diff --git a/CITATION.cff b/.github/CITATION.cff similarity index 100% rename from CITATION.cff rename to .github/CITATION.cff diff --git a/CODEOWNERS b/.github/CODEOWNERS similarity index 100% rename from CODEOWNERS rename to .github/CODEOWNERS diff --git a/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md similarity index 100% rename from CODE_OF_CONDUCT.md rename to .github/CODE_OF_CONDUCT.md diff --git a/CONTRIBUTING.md b/.github/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to .github/CONTRIBUTING.md diff --git a/.github/clear-local-branches.ps1 b/.github/clear-local-branches.ps1 new file mode 100644 index 000000000..f06055c08 --- /dev/null +++ b/.github/clear-local-branches.ps1 @@ -0,0 +1,2 @@ +# Delete local branches that no longer exists on remote (windows command line) +git branch -vv | Where-Object { $_ -match 'gone\]' } | ForEach-Object { $_.Trim().Split()[0] } | ForEach-Object { git branch -D $_ } diff --git a/.vscode/settings.json b/.vscode/settings.json index 62481957c..e36a259e0 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -5,12 +5,14 @@ "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "autoDocstring.docstringFormat": "numpy", - "cSpell.enableFiletypes": [ - "python", - "jupyter", - "markdown", - "restructuredtext" - ], + "cSpell.enabledFileTypes": { + "plaintext": true, + "python": true, + "jupyter": true, + "markdown": true, + "tex": true, + "rst": true + }, "cSpell.language": "en-US", "cSpell.words": [ "Abdulklech", @@ -231,6 +233,7 @@ "polystyle", "powerseries", "Prandtl", + "pressurant", "prettytable", "Projeto", "prometheus", @@ -263,6 +266,7 @@ "SBMT", "scilimits", "searchsorted", + "seblm", "seealso", "setrail", "simplekml", diff --git a/CHANGELOG.md b/CHANGELOG.md index d4d083a77..3aa74969b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,7 +32,7 @@ Attention: The newest changes should be on top --> ### Added -- ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) +- ### Changed @@ -40,7 +40,42 @@ Attention: The newest changes should be on top --> ### Fixed +- + +## [v1.7.1] - 2024-12-07 + +### Changed + +- REL: update version to 1.7.1 in configuration files [#750](https://github.com/RocketPy-Team/RocketPy/pull/750) +- MNT: Refactor Tank's testing Assertion with CAD data. [#678](https://github.com/RocketPy-Team/RocketPy/pull/678) + +### Fixed + +- BUG: Correctly update atmospheric conditions after changing date and location [#743](https://github.com/RocketPy-Team/RocketPy/pull/743) + + +## [v1.7.0] - 2024-11-30 + +### Added + +- DOC: GENESIS Flight Example [#734](https://github.com/RocketPy-Team/RocketPy/pull/734) +- DOC: Camoes Flight Example [#733](https://github.com/RocketPy-Team/RocketPy/pull/733) +- ENH: Callback function for collecting additional data from Monte Carlo sims [#702](https://github.com/RocketPy-Team/RocketPy/pull/702) +- ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) + +### Changed + +- REL: update version to 1.7.0 in configuration files [#741](https://github.com/RocketPy-Team/RocketPy/pull/741) +- MNT: Place filename save parameter to the end. [#739](https://github.com/RocketPy-Team/RocketPy/pull/739) +- DOC: improvements to developers documentation [#732](https://github.com/RocketPy-Team/RocketPy/pull/732) + +### Fixed + +- BUG: Allow multiple sets of stochastic fins [#737](https://github.com/RocketPy-Team/RocketPy/pull/737) +- BUG: forecast and reanalysis models - Update ECMWF dictionary values [#736](https://github.com/RocketPy-Team/RocketPy/pull/736) +- BUG: forecast and reanalysis models - move wind_speed to correct position [#735](https://github.com/RocketPy-Team/RocketPy/pull/735) - BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) +- DOC: fixed documentation about spherical caps [#728](https://github.com/RocketPy-Team/RocketPy/pull/728) ## [v1.6.2] - 2024-11-08 diff --git a/data/rockets/camoes/actuation.csv b/data/rockets/camoes/actuation.csv new file mode 100644 index 000000000..d33391687 --- /dev/null +++ b/data/rockets/camoes/actuation.csv @@ -0,0 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b/data/weather/ndrt_2020_weather_data_ERA5_new.nc new file mode 100644 index 000000000..7fea7c5e2 Binary files /dev/null and b/data/weather/ndrt_2020_weather_data_ERA5_new.nc differ diff --git a/.dockerignore b/docker/.dockerignore similarity index 100% rename from .dockerignore rename to docker/.dockerignore diff --git a/Dockerfile b/docker/Dockerfile similarity index 100% rename from Dockerfile rename to docker/Dockerfile diff --git a/docker-compose.yml b/docker/docker-compose.yml similarity index 100% rename from docker-compose.yml rename to docker/docker-compose.yml diff --git a/docs/conf.py b/docs/conf.py index e834d180e..5a907a639 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "RocketPy Team" # The full version, including alpha/beta/rc tags -release = "1.6.2" +release = "1.7.1" # -- General configuration --------------------------------------------------- @@ -40,6 +40,7 @@ "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.mathjax", + "sphinx_tabs.tabs", "sphinx_copybutton", "sphinx_design", "jupyter_sphinx", diff --git a/docs/development/build_docs.rst b/docs/development/build_docs.rst index f94c4f0fb..398781399 100644 --- a/docs/development/build_docs.rst +++ b/docs/development/build_docs.rst @@ -31,64 +31,67 @@ requirements that are needed to run the sphinx generator. All these requirements are listed in the ``requirements.txt`` file inside the ``docs`` folder. -To install the requirements, navigate the terminal to the ``docs`` folder and -run the following command: +The easiest way to install the requirements and build the documentation is by +RocketPy's ``Makefile`` command: .. code-block:: bash - - pip install -r requirements.txt -After installing the requirements, you can build the documentation by running -the following command in your terminal: + make build-docs -.. code-block:: bash - - make html +This command will install the requirements and generate the documentation in the +``docs/_build/html`` folder. + +.. note:: -The file named ``Makefile`` contains the commands to build the documentation. -The ``make html`` command will generate the documentation in the ``docs/_build/html`` -folder. + This requires the ``make`` command to be available in your system. Check out + GNU Make documentation on how to install it. To see the documentation, open the ``docs/_build/html/index.html`` file in your browser. -.. note:: Watch out for any warnings or errors that may appear in the terminal - when building the documentation. If you find any, fix them before - pushing the changes to the repository or at least warn the team about - them. +.. important:: + + Watch out for any introduced warnings or errors that may appear in the terminal + when building the documentation. If you find any, fix them before + pushing the changes to the repository or at least warn the team about + them. Sometimes you may face problems when building the documentation after several -times of building it. -This may happen because sphinx does not clean the ``docs/_build`` folder before -building the documentation again. -To clean the ``docs/_build`` folder, run the following command in your terminal: +times of building it. This may happen because sphinx does not clean the ``docs/_build`` +folder before building the documentation again. + +One can use the internal Makefile inside the ``docs`` folder generated by sphinx +to have specific commands to build and clean the documentation. + +To clean the ``docs/_build`` folder, run the following commands in your terminal: .. code-block:: bash + cd docs/ make clean After cleaning the ``docs/_build`` folder, you can build the documentation again -by running the ``make html`` command. +by going back to rocketpy root directory and using the ``make build-docs`` command +described above. If the error persists, it may be related to other files, such as the ``.rst`` files or the ``conf.py`` file. .. danger:: - Do not modify the Makefile or the ``make.bat`` files. These files are \ - automatically generated by sphinx and any changes will be lost. + Do not modify sphinx internal Makefile or the ``docs/make.bat`` files. + These files are automatically generated by sphinx and any changes will be lost. How to integrate the documentation with ReadTheDocs --------------------------------------------------- The documentation is automatically built and hosted by `ReadTheDocs`. -Every time a commit is pushed to the repository, `ReadTheDocs` will build the -documentation and host it automatically. -This includes other branches besides the master branch. -However, the documentation will only be available for the master branch, and you -need to configure the `ReadTheDocs` project to build the documentation for other -branches. +Every time a commit is pushed to selected branches of the repository (such as `master` and `develop`), +`ReadTheDocs` will build the documentation and host it automatically. + +However, custom branches need to be manually added. If you need to have your branch +available on `ReadTheDocs`, ask to a maintainer for help. The connection between the GitHub repository and the `ReadTheDocs` project is already configured and defined in the ``readthedocs.yml`` file, available at the diff --git a/docs/development/conflicts.rst b/docs/development/conflicts.rst new file mode 100644 index 000000000..dbdb09e61 --- /dev/null +++ b/docs/development/conflicts.rst @@ -0,0 +1,123 @@ +Solving Git Conflicts +===================== + +What Are Git Conflicts? +----------------------- + +Git conflicts occur when Git is unable to automatically merge changes made to the \ +same lines of code or when changes overlap in a file across branches. +This often occurs in RocketPy repository when two developers make changes to the same \ +file and try to merge them. +In this scenario, Git pauses the operation and marks the conflicting files, \ +requiring manual intervention. + +Merge vs Rebase +--------------- + +**Merge** + +The ``merge`` operation combines the changes from one branch into another. \ +It creates a new commit that marks the merging of the two branches together. + +* Retains the full history of both branches. +* Non-linear history shows how changes were combined. +* The history can become cluttered if there are many branches, due to the number of merge commits. + +**Rebase** + +The ``rebase`` operation integrates changes from one branch into another by reapplying \ +the commits on top of the target branch. It results in a more linear history. + +* Produces a cleaner, linear commit history. +* Easier to follow the sequence of changes. +* Can rewrite history, leading to potential issues when working on shared branches. + +Example Commands +---------------- + +Let's consider a common scenario from which conflicts can arise: +updating a feature branch with the latest changes from the branch it was created. +Both ``merge`` and ``rebase`` can be used to update the feature branch. +However, the ``rebase`` option is highly recommended to keep a more linear history. + +Merge +~~~~~ + +Suppose you have two branches, ``enh/feature`` that was branched off ``develop``. +It is likely that ``develop`` has been updated since you branched off ``enh/feature``, \ +therefore before merging ``enh/feature`` into ``develop``, you should update ``enh/feature`` \ +with the latest changes from ``develop``. + +One way to do this is by merging ``develop`` into ``enh/feature`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git merge develop + +If there are conflicts, Git will pause the operation and mark the conflicting files. \ +VS Code provides a visual interface to resolve these conflicts in the **Merge Editor**. +If you want to abort the merge, you can use ``git merge --abort``. + +Rebase +~~~~~~ + +Similarly, another way to update ``enh/feature`` with the latest changes from ``develop`` \ +is by rebasing ``enh/feature`` onto ``develop`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git rebase develop + +Differently from merge, if there are conflicts, they must be resolved commit by commit. \ +After resolving each step conflicts, you can continue the rebase with ``git rebase --continue``. \ +If you want to abort the rebase, you can use ``git rebase --abort``. + +When to Use Merge or Rebase +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Generally, the maintainers will inform you which operation to use when merging your PR. \ +Notice that there is no wrong way to merge branches, but ``rebase`` is usually preferred \ +to keep a more linear history. + +Furthermore, it is important to avoid letting conflicts accumulate, since they can become \ +harder to resolve. It is recommended to update your feature branch frequently with the latest \ +changes from the branch it was branched off. + +Solving Conflicts +----------------- + +When conflicts arise, Git marks the conflicting files. The file will contain markers like: + +.. code-block:: console + + <<<<<<< HEAD + Current branch changes + ======= + Incoming branch changes + >>>>>>> branch-name + +The ``HEAD`` section contains the changes from the current branch, while the ``branch-name`` section \ +contains the changes from the incoming branch. +The ``=======`` line separates the two sections. +One can manually edit the file to resolve the conflict, removing the markers and keeping the desired changes, however \ +for convenience it is recommended to use a visual tool like the *Merge Editor* in VS Code. + +Resolving Conflicts in VS Code +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When a conflict occurs, VS Code will open the *Merge Editor* to help you resolve it.\ + +1. Open the conflicting file (marked with a ``!