
self._pull_raw_image()
def get_current_raw_image_frame(self):
"""Return the current raw image"""
self._pull_raw_image()
return self._raw_image
def get_current_image_frame(self):
"""Get the processed image"""
# If the current raw image hasn't been procssed, process and return it
if not self._current_frame_processed:
self._process_raw_image()
self._add_image_text()
self._current_frame_processed=True
return self._image
def save_image(self):
"""Save the current frame as a snapshot to the output folder."""
fname = self.output_folder + 'pic_' + dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpg'
cv2.imwrite(fname, self._image)
self._file_saved_notification_start = time.monotonic()
print('Thermal Image ', fname)
def _temps_to_rescaled_uints(self,f,Tmin,Tmax):
"""Function to convert temperatures to pixels on image"""
f=np.nan_to_num(f)
norm = np.uint8((f - Tmin)*255/(Tmax-Tmin))
norm.shape = (24,32)
return norm
def display_camera_onscreen(self):
# Loop to display frames unless/until user requests exit
while not self._exit_requested:
try:
self.display_next_frame_onscreen()
# Catch a common I2C Error. If you get this too often consider checking/adjusting your I2C Baudrate
except RuntimeError as e:
if e.message == 'Too many retries':
print("Too many retries error caught, potential I2C baudrate issue: continuing...")
continue
raise
if __name__ == "__main__":
# If class is run as main, read ini and set up a live feed displayed to screen
output_folder = '/home/pi/PiThermalCam/saved_snapshots/'
thermcam = pithermalcam(output_folder=output_folder) # Instantiate class
thermcam.display_camera_onscreen()
================================================
FILE: pithermalcam/templates/index.html
================================================
<html>
<head>
<title>Pi Thermal Video</title>
<script src="//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js"></script>
<script type=text/javascript>
$(function() {
$('a#save').on('click', function(e) {
e.preventDefault()
$.getJSON('/save',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#units').on('click', function(e) {
e.preventDefault()
$.getJSON('/units',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#colormap').on('click', function(e) {
e.preventDefault()
$.getJSON('/colormap',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#colormapback').on('click', function(e) {
e.preventDefault()
$.getJSON('/colormapback',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#filter').on('click', function(e) {
e.preventDefault()
$.getJSON('/filter',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#interpolation').on('click', function(e) {
e.preventDefault()
$.getJSON('/interpolation',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#interpolationback').on('click', function(e) {
e.preventDefault()
$.getJSON('/interpolationback',
function(data) {
//do nothing
});
return false;
});
});
</script>
<script type=text/javascript>
$(function() {
$('a#exit').on('click', function(e) {
e.preventDefault()
$.getJSON('/exit',
function(data) {
//do nothing
});
return false;
});
});
</script>
</head>
<!-- Center align things and add margins -->
<style>
h1 {
text-align: center;
}
.container {
max-width: 800px;
margin: auto;
}
.container2 {
max-width: 50px;
margin: auto;
}
.form {
max-width: 800px;
margin: auto;
}
.form-inline {
margin: 5px;
margin-left: -10px;
margin-right: -10px;
}
.btn {
margin-left: 3px;
}
</style>
<body>
<div class='container'>
<h1>Pi Thermal Video</h1>
<img src="{{ url_for('video_feed') }}" class='video'>
</div>
<div class='container'>
<form class="form-inline">
<a href=# id=save><button class='btn btn-default'>Save</button></a>
<a href=# id=units><button class='btn btn-default'>Change Temp Units</button></a>
<a href=# id=colormapback><button class='btn btn-default'>Previous Colormap</button></a>
<a href=# id=colormap><button class='btn btn-default'>Next Colormap</button></a>
<a href=# id=interpolationback><button class='btn btn-default'>Previous Interpolation</button></a>
<a href=# id=interpolation><button class='btn btn-default'>Next Interpolation</button></a>
<a href=# id=filter><button class='btn btn-default'>Toggle Filter</button></a>
</form>
</div>
<div class='container2'><a href=# id=exit><button class='btn btn-default'>Exit</button></a></div>
</body>
</html>
================================================
FILE: pithermalcam/web_server.py
================================================
# -*- coding: utf-8 -*-
#!/usr/bin/python3
##################################
# Flask web server for MLX90640 Thermal Camera w Raspberry Pi
# If running directly, run from root folder, not pithermalcam folder
##################################
try: # If called as an imported module
from pithermalcam import pithermalcam
except: # If run directly
from pi_therm_cam import pithermalcam
from flask import Response, request
from flask import Flask
from flask import render_template
import threading
import time, socket, logging, traceback
import cv2
# Set up Logger
logging.basicConfig(filename='pithermcam.log',filemode='a',
format='%(asctime)s %(levelname)-8s [%(filename)s:%(name)s:%(lineno)d] %(message)s',
level=logging.WARNING,datefmt='%d-%b-%y %H:%M:%S')
logger = logging.getLogger(__name__)
# initialize the output frame and a lock used to ensure thread-safe exchanges of the output frames (useful when multiple browsers/tabs are viewing the stream)
outputFrame = None
thermcam = None
lock = threading.Lock()
# initialize a flask object
app = Flask(__name__)
@app.route("/")
def index():
# return the rendered template
return render_template("index.html")
#background processes happen without any refreshing (for button clicks)
@app.route('/save')
def save_image():
thermcam.save_image()
return ("Snapshot Saved")
@app.route('/units')
def change_units():
thermcam.use_f = not thermcam.use_f
return ("Units changed")
@app.route('/colormap')
def increment_colormap():
thermcam.change_colormap()
return ("Colormap changed")
@app.route('/colormapback')
def decrement_colormap():
thermcam.change_colormap(forward=False)
return ("Colormap changed back")
@app.route('/filter')
def toggle_filter():
thermcam.filter_image=not thermcam.filter_image
return ("Filtering Toggled")
@app.route('/interpolation')
def increment_interpolation():
thermcam.change_interpolation()
return ("Interpolation Changed")
@app.route('/interpolationback')
def decrement_interpolation():
thermcam.change_interpolation(forward=False)
return ("Interpolation Changed Back")
@app.route('/exit')
def appexit():
global thermcam
func = request.environ.get('werkzeug.server.shutdown')
if func is None:
raise RuntimeError('Not running with the Werkzeug Server')
func()
thermcam = None
return 'Server shutting down...'
@app.route("/video_feed")
def video_feed():
# return the response generated along with the specific media
# type (mime type)
return Response(generate(), mimetype="multipart/x-mixed-replace; boundary=frame")
def get_ip_address():
"""Find the current IP address of the device"""
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
ip_address=s.getsockname()[0]
s.close()
return ip_address
def pull_images():
global thermcam, outputFrame
# loop over frames from the video stream
while thermcam is not None:
current_frame=None
try:
current_frame = thermcam.update_image_frame()
except Exception:
print("Too many retries error caught; continuing...")
logger.info(traceback.format_exc())
# If we have a frame, acquire the lock, set the output frame, and release the lock
if current_frame is not None:
with lock:
outputFrame = current_frame.copy()
def generate():
# grab global references to the output frame and lock variables
global outputFrame, lock
# loop over frames from the output stream
while True:
# wait until the lock is acquired
with lock:
# check if the output frame is available, otherwise skip the iteration of the loop
if outputFrame is None:
continue
# encode the frame in JPEG format
(flag, encodedImage) = cv2.imencode(".jpg", outputFrame)
# ensure the frame was successfully encoded
if not flag:
continue
# yield the output frame in the byte format
yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + bytearray(encodedImage) + b'\r\n')
def start_server(output_folder:str = '/home/pi/pithermalcam/saved_snapshots/'):
global thermcam
# initialize the video stream and allow the camera sensor to warmup
thermcam = pithermalcam(output_folder=output_folder)
time.sleep(0.1)
# start a thread that will perform motion detection
t = threading.Thread(target=pull_images)
t.daemon = True
t.start()
ip=get_ip_address()
port=8000
print(f'Server can be found at {ip}:{port}')
# start the flask app
app.run(host=ip, port=port, debug=False,threaded=True, use_reloader=False)
# If this is the main thread, simply start the server
if __name__ == '__main__':
start_server()
================================================
FILE: requirements.txt
================================================
numpy>=1.16.5
matplotlib
scipy>=1.6.0
RPI.GPIO
Adafruit-Blinka
adafruit-circuitpython-mlx90640
flask
opencv-python
cmapy
================================================
FILE: requirements_without_opencv.txt
================================================
numpy>=1.16.5
matplotlib
scipy>=1.6.0
RPI.GPIO
Adafruit-Blinka
adafruit-circuitpython-mlx90640
flask
================================================
FILE: saved_snapshots/note.txt
================================================
This folder should be excluded from the git, apart from this file to keep it in existance.
================================================
FILE: sequential_versions/Method Comparison.ipynb
================================================
{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Matplotlib v. OpenCV Method Comparison\n",
"\n",
"This notebook is used to look at the details of the matplotlib and opencv methods in a comparative way. It's useful for debugging. It's based off the code in the examples folder.\n",
"\n",
"The first cell does the imports and performs a single image data pull from the query.\n",
"\n",
"The second cell performs the matplotlib code to transform, scale, and present this result.\n",
"\n",
"The third cell performs the same via the opencv approach.\n",
"\n",
"This enables the two to be compared easily on the same input data.\n",
"\n",
"Of note; in video mode the OpenCV method seems to run MUCH faster, but the Matplotlib color schemes are more useful (and are thus applied to the OpenCV version using the cmapy library)."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import time,board,busio\n",
"import numpy as np\n",
"import adafruit_mlx90640\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import colors\n",
"import logging, configparser\n",
"from scipy import ndimage\n",
"from util_functions import *\n",
"import datetime as dt\n",
"import cv2\n",
"import cmapy\n",
"\n",
"# Setup camera\n",
"i2c = busio.I2C(board.SCL, board.SDA, frequency=800000) # setup I2C\n",
"mlx = adafruit_mlx90640.MLX90640(i2c) # begin MLX90640 with I2C comm\n",
"mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_2_HZ # set refresh rate\n",
"frame = np.zeros(mlx_shape[0]*mlx_shape[1]) # 768 pts\n",
"mlx.getFrame(frame) # read mlx90640"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 648x360 with 2 Axes>",
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"358.578125pt\" version=\"1.1\" viewBox=\"0 0 555.1525 358.578125\" width=\"555.1525pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 358.578125 \nL 555.1525 358.578125 \nL 555.1525 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 33.2875 334.7 \nL 465.2875 334.7 \nL 465.2875 10.7 \nL 33.2875 10.7 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g clip-path=\"url(#p85fff64ca8)\">\n <image height=\"240\" id=\"image953f08c4c7\" transform=\"matrix(1.35 0 0 1.35 33.2875 10.7)\" width=\"320\" xlink:href=\"data:image/png;base64,\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\"/>\n </g>\n <g id=\"matplotlib.axis_1\">\n <g id=\"xtick_1\">\n <g id=\"line2d_1\">\n <defs>\n <path d=\"M 0 0 \nL 0 3.5 \n\" id=\"mc01d7c4bf5\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n </defs>\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.9625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_1\">\n <!-- 0 -->\n <defs>\n <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n </defs>\n <g transform=\"translate(30.78125 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_2\">\n <g id=\"line2d_2\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"101.4625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_2\">\n <!-- 50 -->\n <defs>\n <path d=\"M 10.796875 72.90625 \nL 49.515625 72.90625 \nL 49.515625 64.59375 \nL 19.828125 64.59375 \nL 19.828125 46.734375 \nQ 21.96875 47.46875 24.109375 47.828125 \nQ 26.265625 48.1875 28.421875 48.1875 \nQ 40.625 48.1875 47.75 41.5 \nQ 54.890625 34.8125 54.890625 23.390625 \nQ 54.890625 11.625 47.5625 5.09375 \nQ 40.234375 -1.421875 26.90625 -1.421875 \nQ 22.3125 -1.421875 17.546875 -0.640625 \nQ 12.796875 0.140625 7.71875 1.703125 \nL 7.71875 11.625 \nQ 12.109375 9.234375 16.796875 8.0625 \nQ 21.484375 6.890625 26.703125 6.890625 \nQ 35.15625 6.890625 40.078125 11.328125 \nQ 45.015625 15.765625 45.015625 23.390625 \nQ 45.015625 31 40.078125 35.4375 \nQ 35.15625 39.890625 26.703125 39.890625 \nQ 22.75 39.890625 18.8125 39.015625 \nQ 14.890625 38.140625 10.796875 36.28125 \nz\n\" id=\"DejaVuSans-53\"/>\n </defs>\n <g transform=\"translate(95.1 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_3\">\n <g id=\"line2d_3\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"168.9625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_3\">\n <!-- 100 -->\n <defs>\n <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n </defs>\n <g transform=\"translate(159.41875 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_4\">\n <g id=\"line2d_4\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"236.4625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_4\">\n <!-- 150 -->\n <g transform=\"translate(226.91875 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_5\">\n <g id=\"line2d_5\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"303.9625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_5\">\n <!-- 200 -->\n <defs>\n <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n </defs>\n <g transform=\"translate(294.41875 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_6\">\n <g id=\"line2d_6\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"371.4625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_6\">\n <!-- 250 -->\n <g transform=\"translate(361.91875 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_7\">\n <g id=\"line2d_7\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"438.9625\" xlink:href=\"#mc01d7c4bf5\" y=\"334.7\"/>\n </g>\n </g>\n <g id=\"text_7\">\n <!-- 300 -->\n <defs>\n <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n </defs>\n <g transform=\"translate(429.41875 349.298437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-51\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"matplotlib.axis_2\">\n <g id=\"ytick_1\">\n <g id=\"line2d_8\">\n <defs>\n <path d=\"M 0 0 \nL -3.5 0 \n\" id=\"mf40674f483\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n </defs>\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.2875\" xlink:href=\"#mf40674f483\" y=\"11.375\"/>\n </g>\n </g>\n <g id=\"text_8\">\n <!-- 0 -->\n <g transform=\"translate(19.925 15.174219)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_2\">\n <g id=\"line2d_9\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.2875\" xlink:href=\"#mf40674f483\" y=\"78.875\"/>\n </g>\n </g>\n <g id=\"text_9\">\n <!-- 50 -->\n <g transform=\"translate(13.5625 82.674219)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_3\">\n <g id=\"line2d_10\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.2875\" xlink:href=\"#mf40674f483\" y=\"146.375\"/>\n </g>\n </g>\n <g id=\"text_10\">\n <!-- 100 -->\n <g transform=\"translate(7.2 150.174219)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_4\">\n <g id=\"line2d_11\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.2875\" xlink:href=\"#mf40674f483\" y=\"213.875\"/>\n </g>\n </g>\n <g id=\"text_11\">\n <!-- 150 -->\n <g transform=\"translate(7.2 217.674219)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_5\">\n <g id=\"line2d_12\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"33.2875\" xlink:href=\"#mf40674f483\" y=\"281.375\"/>\n </g>\n </g>\n <g id=\"text_12\">\n <!-- 200 -->\n <g transform=\"translate(7.2 285.174219)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"patch_3\">\n <path d=\"M 33.2875 334.7 \nL 33.2875 10.7 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_4\">\n <path d=\"M 465.2875 334.7 \nL 465.2875 10.7 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_5\">\n <path d=\"M 33.2875 334.7 \nL 465.2875 334.7 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_6\">\n <path d=\"M 33.2875 10.7 \nL 465.2875 10.7 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n </g>\n <g id=\"axes_2\">\n <g id=\"patch_7\">\n <path clip-path=\"url(#p14f5a35b06)\" d=\"M 494.4475 334.7 \nL 494.4475 333.434375 \nL 494.4475 11.965625 \nL 494.4475 10.7 \nL 510.6475 10.7 \nL 510.6475 11.965625 \nL 510.6475 333.434375 \nL 510.6475 334.7 \nz\n\" style=\"fill:#ffffff;stroke:#ffffff;stroke-linejoin:miter;stroke-width:0.01;\"/>\n </g>\n <image height=\"324\" id=\"image997668b39a\" transform=\"scale(1 -1)translate(0 -324)\" width=\"17\" x=\"494\" xlink:href=\"data:image/png;base64,\niVBORw0KGgoAAAANSUhEUgAAABEAAAFECAYAAADBWdUKAAAABHNCSVQICAgIfAhkiAAAAO9JREFUeJzt2kEKg1AQBcGv5P5H1qzDZKcNIvUO0DC1nm2t81wX99n3q4nXRW5IPOkcJk+OgI0iYKMI2CgCdo5JFAEbRcBGEbBzTKII2CgCNoqAnWMSRcBGEbBRBOwckygCNoqAjSJgowjYOSZRBGwUARtFwM4xiSJgowjYKAJ2jkkUAfsnsq3LP7Y3wa7jEPkd2DkmUQRsFAEbRcBGEbBzTKII2CgCNoqAnWMSRcBGEbBRBOwckygCNoqAjSJg55hEEbBRBGwUARtFwM4xiSJgowjYKAJ2jkkUARtFwEYRsHNMosjLYLdzXf+yvcXkC//QBZI2FOHTAAAAAElFTkSuQmCC\" y=\"-10\"/>\n <g id=\"matplotlib.axis_3\"/>\n <g id=\"matplotlib.axis_4\">\n <g id=\"ytick_6\">\n <g id=\"line2d_13\">\n <defs>\n <path d=\"M 0 0 \nL 3.5 0 \n\" id=\"m302939bd38\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n </defs>\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"297.887644\"/>\n </g>\n </g>\n <g id=\"text_13\">\n <!-- 20 -->\n <g transform=\"translate(517.6475 301.686863)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_7\">\n <g id=\"line2d_14\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"248.689469\"/>\n </g>\n </g>\n <g id=\"text_14\">\n <!-- 22 -->\n <g transform=\"translate(517.6475 252.488688)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_8\">\n <g id=\"line2d_15\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"199.491294\"/>\n </g>\n </g>\n <g id=\"text_15\">\n <!-- 24 -->\n <defs>\n <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n </defs>\n <g transform=\"translate(517.6475 203.290512)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_9\">\n <g id=\"line2d_16\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"150.293118\"/>\n </g>\n </g>\n <g id=\"text_16\">\n <!-- 26 -->\n <defs>\n <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n </defs>\n <g transform=\"translate(517.6475 154.092337)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_10\">\n <g id=\"line2d_17\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"101.094943\"/>\n </g>\n </g>\n <g id=\"text_17\">\n <!-- 28 -->\n <defs>\n <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n </defs>\n <g transform=\"translate(517.6475 104.894162)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-56\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_11\">\n <g id=\"line2d_18\">\n <g>\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"510.6475\" xlink:href=\"#m302939bd38\" y=\"51.896768\"/>\n </g>\n </g>\n <g id=\"text_18\">\n <!-- 30 -->\n <g transform=\"translate(517.6475 55.695987)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-51\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"text_19\">\n <!-- Temperature [$^{\\circ}$C] -->\n <defs>\n <path d=\"M -0.296875 72.90625 \nL 61.375 72.90625 \nL 61.375 64.59375 \nL 35.5 64.59375 \nL 35.5 0 \nL 25.59375 0 \nL 25.59375 64.59375 \nL -0.296875 64.59375 \nz\n\" id=\"DejaVuSans-84\"/>\n <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n <path d=\"M 52 44.1875 \nQ 55.375 50.25 60.0625 53.125 \nQ 64.75 56 71.09375 56 \nQ 79.640625 56 84.28125 50.015625 \nQ 88.921875 44.046875 88.921875 33.015625 \nL 88.921875 0 \nL 79.890625 0 \nL 79.890625 32.71875 \nQ 79.890625 40.578125 77.09375 44.375 \nQ 74.3125 48.1875 68.609375 48.1875 \nQ 61.625 48.1875 57.5625 43.546875 \nQ 53.515625 38.921875 53.515625 30.90625 \nL 53.515625 0 \nL 44.484375 0 \nL 44.484375 32.71875 \nQ 44.484375 40.625 41.703125 44.40625 \nQ 38.921875 48.1875 33.109375 48.1875 \nQ 26.21875 48.1875 22.15625 43.53125 \nQ 18.109375 38.875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.1875 51.21875 25.484375 53.609375 \nQ 29.78125 56 35.6875 56 \nQ 41.65625 56 45.828125 52.96875 \nQ 50 49.953125 52 44.1875 \nz\n\" id=\"DejaVuSans-109\"/>\n <path d=\"M 18.109375 8.203125 \nL 18.109375 -20.796875 \nL 9.078125 -20.796875 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.390625 \nQ 20.953125 51.265625 25.265625 53.625 \nQ 29.59375 56 35.59375 56 \nQ 45.5625 56 51.78125 48.09375 \nQ 58.015625 40.1875 58.015625 27.296875 \nQ 58.015625 14.40625 51.78125 6.484375 \nQ 45.5625 -1.421875 35.59375 -1.421875 \nQ 29.59375 -1.421875 25.265625 0.953125 \nQ 20.953125 3.328125 18.109375 8.203125 \nz\nM 48.6875 27.296875 \nQ 48.6875 37.203125 44.609375 42.84375 \nQ 40.53125 48.484375 33.40625 48.484375 \nQ 26.265625 48.484375 22.1875 42.84375 \nQ 18.109375 37.203125 18.109375 27.296875 \nQ 18.109375 17.390625 22.1875 11.75 \nQ 26.265625 6.109375 33.40625 6.109375 \nQ 40.53125 6.109375 44.609375 11.75 \nQ 48.6875 17.390625 48.6875 27.296875 \nz\n\" id=\"DejaVuSans-112\"/>\n <path d=\"M 41.109375 46.296875 \nQ 39.59375 47.171875 37.8125 47.578125 \nQ 36.03125 48 33.890625 48 \nQ 26.265625 48 22.1875 43.046875 \nQ 18.109375 38.09375 18.109375 28.8125 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 20.953125 51.171875 25.484375 53.578125 \nQ 30.03125 56 36.53125 56 \nQ 37.453125 56 38.578125 55.875 \nQ 39.703125 55.765625 41.0625 55.515625 \nz\n\" id=\"DejaVuSans-114\"/>\n <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n <path d=\"M 18.3125 70.21875 \nL 18.3125 54.6875 \nL 36.8125 54.6875 \nL 36.8125 47.703125 \nL 18.3125 47.703125 \nL 18.3125 18.015625 \nQ 18.3125 11.328125 20.140625 9.421875 \nQ 21.96875 7.515625 27.59375 7.515625 \nL 36.8125 7.515625 \nL 36.8125 0 \nL 27.59375 0 \nQ 17.1875 0 13.234375 3.875 \nQ 9.28125 7.765625 9.28125 18.015625 \nL 9.28125 47.703125 \nL 2.6875 47.703125 \nL 2.6875 54.6875 \nL 9.28125 54.6875 \nL 9.28125 70.21875 \nz\n\" id=\"DejaVuSans-116\"/>\n <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n <path id=\"DejaVuSans-32\"/>\n <path d=\"M 8.59375 75.984375 \nL 29.296875 75.984375 \nL 29.296875 69 \nL 17.578125 69 \nL 17.578125 -6.203125 \nL 29.296875 -6.203125 \nL 29.296875 -13.1875 \nL 8.59375 -13.1875 \nz\n\" id=\"DejaVuSans-91\"/>\n <path d=\"M 31.296875 40.671875 \nQ 27.390625 40.671875 24.703125 37.953125 \nQ 22.015625 35.25 22.015625 31.34375 \nQ 22.015625 27.484375 24.703125 24.828125 \nQ 27.390625 22.171875 31.296875 22.171875 \nQ 35.203125 22.171875 37.890625 24.828125 \nQ 40.578125 27.484375 40.578125 31.34375 \nQ 40.578125 35.203125 37.859375 37.9375 \nQ 35.15625 40.671875 31.296875 40.671875 \nz\nM 31.296875 46.96875 \nQ 34.421875 46.96875 37.296875 45.765625 \nQ 40.1875 44.578125 42.28125 42.328125 \nQ 44.53125 40.140625 45.65625 37.34375 \nQ 46.78125 34.5625 46.78125 31.34375 \nQ 46.78125 24.90625 42.265625 20.4375 \nQ 37.75 15.96875 31.203125 15.96875 \nQ 24.609375 15.96875 20.21875 20.359375 \nQ 15.828125 24.75 15.828125 31.34375 \nQ 15.828125 37.890625 20.3125 42.421875 \nQ 24.8125 46.96875 31.296875 46.96875 \nz\n\" id=\"DejaVuSans-8728\"/>\n <path d=\"M 64.40625 67.28125 \nL 64.40625 56.890625 \nQ 59.421875 61.53125 53.78125 63.8125 \nQ 48.140625 66.109375 41.796875 66.109375 \nQ 29.296875 66.109375 22.65625 58.46875 \nQ 16.015625 50.828125 16.015625 36.375 \nQ 16.015625 21.96875 22.65625 14.328125 \nQ 29.296875 6.6875 41.796875 6.6875 \nQ 48.140625 6.6875 53.78125 8.984375 \nQ 59.421875 11.28125 64.40625 15.921875 \nL 64.40625 5.609375 \nQ 59.234375 2.09375 53.4375 0.328125 \nQ 47.65625 -1.421875 41.21875 -1.421875 \nQ 24.65625 -1.421875 15.125 8.703125 \nQ 5.609375 18.84375 5.609375 36.375 \nQ 5.609375 53.953125 15.125 64.078125 \nQ 24.65625 74.21875 41.21875 74.21875 \nQ 47.75 74.21875 53.53125 72.484375 \nQ 59.328125 70.75 64.40625 67.28125 \nz\n\" id=\"DejaVuSans-67\"/>\n <path d=\"M 30.421875 75.984375 \nL 30.421875 -13.1875 \nL 9.71875 -13.1875 \nL 9.71875 -6.203125 \nL 21.390625 -6.203125 \nL 21.390625 69 \nL 9.71875 69 \nL 9.71875 75.984375 \nz\n\" id=\"DejaVuSans-93\"/>\n </defs>\n <g transform=\"translate(545.0125 234.3)rotate(-90)scale(0.14 -0.14)\">\n <use transform=\"translate(0 0.015625)\" xlink:href=\"#DejaVuSans-84\"/>\n <use transform=\"translate(61.083984 0.015625)\" xlink:href=\"#DejaVuSans-101\"/>\n <use transform=\"translate(122.607422 0.015625)\" xlink:href=\"#DejaVuSans-109\"/>\n <use transform=\"translate(220.019531 0.015625)\" xlink:href=\"#DejaVuSans-112\"/>\n <use transform=\"translate(283.496094 0.015625)\" xlink:href=\"#DejaVuSans-101\"/>\n <use transform=\"translate(345.019531 0.015625)\" xlink:href=\"#DejaVuSans-114\"/>\n <use transform=\"translate(386.132812 0.015625)\" xlink:href=\"#DejaVuSans-97\"/>\n <use transform=\"translate(447.412109 0.015625)\" xlink:href=\"#DejaVuSans-116\"/>\n <use transform=\"translate(486.621094 0.015625)\" xlink:href=\"#DejaVuSans-117\"/>\n <use transform=\"translate(550 0.015625)\" xlink:href=\"#DejaVuSans-114\"/>\n <use transform=\"translate(591.113281 0.015625)\" xlink:href=\"#DejaVuSans-101\"/>\n <use transform=\"translate(652.636719 0.015625)\" xlink:href=\"#DejaVuSans-32\"/>\n <use transform=\"translate(684.423828 0.015625)\" xlink:href=\"#DejaVuSans-91\"/>\n <use transform=\"translate(724.394531 38.296875)scale(0.7)\" xlink:href=\"#DejaVuSans-8728\"/>\n <use transform=\"translate(770.947266 0.015625)\" xlink:href=\"#DejaVuSans-67\"/>\n <use transform=\"translate(840.771484 0.015625)\" xlink:href=\"#DejaVuSans-93\"/>\n </g>\n </g>\n </g>\n <g id=\"patch_8\">\n <path d=\"M 494.4475 334.7 \nL 494.4475 333.434375 \nL 494.4475 11.965625 \nL 494.4475 10.7 \nL 510.6475 10.7 \nL 510.6475 11.965625 \nL 510.6475 333.434375 \nL 510.6475 334.7 \nz\n\" style=\"fill:none;stroke:#000000;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"p85fff64ca8\">\n <rect height=\"324\" width=\"432\" x=\"33.2875\" y=\"10.7\"/>\n </clipPath>\n <clipPath id=\"p14f5a35b06\">\n <rect height=\"324\" width=\"16.2\" x=\"494.4475\" y=\"10.7\"/>\n </clipPath>\n </defs>\n</svg>\n",
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAFmCAYAAACla3p9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXu0bctZF/irvc8595kbxEA6hmhCk0ho5aExttIoiCDYdmd0DwdDRYwISbcNDSjDgGC3NBrFGKGhZdBeJRrsaEwLKio2pjE0ZgyD3ETkkfAMYBISwlXCfeWec/be1X+sVXfX+vb3rKq51lz31G+MPfaas2pWfXPOevzq931zzpRzxsTExMTExMTEseDk0AZMTExMTExMTEQwycvExMTExMTEUWGSl4mJiYmJiYmjwiQvExMTExMTE0eFSV4mJiYmJiYmjgqTvExMTExMTEwcFRYjLymlz0kp/WRK6WdSSl+9VD0TExMTExMTdxbSEu95SSmdAvgpAJ8F4L0AfgjAH845v3N4ZRMTExMTExN3FJZSXl4K4Gdyzu/OOd8C8EYAL1uoromJiYmJiYk7CNcWKve5AN5Tbb8XwG+vM6SUXgnglQBw3333/daPf9GL5NJS0re3oCKSJSpFRKeStz6G/i5/dJ9UBv1/cXGZ9+Jid3/Ol+kXF3x6/bsuW7PBgnCpr5Qn7eup2wPNPsv2JeqMtrkR16K2h/vN2cvt02wbYffI+2GVJaVrdlttOXq8p21aQ5v3mkXulefeSW3Kky7Z3Hr/LVskcOOgdwxKafcepQScnOxul/THH/953Lz58EKjDY/PSSk/3FnG24HvzTl/zhCDDoClyIuJnPODAB4EgJf8lt+SH/rX/3o3w8mJ/Ltsn5wg47LNlEm9nuQB4Py81Lm7XwMtoy4fAG7f3pR3dnaZdna22V/qPDu7zH92djUvTbt163L71q3L7Q9/GLh5c/Mf2Px+/HHgySc3208+efn79u3N71JXKbuum56ThhNDm+OuDyVZ9XlK15Mrj6ub7pOaiWfbmxbJK51P9L/XDtIddravXePTpXPQ7p3UtyR793XtpfORyuTsjdybaP+p7amvf72PS+fOSzunSD+M9PleuyPtQKq/kITT06u2UDJTj/G3b1+O/XQcLL/ptbh27fL/jRu7v8v2jRubfaXu7//+l9gnNRgPA3ios4wEPGuELYfCUuTlfQCeV21/zHbfxMTExMTERC8irJ9DZKW0QixFXn4IwAtTSi/AhrT8IQB/JFRCubAnJ5vf5UaRC54q9UVaTXqqkfZrSkF0BVavEspp1ahXK3XesuI4Pb1MPz293K5/n59vVgVlxVHKotemvrye/SPA3UK6L7JCH7WaXxO4dqFBakN1mtQvrOun2UG7pQaPgtYLeq7SuWvnxZ2TpgSWvNZ12gci7ca6BvV/KV2Cdf24epe8RpoCpLVLTfE6P989/mDfNp7kZTxyzmcppS8F8L0ATgG8Luf8402FOdhHqlrcyUlSD4m4LejvQgiAzW9LQq4768nJLqGoOzlHMAphKelUmr12bVfiLL8LedEGp7Ozq4N8C2lpcXVQW6L1tg6qFrwTcTQvBR3Ao2TFskNrg9x+YU3wVJ5I3RpGTVDc5Bi1UyI4lIyMnFSj7pOeNmbZ4CGu3G8Jre05snBpwenppZsIuBwjgc1+bpzUyBtdTxcchLxwK9I7DIvFvOScvwfA93QXpDmpGfqctgSmRk066sMtH3Z9HI2X4fzI5+d6Q67VETpAnJ5eHls6VelsZ2eb9LJdfnP+2YuLTTn0nAtu39bJDb0GkfSIL70XS5CYpSYtjiRYE2idZsFSEizbvDjEWCkRNInARCb93snTQwC5+jS02MIRYk/Z2jF0fLIITc8iPkoe6/QyZlpBwpSUlDFQIy+c4lLSzs4uyz5a5eXIcWef/cTExMTExMTR4WBPG6ngfDlSoATjTzl9ipFedSFRtaTepsqJFe/CMXcKjhxzyktxHXHb165dypwAcP36pXuo3gYuFZiCmzev2nN2dtWFJaFVhaF11nlbVslW2R54V7Ueu7RVrAVudclJ0ty94SR6yYZWm+j+uryRi72Ie5faY12/iN3WOUXcTD0KhOYG9Fz3HiXJUl4kcNdassFzH1rcgMBV9UMqo4yZ1vhNlRfaz5Zw64Uw3UYrJC+09dPfdLtuZVf8IadXDqsbbJnEa5cQrV4yoyY8QOwx7NPTXQJTTqG4lXLePZWyXeoqbqLaRk26rGNcig+47oi1X5ieHx2Qo4PzUjEEPRg58PRMVjVa3Eac5B29V1pd2r3rmagt4uchNSMIcMtxI8lKDY97LGJXOb5O18rWyAu3TcvS0jyExVPP+fnlGOkpu0b9sEM51uorUtnFZUTH/r1jLQPqgbA+8kKhtTJ688osvJUfOL9lPVmfn18G3tKqaLVWQ7UGYK6d1USitrWQ6nq7JjPF5rKKqNUWjshQf/DJyeX7aIBdYkSP9azsvYPAkquV6CRiDZhL2cqpJdqE6JkgpfwtRNOzL2JbgXZOLSRAUlq468nZrYFO6lpddX4urYfgRVWYkcqLtZ+zy0qXlBWrDiuuRYJFhDjFSIOUZ5KXw2D95GViYmJiYmLiEtNttFLyovlv6DL/5GT3Gbg6z8nplewXF5cqA/cWXM0NNEoiLqAxLqUtliePav9s/XmA8rRRrbhIMS8UVHUp++pypBVfyeu9DtYKu6fvScpWyz06pP9aWs3XaV5Y7piIHVIeaV/EdUDVEQuaiulRWiy0xFi0IqJS9NhhKUYR9UMbDyIuPy+s89RUSm9Z9FhOeZbOk57jwZSXOxzrJC8UHJkBLlug4YSmMS1lm74iuv7t8YnWiA5wdf4Sk1L2nZzsEmu6ff361W8d3XVXrP4a5fHpAo7AFLS4IpYgLD1kRSo7KtN7y6WwJm7vuXGTems7bSEutQ2S661OM7qpWn7E1shEH3EhRQmE5WLS8tf7PHbWaHGl1ce2oGeRp5EjzSaJFHvrkdyDwO4YaK2jp9voMDgO8iLBWEIUAlI3tqK21On19y4i5KVnYKvBvYugfu8L3abnVQfklnx1cC8gv/eloKgxNH9LPALFiAGRlhNVGbwDYi9arhW3SqbpnrK5wbw+XitDmmQjk7tki7fOVmh9b6RyIt0bTQmwCJtF/iL7LbtGEDpONeN+W/eclmnVW8AF7dbHSgslK16G1m2Nl3R+2Dum2+jIycvExMTExMSdiElejhi1r6VsV/+L0lIrFLXywn1xuf4N6AycrgIkEwvoCsX7qB09TbqvfhKplFs/dn1+vnE1eRBxIZX6loJHMfCgxfXQU/bIY0as5j2r4BEuMgmWnYfESGWmhsc1qKWPdmN6XZUjINVjqddelHFTU2DKvrKds66+FCGjlEM/LVDXcXDVpWCSl5Uh6tw9Obmcbbe/88mmVV8QskK/R6TFvFiv+i+g3zuiZEZq5JYsyYG6lySU98iUY2qXU/ldkxtq8+3bu3VIQWwFkm99JDwTIE33upIkl0mdL0KkRp2/N4Yl6uopkCT2kuaNk4jUMyIfhcc2jvC1uq08+Uf3Aa0Naq4YDd74EIk8a/uWACUg9RhaxjCN6GsEZgbdHh/WR14mJiYmJiYmZMyYlyMhL5xrqPyulZft1wqL0nL79q5bqKgwJTiVBuxSV07L6qWUZQWIUTWjVoQkBaF1dXBysqva1O2ePmWe81UXE32km6I1wNR6ysCb5slvrVSj5UXTRxxHAy8tVaTnPC31yVNGa/q+MOqeRdwk3rK1exxVAqPgXNRSH+fql5SYMn5EFUIPqJJ9ehrrKyWPFi4QGa/2gkleVgLqe5DS6b6avNy4gfOLJJKX8rt+VJo2WOk1/0s0VOryqLfrWB0pr0R2NJfUycmuGwm4SmbKV6yBXbeS9VZKLT6D5qPwTgDaNdHSaRnaZBDF0m1Fcst5bI6cl3VdLDdT63b0eilhbqxtmh0t8Nxvb5uwbJXikjxk/BCTKyXY3mO8sGJXCup3eQFX3UpS+/W68w/OG6bysiLyUkBbv9ZTC3HZkpfzi4Rbt4BbtzbJRWWxAnQ1tWWJVaRUR/2fvseFfsaA2/baQ5UYGvNS94vyaYKC2i7PSoQjMK0xEBpZablvI/p+y0TWE6MQIRR0Yo9OlFrXi07YFtG0sOQ43dvHW5SXFiVRWt/1KkgREuxpF9aQXbdHrs1a7bVFgaakRCIpVnwfZ1f0swUTY7A+8jIxMTExMTGhYyovK0C91OeWxAp1zyenO8pKrbzcvLmrvNy+zbtbvB9btNIiKyBulUoVIE2J4ZQZLk2K3SlqS/2Cu5Kfe/oIuBovw52H5h9vuT7Wal5zP3gUj1b0uEF65HzLZaCtZLl0yTZL5fGUQbcj96rAE8/Tcn6eulsUlUi9rWVYMShSnZp7qdWNKrmKvZ7/ev8SSugISApVrU4fBJO8rAxWq69G4/OLhLNbl5Pr+fmGuNAA3Zs3d7e5AN2ybSEqjUugsiWVK2s7C+nSYnlKfZxrTAN9vwElJ9xj1XVc0MggtoibyJMulT+6z1v3XXKneeCdqKS0nkleilmyYBGTVjdigRXzQvNaJGS0fV5EJvgarf1OI71eUHISJTr0/6i+GL1nvX3y4JgxLysiL05anpF2Jubbty/JS5m0i/JSiIwVoNuqonAr/5qURBWdejCty87Zfmqq/s3Fw9BPC1BQwsLlKf2Fvm9mpNJCtyNkJaIMLN3vW9WX1glNy99LYKw81v7W/sWBIyvRa6a1qaWIS2t7kwh3LxGRVDXad62yo2SGi3mxYmCs85C2ewiNRZBXwRtWYcThcGef/cTExMTExMTRYT3KSw1CcTPSjltEcpEU1WXJp4ukfB6Fw8v8y7HaeVxc7CpK5Zxu3978LkoMPcdaddJQ+3PLb3qM5N+WfPOchN+qrFhuooirZQRGrdBbVCFPXAjd9tbR0x9aypEQsT2qukj/6e8IuD7h2deCVrdbqxtV69eWm9NTV69KVf+2xg+tXk2xrONcDhLzMt1GKyEv9EZUv4ubqG6M0qReiAt1r2gulBrRQUBzQVHXj6d8WlZ9niXupd6m6eXY27evkijPo4EFUmeknxYowb6tA7zlToi6iUYRiF702mFNKpH9Xhm+1fXQsy0h4jqw8nr6XCtB1mzh7qHHpeLZ14sRpEkiQJ6yvTEvvW3Sc2+94Gy1vmu3OCZ5WQkIYZFIAPfSOapI1BN5fax3lS6BllWTgkI2NPJSYBEJTnmhH5Ss1RVOeaFESDun+r/3m041pD5UBjLP9ZUmulETy9Kgg/dSdrUQlJGERcK+SCUXfxCFdxHhyUuP01TIKPY1N3F2R4JXW0kp3b/k+XrGC4141Wmrea/LJC8TExMTExMTR4PpNlofealVF+CqwkHVD/pkjeWKkGJALEj5qFqiKTNWefRYSWEqv0vZ9GvY9bHeVTB9ZNxzXawVSFSB0Gz1uI282Eefb3niqqee1m1vWst5RO7V6HvSo/5ILpC1qXw1emxriSGS6utVHal7hhtjRn0BurbT0/5qFW016ssdjNWQl4xNa+AmKSkQ9uKCjzORgma9360o4AZAiziV7RJ3EpHTLfJCHwmn2+VYLs5HA/eRSI50Rb/7QSVzj0vFQ2C4fIeEFrDYQmJa3TwjiYxUtvfe9dyrmjREXQot7UJqn72Bra3HcMdF7/2o/hG1v9UtE0EhDjnrY02Ncl8jsTkaco7PJ8MxlZd1gJIWiaAsUae0TffV5OT8fJe8FMJAn/LxKAY0jxSwW8e11MoLjYehT1RJ5yadp/bUFDcoaGVZE7tkl3WNIliij0cCFkcN2q3wBoJ6lJdWIqrloxPL0hg1gZUyrHp6ELlPkpIQ7TMeuy0yy+2PXo9CUurA2JowpLS7uOLao0VMIzbS61vqHqUEhTDdRushLxMTExMTExNOTPJyeNQqQ0GtQnD7R8Lr2qHuFKqscC4rms6Vy6kk9H02tbpC37DLPYlEvzLtlTg5V5cUt2Oh1//tUcas+qVta4UVedqits2jurReE8suj909T5WUOsq2tso9Bkj3zuuK2Ces+0bvvea6jD6xFm2/I9UY+jjy6Sk/llnu2ug8r13fMpYfnDsc3IDDYjXkhTZsiSQA8fgBS9r2kBfpPS5SrAn9FEFNAsp50Trq/xw5od82KmXU74Ch7iz66LN34OEISzkfi8xYg39kMvC6JSKTdk/sh6d8YD9kRirPi+h1oAM4PcaatDS3xtIBuxo0F4I2IXJ5NBt6J1DteO56WvVHSYwGbgzjyuZIFq3DCoYthOb8fDf+pbZ3H22qbB/EbTSxDvIC6BM7JQG90NQQqohwtgBXyUnZlt45U5+XVC8lOvXTRNoHJut3vnBPG3EqjwTtqShKwGg6YMe19KoxUr0UHFlZgrR46gd04haJT1hqxeeNcxilRvSeR2u9PQQicu7e87OUgZaYF0oSehXLKOiYQ8uWxgNabyEpUv+gKgwti7sHrddkdWrijHlZD3mZmJiYmJiYcGKSl8NDe+xMY7yeOA5NXaGKB1V/uGNrRaNWZjjlRXvEm7NJeltvUV3o00a0rtpGquJ4XUdcGr3OXpl0pOqireSk7R5XkbVyjowbllQevUaaO46uuK0VfeS69KzmufyWW3EfiNQVvTct10i7X1Yciqb2abaMVFo01WXE/ZXOpTx55HUbcWmSW8jK93R82iildDeAHwBwFzY84R/mnP98SukFAN4I4NcCeDuAL8g531rMEAWrIC+A3XC0id8LKciW+1Aj58rh8kpuIqkurqySn5IR6WOLlLxwH6vUYoYi7yfwfFrAwsiAxxFl9Pb5qJTviUdowWgXkiduSJqAVierO+AhcJHz4ojjEtfFan8cKZTid1pRj2VaOmej5drhjonCE/OjgSPqrfPOkeImgN+Tc34spXQdwFtTSv8CwJ8G8E055zemlP5PAF8E4NsOYeAqyAv3tFEN2mikd5iUtHqbezNtSS8vkuPiQ7gJn8tL67LIT12WRGZo3Ir0hl26XX7Tp424eKIlO99IstJSr/R7qYWKZ/C11JEeRUoqq3eFS+up93MTQ8/97iVircpCjzpX6pWIg6Y20fJbzl8jWdrkvUS/1BaXkrI2mnxTcGVrwcBeQq6pS3vFghcv55wBPLbdvL79ywB+D4A/st3/egBfhzuZvExMTExMTEwE0E9enpVSeqjafjDn/GDZSCmdYuMa+jgA3wrgZwF8KOdcpIb3AnhurxGtWAV5sZQXQFYPqNpB30zLvX1We5W+5Pap85ffXEwLVWY0l5Tk2uHe1htRXjSVp9TfgrXFh3ncHN68S9mwBoxSYlax2mQQtcsbJ0W3JWVrhGrWC+5JGy7NYwen5tVpmkJd56O/DxnbBFxVXeibe+kbey2U8zjimJeHc84vkRJzzucAPjml9BEA/hGAj++tcCSOhrwUaK6aszPg1q3d96GU+BDgMuiVTvTUXVPXQ11S1kvotJgXiaxQgkHdWcUNVD8OXX8ugLqYIq/3t9Di/+91H/SU4YlH2adUHZ3orImAq6MF1sSpSeaR/S11L41DTZ7avR0Vg8LVw/Up6R5wBG1fpLXUTR8u8IxBFoHQiEvZrt8bs2ay/hT21IBzzh9KKb0FwO8A8BEppWtb9eVjALxvL0YwOIJ148TExMTExMS+kFL6qK3igpTSPQA+C8C7ALwFwB/cZns5gH9yGAuPSHnhVhY0OLW4T27duty+dWt3WwvgrX9LQbVe14+k1Ehlc0G2VFmhihF9MV2dRldbrYoLlZ9b4Kmbk58jNnvUlX0sVOjKtd6vHRNJi8jwXoXF8+QHPU5yGewT3rblvQbRur1KlNdOCVFXkAecusIFlHPHaeOiBe91Pj/fKCJSmdxTk9J4lbP95l4KSy09qMt4+ZfUPQfA67dxLycA3pRz/mcppXcCeGNK6S8C+HcAvn1JIzSsgrwAMnnhBgH6PpR6Uq/dRoW41NscefG6jUqeUreHrEgxLzRvcRVx29wTRPQNu/R9M62uImuQt/qLJV9zA9+xIDKxR/N43EUjoE1SngkxQkRH2M+5MUaUp21rx3J5rP3aPu+TN5bdtOxR5FnKP2pMsVAITL1dQ3t/FSUyNYEp5dSfGpCgPS0FxEnRMCz7tNGPAPgUZv+7Abx0sYoDWAV5ubi4fPU9B+sV/VRpqd+HQpWXOl17lb6kvBRI6kkreeFImfToNEd26jTuvwSp/bdMFktMwKMm8KX6eQuhs7ajiJAq7bFqadXK2RUhokuSsN4yogqG9/5x/U/rH9HgX81uq+9LBMpbv3b+nJ31byuIvm6jhWy0Epa6jlJufaxVtnQunj6/F6zCiMNhFeRlYmJiYmJiwon5baP1kBfrg3/cCq88caO5iWrlpTxtVLY590ytvADyUzse5aX+71FlqKIU3ebqlTByFWGtfrw2RXDop4pqWBJ+y/Y+QJUYwI51qH+32txzX1rr5BQmT3wHV0ar8uKx0XNtWtQR7biojdy2puZEUd8f7xu+o+cWKbs+ZmI9WAV5ubi4JBRcWt2QuFfhS24iSl64mBfqfuHIBmcHR17KfkB+HFori8aw1GVTgsK9zbe2VYMnhqA1vsMjk3N2l9+HmMRHQLPbM9lpZY0il9o7SjwTuDVJ95DUQxDN8ttrb4S80N/cNqC/T8UL6d5F21ldnpcMeOws9hW3UYub01P/EkSxQBszn44xL8eAVZCXnOWYFy4wtn6PC6e8FLLy5JM2eeE+YkjrLuAIR51WH88dqxGbsr/k5UhVTdqkycJDXEbBM8C1EhJtYB0Byd+uwSJ8Wlnaitw7ySxx7yKxFhZ5kfJKONTYy5E5T35u2/odaVN1Xu2+eJ8I8tTrTWtdVLTcY+v+RAniKFgB03vFdButg7xMTExMTExMBDDJy+GR867iwa3cyz4ao2K5iW7evHQrle36a81c3EqxqQa3kvLEuHCrBClNewxbU164aybFf/Ss4iNP1Ujbve6FqE2tZbXaZiku0u8lVoycK8KS4a3y6O9W2d5bV/T9My11RPK0qDAt4Prp6DZp7fOUReGJRZPSODVQO/8lFBhvO6vzHcRtNJWX9ZCXOuaFm9RrwkLf61IH5Ja4F2DjNqLkpXwyALh8dwp13xSbKCQiUuenRIh7rI87ViNsnm3JTiD+noyRga9e+XpJubcFEb+/lWefk74Frm1o6Vpaz8QdbVM9pJsrpzV/dFvbzxFLbgKn+WleWpZliydfD1HS4ujqmJfWc2htd9y9oo9uW/Zo+yf2i9WQFxrzon3w0ArQffLJy21KXuqnkTgFQ3pZ0Qglpi6LIyt0WyIr9Bitgy+5cuWg2RIdXJaE55pE371Rl30oeNQWmj+S1rLa9V5nyQZtde7B6Ek8ssq30qR7ZZEYS5XoQVTFaCWi0rHSPfaQ51HXgSOSqyMtqzNov1gFeZmYmJiYmJgIYJKXw6OOeaEMuigMtatHchsV1UVzG9XKi/S4swbJtcQpLdKbILlz5PLQa0DTLMWlbHvk2H0qM2tzEY2EtDpc0k0krdI114NWjrWP27/kPeXasLf99ihLkbTW/JaSxKVLLiYvOPdIz/3zqn0eezn1SSpHy2OlFTu0PJKbbhWcYca8rIe80JgXSgLo483lN/dodHEb3by5+U1jXug3gup6o6BuIclt5PltdcoRA2t0AFkSvROeN4BYcp2NHIwik/moycOaNKxJ36rXc06t9zDa/ryuBg+8hCxaLkXkvnITZI/LzEP2NDdfK1GL3jead1R8XYQ4Avrj51zaHc4bVoHVkJc65oUqFvTFdNJXo0uMSymrBOdK73kpJEn7KFdB/XEwgP8mRoSQaBNDRAkqiEzkVj5uEOWO5Qa53lUchbQKWyKouPVJnBHn2xq/0EJgPPVYxGVk27Tq75kQo3V59lNo9641bqoH3DXhrqHWLrTgYA3a+OG1ewmFQyNqXPpocrcI7nAGtQryMjExMTExMeHEdButg7xYMS/0C8v0jbpFaSnxLbWbqP58QFFqahWnrouibhu1e6lGrytI2y/ZY8nq3DHe1bf2dNIh5dKRK+wWJaIX2qrSUpUs0DKt7ZaypW0JS66ae54+iroTopDuq2e13tu/LDVSUqw0pUVzp0iIqE+Hnn+990hL076RtCgOffEOjNWQl5aYF0pOykcX688H1AG79FtGGnnRJnEOUTeRtI+r2zOgjJrw6n1c2VpdI+XUJaXZkSQsaqPkavOWF5nID0U2tfbBIXoNubbaStCWmlClfhN1v2io06XfUv5Sh5a/tkNzA2nX0Lq+Ldefa9+9C0OuvEO4/EKY5OXwuLiIxbzQgN2arNy+LX/