``). +2. The *Merge Editor* will show the conflicting sections side by side. +3. Click on the ``Accept Current Change`` or ``Accept Incoming Change`` buttons to keep the desired changes, sometimes both changes will be kept or even a manual edit will be necessary. + +More details on VS Code interface and conflict solver can be found in `VS Code Docs `_. +After resolving the conflicts, save the files, make sure all conflicts are resolved, and then \ +commit the changes. + diff --git a/docs/development/docker.rst b/docs/development/docker.rst index 91aafb1d2..bc1de8745 100644 --- a/docs/development/docker.rst +++ b/docs/development/docker.rst @@ -2,7 +2,7 @@ RocketPy with docker ===================== RocketPy does not provide an official docker image, but you can build one -yourself using the provided `Dockerfile` and `docker-compose.yml` files. +yourself using the provided ``Dockerfile`` and ``docker-compose.yml`` files. Benefits -------- @@ -14,13 +14,13 @@ using docker are: worrying about dependencies. 2. **Portability**: run RocketPy on any operational system that supports docker, including the 3 main operational systems (Windows, Linux and Mac). -3. **Reproducibility**: ensure that tour code is working regardless of the +3. **Reproducibility**: ensure that our code is working regardless of the operational system. Using docker will be specially important when you are not sure if the code additions will still run on different operational systems. -Although we have a set of GitHub actions to test the code on different +Although we have a set of GitHub actions to test the code on different operational systems every time a pull request is made, it is important to submit a PR only after you are sure that the code will run flawlessly, otherwise quota limits may be reached on GitHub. @@ -28,7 +28,7 @@ otherwise quota limits may be reached on GitHub. Requirements ------------- -Before you start, you need to install on your machine: +Before you start, you need to install on your machine: 1. `Docker `__, to build and run the image. 2. `Docker Compose `__, to compose multiple images at once. @@ -40,11 +40,11 @@ Build the image To build the image, run the following command on your terminal: .. code-block:: console - + docker build -t rocketpy-image -f Dockerfile . -This will build the image and tag it as `rocketpy-image` (you can apply another +This will build the image and tag it as ``rocketpy-image`` (you can apply another name of your preference if you want). An image is a read-only template with instructions for creating a Docker @@ -82,7 +82,7 @@ Run the unit tests -------------------- You might have noticed that the container is running in an isolated environment -with no access to your machine's files, but the `Dockerfile` already copied the +with no access to your machine's files, but the ``Dockerfile`` already copied the RocketPy repository to the container. This means that you can run tests (and simulations!) as if you were running RocketPy on your machine. @@ -90,37 +90,37 @@ RocketPy on your machine. As simple as that, you can run the unit tests: .. code-block:: console - + pytest To access a list of all available execution options, see the `pytest docs `__. -Compose docker images +Compose docker images --------------------- -We also made available a `docker-compose.yml` file that allows you to compose +We also made available a ``docker-compose.yml`` file that allows you to compose multiple docker images at once. -Unfortunately, this file will not allow you to test the code on different +Unfortunately, this file will not allow you to test the code on different operational systems at once, since docker images inherits from the host operational system. However, it is still useful to run the unit tests on different python versions. -Currently, the `docker-compose.yml` file is configured to run the unit tests +Currently, the ``docker-compose.yml`` file is configured to run the unit tests on python 3.9 and 3.12. To run the unit tests on both python versions, run the following command **on your machine**: .. code-block:: console - + docker-compose up Also, you can check the logs of the containers by running: .. code-block:: console - + docker-compose logs @@ -131,7 +131,7 @@ After you're done testing, or if you wish to stop the containers and remove the services, use the command: .. code-block:: console - + docker-compose down @@ -142,31 +142,31 @@ images created by up. Changing to other operational systems ------------------------------------- -The default image in the `Dockerfile` is based on a Linux distribution. +The default image in the ``Dockerfile`` is based on a Linux distribution. However, you can alter the base image to use different operating systems, though the process may require additional steps depending on the OS's compatibility with your project setup. For instance, certain dependencies or scripts may behave differently or require different installation procedures, so use it with caution. -To change the base image, you will need to modify the `FROM` statement in the -`Dockerfile`. +To change the base image, you will need to modify the ``FROM`` statement in the +``Dockerfile``. For example, to use a Windows-based image, you might change: .. code-block:: Dockerfile - + FROM python:latest to .. code-block:: Dockerfile - + FROM mcr.microsoft.com/windows/servercore:ltsc2019 Please note, the above is just an example, and using a different OS may require -further adjustments in the `Dockerfile`. +further adjustments in the ``Dockerfile``. We recommend you to see the official Python images on the Docker Hub for different OS options: `Docker Hub Python Tags `__. diff --git a/docs/development/first_pr.rst b/docs/development/first_pr.rst new file mode 100644 index 000000000..5f3eb876c --- /dev/null +++ b/docs/development/first_pr.rst @@ -0,0 +1,139 @@ +Your first Pull Request (PR) +============================ + +This page describes a few steps to help you get started with your first PR to RocketPy. +We are excited to have you here! + +Picking an issue +---------------- + +Before you start coding, you should pick an issue to work on. You can find a +list of issues in the ``Issues`` tab on GitHub (https://github.com/RocketPy-Team/RocketPy/issues). +If you are new here, it is really recommended that you talk to maintainers +before starting to work on an issue. +That way you avoid working on something that is already being worked on or +something that is not in line with the project's goals. + +If you want to develop a new feature that is not in the issues list, please open +a new issue to discuss it with the maintainers before submitting a PR. + +Once the issue is assigned to you, you can start working on it. + +.. note:: + + In order to open an issue at our repo, you must have a GitHub account. \ + In case you do not want to open an account yet, you can contact the maintainers \ + through our Discord server. But keep in mind that you you need an account to \ + open a Pull Request (PR). + +Creating a new branch +--------------------- + +At your local machine, make sure you already have `git `_ \ +installed and set up. +In order to create a new branch, you should run the following command in your +preferred terminal: + +.. code-block:: console + + git checkout -b + +This command will create a new branch named ``branch_name`` from the current branch you are in. +Your branch name should follow the guidelines described in :doc:`/development/style_guide`. + +.. tip:: + + VS Code has a built-in integration with git, this allows you to run git commands \ + quite easily through the editor's interface. For example, you could search for \ + "git checkout" in the command palette and run the command from there. + + +Opening the PR +-------------- + +After you finish your work, you are more than welcome opening a PR to the RocketPy repository. +Here are some checks to do before opening a PR: + +* Check if the test suite is passing. +* Format your code using ``black`` and ``isort``. + +Check the :doc:`/development/pro_tips` section for more information on how to run these commands. + +When you open a PR, you should: + +* Provide a clear and concise title. +* Fill the PR description following the standard template. +* Use labels to help maintainers understand what the PR is about. +* Link any issue that may be closed when the PR is merged. + +Remember, the PR is yours, not ours! You should keep track of it and update it as needed. + +.. important:: + + See the :doc:`/development/style_guide` for more information on how to name and \ + describe your PR. + +Continuous Integration (CI) +--------------------------- + +There are several automation on our repository to help us maintain the code quality. +Currently, our CI pipeline runs the following checks: + +* **Linting**: we run ``flake8``, ``pylint``, ``black`` and ``isort`` on top of your latest commit in order to check for code style issues. To understand more about these tools, please read the :doc:`/development/pro_tips` section. +* **Testing**: we run the tests defined in the ``tests`` folder to make sure your changes do not break any existing functionality. The tests will be executed 6 times, each time with a different Python version (the oldest and newest supported versions) and with three different operating systems (Windows, Linux and MacOS). +* **Coverage**: based on the tests results, we also check the code coverage results from our test suite. There is an automation to check if the code coverage increased or decreased with your PR. It also points to potential introduced lines of code that should be tested. + +Once you open your PR or commit and push to your branch, the CI will be initialized. +Please correct any issues that may arise from the CI checks. + +.. important:: + + All the commands we run in the CI pipeline can also be run locally. It is \ + important that you run the checks locally before pushing your changes to \ + the repository. + +The CHANGELOG file +------------------ + +We keep track of the changes in the ``CHANGELOG.md`` file. +When you open a PR, you should add a new entry to the "Unreleased" section of the file. +This entry should simply be the title of your PR. + +.. note:: + + In the future we would like to automate the CHANGELOG update, but for now \ + it is a manual process, unfortunately. + + +The review process +------------------ + +After you open a PR, the maintainers will review your code. +This review process is a way to ensure that the code is in line with the +project's goals and that it is well written and documented. + +The maintainers may ask you to make changes to your code. +You should address these changes or explain why you think they are not necessary. +This is the best time to learn from the maintainers and improve your coding skills. + +In case you do not address the comments in a timely manner, the maintainers may +either close the PR or make the changes themselves. + + +Merging the PR +-------------- + +There are 3 different ways of merging a PR: + +1. **Create a merge commit**: this is the default option on GitHub. +2. **Squash and merge**: this option will squash all your commits into a single one. This is useful when you have many commits and you want to keep the history clean, therefore this is the recommended option. +3. **Rebase and merge**: this option will add your commits directly to the target branch, without creating a merge commit. This is useful to keep the history linear, however it also requires handling potential conflicts one at a time, which can be a bit more complex. + +.. note:: + + Overall, you will not have permission to merge your PR. The maintainers will \ + take care of that for you. This is here just for you to understand the process. + +All in all, there is no right or wrong way to merge a PR. +The maintainers will decide which option is the best for the project. +What you should care though is about conflicting changes, let's talk about that next in :doc:`/development/conflicts`. diff --git a/docs/development/index.rst b/docs/development/index.rst index 4837fa375..5d8cc490f 100644 --- a/docs/development/index.rst +++ b/docs/development/index.rst @@ -1,14 +1,27 @@ Contributing to RocketPy ======================== +Here you will find information on how to contribute to our code base. +RocketPy is an open-source project and we welcome contributions from everyone. +There are a few guidelines we would like to share with you to make your +contribution as smooth as possible: + .