7qCg4NdHo8d1rnZgjMxFosSYWoit5bqDV7Ki3e0gGPbalLM+1kZSqEXEyLfCqLtFVLnd/ekluBFHS4oHVlqW2661zdJvT+krvhCipqNZ+yU7pumntxEvCIoRmVJscseDRSFqdp7b5YMrLHY5VkJeJiYmJiYkJJ2bMSx95SSn9PIBHAZwDOMs5vySl9JEA/gGA5wP4eQCfl3P+Faus1ve8WEpLnV4UHCvWhYPF6ntWUlJ5XneNdpyVh1tJcdsRG+hxdb20POv4UZKtpL7U9kTK6kmvYcnrluuRq/fQbqOCHvXDcgNYfSPa1rx2RvIs6XrQVJaonRY0ZZbLG0GPwuwtH4i5umj6ki7sLkzy0o3PyDk/XG1/NYDvyzl/Q0rpq7fbX6UVcHFhx7xwbqPykUWJrFxc7AYDl2O9Lh1pErfOZQT2GZPBDbIeKV4q25KHR07uEiJkxTt4Rt04EUTdRVw+6R5q57dGv37kOlvn6Ckr2sYiiwjNBavZFiEgLW6jYiPNS+32Xl+JZGvn6XVrtpCKFsIhjfVr6htPYSovi7iNXgbg07e/Xw/g+2GQF+s9L1R5kQJ0S0xLrcTUTyqVsjTyoq3otAlRQuvkq4EbYKzBRlt5aMdTO5dazVsxAS11RslKzyC1zwHO64u3QFfrXJtYA7nR2kKrQsnBOq5nYuUmU2tx1NPPWlQi7riI6sKla+OeNI5ptkukZLQ6YhHLOn0+bXQY9J59BvAvU0pvTym9crvv2Tnn929/fwDAs7l+FzVwAAAgAElEQVQDU0qvTCk9lFJ66PHHf7nTjImJiYmJiYk7Bb3Ky3+Vc35fSumjAbw5pfQTdWLOOaeUWF6ac34QwIMA8Ot+3UtyUVMKJLdR/Xr/oqzU3yq6uLjcLi6jWmnRlBdtJSTJqV70kuRWH/UItxGXzypvTeh1F3nKHoXoypZLb1UiDuXb15RQad8+F51SXZxSMsKV56lby+dxdXlduq1tQlOUIkrLGrGa8e3YLtxgdJGXnPP7tv8/mFL6RwBeCuCXUkrPyTm/P6X0HAAftMvZjXkBdge0+iOH3AvrisupbNcxLhp5oWiJk6Boldmj/mnJVzzaLskvztnskYy9WGIi9UrjkbK8+2tIxFDa563r0GNZdIIedX/XQmY8x3ndzNz+kedouV+WcBe2LH56z7lnTBqxuFkMM+al3W2UUrovpfSM8hvAZwP4MQDfDeDl22wvB/BPeo2cmJiYmJiYqFAYbevfkaNHeXk2gH+UUirl/L2c8/+TUvohAG9KKX0RgF8A8HlWQfUbdOnXmYHdgF3ubbvUjVQrM5qbyCORjlqRa/AG13nLblEtPKtmKwiY26cpXSNgBdZpx2jBlpG6etQsK537vRrZWoAU5KkFfq79nGqMWJGP6KM0jVNi6bHeOg7lPvSqklaekX1ylQrMVF7ayUvO+d0APonZ/x8BfGasrKuPShdwMS+1C4l+HqB+FJojL1G3USQ9iqXaXu/AY00+0kQfdXPQMj1PaFjlaem9LkFPnhGDvjYQj3JHjmp7nmtouSA8JLjGyKdyPLDaeyusp2q8oH2nJZZEI8y97l/u9whECUu0nRXc4RxhtVjFG3Y18nJxsRvzUqsw5bHpWnm5uNh9z0uEvHiC2kYOZBFGb3W8pVZKmv+bIxeWTSPjHHqOaQ1q5eoerSKMJkajiQq3bRHTXixNwrzoXdF7Y9Mi5UoxcBp5t9qYdK+5MZXDaGLG2aLZGcknYdQ5LIZVGHE4rIK8TExMTExMTAQwycvhcXFhv6SujoWpXUj0pXVFfamPjayWpNV5SRspG7fYZG1bK6cRtkTcAL2ycxQRVS2qvmhK0kgVbJ/XqxU9rjUPrHuxpBLjtbs1roQ7NpJHUlassryKHu2zkT6sqdNcnKjHTe8dZ7i01nHQY2fZtwn73DNmzMs6yEvOlwSkoJCVkl43xtotRN1GNK/WCanbxpIotY5JocnoVl6pw3rOhSuvRSKtrw13nTT0prccG6lTCkq2iKu0zQ3uo10/HLQB1ZvfwhL3MuIm9Z7jPklLa/4IImSbTvLR2LFShvQ7SmSkiZ8+6CLl0+591FbpOK/t9b59x1pN6FgNeZE+llhUlzrmpeQ5P999j0spR4p5qcu0wHWgXuWllfhYq3uP8jICvYO7Rcha0HK8R3XRyvUMmCOVr6gK0TqoRtWUfZ6jlq93EvHe6xHoUYwksi2Rk0jMUZQQcAuusj/SHqkaExljtbao5fWiRzXaCw5uwGGxCvIyMTExMTEx4cR0G62DvNAPM5Z9BVRtoSqM9nQR95/C6yKxIvgjsFSYiJsisgKJYrQyMtJlFFVyrBVeJObFamOjztt6wq31yQ3r2Kj9nnthPbXW4vLap+qyhIusx5XncX9Gy5R+e8cYzsUiKRie/hZ132p2W/DYXKcdNOalNuAOxWrIS/3l5xr0pXUckSnb9TteaN56nwRtQvMOxBKi8TF0f2RA8U7go9Aq2Y6Wer3lWYO+RSatNtU7iBZIsQuj7mHP9Y9Oaly6FrOxD0T7nXYMB+ucRhIvT5yLxzXaM7ZwdXAkIEq4PWk9/azYVv+n+zTycjAc3IDDYjXkhX7bqIA2Si6Qt87DEZYWcANqtOPS8qy81mDqVV80SIOptrr3ImpD5PgIYfDU3+Jn90xqvYM/oJMrTR20AhytyXQEmfGWxfUtryrUi8h5cGktq3nPOUXiriTbSjlcfx7RriVoqkX924ojqetqUaS1vBFwCpH0/2DKyx2OVZCXiYmJiYmJCSdmzMs6yEv9tBEH7bHp+v8IWEqLttr1qCtSXitdU1o8K3tpVRddgbdca88x3lXTCAWmxR2j2dPiNtLairSK9o5VHkXD2/5a2oL3fnvcRloaLceqL5rHUtG4baufRe+lBktFjbi2W1ULyU0kqS/cttemaLoFzi7JJSS5jg6KVRhxOKyCvABXCQqFNlnXgwzdpvC4f+pje2JbImSGy28RlIg7gpOR6/2WyyGa5sU+XRV1Pm2CBPT7yNXTK2dLdXP5LJeXRgq8pMljpwUtr+Y2qo/1uJRGLl4i0K6PdE6R+SYyVlmLkJ6FlHW8FefiKaPYER0jrf0UXlvr7VXGvEzlZR3kpQ7YlSANFN4B0gOus0urYO+2VH7EFousRJUX6zw9No3IFz12JJHykACtjn1NmNrELZEubeKvofWPVmLohee86t8SARupYEjb1n4pr0VcRpCbiD0RWOOCpaYsQWCiZDpKuKidEaJzEKzCiMPhzj77iYmJiYmJiR2klJ6XUnpLSumdKaUfTyl9+Xb/J6eU3pZS+uGU0kMppZceysZVKC/A2FW9tdKk6b3yeF22tm1hX26j2p6WFfUSqkP0HvS4m6T4A82F1rNy9ShyLatoS1mx7Buptkl1RMrg1BXpHLWyl1IkokquVS89z6g9+4B0LSOqRhRLXQOPksK5hDgVZhVY1qgzAF+Zc35HSukZAN6eUnozgNcA+N9yzv8ipfT7t9ufvqQhElZBXuog3F5EYhcsaGREq78c2+qqkUhMxFVkyb2HHCRbJs2R7o0Wl5EFL2GW8ljlcrZaLpWlIdUdJUZaXItETrRruAYCECXIUQK7JEa6rka7yqKIurNW5xqSsHDMS875/QDev/39aErpXQCeCyADeGCb7ZkAfnExIwysgrwA7b5Lms+jfrSs6D0qjrZibKkjorx4yrfSOCzpV/cc30JOvHVZ5FI7VkOLChdV6by21Hk8k8ao8bCHzGhledP3MfFw44lWr4c0tygd1IYWtNbbkl9Tm0a2P247Ql40m1ZBNvsNeFZK6aFq+8Gc84M0U0rp+QA+BcAPAvgKAN+bUnotNmEnv7PXiFashrxMTExMTExMODBGeXk45/wSvZp0P4DvBPAVOedHUkp/EcCfyjl/Z0rp8wB8O4Df22tIC1ZBXqjbiJMapTRt1WX5k60VXa/7gPNpe+rxuI28ZVn7KbRrPxpW2T1uPi7N266iilkNyU1kXdcetWWUK7DX7RR19RRIypC3Tktd7R3jLVdwS/l0fCjltJbZ02+9rpXRkPrFUm6riJsooiA9XZFSuo4NcXlDzvm7trtfDuDLt7//bwB/6xC2ASshLxTRgcciDN7B3uOa8dZlTZRRF4mX4FhlWhjhvmst30qPupC0/B5ffCs0N1ELmbXQQmZaXFtSObQuzqXqsStKejQ7Wl2CEbSQPcmVJrmhrfrrMnttq8scOZFH+l2dfxTprH9L/yX7Vo0FjU0pJWxUlXflnL+xSvpFAL8bwPcD+D0AfnoxIwyshrwsNcDU8AzqGmGo91kDdNnnXcFHiIsHo8jMiJXmCHVrCZVm1KBpxVxYbdt7rLbPsk86ziIYI/pij6ITVUtpnoiKy9Vn1S1dH+v6edQ5zQ5ue5QSYCkUFNw4WKdJ254FX2v785CwVoWJnhP3UtW9YFmm9akAvgDAj6aUfni772sAvALAN6eUrgF4EsArlzRCw2rIy8TExMTExIQDyz9t9FYA0icnf+tiFQewevISXZG0yNVeO2hZ1grPa7e0Ohmlnmh1cGhdEXrqter25huhxIxWF6R6rXiEiLsqcl4cvK6GqAJjuaFa7dXqK8dSuy1FdES91vER90jJr7mRIrb1nhfdbukf3nY6cqzxYPRYdlAclY9rPFZJXvbRiD021P97y/EMnl6XlXe/px4rv0bQWjDa/TPi+JGDqNetUdelHW+VFyEq1r3UYi6saxJtYxo87i0tvYeERdDrhtau7xrGQEB2G43oZ9K+SDqFlLf3Wo5w306MxSrJy8TExMTExISA+WHG4yMvGhOX1JIeFWV0MFxBjxoysl6671D9odXF0HLcPqRpr10RZaW1zXBqiuX2tGyJ9AuPYtijBmrulrUqGoDuShoZvErLlsrX6o2MP15F2VL/RsLrXoyUd3CspSEfCEdBXrgBSMur5bu48JEZblCU8kX2e+AlW61upNHoIXg9g6I3nRtMtfuzhMTszdtLBkaOZ56FQiRN6pseSV4iIB5yoo0fhx7/pcmUS6/3RWN4tD5gXZ+WBaPHJppnKdJSytbIO0UkDu1gOHTjPTCOgrwAvgbOdaSarGjHRKEFuHn2cza0qg+94GyUAvaswL59dvR9qVVAG3mOpGsTeHSiHwmr30UII/0dIW5eO6IxLtYCpvVYTzrNy7UxaZ+1oPOoWpZ65bXbqr8H0flZU5C4vHf4/H/UOBryMjExMTExMYEZ84IjJC+e1ZkmT3NKjEc6lNKp8jCqPS25krHK8p6DpNh4V3EjXUY9+Vtcgr0qWY/r01sOYCto3nvd4s6KqAGR6xmN1anLj/TPnj7XeqymHHhdHl41pCVuyVI0NGVwiTgeCZwd3DWTXIicbavjCqszaBcppf++4bB/kXP+sCfjKslL64DqIS/a8XXdHkKjuVOkfS0YPcl7fb102zvheQfFkW6mEeVY9zNSf4tLRNrvJUDSvWtxAXrQ4jLybPeCm+i5ukaP/VGiGXEpc8Gm3ro896m+Xt7rZ5XJpR1yvj07u7rPE0tU8rUQ4MVwHMrLPwzmzwBeCODdnsyrIS+eWBYrzUtevBO8Jz1CZrxoGcwt331k4PQQGG579CSklTlCzZHguXcaQWtRGCL7W1WlEcRlScLSqoC2TopLtNdIHRwxkLY1QiHVNYokHpp0tCLa5iyCsrprsDqDWPxnOecPejKmlB6NFHwUZz8xMTExMTFxVHg9AJcLaIv/C8Aj3syrUV4kRFa2lvJipVnwuFBayXB0hb3UqrHHbSTFu3h99YeEZ6UViQ1oUV9a06w2OEod7FVXllTLNOxTORjpBpVcRVYf6lEcaHrEjS7B65oZiWg/pGNVDSlW56DCxxG4jXLOXxjM/ycj+VdPXihoY+whLx70TARLTdQtZUbaecRtxB3b4tawBsclJr7oeXpjeSKuytEE1esaktKibiEP6e51fbVAmnz37c4dBboI8B6jbVv76zTpunkm/H3GimgLW2teoATxmjA7roYzrMaQw2AV5IWSSK/aIpGVEb5e72rWm2/EAL6PwVNTV/ZJ0Fowyo4RgasRAj2SgNF9dIXYS9B6VM0R96c3/oWutrX0Q4CqK9K4GLF99NgjKRF1+j4Ii2fskeYK6Zpo89DquMLqDLqKlNLnAvg2AJ+Yc36EpD0TwL8H8Iqc85ujZa//7CcmJiYmJiYuUVb8PX/7wZcC+KuUuABAzvlXAfwVAF/RUvAqlJcIOPlPkwrpsRy0+9givR8bIjET2jYn1XO+em51v08Fx3vfpJiXHlstN1IPWmNfpLIiNi6lunjvTQtG3ANvGZrbpSXdU+8It5EHVh/XxgMLnJLGtWPv9Yj0P0vpmnDhEwH8aSX9XwH42paCV0levAOnxw8/cpIY1YAPLU3X6CUuNK3XBXKoa9NzbyPytQda3rUMoq2TwL5huZFaytuHHVGXET3W2tdKZGhb59o+R2bKb66+HnJD64ySGVoGl64tzg6KtQwGOj4KgHalMoBf21Lwas6+VcmK+t2lMpZqiEuWvW947g+3KqrvbWT1H617JKR4kKXVtqdLW1kaWr8aGVM0spwl7SjXo4W4aMdK9Vi2eOpuuR5LjwOea7iKMf143EbvxUZ9kfCJAN7XUvBqyMvExMTExMSEE8dBXv45gL+QUrqHJqSU7gXw9ds8YazObeSRJSkiCgutS8ur+XJ77DgGRFSWJVxzrauyXrfVKFhSs3SekqtAghRfsDQi8Ust93QpRevQSrsWwxJ1K0XzLKkua64fqW1Iaa3t2eOiqvPSfdpxre6sRbEaQ1S8GsAfBPBTKaW/DuAntvtfjE0wbwLwl1oKXh15KTiEX1HrLJ5O6Sl/bZDOOdJRtYnZIhSt97nFx83V2wpr8IsO2EvYyMVNLDXeeWMhOIywyyKJI+rqJcdRO3ps9RCX1nOJkiypDS7RPrXxYFRdtOyc28q5E5Bz/mBK6Xdi87j0X8KGrACbWJfvBfAlOedfail7teQlAu8ERgcfrgFrSgs3WXoZfMsqyFsXhYcs0PyR1Y6U7iEqXsIhTX6eeyyVveTEXcBNmtEBe1RshTaRt65qNWLqIQ5S2a3YJ0EDliUwEdt728xIsu8liBFVpi5Xak+9iy4Oo9WvxXAEb9gtyDn/AoDfn1L6NQA+DhsC89M551/pKfdpQV4mJiYmJibuKBwJeSnYkpUfGlXeKshLSrvbkdVNxKda9nkgrSw1FSQar7AEvKrMPtp9z33srcvj1lvCfWCtFgtGtiOvWmjljUCqi9rv2b8kLHW1BYdwaXP1H7J8j8ur5JPanFeFjCqEI3ByclnW6PYzBEegvKSUrgF4+Xbz7+acb40sfzVnXzcWLZ3+ecptBefqoX9SeqRcila3TQTUznq7l0Rw+1ps7r0OkXbSinqAq+uRBj+pDUu2jm6/9Le3zXK2aOegnbO1PwrLJXtQed+Adu2l6+3JG4V2H1rG3QKtzdW/uWsQuW9Wn6J/167p6dw5amPmBIvXAHgYwC8D+IbRha9CebFgrcol9h6NFeGgrYi11b0UZ9CCXlVCg7WyWTqGIArtXnqv0+jzoatLC5HVp6RuWOXXtnhWwd6ypfyRti/dQ2nC2De818TT3iz1r8Umb/6I6mnts8YIDV7lhZbVszjl2rd2vDSeSFjFuHhwA0wkAOUR6eHGrv7sJyYmJiYmJggs6SgqLY3HqwDcBeBuAF81uvCjUF40cOqHN5qdwmLfVkyFtZJYK6zV0JohqWz1PppXKmNEvVSR86w2aTmedsZhRMxABB4VxmuX13WyNDjFobUPc+6rUeejKV09igvd71U/vIogp1KOUqc0u5ZQvUrZB3lU+ghiXnLOtwG8fqnyV0NeRkzy0kQRITCaXR55fxXBXAMgkYFDTSYcEbHuq2ardR7ec7VIrJescPlHE1+tb/TeV4/8b5Ey7tiJqxhJsKSyLbeR1NeidoweU6xFxdMKT7sTiuHOPvuJiYmJiYmJ4UgpvTSldBrI/1tTSte9+VdBXlplNy/xpC4+yeXnWY1r2z2wVjqRALo7Bdw18bhzewIBvXZYabWdUn4uL7fdYjOF5rZpcZlL9i1xHiP7QqSPt6ZFQNvJEm6nlrwjbRkJ6QkgT7+zlNpVnfNxfJjx3wD4yED+twB4njfzatxGNUZ2/Nb4Acu21TTihTByclzSDaC5lbzHevNH7NBcl6Nw6DZouXpGufyiT4L0Ev3IE0TRYyks4roG7CtuqgW947HUb48C6zc2AfjLKaUnnPlvRApfJXmh0AIUo/AQGq3s3k5So2dCW0r1GTWY0N9SHitvgUUAIj73JVUsqX2NIDPe49Y/pl2Fdm69KsFItLTdY0Q0CLjnGlBVpOUeWrFlIyDZRV+yujesv6P/AID/PJD/3wD4sDfzUZCXiYmJiYmJiS2O42mjT1+y/NWRl8KUR8WXaJK055E97TE+KT5Bys/ZZtlRsNTqzht3MQL7OK8Rq/TIfsmlseT9iigU3nu5BpdgRD2jx3j3W2kUS7mI6HbL+LEEpKcorWM8oOfoGR9pHu1JvhZ1yKPmRtMm9oPVkBfJ3WCRmIiMPvJx231O+iPhneBazoUjniODnLmBbEQ50r5IWuvj95E0jz1cek/8khQL4LFz5P3Zx7EFUTd1NCZnDdAWZb1xgRTaWFITFMllpF3LUf3OQk8fWgwHN+CwMMlLSul1AP4AgA/mnH/Tdt9HAvgHAJ4P4OcBfF7O+VdSSgnANwP4/QCeAPDHc87v6DVy6ad8vO+bGLlyGj1AaPBMvj39oNUf7lkJLbn62efKSmvDo2K6LNIygsSMyDuirY+Mg4mSyQhx28fip/d6RoOlNXjstmJcesaknoVB5F7V2weJeTkCt9HS8Jz93wHwOWTfVwP4vpzzCwF833YbAD4XwAu3f68E8G1jzJyYmJiYmJh4CjX7a/k7cpjKS875B1JKzye7Xwbg07e/Xw/g+7H5dsHLAHxHzjkDeFtK6SNSSs/JOb/fqqfFPRRVZDT3kbZf20dXS942sZTaEln5cNue1XqNiKIgxTNJ+ZdCS5yEt8zoOXhcbZ56pe3Ivh4spbZE+lMLIiv1XqWwF/tQtKLlaDEpNN2aN7l0Lm+LuuJ9mtEzNoxQq4fg4AYcFq0xL8+uCMkHADx7+/u5AN5T5Xvvdt8V8pJSeiU26gxu3Pj1O2nRmIlWadMaeDzBuq2yLxd45j2PlkEq6lLwdGLPQLGvAddCNE5kKVjtdyRxiRLROs+ItrgUYdknQYgukCR3sEUgeyfEUa71aPCtFEfobWs1WWlpr97zlvZLY7B2v2jawR6VPjKklD4XwJcA+FgAvy/n/J6U0hcD+Lmc8/dFy+seBrYqS/gduTnnB3POL8k5v+TatY96ar9XYRnlN7fiHriVgEZctPyHwuhJTrr+2sDRqzB4EVFItfRRyqt0nXrasNUmtW2uHO+KlzsPaX8rwW65VzTde3+8RITer3qb/h0CI+rV+jR3jpHzjvSh1r5F7SjbZ2ebP+2etdy/iJ2LYOE37KaUnpdSektK6Z0ppR9PKX15lfY/p5R+Yrv/NT5z0+cDeBOAnwbwAgDlMwCn2Hx9OoxW5eWXijsopfQcAB/c7n8fdl/v+zHbfRMTExMTExOjsCxzOgPwlTnnd6SUngHg7SmlN2PjZXkZgE/KOd9MKX20s7xXAXhFzvmNW7Wl4G0Avr7FwFby8t0AXg7gG7b//0m1/0tTSm8E8NsB/Kon3gUYJ3u2gGsDmnxY75dWq5qawUmUUdYflXdbtykibo/ee+qVnltgyfgj4TnvJVwsveeotbMR7luvDZH9Ur7IPehpw7RPH6JN9d4H7d5ybYLui56zpFrTslrGHu1a0DHYspGO+QdV1Bd+2mg7b79/+/vRlNK7sAkBeQWAb8g539ymfVAuZQcvxOYNuhSPAXigxUbz7FNKf39b6W9MKb03pfRF2JCWz0op/TSA37vdBoDvAfBuAD8D4G8C+J9ajOKwb1nWktrL73q/dhwtoyfdyiO5ASLbXmhSsye9FaMlW8/1jB7nwcg2PaJdtWLE/fTmO9SkIbmvufY8mtxZ7o0e15VF1jh3jNXHR0C6z9r1lq6TZXeL2+hpgGellB6q/l7JZdo+sPMpAH4QwIsAfFpK6QdTSv9fSum3Oev6xe2xFL8LwM/GTfc9bfSHhaTPZPJmbAJyusA1onoFRVdT+5pAJLKipWv1RFaFlj2RfFG1xYvIKt1Cq2og1UWPtVbFVv3Rc9LsWnrQPMaBdrTNPX3Nq75wbSqqSETsW3Ixp50zpyJrNrUqMRY8REs7llNeRs0pe0G/kQ/nnF+iZUgp3Q/gOwF8Rc75kZTSNWy+FP1fAvhtAN6UUvrY7dyv4UEA31K5jJ6XUvo0AK8B8HUtxq/mDbsTExMTExMTTizMsFJK17EhLm/IOX/Xdvd7AXzXlqz825TSBYBnAfhlrayc82tSSs8E8GYAdwN4C4CbAF6bc/7WFvuOmrxEV61aOcDybqloPVRtGrFCH9HetVWZd+UjocdubxzASLddy72xymuFpCZpq0nvqnnJvrFkbFPBKPtHxbxwK/6WeLN9gOvvksJkKc6j59t9XRNOYXo6x7xs35b/7QDelXP+xirpHwP4DABvSSm9CMANAA8bZV0H8FYAfwzAqwF8AjYhK+/MOT/WauMqyQsXACYFjEXun9XQR5EhD6TAN8Dv+ojWJ8F7PUf78Wu0uok0Oyyp25KIrYknAkmW1mzx1KMNqtEBtqf9t7jUWu7v6Dgnj62jiCrXHqVxbSQkUuqtm0v3kjTOliXmXK9rj6J1jNnnXCFiWfb0qQC+AMCPppR+eLvvawC8DsDrUko/BuAWgJdbLqOc8+2U0gs2P/MTAB4aYeAqyQsHbUBubahLrHJbESFs0XIj6PG5c4FwrTa1XltP3I1nwLXSeu4NdzxHXLn26omr0Mr2qk4csfYO3r39wiLQxxCP4FmptxLNpVUwrj16Y0IsEkTzRJVCLzyEjG6XPwu0XDPa4wiRc34rAOn1e3+0ocjXY/Ok0p9pNorgaMjLxMTExMTEBI7xw4z3Afj8lNJnAXg7gMfrxJzzl0ULXC15kWRobv++fMVRX+9IHLKdev38I8CtiLQ6I/E21io3UrdXoaD7JDXE4zYq+SR3I2ebpBKNaE8triJveRq0e9tjj3VvWiApF3V99Pea0OOmp7DauKSILuWablVaVnOvjou8vBjAO7a/P5akNWlXqyUvNTxEhkv3HB9thNrg3yoDS5K8NEGOwEhCNwJe4mDBIjClbI0AeFwr0vGtLiSJlHvjXjjbovEInF10H1cnTTsElugjvf1Scx2NcK2OhEbYouOk5a7RFqMWIW7t09w++rt1EXwwt9GhO10AOefPGF3masiLl21b+XpYu9VZNJ/1ktBW+RK0896nkgLoA3YraaGDFS07ei81u6wJxzO4S6TWmuCssq1JpmflrNW9mtVnJzwTtUfJsiAdS9tWz4JllJrmaaMULSq41DZ71EHvWFm2pf8USy8mwzg+t9FwrIa8TExMTExMTDz9kFL6bi095/zfRstcLXnRGLCWz7PyjErx9T7vClqDZ9XllUG1OuqyNexrJRFRO7h9UcWo17UhrdY8K9H6mJ7Vprbik7Y97dLbXpd0FS3RNkes1i2lJaKM1dCObVFXufJbjqPwttm63ojibfWjEeOc12UkuZE4rEJxqXFcytBrqPUAACAASURBVMt/JNvXAXwSNh9y/q6r2W2slrxIiJIVDzT3g5R3hMRZQxskPXZ54XGvHALWYFO2R7i8et19vS4Ta8DuiTfgjh+F0a4ijWC31tPrcuCufYTMROzTFjA0P7XTW08rIm6j6LWW3G4eUqTBcnPSOk5O5PIjZOwgMS9H5jbKOX8htz+l9NcAPNJS5vGc/cTExMTExMQGhX21/q0DfwON30M8OuXFA+9KKJoekUs9LpKIHK2V17LKko5pXelJ+S3bvCsfihEKjATatyMr65a6pLKllaSmFC49Jo1YgY9QS5eC5jKRXLk9ak/EPVMfs8+5J6LCcJDaSY/rXYJUdkmT3ETR8fXg7fXIlBcFv7H1wFWQF+0+jJyYI2lSg9U6RxSW79w7aY5wpyzVGZeQm731WhIy3Zb27wNcu1pygPS46SwbWq/PmsgKB+v6S66eyDlJCx/vZL5vAlOgkTsOlp0aGep17Xq2PXXQc1xz210rUkrfQncBeA6Az8XmkwNhrIK8RBFRDbhG19MQrVWWdwK0VlzRAcKzz5NG4e3cnnIjg5FnhddTn7YKs8o79OA1yg7P+S91zksRGI/iKUFSVaRFhkRiuLJa0aPqaLb0EgPv+DSSYLVcU62NW+3fW998z4sLv5lsX2DzJeo/hTuJvExMTExMTNzROCLy8rR+SZ0XlA1b29K+er+mxLSufKyYKMuP7FndeBUozwrCe37ec4rULaFXbZEkaCoZH9JdVNCqBmrun9IG6TbN463n0IqTBq8SapVhuck4tx6nTkl9oWUciaInDsUL6xp4Y0usuJTRWKKvJ+nzhUviyGJeUkq/HsB76BeoU0oJwPNyzv8hWubRkBdpcG8hK55yubSWtuKRqemgSOvznGPknCMYFdfTa0uUgNXXj5uor12TJ3maN2KDFy0TjAeRwX+f57tU2d7YnZ7ypUVE1G3U49Ly5I3EtXHxNlEbSn5vnIo3DmUfiLqJ6X1fM5FfKX4OmxiXD5L9H7lNO40WuBryQgcCjay0khcrrSVfDW5lS9Pr8qXOUfK22jo6PiSSR6s3Ej8RGYhL2bQuaXCUFIklBs9ekqmdp9XGLOXFM5mudZD2TnyexUNr3RppiZKLCAnzttPIOKidkwWtnXjOK9rvvAtOq69YdkmLy9aF7HCswgg3EvgPMN4P4MmWAldDXiYmJiYmJiYcOBK3UfWUUQbwl1NKT1TJpwBeCuCHW8peHXmRWHytyHiZtwVPzIsX0grDu3ri3EYSIrE6EiLyrjeNq5e7n5IC06uYaSs6S5HQyuFskdqjdoxnf6viJ/3WlBea37KDu5deNWGEGkrL5PaNUgwitnjcMS22adst0OJcPK4wDZ4xg3PN9ow1FNGx32o3mtsoWt8iOALygsunjBKAFwO4VaXdAvAOAK9tKXh15IWiTA7ShN07+O8rJkQb1Eq6Jr1a8ThSuV6MGiw1UtZCLFvr5n57/7fYw7VPb1nRbYsQW+4xq5yS1nrvJLt74D1nKz+gu2cjZFury5r8o/c0iqgrebRLROp/1oJujfMx7QvA7vZBAnYBZByo4gDKU0Yppb8N4Mtzzk2fAuCwevLCQVv1RlY6LcRHmgi86XU+6lOlZVh2RQiMd4UzgsRofmfrXCKxH9w2N5l5Vnye82ydjCMKWY+CZpEX7jivOsKhl5x4j4+oLZ77qJGRUYTLs1iJHN8DqU1JyosnvmOEIlPSlyIsnO0tMT00/2piXo4IOecvTCldSyn9TgC/HsANkv4d0TKPkrxMTExMTEzcyTi42yqAlNLHA/inAF6AjQvpHBv+cRvATQDHSV48shuntljxBvu+udEViuRf7lkBR4+VlAotr2WHdl6a+iKVp0FSVCy1JbJyWqId9cQrFVgqU/QaRO5ND7wuOAma2sTliZTtrddCVGlpqbtXKRuhQnjyaG7M1vJbMNoldkjykPNxkRcA/zuAtwP4ZAAf2P5/JoBvA/DnWgpcBXkB+qRcb9DgmhGd1L3uBolIaHZo255jPW4iT3oE3MDYSl5621vk+EisTF2H9NvrIvJOQCMHyF7S4kXEpRGJPeu1ofUeW3mibmUun7TAaBk/1zLmRsmm1T9GE9JeHBl5+W0AfnfO+fGU0gWAaznnd6SUXgXg/wDwidECV0NeIogGR45k29G6vIPAiEBPbr/n/L2KUYvyQgfC0UGcEtniJvVoOxihjpT6l1QIPddgCYxU0UYpIp62Lk1CS08Gh57s9oGexU9BjyKkHTtK/VoDaThC5SUBKI9J/zKA5wL4SQDvBfBxLQUeJXmZmJiYmJiYOBr8GIBPAvBuAP8WwFellM4BvALAz7QU+LQnLy1uD2DMkyV1mZoq46nzkCy7R4aP1GFJ+JodlgozYtXrcU+OcEN45WnPKnfUar+ch+SG7GmfHlXGcvNp/exY4XVtSm1MUkC1eg4VjxIBd68lxaVVCTqGdnRkysurAdy3/f3nAPxzAG8B8DCAz2sp8CjIizaJeRowLauGp0O3olVSLzEtlnvMM3G0urtawbmJIhOe915KMS49LhPJTdQySCwlMbe4vryDfY8rzCqn12WotSmuT0vXac0DfnSh1ev+isTVtI4P9F54bGvpO1HXuHdcXDPWbl+NnPP3Vr/fDeDFKaWPBPAr9GONXqyOvEiBUq2dJ9Koe1fIrYgGbXKde9TKsydupwVafIx1XHTA0tATRMtN5JZi5Fk590KLOfJMEB4lzENArf0RWHFUUt9Y40C/5MpeW0RYttC+1dKPpHHVKiuSV1uIaiqst3wLh2xTxxTzklK6DuCtAP5Yzvkny/6c83/qKXd15GViYmJiYmJCx7GQl5zz7ZTSC8B/mLEZR0FeehhyK9PXVpQlParaSKqCN/bFE1dhrYY5mzRlYPTqUFMCIqoRXUlZ5+BZiUdVCGl/pG1o8r9kh6SySaqE5jaS6vW2QW/biSghnuslKXbUTq28VnfsSLfMvmBdI89vbhuw+1JEcdPG2Mixnm0vjoUkrByvxyY498+MKnD15GWUH7iGJi9GJ22LNGjH9OaRJqWWTuqd8JaWuqN5PTKwRSa85NFjDzeZWmSEHh+Fde8sMkDL8cAj2S/huvEStCVcVtHjIy4QCmuBw+XXrolmA9eHlnCvRNsAtxDoITPW8ZZtayIxx+Q22uI+AJ+fUvosbF5W93idmHP+smiBqyQv2oDKsenWiYKrIzLIaYMmN8hG4V2xc/VEB1npPHrUEQ8iape2P6KwedO9BJKbpCPKS50nOsBHyIpHgWq9t1yfklbRkp2ee9AyAUple46tj4u2kRYVoWVylvpNZOzxKi8ejJxUtfpb07zwKm9t4ab9ODLy8mJsviANAB9L0p4eAbsTExMTExMTMo5NeSlflx6J1ZAXaRURWTVYTFlyIXGrZO+KzbtiHqlYaK4daZuzp5RFty1XkXU9WxGVeXsk4FHHFDs0RSPqNoq2Z6kNRlQKj4shom5FbPGqMJx90Xs2UjWMqHm9qqXnXkr3ULvu1m9u2xrntLQRap9HGfKW6VVXvO1zn1iDDYfEashLgWfypHmlbQmeiYYbxCOdtmXisPJIdlvEJTI4aO6u3rJr28vx3H7tmB4s2dlb241X+ufgJd5cHVw/o3l7JhWNhEVdP7QMqy9YxF6yucWWCKHQ7FwSVh0cGbD6ojaZe6+JlN9LSCKEy2MXzeM9p0O5jY4NKaXPBfCl2LiNPjvn/J6U0hcD+Lmc8/dFy9tD15mYmJiYmJgYiYuLvr99IqX0+QDeBOCnADwfwPVt0imAV7WUuTrlhcKzaoiuTj0rREne9N50T76oAuFZ3fe4DiRoq8VWCbm2z7Pfs0KNSLuS0tEL6XpLZfeqEtbxlrLosbEFlhoSgXZfJaVF6w9a2VyadzVuKbgeRZnr25adFBEXE7c92rVmjRfSuUquy6hSVOf1jgne+3woHFvMCzYE5RU55zdu1ZaCtwH4+pYCV0leNCk8AjqQlXKkm64NdFF3gGQPV1erPC8NlhZZ2ze0623tax1IWwYcbZKLIEogR96fiHtFO97r1mglJBoiLgjLHWP1h6Xso9eZs5PLZ5XrhTXOSdtW/7P6lUb4PG0uAonIeOqg6kOUqK4Ba7RJwQsB/Btm/2MAHmgpcJXkpYY2UZf9Xr9yVAmwVqwjVI1SZsuA6rGpZyJtxahOFR1Il7anxQ7P9ZaUN458a/VxdkX6SgvWMIBGxoCSp6RJ5Um/rQmvhscOS4nxIKr2HQrec6SEpFZX6DY9Rhu/6f2jeSLEZQ3t/sjwiwBeBOAXyP7fBeBnWwpcPXmZmJiYmJiYuMQRuo0eBPAtlcvoeSmlTwPwGgBf11Lg0ZAXi1HT3zVa5T+P26hF8pcUBWlVYq30o2qL1xXAbUdXc5ZqcIjVYa+7jtu/1EAScX+1rN6Xuv6R1eo+BuEWt6+krHjHE00RKrZE3XT7xsh7I7mJpfGFG4ci+Wt43F2RvraG+3RM5CXn/JqU0jMBvBnA3QDeAuAmgNfmnL+1pcyjIS81vG4gjz82WmddluVL1whDD0HwTJaWT92CNNBYiF5bzb4IOYzc2yh5s45pndy85UWP9ZDglsHXIsBecrK0G68Xh3Qb9brxOFgLhqj7Kno/NTcQ/a+5ibhyI9eJu3/0Hh4TIVjS1pTS8wB8B4BnY/MG3Adzzt9cpX8lgNcC+Kic88OeMnPOX5tSejWATwBwAuCdOefHWm08SvIiQWLPvZMJh54YFW7bmjAttUWrs8UfPmLwbFEN6OC9xGDOoZWseWCdt7dNjiZmVsxAqy0e1cVa1VpqqkXQeslZva1NcEspLyPavUUmJWVZIzveSV5TWbxkxbNtQbsGPXNDXXdK/uNGYQ9uozMAX5lzfkdK6RkA3p5SenPO+Z1bYvPZAP5DtNCc8xMppZ/Y/m4mLgDme14mJiYmJiYmLpFzfn/O+R3b348CeBeA526TvwmbR59Dr+dLKX1FSuk/APhVAL+aUnpPSulPpdRG/45SebHYcr060GRfCsu1E0XrKljK28q0tbI9LgYNI9m/tvLsqcdaTXL5W2HFSXhdKyMgyfPWvhF2jXQVRVVCr4rmVYg0N5KFXrfRksoj5wbUVJlWeN1E165tfl+7xqdbbqQa0n2kc4N2P612tbQabGFfLq6U0vMBfAqAH0wpvQzA+3LO/z7COVJKrwHwSgB/FZePTP8OAP8rgOeg4UV1R0dePGRFIy9cGR5/72hyEXVTcBN7C1oH+p56I8dJsnq9r6WepclKJN3rKmr1w0sxBnVaj3ulRtR9VO/vced6J5IIIbPssoipVm+L20giOh60uumi8PRPqT0WohJ1G0Vsi84NEiILzX1gkNvoWSmlh6rtB3POD9YZUkr3A/hOAF+BjSvpa7BxGUXxxQC+OOf8D6t9/yql9JMA/gbuBPJSw2LTmq+algP4JnJrEvQ0qCVVFk99VkeUVl0ceWqx1UsyuIFxnyt4Cu9Eba3etTKXJKYSkeHySra0KEeRcjR7PPlaJpYIWfHeLw/ZsJQXjsCUtAiiBFvrZz2LNIms1EqLRWZomfQcLLKizQnW9Zb6yyFiXoAhc8TDOeeXSIkppevYEJc35Jy/K6X0mwG8AEBRXT4GwDtSSi/NOX/AUd+PCPua6OBRk5eJiYmJiYk7EUu6jbZxKN8O4F05528EgJzzjwL46CrPzwN4ifNpo+8A8CUAvpzs/5MA/m6LjUdDXlolZo5tU3hXNx7p0FIOvOAkY7oq4/J7/bZeGVS7Nkt0Hs1+jzw/Is06JuLy8exfWn2RZPeoAmj1wRYXWgtGxh5ElJYWhVFy/VhKC5feC68qHO2DUjn0N1VSisoScRuNBKeI1tfbGuPX4D5aEJ8K4AsA/GhK6Ye3+74m5/w9jeXdBeCPpJR+HzbfMwKA3w7g1wF4Q0rpW0rGnPOXeQo8GvLCwSMNWr5Nr185Oul70iVEBwjOtSPZ0XIeGmlaGhbhtM6dOyaaFiXLdNu7r7VujaBKg782OHvt6bE5mt/jEmutI+I28pZp1SWRBM1tVJfhuXcRd1fvgkQbNyW3T5TMWIjMBVwf1M5tJGEegaUflc45vxWA6hDLOT8/UOTHA3jH9vdv2P7/wPbvxXWx3gKPgrz0qC7aNt0fmfxa2Pe+J37AJiuH7oQUHnWszue1v2fwiZK2FlViBDzn1LpqlPpSjwqlIUI0l1iRc785O1rvqxVfoak2dHsEWdT6nUfx9CoWFjnxKIU9algPJBJ2KBxiPmlFzvkzRpd5FORlYmJiYmJiYoMj/LYRUkrPxsYd9dHATpBuzjl/W7S81ZCXFr8qB8tNxOWPrsK539w+K/7EUoYiK0ANPUqLtUprWQW1rBg9Sov3Xi65Ut83OBXCiiGQjgX6XIRLxbRwaZZ7l9rkaXOjFSIL1OXp3e518Wh21PVpaV53rdXmLLeSVX6BNE5F3USHVlMiOCbyklL6owD+FjauqF/BrnsoAzhe8lLD6shrhEVkAP+kH5HkW+yQ9ln2tEIaCCW/v1anNIAf06ATwVJtv+V6jW4XFBFXkTf9GMYOIOYmGllnNE0amyO29txnDZSscGVbBNFjRzm/Y2hXK8KrsfmC9NfnnM9GFLhK8qLBajTR1eS+Jj2OrNB9kckh6veng0tk4GqdtKTBwdrn2d+D0ROBh2xLZEuLdbBW2ku03ciKe1+w2vqo6zBifPAovVL53ok1qjBqRERK1+qmx0aUGK5+C9ZCxluXx0aNwNB55NALpiMjTw8A+DujiAuA+W2jiYmJiYmJY0KJeen52zPeAOC/HlmgqbyklF4H4A8A+GDO+Tdt930dgFcA+OVttqee/04p/VkAXwTgHMCX5Zy/t8Uwz+pTYr6SK0Fiyy0rgF7WTRsQt12DWxFIqwRNfSrpXheWtM+TxtVF7dPuFbefK9+DfcjwEqLxDJJS6I3zWALSfVzD6k+7lz2u1163red471hluWqoDdq9su5bi9KqXX8rLaqMWyo2d3y0v0fc6ofCWuxw4k8D+Mcppc8E8KMAbteJOeevjxbocRv9HQB/HZs35NX4ppzza+sdKaVPAPCHAPwX2Lx85v9NKb0o53weNaygJS5iqQbrJUNaGeV/hLxwg9XJyS6JseTO+pponb/VRaRBIiZamnaMdCyFx0XjtdOLCFGptyW30aEJgnatJLu5/N56PJD6omavp25uMo1Cu9fWcVzdnC1RO6V71UJmOLu0Y7l66Hb0nnkXXlzdksvRqltb1OXQt5XvWPwPAD4HwMMAPg5XA3bHk5ec8w9svyrpwcsAvDHnfBPAz6WUfgbAS3H5FUkWKV39PkTdILSVqYaWgT8ymY5ayXNkhrOLkhX69VXOzrp8uo9Lt1YynF1RRFZp0YlEGuzrfVEyrEEagLVJ3ZqIIoO7pbhJaG2vFpmRbLHa9ihI9UgTsGeCayFCdfl1GZG2JhFCzyJKI80R0iCNG94Fokaaesm5RlwKvHZydnjaEnAY8nKEj0r/LwC+Muf8TaMK7Bk2vjSl9CMppdellH7Ndt9zAbynyvPe7b4rSCm9MqX0UErpoVu3fpnLMjExMTExMcGgXvS2/O0ZpwC+e2SBrU8bfRuAv4CN3PMXAPw1AH8iUsD209sPAsADD7zkCnetlZic/aszyuyltGNEuQblNdpFeTk93fzVeQouLoDz890VyNmZLvFbUizNK92bEa4ATaVpKY+zzTrHKDRJnlvda6vgXjcGRURl8Li86uOWuqYjFDkvuDEi2o65sjxjD6dScGncNgetjUn30qtcRZRZTfHpGY+5Yy0FJlK2ZOda5pC12OHE3wbw+WhwD0loIi85518qv1NKfxPAP9tuvg/A86qsH7Pd1wWJyHilb63Teo/15ou4nSw76/3Xrl2SlfK73i7X6PR09xzPzzfpZfv2TpjUJZHxXpPooOlFj1uo3m/tK8dqE5SU5rlG3ACtuYmoPRF3lxet7pwaHiLjJYcj4SExVv1RUqCV771X3j4ntYsI0af1SQTasoHb5s5XKpdbVLYsQjTbJLSML9J50//TbeTCvQC+ePthxh/B1YBd18cYazSRl5TSc3LO799u/ncAfmz7+7sB/L2U0jdiE7D7QgD/tqWOiYmJiYmJiacFXgzg321/fzxJa6J/nkel/z6ATwfwrJTSewH8eQCfnlL65G2lP49NJDFyzj+eUnoTgHcCOAPwJd4njYrbg8N5VUKtwniD1DTZkkJayXHbWh6L6ddqCPVD0rKp0nJ6Ctx1l195yXmjsJSyb968es6lrJIvIndbaFFiPKvLVinbcidG1QlLSWl1G1nqhgfSyrVFYSg2cMdb94bui8JSWFrKjrqIPC4Jbazp7UuSEhdVh0fYItlU7+PqpelnZ5djD1d2r9vUGsdpXfU2TZeUl0Ph0PVHcJAPM+ac/zCz+9uV/K/G5lXAw0CJTSEzZcL2ujGsSUs6rt6WOgNNq8kEZ4fVGeryrl3blHP9+mb7xg3ejcTZfHGxGSAKKbl1a2NbcR+V/Gdnu8d4O6iH5FnHUHu5PB73i1QWB2vijQ6U1gStTRwSefLk1eAhe5HJ2kPKNDtHuJJ6JjIOEZskEjiq7lHnJJVf79fIOs3P7bcIfmSRUS+s6nFPWmx6rlN03NH6lbTInW6jw2IVnwdIKTaI1oTg/PyqGtMz+FsNnKordUdLSQ6c5c6nkDCu8ddlFXJSyMv165cEpqRTO0qZFxcbolLqunZto7yUsgqZKWXdunVJeMrx9fVqvXaedC6Pttq0VDQNnom3ZzLxrnJbyEnryruGZFfkemqki9oVXTgcAi3kxEs8rfYavYfR9tl7r6O2RIjAKGiLm5Y+zakv5T8dFw+BQ9cfRUrpcwF8KYCPBfDZOef3pJS+GMDP5Zy/L1reYK4/MTExMTExsTRqEtXyt0+klD4fwJsA/BSA5wPYLp9xCuBVLWWuQnmxoPkpLRWGlkNXP17ZuFY2yv+icgAbO+rYk7JPs12rqy77+vXLOBfgUom5ceMyP3Ub1erO+fmlm+jGjc3frVu7dhel5fR0k79WXmgcTKtLKZIuKS5eRPJLK7IWZcOj7HltG7Va97riuDo8NtLjvOfYs/If7T6SsJRC1KMA76N+C63X31NnXTZVT7x1S2O9Nq7Uk7qkukw041UAXpFzfuNWbSl4Gxofn14NebE6Add4aAMsRMYbE+Px/dK08r+4dQppkLbpcQX0jcI0L+c20h6Vru2q3VCFfBQCcvPmhsgU8nLr1uU+ALj77s12uYa1y6mUQ+XTGr3ulgLLlSO1B69cbRGn1nMYOcgtMWDua9KniLjOvOX1pBeMcJl4y4uMN1w6dVlLtnhIYet5e9wvvaSIc/9E7ZLcRpqNXiJT13koHGHMywvBv2n/MWy+OB3GasiLBe/KmKoxwO7TSqenVz89QMvj6uPISEqXsSNF/ajJy/XrV8lOyUtVm5OTy3RuuwTtApflcp8HoOdCycvZ2YawSGTmyScvCU25diXt5s1dlaYQmbou70Czb3CrLi3vUrAmu30PSBEyYw34+4JnkREpKzrJ98RztLatJdpkVAGUbFlCfZGOi8asSCq7ZFNEVT40eTh0/UH8IoAXAfgFsv93AfjZlgKPhrxMTExMTExMHKXy8iCAb6lcRs9LKX0agNcA+LqWAldDXqgbpYb0KJrkby+g7iSKc/IGmjof99QQVTvq1/J7XDv02KLalFgZquKUuks9VGmRXFL1uRdlpKglt29v/jTl5ebNzf+yXWJrrl3b5Ct5T052VZ26Xg37cF1wK6N9Kj6aRB1VWyKDVI+7Kxr/sibVZR/KROS+9V6bHnXJiuHy7h+N3ji2UfV70rT+S11U2ty1JI6JvOScX5NSeiaANwO4G8BbANwE8Nqc87e2lLka8qKBfh6AojWg0TNAaK4djpxIjzPfuKE/6lxve8kK12nqR6TL/9q1U8hG/d6XmpB8+MMb8vL445vtJ5/c7Ct2PvnkZf30ZXc0sJdiRCzJUsGTvYhO9N7Bcp8DVKSu0RN3awArt78lfk5K9xCW1vMdkUdCb3usbeiNY5PiTlpdL5wrqJWYees/5ELomJFSeh2AL885P5pz/tqU0qsBfAKAEwDvzDk/1lr2KshLHQ9CQdURGpBao7dB0SeF6jIpoaDKyvXrl6SkbN911yYAFtglL/fcc/nUT51GX0JHiZOEi4vd60QlRRqXopGXJ58Ennji8smmJ564qhDVqOuKDkbaoNg6qHjhmfRagz6tyU+6N9axUYweYHsIS8+5jLxXdV5P0CaXNvpctP29eQu8pIvb5+nXWj+Wgmi9ZUfyeseilvgk6Ryn8qLi5QC+GsCjAJBzfgLAQyMKXgV5mZiYmJiYmPDhiGJeFqN2qyEv0lM/9PFn4JLptryWmWPJ3Cv8y9NAtQJTx6nQ7wtxSsvdd19uc7/rbarE0Lfm1qCuoPo3fWsvF0Vfx8CUp4+Ka+jmTeCxxy5tK++Yqe2oy6aPTkv3sd7H5ZFWSj2qRM9qdqR7i6orNJ3L34Il4kFGuIeWUCqsPNH4HQlRF5Jlh5U2Si1rje+gdtA4FQ8slVhTvkY9xeNVzagKxP2W0g7tOjoS8gI0fnjRwmrISw0t6DZCYiQ5j7qouMZIH3WW3EQ1Wbnrro1LCLj8fe+9m+17773Me++9m7/imrnvvksCU8qqyQu9FvRjizUZKS4k+q4XWg4lLyVAt7iM6hfg0fshPXZd8rZ0Ki9xGdlhR0z2I2NdPHUdKuhY2+c9dhS8kwy332NX1A3oqa8n/i4Kz30b6arh6pIWKHQ7Smbq7YgbSVpU0Lrq3/XYz9l16IDdI8IHknGRcs7Kp5l5rIK80G8baT7R8gZYerwGiazQ39y7WKSA3DLBUzWlJivlr2zff/9uWtkuROf0YvumuFu3gFtnVy9E3ZtOToD7Nwzj/CKp5IWiVgLOzjZqSwm+LTEuHe0hGwAAIABJREFU9TWr89bxMTdvbq5BTWZGoWWSsdDzBEcEHsLiVZS47RGB6C0YocIshSWCrnuIZy/RbAk6luzx2K2dC2dLJPZE2leukUVmrG1pH9fPtEUQXajVx9Vp5fchlZcjchsBwCsBfGh0oasgLxMTExMTExN+HBF5+ac55w+OLnSV5IWydU59Aa4qMDSdK49uS98iKjEvtTRYKy80pqWoKcVtdN99G2WlqCvc73vu3sojTzwBfOjJS99N8cXQpQWVgba+ndNr13Ba+3luOG7r9sTOL9KO2+jGjV0Fqv7OUclX8haFRlqRWCtBz4pun3EuWlrUPTQifcTg1Bu7MyrfvsqxEHWDRF0w3NhFf49AxGUibVv7rfo9x3OuGU1poel1GR7FRVNaIsoL57aX8h7CbXREyssi8S7AisiL9C4XzS8qPV5dH8ttc98Vovnp5wCuX999RX8J3q3dRvfcAzzjGZvt++8HHnjgkrA84xm7aae3Pgw8vH3E/YkndllBHUhSG1+/NKb+MiN9aQynaQoO3tNr13DPjRu48cBdO9eg3IP6UwHlMWr6iDd1tdVV9HSwpdwULbEuowIJKQ5JUNaCJd0vI23h0qVtj+ujl9x4JnfJTq4cD3rbq2UHFy9jbUv1WOSFC08ow6h27Fr625GQl6f/00Y1rHe5eDsibWT1e1M8x9fHlLT6RXP100X33nuptgAb4lL+gA1xuf+erVT02GOXf8Aleam/lkh7S01eSuWFRdBPTnvkj3qJceMGTu/ekKX7778PFxe7730pcTuPPbb7vppSVbmeRanyVh3BkpO81x5p5Xckg8gisO71CGgTmLRa18rS4BlfWs/VsluL9+jFqHZqlRNRRzlV3UNcpGvDqS5e8kLPjUufiCPnvBjVWyV5mZiYmJiYmJBxp5OrVZAXGmNR9gEbBUZj5xRcPEtEaaGgClAtWNSem/KkEVVePuIjNtv3XLsNfOiRzcZjjwGPPrr7Dv5aeaEfDCqqS/1Z6du3dx99un370jD6ymIuOKj+KFORj7BxI919911PCTn0/TNUeakfHy9PgtUxMMDhO1mv4hKpI3qu2kqWS9uXZC3V7VXTRt9zTYnYV2yJ5erxuL9oWd66y3bUdsuuXkixJhFIbadFRbOUFinmRXO9cSrMoce0I4p5WQyrIC81OBJTCEyB1qG1V/x7YHWcUn4J3qXk5b77Ntv33bchLvdc25KKD30IeISQl/r55PoZZPqscyEjhSWcn28MLQTn/HyX2FgnTCPT6hO+cQM37t0lLzTUhsYN19fk7OwqeRkBbiD3DP4taVFbrHSu7XrjFUaTlYg7wiJPkTiKUQOt5GqIkAIJUrmWq6fOb9neigiRtc6jBV5XkUQOOLSSFo8drdDaET23YmfLy1JHYJKXlaImMTQGxiIsUtwL91I7rQGcn/MvKCrlcuSlBOU+85lFbdk+3v7II7vk5YknNn/AJXGpX9ZSjzYnJxtjCqMr6XV0Wfl9+/YuOeFQKzPl8aJy/K1bOL33HNeubdJrwacIQOU6prRbVNmuzbb8+NKAzE1SFBElwJvmKVvL6yFZIydfz4TZS9TqsrS6o/cqEjuhLV64NtZiIz1GIisR5WWJCcajqHgI9Mg66cQeURM5G1uxxPWWVJuR77WKYiovwJ5E6ImJiYmJiYmJMViN8iKtDEochfYsPXUPlf9WzEuJz5BWTKenV9OoCkGVl/Jkzv33A/hPldryoQ9dPl1UXEblg0K3b1+Nc6lRKuOMrI0tBnJLGCpH1eWdnOy+Jvfi4inlpShMwOXTRfUTV3V15cvbRSAqApAmB0dWhPuKAeHcXppCpJVhqU3aObW4xZa4HlrZmtukdWVoKRieNtMa76EpLRJaVDNOhfL2Fc4mzTVJ62txo0v7qSpR75OwRBuNqHrFBnpNLMFaUpWm2+gwWA15qUE7nfd9LicnV2NV6ZxNb3gdYKo1yjJglLwl3qN+z0sdsJsee3RDVgp5eeQR+dFoSlxKZTVjo8Sm1g2pi0nymwGXH22iF4V8KCml60+dZ00G6+ILB6LveZFM4WC5T1onIQ6WLZaribNBciVECQp3nhwZGDXwjyorQvC8rgcLETecZq9Up+YWsiY37Ryt8/Pek2g7jtxr69p6jpfIi9aHuG2PzRHXY11mtP2PHotGYC12HAqrJC8TExMTExMTPGbMy4rIiyVxaivSWhngXvFf4/T0qsznCbyiq4HiMipuo/IF6XtubH0mH3psN0j3kUcuA3SfeGLjMqIBuHVlFxe7TxdRQ+uLUD9tJC1b6v00+vns7Iot3FtzywNPxSwasFvcRuWac8pLq4ukNf++YakAUnv2uB56lKMoWlbqERegF55r4VW5onZJ10BzK0n3UXI59MB7jyx3H5c3ooLVwxfnQoqoHZo7LKLiSmVbbjlu+OTU1Lq+6TY6DFZBXsokCFx9LBq42ujr3zU5KU8J01gXafAqXKCebOt0SnxqF0khL+WR4nvu2X7XqLiGHnsM+NVfvSQv9L0u5S26tVHU31IMvHZt910t5Xnk+rg6nbtAteH1/mvXNuWUHriNeTmpstWErd6+fv3yswllu379TM6XxdenWUNzE2mTelTOHgFpYG9xC0nuiBZ52nJ3RdCTv4WQccdy21Z+771puVbahOe1j+4bcW8jx2mEjsvX6+LjzpUjex4sQcqj91Q730ORlyWRUnoegO8A8Gxsvk/0YM75m1NKfxXAfwPgFoCfBfCFOefhX4z2YBXkpQYXmEsbmPUxRelbO2XiLQ2xvJekfiybqjg0Drb+MOONG5cfYrz3XuD++zLwga268thjG7JSyEz9uzwWTZWUmiXUAbWlx9cERWN0dLs+trwjpqQXO8hj2lRtKcXU3zIqZK5+SV3Ns8r1lRAhK/uAd0CzyIpHffGcmzWB9Cgx0sozkr9XRRuJKHms9/UqWtGYkOgxHEbe22jb5IimpLxY4IYw733x2FaX7VHvvdephYSNxB7cRmcAvjLn/I6U0jMAvD2l9GYAbwbwZ3POZymlvwLgzwL4qkUtEbA68jIxMTExMTGhY0nyknN+P4D3b38/mlJ6F4Dn5pz/ZZXtbQD+4HJW6FgFeakfgOFuSC040CePasGilEPVE6k8jn3XK7VaaSnfQSwxLnfdtVFbitvo3nuxiWWp3Ub1xxfLi+mAS+Wlrow+BQRcShrFTVQMpjql9Rw5VXXqkz893bVlq8LQt+jWv+tHpzm3EfUFe1/mxLlMIorGGuGx0VKcIiu8XhUhWtah1DEJksKiqWpSmuR64Vx8rfZ627PmBupVADz9agnFiFM7rHvA2dRqV9QNqI1Fh8CA+p+VUnqo2n4w5/wgzZRSej6ATwHwgyTpTwD4B91WNGIV5AXYbcjewVtyE9VhHtR1VJfHDUY1UarfoFviW8qj0ffcs9kub9S9914A/+kxP3mho8W1a5ezfdFeizGFkUV6dkHxhZWLQi/utWu7nybYkpd0dnt73td3Pg/AfeuobN+8edVtdHGxy8Eo1khGPJNSq4tk9Dl6msCoPMeAaExRxMXSQp4j7URrd6NjRZZ0+XldNNJvzX2k2RchM9Z9t0huXf4Rx7w8nHN+iZYhpXQ/gO8E8BU550eq/V+LjWvpDcuaKGM15IXGsWgNQot5qd9LQslNgfQStfPzXfHj+vVLslK+W1ReQlc+BVC2r1/cvEpWHn10990ulLxQw2uWUBuTsywjceBGi1I2vbC3bl0SmLJdvYPmxo3r4ocZKXm5667LkJpSVU0GC7GR7q0VbLlPWCtbr2/dc1zrCjIa2xDJ2xtvwP3uXSm32DKalHnVkZa27FVxRpyTZk8LIafHawSlHva4ba3sKEa0M3o+h453AfbzqHTavOzrOwG8Ief8XdX+Pw7gDwD4zJwPR91WQ14mJiYmJiYmfFiSvKSUEoBvB/CunPM3Vvs/B8CrAPzunPMTy1lgYxXkhYZ7eG8KlRWL6iJtF9TfM6TemYLy5EytvNx//+UbdMvvsn3lY4uPPbZ5l0vZfvTRjaIB7MoT5QROTy8DaC4udt+zXx574nRUz0VKafexa1r3ycmlfFJUl62td3/kfU9dgxL3U39xmioxN2/uPo2kxTBZq9g1uJCAtqdJRpXHwbs6XXp1uOT9kdwYre4NTvLXXDRaOoUVZ0Pz1fWMVhpaIcW4RKGpgZLSom332G0poIdWT3qwB+XlUwF8AYAfTSn98Hbf1wD4FgB3AXjzht/gbTnn/3FRSwSshryUCU96z0uNOp2LealjUmkMDAV1FRWUd5jsPApdkZUHHtj8fuqldB8kMS6PP75LZp588vJbRvVL4Uplta/s7GyzzyIvWqBuAX2/CyVOpbyavDzxxFP+sHTrJu6+e8NW7rlnQ+bqTyLUZObJJze/y2lQtxFFiUPmsAQJWMqV0ALqSokSdvqb226B13Wh7bfcRFHXGJd/VIyGJwamFR6yoh1ruVSssj1Bytb+ssCzbNW26/3a4pKmS2VIrletjVmxUHT7GMjNkuQl5/xWANwE8z3L1RrDasiL9LRR/fJYCvoSOjrHS3O+1igLHyjBuiWm5b77NmTlgQc22/ffv9m387HFOii3qC71dlFebt7crfT27d1I1xs3dgkOdyIScaEX6/btTX76wjsuYLjUfffdO6Tr3ns37KQELBfyUrKW0yoxL1TkKWSGxjLVL7yLkpWejrvPgckz+FvnZdmqEZpeROJ3IjEvEVixJD1la/CWGSGeXgIduY9WTEak/Un5LYXUEz8lETK62KTHWTZIeazzoEQoqrRNHBarIC8TExMTExMTfqzFrX4orIa81G+ypZ/e0UDdQiV8pN6u34jPvSem/l2/0+Tuuy/dRs94xqWrCNi6kO45Bz6wVV6o0lJ+F1niySd3lZfyBFExtH4d7e3bGxmjljC4x6joxSvHcheu1mq5YJRy4k88cfmJ7O329e1J33PPKes2qp9GOju7NKGIR9dIKyunVYf1LLmC3ge8K76ixLS6jSgs1aVl9arljcQYRJQyb4yTFQvTi4gqF3V/ReFxFdVl07ieiAswGvNiuVe4dlkPQWWbi3HxnLMF63wk1SXiWo5470djfphxJeSFez9bfWO0uAngasxLvV2+x1PyWQFiZSK+dm3DH+67b7NNA3Tvuw9XX0pHY1xqwsKRF3oBiuHF/1K2S1q5SIW8lPTiGqpBt8tFODvjg3br2JjyBr5i9/ac7rrrGSp5KS+sK2YW71Q5VfrKmZOTq8G7rSQmGs/SIhcvBWkSkQZU7VZH3RE9ZMYb09ISz2PVHXGLWNBIgGWHdt7e+AoJve5A73mMIDHSPvrbinFp7YsRV1edJt1zrh8u6Z5twSQvK0BNGoDdedsK4K0bv/RV6XpervMXYkM/tghcxnMUsnLvvZdBu0D5jtFjOlmpX/528+ZlGv2uUUq7J10CR+og29PTS0mDGk5HAOuNu9zXEsvxd921sbWc1+OPP3XS9z3rGXj0UfkldeXQ2qz6K9NFVavvbfkdeQuvd7t1BR0dmFoHTs++3kHSIiteEiApRN5J0Co/MpG3nIM0gfes8qPKS8v99E6YlPj3kLsWwiKlc2SFpmnn6FEx6/2S7dz+Wn2RlJi1YiovwMpv0cTExMTExMTELlahvKR0uXq/uOCFgeJe4JQYyS1UHtKpfax16Ej5Xce5FDuKe6R+z8u9915ui26h2jVUvakWTz55+ZTR7dtXfWFUeSlxL8ClCkM/MlT7XLTnwWk99beMyjJNinmpFKN0dht3331952mjWnkp16+c8u3bl099A5ePTtdKGf0trYQ4eNNHKCledwunTGgxLp5zkOq2PIWWpB9VWrjf0biYEfCoMK0KXCRORNvHgbbvCFracORceu4Rp5xI+yRVhh4zChEF1Fu/5L7dN+505WV15AXY3JSarHhlUCpDSjEulMzQb/cAl49KUxfJU+TlMeIWKr+La+f27c1fISyFkJS89UmWyNWavNSRyyWQpGZy9UWpg4asAKFCdCh5KXXfuLFLwj784R0Cdv367ucC6g8zcm64Og65pI14ofS+O+6S9UXjTqRBM+qW4KTyCGHscRONgmSz5zp4Ylos9MSItLpntPtfp0didyxILimaLv0vvy2iMwqe9ui9F4cmKRImeVkBaMwLXalyZKaklePrsuqAUEpe6tV+SeNCR4piQ8WOp56cKY/V1E8I1e9mKb/LNs1bv2uFRrKen28uSJnly7eOajKT8y4LK2neF9fRR7rKRbjnng3hquWTiqDd9cDuNaHkpY47Tunyr1RLvxBO3wHDrZx7MSJ2ZJ+QJh3pPFpX8/UxUZUrorx40HOPPKRrJCJxFRqWVl8KemNgNBtaSMrSaosXXnWup38thRnzshLyMjExMTExMeHHJC8rQEqX7pgiMNQru/o18rUKwzFh+i4XyvJpmrRisOIFAOwaWn5Tyai4iupXzxYXEve4tITy4aBSdg365BGHevlA39tfP8lUXF/UHQYAZ2c76hOnbHExR7U3jCpjGiIramt1tGb1hXPf1JDcAVz++jxHn3NvDJK3/CUVpJ7rEVVc1jS5tNriiSHSlBRP3I01ZEVs71GYrBiYljiqieWwOvJS5vx6nj85udymDYyGeNBHpevJ1Jo0Oe4hbZ9SHxQ1qLCsQlBq1kXdRsWo2i1EQV9KVz9brPUkKTC4HEPdX+Ux7vqCVxchIePkJD1VrUZeJCJYk5n6nlCXnmfwkdwZo90rPfAM4F5EXADlGi51vktNzj2TxL7iJ6y0FqK3lgmRa19W/I3H9tIWLTJOj+F+S3ZIBMRzPyJk5dDEdLqNVkJeTk6uKi90Lq3nWvqbdgZr8qxBV6qlrPJATh2mcvNm9XbYEuBRMyMq7dQtrI5/yVlXXgpjo5JF/VKUk5NdYlJflJR2y6atvI6XKXbRE6/tpgwOpztVaqCrspqD0VPUjm9ZRa1BhfGSMO9AbpXnSe/BvlUGb7mHmPgl4mzt85QX7VveYzxljVQYOLLd2qc5REhLRMml4Mo/NHk4dP2HxirIy8TExMTExIQfk7ysALXyQt1Gt25ddf3Uq3RuFU3dGAW1t6RGLTKUek9ONnWXp6CKp6eEhtxFHz+qVZjaiAIayEN1Pyoh0eAe+iGg+sTrl6fQsuoTrFHKKk8uSfkX0Ce5e7Ck3C/d8yXqpLfG6x6gt16CVTY9L65ZSTZw53Es2KdLsFVxicTjjD4fS30endfbnq2yPNeaO567fh7VR9qvlT3i1Q9RTLfRishLeSda8aaUGNFCIqQJj3utvOaqqGF1hLMz+TUuuP8G1OhVrTIaQFNQkxPui4Z1L6kJy+mpPFrUzKzkrcuK+DTI75qLeYuqORi9L/Wj0+VStsQbcPkOGQNj+ct7BljP5FCXx/0egV6yEz22xyW4ZPyCd7K13BC0DVjEs2dBMIrYeI4fCYmYl21av5bfglX2xP6xCvIyMTExMTEx4cf/397XxdqSVOd9a/+cc+6dsTMmWKMRTIKVjBRNHjxYKCEiiohREuBlEslC+IFgayTyMEhG4sFjXpJIeSAPwXKkxNJEIA8RCR4ZLFCEnKAJVuQHY2MyCYaR5UkCMmhgjIeZ+zfn3vNTeehed69eZ1XVqt69z97nnvVJR6eru3fXT1dXffWtVVWXnTztBHmRZqO9veGehKy68CxhKW5YvqkSeqNG/r2GVQlYdeF0sMMuKzF318KXK7RZyot8OEsO7ASrvVQlpdfL/EslRl/XU5815LXSPHOdDuCMh20CnfXfFZDnrOulqO4V1EZ4VhXJydI169+U6fI+I5eWsSPSMelY17k1d23KtLe8O6950bq2STOsR0i2wiVsSvHMKZjWOT5et+5tC2E22hHyQrQyG52erlbWB85aY+6SB6wqnzZb5D7s2sciKwNP6JGzjWS6jo4JS202Kq1uy34uwDRGUll7axmTthpmc6U5yrPZKl9yc6jFYsC5uOy5THLWMIna7gXroGQymMJM4m3Ia7/1yNve505lbhmDXOO/aRNSjfzV7q89e2zaLbLSSj4tc1ErWn/juT9HwMf49njRSkpzgwB53xgzWO032/B5AYK87AR5mc26VemBlcDAs4ZZ0NBL/gP2cilWQ1bqdEo26dPTs8udyI57kJjZrOvoNYHJ9dZyDRjPkv7rQu+BINfwlyvJMVnhfC0Wg9/qHRDkjG+eZS0FJj3TelMoEdgpMHWnz7DSO2aULp9d86mxznvQ0rF78jN29L5OHsZgLJmskRkrnlz9rbVrrQStFaX32ZpPb1ytA46c34uVnikI9r2oGl8k7AR5CQQCgUAg4EcoLzuA2Qy4777uWC/wyqqL9IGRxzzaZ1gr7mroGcfeEZVlojqD3OJwJQnCozvqfQ2s1X3l9Rx0gfLOiix1cVhvHd0fW/tLSnXq+HhoHdPuONaOCpvAlCOjKcxNOV+T2qi+xe+lNGrX8fF5b96sdGsfAu9vW+FRWnT5rvPOWn13SqaT3P/WuM/DfORJRw41xckbl/WteJT0moJZ+iYuIsLnZUfIy3w+9HmRU5TZrUSH+ViSF6/cLv9LM4de8NaaCS3jPsNmrNVoZU8ur01tKM3tbaTDvJIvZ2Rvr/vj3y+Xq3PyOgAsFjg6XL0LJi65nQX43bTMytbFp6+1dgbrYNuysKfTtxp3T8eX6yisNMhnljrXlnfhNRmNyf+YezaBMWaxktlTWqg958dCm6NK5Mw6XyMvJbLSkjZ93msuLf0m9+xSs3oeVn8LQV4qIKKHAXwawIMAEoCnU0q/RkRvAPCbAN4C4NsA3pdS+hEREYBfA/BeALcA/EJK6eulOGazs+RFkoTFYjXbSBIKnplU6/Ak9HZDWrmRaZICxP5+9yf68TObFg48erkX1+TGStxsNpxNlPOfyX0l+rz1ZUlyI2dJMVnh6V6cSaG23M30wQHuXDu79o0mK9Ln5eholU09ySp3bBXRJjCFk2dLh+S9vzTqLdn1PfHnfudNl6W+6GfWvsExnXorWjrEHNYlZLlz1vmaYmR1nmN9XmrwqHsy7Gl3rWselawljx4yXoMnLRaZOW+E8gJ4XsExgI+mlB4F8HYATxLRowCeAvBcSukRAM/1YQB4D4BH+r8PAfj1yVMdCAQCgUDg0qKqvKSUXgLwUn98nYheAPAmAI8DeGd/2zMAfhfAL/fnP51SSgB+n4geIKKH+ueYmM2A++/vjrXZiEf3UgFhFYb9L3LihrbM6FECH99dNVeAJ92wIHHffd2MKA7T4evAobChvP56dyyn4rAaw4nlBFlTpCznHFlAVgKtYQn7tGgfGb7O0pVUWpbL7j/QnT84WE3/2t+/e+/RMQ3EJt6oUr8rqbzwHpQclsWgTUraslZTLFpGHh67ueeaJ+4WpaU1Dy3KTQ3rmFM8Pi9TKCye0bYHUygxuWfWzsnzrWWwjillKpTMt9a9ufdeMoWVzrX4vEwF2WTycS4cZqPtoMnnhYjeAuCtAL4K4EFBSL6PzqwEdMTmz8TPvtufG5AXIvoQOmUGf+XhhzsyAGA+m2G+WGBvr+t8uUOUHST3u9phlzvLXOdhkRdtRmLM512fznFdvQr8+I93fwCAH94Abt0Cbtzowrdvd2Tm8HCYOOlQwxFY+xpJ1HR+fb8kMnplPiYzbAaaz4f2r4ODjpmx3Y7JC5MZwdg4e5I8arPRYC2co6EDLxMUvl9yOD7W5KUUnhJTdQC59JUITS0vOb+TGqbs1Cyy0iLNT20qKjnual+HUnhTGEvUPKaJ3LUWU2Br+rxtqieeks+Obua8ZiMPma6hli45/tu22QgI8uImL0R0P4DPAfhISukaCbqZUkpE1OSBmlJ6GsDTAPC2xx5LuHWru9DXEup9Mvb39rB//wJHx118t2+v+MCdO0PlhDmB9XFZYU12ZOVn1xBJXh54AKAb17sT164Br72Gu+m+dessebl9e+gDU+t5W2ujXPyGwZKR9HXRXs4yY0xUOHzlSpdZnv7FZAbA4Stdlpi8WMRSbl6Zc+jVPjHA2XdnFVWtAxzbqbc2RLURt4e4jO1YrA6qlJ9cR6fJR64z1Bw751NQIjLnocCUYBGYdZ85FVrqbO09557dWv4t9dujkI4lYfq/vr6JztuKk9OZ28ZuG8pL+Lw4yQsRLdERl8+klD7fn/4Bm4OI6CEAL/fnvwfgYfHzN/fnAoFAIBAITIAgLxX0s4c+CeCFlNInxKUvAvgggI/3/78gzn+YiD4L4G8DeK3k7wKgewtsfmGKqxZ2Wfbh5ZU9pFlnUrJG8iWlRSougK3UMJtmsxFbU65eBeaHN4FXX+1OXLsGXL/e/Qe69LP6AnT/eYlZjlQuiOJZkEZD+7HIY6msaDPRcrn6LZuMpPIizUZ8LCSno9P53SzJ/Z208sL+L3IdGK28yCJhMxJf85iJSspFq0lgalORPC6NVPU5K2zBGnHWzAtejDWntIzuWxrbKZUWGffUJqTWDqTlXXnNQ5vIg9fsWVMSrbR6zTM55cV69tRmSJ0Ovej4tpWXgE95eQeADwD4BhE935/7GDrS8iwRPQHgOwDe11/7Erpp0i+imyr9i9UYTk9XnT5wtqYosweblJZMavZWtezklAaP1WYi4KyPi2zYZLR7e8D8tLdr3LjVEZVXXunCr77ahZl0Xb8+JC/cy8tFUbS9Stb6lnVfeIE6ucS//JI0WZHld3DQZUyysoODYZj/+vvZMnZ42PklS37G1jFgRVykT4u17gsXgzXN3Rseg02YCcaQlVZTWM085u0Qa/kvmaB056/NTrnn1Y496fSku3RuF3xgPLA6buuaFa49dx1TZ+u9pXTosM5zrQxK8baYLq1nWmUsz9f24D1PhNnIN9vo9wDkuOW7jPsTgCebUnF6Cu3zUvTZ0MRG1LK5qGVzft6iv743/CqSyhZB1Ig7d4Abd1Y99a2evLDy8sqBPWSIAAAe2ElEQVQr3fFrr3Xhmze7P9mz3759tvftCmn438LJyUo9yUHKRHqRObkYDjvpAisfFu3jkiEv6eAKDq8NsyRnG0m3HssHxlJe9Do78tgqrhxyHZQHnhGtFY8XY8mMhZZOquW8V6HgsNfHxYpH/m+BJ+8eEmWN0nPEbBPIpbEUZytBkWj5dlqVQY9SY5EAPi6RlRJ50fDUwZbvTMbZks6YbbQd7MQKu4FAIBAIBHwI5WVXyEtKK8XCMnhaBkd5nAtriiyfC0NOklLA8fFw9pBWXq5d6/5u3lxdlzYV3vhHzjZaBzIfvPquzKfeFVrvTaSVF+njcv/9WeXl5k1kzUZHR0MlJrfIsDQjyanSLWYircRYI0M5EptCiRmD3IjVa0LxoFVhmQotaotGy6i3dJx7nlZRcvdv02RkqVfyuKY6WKpA6d6Wd7WOEuOFR2nx5M1Kgye/pXtKZjnuguRsI9kNhfKyHewGeTk9XfWAwLAWWeuW6DlrXHuWy/avQfeQ3LOyPYR76hs3uj920GV/F/Z5YZOR7smlacha/34MmKxwObBTLrByyGXywovVSPJimYl4UTrlA3P7lbOWMO3zIs1G0iFXk5cSIcn5tOjGcxsfrMcs4Q23dAYe09Y27e7ngRpRa60PJcIyhsyUzEGtZqJaPFOjZP6Z6rvzkLDW5tp6lsenpWZmttKi05tLZ+D8sRvkJaUheQGGNeXoaBiW/jCyFvF9khLnZugwTk/tGUHsvCHXcZHk5fr17o+VF+7Z2XuVV2ebotfV67noocByeXYvIvZpWS6HSwNr5YXJCq/r0pOZ23fobrY1WdGzjeSidDJsbe9UIi/6f+tor8W3obWjWmckq8Ne5UWnsUZc7pXGdMr3UvPrydWZqTCWZLU8e924rbo6VilsUYz0uVZFaYwSaLUFtbS2qEHniVBeAoFAIBAIXBiEz8uukBfpCKGR0+yAs0qLNYetpO/x25cLk8jle7XPC0+H5vDh4VB2kL/X+xdZaSgZS/W6L0Sr+/lYziiSPi4HByslhlUXqbxoJUZNjcbVqzjsrWGyCPg4t7cRr+sizUbSEldSVyylpQavCWCsH0wOLSO+KXwFrPhL4XsRreqIVgvOq4w2qbR44mVY5pWSaaVVnZkCNZNgTigfU8a5tqDk52Kd3yUEedkVSPIiO/WTk64jt3ogbXzUrZQ2G2no/QGsXQZzDh9MVKSPi/Zp0TWe02LdZxEZ6YSrp0Mvl8Pp40xW+Br7sGgz0f7+cFE6niqtFqWTTro5s1HOTMRhJi5T29ElaqYh61ruukauwWvpGKxnrpvvUp7XxaYa6Vq+a/HmfruJOuSFztMYElDqiDVhG/turHRt0lTmSY8V9pAEq8w9yJVfiaSMrZOB88HukBcJvf6J7OyZzDBkrbRq38lJ/kvl4X5OedHLyepFS46O8jWYZwRxWhcLv8E495XJ69Jhd29v6JDLhATozl+5srp+331nZxspZUaqK3oXab2iriwyveKxtWqu/K+Payh1DpbvQo18lDoHfTy28fd02i0qTinN65AYz2/GvjcdxyaUkBIpWLeDq2EdtaX0ztZ5v+sSFi9x3wRy32HroKFGVvT5nPLCcefKoGV90akQZiNgB8WwQCAQCAQCJfDAcOxfCUT0MBF9hYi+RUTfJKJf6s+/gYi+TER/2v//ifPIq4XdUF40jdS0Vy+lz2A6nDNw567LY705klZWdFhuzKPTqqd0S1OPVIws7VObt/QwQN6zWHRKCpuKrBVzWWnRZiFWXfTsoz58MlsOrGWWmYgnVPFscmk2kq4+1ofSOlpoGXmVzEJW9WqRn0vPK6XbGrmWpO8Ws8M6+WpVGHKqmed9jpX6OY6cKuHBmHxao+4xKNVPHVfObFR7v/K89XsZj/d3Oo7Wul/Duma6Maa2XLpzZiPLhJXrZraFDSsvxwA+mlL6OhH9GIA/IqIvA/gFAM+llD5ORE8BeArAL280JRnsBnmRC67lrreg1lvKlthamISPa62zJCt6DyZee8Va24Xj1c+xpkTzf7nZIsfDBEVvtMh+LRyWZiI+1j4x/e/v3OkWomPCYpmQNNeTxVcjL17UGuqWTr4Wxzppyj2jldyMSVMuDq85bJ04rI44l44cISiVXQuhmxot767UKZaaHk9cJQKxDrydv0yHJ88WxpLWVpRMwp74c+YihkVc+Ny9aDbqN1N+qT++TkQvAHgTgMcBvLO/7RkAv4tLTV4CgUAgEAicJ95IRF8T4adTSk/rm4joLQDeCuCrAB7siQ0AfB/Ag5tOZA67SV5KM2+As/S4xSNP/tezjTwygTT9SLWE1RC50q1UdSQ9Pz6286B3iZZTo+VO0bzkP0+P1lOh5WwivREjm4yU8pL2OhXn8NWVeQg4azaSs4l0mFUXa/l/DntGP3yvPifPt8AzWiyNbNcx81gjQo8pwPv82jO85e2Jr0U1qJkaap9si8pw3spMCWOVQateWIqHPvamyTK3lN5/zaQ05v1Zitq6ipInT6Xf1c5J5AT9bSgvOg0j8cOU0ttKNxDR/QA+B+AjKaVrJPrmlFIioi3lfpfIiyYsLWSl1sPpWicJRYvxXJuJ5LL8R0fDPYTYppJ7vjYn6eX++fnAirhwXLxLNBMSeczkJBfO+LxoHxft58LHmqxIfqazbHHBnBmj1TxkdYheImHBapB1Olsai5rPi4zXur+Uzlq8U3YGOt7c/1raxpLQdUyOm0JLnZS/qRFDq15YBFCTEev3tbSVCLmO1wrr+7xoMRfW0JqnsWNeTtsu4TxmGxHREh1x+UxK6fP96R8Q0UMppZeI6CEAL282FXnsDnmxHFZrxzpc6wlah7fSa2s2G/q0sMrC6sfR0UptAc46+ErwAnP6S5PPtvxnOC7ev0g66UqyIhedu3JlOFWa7xXKDDvpAmcXotM+yzxjnK9ZPi61YrWQGx3K6xJjGs1S3B6M9XkpqRC1cO6ZpfOeMhzTwY8lizVFaIpGeB3CsslOoDaOKkHWBatj99TD0nM9SmcOJVWt9bfrEBZ9PvcsL5HxvB99/gIrL1lQJ7F8EsALKaVPiEtfBPBBAB/v/39hc6koY3fISyAQCAQCARc2rLy8A8AHAHyDiJ7vz30MHWl5loieAPAdAO/baCoK2A3yYs020sPEMUZKoN1pgI85TTKslZe9vZUNhVUXKUuwGqLj4WGCTltOeWHVhcNsBpL+NbnZRMYidDKcDq7g9Ztnp0ZbPi96o0WWLvXs8dzopXV0vM7ofJ14PfCaeXK/9aotGmNMV957PM9uVYU4jm0oZbsMb1nn/FLWLYOciaVmjvKE18FU7UXu2hj1kfM4VlW+iEgp/R6A3DTfd51nWnLYDfIikatlLbWupJe31jr+7Xze9dKaYDCBYOLCZObgoOvdpabI12az4RxjXsdG+rjIZ7ODLhMQNhkxQdFmoytXhrtES/LCv+3DetcD3hib13LRZqOTk7PbAXAWmY9ZJIbDXru8t8PzkIgpGnsd59hqVPMlsExnreai1jTJcCuJ8t6/KYLGzy6Fxz7fU29KHdrUhLtm2mk1/ci4Wkw5JcIyxXfWakbSacg9y9ONeD0OxrQBUyJW2N0l8lLyccldYydf7j1zX96YFnY+757Lz1oshlsNsFOt9HmRDruauEhycufOcGNFnR/2aZFL/vM5Duv9ipjIXL06nG1kkJe0tz/Yr0iu66IXnrPIC2eL/V1aRyRjG9nzwq6mi1EjahJj8zJl4+wlX2NVrFK4FVMRkLF5aVUDvNda/JM8ZCZHwtdFi1pYUy9r3cgufBvrYBfSsE3sDnkJBAKBQCBQRSgvu0xeSkMqPa1aKzAlWLN/rHhOT8/64sip0rx6Lqsn+/td/NIBpKRlsrLD985mq3ywiYjVEh3Wa7fcd99w3RY524iVl161OZktcecwv1n266/bq+oCK0WGlRj+gPRoUysxrTMY9PEYtPp76HBNNm/FlA3N1KPdqTHGd6ZFHJX/9bEV1vHI8pvS7NMCz+h9bL44XLrXkz753Jxauo7Q7U3DOr+tlaHHT0abw/kco3UB+Kmwq9//eWF3yUvJOYA7/Rxpkb2o1btK0pCLV0N/Daw7ah8YNu1Y5IXTy/sc5ciLngqtyYoVllOj9XYABwc4Ou1MVoe3VlsAAB1xYcICrNZ10YSFky3XdWn1YdASdO33Y3wfSq9Pvzp9vQWlKuYxpbXkLVdelqTf6ho2pXTuNQ/VzufiKx3n3uemiWYr4bF8m+Q1q5kpPUc+q0QwSihdz32zVvz6uPTM2nHuXMlUVKqTnu9dp0W3W1Yacs8KbB67Q15KRtbc15EjLa3x5gzVlpIjWxWpzFgzhDRy5IWhZzJJZUUSEr2Wi1x0Tm/EePUqbt+hwWwi6ZD7+utD5YVVF0lemKwcHXVFJDnXVOu86PstNadlxGid1+Sl1kGU0ufFuh28htVgW/d40tVqu/eO7K3r64wSS2TFO7LOdUQaY+qvl5yWCAaf89RRDyltOd+KEmko1U89aMjlrfRdeutsjUTlSH9LOTNCedkOdoe8BAKBQCAQqCJ8XnaRvIwZEsrf6v9j33Bp8RIGT58GVrOH2GykMZutZhtZyoueKi1nEPG0aI/ZSM0uYtVFKi/s5wKcNRuxMiN9XtjHhU1GXBQ196EcWka+3hF8TZGojWotU1IpjZs0G9VGtR47fe3ZraP33CdpmWesT2asapZTWrymB51f76jdOvbcXztfMwPV6mgpDV5lrCVd1n1eYVw/u/bdec1GVlyt76mmfOl7SgrftsxGQV52AVPRyBzRkL2ttRHjWLIjazuTEe7RreX/JXnZ2xv2/tIZeD5fbbAInDUTWeSFSVNvbrrr43KIM+TFMhtx+PBw6JQrj9lkVCIQNVtxrQGWx7WwFX8uPMZMlEtb6XouneuYICQ8affms/Y+5DNypMfTcXs+K11vauYGb+fhGQd5ycc6RMZTzh6CPSbuMenW5Zsj1DrcUj+t46nMRkCdVJXSptMhx5alPATOD7tBXoD6MN5rtJb38vEYiaBGqLjGyl2lT05Wvi5y7RlguJM0t+aavEifF725ol6IznLg7Y/TYonbN7ugXjGXiYreeFGu6yLvl4sG8+q6LRO2aq8rR0gsEU3fU4rbOi6N4j2jd4ug5DrmGo9uQW1Evc6osDYa1c9tSb93TFBSlLzEJZf2Wh30qBVTvMNcvKU6muvIPZ1la5pLpNRDDnMKSI2ctCoxLQqTvD6GPDJZ4aaZm3oeZ64zGFoXYTbaJfISCAQCgUDAhSAvuwLp/yGH9uwfwvDof/Ktymd56KqWFWpDRk5bSsMZRnt7w9lIckVdaygqn8V7GUkz0f7+2SX+palIHEslhVUWudy/tXdRbi0XqbTkRtAtpofa6LbVVJRLR6vpoZSmUtw5y2NLultQGwV7YX1GY9SRWrno49LzPddbTEZ8vaQitIq4pes63tI5j/pXymsp317VL6dglNKulQytbJV+61HNSnkuqWitgryVVhn3fL5qzvmYry0Ww3tjttF2sBvkRZMKXauOj4c1RJtbNDQBqU2p9nztuS9G1uDZbEhgZrMVC9DrupTIi17nRZuNmLhwXGrDyKObQydbuY0S70ekfVrkfkXSKffoyL/lu0UISrZyjSmIS6lBbOkIaqagFrt77nkWvJ34mOslyd1DfKz3Z5kLvOaXdeT21veXM2t44vHmR1+rkRX930O4S/B+W9Y577uwyKDXbMRhr5nICufGrhapGgNJXuRY0iIzuXSeB8JstCvkBRjfunMvzCDK97YWOVmnlssviWu67h2kT0yuJ+MvOLdIHe9zpJUW3vtI7DidQEWfZPZbyZEbvtfrJiQnXHE2SoRFh6f0J8g1ft7zpTSV+G5NZWjJU6lDqY3m14FH0BzzTP6/jmpR6uA2gRLBa22mcmTEq7xYafJgHeVvnbpQU170PaVBhvf7lL+XJCZHIHNpkk2wHEsyWeHml/1f+Pq2lJfLjt0hL4FAIBAIBFwI5WUX0aLHanhtANb9liKiqbyk5vK3bAjVQ56cn4v25ZnNVveysiLNRFJ5YTORMWyzfC6kGchKioRHjJIjErnRtkrK3eetI+W2+kqU1JbWkazXuuj5bela6yi3pfrn8tBiJij9Tn8eJYwxU5TSNOVzJHLptPw7vO8yp0h4zEm1Z03didXKtKamlp7lUT9yea81/bqtqZWdZcKSbZs2IXGYaOgDsw3lJcxGu0JeWt5E7UtpeaPWV8hgu0qupeRNGbXZSn4N0v5S+upYg5RTpaVOydfkdd0bFyA/PCsrGqUG2PrgJZ+zfp+Tcj3EZh3iU4KnIbSON4GpTDa1xvq8GrvcZzjVu7TqTUs+a4RM3je1GbNGSkrEzEMqAB/J8mLKemnl3cpzqQy8362HLJbCss1kUqOvbdtcFOTlosHTKrZ85TnoHaWt58pdpbVkwWF5Xech9+XwCrtynySttGS+HH4M/9T6SCXnyn3EcgTCUXGR6GTICVdyORuNWqOqO6BaA1xq5HLxy3tblALt13IeyDXUreoJY4qOKPfM0mfJGJvumiCai9+C9Vvrnlq83ris49K5MfCQRYvcldI0RRPailpz29rM699a7aBuy+QzS+RkEz5irbjs5GXLxR8IBAKBQCDQht1VXrx+KvLesQpMDtqhQ88YkkMefa+lxJTSIeUOVlo4zO7tJUmjfz4hYbGgu8oLW5zko6QgpDfD5tGItmAB3bRpfa+cqaSVmRpyI0IuEo8N2/PsVr+ZnJ9I7Zz1TF1NrPi8sMorV35WWsbG6UUpz6V0lMp/HZNHDqU6Zal9uU9ep7WmPnlMIDkTqydPtWYw1yzWwpuC1VZodWTMM4GzZcfHWpGW7eRyedYEzpDNO6/cIRXn80b4vOwyeSmhZmvIwerNStqkRU5kSyZbNt2qWWSmlC72AgNWDEJOv9br2ej50GIN/8Viefej3N8f3srHJfIiw3K9A+1qwx+/nGadUvkVtEjY1nWvqcfqhGqNeu5ZnnglPHF74SEstUbew+VbCGcNtY7XSk/p3nXMNS335QizNhfmyExLvdaw6qRVj0rPbK0X1u9K53RaPfCQdm+9bKn7mgDlzEZMVtgB11rxAujaNV4Li31euN3bBnkBgrxcTPISCAQCgcAlRSgvl4W8eHXR0hCPr8nZQ1pTLundpVXfrJ2+SvYXrfrIJXOPj7E8WOB0j8xbc2H++d7e8HHL5dBsJJUYduaVm2WXrFsleEZVLaNJj9rROjouIZf23Oh8zOg1Jxx6zTO1dLfAitNrGsvda/0mZ7IrmX6s+Erh3LWS4mL9l8detSh3f4vZaJ3vxPp9K7x12SuM5+qD5z3rb6WmvHBYT4fW6ZaoLdh+XgjysqsYq7N7IN3Ic3YOi5zI2s9ERoYtM5JXU9ZflmQBuS9KbkDEx3fuAIsFFouOccznHSEpkZfj4xVBOT62zUh8vFyueNjR0TA8n7c32GPREs8mUKqeLSarlnR6CYO3fFvLX8ffQppaTT+tJKAW75gOUZen53+pXuRITu7+0vstjW1q51vJTS59nvta42klLKXfy2MmKtpMlNspWu9GUxp7bstsdNmxu+SF0dJ6WS2UPLbmvulzua/HUlZk2iyfGP37XBzyeXxs7dkk45IqkFJecOcO5gfds/b350hptWSM/imTF/no27fz5EWOTthhl5PKz9VL2+Q60zFKSq3RL1WVdTv1mignj1uUl7EddO6Znk5qbOfV+imuC49qpsmJV5mxCECpjlpkRV8vpdd6zy0qWq3+esu85d14SJbn9/wMPXAqQTfnrYOeFqVFEhmGJCUW8ZRt6DYQyksgEAgEAoELg/B5uSjkRQ8/1h3WWaoGQ+9YzfTbM2yw1sqvDcXkvaUhv0wX19yc8iLCy70ZTvfobjLYzMNmopOTlZ8LZ3l//6xnPSdRL1qnfV6kaORVYHJFUnrVNVneCtdGj7nrnupWUlpKZiPPyLbUSFmmhZYRaqvK5IHOcw2ePI8d+edETuu/db+uj5bS4lH7Wq9Z8Ji4PPeWUDNNen+Xu6e1TlrHlknRyr82C1mzi6TaIrsF2dzyO5azi+R7j9lG28FukBfp3+HVElufzygRF+u6rMWeuLVDr/WbsQRM69UynCEvmM2wWCwH0501WZGmoeVyaDaSuxZwgyAddufzFdEBhq9SczmdDU/H67m3VFSlcznCIa/lnpVLh8dsVMqHDtc+h1LcMn4rX2NNR7l01Uxlued5Osix764GPWao/XasiQ+ol9/YZsEiYWORM5u15LnlXYwpS69JUA6sOMyDNut6Lj1MXKSZ6ORkRVrC52U72A3yIjGmdRhrBG792nO9ceneXCs/prXVrZ80vmpiw+xkscBsMVRa2CkXOLtPhx6xaLtxLSwxn5/d+kneV+v09b0WodD3e5WLkjrSCk+VLXW2U2EKUdJ6Zuv1GonKXcu9Q48akCObrUSm9Tc5eN9vCyFvTdc646Pac3P3bVINIBoShVo9kGEedHGYiYu3jKSZhn0Et6l8hNloF8lLIBAIBAKBIoK87CI2NTSdEl4zl7x/7JBQQ8dZWnjg9BSEBCC/kWPpnDz27KI6n5enFebi8ghUrWgZ/U6tWDC8qswmZPlN5mubKOWrZjaroXRvy3saYwqbElOpiFM0xTXzYQ2y3bHUFxmHvE96ANTaLiuf3nyHz8t2QGkHDHZE9OcAbgL44bbTsiN4I6IsJKI8VoiyGCLKY4UoiyHOqzz+akrpJ88hnrsgot9Bl7918MOU0runSM82sBPkBQCI6GsppbdtOx27gCiLIaI8VoiyGCLKY4UoiyGiPO5t3IOiciAQCAQCgXsZQV4CgUAgEAhcKOwSeXl62wnYIURZDBHlsUKUxRBRHitEWQwR5XEPY2d8XgKBQCAQCAQ82CXlJRAIBAKBQKCKIC+BQCAQCAQuFLZOXojo3UT0J0T0IhE9te30bANE9G0i+gYRPU9EX+vPvYGIvkxEf9r//4ltp3NTIKJPEdHLRPTH4pyZf+rwb/v68r+J6Ge2l/LpkSmLf0FE3+vrx/NE9F5x7Vf6svgTIvpH20n1ZkBEDxPRV4joW0T0TSL6pf78Za0bufK4dPWDiA6I6A+I6H/1ZfEv+/M/RURf7fP8m0S015/f78Mv9tffss30B9bHVskLEc0B/DsA7wHwKICfJ6JHt5mmLeLvp5QeE+sSPAXguZTSIwCe68P3Kn4DgF4sKZf/9wB4pP/7EIBfP6c0nhd+A2fLAgB+ta8fj6WUvgQA/bfyfgB/s//Nv++/qXsFxwA+mlJ6FMDbATzZ5/my1o1ceQCXr37cBvCzKaWfBvAYgHcT0dsB/Gt0ZfHXAfwIwBP9/U8A+FF//lf7+wIXGNtWXv4WgBdTSv83pXQHwGcBPL7lNO0KHgfwTH/8DIB/vMW0bBQppf8B4BV1Opf/xwF8OnX4fQAPENFD55PSzSNTFjk8DuCzKaXbKaX/B+BFdN/UPYGU0ksppa/3x9cBvADgTbi8dSNXHjncs/Wjf8c3+uCy/0sAfhbAb/Xndd3gOvNbAN5F5NnwJLCr2DZ5eROAPxPh76L8Md6rSAD+GxH9ERF9qD/3YErppf74+wAe3E7StoZc/i9rnflwbwr5lDAhXpqy6GX+twL4KqJu6PIALmH9IKI5ET0P4GUAXwbwfwC8mlLivexlfu+WRX/9NQB/+XxTHJgS2yYvgQ5/N6X0M+hk7yeJ6O/Ji6mbz35p57Rf9vyjM3/8NXTy+EsA/s12k3O+IKL7AXwOwEdSStfktctYN4zyuJT1I6V0klJ6DMCb0SlKf2PLSQqcI7ZNXr4H4GERfnN/7lIhpfS9/v/LAH4b3Yf4A5a8+/8vby+FW0Eu/5euzqSUftA31KcA/gNW0v89XxZEtETXUX8mpfT5/vSlrRtWeVzm+gEAKaVXAXwFwN9BZypc9Jdkfu+WRX/9LwH4i3NOamBCbJu8/CGAR3oP8T10zmVf3HKazhVEdB8R/RgfA/iHAP4YXTl8sL/tgwC+sJ0Ubg25/H8RwD/tZ5a8HcBrwoRwT0L5bfwTdPUD6Mri/f1Mip9C56j6B+edvk2h90n4JIAXUkqfEJcuZd3IlcdlrB9E9JNE9EB/fAXAP0DnA/QVAD/X36brBteZnwPw31Os0HqhsajfsjmklI6J6MMA/iuAOYBPpZS+uc00bQEPAvjt3ndsAeA/pZR+h4j+EMCzRPQEgO8AeN8W07hRENF/BvBOAG8kou8C+OcAPg47/18C8F50zoe3APziuSd4g8iUxTuJ6DF05pFvA/hnAJBS+iYRPQvgW+hmojyZUjrZRro3hHcA+ACAb/S+DQDwMVzSuoF8efz8JawfDwF4pp89NQPwbErpvxDRtwB8loj+FYD/iY7sof//H4noRXQO8e/fRqID0yG2BwgEAoFAIHChsG2zUSAQCAQCgUATgrwEAoFAIBC4UAjyEggEAoFA4EIhyEsgEAgEAoELhSAvgUAgEAgELhSCvAQCgUAgELhQCPISCAQCgUDgQuH/A+8mdlJjXnXVAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"mlx_shape = (24,32)\n",
"\n",
"# Set up figure\n",
"fig = plt.figure(figsize=(9,5)) # start figure\n",
"ax = fig.add_subplot(111) # add subplot\n",
"fig.subplots_adjust(0.05,0.05,0.95,0.95) # get rid of unnecessary padding\n",
"\n",
"# Interpolate\n",
"mlx_interp_val = 10 # interpolate # on each dimension\n",
"mlx_interp_shape = (mlx_shape[0]*mlx_interp_val,\n",
" mlx_shape[1]*mlx_interp_val) # new shape\n",
"therm1 = ax.imshow(np.zeros(mlx_interp_shape),interpolation='none', cmap=plt.cm.bwr,vmin=25,vmax=45) # preemptive image\n",
"\n",
"# Add colorbar\n",
"cbar = fig.colorbar(therm1) # setup colorbar\n",
"cbar.set_label('Temperature [$^{\\circ}$C]',fontsize=14) # colorbar label\n",
"\n",
"# Raw Figure\n",
"fig.canvas.draw() # draw figure to copy background\n",
"fig.show() # show the figure before blitting\n",
"\n",
"# fig.canvas.restore_region(ax_background) # restore background\n",
"data_array = np.fliplr(np.reshape(frame,mlx_shape)) # reshape, flip data\n",
"data_array = ndimage.zoom(data_array,mlx_interp_val) # interpolate\n",
"\n",
"therm1.set_array(data_array) # set data\n",
"therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array)) # set bounds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x9a73e6b0>"
]
},
"metadata": {},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 648x360 with 1 Axes>",
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"306.450844pt\" version=\"1.1\" viewBox=\"0 0 412.65775 306.450844\" width=\"412.65775pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 306.450844 \nL 412.65775 306.450844 \nL 412.65775 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 33.2875 282.572719 \nL 395.6875 282.572719 \nL 395.6875 10.772719 \nL 33.2875 10.772719 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g clip-path=\"url(#p0430ad18cc)\">\n <image height=\"272\" id=\"image736e31cb12\" transform=\"scale(1 -1)translate(0 -272)\" width=\"363\" x=\"33.2875\" xlink:href=\"data:image/png;base64,\niVBORw0KGgoAAAANSUhEUgAAAWsAAAEQCAYAAAB2o1V6AAAABHNCSVQICAgIfAhkiAAAIABJREFUeJzsve2y7CquLTgEds61z4n7/m9wH6+ju6tqr5npD/oHYMuakpBzzlWnbkQrwukvDALE0DCJgcr//t8FlpRy7kv5er0L0blPqe75pl3rm3xeplkKsO/ntm3X877x8HzjOqZ0btNUt48H8JiAeT73vz7qfp6B6QHkGfv0C3uasaWMnSasmLCVhK0krFvCsiX8/Uz4fCX8/Un4/Qn8/Qn84x/A79/A5+9z/4//93rt+az7dT2z17Mti2Fd9SzL6pJFEymmUq5VoVWfrC7LZGQVauaknfPr+/7V/GSVAlf9eBVrpmfpwctY09cS7VmtzGVeeNlZzSXnMy85n+f8Os8rz+NIv96Uur31PbcvqTvXVabP9fP0kvmWukq9+bHWHnh74ZvUc56rfr3ZTxPweJzHXf+cr3pF4KTHK8uAx9Wf72W+bbWs1xVYlvOcw5tMM6mlpkkkzHfEQ4II0OuR+mk5QU4p6jHxR61knNt/uDTPdIIJeYB0J8x3dPjJNP+d8k4Z/1S+tLRl8/jTTTcqHlDfff4/RSyd7uoq7UECNdDB+k+21Kh4NCaSEz1SPy0niB9lUR77PwBV/n/5I/Kf6FAibwX/p8l/Yl7+nTqlcMg/4dY0EPbuaSBtWaUF+pHCJf2koNMUauc8WrqqI4+B6zMDVd+VSDFocvflahT+p1jkHbP7T2ReXP4E273Lbf5d7N7SQ9PpJ3X5n5aRk5TXtW5Hed636ce0vPMu2PumeYeO7Cjq++gmw/N4Ljrye0qJUvtxul4K6+DgIO2qZ+1ZlmU2ZLFZ/kpmNfziIbMeAJPvgsx3G6lM3+oGsNIe6S/7tT3x6kEzPS2811hlGJ6GtBcrfqt5aHVg5VmL+087G68t/GmR5alBivX3mnYu45Ci/WehhY2Btaytd1uyBGqr1fZj+Y/IaOvPaqUlrf8okcIwu7Hmo+RS3VJCIb4R9pKwg7AXwlYIewE2/h8o+yNhZ/ud/z9avv6Z4P0JeMdf8WL0jMS6bnl57znLwchjGdYyZG4iHmCE/8JQdLwrlt7aH75SvP5lb/N04elrZSTtSmtSHr+Rur/TH363zDVdtHxx3Tx4ititB7DavX2vENJhp5e/lMT6LmQ8EpzldZnne8zact8j4Vrwcy+sZVnW5qFXZ9SFRJieL7CSI+Uv7YSS8gnWSNiRsJcK1vtet22jy7+9+86AWqpcgPIGMPNisrKr7blYLzT8vtUo5V6LPwraXgOU+ni6asfvyMi8rTxE6kvLg/fK64F1j1uyOy+sN1LFsqF3nIuWvsXPLImSjZ8QrU11Pb065UANXME6aocak/bY9c90g9xpJVataaWmjT3zwJo/e8TXQdmyFMamEx1M+jKWJ2cgZYAydrSNMet9J2z7Fai3NixnW6/DdTZDda3hWC8LnhFpgCKrymrY/X7faw1RYy2y6jSdtaq50xijbwFSJGB617VyscpVy5c23MrKgzz2wE/qzcGkg4QGiJo9jYA7ordX5tKJvwvU8vyOPfdzj2TINDRb9NoZZ9ZEV+CWenM9PFDu8WnOHvDAWmuB3xWt1rTS6ccaKG/b13tW6zkjPveXVgrGplvJ5XSCc+pgPaGkjJ0yygHUCVupIL3uwLoRVj52cj2PNwHalq+JdIdEXiQ8I+PiAXDfaw0zwkA9kJPXtb3UxQLduyzvOxJ1mjIvHqiNmOoIsK28WY7EGgNu2YlmG9Hy1vSK3vPyNXrOsue7cVtOmDuHlGqbjryFaPd7N0kHbU7QpOhg/VPgLOO0WnmECnjodqEFBZehGF0I+Pp/Yi/J3uUhQHqq+5Jz7QZBxl5yBeoG1mupbLkCNVWQXtgmQZv3Xyv+xgNjq3g8YJQgxw1NEwki8ljGpVWjV6383HrO00s7joLG6AXQYtUyD54z9fKjsbwoaGv50Y75udWUInnQyoPrM/rghesyciwjiYAzd4w9TY1keHrKY88RAyewEp0cUsZhfQ8odeXpWTINS+IngNsC6lGpaCza66f+onPfE/r46OMat7oO1P1TpLl9yTg/2heMD+z0wI4J256wbQkrgLUAywa8NuC1AK9XwetJeD7rV4kvti2v+qXSAeCCZUvGbYGzxYhkcfJi5eKxDuuVMdIIR/7W02kEbto1C/A08XgCj1dr8MDX7iitDqx8jABZXrfC8QbN86XllevjdbNZTkY6Nk0vDYQ0XTQ9vTyMHKoUi4DcceKyvHqclqPj8ffuEOC0i96LWor9RW1/ZuRM+PH3+6xHpStLctS6NWTircP6vtSyiJ506WlBtxDt29neBZInFJqwl4wCquoA2Erb+uejW2XWC/uMlHeHdHb9ZVRIkPWM7mvFeykKpwFq10fV64FFBBDuiOdIos7FuifT0MB9VBdaGtYbAG+oFlDLYy0v2jVLz3feBCx9PGfC9dGaPddRE6ueNGcl72npemFluhY0aWWn5ZuP/NDilfe9WTi0Mr0P1lZuI6DNj70S0YBZ9lnLZ74m2HYEUAH2BCTFUmUr4iw7s24QZJQ2RG8nBtY7ap/1fn7bv3FwXr72WY/6raPdIO+CoQcO3l5Wobxu+Uur2j3xAE/et5iIJp7j4vc0wOZ50BoxD2PpaTVI7dzLj1XfVlOy/tqR+mppfgewI/rKtKRo9WAJr7uRflw3DsJaWVr91j38qD32tCVA32HeP/dRTI850lpkjqx+aK0bRIJ1j/OaSMXqrgpRPd4JIKf7hCCG7p192AVUg+8V/zdcAbsy668set3OLo8O2Fq3hwbS/LXLA3OtGC7ZMhqiBRreMz1ubtQWC7WqO9rwuIz0HIHayCx5mDtALRuxV34joBsBplZuFlB4gM3Lhcej6SCZYxQAZdxSV00HroeW70had8JpTra3JWDw1xi+loEJKyycdl2bAOs9sNZolbznXY+4M25V3p+KEsBlqZyR4+sY6nIeb+kr6p0Kt/DdxVEbX30C9U41+r2Ibbc/hIn0T4/6rL1GaVVRv+6BtMacRtXnpcfvR8HaYlTaPUvXkS78WQ2cIs9bgCgb4ajMtZkMR45H6jOyCa/bTMqo/LU6iOhrOe5+3vejPEfYtQT2d0GbX/PeTCQI849bZHyShcvrcjZFOfsfcIdZj2omYnWedWksms/XyCnpyPq0kkoE7NP5XC+hlfdP7Ff0IwLQvmKkCtSlA3XbCgNq6VOszQpjgXXPksW8TZ/Fsm81CA+sR+xOAp9XBZHrXuOyHIump5fWO8AgwcYCQa8MeKMcjRDw8hHRxXpR5c/J/HL9eBlIu7CcOb/m6esBtkzXctLfEc/JaWGsN9+uT9eJj7nudayVdQdkaRscpDlYf2XWXmuWcgesedyyxLSSs5i0trfeTeTQPWo/idpz7T61Ejn6LLTO5IIW2Zcv0sHPWyiZPS2LsjFZjUwzas+