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :caption: Contents: - Running RocketPy as a Developer + setting_up.rst + Your First Pull Request (PR) + Solving git conflicts Style Guide Testing Guidelines RocketPy with Docker Building the Documentation + Pro Tips + + +.. important:: -This section is still a work in progress. Here you will find information on how to contribute to our code base. \ No newline at end of file + Our Development Documentation matters as much as the Usage Documentation to us. \ + In case you find any trouble when following this guide, please let us know so \ + we can improve it. diff --git a/docs/development/pro_tips.rst b/docs/development/pro_tips.rst new file mode 100644 index 000000000..465ff7982 --- /dev/null +++ b/docs/development/pro_tips.rst @@ -0,0 +1,99 @@ +Pro Tips +======== + +Last but not least, here are some pro tips that can help you to be more +productive when developing in the project (or any other project). + + +Makefile +-------- + +We have a ``Makefile`` in the repository that contains some useful commands to +help you with the development process. + +Some examples of commands are: + +* ``make black``: runs the black formatter on the code. +* ``make format``: runs the isort and black formatters on the code. +* ``make lint``: runs the flake8 and pylint tools. +* ``make build-docs``: This will build the documentation locally, so you can check if everything is working as expected. +* ``make pytest-slow``: runs only the slow tests (marked with the ``@pytest.mark.slow`` decorator). + +These commands are meant to be system agnostic, so you can run them on any +Operational System (OS). + +Knowing your editor +------------------- + +Something we usually notice on beginners is that they don't know all the power +their editor can provide. Knowing your editor can save you a lot of time and +make you more productive. + +As stated earlier, we recommend using VS Code as your editor, as most of the +RocketPy developers use it. Therefore it is easier to help you if you are using +the same editor. + +Some of the features that can help you are: + +1. **Code navigation**: Jump to definitions, find references, etc. +2. **Integrated tests**: Run tests directly from the editor. +3. **Python Debugger**: Debug your code directly from the editor. +4. **GitHub Pull Requests**: Open, review, comment, and merge pull requests directly from the editor + +Extensions +---------- + +We have listed some recommended extensions in the ``.vscode/extensions.json`` file. +These are general recommendations based on what our developers usually install +in their VSCode. +Obviously, it is not mandatory to install them, but they can help you to be more +productive. + + +Code assistance +--------------- + +Artificial Intelligence (AI) assistance has becoming more and more common in +software development. +Some editors have AI assistance built-in. +Famous options are GitHub Copilot, JetBrains AI and TabNine. + +At this repo, the use of AI tools is welcome, we don't have any restrictions +against it. + +A few possible applications of AI tools are: + +* Writing documentation. +* Writing tests. +* Getting explanations of code sections. + +.. tip:: + + As of today, November 2024, GitHub Copilot still provides free access for \ + university email addresses. We can't guarantee this will still be the case \ + when you are reading this, so check the GitHub Copilot website for more \ + information. + + +If you are against the use of AI tools, do not worry, you can still contribute +to the project without using them. + + +Engaging with the community +--------------------------- + +The most important part of contributing to an open-source project is engaging +with the community. +Our developers are frequently available on our server, and you can +ask any questions you may have there. +Either a question about the project, or a question about how to contribute, or +even a suggestion of a new feature. +Any kind of interaction is welcome. + + +.. tip:: + + The official supported language in our server is English. \ + However, the RocketPy Team is lucky to have developers from all around the \ + world, so you may find people speaking other languages as well. \ + Don't let the language barrier keep you from engaging with the community. diff --git a/docs/development/rocketpy_as_developer.rst b/docs/development/rocketpy_as_developer.rst deleted file mode 100644 index df3608c77..000000000 --- a/docs/development/rocketpy_as_developer.rst +++ /dev/null @@ -1,277 +0,0 @@ -======================== -Introduction to RocketPy -======================== - -This tutorial part shows how to open rocketpy files and run a simple simulation. - -Opening rocketpy folder -======================= - -Go into the cloned repository folder by typing on a terminal - -.. code-block:: console - - cd - -Open your preference editor by typing on a terminal - -.. code-block:: console - - . - -For example, to open VS Code type on terminal - -.. code-block:: console - - code . - -Alternatively, you can open the folder directly through your editor's interface. - -Preparing directory for code editing -==================================== - -You may create a testing file in any directory, but you must remember that they -should not be included in the commits and pull requests unless they are part of -the proposed solution. -With that in mind, we suggest you to create a folder with all testing files, so -they can be added in the ``.gitignore`` file, which contains the name of all the -files and folders that will not be added to the commits. -To create the folder, type on the terminal: - -.. code-block:: console - - mkdir - -And, to add it on .gitignore, type: - -.. code-block:: console - - echo / >> .gitignore - -It is important to remember that all the files inside this folder will not be included in any commit so, if it is important to the solution, do not add them inside it. - -Running a simulation with RocketPy -================================== - -Importing the RocketPy files ----------------------------- - -First, create a python (or .ipynb) file to make the simulation. -To ensure you are using the local files and not the files as a python package (if you installed the library via pip for example), add - -.. code-block:: python - - pip install -e . - -Alternatively you can use the following command to pip install the local library: - -.. code-block:: console - - import sys - sys.path.append('../') # if you are using a notebook - sys.path.append('../rocketpy') # if you are using a script - -Import the classes that will be used, in case: - -.. code-block:: python - - from rocketpy import Environment, SolidMotor, Rocket, Flight, Function - -If it is the first time you are using rocketpy and you do not have all required libraries installed, you could use the command: - -.. code-block:: python - - pip install -r - -Alternatively, if you are in rocketpy folder, just type - -.. code-block:: python - - pip install -r requirements.txt - -Creating an Environment ------------------------ - -Here we create the environment object that will be used in the simulation. -It contains information about the local pressure profile, temperature, speed of sound, wind direction and intensity, etc. - -.. code-block:: python - - env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) - -RocketPy can use local files via the Ensemble method or meteorological forecasts through OpenDAP protocol. -To work with environment files, it will be very important ensuring tha that you have the netCDF4 library installed. -Assuming we are using forecast, first we set the simulated data with: - -.. code-block:: python - - import datetime - tomorrow = datetime.date.today() + datetime.timedelta(days=1) - env.set_date((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time - -Then we set the atmospheric model, in this case, GFS forecast: - -.. code-block:: python - - env.set_atmospheric_model(type="Forecast", file="GFS") - -Weather forecast data can be visualized through two info methods. - -``env.info()`` or ``env.all_info()`` - -Creating the motor that boosts the rocket ------------------------------------------ - -Now we need to create the motor. -For example, we will use a solid motor called Pro75M1670, but other configurations are also possible. -The motor class contains information about the thrust curve and uses some geometric parameters to calculate the mass variation over time, as well as the total thrust and other important outputs. - -.. code-block:: python - - Pro75M1670 = SolidMotor( - thrust_source="../data/motors/cesaroni/Cesaroni_M1670.eng", #copy here the path to the thrust source file - burn_time=3.9, - grain_number=5, - grain_separation=5 / 1000, - grain_density=1815, - grain_outer_radius=33 / 1000, - grain_initial_inner_radius=15 / 1000, - grain_initial_height=120 / 1000, - nozzle_radius=33 / 1000, - throat_radius=11 / 1000, - interpolation_method="linear", - ) - -Motor data can be visualized through the following methods: - -``Pro75M1670.info()`` or ``Pro75M1670.all_info()`` - - -Creating the rocket -------------------- - -The Rocket class contains all information about the rocket that are necessary to the simulation, including the motor, rocket mass and inertia, aerodynamic surfaces, parachutes, etc. -The first step is to initialize the class with the vital data: - -.. code-block:: python - - calisto = Rocket( - radius=127 / 2000, - mass=19.197 - 2.956, - inertia_i=6.60, - inertia_z=0.0351, - power_off_drag="../../data/rockets/calisto/powerOffDragCurve.csv", - power_on_drag="../../data/rockets/calisto/powerOnDragCurve.csv", - center_of_dry_mass_position=0, - coordinate_system_orientation="tail_to_nose", - ) - - calisto.add_motor(Pro75M1670, position=-1.255) - -Then the rail buttons must be set: - -.. code-block:: python - - calisto.set_rail_buttons(0.2, -0.5) - -In sequence, the aerodynamic surfaces must be set. -If a lift curve for the fin set is not specified, it is assumed that they behave according to a linearized model with a coefficient calculated with Barrowman's theory. -In the example, a nosecone, one fin set and one tail were added, but each case can be designed differently. - -.. code-block:: python - - nosecone = calisto.add_nose(length=0.55829, kind="vonKarman", position=0.71971 + 0.55829) - - fin_set = calisto.add_trapezoidal_fins( - n=4, - root_chord=0.120, - tip_chord=0.040, - span=0.100, - position=-1.04956, - cant_angle=0, - radius=None, - airfoil=None, - ) - - tail = calisto.add_tail( - top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 - ) - -If you are considering the parachutes in the simulation, they also have to be added to the rocket object. -A trigger function must be supplied to trigger the parachutes. -Currently, the pressure `(p)`, the height above ground level considering noise `(h)`, and the state-space variables `(y)` are necessary inputs for the function. -The state-space contains information about the rocket's position and velocities (translation and rotation). -For example: - -.. code-block:: python - - def drogue_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate drogue when vz < 0 m/s. - return True if y[5] < 0 else False - - - def main_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate main when vz < 0 m/s and z < 800 m - return True if y[5] < 0 and h < 800 else False - -After having the trigger functions defined, the parachute must be added to the rocket: - -.. code-block:: python - - Main = calisto.add_parachute( - "Main", - cd_s=10.0, - trigger=main_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - - Drogue = calisto.add_parachute( - "Drogue", - cd_s=1.0, - trigger=drogue_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - -Simulating the flight ---------------------- - -Finally, the flight can be simulated with the provided data. -The rocket and environment classes are supplied as inputs, as well as the rail length, inclination and heading angle. - -.. code-block:: python - - test_flight = Flight(rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0) - -Flight data can be retrieved through: - -``test_flight.info()`` or ``test_flight.all_info()`` - -This function plots a comprehensive amount of flight data and graphs but, if you want to access one specific variable, for example Z position, this may be achieved by `test_flight.z`. -If you insert `test_flight.z()` the graph of the function will be plotted. -This and other features can be found in the documentation of the `Function` class, which allows data to be treated in an easier way. -The documentation of each variable used in the class can be found on `Flight.py` file. - -Further considerations -====================== - -RocketPy's classes documentation can be accessed in code via `help()` command. -For example, to access Flight class parameters, you can use: - -.. code-block:: python - - help(Flight) - -More documentation materials can be found at `read the docs `_. -It can also be found on RocketPy's GitHub page on the badge "docs". \ No newline at end of file diff --git a/docs/development/setting_up.rst b/docs/development/setting_up.rst new file mode 100644 index 000000000..9bf0981fd --- /dev/null +++ b/docs/development/setting_up.rst @@ -0,0 +1,122 @@ +Setting up RocketPy as a Developer +================================== + +Your first step to contribute to RocketPy is to set up your development environment. +If you are an experienced developer, you may skip to the final sections of this page. + +Forking the repository +---------------------- + +First you need to fork the RocketPy repository. You can do this by clicking +the **Fork** button on the top right corner of the repository page on GitHub. + +By default, the fork will only have the ``master`` branch. +You should also fork the ``develop`` branch. +You can do this by **disabling** the "Copy the master branch only" option when +forking. + +.. important:: + + Our ``master`` branch is the stable branch, and the ``develop`` branch is the \ + development branch. You should always create your PRs against the ``develop`` \ + branch, unless it is a really important hotfix. + +Alternatively, one may clone the repository directly from the RocketPy repository +and then add the fork as a remote. +However, this option is less frequently used. + +Cloning the Repository +---------------------- + +Next step is to clone the repository to your local machine. +There are different ways to do this, but most of them will involve the following command: + +.. code-block:: bash + + git clone https://github.com//RocketPy.git + + +After cloning the repository, you will have a copy of the RocketPy repository on your \ +local machine and, by default, you will be on the ``master`` branch. + +.. tip:: + + Cloning a repo means downloading the repository to your local machine and \ + creating a local copy of it. We call this local copy a "local repository", \ + and the original repository is called the "remote repository". + + When you clone a repository, you are creating a connection between your local \ + repository and the remote repository. This connection allows you to push and \ + pull changes between the two repositories. + +Navigating through the project +------------------------------ + +In order to work on your local repository, you will need to open the directory where you \ +cloned it. We recommend using VS Code as your editor, but you can use any editor you prefer. +If you are using VS Code, you can open the project by running the following command: + +.. code-block:: console + + code + +After opening the project for the first time, you will be at the ``master`` branch. +To switch to another branch, such as the ``develop`` branch, you will need to fetch \ +it from the remote repository and then checkout to it. + +.. code-block:: console + + git fetch origin develop + git checkout develop + + +Installation +------------ + +We highly recommend creating a python virtual environment first. +However, we will not describe how to do this here, since this is a common task. +Run the commands below inside Rocketpy folder to install the library and +development requirements: + +.. code-block:: console + + pip install -e . # install the rocketpy lib in