4ZLqy+KNVO/K1GkDzNCyAtEwgApz/CWI16H7M9yOQ0YBaA0DPsVlO23pj0+zJcgxafY5syLvv2asELWlj/LoVt3fsORFZhhxER+1SyzOvR+5w+jhsXjcSsDXHLtvX5NIoKdEwI0qmlZYH3BxIrdnRv7ScnStQwbqz58Jc4OvVZiJfgHWuwzcufRk7QAWU0HH78nX65ePHvhmTwXugLRsXD8+zpRWhJ5bx3xEN8DWj5Pe8BvZdiTqfd+Py9LacjiV39Bw1mztNxrMpT28NjDRHzM+jeYzYs4xXHmvgG2kDow04mbHnCHl4rlN/tl/jTFsCtbbJ8Pwtp6c3uSU9et8ZhZWlqp1rblezPtlvYL2fXGqgx8vBemtA3krk9arjql99tZjlyrRzA22aLqT6KFD2CnMMJDmnFPkC3lY2S9u0bvSfBj2PsVhM3AJn+UKlNWKNKXp+X5bTHaalxR29x4UDQSQtrVz+5FtBBIAkGGl2NWLX3EblpEQesPYwP5lfy3lpebOe0xwcYE+45JXnSN8RWHfGnXM9ltKf76KDtQfSI9C2NOfHniVZdIGvhSNB+0s/Nkc9nFS4r+GzLHWIxr4DjxnHKJCc6leLzyeQp7qlGaCtFnqLKrOg+wRsc93mBzAv9Tuaaa1bnoG0ApQraPfvc75kbz9J/x40cgvUeJF7flM2wH7N87uy+jVGoIH53fxYOvdzyfjkK6MXn1cmo0ZpgbHmtCwZgaSlF9dP6mp1n1n6W2n1vWYHnTHKvHJHZTnSSH69sNqzcm+BtWSuFoPm9cbLNFq2Ml0LxmQY4OuybfofjF5paZbpUSBLa83VWsdajnmpjf614+H68x2s1w2YtoaGjRI/n41Zz21rnxouCzCvQF5BaQOlAiJCwonrO/sqfZsrtk9z3fJUgfoyD1Rj28AVsC+VyiqQjh+9eizRGoHGgAD71UtLz2roGlC/A9ZePkaArek7uqalZ7E1nr4HyKO8jxyBBwQWeEfFqj8rDeBknqO3KS0dfu45UO2eF06ee2XqATU/ttLyyjnqbHgcPU0O2l2vbTtZtnSKpXSwtkpT1pBFJzw37VngHUvzukTMUSOcXVOlutuEoz+bCHj+Oj8vfzCw7rMxTSuwr6CyIWH6AtbT1Nh1i2J5ANPSvlRvzDovbbi26A6xAPvyPY8oBglemmF4PtSqks6aJNvWAFFrjHfBOtJ4Pf21vEqdRscec/WYFHds3n0tPcv5WLpr8d8B6qiztPiUx9ekc/fqxbPbSB1KserIAlWN6XIQt57hcUZYtVZe/FmeHtend6Ou63XEEJfJjN0Cac+Vei5qdE3GJXMpS1rrGrmA+Ipz0mhU1Funk1kX1BL5fLb5QBpYP16tH/tVEXddQXkF5Q2U88GuMwF7AkrrCtl7N8h6AvW8NLCe2ER+rSuk5u1rFuUQcVk0FpvURIKDBXx94wbM/9zw2IOMowOY1xBGIp2JBVa8LCyA9JyGlk4EpLU0JAhpjbWHu8NR+PN874nlIOR9j0NJp85tj88UJ21Fph9xrHfFqjMrTxIoLeDWnonW1Yig8LTkOWfSvQ1pMDuZrVIDa08jXruyFXjsWqZlxS1zJ9dzv0wkzdh2Z9cgIK+NCrN/FZ6fwJyBx9TAemZg/WoTOa2geUWijJTqaMcO1NiBMgFla3j/qIT8tTaG3XpX8oQ+JfaVXXMjKwWlEAoKCCetthqbVZRaY5PFyY9l1fJGyOOSacgq48/JPsA7YjFeLZzVMKS+mknxY9ngrXQ5YPHnIg6ix9XLRuty8iQK1JIRS6ctw/Lrmm3IrjKZHnAdiuY5d6lTFAw1hx2pL35/BNp3OaeXT62seFcM18PjwzwOfeieB9RaTLLULEriWZCWpiwtyaZ7d8XzeQL2MaH0cu3HBs5Z9ZbXCeYz+4Mxpbr/66/L5+ZEGZQ/kGZgygVbmgCi+mfAhNo1guonCtgETzvw+7OC97IA06v2b3/xnAdQK5Xftg7cEiRkA9EYHG90vLq0Kur3u47SmGU1cWYigZ8DOE9HA1nZ2LhR9+ue3hL8RoxSA4qeF96oNQCS3R8aUHjOoYv1x6xXPta51ZQ8h9b3Vj5kfnh98lnlZBiNAPAwktdF88j1lXWk5UODqn6tQwO3cfkmaMGZzI8HkTIv2qiPqIz/YOS1ZEG+rAGeex6PjLvnwrvfw/T4LGa9iMmjl1f7IrF1exAq+B7Mmq7MmvdbPz/r8fQAphmUZ6THi5UBAJpARPUPwwygP77VfuvXUqOa2zZNJ7vuw/qui6UTICbcPi6zRqBVkyZWQ/AYCHAFXs14JRDIKosYrtRTi08yPA1cejj5cqgxXc1cNT1kGvKezK+WZhS0uelHWPao7rT0rPStMtDKWuZP410aQ/X0sHTgukTsQCMuXr68jevplbElGnTxuCJ58tKOg7XW+kZUZTSui4eR32tabo4D9jEPqdjWtXZl8Fn7gPOvVu5EPj8r3e2o+phrP/b8aChbN3q9QJSQagcFKAM7JaREoEJAJjzaML5lrd0hr+UE6mk61jI4CPzxIQ2AY8QKUONjIG35SCmyYXkNQTNobW4DLQ7Ln94Fawlwo4Zi6aUBudSVH8vy0Z63GNWIKXqi6RYpZ89xjPR7N04LgGX3mDeiwtJJ01GzBc8ONB2/C9YekH5HeByyz7qXoWWDXK5zg2h0JNICucXKsTH8/Vj++9RFA2qeU54rzqY7WL9eYhTHcgVrHrfssz7Ami068Pv3ibDtH0J6fIIS1axRAVFBSnXVGGpxP6aEbSMsrPubM+sjSsauvxRpQR0FogC0LHbLwD3mGDVqec7T0sQyHXlP6imvaaI1SCuuiH4jsYBMcyx34rLilPnyWK8UjcXdyStPj+dPswVZp1L3qGOXuo5sIQq23wXriP1pOkoHqOlht
gitextract_801zmmcq/ ├── .github/ │ └── workflows/ │ ├── publish_on_release_update.yml │ └── publish_to_testpypi_on_setup_change.yml ├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── examples/ │ ├── cam_test.py │ ├── live_video.py │ └── web_server.py ├── icons/ │ ├── MatPlotLib Thermal Camera │ ├── Run Flask Thermal Camera │ └── Run OpenCV Thermal Camera ├── pithermalcam/ │ ├── __init__.py │ ├── pi_therm_cam.py │ ├── templates/ │ │ └── index.html │ └── web_server.py ├── requirements.txt ├── requirements_without_opencv.txt ├── saved_snapshots/ │ └── note.txt ├── sequential_versions/ │ ├── Method Comparison.ipynb │ ├── matplotlib_therm_cam.py │ ├── opencv_therm_cam.py │ └── sequential_config.ini ├── setup.cfg └── setup.py
SYMBOL INDEX (53 symbols across 5 files)
FILE: pithermalcam/__init__.py
function test_camera (line 8) | def test_camera():
function display_camera_live (line 28) | def display_camera_live(output_folder:str = '/home/pi/pithermalcam/saved...
function stream_camera_online (line 34) | def stream_camera_online(output_folder:str = '/home/pi/pithermalcam/save...
FILE: pithermalcam/pi_therm_cam.py
class pithermalcam (line 22) | class pithermalcam:
method __init__ (line 38) | def __init__(self,use_f:bool = True, filter_image:bool = False, image_...
method __del__ (line 52) | def __del__(self):
method _setup_therm_cam (line 55) | def _setup_therm_cam(self):
method _c_to_f (line 63) | def _c_to_f(self,temp:float):
method get_mean_temp (line 67) | def get_mean_temp(self):
method _pull_raw_image (line 83) | def _pull_raw_image(self):
method _process_raw_image (line 102) | def _process_raw_image(self):
method _add_image_text (line 120) | def _add_image_text(self):
method add_customized_text (line 135) | def add_customized_text(self,text):
method _show_processed_image (line 140) | def _show_processed_image(self):
method _set_click_keyboard_events (line 146) | def _set_click_keyboard_events(self):
method _mouse_click (line 174) | def _mouse_click(self,event,x,y,flags,param):
method _print_shortcuts_keys (line 180) | def _print_shortcuts_keys(self):
method display_next_frame_onscreen (line 193) | def display_next_frame_onscreen(self):
method change_colormap (line 203) | def change_colormap(self, forward:bool = True):
method change_interpolation (line 214) | def change_interpolation(self, forward:bool = True):
method update_image_frame (line 225) | def update_image_frame(self):
method update_raw_image_only (line 233) | def update_raw_image_only(self):
method get_current_raw_image_frame (line 237) | def get_current_raw_image_frame(self):
method get_current_image_frame (line 242) | def get_current_image_frame(self):
method save_image (line 251) | def save_image(self):
method _temps_to_rescaled_uints (line 258) | def _temps_to_rescaled_uints(self,f,Tmin,Tmax):
method display_camera_onscreen (line 265) | def display_camera_onscreen(self):
FILE: pithermalcam/web_server.py
function index (line 33) | def index():
function save_image (line 39) | def save_image():
function change_units (line 44) | def change_units():
function increment_colormap (line 49) | def increment_colormap():
function decrement_colormap (line 54) | def decrement_colormap():
function toggle_filter (line 59) | def toggle_filter():
function increment_interpolation (line 64) | def increment_interpolation():
function decrement_interpolation (line 69) | def decrement_interpolation():
function appexit (line 74) | def appexit():
function video_feed (line 84) | def video_feed():
function get_ip_address (line 89) | def get_ip_address():
function pull_images (line 97) | def pull_images():
function generate (line 113) | def generate():
function start_server (line 131) | def start_server(output_folder:str = '/home/pi/pithermalcam/saved_snapsh...
FILE: sequential_versions/matplotlib_therm_cam.py
function c_to_f (line 46) | def c_to_f(temp:float):
function print_mean_temp (line 52) | def print_mean_temp():
function simple_pic (line 70) | def simple_pic():
function simple_camera_read (line 92) | def simple_camera_read():
function interpolated_pic (line 122) | def interpolated_pic():
function interpolated_camera_read (line 165) | def interpolated_camera_read():
FILE: sequential_versions/opencv_therm_cam.py
function c_to_f (line 46) | def c_to_f(temp:float):
function temps_to_rescaled_uints (line 52) | def temps_to_rescaled_uints(f,Tmin,Tmax):
function take_pic (line 58) | def take_pic(use_f=True):
function save_snapshot (line 89) | def save_snapshot(event,x,y,flags,param):
function camera_read (line 99) | def camera_read(use_f:bool = True, filter_image:bool = False):
function print_shortcuts_keys (line 210) | def print_shortcuts_keys():
Condensed preview — 25 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (361K chars).
[
{
"path": ".github/workflows/publish_on_release_update.yml",
"chars": 2607,
"preview": "# This is a basic workflow to help you get started with Actions\n\nname: Publish distribution 📦 to PyPI 🐍 on new release\n\n"
},
{
"path": ".github/workflows/publish_to_testpypi_on_setup_change.yml",
"chars": 2564,
"preview": "# This is a basic workflow to help you get started with Actions\n\nname: Publish distribution 📦 to TestPyPI 🐍\n\n# Controls "
},
{
"path": ".gitignore",
"chars": 757,
"preview": "# These are some examples of commonly ignored file patterns.\n# You should customize this list as applicable to your proj"
},
{
"path": "LICENSE",
"chars": 34523,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C)"
},
{
"path": "MANIFEST.in",
"chars": 97,
"preview": "recursive-include pithermalcam/templates *\r\nrecursive-exclude lib/pithermalcam/templates *\r\n*.ini"
},
{
"path": "README.md",
"chars": 2958,
"preview": "# README #\n\nDocumentation of the pithermalcam project and accompanying [PyPI package](https://pypi.org/project/pithermal"
},
{
"path": "examples/cam_test.py",
"chars": 101,
"preview": "import pithermalcam as ptc\n\n# Run a camera test, getting the mean temperature back\nptc.test_camera()\n"
},
{
"path": "examples/live_video.py",
"chars": 119,
"preview": "import pithermalcam as ptc\n\n# Run this file locally to display the video feed live on screen\nptc.display_camera_live()\n"
},
{
"path": "examples/web_server.py",
"chars": 219,
"preview": "import pithermalcam as ptc\n\n# Run this file to start a flask server streaming the video to the local network\n# IP Addres"
},
{
"path": "icons/MatPlotLib Thermal Camera",
"chars": 310,
"preview": "[Desktop Entry]\nVersion=1.0\nName=MatPlotLib Thermal Camera\nIcon=applets-screenshooter\nPath=/home/pi/PiThermalCam/sequent"
},
{
"path": "icons/Run Flask Thermal Camera",
"chars": 251,
"preview": "[Desktop Entry]\nVersion=1.0\nName=Flask Thermal Camera\nIcon=screensaver\nPath=/home/pi/PiThermalCam/\nExec=/usr/bin/python3"
},
{
"path": "icons/Run OpenCV Thermal Camera",
"chars": 285,
"preview": "[Desktop Entry]\nVersion=1.0\nName=OpenCV Thermal Camera\nIcon=xfce4-ui\nPath=/home/pi/PiThermalCam/sequential_versions/\nExe"
},
{
"path": "pithermalcam/__init__.py",
"chars": 2119,
"preview": "\n# Imports here to enable access to these functions from the library import without having to import those files.\n# Effe"
},
{
"path": "pithermalcam/pi_therm_cam.py",
"chars": 13293,
"preview": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n#"
},
{
"path": "pithermalcam/templates/index.html",
"chars": 3858,
"preview": "<html>\n\n<head>\n <title>Pi Thermal Video</title>\n <script src=\"//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min."
},
{
"path": "pithermalcam/web_server.py",
"chars": 4539,
"preview": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# Flask web server for MLX90640 Thermal Ca"
},
{
"path": "requirements.txt",
"chars": 121,
"preview": "numpy>=1.16.5\nmatplotlib\nscipy>=1.6.0\nRPI.GPIO\nAdafruit-Blinka\nadafruit-circuitpython-mlx90640\nflask\nopencv-python\ncmapy"
},
{
"path": "requirements_without_opencv.txt",
"chars": 101,
"preview": "numpy>=1.16.5\nmatplotlib\nscipy>=1.6.0\nRPI.GPIO\nAdafruit-Blinka\nadafruit-circuitpython-mlx90640\nflask\n"
},
{
"path": "saved_snapshots/note.txt",
"chars": 90,
"preview": "This folder should be excluded from the git, apart from this file to keep it in existance."
},
{
"path": "sequential_versions/Method Comparison.ipynb",
"chars": 260608,
"preview": "{\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_ext"
},
{
"path": "sequential_versions/matplotlib_therm_cam.py",
"chars": 10192,
"preview": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n#"
},
{
"path": "sequential_versions/opencv_therm_cam.py",
"chars": 10077,
"preview": "# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n##################################\n# MLX90640 Thermal Camera w Raspberry Pi\n#"
},
{
"path": "sequential_versions/sequential_config.ini",
"chars": 153,
"preview": "# Note: Inline comment prefixes must be enabled in config parser for this file to work\n[FILEPATHS]\noutput_folder = /home"
},
{
"path": "setup.cfg",
"chars": 526,
"preview": "[flake8]\nignore = D203, E265, E401, E231, E226, E225, E252, E305, W191, W605\nmax-line-length = 160\nexclude =\n # No ne"
},
{
"path": "setup.py",
"chars": 1316,
"preview": "import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n\nsetuptools.set"
}
]
About this extraction
This page contains the full source code of the tomshaffner/PiThermalCam GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 25 files (343.5 KB), approximately 188.5k tokens, and a symbol index with 53 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.