editable mode + pip install -r requirements-optional.txt # install optional requirements + pip install -r requirements-tests.txt # install test/dev requirements + +.. tip:: + + When installing the ``rocketpy`` library, the requirements listed in the \ + ``requirements.txt`` file will automatically be installed. + +Running the tests +----------------- + +One simple sanity check you can do after installing rocketpy is to run the unit and integration tests. +One may achieve this by running the following commands: + +Running all tests: + +.. code-block:: console + + make pytest + +Running the slow tests only: + +.. code-block:: console + + make pytest-slow + +Creating a .html coverage report, where you could see the coverage of the tests: + +.. code-block:: console + + make coverage-report + + +.. note:: + + The slow tests are the tests marked with the ``@pytest.mark.slow`` decorator. \ + These tests are usually the ones that take longer to run, and therefore are \ + not run by default. More about the tests can be found in the \ + :ref:`testing_guidelines` section diff --git a/docs/development/style_guide.rst b/docs/development/style_guide.rst index de738d53a..510a49560 100644 --- a/docs/development/style_guide.rst +++ b/docs/development/style_guide.rst @@ -1,31 +1,41 @@ Style Guide =========== -This page describes code, documentation and git branching styles used throughout RocketPy's development. +This page describes code, documentation and git branching styles used throughout +RocketPy's development. Code Style ---------- -We mostly follow the standard Python style conventions as described here: `Style Guide for Python Code `_ +We mostly follow the standard Python style conventions as described here: +`Style Guide for Python Code `_ Naming Conventions ^^^^^^^^^^^^^^^^^^ -RocketPy was, unfortunately, initially coded using `Camel Case `_. +RocketPy was, unfortunately, initially coded using +`camelCase `_. However, PEP8 specifies the following:: - Function names should be lowercase, with words separated by underscores as necessary to improve readability. + Function names should be lowercase, with words separated by underscores as \ + necessary to improve readability. Variable names follow the same convention as function names. mixedCase is allowed only in contexts where that is already the prevailing style (e.g. threading.py), to retain backwards compatibility. -Therefore, `Snake Case `_ is preferred. -For this reason, RocketPy is being converted from Camel to Snake as of version ``1.0.0``. -New contributions should strive to follow Snake case as well. +Therefore, `snake_case `_ is preferred. +For this reason, RocketPy has being fully converted from ``CamelCase`` to ``snake_case`` as of version ``1.0.0``. -Furthermore, when it comes to naming new variables, functions, classes or anything in RocketPy, always try to use descriptive names. +.. important:: -As an example, instead of using `a` or `alpha` as a variable for a rocket's angle of attack, `angle_of_attack` is preferred. -Such descriptive names make the code significantly easier to understand, review and maintain. + New contributions should only follow ``snake_case`` naming conventions. + +Furthermore, when it comes to naming new variables, functions, classes or +anything in RocketPy, always try to use descriptive names. + +As an example, instead of using ``a`` or ``alpha`` as a variable for a rocket's +angle of attack, ``angle_of_attack`` is preferred. +Such descriptive names make the code significantly easier to understand, review +and maintain. In summary: @@ -38,18 +48,37 @@ In summary: angle_of_attack = 0.2 # in rad -Linting -^^^^^^^ -As far as line wrapping, parentheses, calling chains and other code style related matter goes, RocketPy currently employs `Black style `_. +Linting and formatting +^^^^^^^^^^^^^^^^^^^^^^ + +As far as line wrapping, parentheses, calling chains and other code style +related matter goes, RocketPy currently employs `Black style `_. Installing Black and running it before submitting a pull request is highly recommend. Currently, there are pull request tests in place to enforce that Black style is used. +Aside from Black, RocketPy also uses `Flake8 `_ +and `Pylint `_ to check for code style issues. + +Running these commands before submitting a pull request is also highly recommended: + +.. code-block:: bash + + make format + make flake8 + make pylint + +These commands will check for any code style issues in the codebase. +The ``flake8`` command will throw a report directly to the console, while the +``pylint`` command will create a ``.pylint_report.txt`` file in the root directory, +which you can open to see the report. Documentation Style ------------------- -Every class, method, attribute and function added to RocketPy should be well documented using docstrings. -RocketPy follows NumPy style docstrings, which are very well explained here: `NumPyDoc Style Guide `_. +Every class, method, attribute and function added to RocketPy should be well +documented using docstrings. +RocketPy follows NumPy style docstrings, which are very well explained here: +`NumPyDoc Style Guide `_. Git Style --------- @@ -86,7 +115,12 @@ Here are a couple of example branch names: Once you are ready to create a Pull Request for your branch, we advise you to merge with the ``develop`` branch instead of the default ``master`` branch. This way, we keep the ``master`` branch stable and use the ``develop`` branch to test out new features! -If you have any doubts, just open an issue! And don't forget that these are recommendations. Don't let them keep you from contributing. +.. important:: + + If you have any doubts, just open an issue or ask in our Discord server. \ + And don't forget that these are recommendations. \ + Don't let them keep you from contributing. + Commit Messages ^^^^^^^^^^^^^^^ @@ -103,9 +137,8 @@ Commit messages should be clear and follow a few basic rules. Example:: Describing the motivation for a change, the nature of a bug for bug fixes or some details on what an enhancement does are also good to include in a commit -message. Messages should be understandable without looking at the code -changes. A commit message like ``MNT: fixed another one`` is an example of -what not to do; the reader has to go look for context elsewhere. +message. +Messages should be understandable without looking at the code changes. Standard acronyms to start the commit message with are:: @@ -120,3 +153,27 @@ Standard acronyms to start the commit message with are:: STY: style fix (whitespace, PEP8) TST: addition or modification of tests REL: related to releasing RocketPy + +.. note:: + + A commit message like ``MNT: fixed another one`` is an example of what not to do; \ + the reader has to go look for context elsewhere. + +Pull Requests +^^^^^^^^^^^^^ + +When opening a Pull Request, the name of the PR should be clear and concise. +Similarly to the commit messages, the PR name should start with an acronym indicating the type of PR +and then a brief description of the changes. + +Here is an example of a good PR name: + +- ``BUG: fix the Frequency Response plot of the Flight class`` + +The PR description explain the changes and motivation behind them. There is a template \ +available when opening a PR that can be used to guide you through the process of both \ +describing the changes and making sure all the necessary steps were taken. Of course, \ +you can always modify the template or add more information if you think it is necessary. + + + diff --git a/docs/development/testing.rst b/docs/development/testing.rst index 032287ee3..fd521167b 100644 --- a/docs/development/testing.rst +++ b/docs/development/testing.rst @@ -1,3 +1,5 @@ +.. _testing_guidelines: + Testing Guidelines ================== @@ -68,6 +70,49 @@ Do not get caught by the size of that docstring. The only requirements it has to that the docstring contains precise information on the expected behaviour and/or behaviour to be tested. +Testing Structure +----------------- + +In order to keep the tests easily readable and maintainable, RocketPy encourages +the use of the AAA pattern (Arrange, Act, Assert) for structuring the tests. + +* **Arrange:** Set up the necessary preconditions and inputs (often done by *Fixtures* as it will be described below); +* **Act:** Execute the code under test; +* **Assert:** Verify that the code under test behaves as expected. + +The following example illustrates the AAA pattern: + +.. code-block:: python + + @pytest.mark.parametrize( + "latitude, longitude", [(-21.960641, -47.482122), (0, 0), (21.960641, 47.482122)] + ) # Arrange: Done by the fixtures and the parameters of the test + def test_location_set_location_saves_location(latitude, longitude, example_plain_env): + """Tests location is saved correctly in the environment obj. + + Parameters + ---------- + example_plain_env : rocketpy.Environment + latitude: float + The latitude in decimal degrees. + longitude: float + The longitude in decimal degrees. + """ + # Act: Set the location + example_plain_env.set_location(latitude, longitude) + # Assert: Check if the location was saved correctly + assert example_plain_env.latitude == latitude + assert example_plain_env.longitude == longitude + +This pattern is a general guideline, of course specific tests cases might +modify it to better fit the specific needs of the test. + +.. note:: + + Parameterization is a powerful feature of ``pytest.mark.parametrize`` that allows + you to run the same test with different inputs. This is highly recommended when + there multiple testing scenarios for the same method. + Directory Structure ------------------- @@ -234,7 +279,7 @@ This test contains two fundamental traits which defines it as an integration tes The motivation behind lies in the fact that it interacts and calls too many methods, being too broad to be considered an unit test. -Please be aware that Integration tests are not solely classfied when interacting with external dependencies, +Please be aware that Integration tests are not solely classified when interacting with external dependencies, but also encompass verifying the interaction between classes or too many methods at once, such as ``all_info()``. Further clarification: Even if the test contains traits of unit tests and use dependencies which are stable, such as @@ -305,5 +350,6 @@ documenting purposes, such as below: [[-10. 10.]] """ -This is not common practice, but it is optional and can be done. RocketPy however encourages -the use of other means to test its software, as described. +This is not common practice, but it is optional and can be done, specially to provide +an usage example for the function under testing. RocketPy however encourages the use +of other means to test its software, as described. diff --git a/docs/examples/camoes_flight_sim.ipynb b/docs/examples/camoes_flight_sim.ipynb new file mode 100644 index 000000000..b0b48cae2 --- /dev/null +++ b/docs/examples/camoes_flight_sim.ipynb @@ -0,0 +1,835 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Camões - Rocket Experiment Division \n", + "\n", + "Launched at EUROC'23, achieved the target apogee of 3000 meters, with a flawless ascent phase, featuring effective air brake operation throughout. \n", + "The recovery phase had only the actuation of a drogue parachute.\n", + "\n", + "Permission to use flight data given by Gonçalo Machado, 2024.\n", + "\n", + "These results were extracted out of the flight card:\n", + "\n", + "1. Team number: `20`\n", + "2. Launch date: `October 14th, 2023. 14h local time`\n", + "3. Simulated apogee: `3003 m`\n", + "4. Official recorded apogee: `3015 m`\n", + "\n", + "The relative error of altitude apogee is only `0.39%`!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Function, Environment, Flight, Rocket, SolidMotor\n", + "from rocketpy import AirBrakes, _Controller\n", + "from rocketpy.motors import CylindricalTank, SolidMotor\n", + "from rocketpy.motors.tank import MassFlowRateBasedTank\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gravity Details\n", + "\n", + "Acceleration of gravity at surface level: 9.8066 m/s²\n", + "Acceleration of gravity at 4.000 km (ASL): 9.8066 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Date: 2023-10-14 13:00:00 UTC | 2023-10-14 14:00:00 Portugal\n", + "Launch Site Latitude: 39.39000°\n", + "Launch Site Longitude: -8.28954°\n", + "Reference Datum: WGS84\n", + "Launch Site UTM coordinates: 44367.90 W 4373424.89 N\n", + "Launch Site UTM zone: 30S\n", + "Launch Site Surface Elevation: 107.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: Reanalysis\n", + "Reanalysis Maximum Height: 4.000 km\n", + "Reanalysis Time Period: from 2023-10-12 08:00:00 to 2023-10-15 18:00:00 utc\n", + "Reanalysis Hour Interval: 1 hrs\n", + "Reanalysis Latitude Range: From 40.0° to 39.0°\n", + "Reanalysis Longitude Range: From -9.0° to -7.0°\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 207.00 m/s\n", + "Surface Wind Direction: 27.00°\n", + "Surface Wind Heading: 0.90°\n", + "Surface Pressure: 1000.00 hPa\n", + "Surface Temperature: 295.59 K\n", + "Surface Air Density: 1.179 kg/m³\n", + "Surface Speed of Sound: 344.66 m/s\n", + "\n", + "\n", + "Earth Model Details\n", + "\n", + "Earth Radius at Launch site: 6369.57 km\n", + "Semi-major Axis: 6378.14 km\n", + "Semi-minor Axis: 6356.75 km\n", + "Flattening: 0.0034\n", + "\n", + "\n", + "Atmospheric Model Plots\n", + "\n" + ] + }, + { + "data": { + "image/png": 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DXk39GVK2vkPX8Nq0qefFMz/vZMORNBIz8th5OoP3Vx1iz5nMSr3P7oQLfPnvMfacySQpM5+V+5LJyC2iof+1bxYFe7sQl5hJYkYeGblFxsRDo1ZxX9tgZqw8TP3abhW61l5LdANf8opKOJJy7RtKT3QJ44W+TRg+Zzvby+qBbo38qOfrygsL4jl4LpsdpzL4oGyAeHlfg5gDyXQNr33DVpVbtTshE0eN2jirpbAt0o1KVIvoBr4UFOto6OdmHDsA0LGBDxcLS2jg51ZhAb2qGNEtjNScAl6cH49KBQ+0C6Fv8wByKpFw3P7helQqcCtb1K97o9oM7xZ23XEK1xPg4czCUZ159++DPPbdVopKdQR5uXBbY3/Ku6k+d3sjHNQqPoo5QmpOAf7uzsZBhQ4aNW/c2ZzP1h7lo5gjtK/vU2Hhw3Iv9W+KXg8T5sdzsWzq25+e6mBSn99rGdo+hDu/2ER2QbFxgODNLN1zlnG9G9/S+wohhNLWH0mjw9trcVCr8HTR0qyOB1MGN+e+NpcW9VOpVPzwZAc+WHWYiQvjycgtwq+WEx3CfK47kciV3J0d2Hoyg+83nSSnsIRgLxdeGdTMOOHHlUZ0a8ALC+Lp8/F6Cop1bJzU09jdami7enz573Hub3vzxe683Rzp2zyQxXFJV3V5Kje8a5hhStoftvPjU+1pG+rDt4+14+VFe7jri/8I8XHhfwObMfzHHTiV3WCKOZBiTMYsZUn8We6KCrJoQiMsR6W/VnuaEKLG+r+5O2le15PRPcNveuy/h1N5e/lBVj7f7aq+uEIIISxr28kMHpm9hc0v96pwI+96Dp7L5rHvtrJ+Yk/crjMF7s3sOJXBfV/Hsn5iD9ydtXR4ew2xkyv3/lWRkVvE7R+uY+mYrjLjoY2Svw6EEBVMHtAMt0rePcovKuX9+yIl0RBCiGpUWFLKuax8PllzhIEt61T6D/1mdTx4qX9TEi9UfrKSlfuS2XjU0GVs09HzTP5jL+1CvQn1dSMzr4hXBzWzWKIBcOZCHm/e1UISDRsmLRtCCCGEEDZkwY5EXlq0h4i6Hswe1p5Az1vrhnwji3ae4Yt/j5GUmY+PqyNdwmvz6qBmeMvMUKKSJNkQQgghhBBCWIT0fRBCCCGEEEJYhCQbQgghhBBCCIuQZEMIIYQQQghhEbLORiX8FHuKb9afIO1iIc3qeDB1cHNah3gpHZYitp5I59sNJ9iblEVqTiHfPNaWfs0DjY/r9Xo+jjnCb9sTyc4vpl19b966uyVhtd2Mx2TmFTFlyX7WHkxFpYIBLQKZcmfzKk/DZ+2+/PcYq/Ynczz1Is5aDW1CvXl5QFMa+l1awKmguJS3lx9k6Z6zFJXo6N7IjzfvblFhho+kzHxe/XMvsSfScXN0YEjbYCb1a2K3M0H9vOU0c7ec5syFfAAaBdTiuV6NjHPRyzkTwvT66btNJ5m75bRhsK+bIwNa1GFS/yZ2sSinKeeiuFTHV/8eZ9GuMyRnF9CgthsvD2hKj+usdWFLblZPX0vs8XTeWn6AoykXqePlzJie4TddkdwWmHouUrMLeGv5QfYmZXEqPZcnOtdnyp3NqzFi+yQ17k0sjT/LW8sO8nzvRiwf25WIOu4M+24r5y8WKh2aIvKKS2lWx4Npd117Ze2v15/gh82nePvuFiwe3QUXrQPDvt9KQXGp8Zjnf4/jSMpFfh7ege+faM+2kxlM/mNvdRWh2m09mcFjnUL5c3QXfh7ekZJSHcO+20Ze0aXFBt9cdoC1B1P46uE2zHsmmpScAkb+stP4eKlOz1M/bKe4VM+iUZ354IFWLNx5ho9ijihRpGpRx8OZl/o3ZenYriwZ04XODX155qcdHEnJAeScCWFq/fRXXBLvrTzE870bsWbCbbw3JJJle87y/qrD1Ry5+Zl6Lj5YfZhft51m6uDmrBl/G490CuXZn3eyLymrmiM3v5vV01dKzMjjqTnbiW7gy4rnu/JUlzBe/mMv64+kWThSyzP1XBSW6PBxc2RMz3CaBXpYOLqaQ5KNm5i96SQPdgjhgXYhNApw5+27W+LiqGH+jkSlQ1NEzyb+vNivCf1bXH1nQK/X8/1/Jxl7ezh9mwfSrI4HHw1tRUp2IasPpABwLDWH9UfSeG9IS6LqedO+vg9vDG7O0j1nSckuqO7iVIufnurA/e1CaBzgTkRdDz64vxVJmfnsPWOo1LILipm/I5FX74igc3htWgZ78v59rdh5+gK7Ei4AsOFoGkdTc/h4aGua1/WkZxN/JvRpzM+xpykq0SlZPIvpHRFAz6b+hNV2o4FfLSb2a4qrowO7Ey7IORMC0+unnacv0C7Um7taBxHi40r3xn4MblWX+MTM6g3cAkw9F3/uSmJ0z3B6NvWnnq8rj3UKpWcTf2ZvPFHNkZvfjerpa/ll62lCfFx49Y4Iwv3debxzfQa0COS7TSctHKnlmXouQnxceWNwc4a0Dcbd2T57WyhBko0bKCrRsS8piy7htY371GoVXcJrs+t0pnKBWanEjHzScgornC8PZy2tQ7zYddrwB+Cu05l4ODsQGexlPKZreG3UKhW7EzKrOWJl5BQYWjS8XA1zlO87k0Vxqb7CeQv3r0WQl4vxvO0+fYEmgR4Vugjd1tiPnMIS451+e1aq07Mk/iz5RaW0qect50zUeFWpn9qGerM3KYu4suQiIT2Pfw+n0rOpbXcdqsq5KCrV4eRQ8U8gZ62a7acuWDJUq7T7dGaFcwfQvbEfu0/XvHMhLEPSthu4kFdEqU5P7VoVV8b0q+XE8bRchaKyXmkXDS0Tftc4X2llTdlpFwuvOp8OGjVeLlrjMfZMp9MzbdkB2oV60yTQHTCcE0eNGk8XbYVja9dyvOK8OV7xuJPxMXt1KDmbe7/aTGGJDldHDd881pZGAe4cOJct50zUaFWpn+5qHURGbhH3f70ZvR5KdHoe6ViP0T3DqyNki6nKuejeyI/ZG0/SIcyXUB9X/jt+npX7k9HVwEbPa9XLfrWcyCksoaC41C7G8whlSbIhRDV67a99HE7OYeGoaKVDsQkNatdixXPdyCkoYcW+c7ywIJ55z3RSOiwhbFLs8XS+/Pc4b97Vgtb1vDh1Po9pS/fz2dqjPNerkdLhVaspd0bw8h976fXhOlQqFaE+rtzfNqTGdpEWwpIk2bgBb1dHNGrVVQPM0i4WXnX3XoBfLWfAcH78PZyN+9MuFhJRx6PsGKerzmdJqY7M/GK7P6ev/7WPfw6lMv/ZaOp4uhj3+9VyoqhUR1Z+cYU79ecvFhnPiV8tJ+ISKw5cLD+P9nzeHB3U1C+byaxlsCd7zmTy/X+nuDOyjpwzUaNVpX76KOYw97YJ4sEO9QBoGuhBfnEJk//Yy5ie4ajVKovHbQlVORe+tZyYNawdBcWlZOYVE+DhxLsrD1HPx7U6QrYq16qX0y4W4u7kIK0awixkzMYNODqoaRHkyeZj5437dDo9m4+l0ybUS7nArFSIjwt+7k5sPpZu3JdTUExcYiZtQr0BaBPqRXZBiXFwNMDm4+no9Hqi6nlVd8jVQq/X8/pf+1i1P5lfR3Qi5IrKrEWwJ1qNqsL37HjaRZIy843nLSrUm8PJ2RUqhI1Hz+Pu5ECjgFrUFDqdoX+2nDNR01WlfsovLkV1RT6hLtuht1Sg1eBW6mpnrYZAT2dKdHpW7kumT0SAhaO1PlGhXhXqbYBNR88TVXYtFeJWScvGTTzdNYwXFsTTMtiL1iGefLfpFHlFJdzf1vbnn66K3MISTqVf6gObmJHH/rNZeLk6EuTlwlNdwvj8n6PUr+1GiI8LH64+QoCHE33LLuDh/u7c1tiPl//Yw9v3tKSkVMeUJfu5M7IuAZe1htiT1/7ax19xZ5k1rB1uThpScwxjWzyctThrNXg4a3mgXQhvLT+Ip6sWdyctU5bso009L9rUM1zsuzfyo5G/O+PnxTF5QDPSLhby4erDPBYdipODfd55em/lIXo09qOulwu5RSX8FXeWLSfT+empDnLOhODm9dOEeXEEeBqmkAbo1TSA7zadpHldT6JCvDiVnstHMUfo1SwAjY22apQz9VzsTrhASnYBEXU8Sc4u4JM1R9Dp9Tx7W0Mli2EWN6un31t5iJSsAj4a2hqARzuG8tPm00xfcZD724UQe/w8y/ee4/sn2itUAvMx9VwA7D9ruBmaV1RKRm4R+89m4ahR0yjAvbrDtxsqvV5vyzc0qsWPm0/x7YYTpOUU0qyuB2/cGUFUvZqZ8cceT+ehWVuu2j+kTTAfPtDKuKjfr9sSyS4opn19b968qwUNLlvALjOviNf/2s/agymoVSr6twjkjcH2u6hf/ZeXX3P/+/dFGhdNKl+gbkl82QJ1jWvz5t0t8He/lICduZDHq4v3seVEOq6ODgxpE8RL/Zva7QJ1kxbG89+xdNJyCnF3dqBpHXdG3taQbo38ADlnQsCN66eh38QS7O3Khw+0AgxdVr/49xh/7k4iOasAXzdHejUL4MV+Ta6abMEWmXIutpxI59XF+0jIyMPNUUPPJv68NKCpXdz0ulk9/cL8eM5cyGPes9EVnvPmsgMcS71IoKczY2+3j0X9qnIurlVnB3m58N/Lt1s0VnsmyYYQQgghhBDCIuT2nhBCCCGEEMIiJNkQQgghhBBCWIQkG0IIIYQQQgiLkGRDCCGEEEIIYRGSbAghhBBCCCEsQpINIYQQQgghhEVIslEJhSWlfBxzhMKSUqVDsSly3kwn56xq5LwJcTX5f3GJnItL5FxcIueielhNsvHuu++iUqkYN26ccV9BQQGjR4/G19eXWrVqMWTIEFJSUio8LyEhgUGDBuHq6oq/vz8TJ06kpKSkwjHr1q2jTZs2ODk5ER4ezpw5c0yKrahEx6drj1JUoqtq8WokOW+mk3NWNXLehLlYc11kKvl/cYmci0vkXFwi56J6WEWysX37dr755hsiIyMr7B8/fjxLly5lwYIFrF+/nrNnz3LvvfcaHy8tLWXQoEEUFRWxefNmfvzxR+bMmcPrr79uPObkyZMMGjSInj17EhcXx7hx43j66adZtWpVtZVPCCGE9ZO6SAghLECvsJycHH2jRo30MTEx+ttuu03//PPP6/V6vT4zM1Ov1Wr1CxYsMB578OBBPaCPjY3V6/V6/YoVK/RqtVqfnJxsPGbmzJl6Dw8PfWFhoV6v1+snTZqkb968eYX3HDp0qL5fv36VjjE7v0gf+tIyfXZ+UVWLWSPJeTOdnLOqkfMmbpUt1EWmkv8Xl8i5uETOxSVyLqqHg9LJzujRoxk0aBC9e/fmrbfeMu7fuXMnxcXF9O7d27ivadOm1KtXj9jYWDp16kRsbCwtW7YkICDAeEy/fv0YNWoU+/fvJyoqitjY2AqvUX7M5U3kVyosLKSwsND4e2ZuASXZ5zlz5gzuzlozlLpmyC0qQVeYx9mzSbg5Kv5VswlyzqrGVs+bTqcjJSWFqKgoHBxsJ257ZAt1UUlJCQcPHiQkJAS1+uYdE2z1/4UlyLm4RM7FJXIuDCxdFyl6Zn///Xd27drF9u3br3osOTkZR0dHvLy8KuwPCAggOTnZeMzlF/fyx8sfu9Ex2dnZ5Ofn4+LictV7T58+nalTp17aoVKDXkfETJOLKICmnygdge2Rc1Y1tnretm3bRvv27ZUOo8aymbqoimz1/4UlyLm4RM7FJXIuDCxVFymWbCQmJvL8888TExODs7OzUmFc0+TJk5kwYYLx98TERFq0aMHmzZupU6eOgpGZR3FxMRs2bKB79+5otbbfUlPZ8qw/doHXlh8j1MeZnx9rWY0Rmqamfj624lrlUe34Ds3mT9A7uFD60ALwDq3Ua507d47OnTtf9UeoqD72XBcVF+Tyf7NWc1gXTFe/PN5+qDsqlariMXby/9NeygH2UxZ7KQfYT1muVw5L10WKJRs7d+4kNTWVNm3aGPeVlpayYcMGvvjiC1atWkVRURGZmZkV7iilpKQQGBgIQGBgINu2bavwuuUzhFx+zJWzhqSkpODh4XHNO0kATk5OODk5GX/39PQEICQkhODg4CqW2HoUFxdz4MAB6tevb9P/acpVtjzfxV1E7eTK7ZGh1K9fv/oCNFFN/XxsxVXlObsbDnwFHmq46yOIuq3Sr1XeXF2ZLjHCMuy9Lvqs9yYGr3dkc7Yrey5ouKtNvQqP28v/T3spB9hPWeylHGA/ZbleOSxdFylWw/Xq1Yu9e/cSFxdn/NeuXTseeeQR47ZWq2Xt2rXG5xw+fJiEhASio6MBiI6OZu/evaSmphqPiYmJwcPDg4iICOMxl79G+THlryFqjv+OnQegS3hthSMRdqMoFxY9DboSiLgLWj+idETCRPZeFzXr9ShjXQwzXk1ZHE9qToFF308IIa6kWMuGu7s7LVq0qLDPzc0NX19f4/7hw4czYcIEfHx88PDwYOzYsURHR9OpUycA+vbtS0REBI899hgzZswgOTmZV199ldGjRxvvBo0cOZIvvviCSZMm8dRTT/HPP/8wf/58li9fXr0FFoo6m5nPifO5qFXQqaGv0uEIe7HqFUg/Bu514Y5P4IouKsL62X1d5OjGqNubsmrFSfYXhfHqH3v5Zli7q7pTCSGEpVh12/3HH3/MHXfcwZAhQ+jevTuBgYH88ccfxsc1Gg3Lli1Do9EQHR3No48+yrBhw5g2bZrxmLCwMJYvX05MTAytWrXiww8/ZPbs2fTr10+JIgmFlLdqRAZ74SEzigkzUB1eATt/AFRwz9fg6qN0SMJCbL0u0nYczgfu89BSwuqDqSyJP2vx9xRCiHJWNc/XunXrKvzu7OzMl19+yZdffnnd54SGhrJixYobvm6PHj3YvXu3OUIUNqo82egqXaiEGTgVZ6JZUTZLUOex0KDy4zSE9bO7usjRjWa33cfYv//ko5L7mfLXfqIb+uLvbl0D4oUQ9smqWzaEMAe9Xs9/x9MB6BwuXajELdLraHP6W1R56RAYCbe/qnREQtxc+6cZ5b6J5qqTZOYX8+qf+9Dr9UpHJYSoASTZEHbvaOpF0nIKcdaqaVPPW+lwhI1Tb5+Ff84+9A7OMGQ2ODjd/ElCKM3RDW2X0Xyg/drQnepAinSnEkJUC0k2hN3bdNTQhap9fR+ctRqFoxE2LXkf6n8M3ad0vaeBXxOFAxLCBO1H0Mwtl7EOfwIwZcl+0nIKb/IkIYS4NZJsCLu3+bhMeSvMoDgf/hiBqrSIZI/W6No8qXREQpjGqRZ0HssozRKaa8+SmVfMlKUHkd5UQghLkmRD2LWSUh1bTmQAMjhc3KI1b0DqAfRu/uyu97RMcytsU4cRaF09+ED1KQ4qPTEHU9mVLt9lIYTlSLIh7Fr8mSwuFpbg5aoloo6H0uEIW3V0DWz9GoDSOz+nSCvfJWGjnNyh81iaqRMZW+tfABaeVEt3KiGExUiyIexa+ZS3nRv6olbL3TtRBbnnYfEow3aHZ9E37KVsPELcqg7PgIs3/1f0A828SskrUZV1p5L+VEII85NkQ9i1TcZkQ7pQiSrQ6+GvMZCbCn7NoM9UpSMS4tY5uUP0aLSqUj5w/AZ1WXcqmZ1KCGEJkmwIu5VXVMLuhAuADA4XVbTjezjyN2gcDdPcal2UjkgI8+jwLDh70Tx7E/f7JgCG2alScwoUDkwIYW8k2RB2a9vJDIpL9QR5uVDf11XpcIStSTsCq14xbPd+AwJbKBqOEGbl7AHRYwB4qfhLmgXWIjNPFvsTQpifJBvCbpWP1+gS7otKZg4SpigpgkXDoSQfGvSEjqOUjkgI8+v4DHpnT3wKzzCjdRoOapUs9ieEMDtJNoTd+u9YOiBdqEQV/PsWJO8BFx+4eyao5VIp7JCzJ7oOIwFosW8GY3o2BGSxPyGEeUkNKuxS+sVCDpzLBmRwuDDRyQ3w32eG7cGfg0cdZeMRwoJ07Z+hWOOK6vxhRgfsJ6KOh6E71eK90p1KCGEWkmwIu7T5uKFVo2mgO37uTgpHI2xG/gX4cySghzaPQ7M7lI5ICMty9uS4Xz8AtBvf4/0hLXFQq1i1X7pTCSHMQ5INYZe2nDAkG9KqIUyydhpkJ4FvOPSfrnQ0QlSL43590Tt5QNohmp9bxJjbw4Gy2amyZXYqIcStkWRD2KXdCZkAtKvvrWwgwnaci4cdPxi27/wUHN2UjUeIalLi4IbutpcNv6yZyuh27jSva+hO9fIf0p1KCHFrJNkQdievqIRDyYbxGlH1vJQNRtgGvR5WTAL00GII1O+qdERCVCtd2+FQNwoKs9DGTOajB1rjqFHzz6FU5u9IVDo8IYQNk2RD2J09Z7LQ6SHQw5k6nrIIm6iEvQsgcQtoXaHPm0pHI0T1U2sMLXoqDez/kybZsbzQtzEA05YeIDEjT+EAhRC2SpINYXfKu1C1CfVSNA5hIwpzYPVrhu1uL4BnkLLxCKGUOq2gU9maMstf4OmOAbSv701uUSkTF8aj00l3KiGE6STZEHZnd8IFAKJCZLyGqIQN78PFZPAOg85jlY5GCGX1mAyeIZCVgGbDu3xwfytctBq2nMhgzuZTSkcnhLBBkmwIu6LX69mdmAnIeA1RCeePQexXhu3+74KDTJMsajinWjDwA8N27FeEFh3nf4OaAfDeykMcT7uoYHBCCFskyYawK0mZ+aTlFOKgVtEiyFPpcIS1WzUZdMUQ3gca91M6GiGsQ5P+EHEX6Eth6fM82j6Ibo1qU1iiY8L8eEpKdUpHKISwIZJsCLsSV9aq0ayOB85ajbLBCOt2ZBUcXQ1qraFVQ6VSOiIhrEf/98DJA87uQrV9NjPui8TD2YH4xEy+Xn9c6eiEEDZEkg1hV8oHh0sXKnFDJUWwcrJhu9MoqB2ubDxCWBuPOtD7DcP22mnU0Z9n6l3NAfh07VH2n81SLjYhhE2RZEPYlfKWjdYhXorGIazc1pmQcRzc/KH7RKWjEcI6tX0SQjpBcS6seJG7W9Wlf/NAikv1TJgXT2FJqdIRCiFsgCQbwm4UlejYm2S42xZVT2aiEteRkwzrZxi2+0wFZw9l4xHCWqnVhrU31Fo4shLVwSW8fU8LfN0cOZySw8cxR5WOUAhhAyTZEHbjcEoORSU6vFy11Pd1VTocYa3WTIWiixDUFiIfVDoaIaybf1PoNsGw/fckfDX5vHNvSwC+3XCcnaczFAxOCGELJNkQdiMu0dCq0TrEC5UM9hXXcmYHxP9q2B4ww3DnVghxY10ngG8juJgCa96gX/NA7m0ThE4PL8yPJ6+oROkIhRBWTGpaYTcuTzaEuIpOByvKxme0fgSC2ykbjxC2QusMd35i2N75A5yOZcqdzanj6cyp9Dze/fuQouEJIaybJBvCbsSfkfEa4gbif4Ozu8DRHXpNUToaIWxL/a7QZphhe+nzeGp1zLgvEoCfYk+z6eh5BYMTQlgzSTaEXbhYDKcz8gBoHeylbDDC+hRkw5o3DNu3TQT3AEXDEcIm9ZkGbn5w/jBs+oRujfx4rFMoABMXxpOVX6xwgEIIayTJhrALpy8axmg08HPD01WrcDTC6mx4H3JTwTccOo5SOhohbJOLt2EBTICNH8D5o0we2JT6vq6cyypg6tL9ysYnhLBKkmwIu3A6x5BsRIVIFypxhfPHYMtMw3a/6eDgqGw8QtiyFkMgvA+UFsHS53F1UPPhA61Qq+CPXUms2p+sdIRCCCujaLIxc+ZMIiMj8fDwwMPDg+joaP7++2/j4z169EClUlX4N3LkyAqvkZCQwKBBg3B1dcXf35+JEydSUlJxZox169bRpk0bnJycCA8PZ86cOdVRPFGNTl80/GwtK4eLK62aDLpiaNQXGvdVOhphZaQeMpFKBYM+BK0rnP4Pdv9M21AfnuneEID//bGX8xcLFQ5SCGFNFE02goODeffdd9m5cyc7duzg9ttv56677mL//ktNsSNGjODcuXPGfzNmzDA+VlpayqBBgygqKmLz5s38+OOPzJkzh9dff914zMmTJxk0aBA9e/YkLi6OcePG8fTTT7Nq1apqLauwHJ1Ob+xGFSUzUYnLHVkFR1cbFiXrN13paIQVknqoCrxDoecrhu2Y1+BiKuP7NKJJgDvpuUW88ude9Hq9sjEKIayGosnGnXfeycCBA2nUqBGNGzfm7bffplatWmzZssV4jKurK4GBgcZ/Hh6XVvtdvXo1Bw4c4JdffqF169YMGDCAN998ky+//JKioiIAvv76a8LCwvjwww9p1qwZY8aM4b777uPjjz+u9vIKyzhxPpf8UhXOWjVNA92VDkdYi5IiWDnZsN1pFNQOVzYeYZWkHqqijiOhTisoyIKVL+PkoOHDB1rhoFaxan8Ki+OSlI5QCGElrGbMRmlpKb///ju5ublER0cb98+dO5fatWvTokULJk+eTF5envGx2NhYWrZsSUDApZll+vXrR3Z2tvGuVGxsLL17967wXv369SM2NtbCJRLVpXzK2xZ1PXDQWM1XWiht60zIOA5u/tB9otLRCBsg9ZAJNA5w52egUsO+RXBkNS2CPHm+VyMAXv9rP+ey8hUOUghhDRyUDmDv3r1ER0dTUFBArVq1+PPPP4mIiADg4YcfJjQ0lLp167Jnzx5eeuklDh8+zB9//AFAcnJyhQs8YPw9OTn5hsdkZ2eTn5+Pi4vLVTEVFhZSWHipz2lOTg4AJSUlFBfb/tR+5WWwh7IA7Eq4AEBkXXe7KJO9fT6KlCcnGYf176ECSnq+hl7jAmZ6f3OW58p+/UIZ1lgPQfXURbf0ffZrjrrDs2i2zkS/fAIlQRt5uks9Yg4ms+dMNhMXxPP9sDaoVCqzxHoj9nTdtJey2Es5wH7Kcr1yWLouUjzZaNKkCXFxcWRlZbFw4UIef/xx1q9fT0REBM8884zxuJYtW1KnTh169erF8ePHadiwocVimj59OlOnTr1q/9q1a6ldu7bF3re6xcTEKB2CWfx3UAOo0KefYsWKk0qHYzb28vmUq87yRJ3+lnpFuVxwbcCGM+6QtMLs72GO8pw/LwuhWQNrrIegeuuiqn6fNaVR3O5YG9esRE7PGcn+4Ie5ozYcTNKw6Vg6r/ywkq6B1Td+w56um/ZSFnspB9hPWa4sh6XrIsWTDUdHR8LDDX2p27Zty/bt2/n000/55ptvrjq2Y8eOABw7doyGDRsSGBjItm3bKhyTkpICQGBgoPFn+b7Lj/Hw8Lju3aTJkyczYcIE4+9JSUlERETQq1cvgoKCqlhS61FcXExMTAx9+vRBq7XtNSnyikoYv+UfAIYN7Eawby2FI7p19vT5QPWXR5W0E4fdmwBwf2AmA4PamvX1zVmepCTp124NrLEeguqpi8zxfVY184B5D9Lw/GpC73wR6rRGVfc0b684zLIzWp69qzOhvq5mifd67Om6aS9lsZdygP2U5XrlsHRdpHiycSWdTleh2fhycXFxANSpUweA6Oho3n77bVJTU/H39wcM2ZqHh4exCTw6OpoVKyre1YyJianQH/dKTk5OODk5GX/Pzs4GwMHBwaa/ZFfSarU2X56Didno9ODlqCfYt5bNl+dy9vD5XK5ayqPTQcz/DNutH8GhfieLvZU5yuPgYHWXYIF11ENQvXXRLX2fmw2AFveh2rcQ7YoJMOJfhndtyD+HzhN7Ip2X/9zPvGej0agt353Knq6b9lIWeykH2E9ZriyHpesiRUfTTp48mQ0bNnDq1Cn27t3L5MmTWbduHY888gjHjx/nzTffZOfOnZw6dYolS5YwbNgwunfvTmRkJAB9+/YlIiKCxx57jPj4eFatWsWrr77K6NGjjRfokSNHcuLECSZNmsShQ4f46quvmD9/PuPHj1ey6MJM4hIzAQitJdMsCiD+N0jaCY7u0GuK0tEIGyD1kJn0nw7OXpC8B7bORK1W8f79kdRycmDH6QvM2nhC6QiFEApRNNlITU1l2LBhNGnShF69erF9+3ZWrVpFnz59cHR0ZM2aNfTt25emTZvywgsvMGTIEJYuXWp8vkajYdmyZWg0GqKjo3n00UcZNmwY06ZNMx4TFhbG8uXLiYmJoVWrVnz44YfMnj2bfv36KVFkYWa7ywaHS7IhKMiGNW8Ytm+bCO4BNzxcCJB6yGxq+UPftwzb/74DF04R7O3K63cYWnc+Wn2EQ8nZCgYohFCKom3433333XUfCwkJYf369Td9jdDQ0Kuap6/Uo0cPdu/ebXJ8wrrp9Xp2J2QCUN9dko0ab8P7kJsKvuHQcZTS0QgbIfWQGUU9CnvmwamNsPwFeGQh97cLZtX+ZNYeSmXCvHgWj+6Co4NMUS5ETSL/44XNOpdVQGpOIRq1ihA3paMRijp/DLbMNGz3mw4OjsrGI0RNpFLBHR+DxhGOrYF9i1CpVEy/tyVerloOnMvmi3+OKh2lEKKaSbIhbFb5eI0mAbVw1Cgbi1DYqsmgK4ZGfaFxX6WjEaLmqt3o0iKaf78EeRn4ezjz1t0tAPhy3XHiy67dQoiaQZINYbPKx2u0DvFUOBKhqCOr4OhqUGsNrRpCCGV1GQd+TSHvPMS8BsAdkXW5s1VdSnV6JsyPo6C4VNkYhRDVRpINYbPKWzZaB3spGodQUEkRrJxs2O40EmqHKxuPEMLQjfHOTw3bu3+BkxsAePOu5vi7O3E8LZcZKw8rGKAQojpJsiFsUnGpjj1nsgBoFSwtGzXW1pmQcRzc/KH7JKWjEUKUq9cJ2j1l2F46DooL8HJ15L0hhimDv//vJLHH05WLTwhRbSTZEDbp0LkcCkt0eLpoqW/hlWmFlcpJhvXvG7Z7vwHOHoqGI4S4Qq8pUCvQcENgg+H/as+m/jzUIQSAFxfEc7GwRMkIhRDVQJINYZPiEg3jNVqFeKGuhlVphRVaMxWKcqBuG2j1kNLRCCGu5OIFA2cYtv/7BFIPAvDKoAiCvV1IysznrWUHFAtPCFE9JNkQNql8fY2oEC9F4xAKObMD4n81bA98H9RyKRPCKjUbDE0Ggq4Elj4POh21nBz44P5WqFTw+/ZE/jmUonSUQggLkhpa2CTj4PB6XorGIRSg08HfZeMzWj0Mwe2UjUcIcX0qleGGgGMtSNwKO38AoFMDX57qEgbAS4v2ciG3SMkohRAWJMmGsDkXcos4cT4XkJmoaqT43yBpp+GPl95TlI5GCHEznsHQ63XD9po3IPscABP7NSHcvxZpOYW89tc+5eITQliUJBvC5sSdyQSgQW03vN1kpegapSDb8McKwG2TwD1Q0XCEEJXU/mkIaguF2caWSWetho8eaIVGrWLZnnMsjT+rcJBCCEuQZEPYnLiy8RqtZbxGzbNhBuSmgk9D6DhK6WiEEJWl1sCdn4HaAQ4ugUPLAYgM9mJ0T8P6OK/9tY/U7AIloxRCWIAkG8Lm7C4brxEl4zVqlvPHYMvXhu3+7xoWDhNC2I7AFtB5rGF7xUQozAFgTM9wmtf1IDOvmJcW7UGv1ysYpBDC3CTZEDZFp9MTXz44PMRb2WBE9Vo1GXTF0KgvNO6rdDRCiKq47SXwDoPsJPjnLQAcHdR8PLQ1jho1/x5OY972RIWDFEKYkyQbwqacTM8lK78YJwc1Teu4Kx2OqC5HVsHR1aDWQr/pSkcjhKgqrQvc8bFhe+s3cGYnAI0D3Hmhb2MA3lx2gMSMPKUiFEKYmSQbwqaUr6/RMsgTrUa+vjVCSRGsnGzY7jQKaocrG48Q4tY07Fm2EKcelj4HpcUAPN2tAe3re5NbVMqLC+LR6aQ7lRD2QP5aEzalfOVwGa9Rg2ydCRnHwc0fuk9UOhohhDn0fRtcfCBlH8R+AYBGreKD+1vh6qhh68kMfth8StkYhRBmIcmGsCm7jTNRyXiNGiEnGdbPMGz3mQrOHsrGI4QwDzdf6F/WJXLdu5BxAoBQXzf+N7AZADNWHuJY6kWlIhRCmIkkG8Jm5BeVcijZMHuJtGzUEGumQtFFw/z8kQ8qHY0Qwpwih0KDHlBSAMvGQ9ksVI90rEe3RrUpLNHxwvw4Skp1ysYphLglkmwIm7E3KYtSnR5/dyfqeDorHY6wtHPxEP+rYXvADFDL5UoIu6JSGQaLOzjDiXUQ92vZbhUz7ovE3dmB+DNZfLXuuLJxCiFuidTewmYcTs4GoEWQJyqVSuFohMXFfmn42WIIBLdTNhYhhGX4NIAeZRNArJwM2YZVxOt4uvDmXS0A+GztUfaeyVIqQiHELZJkQ9iMo2V9dxsF1FI4EmFx2edg3x+G7fJFwIQQ9il6DNRtA4VZsHScsTvVXa3rMrBlICU6PePm7aaguFTZOIUQVSLJhrAZ5QMFw/0k2bB7O74zLOBXLxrqRikdjRDCkjQOcPdXoHGEo6sg/nfA0J3q7btb4ufuxPG0XN5beUjhQIUQVSHJhrAZl1o2ZDE/u1acDzu+N2x3GqVsLEKI6uHfDHq8bNhe+ZKhdRPwdnNkxn2RAPzw3yk2HzuvVIRCiCqSZEPYhKy8YtJyCgEI95eWDbu2dwHkpYNnPWgySOlohBDVpfPzhpbMgixYNs7YnapnE38e7lgPgBcXxJOVX6xgkEIIU0myIWzCsTTDlLd1PJ2p5eSgcDTCYvR62DLTsN1hhKF7hRCiZtA4wF1l3amOrIQ984wPvTKwGaG+rpzNKmDqkv0KBimEMJUkG8ImHE0pG68hrRr27dQmSD0AWjdo85jS0QghqltABNz2kmH770mQlQSAm5MDHz3QCrUK/tidxIq95xQMUghhCpNuG+p0OtavX8/GjRs5ffo0eXl5+Pn5ERUVRe/evQkJCbFUnKKGO5dVAECIj6vCkQiLOrne8LPZneAiq8SLq0k9VAN0GQeHlsHZ3fDnszDsL1BraBvqw6geDfny3+P878+9tKnnTaCsuSSE1atUy0Z+fj5vvfUWISEhDBw4kL///pvMzEw0Gg3Hjh1jypQphIWFMXDgQLZs2WLpmEUNlJlXBICPq6PCkQiLOrvb8FPW1RBXkHqoBtE4wL2zQesKpzbC5s+MDz3fqzEtgzzJzCtm4sJ4dDq9goEKISqjUslG48aN2bNnD7NmzSI7O5vY2FgWLVrEL7/8wooVK0hISOD48eN069aNBx98kFmzZlk6blHDZOQZBgR6uWoVjkRYjF5/KdkIaqNsLMLqSD1Uw9QOhwHvGbb/eQuSdgLg6KDm46Gtcdaq2Xj0PHM2n1IuRiFEpVQq2Vi9ejXz589n4MCBaLXX/mMvNDSUyZMnc/ToUW6//XazBimEsWXDTVo27FZmgmEWKrUWAlooHY2wMlIP1UBRj0HEXaArgUUjoPDS2L1XBkUA8O7KQxxOzlEySiHETVQq2WjWrFmlX1Cr1dKwYcMqByTEtWTkGpINb+lGZb/KWzUCmoODk7KxCKsj9VANpFLBnZ+CRxBkHDesv1Hm0Y716NnEj6ISHc//vpvCEp2CgQohbqRK80oWFBSwZ88eUlNT0ekq/gcfPHiwWQIT4nKZZd2ovKVlw36d3WX4KSuGi0qQeqiGcPGGe7+FOXfA7l8gvDc0vweVSsWM+1rR/5MNHErO4eM1R4lUOlYhxDWZnGysXLmSYcOGcf781at4qlQqSktLzRKYEJe71LIhYzbslozXEJUk9VANU78rdJsAGz+Epc9DUDvwCsHP3Yl3h0Qy4qcdfL/5NP/XTMVApWMVQlzF5HU2xo4dy/3338+5c+fQ6XQV/pl6gZ85cyaRkZF4eHjg4eFBdHQ0f//9t/HxgoICRo8eja+vL7Vq1WLIkCGkpKRUeI2EhAQGDRqEq6sr/v7+TJw4kZKSkgrHrFu3jjZt2uDk5ER4eDhz5swxtdhCQQXFpeQXG75bXtKNyj7pdHA2zrAtLRviJqQeqoF6TIagtobVxf98FnSGz7lPRAAPdaiHXg9zj6lldXEhrJDJyUZKSgoTJkwgICDglt88ODiYd999l507d7Jjxw5uv/127rrrLvbvN6wOOn78eJYuXcqCBQtYv349Z8+e5d577zU+v7S0lEGDBlFUVMTmzZv58ccfmTNnDq+//rrxmJMnTzJo0CB69uxJXFwc48aN4+mnn2bVqlW3HL+oHhfKBoc7qFV4OMuK0nYp4zgUZoODM/hVvm++qJmkHqqBNFq4dxY41oLT/8Gmj40PvXZHM+r7upJZpGLKkoPo9TIdrhBWRW+iJ598Uj979mxTn1Zp3t7e+tmzZ+szMzP1Wq1Wv2DBAuNjBw8e1AP62NhYvV6v169YsUKvVqv1ycnJxmNmzpyp9/Dw0BcWFur1er1+0qRJ+ubNm1d4j6FDh+r79etX6ZgSExP1gD4xMfFWimY1ioqK9IsXL9YXFRUpHUql7EvK1Ie+tEzf9s2Yaz5ua+W5mRpZnvh5ev0UD71+Vu/qC6yKzPn52Nu1pbrUxHpIr7fM98Xmrje75xquFW946/WJ2427t59I04e9tFQf+tIy/R+7bPv/k819JtdhL+XQ6+2nLNcrh6XrIpNvE3/xxRfcf//9bNy4kZYtW141BeFzzz1XpaSntLSUBQsWkJubS3R0NDt37qS4uJjevXsbj2natCn16tUjNjaWTp06ERsbS8uWLSvc3erXrx+jRo1i//79REVFERsbW+E1yo8ZN27cdWMpLCyksLDQ+HtOjmFavZKSEoqLbb+JtrwMtlKW89n5AHi5OFwzZlsrz83UxPKoE3egAUrrtEZn5eU25+dzZVcbUTk1oR6C6qmLbO56E3EfmiOrUB9YjH7hcEqe/hec3IkIcKV/iI4ViRpeW7yf1kEeBHu7KB1tldjcZ3Id9lIOsJ+yXK8clq6LTE42fvvtN1avXo2zszPr1q1DpVIZH1OpVCZf5Pfu3Ut0dDQFBQXUqlWLP//8k4iICOLi4nB0dMTLy6vC8QEBASQnJwOQnJx8VTN6+e83OyY7O5v8/HxcXK6+GE2fPp2pU6detX/t2rXUrl3bpPJZs5iYGKVDqJRd51WABn3BRVasWHHd42ylPJVVk8rT9cg/+AJxqSrO3OAztibm+HyuNcBZ3FxNqIegeusiW7reOGj60VO7EdfMU5z7/lF2hz4LQO8gOJip52ROCU/P2sCY5qWoVTd5MStmS5/JjdhLOcB+ynJlOSxdF5mcbLzyyitMnTqVl19+GbXa5CEfV2nSpAlxcXFkZWWxcOFCHn/8cdavX3/Lr3srJk+ezIQJE4y/JyUlERERQa9evQgKClIwMvMoLi4mJiaGPn36XHdxLGtyYWsCHD1Eg+AABg5sfdXjtlaem6lx5dGV4LDX8MdCZP8niKzdqJojNI05P5+kpCQzRVWz1IR6CKqnLrLV640qKgT9z4Opl/EfdbsPo6jxYGJiYvj6iWju/WY7x3NKSXJvxrPdw5QO1WS2+plcyV7KAfZTluuVw9J1kcnJRlFREUOHDjXLBR7A0dGR8PBwANq2bcv27dv59NNPGTp0KEVFRWRmZla4q5SSkkJgYCAAgYGBbNu2rcLrlc8ScvkxV84ckpKSgoeHx3XvJjk5OeHkdGlRsezsbAAcHBxs+kt2Ja1WaxPlySowzKFf293phvHaSnkqq8aUJ/kwlOSDozvagKZgpmuLpZnj83FwkAkPqqIm1ENQvXWRzV1vGnSD7hNh/Xs4/D0RfVB7w25/D94Y3JyJC/fwydpj3NYkgJbBngoHWzU295lch72UA+ynLFeWw9J1kclX6scff5x58+ZZIhYAdDodhYWFtG3bFq1Wy9q1a42PHT58mISEBKKjowGIjo5m7969pKamGo+JiYnBw8ODiIgI4zGXv0b5MeWvIaxf+WxUMu2tnSpfX6NOK5tJNISypB4SAHSfBMEdoDAbzV+jUOkN0+He1zaYAS0CKdHpGTdvN/lFsu6KEEoyOZUpLS1lxowZrFq1isjIyKsyvI8++qjSrzV58mQGDBhAvXr1yMnJ4ddff2XdunWsWrUKT09Phg8fzoQJE/Dx8cHDw4OxY8cSHR1Np06dAOjbty8RERE89thjzJgxg+TkZF599VVGjx5tvBs0cuRIvvjiCyZNmsRTTz3FP//8w/z581m+fLmpRRcKKU82fCTZsE/lK4cHyfoaonKkHhIAaBxgyCyY2RX1ma00LqkL3IlKpeKde1qyK+ECx9Nymf73Qabd1ULpaIWosUxONvbu3UtUlOGPgn379lV47PJBepWRmprKsGHDOHfuHJ6enkRGRrJq1Sr69OkDwMcff4xarWbIkCEUFhbSr18/vvrqK+PzNRoNy5YtY9SoUURHR+Pm5sbjjz/OtGnTjMeEhYWxfPlyxo8fz6effkpwcDCzZ8+mX79+phZdKORCnmHWBG83STbsUnnLRl1ZOVxUjtRDwsi7PtzxEfwxgibJiylNfBoadMXbzZEP7m/FY99t46fY0/Rs4k/Ppv5KRytEjWRysvHvv/+a7c2/++67Gz7u7OzMl19+yZdffnndY0JDQ284QxFAjx492L17d5ViFMq7kGto2fB2tf1+kuIKJYWQXPbHoqwcLipJ6iFRQeQD6I6sQr1vIZq/RsKo/8DZk26N/HiqSxjf/3eSiQvjWTmuO7VrOd389YQQZiUdpIXVK+9GJS0bdihlP+iKwcXHcIdSCCGqoLTfDHId/VBlJcLyF4z7J/VvQuOAWpy/WMTLi/bK6uJCKKBSycbIkSM5c+ZMpV5w3rx5zJ0795aCEuJyl1o2JNmwO8YuVFFgYvcXUbNIPSRuyNmDnfVHoldpYO8CiDdMIOCs1fDJ0CgcNWrWHEzh9+2JCgcqRM1TqW5Ufn5+NG/enC5dunDnnXfSrl076tati7OzMxcuXODAgQNs2rSJ33//nbp16/Ltt99aOm5RQxSWlJJbNpOIDBC3Q+WDw6ULlbgJqYfEzVxwa4Su24toNrxnaN0I6QA+YUTU9WBivya8veIg05YeoFMDX8JquykdrhA1RqVaNt58802OHDlCly5d+Oqrr+jUqRP16tXD39+fJk2aMGzYME6cOMG3337Lli1biIyMtHTcoobILBscrlaBu7OsSWB3zsYZfkqyIW5C6iFRGbou46FeNBTlwKKnodRQhwzvGkbnhr7kF5cybl4cxaU6hSMVouao9F9vAQEBvPLKK7zyyitcuHCBhIQE8vPzqV27Ng0bNjR5BhAhKsM4XsPVEbVavmN2pSgPUg8atoNkJipxc1IPiZtSO8C938LMrpC0A9a/B7e/ilqt4oP7W9H/kw3EJ2by+T/HmNCnsdLRClEjVOlWsbe3N97e3uaORYirZOTK4HC7lbwX9KVQKxA86iodjbAxUg+J6/KqZ5gOd9Fw2PghNLwdQjtT18uFt+9pydjfdvPFP0e5rbEfbUPlOySEpclsVMKqlXejkmlv7ZCM1xBCWErL+6DVw6DXwaIRkH8BgDtb1eWeqCB0ehg/L46LhSUKByqE/ZNkQ1i1DJmJyn6Vz0QlXaiEEJYwcAZ4h0H2GVg2HsqmvZ16V3OCvFxIyMhj2tL9CgcphP2TZENYtcyyMRs+0o3K/iRJy4YQwoKc3GHId4ZxHPv/hLhfAfBw1vLRA61QqWD+jjOs3HdO4UCFsG+SbAirlpFr6EblJS0b9qUgG9KPGrYl2RBCWEpwW+j5P8P2iomQfhyAjg18GXlbQwBe/mMvKdkFSkUohN2TZENYtUzjbFQyZsOunIsz/PSsB261FQ1FCGHnuoyD0K5QnFthOtzxvRvTIsiDzLxiXlwQj04nq4sLYQmVmo0qKiqq0lMK7tq165YCEuJyGXkyG5VdMo7XkFYNUTlSD4kqU2vKpsPtbJiY4t93oPcUHB3UfDK0NYM+28TGo+f5MfYUT3YJUzpaIexOpZKNu+++28JhCHFtF8oGiMvq4XZGxmsIE0k9JG6JZxAM/gzmD4NNH0PDnhDWnXB/d14d1IzX/trP9L8P0SW8No0D3JWOVgi7UqlkY8qUKZaOQ4hrulA+9a2bdKOyK+UtG3VlJipROVIPiVsWcRdEPQa7f4Y/noVR/4GrD492CmXtoVTWHU7j+d/jWDy6M04OGqWjFcJuVGnMRmZmJrNnz2by5MlkZGQAhmbrpKQkswYnxAWZ+tb+5KZD5mnDdp1WysYibJbUQ6JK+r8LvuGQcxaWPgd6PSqVihn3ReLj5sjBc9l8tPqI0lEKYVdMTjb27NlD48aNee+99/jggw/IzMwE4I8//mDy5Mnmjk/UYEUlOnLKFlySZMOOnCtr1fANBxcvRUMRtknqIVFlTrVgyGxQa+HgUtj1EwD+7s68e29LAL7deILNx88rGaUQdsXkZGPChAk88cQTHD16FGdnZ+P+gQMHsmHDBrMGJ2q2zHxDq4ZaBR4u0o3KbiSVd6GS8RqiaqQeErekbhTc/qphe+XLcN4wDXff5oE81CEEvR5emB9PVlk3XiHErTE52di+fTvPPvvsVfuDgoJITk42S1BCAFwoW2PD00WLRl25WWiEDTgrg8PFrZF6SNyyzs9BWHcozoNFw6HEcHPr1UER1Pd15VxWAa/9tU/hIIWwDyYnG05OTmRnZ1+1/8iRI/j5+ZklKCEALsi0t/ZJBoeLWyT1kLhlajXc8w24eMO5ePjnTQDcnBz45MEoNGoVS+LP8lecjAES4laZnGwMHjyYadOmUVxsuOusUqlISEjgpZdeYsiQIWYPUNRcMjjcDmWfg5xzoFJDnUiloxE2SuohYRYedWHwF4btzZ/B8X8BaB3ixXO3NwLg1cX7OHMhT6kIhbALJicbH374IRcvXsTf35/8/Hxuu+02wsPDcXd35+2337ZEjKKGMk57K8mG/Shv1fBrCo5uysYibJbUQ8Jsmt0BbZ80bP850jBbHjC6Z0Oi6nmRU1DCC/PjKZXVxYWoskqts3E5T09PYmJi2LRpE3v27OHixYu0adOG3r17WyI+UYMZu1G5yuBwu2EcryFdqETVST0kzKrfO3D6Pzh/BJaMhQfn4qAxrC4+4NONbD2ZwbcbTjCqR0OlIxXCJpmcbCQmJhISEkLXrl3p2rWrJWISArhs9XAZs2E/jOM1WisahrBtUg8Js3J0NUyHO7s3HF4OO76H9sMJ9XXjjTubM2nRHj6KOUy3RrVpEeSpdLRC2ByTu1HVr1+f2267jVmzZnHhwgVLxCQEABllLRte0o3KPuj1kFTWshEkLRui6qQeEmZXpxX0KlulftUrkHoIgPvbBdOveQDFpXqe/303+UWlCgYphG0yOdnYsWMHHTp0YNq0adSpU4e7776bhQsXUlhYaIn4RA2WnW9Y0M9T1tiwD7lpkJ8BqCCghdLRCBsm9ZCwiE7/Bw1vh5J8WDwKSktQqVRMvzcSP3cnjqfl8vaKA0pHKYTNMTnZiIqK4v333ychIYG///4bPz8/nnnmGQICAnjqqacsEaOooYpKdQA4Opj8NRXWqLBsqlInD3BwUjYWYdOkHhIWoVbDXV+Bk6dhfFmsYaYqHzdHPnqgFQC/bElg9X5Zy0UIU1T5rziVSkXPnj2ZNWsWa9asISwsjB9//NGcsYkarqQs2dBqZEE/u1B00fDTqZaycQi7IfWQMDuPOtD/HcP2v+9A2hEAujXy45nuDQCYtGgPyVkFSkUohM2pcrJx5swZZsyYQevWrenQoQO1atXiyy+/NGdsooYrKTVMNajVSMuGPVAV5hg2nNyVDUTYDamHhEW0fgQa9oLSQvhrNOgM4zRe7NuEFkEeZOYVM35enEyHK0QlmfxX3DfffMNtt91G/fr1+emnnxg6dCjHjx9n48aNjBw50hIxihqqWGdo2XBQS8uGXZBkQ5iJ1EPColQqGPwZOLrDmW2w9RvA0KX3swejcNFqiD2RzjcbjiscqBC2weRk46233qJjx47s3LmTffv2MXnyZEJDQy0Rm6jhpGXDzpR3o3KUblTi1kg9JCzOMxj6vmnYXjsN0g2JRQO/Wkwd3ByAj1YfIS4xU6EAhbAdJq+zkZCQgEold5qF5RWXjdlwkDEbdkFVWD5mQ1o2xK2RekhUi7ZPwP4/4OQGw2J/jy8DtZr72wWz/mgay/ec4/nfd7P8uW7UcjL5zykhagyTbxmrVCo2btzIo48+SnR0NElJSQD8/PPPbNq0yewBCqGXbrH2QVds+FkiAyvFrZF6SFQLlQoGfw5aN8MK4wf+LNut4p17WhLk5cLp9DxeWrQHvVRUQlyXycnGokWL6NevHy4uLuzevds4r3lWVhbvvPOO2QMUNVegpzMA57LyFY5EmIOufjfDxol1kC8LsYmqk3pIVBvv+tDlecP2uveMg8U9XbR89lAUDmoVy/ec46fY08rFKISVq9KYja+//ppZs2ah1V5abK1Lly7s2rXLpNeaPn067du3x93dHX9/f+6++24OHz5c4ZgePXqgUqkq/LtyAGBCQgKDBg3C1dUVf39/Jk6cSElJSYVj1q1bR5s2bXByciI8PJw5c+aYVnBR7er5uAKQkJGncCTCLPwjwL85lBbBgb+UjkbYMKmHRLXqNBKcPeH8Ydj/p3F321BvJg9sBsBbyw+wO0FuoghxLSYnG4cPH6Z79+5X7ff09CQzM9Ok11q/fj2jR49my5YtxMTEUFxcTN++fcnNza1w3IgRIzh37pzx34wZM4yPlZaWMmjQIIqKiti8eTM//vgjc+bM4fXXXzcec/LkSQYNGkTPnj2Ji4tj3LhxPP3006xatcq0wotqdSnZkJYNuxH5gOHnnvnKxiFsmtRDolo5e0L0WMP2uneNrRsAT3Wpz8CWgRSX6hk9dxcZuUUKBSmE9TJ5RFNgYCDHjh2jfv36FfZv2rSJBg0amPRaK1eurPD7nDlz8Pf3Z+fOnRUqEldXVwIDA6/5GqtXr+bAgQOsWbOGgIAAWrduzZtvvslLL73EG2+8gaOjI19//TVhYWF8+OGHADRr1oxNmzbx8ccf069fP5NiFtUnRFo27E/L+2DNG4b+z5mJ4BWidETCBkk9JKpdx2dhy5eQfhT2LoRWQwHD+I33hkRy8FwOJ8/nMm5eHHOeaI9apmwXwsjklo0RI0bw/PPPs3XrVlQqFWfPnmXu3Lm8+OKLjBo16paCycrKAsDHx6fC/rlz51K7dm1atGjB5MmTycu79MdnbGwsLVu2JCAgwLivX79+ZGdns3//fuMxvXv3rvCa/fr1IzY29pbiFZZV3rKRKMmG/fAMhvpdDdt7Fygbi7BZUg+JaufsAdFjDNvr34PSS13k3J21zHy0Dc5aNRuOpPHFv8cUClII62Ryy8bLL7+MTqejV69e5OXl0b17d5ycnHjxxRcZO3ZslQPR6XSMGzeOLl260KJFC+P+hx9+mNDQUOrWrcuePXt46aWXOHz4MH/88QcAycnJFS7wgPH35OTkGx6TnZ1Nfn4+Li4uFR4rLCw0DjgEyMkxLEZWUlJCcXFxlctoLcrLYO1lCXQ39MXOyC0iIycfd+drf11tpTyVZe/lUUXci8Opjej3zKOk4xjDjC82xJyfz5V9+kXl1IR6CKqnLrKX6021lKPNUzjEfokq4zglcb+jjxxqfKihrwtT72zGS3/s5+M1R2hZ152u4b5Vehv5TKyPvZTleuWwdF1kcrKhUql45ZVXmDhxIseOHePixYtERETg7OzM2bNnqVu3bpUCGT16NPv27btq2sJnnnnGuN2yZUvq1KlDr169OH78OA0bNqzSe93M9OnTmTp16lX7165dS+3atS3ynkqIiYlROoSbquWg4WKJit+WribY7cbH2kJ5TGGv5XEocaa/ygFN2iE2LfqGbNd6CkdWNeb4fM6fP2+GSGqemlAPQfXWRfZyvbF0OcK9e9M8fz4Fq6fxT6IrepXG+JgzEO2vJjZVzZi5O5gUWYqXU9XfSz4T62MvZbmyHJaui6q8Co2joyMRERHG3+Pj42nTpg2lpaU3eNa1jRkzhmXLlrFhwwaCg4NveGzHjh0BOHbsGA0bNiQwMJBt27ZVOCYlJQXA2L82MDDQuO/yYzw8PK55N2ny5MlMmDDB+HtSUhIRERH06tWLoKAgk8tnbYqLi4mJiaFPnz4VZnKxRt8nbiX+TBahEW3p1zzgmsfYUnkqoyaUR1W0HA4vo7t3MrpeI2/yCtbFnJ9P+foQomrsuR6C6qmL7OV6U23lKOqO/su11MpLYWDwRfStHqrwcK/iUh6YtY0D53JYnObL3OHt0WpM67Eun4n1sZeyXK8clq6LFF3yUq/XM3bsWP7880/WrVtHWFjYTZ8TFxcHQJ06dQCIjo7m7bffJjU1FX9/f8CQsXl4eBgroejoaFasWFHhdWJiYoiOjr7mezg5OeHkdOl2RHZ2NgAODg42/SW7klartfryhPq6EX8mi7PZhTeN1RbKYwq7Lk/rB+HwMjT7/0DTdxqoNTd+shUyx+fj4CCrDivNWushqN66yF6uNxYvh9bbsO5GzOs4bPoAoh4CzaX302q1zHy0LXd8vondiVl8uOY4r90RcYMXvMFbyWdideylLFeWw9J1kckDxM1p9OjR/PLLL/z666+4u7uTnJxMcnIy+fmGqU6PHz/Om2++yc6dOzl16hRLlixh2LBhdO/encjISAD69u1LREQEjz32GPHx8axatYpXX32V0aNHGy/SI0eO5MSJE0yaNIlDhw7x1VdfMX/+fMaPH69Y2UXlyFobdqpRX8N0kjlnDTNTCaEQqYeEydo/DW5+kHka4n+76uFQXzc+vL8VAN9tOsmKveeqO0IhrIqiycbMmTPJysqiR48e1KlTx/hv3rx5gKGJfM2aNfTt25emTZvywgsvMGTIEJYuXWp8DY1Gw7Jly9BoNERHR/Poo48ybNgwpk2bZjwmLCyM5cuXExMTQ6tWrfjwww+ZPXu2TDdoA2StDTvl4AQRdxu298xTNBRRs0k9JEzm6HZpVfEN70PJ1Wtr9G0eyLO3GaZhnrRwDyfSLlZnhEJYlUq3m+zZs+eGj1+54mpl6PX6Gz4eEhLC+vXrb/o6oaGhVzVPX6lHjx7s3r3bpPiE8kJk+lv7FfkA7PoRDiyBgR+C1lnpiISVk3pIWI12w+G/zyAzAeJ/hbZPXHXIxL5N2J2QybaTGfzf3F38+X9dcHG0vS6jQtyqSicbrVu3RqVSXfPCXL5fZWNTWArrV8/XkGycuZBHqU6PRhZKsh/1OoNHMGSfgSMrofndSkckrJzUQ8JqOLpC1/GwajJs+ABaPQwOjhUOcdCo+eKhKAZ+tolDyTm8ungfH9wfKd9RUeNUOtk4efKkJeMQ4poCPZzRalQUl+o5l5VPsLer0iEJc1GrDSuK//cJ7JkvyYa4KamHhFVp9yT89ylkJULcL9DuqasO8fdw5vOHonhk9hYW7TpD+/rePNjBNqf7FqKqKp1shIaGWjIOIa5Jo1YR7O3KyfO5JGZIsmF3Iocako2jqyEvA1x9bvoUUXNJPSSsitbF0Lqx8iXY8CG0fsQwHu0K0Q19ebFfE2asPMzrS/bTIsiTFkGeCgQshDIUHSAuRGXIuA07FhABAS1AVwwH/lI6GiGEME3bJ8C9jqE76K6frnvYyO4N6dXUn6ISHf83dxdZ+ba9ErUQppBkQ1i9ej6GBa9OZ+QqHImwiMgHDD/3zFc2DiGEMJXWGbq9YNje+BEUF1zzMLVaxUcPtCbY24WEjDxeXBB/08kJhLAXkmwIqyfT39q5FvcBKkjYbJjZRQghbEmbYeARZFg36AatG56uWmY+0hZHjZqYAyl8u+FENQYphHIk2RBWTxb2s3OeQVC/q2F770JlYxFCCFM5OEG3CYbtTddv3QBoGezJlMGGFcVnrDrM1hPp1RGhEIqSZENYPRmzUQNc3pVKuhYIIWxN1GOGqbxzzsHOOTc89OEO9bgnKohSnZ4xv+0mNef6yYkQ9qDSs1FdbuHChcyfP5+EhASKiiqunLlr1y6zBCZEufKWjYzcIrLyi/F00SockTC7ZoNh+YuQdhASYiG0s9IRCSsn9ZCwKg5O0P0FWDYeYr+Ajs/CddbTUKlUvH1PC/afzeJIykX+75ddzB3REScHWfBP2CeTWzY+++wznnzySQICAti9ezcdOnTA19eXEydOMGDAAEvEKGo4d2ctQV6GQeL7k7IUjkZYhIsXtH7IsL3qf6DTKRqOsG5SDwmrFPkgqDSGdTeyz97wUFdHB2Y+2hZ3Zwd2nL7A5EV7ZcC4sFsmJxtfffUV3377LZ9//jmOjo5MmjSJmJgYnnvuObKy5A9BYRmtQ7wAiDuTqWgcwoJ6vgKO7nB2N+yVmanE9Uk9JKySoyv4G8ZjcPbmrWsN/Wrx1SNt0KhV/LE7ia/WHbdwgEIow+RkIyEhgc6dDV0cXFxcyMnJAeCxxx7jt99+M290QpRpFWJYAGlPovwhYbdq+V8aZLlmKhTJGB1xbVIPCasVFGX4mbSzUod3a+TH1MHNAXh/1WH+3nvOUpEJoRiTk43AwEAyMjIAqFevHlu2bAHg5MmT0gQoLKZVsBcA8dKyYd86/R941TNMIbn5c6WjEVZK6iFhtYLaGn4mVX7c0KOdQnmic30Axs+PY690FxZ2xuRk4/bbb2fJkiUAPPnkk4wfP54+ffowdOhQ7rnnHrMHKARAiyBP1Co4l1VASrbM3GG3tM7Qe6ph+79PbtrvWdRMUg8Jq1W3jeHn2d0mjT17dVAzejTxo6BYx8i5cWQWWig+IRRg8mxU3377Lbqy/0CjR4/G19eXzZs3M3jwYJ599lmzBygEgJuTA40D3DmUnEN8YiZ9mwcqHZKwlOb3wNavIXErrH0T7pmpdETCykg9JKyWfzNwcIHCbEg/Bn6NK/U0B42azx+KYsjMzRxJuciswxruLirBUyuzLwrbZ3LLhlqtxsHhUo7y4IMP8tlnnzF27FgcHR3NGpwQlyvvShWXmKloHMLCVCroN92wHf8rnKlc32dRc0g9JKyWRgt1Whm2KzFI/HLuzlq+e7w9Pm5azuSqeGHBXkp10i1Q2L4qLeq3ceNGHn30UaKjo0lKSgLg559/ZtOmTWYNTojLta7nBcDuhExF4xDVILitYRpJgBUvgK5U2XiE1ZF6SFitAMOAb9IOmfzUEB9XZj4chYNKz5pDaUxfcdDMwQlR/UxONhYtWkS/fv1wcXFh9+7dFBYaOhZmZWXxzjvvmD1AIcq1qecNGAaJl5TKOgx2r880cPIw9H3e9aPS0QgrIvWQsGp+TQw/045U6elt6nnxSLihjpu96SQ/bzltrsiEUITJycZbb73F119/zaxZs9Be1pewS5cusmqrsKhG/rVwd3Igr6iUQ8k5SocjLM09wLD2Bhimws1NVzYeYTWkHhJWrXbZOI3zh6v8Em1q6xnXKxyAN5bsZ93hVHNEJoQiTE42Dh8+TPfu3a/a7+npSWZmpjliEuKa1GrVZV2pLigbjKge7Z+GgBZQkAlr31A6GmElpB4SVq082cg4CSVFVX6Z/7stjCFtginV6Rnz624OJWebKUAhqleV1tk4duzYVfs3bdpEgwYNzBKUENdT3pVql4zbqBk0DjDoQ8P2rp8gcbuy8QirIPWQsGoedcHRHfSlkHGiyi+jUqmYfm9LOob5cLGwhKd+2E6qTP0ubJDJycaIESN4/vnn2bp1KyqVirNnzzJ37lxefPFFRo0aZYkYhTBqG2pINnaelpaNGqNeJ2j9iGFbBosLpB4SVk6lgtqNDNu30JUKwNFBzTePtaVBbTfOZhXw9E87yCsqMUOQQlQfk9fZePnll9HpdPTq1Yu8vDy6d++Ok5MTL774ImPHjrVEjEIYta7nhUoFCRl5pOUU4ufupHRIojr0ngoHl8G5eNj5g6F7laixpB4SVs+viWHq2yoOEr+cl6sj3z/Rnnu++o89Z7IY93scXz/aFrVaZYZAhbA8k1s2VCoVr7zyChkZGezbt48tW7aQlpbGm2++aYn4hKjAw1lLI/9agIzbqFFq+UGv1wzba6dB7nll4xGKknpIWD0ztWyUq1/bjW+HtcNRo2b1gRTeXWn6tLpCKKVK62wAODo6EhERQUBAAAkJCcbVXIWwNGNXKkk2apZ2T0FgSyjIgjVTlI5GWAGph4TVql02/e35W2/ZKNe+vg/v3x8JwLcbTjB3q0yJK2xDpZON77//no8++qjCvmeeeYYGDRrQsmVLWrRoQWJiotkDFOJKUWWDxHefzlQ2EFG91BoYWDZYfPcvkLhN2XhEtZN6SNiM8rU2zh8FMybBd7UOYnxvw2xXr/+1n/VH0sz22kJYSqWTjW+//RZvb2/j7ytXruSHH37gp59+Yvv27Xh5eTF16lSLBCnE5S5f3K9YFverWep1hNaPGraXT5DB4jWM1EPCZniHgVoLxXmQfcasL/1cr3DujQqiVKdn9NxdHJZ1p4SVq3SycfToUdq1a2f8/a+//uKuu+7ikUceoU2bNrzzzjusXbvWIkEKcbkGtd3wctVSWKLjwFmZd7zG6f0GOHtC8l7Y8b3S0YhqJPWQsBkaB/BtaNg2wyDxy6lUKqYPaUmH8ilx52wnNUemxBXWq9LJRn5+Ph4eHsbfN2/eXGFRpQYNGpCcnGze6IS4BrVaRVSIFwC7ZNxGzVPLD24vHyz+JlyUbgQ1hdRDwqYYB4mbN9kAcHLQ8M2jbQmr7UZSZj4jftxBfpG09ArrVOlkIzQ0lJ07dwJw/vx59u/fT5cuXYyPJycn4+npaf4IhbgGWdyvhmv3FARGQmEW/CMzENUUUg8Jm2IcJG6eGamu5O1mmBLXy1VL/Jksxs+Lo1Snt8h7CXErKp1sPP7444wePZo333yT+++/n6ZNm9K2bVvj45s3b6ZFixYWCVKIK0WWtWwcOJulbCBCGWoNDJhh2I77FbKSlI1HVAuph4RN8Q03/Mw4abG3CKvtxrePGabEXbk/mXdWHLTYewlRVZVONiZNmsSIESP4448/cHZ2ZsGCBRUe/++//3jooYfMHqAQ19I00B2AU+l5FBZL03GNFBoNoV1AVwyxXyodjagGUg8Jm+Idavh54ZRF36ZDmA8fPNAKgO82neT7TZZLboSoikqvIK5Wq5k2bRrTpk275uNXXvSFsCR/dye8XLVk5hVzLC1X6XCEUrpOgNP/wc450P1FcPVROiJhQVIPCZviVZZsZJ2B0hLDoHELGdyqLmcz83n370O8ufwAdb1c6N8i0GLvJ4QpqryonxBKUqlUNAkwtG4cSbmocDRCMeG9DAv9FefCtm+VjkYIIS5xrwMaR9CXQrblu3o+270Bj3aqh14Pz/++WyZQEVZD0WRj+vTptG/fHnd3d/z9/bn77rs5fLjiQKqCggJGjx6Nr68vtWrVYsiQIaSkpFQ4JiEhgUGDBuHq6oq/vz8TJ06kpKSkwjHr1q2jTZs2ODk5ER4ezpw5cyxdPGFhTcq6Uh1OkTnGayyVCrqON2xv/RqKpJVLmEbqIWExajV4hhi2My2/2rdKpeKNO5tze1N/Ckt0PP3jDk6nyzVRKE/RZGP9+vWMHj2aLVu2EBMTQ3FxMX379iU399J/jvHjx7N06VIWLFjA+vXrOXv2LPfee6/x8dLSUgYNGkRRURGbN2/mxx9/ZM6cObz++uvGY06ePMmgQYPo2bMncXFxjBs3jqeffppVq1ZVa3mFeZUnG0dTpWWjRmt2l2EBrfwLsPNHpaMRNkbqIWFRxnEblk82ABw0aj5/KIqWQZ5k5BbxxA/bycgtqpb3FuK69FYkNTVVD+jXr1+v1+v1+szMTL1Wq9UvWLDAeMzBgwf1gD42Nlav1+v1K1as0KvVan1ycrLxmJkzZ+o9PDz0hYWFer1er580aZK+efPmFd5r6NCh+n79+lUqrsTERD2gT0xMvKXyWYuioiL94sWL9UVFRUqHckt2nErXh760TN/h7Ri7KE85e/l8ylVLebZ/r9dP8dDrP2ym15cUW+599OYtj71dW+yBtdZDer1lvi/2cr2x2nIsed5wbVr7ZqWfYo6ypGTn6ztPX6sPfWmZ/p4vN+nzi0qq/FpVZbWfSRXYS1muVw5L10VVHq1UVFTEyZMnadiwIQ4O5hn0lJVlmMbUx8cwyHPnzp0UFxfTu3dv4zFNmzalXr16xMbG0qlTJ2JjY2nZsiUBAQHGY/r168eoUaPYv38/UVFRxMbGVniN8mPGjRt3zTgKCwspLCw0/p6TY+imU1JSQnFxsVnKqqTyMth6WUK9nQFIyS6ksNT2y1POXj6fctVSnub34/DPm6iykyg5ugZ9w14WeytzlufKbjbCNPZcD0H11EX2cr2x1nKo3YPQALrMREorGZs5yuLtrGH2Y1EMnbWNXQmZjP11F58/2AqNWlXl1zSVtX4mVWEvZbleOSxdF5l8dc7Ly2Ps2LH8+KOhu8KRI0do0KABY8eOJSgoiJdffrlKgeh0OsaNG0eXLl2M86QnJyfj6OiIl5dXhWMDAgKMq8QmJydXuMCXP17+2I2Oyc7OJj8/HxcXlwqPTZ8+nalTp14V49q1a6ldu3aVymeNYmJilA7hlrlqNOSVqkgvsI/yXE7KY5qWrlE0yFvDuVWfsKt+4c2fcIvMUZ7z58+bIZKapybUQ1C9dZG9XG+srRwh6am0Ac6f3EvsihUmPdccZXmiIXx1QEPMwVSe/moV94XpUFVfvgFY32dyK+ylLFeWw9J1kcnJxuTJk4mPj2fdunX079/fuL9379688cYbVb7Ijx49mn379rFp06YqPd+cJk+ezIQJE4y/JyUlERERQa9evQgKClIwMvMoLi4mJiaGPn36oNVqlQ7nlsxO2MLepGzSC1U8fndvmy8P2NfnA9VXHlVSAMxZQ3BOHIG9uoGTu0Xex5zlSUqSxQiroibUQ1A9dZG9XG+stRyqk27w67f4ORUzcODASj3H3GUJ35fM8/P3sClFTafIJjzbPeyWX7MyrPUzqQp7Kcv1ymHpusjkZGPx4sXMmzePTp06obosPW7evDnHjx+vUhBjxoxh2bJlbNiwgeDgYOP+wMBAioqKyMzMrHBXKSUlhcDAQOMx27Ztq/B65bOEXH7MlTOHpKSk4OHhcc27SU5OTjg5ORl/z87OBsDBwcGmv2RX0mq1Nl+eer5u7E3K5nyBfZTnclIeE4V2BJ+GqDKOoz22ClpbdnE3c5THXF1/apqaUA9B9dZF9nK9sbpyeBtmo1LlJJscl7nKMjgqhPS8EqYuPcAHMUep6+3KvW2Cb/5EM7G6z+QW2EtZriyHpesik2ejSktLw9/f/6r9ubm5FS76laHX6xkzZgx//vkn//zzD2FhFbPttm3botVqWbt2rXHf4cOHSUhIIDo6GoDo6Gj27t1Lamqq8ZiYmBg8PDyIiIgwHnP5a5QfU/4awnaF+rgCkF5Qze3CwvqoVBA51LC9Z56ysQiLknpI2Az3OoafhdlQqNzMiU92CeOZ7g0AmLRwDxuPpikWi6h5TE422rVrx/Lly42/l1/YZ8+ebfJFc/To0fzyyy/8+uuvuLu7k5ycTHJyMvn5+QB4enoyfPhwJkyYwL///svOnTt58skniY6OplOnTgD07duXiIgIHnvsMeLj41m1ahWvvvoqo0ePNt4RGjlyJCdOnGDSpEkcOnSIr776ivnz5zN+/HhTiy+sTL2yZOO85bvoC1sQ+YDh58n1kH1O2ViExUg9JGyGswc41jJs5yQrGsrL/ZsyuFVdSnR6Rv68k31JWYrGI2oOk9tN3nnnHQYMGMCBAwcoKSnh008/5cCBA2zevJn169eb9FozZ84EoEePHhX2//DDDzzxxBMAfPzxx6jVaoYMGUJhYSH9+vXjq6++Mh6r0WhYtmwZo0aNIjo6Gjc3Nx5//HGmTZtmPCYsLIzly5czfvx4Pv30U4KDg5k9ezb9+vUztfjCytSTlg1xOZ8wCOkEiVtg7wLo8pzSEQkLkHpI2BT3QEg/BjlnoXa4YmGo1Srevz+StJxCYk+k8+Sc7fwxqjMhZfWoEJZicrLRtWtX4uLiePfdd2nZsiWrV6+mTZs2xqn/TKHX6296jLOzM19++SVffvnldY8JDQ1lxU1meejRowe7d+82KT5h/er5liUbhVCq02P7PSnFLYt8wJBs7JkvyYadknpI2BT3OmXJhrItGwBODhq+GdaWB76O5VByDo//sI1FIzvj7eaodGjCjlVpREjDhg2ZNWuWuWMRwmR1PF3QalQUl0JqTiH1nOSCWeM1vwf+fglS9kLKfghornREwgKkHhI2w6Ou4Wf2WWXjKOPhrGXOkx2496v/OJGWy/Aft/PriE44azVKhybslMljNnr37s2cOXOMs2IIoSSNWkWQl2Eml4SMPIWjEVbB1Qcal3VNif9d2ViERUg9JGxK+SDxHOsZRxbo6cyPT3XAw9mBXQmZPPfbbkp1N2/lE6IqTE42mjdvzuTJkwkMDOT+++/nr7/+svkVFYVtC/EuTzbyFY5EWI3yWan2LgRdqbKxCLOTekjYFCtMNgAaBbgz+/H2ODqoWX0ghTeW7K9Ut0IhTGVysvHpp5+SlJTE4sWLcXNzY9iwYQQEBPDMM8+YPDBPCHMoHySeKC0bolzjfuDsaRiQeWqj0tEIM5N6SNgUj7JkwwpnyOsQ5sMnQ1ujUsHPW04zc33V1qkR4kZMTjYA1Go1ffv2Zc6cOaSkpPDNN9+wbds2br/9dnPHJ8RNhfiUtWxckJYNUcbByTB2AyBe1tywR1IPCZvhXjZmw8paNsoNbFmH1+8wrAczY+VhFu08o3BEwt5UKdkol5yczNdff817773Hnj17aN++vbniEqLSyrtRScuGqCDyQcPPg0ugSL4b9krqIWH1yls2cpJBp1M2luu4fNG/lxbJon/CvExONrKzs/nhhx/o06cPISEhzJw5k8GDB3P06FG2bNliiRiFuKG6noZkIyVbVvYTl6nXCWoFQtFFSN6jdDTCjKQeEjalViA4uICuGNKPKh3NdV2+6N///bKLQ8kyAYMwD5Onvg0ICMDb25uhQ4cyffp02rVrZ4m4hKg0DxfD1zinsEThSIRVUakM095eTIbUg4bkQ9gFqYeETdE4QEh7OLnBMIbMr4nSEV1T+aJ/ydkFbDuZwZM/bGfx6C4EeDgrHZqwcSa3bCxZsoQzZ87w8ccfywVeWAUPZ8NSfnlFpRSXWmcTtVCIfzPDz9SDysYhzErqIWFz6ncz/Dy1Sdk4bsLJQcO3j7WlgZ8b57IKePKH7VyUG3niFpmcbPTp0we1Wk1aWhqbNm1i06ZNpKVJ3z6hnFpOlxYiyimQi6K4jL9h0CNpkmzYE6mHhM2p39Xw89QmsPLpZb1cHZnzRAd83Rw5cC6bsb/uokRu5IlbYHKykZeXx1NPPUWdOnXo3r073bt3p27dugwfPpy8PBmEKaqfg0aNk8Zw8c7Kl7n2xWX8mxp+SsuGXZF6SNicoLbg4Ay5aXDeesdtlKvn68rsx9vhrFXz7+E0psgaHOIWmJxsjB8/nvXr17N06VIyMzPJzMzkr7/+Yv369bzwwguWiFGIm3Ita9zIlmRDXM6vLNnITYPc88rGIsxG6iFhcxycIKSDYdtG1v6JqufNJ0OjUKlg7tYEZm08oXRIwkaZnGwsWrSI7777jgEDBuDh4YGHhwcDBw5k1qxZLFy40BIxCnFTZWPEyS6QZENcxtENvEIN29K6YTekHhI2KbSsK9XJDcrGYYL+LQJ5dZChO+o7Kw6xYq91rhUirFuVulEFBARctd/f31+ar4Vi1CrDTxkgLq6iNawwT9FFZeMQZiP1kLBJ4b0MPw8th4yTysZigqe61OeJzvUBmDA/jn1JWcoGJGyOyclGdHQ0U6ZMoaCgwLgvPz+fqVOnEh0dbdbghKisi2UNGj5uTsoGIqxLQRakHTJs141SNhZhNlIPCZsU3A4a9DSst/HPm0pHU2kqlYpXBzWje2M/Cop1PPPTDtJyZF0rUXkmr7Px6aef0q9fP4KDg2nVqhUA8fHxODs7s2rVKrMHKMTN6PV6csuSDV83R2WDEdblzA5Ab+hK5R6odDTCTKQeEjarzzT4pjvsWwTRow0Dx22Ag0bN5w9Fcc+X/3HifC4jf9nJryM64uSgufmTRY1ncstGixYtOHr0KNOnT6d169a0bt2ad999l6NHj9K8eXNLxCjEDeUWlVKsN/Sj8q0lyYa4TOI2w09Z0M+uSD0kbFadSGj1oGF79etWPw3u5TxdtMx6vB3uzg7sPH2BV//cJzNUiUoxuWUDwNXVlREjRpg7FiGqJD23CAAXrRpXxyp9pYW9Stxq+Fk+C4ywG1IPCZt1+6uw7w84vQmOrIQmA5SOqNIa+tXii4fb8OQP21iw8wxN63gwvGuY0mEJK2dyywbA4cOHGTNmDL169aJXr16MGTOGQ4cOmTs2ISol46Ih2ZAuVKICXWlZNyogpKOysQizk3pI2CzPYOg0yrAdMwVKbWsx2tsa+/G/gc0AeHv5ATYckQU1xY1VaerbFi1asHPnTlq1akWrVq3YtWsXLVu2ZNGiRZaIUYgbyihr2fCRZENcLvUgFOWAY61LK4kLuyD1kLB53SaAiw+cPwy7f1Y6GpMN7xrGfW2D0elh9K+7OJEms/2J6zO5z8mkSZOYPHky06ZNq7B/ypQpTJo0iSFDhpgtOCEqo7wblbckG+JyiVsMP4PbgVoGMdoTqYeEzXP2hNsmwcqXYd10aHk/ONVSOqpKU6lUvH1PC06ez2Xn6Qs8/dMO/vy/Lni6aJUOTVghk1s2zp07x7Bhw67a/+ijj3LunCz2IqpfebJRWwaHi8uVDw4PkcHh9kbqIWEX2g0H7/pwMQViv1A6GpM5OWj4+tG21PF05kRaLs/9tptSnQwYF1czOdno0aMHGzduvGr/pk2b6Natm1mCEsIUxm5UrpJsiMvI4HC7JfWQsAsOjtBrimH7v88gJ0XZeKrAz92JWcPa4axVs/5IGu/+fVDpkIQVqlQ3qiVLlhi3Bw8ezEsvvcTOnTvp1Mlwx3DLli0sWLCAqVOnWiZKIW6gvGVDpr0VRjkpcOEUoDJ0oxI2T+ohYZea3wOxX0LSDkN3qjs/UToik7UI8uTD+1sz+tddzNp4kiaBHtzXNljpsIQVqVSycffdd1+176uvvuKrr76qsG/06NGMHDnSLIEJUVnGZEPGbIhyZ8q6UPlHGPpGC5sn9ZCwSyoV9H0TfhgAu34yzFLl1UDpqEw2KLIOh5PD+eyfY/zvj72E1XYjsq7tjEERllWpblQ6na5S/0pLSy0drxBXyShbPlxmoxJG0oXK7kg9JOxWaGdoMgj0pbDmDaWjqbJxvRvTr3kARaU6nv15J+eyCpQOSViJKq2zcS2ZmZl88YXtDXASti9DWjbElWTl8BpJ6iFhs3q/ASoNHF6B6vR/SkdTJWq1io8eaE3TQHfOXyxk1K+7KZLcX2CGZGPt2rU8/PDD1KlThylTppgjJiEqrahEJ7NRiYoyE2UxvxpG6iFh8/waQ9vHAdCsehmV3rYW+ivn5uTArGHt8HFzZP/ZHH47rkavlxmqaroqJRuJiYlMmzaNsLAw+vbti0ql4s8//yQ5Odnc8QlxQ6fScynV6XHS6PF3d1I6HGENtn1j6I4Q1h18wpSORliI1EPC7tz+Grj6oko7SMPUlUpHU2UhPq7MfKQNDmoVu9LVzNp0SumQhMIqnWwUFxezYMEC+vXrR5MmTYiLi+P9999HrVbzyiuv0L9/f7RaWcxFVK+jKYZVSwNdDIsMiRquMAd2/mjYjh6jbCzC7KQeEnbN1Qf6vg1Ak3OLy2bUs00dG/jy6sAmAHwQc5R1h1MVjkgoqdLJRlBQEJ9//jlDhgwhKSmJP/74g/vuu8+SsQlxU0dTcwAIdJFmWgHsnguF2eDbCML7KB2NMDOph4Tda/UguvrdcNAXoVk5CWy4C9LDHUKI9teh18PY33ZzIu2i0iEJhVQ62SgpKUGlUqFSqdBoNJaMSYhKO5pa1rLharsXZGEmulLYUjYNaqdRoDbb/BfCSkg9JOyeSkVp//cpVWlRn/gH9i1SOqIqU6lU3Bemo009L3IKShjx0w5yCoqVDksooNK18dmzZ3nmmWf47bffCAwMZMiQIfz555/SdUUo6mhKecuGwoEI5R1eAZmnwcUbWj2kdDTCAqQeEjWCbzhHAu80bK+cDPkXlI3nFjio4YsHWxHo4czxtFzGz4tDp5ObgzVNpZMNZ2dnHnnkEf755x/27t1Ls2bNeO655ygpKeHtt98mJibG5PnNN2zYwJ133kndunVRqVQsXry4wuNPPPGE8S5W+b/+/ftXOCYjI4NHHnkEDw8PvLy8GD58OBcvVmyq27NnD926dcPZ2ZmQkBBmzJhhUpzCOhWX6jh5PheQlo0ar7QE/vvMsN3uKXB0VTYeYRGWqIdA6iJhfY75D0Lv2whyUyHGtmdY83N34tthbXF0ULPmYCofrzmidEiimlWpn0HDhg156623OH36NMuXL6ewsJA77riDgIAAk14nNzeXVq1a8eWXX173mP79+3Pu3Dnjv99++63C44888gj79+8nJiaGZcuWsWHDBp555hnj49nZ2fTt25fQ0FB27tzJ+++/zxtvvMG3335rWqGF1dl07DzFpXpqOTngJbPe1lzZZ+HHOw2rhqu10H6E0hGJamCuegikLhLWR6fWUjrwQ8Mvu36EzZ8rG9Atigz24t17WwLw+T/H+OdQisIRierkcCtPVqvVDBgwgAEDBpCWlsbPP/9s0vPLn3sjTk5OBAYGXvOxgwcPsnLlSrZv3067du0A+Pzzzxk4cCAffPABdevWZe7cuRQVFfH999/j6OhI8+bNiYuL46OPPqpQEQjbotfr+XTNUQCGtgtCrTuucERCEcfWwB/PQF46OLrD3V+BRx2loxLV6FbrIZC6SFgnfb3Ohulw/3kTVr8KTu7Q9gmlw6qye9sEE5+YyY+xp5kwP57lz3UjyEv6QNcEZhtB6efnx4QJE8z1ckbr1q3D39+fJk2aMGrUKNLT042PxcbG4uXlZby4A/Tu3Ru1Ws3WrVuNx3Tv3h1Hx0u3vvv168fhw4e5cMF2+0HWdBuOnicuMRNnrZqnu9ZXOhxR3UpLYO00+GWIIdEIbAnProeIwUpHJhRkqXoIpC4SCun2AnR53rC9dBzsXahoOLfqf4OaERnsSWZeMWN+3UVRiU7pkEQ1uKWWDUvr378/9957L2FhYRw/fpz//e9/DBgwgNjYWDQaDcnJyfj7+1d4joODAz4+PsaFnZKTkwkLq7iwV3kze3JyMt7e3le9b2FhIYWFhcbfc3IMg5BLSkooLrb9mRTKy2CrZdHr9XwccxiAh9qH4OlkyJlttTxXsvXP50pmL0/2OTSLR6BO3AJAadun0PWeBg7OUA3nzJzlKSmxzVWCaxp7rovs5XpjL+WAa5TltldR52eh2TUH/Z/PUqp2Qt+4/w1ewTpc6zNRA5880JK7v9rC7oRMpq84wP8GNFEowsqzl+/X9cph6brIqpONBx980LjdsmVLIiMjadiwIevWraNXr14We9/p06czderUq/avXbuW2rVrW+x9q1tMTIzSIVTJoUwVcYkatCo9DQqPExNj6EJlq+W5HinP1fyy99D29DdoS3IoVjsTV284Z3UdYfU/ZojQNOYoz/nz580QibC0mlAX2cv1xl7KAVeURd+DNt5HCLmwGdXCJ9na8AXOu0coF5wJrvWZPBCqYvZhDT9sPo3q/Ala+drGJC/28v26shyWrousOtm4UoMGDahduzbHjh2jV69eBAYGkppacVXKkpISMjIyjH1rAwMDSUmpOBCp/Pfr9b+dPHlyhab4pKQkIiIi6NWrF0FBQeYskiKKi4uJiYmhT58+Nrfarl6v58fZ24FMHu4UyoMDm9p0ea5FynMNuhLU699Ds/tjAPQBLeHe72jt04DW5gu1Usz5+SQlJZkpKlGd7Kkuspfrjb2UA25QFl1/dIueQnNkBZ1Pf07pI4vQB7W7/gsp7EafyUBAv/Iw3/13mgUJTjw8qBOhPtY7i6C9fL+uVw5L10UmJxvTpk3jxRdfxNW14pciPz+f999/n9dff91swV3pzJkzpKenU6eOYQBodHQ0mZmZ7Ny5k7Zt2wLwzz//oNPp6Nixo/GYV155heLiYuOJjYmJoUmTJtdstgbDQEAnJyfj79nZ2YChWdyWv2RX0mq1NleeTUfPsyshE0cHNf/Xs1GF+G2xPDci5SmTfRYWDoeEzYbf2z+Nqu/baLXO5g3QROb4fBwcbOp+j9VQsh4C+6yL7OV6Yy/lgGuVRQsPzIFfh6I68S8Ovw+FJ5YbxqxZset9Ji8PjCDuTDY7T1/g+Xl7WDSqM85a616s016+X1eWw9J1kckDxKdOnXrV3OEAeXl512zuvZGLFy8SFxdHXFwcACdPniQuLo6EhAQuXrzIxIkT2bJlC6dOnWLt2rXcddddhIeH069fPwCaNWtG//79GTFiBNu2beO///5jzJgxPPjgg9StWxeAhx9+GEdHR4YPH87+/fuZN28en376qcUGEQrL0ev1fLrWMD/3wx3qEeCh7B+bohocWwNfdzUkGo7ucP8cGPQhKJxoCGWZsx4CqYuEDXFwggfnQkhHKMiCn++B80eVjqpKtBo1Xzwchberlv1ns3lr+QGlQxIWYnKyodfrr7laa3x8PD4+Pia91o4dO4iKiiIqKgqACRMmEBUVxeuvv45Go2HPnj0MHjyYxo0bM3z4cNq2bcvGjRsr3OmZO3cuTZs2pVevXgwcOJCuXbtWmLfc09OT1atXc/LkSdq2bcsLL7zA66+/LlMN2qDY4+lsP3UBR42akbc1VDocYUmlJbBm6mWzTUUaZptqfo/SkQkrYM56CKQuEjbG0Q0enm9o0chNg5/ugswEpaOqkjqeLnzyYBQqFfyyJYG/4qRrqT2qdLuJt7e3ceXUxo0bV7jQl5aWcvHiRUaOHGnSm/fo0QO9/vqDglatWnXT1/Dx8eHXX3+94TGRkZFs3LjRpNiE9flkreHuzYMdQgj0lDvbdiv7LCx8ChJiDb+3HwF935LWDGGRegikLhI2yMULHlsMPwyA80cMCceTK8Hd9EUtlXZbYz/G9Azn83+OMfmPvTSv60m4fy2lwxJmVOlk45NPPkGv1/PUU08xdepUPD09jY85OjpSv359oqOjLRKkELHH09l2MgNHjZpRPaRVw24dXQN/XrZI312fS2uGMJJ6SIjLuNUuSzj6Q8YJ+PluwxgOV9Nb95Q2rndjdpy6QOyJdEbP3cXi0V1wcbTu8Rui8iqdbDz++OMAhIWF0blzZ7sYICNsR/lYjaHtQ6jjKSuO2p3SEvj3LdhkmG2KwEjD+AxfSSzFJVIPCXEFzyAY9hd8PwBSD8Dc+wy/O7krHZlJNGoVnz7UmoGfbuJwSg6v/7WP9+9vpXRYwkxMHn5+2223odPpOHLkCKmpqeh0FVd/7N69u9mCEwJg64l0tpzIQKtRSauGPcpKgkXDpduUqDSph4S4jE8DGLYYfhgISTvht4fgkQWgta0bc/7uznz2UGsenb2VBTvP0D7MhwfahSgdljADk5ONLVu28PDDD3P69Omr+riqVCpKS0vNFpwQAJ+WjdV4oF0Idb1s6+IpbuLw3/DXaOk2JUwi9ZAQV/BvBo/9AXPuhFMbYf7jMPQXcHBUOjKTdG5Ymwl9GvPB6iO8/tc+IoM9aRrooXRY4haZPBvVyJEjadeuHfv27SMjI4MLFy4Y/2VkZFgiRlGDrd6fzObj6Wg1Kv6vZ7jS4QhzSdlvmLLxtwdltilhMqmHhLiGulHwyHxwcIGjq+CPEYYuqjbm/3qE072xHwXFOv5v7i4uFtpeGURFJrdsHD16lIULFxIeLn/4Ccu6kFvE//7cB8Dwrg0IklYN25eTYhibsfsX0OtA4wgdR0LPV6TblKg0qYeEuI7QzvDgL4auVAcWG7pS3fUVqE2+t6wYtVrFJ0NbM+izjZxIy+V/f+zl0wdbX3O6a2EbTP72dezYkWPHjlkiFiEqmLp0P+cvFtLIvxbjejdSOhxxCzS6QtQbP4DPomDXT4ZEI+JuGL0N+r4piYYwidRDQtxAeG/DBBsqDcT/BitegBtM7WyNfNwc+fyhKDRqFUviz/LrNttcR0QYVKplY8+ePcbtsWPH8sILL5CcnEzLli2vmg0kMjLSvBGKGmn1/mQWx51FrYL372+Fs1amwLNJOh2qPb/T68BraIovGPYFt4e+b0O9jsrGJmyK1ENCmKDpILj3W1j0NOz4HrSuhok3bKh1oF19H17q34R3Vhxi6tIDtAr2okWQ582fKKxOpZKN1q0NzVeXD8R76qmnjNvlj8nAPGEOmXmXuk89070hrUO8lA1IVM3JDbDqFRyS9+AA6D3roerzBjS/16YqPGEdpB4SwkQt74PifFgyBmK/MKw83vN/SkdlkhHdGrDtZAZrDqYy+tddLB3bFQ9nmfLa1lQq2Th58qSl4xDC6I0lhu5T4dJ9yjadPwqrX4MjfwOgd3LngO9AGj/2IVoX25r7XVgPqYeEqII2jxkSjr8nwvr3DGM4uo5XOqpKU6lUfHB/KwZ9tonT6Xm8tHAPXz3SRsZv2JhKJRuhoaGWjkMIAFZd3n3qvkjpPmUrSosheQ/E/25osteVGPoLt3uKki4vcGz9Nho7yLgMUXVSDwlRRR2fgeJcWPOG4Z/WFTo+q3RUlebl6siXj7Th/q838/e+ZH7cfIonuoQpHZYwgcmzUS1ZsuSa+1UqFc7OzoSHhxMWJl8CYZqcgmI+WXOUOZtPATCiewOi6nkrG5S4votpcGYbJG6FxO1wdheUFFx6vPEA6DMN/BpDcbFycQq7JPWQECbqOh6K8mDDDPh7kmGfDSUcrUO8+N/AZkxdeoC3Vxwkqp43raSLtc0wOdm4++67r+o3CxX7y3bt2pXFixfj7S1/LIob0+v1LIk/y1vLD5KWUwjAoJZ1GN+7scKRCaPSEkg9YEgszmyHxG1w4RpdWly8IaQjRI+GMFnBWViO1ENCVEHP/0FpIfz3qSHhKCmELs8pHVWlPdG5PltPZLByfzJjftvFsrHd8HSR8Ru2wOSpb2NiYmjfvj0xMTFkZWWRlZVFTEwMHTt2ZNmyZWzYsIH09HRefPFFS8Qr7MiRlBwemrWF53+PIy2nkPq+rsx5sj1fPtJGuk8pKS8DjqyCtW/Cj3fCu/Xgm26w4kXYM68s0VCBXzNo8zjc9SWM2QGTTsLD8yTREBYn9ZAQVaBSQe+p0H2i4feY12D9+8rGZAKVSsV790US4uNCYkY+Ly3cc9UNB2GdTG7ZeP755/n222/p3LmzcV+vXr1wdnbmmWeeYf/+/XzyyScVZgkR4nIXC0v4bO1Rvt90khKdHmetmjE9wxnRvQFODpJkVCudDtIOlXWJ2m5ovUg/evVxTh4Q3A6CO0BIewhqBy5e1R6uECD1kBBVplLB7a+CxsmwwOq/bxm6wN7+qk3MEujpouWLh9pw39ebWbk/mZ9iT/N45/pKhyVuwuRk4/jx43h4eFy138PDgxMnTgDQqFEjzp8/f+vRCbui1+tZtuccby0/wP+3d9/hUVRtA4d/m00nDRISQgoldAhVqiBIR3oVRERAEAQblk9eFcGury8IiopKL1IUkSa99xp6JxASSCghFdL3++MkWUJ6yGYLz31duTa7MztzTiYzZ545LSJGNZnqWMuLj7vVwq+Mo5FT94RIiIbQw+nNoQ5A6BFIjM6+nntV8Gui5sTwawplq4OVBILCNEg5JMRjav0eWNup2o1d36nmVR0+M4uAo56fGxO61OTTNWf4Yu1ZGvqXJtBX5t8wZYUONho1asR7773H/PnzKVu2LAC3b9/m/fffp3HjxgBcvHgRPz+/4k2pMGuXbsXxyapT7Ll0FwD/Mo5M6lGLtjW8jJwyC6bTwd1Lqo9FRn+LW2eBR6qdbUqBT0MVXPg1VQGGYxmjJFmIgpBySIhi8PQbYG2vhsXd+4Pqw9H5G7AqdAv7Ejfs6Yrsv3KXjWciGLv4KGvekPk3TFmhg41Zs2bRs2dPfH19My/k169fp3Llyvzzzz8AxMXF8dFHHxVvSoVZup+UwvQtl5i1+wrJqTrsrK14rU0VXm1dWfplFLfEODUqVMYIUaEH4cG97OuVrqgPKvyagGdt0Bb6UiCE0Ug5JEQxaToKtDaw5m04+KsKOLp9b/IBh0aj4b/96nF6+i5CIu8z4a+T/PhCA5l/w0QV+g6jevXqnDlzho0bN3LhwoXMzzp06IBV+j9nr169ijWRwvzodDrWnwrnszVnuBGthkRtV8OTT7rXxt9dmkw9Np0O7l1VtRYZQ9BGnAZdWtb1rO2hfEPVz8K3iQounDyNkmQhiouUQ0IUo6eGqSZV/4yFo/MgNUkN/GHiTWddHW348YUG9P9lH2tP3qTZ/jIMaV7R2MkSOSjS40wrKys6d+5M586dizs9wgJcuR3HJ6tOs+uiai/tW9qBSd1r076WNJkqsuQHcONYepOo9AAj/nb29Vx8VWDh11QFFl6BYG1b8ukVwsCkHBKiGNV/AbS2sGIUHP9DBRy9Z6paDxPWwL80H3Spwedrz/LZGjX/Rh0f6b9hagoUbEyfPp1Ro0Zhb2/P9OnT81z3jTfMZ8xmUbweJKUyY9slft15haTUNGy1VoxuXZnXnq0iTaYKQ6fDIekOmjN/w40jKrgIP6Fm5X6Y1ha86+lrLPyagEt546RZCAOTckgIAwvsp2o4lg+DU3+pgKPvbJN/YDWiZSX2X4lk89kIxi0+yurXW+Is/TdMSoGCjalTpzJ48GDs7e2ZOnVqrutpNBq5yD+BdDodG89E8OnqM4RFPQCgdbWyTO5Rm4oepYycOjOQkgg3T6R34j6IdcgBOsaFw+lH1nMqp6+18G2iAg0be6MkWYiSJuWQECWgZnd4fiEsGwJnV8PSF2HAfJMuazQaDd/1r0vX6bu5evc+E1ac5IdB0n/DlBQo2AgODs7xdyGu3Y1n0qrTbDuvmvT4uDkwsXstOtbykhM9NzE30/tZpP/cDFJPkNJpgDSsoFwgVv7N9LUWrn5mMSyhEIYg5ZAQJaR6Zxi0BJa8ABc3wJJB8PwisDXd/pZujrZMH9SA52fuY82JmzQPcGdw0wrGTpZIV+QhaJKSkggODiYgIABraxnJ5kmTkJzKz9sv8/OOyySlpGGj1TDqmcqMfbYKjrby/5ApNRnCTz7UkfsQRIdkX8/RPXOEqBTvhqw/cYtO3XtjZSNVwULkRsohIQykSjsY/Ccsfh4ub4XFA1QAYudk7JTlqlGF0rzfuTpfrjvH5NVnqO/nRu3y0n/DFBT66nz//n1ef/115s2bB8CFCxeoXLkyr7/+Oj4+PnzwwQfFnkhhWracjWDS6tNcj1RNplpV9WBSj9oElDXdi1CJib+bPvRs+rwWYUch5UHWdTRWarjZh0eIKlM5s9ZCl5xM6ul1Rki8EOZByiEhSkClVjBkBSzsB1d3wcI+MHg52JvuDfwrLSuz/0okW8/dYtziY6x+vSVOdvIgwtgKPZDyhAkTOH78ONu3b8feXt+Gr3379ixdurRYEydMy62YBMYsPMKIeYe5HvkAb1d7fhrckPnDmzyZgYZOB/euwfElsPpN+LEJ/LeyqnLe8z1c26MCDXs3qNIBnv0QXvoHPgiBMbuh21SoPwjcA6R5lBCFIOWQECXEv5kqt+xd1UO0+T3hfqSxU5UrKysN/+tfD29Xe4LvxPOfFSfR6XT5f1EYVKHDvZUrV7J06VKaNWuWpU1+7dq1uXz5crEmTpiGtDQdSw9f58t1Z4lNSEFrpeGVlpV4o11VSj1JTwzS0uD2WQjZB9f2qdeYsOzreVTXz8bt1wTcq5r8BElCmBMph4QoQb6NYOhqmN9LDcE+rzsMWQlOZY2dshyVLmXLjy80YMDM/aw6foPmAe4MauJv7GQ90Qp9p3j79m08PbNPChYfHy8dgi3Q5dtxTFhxkoPB6klGXV9Xvu5Tl1rlXYycshKQkqQ6b1/bqwKLkP2QEJV1HStrNSqUf3Oo0AL8mkEpd2OkVognhpRDQpQw73owbJ2q2Yg4BXO6wNBVJjvceqMKZXi3Y3W+WX+OSatOU9/PjZreT8B9i4kqdLDx1FNPsXbtWl5//XWAzAv777//TvPmzYs3dcJoklLS+HXnZaZvvURSShoONlre6ViNYU9XQmtloYV5YqzqyJ1RcxF2GFISsq5j4wi+jVVg4d8cfJ8CWxneV4iSJOWQEEbgWROG/QvzesDdiyrgeGkVlDbNUZ9efaYyB4Lvsv38bcYuPsrqcS2frNYYJqTQf/Uvv/ySLl26cObMGVJSUpg2bRpnzpxh79697NixwxBpFCXsWMg9Jqw4ybnwWEDNmfF5rzr4lTHdYe+KJO5W1iZR4SdAl5Z1HUd3FVRk/HjXNfkZVYWwdFIOCWEk7gEwPD3guBesDzg8qhg7Zdlk9N94bvourtyO56OVp5gyoJ7UfhpBoYONli1bEhQUxNdff01gYCAbN26kYcOG7Nu3j8DAQEOkUZSQ+MQUvtt4nrl7r6LTQZlStnzSvRY96pU3/5NTp1MXxozAImQf3L2UfT03f31gUaEFeFSTzttCmBgph4QwIjd/VcMxvyfcOZ8ecPwDXrWMnbJs3J3s+GFQQwb+uo+/j4XRvLI7Axr7GTtZT5wi1ScFBATw22+/FXdahBFtO3eLj1aeypwBvE9DHz7qWosypWyNnLIi0ukg4nR6f4u9KsiIC8++nmctfWDh3xxcfUo+rUKIQpNySAgjcvFO78PRCyJOwtyuMORvKF/f2CnLpkmlMrzTsTr/3XCeiatOUd/fjWpezsZO1hOlwMFGTExMgdZzcZEOOOZEp9Px/eaLTNtyEQC/Mg580SuQZ6qZ5igT+bp7GU4sg5PLIfKRUWmsbKB8A6jQHPxbgH9TcChtnHQKIQpNyiEhTEgpD3h5NSzsC2FH1ChVAxdBpWeMnbJsxrQOYP+Vu+y6eIfXFh1l1binZQLiElTgv7Sbm1ueTWl0Oh0ajYbU1NRiSZgwPJ1Oxxdrz/L77mAAhj9diXc7VTO/EzAugsq31qOdPUWNHpXB2uGhwKIZ+DQCWwvrdyLEE0TKISFMjENpNQzuH4Pg2m4VePSeCXX6GDtlWVhZaZj6fH2em7aLS7fimLTqNN/2q2fsZD0xCjz4/7Zt29i6dStbt25ly5Yt2NnZsWDBgszPMpYXxs6dO+nevTvly6s+AStXrsyyXKfTMXHiRLy9vXFwcKB9+/ZcvHgxyzqRkZEMHjwYFxcX3NzcGDFiBHFxcVnWOXHiBK1atcLe3h4/Pz++/fbbQqXTEqXpYOJqfaDxSfdaTOxey3wCjYQYOLYI5vfEenoggWGLsboZBBotVGkPvX+F9y6qat3W76mZUCXQEMKsGaIcAimLhHgs9i7w4l9QqyekJsGfw2H/z8ZOVTYeTnZ8P7A+Gg0sOxzK38dCjZ2kJ0aB7yxbt26d5b1Wq6VZs2ZUrly5yDuPj4+nXr16DB8+nD59skfB3377LdOnT2fevHlUqlSJjz/+mE6dOnHmzJnMWWMHDx7MzZs32bRpE8nJyQwbNoxRo0axePFiQFW7d+zYkfbt2/PLL79w8uRJhg8fjpubG6NGjSpy2s1ZSmoaiy5ZcfhOKBoNfNOnrnl0mEpJhIub4OQyOL8eUhMB0ACRjgG4thyJtm4/k51oSAjxeAxRDoGURUI8Nht76DcH1n8AB3+F9R9gFR0GukbGTlkWLQI8eKNtVaZtuciHf5+inq8blcs6GTtZFs+oj7G7dOlCly5dclym0+n4/vvv+eijj+jZsycA8+fPx8vLi5UrVzJw4EDOnj3L+vXrOXToEE899RQAP/zwA8899xzfffcd5cuXZ9GiRSQlJTF79mxsbW2pXbs2QUFBTJky5Ym8wCempPLG0hMcvmOFtZWGKc/Xp0c905yUB1Czdl/bowKMM/9AQrR+mUc1CBxAcs1e7Np3hucaP4fWRoalFUIUjpRFQhQDKy10+RacvWHLZLT7fqBh6RaQ2hFMqGx+o11VDgTfZf+VSMYtPsaK11pgb6M1drIsmsm2mQkODiY8PJz27dtnfubq6krTpk3Zt28fAwcOZN++fbi5uWVe3AHat2+PlZUVBw4coHfv3uzbt49nnnkGW1v9qEqdOnXim2++4d69e5Qunb2DcGJiIomJiZnvY2PVfBMpKSkkJycbIrsl4kFSKuOWBLHz4l20Gh3f9w+kU62yppcnnQ4iTmF1+k+sTq9AE3tTv8ipHGm1+5BWpx94BYJGk57+M6aXjyLKyIfkxzQVZ35SUlIeexvCsCy9LLKU89NS8gEWkJdmr6NxLIt2zZv43dtL6pJBJPebC3amMwLUd33r0H3GPs7cjOHzNaf5pFvNPNc3+2OSLrd8GLoseqxgw5BzL4SHq2FKvby8snzu5eWVuSw8PBxPT88sy62trSlTpkyWdSpVqpRtGxnLcrrAf/XVV0yePDnb51u2bMHDw6OIOTKuhFT47ZyWSzEabK10vFI9jdSQY6wLOWbspGVyTLyN7719+N7bi3PCjczPk7WO3HBrTGjp5txxqgFJVnA0FMja3nLTpk0lnGLDkvyYtuLIz507d4ohJU82Q88B9KSURZZyflpKPsDc8+KCZ+W3aRz8A9ZXdxA741n2B7xDoo2rsROWaYC/hpnntCw8cB2be1ep767L9zvmfUz0Hs2HocuiAgcbj7ZjTUhIYPTo0ZQqVSrL5ytWrCielBnRhAkTGD9+fOb7sLAwatWqRbt27fDxMb95GKLuJzNiwREuxcTgZGfNL4MCuXv+EB06dMDG2FWb8XewOvsPmlN/YhV2KPNjndYOXdWOpNXuB1XaUd7antwaeyUnJ7Np0ybTyE8xkPyYtuLMT1hYWDGl6snwJJVDUDJlkaWcn5aSD7CcvCQnd2DPSieeCf0Bt/tX6RT6P1IGLYUyAcZOGgDPAWkbLvDb7qv8GWLHkG7N8Cud80AylnNMcs6HocuiAgcbrq5Zo9EXX3yx2BPzsHLlygEQERGBt7d35ucRERHUr18/c51bt25l+V5KSgqRkZGZ3y9XrhwRERFZ1sl4n7HOo+zs7LCzs8t8nzG2u7W1tdn9k92OTWTInMOcC4/FzdGGBcObUsPLkXXnwcbGxjj5SYyD8+vUfBiXt4IuY5hKjRqfu+4ANDW7o7F3LfhwaRgxPwYi+TFtxZEfa2uTbclqkkq6HIInpyyylPPTUvIBlpGXqFKVSRn6LzZLBqC5dxWbeV1h8DI1FL0JeL9LTQ6HRHEsJIq3l59i+avNsbXO/c7DEo4JZM+HocuiAm99zpw5hkxHNpUqVaJcuXJs2bIl84IeExPDgQMHGDNmDADNmzcnKiqKI0eO0KiR+sfdunUraWlpNG3aNHOdDz/8kOTk5Mw/7KZNm6hevXqO1daW5Gb0Awb/doArd+Ip62zHoleaUs3L2ThtDlOTVWBxYpkKNJLv65eVbwCB/aFOX3DOudAVQoiSLodAyiIhHluZyjBiEyzqBzePw9xuMGABVG2f/3cNzEZrxfSBDeg6fRfHr0fx3cbz/Oe5vPtviMIrzIPjYhcXF0dQUBBBQUGA6ogXFBRESEgIGo2Gt956i88//5xVq1Zx8uRJXnrpJcqXL0+vXr0AqFmzJp07d2bkyJEcPHiQPXv2MG7cOAYOHEj58qrRzQsvvICtrS0jRozg9OnTLF26lGnTpmWpmrZE1+7G0/+XfVy5E4+PmwPLX21ONa8S7pyVlgYh+2HNePiuGiweAKf+VIFG6UrQ+v9g3GEYtR2aj5VAQwhhFFIWCWFgTp7w8loIaKvuAf54HoIWGztVAPiVccyc4O/XnVfYei4in2+IwjJqHf7hw4d59tlnM99nXHSHDh3K3Llzef/994mPj2fUqFFERUXRsmVL1q9fnzmuOcCiRYsYN24c7dq1w8rKir59+zJ9+vTM5a6urmzcuJGxY8fSqFEjPDw8mDhxokUPNXjpViyDfz9AREwilTxKsfCVpvi4OZRcAm6dVTUYJ/+E6BD956XKqtqLwAHg0xAM3LFTCCEKQsoiIUqAnTMMWgqrxsGJpbByDMTehJbjjX4/0LlOOV5uUZG5e6/yzrLjrHuzFd6uJXjfZOGMGmy0adMGnS733v8ajYZPP/2UTz/9NNd1ypQpkzlpUm7q1q3Lrl27ipxOc3IqLJqXZh8kMj6J6l7OLHilCZ7O9vl/8XFFh6rg4uSfEHFS/7mtE9TsrppJVWoNWmmjLoQwLVIWCVFCrG2h1y+qJcOeabDlU4gNh85fq3k6jGjCczU4dDWS0zdiePOPIBaPbIq11qgNgCyG3PlZkCPX7vHynIPEJqRQ19eVecOaULqUbf5fLKr7kWqivZN/qon3SC+srWygagcVYFTrDLY5j+4ghBBCiCeMlRV0+FRN/rd+gppxPC4Cev+qZiI3EjtrLT++0JBu03dx8Gok07dcZHzH6kZLjyWRYMNCHL4ayUuzD3I/KZXGFUsz++XGONsbYMSEtDQI3gFH58O5NZCapF9W4WkI7Ae1eoFjmeLftxBCCCEsQ7Mxqi/H36PVg8u42zBwkVHvHyp5lOLLPoG8uSSIH7Zdollld1pUMc/51UyJBBsW4FRYNMPmHOJ+Uiotq3jw60uNcLQt5kMbcxOCFsLRBRB1Tf+5Z22o2x/q9AM3v+LdpxBCCCEsV52+qj/nksEQshdmdVRD45apbLQk9azvw95Ld1l6+DpvLg3i3zdb4WonzakehwQbZu5iRCwvzT5IbGIKTSqV4beXnsLBtpjaPaamwKVNqhbjwgb9fBh2LlB3ADR8CbzrFc++hBBCCPHkqfQMDN8Ai/rD3Yvwe3sYtAT8mhgtSZN61OZIyD0u3Yrj3eXHmflCfaOlxRJIsGHGQu7e58VZB4iMT6KuryuzhhZToHHvqqrBCFqkRorI4N9cBRi1ekk/DCGEEEIUD69aMHKLGiL/5nGY1x16z4TavYySHAdbLT++0ICeP+5h+/nbzNl3De/8vyZyIfVCZio8OoHBs/YTEZNIdS9n5g1r8nh9NFIS4dQKmN8LptWDXd+pQMPRHZqPg7EHYfh6qP+CBBpCCCGEKF7O5eDldVCtC6QkwPKhasSqPEaKM6Qa5VyY2L0WAN9tvMi1OKMkwyJIzYYZuhuXyODf93M98gEV3R1ZMOIxRp26fV41kzr+B9y/q/+88rPQaChUfw6s7Yon4UIIIYQQubFzUp3E13+gRqnaNFG1tujyX6MMnf9CE3/2XLrDupPhzLug5cWEZMrYGGDwHQsnwYaZiX6QzEuzD3L5djzlXe1Z+EpTPF0KOVRc8n387u5CO28GhB7Qf+7sDQ1eVD+lKxZruoUQQggh8mWlhS7fQulKsOE/cHg2RF2H/nPUxIAlSKPR8FWfuhy/HkVYVAIfrzrLjy80RCOTEheKNKMyI2lpOkbNP8zpGzF4ONmy8JWm+JYuRJOmtFQ48CvW0wNpGPIbVqEHQKOF6l3VrJ5vnYK2H0mgIYQQQgjj0Wig+Wvw/AKwdlCD1czuDJFXSjwprg42TB1QFyt0rD0ZzvLDoSWeBnMnwYYZWXb4OgeCI3Gys2bBiKZULutU8C/fPKFGePj3PTQJ0cTbepLa5iN4+zQMWgzVO8vs3kIIIYQwHTW7w8troZQnRJyCmW3g3LoST0YDPze6+qcBMGn1aYLvxJd4GsyZBBtmIup+Et+sPwfAW+2rUtPbpWBfTIqHDR/Cr23gxlGwcyG187dsrvUtaU+/BS4yvoIQQgghTJRvI3h1B/g2gcRoWDIINk9Ww/OXoLbldTSrVJr7Sam8teQYyalpJbp/cybBhpmYsukC9+4nU9XTiaEtKhbsSxc2wIymsO9HNUdG7d4w7hBpjYaDRg69EEIIIcyAS3lVw9F0jHq/ewos7K1mHS8hVhr4tm8grg42HA+NZtrmiyW2b3Mnd5xm4PSNaBbuV7N2T+5ZGxttPoct5iYsG6rGq46+Dq7+8MJy6D9XDS0nhBBCCGFOrG2hy9fQbzbYlILgnTCzFYQcyP+7xcTb1Z4vewcCMGP7JQ5cuZvPNwRIsGHydDodn/xzmjQddKvrTYsAj9xXTkuFg7/BjCZwZqXq/N3iDRi7H6p1LLE0CyGEEEIYRJ2+MHIreFRT84HNfQ72/1Ji83F0retN/0a+6HQwftlxoh8kl8h+zZkEGybu72NhHL52DwcbLR92rZn7iuGnYFZHWPcuJMaATyMYtR06fga2pUosvUIIIYQQBuVZQwUctXtDWgqs/z/4czgklszMe5/0qE0Fd0fCoh7w0cpT6Iw08aC5kGDDhMUmJPPlOtUp/PV2VfB2dci+UtJ92PQJ/Noawg6DrTM89x2M2ATedUs4xUIIIYQQJcDOGfrNgc5fg5U1nF4Bv7VVkxUbmJOdNd8/Xx+tlYbVx2/w97Ewg+/TnEmwYcKmbb7InbhEKnmUYkTLStlXuLgZfmoKe75XkX3NHjDuIDQZqSbFEUIIIYSwVBoNNBujOo87e8Od8/Drs3BqhcF33cC/NG+2qwrAxH9Ocz3yvsH3aa4k2DBRFyJimbP3KgCfdK+FnfVDwUNsBCwfBov6QlQIuPjCwD/U5Dcu5Y2TYCGEEEIIY/BvBq/uhIqtIDke/hwG/34AKUkG3e1rbQJ4qkJp4hJTeGtpECkyHG6OJNgwQTqdjkmrTpOapqNDLS/aVPdUC9LS4PBs+LGxqi7UWEHzcTD2ANR4zriJFkIIIYQwFidPGLISWr6t3h/4GeZ1g5gbBtultdaKqc/Xx9nOmiPX7jFj22WD7cucSbBhgtadDGfv5bvYWlsxsVst9WHEGZjdCda8rSa1Kd8ARm6DTl+AXSFmEhdCCCGEsERaa2g/CQYuBjtXuH4AZj4DV3YYbJd+ZRz5rFcdAKZvvcjRkHsG25e5kmDDxNxPSuHztWcAGNM6AD8nYPMkNZZ06EGwdYLO38ArW6B8fWMmVQghhBDC9NToCqO2gVcdiL8NC3rBrimqhYgB9GrgQ4965UlN0/HWkiDiEkt2dnNTJ8GGiflp22VuRifgW9qBMX7X4OfmsHuq6gBeoxuMPQjNRksHcCGEEEKI3LgHqJE5670AujTYMhmWDoYHUQbZ3We96uDj5kBI5H0mrTptkH2YKwk2TMi1u/H8uvMKABPL7sJ+ST+4dxVcfFSV4MBF4Opj3EQKIYQQQpgDW0fo9RN0nwZaWzi/Tk0VcPNEse/K1cGGqc/Xx0oDfx4JZc0Jw/UVMTcSbJiQmTuvkJSaRiuHq3S4NkV1AG86Jr0DeFdjJ08IIYQQwrxoNNDoZRi+AVz91UPcWR3g2KJi31WTSmV4rU0VAP6z4iRhUQ+KfR/mSIINExEZn8Rfh68DMC51PhoHN3jpH+jytZq4RgghhBBCFI1PQ3h1B1TpACkJ8M9rsOoNSE4o1t282b4q9XxdiUlIYfzSIFLTZHZxCTZMxOK1m0lM1VFHE0wTjxQYuRUqPWPsZAkhhBBCWAbHMvDCMnj2Q0ADR+fB7I6qtqOY2GitmDawAY62Wg4ER/LLDhkOV4INY9PpSNw5nXlHIwEY4R2MZuRm1bFJCCGEEEIUHysraP0+vPgXOJSBm8dhZmu4sLHYdlHRoxSTetQGYOqmCxy/HlVs2zZHEmwYU0oSrBrHmk0buU1pPG0T6frql+BQ2tgpE0IIIYSwXFXaqVnHfRpBQhQs7g9bv4C01GLZfP9GvnQN9CYlTcdbS4OIf4KHw5Vgw1juR8KC3uiOLmRWShcAhj4biK2dnZETJoQQQgjxBHDzg2H/QuNX1Pud38KifhB/97E3rdFo+LJ3IN6u9gTfiefT1Wcee5vmSoINY4gNh9/awrXd7Nc24oyuIvY2VgxuWsHYKRNCCCGEeHJY20HX/0HvX8HaAS5vVbOOhx557E27OtowZUB9NBpYevg6/568WQwJNj8SbBjDzu/gXjC4+rO5xmQAetQrj5ujrZETJoQQQgjxBKr3PIzcAmUCICYUZneCQ7+D7vFGk2oe4M7o1qof7gcrTnLjCRwOV4KNkhZ/F44tVL/3/JELsSrAaOgv/TSEEEIIIYzGqzaM2g41u0NaMqx9B/5+FZLvP9Zmx3eoRj1fV6IfJPP2EzgcrgQbJe3Qb5DyALzrQ6VnOB8eC0C1cjKXhhBCCCGEUdm7wIAF0OEz0GjhxFKs53amVEJ4kTf5pA+Ha9LBxqRJk9BoNFl+atSokbk8ISGBsWPH4u7ujpOTE3379iUiIiLLNkJCQujatSuOjo54enry3nvvkZJipBEBku7DgZnq96ffIOpBMrdiEwGo6ulknDQJIYTIk8WVRUKIvGk08PQbMHQVlPJEc+sMrc9/gubc2iJvsqJHKSanD4c7ZdMFjoXcK67UmjyTDjYAateuzc2bNzN/du/enbns7bffZvXq1SxfvpwdO3Zw48YN+vTpk7k8NTWVrl27kpSUxN69e5k3bx5z585l4sSJxsgKBC2CB5HgVgFq9uRCRBwAPm4OONvbGCdNQggh8mVRZZEQomAqtoTRu0jza4ZN2gOs/xoKmyZCatEeFPRr5Eu3ut6kpul4c0kQcU/IcLgmH2xYW1tTrly5zB8PDw8AoqOjmTVrFlOmTKFt27Y0atSIOXPmsHfvXvbv3w/Axo0bOXPmDAsXLqR+/fp06dKFzz77jBkzZpCUlFSyGUlLhX0/qt9bvA5aa85HpDeh8pJaDSGEMGUWUxYJIQrHuRypg//mkqeapoA902B+T4iNyPt7OdBoNHzROxAfNwdCIu8z8Z9TxZxY02TywcbFixcpX748lStXZvDgwYSEhABw5MgRkpOTad++fea6NWrUwN/fn3379gGwb98+AgMD8fLyylynU6dOxMTEcPr06ZLNyNlVcO+qmq2y/mAALkZIfw0hhDAHFlMWCSEKT2vDaZ9BpPSZDbbOcG23Gh732r5Cb8rVwYbvB9bHSgMrjobxT1CYARJsWqyNnYC8NG3alLlz51K9enVu3rzJ5MmTadWqFadOnSI8PBxbW1vc3NyyfMfLy4vwcNWJJzw8PMvFPWN5xrLcJCYmkpiYmPk+NlYFBSkpKSQnJxc+Izod2t3fYwWkNhpOmsYGkpM5dzMGgAB3x6Jtt4gy9lWS+zQkyY9pk/zkTtrsmweLKYtyYCnnp6XkAywnL5aSD9DnIalKF3TDa2L958to7pxHN7crae0mkdZktOrnUUD1fZwZ26YyP2y7wod/nyKwvBN+pR0NlfxMuR0TQ5dFJh1sdOnSJfP3unXr0rRpUypUqMCyZctwcHAw2H6/+uorJk+enO3zLVu2ZFadF4Z77Fla3gwiRWPLpqiKJK1bh04Hp0O1gIZbF4NYdzPo8RNeSJs2bSrxfRqS5Me0SX6yu3PnTjGkRBiapZRFebGU89NS8gGWkxdLyQfo86It/w71Uufgd28f2s0fE354NUH+I0jRFvx6UEkHlZy1BMemMPzXXbxRJxVtweOVx/LoMTF0WWTSwcaj3NzcqFatGpcuXaJDhw4kJSURFRWV5YlSREQE5cqVA6BcuXIcPHgwyzYyRgjJWCcnEyZMYPz48Znvw8LCqFWrFu3atcPHx6fQ6dYumQ+ApuGLtO88EIDbsYnE79+BRgNDe3fC3kZb6O0WVXJyMps2baJDhw7Y2Jh/x3TJj2mT/OQuLMzyq88tkbmWRTmxlPPTUvIBlpMXS8kH5JIXXW9SD8/CavPH+EQdpLw2kpS+86Bs9QJvt36L+/SYsZ+rcSkEO1TjjbZVDJQDJbdjYuiyyKyCjbi4OC5fvsyQIUNo1KgRNjY2bNmyhb59+wJw/vx5QkJCaN68OQDNmzfniy++4NatW3h6egIqmnNxcaFWrVq57sfOzg47O7vM9zExqrmTtbV14U+YiNNweTNorNA+/Tra9O8HR0YDUNG9FM6O9oXbZjGxsbEx+wvAwyQ/pk3yk521tVldgkU6syyL8mEp56el5AMsJy+Wkg/IIS8txoBfI1g2FM3dS9jM6Qg9pkNgvwJtr7KnK1/0rsObS4KYsf0Kz1T3onHFMgZKvd6j+TB0WWTSHcTfffddduzYwdWrV9m7dy+9e/dGq9UyaNAgXF1dGTFiBOPHj2fbtm0cOXKEYcOG0bx5c5o1awZAx44dqVWrFkOGDOH48eNs2LCBjz76iLFjx2a5gBvU3h/Ua80eUKZy5seZk/nJSFRCCGHSLKIsEkIYhl8TGL0LKrWG5Hj4awSsex9SCjbSXM/6PvRp6EOaDt5aEkT0A/Pv4/Iokw42QkNDGTRoENWrV2fAgAG4u7uzf/9+ypYtC8DUqVPp1q0bffv25ZlnnqFcuXKsWLEi8/tarZY1a9ag1Wpp3rw5L774Ii+99BKffvppyWQgOhROLle/P/1GlkUXMoe9lZGohBDClJl9WSSEMKxSHjDkb2j1jnp/cCbMfQ6iC9Y86dOedfAv40hY1AM+/PskOp3OgIkteSZdh79kyZI8l9vb2zNjxgxmzJiR6zoVKlRg3bp1xZ20gtn/M6SlQMVW4NMoyyIJNoQQwjyYfVkkhDA8Ky20mwi+jWHFqxB6CGa2gn6zoXKbPL/qZGfN9EEN6PfzXtacuEnramXp/5RfyaS7BJh0zYbZO5X+ZKv5uGyLgu/EAxBQVppRCSGEEEJYhOpd4NXtUC4Q7t+FBb1h53eQlpbn1+r7ufF2h2oAfLLqdOZ9oiWQYMNQEmIg9ob6vULzLIt0Oh0xCWpM4zKlbEs6ZUIIIYQQwlDKVIYRm6D+i6BLg62fwZJB8OBenl8b3TqAZpXLcD8plTeXHCMpJe8AxVxIsGEody+p11KeYO+aZdGD5FRS01R7PGd7k27JJoQQQgghCsvGAXrNgB4/gNYOLqyHma3hRlCuX9FaaZj6fH3cHG04ERrN/zadL7n0GpAEG4aSEWx4VMu2KC69VsNKA462JTe/hhBCCCGEKEENX4IRG8GtAkRdg1kd4ej8XFf3dnXg6z51AZi54wq7L5r/5K8SbBjKnQvq1SP7BC0ZTaic7KzRFGJ6eyGEEEIIYWbK14dXd0C1zpCaCKteh5VjIflBjqt3rlOOF5r6AzB+WRB34xJLMLHFT4INQ7lzUb26V822KC5RBRvO9pYxyY0QQgghhMiDQ2kY+Ae0/Rg0VhC0EGZ1gMgrOa7+cddaVPV04lZsIv/31wmzHg5Xgg1DyWxGlT3YiE1QE7ZIfw0hhBBCiCeElRU8866ak8PRA8JPwsw2cC77sNgOtlqmD2qArbUVm8/eYv6+ayWf3mIiwYahRF9Xr27+2RbdT0oFpL+GEEIIIcQTp3IbNeu4bxNIjFYjVW36BFJTsqxW09uFCV1qAPDFurOcC48xQmIfnwQbhuJQWr0+iMq2yNZa/dmTU823SkwIIYQQQhSRS3l4eS00HaPe7/keFvSCuFtZVnu5RUXa1vAkKSWN1xcf40H6A2tzIsGGoTh7q9fYm9kWOdioGo0Hyeb3DyOEEEIIIYqBtS10+Rr6zQFbJ7i6C35pBdf2Zq6i0Wj4b7+6lHW24+KtOD5fe8aICS4aCTYMpSDBhhlGp0IIIYQQohjV6QMjt0HZGhAXDnO7wd4fIL1TuLuTHVMG1ANg0YEQ1p8KN2ZqC02CDUPJCDZibmRb5JDeVyNBajaEEEIIIUTZavDKFgjsD7pU2PgRLBsCCdEAtKpalldbVwbggxUnuBmd87C5pkiCDUNxyajZyB592ltLMyohhBBCCPEQOyfo8xs89x1Y2cDZ1fBrGwg/BcA7HapT19eVqPvJvLUkiNQ08+j7K8GGoeTRjMreVv3ZE5JTzXrcZCGEEEIIUYw0GmgyEoZvAFc/NQ/H7+0h6A9sra2YPrABpWy1HAiO5Kdtl4yd2gKRYMNQ8mpGld5nI00HiSlpJZkqIYQQQghh6nwbwagdENAOUh7AytGw+k0qumr5tGcdAL7fcpEj1+4ZOaH5k2DDUB5uRvVI7YW9jX5+jcRkCTaEEEIIIcQjSrnD4OXQZgKggSNzYXZH+lRKplf98qSm6XhzyTFi0ieLNlUSbBhKRs1GygNIiMqyyEZrhY1WA0i/DSGEEEIIkQsrLbT5AF78ExzKwM3jaH5tw2d1IvAr40DovQf8Z8VJk26WL8GGodg4gL2b+j2nTuIy14YQQgghhCiIKu3h1Z3g0wgSonD+83mmVzmGtZWGNSdusvxIqLFTmCsJNgzJpbx6zaHfhr3MtSGEEEIIIQrKzQ+G/QuNRwLQ4MSnvO2+H4BJq05z5XacMVOXKwk2DMm5nHrNoWZDZhEXQgghhBCFYm0HXb+DvrPAxpHRMdNpbnOJ+0mpvLHkGIkppndfKcGGITmn12zE5j4ilUzsJ4QQQgghCiWwH4zchrZsVaZaTaU0sZwKi+G7DeeNnbJsJNgwJGcv9RobkW2RvY3600szKiGEEEIIUWieNWDkVsrVeZZvbWYC8NuuYMKjE4ycsKwk2DAk21LqNSX7lPJaKzUaVaoJjx4ghBBCCCFMmJ0z9JtNhzo+VNNcB+BseIyRE5WVBBuGpLVVr6nZxz+20qhgI81MppoXQgghhBAmSKOBGt2polHN9i/fMq2O4hJsGFJmsJGUbZFVes2GxBpCCCGEEOKxVGlHFU0YAJfCbhk5MVlJsGFIWhv1mmPNhnpNk2ZUQgghhBDicZTyIMDdDoDLodlHQTUmCTYMqSDNqCTYEEIIIYQQjykgoCoAl+6lGTklWUmwYUh5NaNKDzYk1hBCCCGEEI8roG5LNKRxL9WOuzH3jZ2cTNbGToA5SEtTEeLNmzcL98U70RCTBrdjIDTrNPKJcdGkJd7n9u1bhIaWbMSRkpLCnTt3CAsLw9ra/P8FJD+mTfKTu4xrSsY1Roi8FLksyoOlnJ+Wkg+wnLxYSj7AjPKi9aRs0hHCde7s3b6eBg2bZFmcWz4MXRZpdDp5tp6fQ4cO0aRJk/xXFEKIIjh48CCNGzc2djKEiZOySAhhSIYqiyTYKICUlBSOHTuGl5cXVlYW0PIsMRZmNIGxB9X4zOZO8mPaJD+5SktLIyIiggYNGpj20zJhEgxSFlnK+Wkp+QDLyYul5AMsJy+55MPQZZGUbgVgbW1tWU8dE2LAxQp8fMDexdipeXySH9Mm+cmTv79/MSRKPAkMUhZZyvlpKfkAy8mLpeQDLCcveeTDkGWRBTymF0IIIYQQQpgiCTaEEEIIIYQQBiHBxpPI2g5af6BeLYHkx7RJfoQwXZby/2wp+QDLyYul5AMsJy9Gyod0EBdCCCGEEEIYhNRsCCGEEEIIIQxCgg0hhBBCCCGEQUiwIYQQQgghhDAImWfDUuz6H5xdDXcugrU9+DWFDpPBo6pafj8Stn8Fl7dCdCg4ekCNrtD2Q7B31W9nkmv2bfedBYH9SiYfGfLLD8CcrnBtd9bvNRoG3b/Xv4+6DmvHQ/AusC0F9QdBu0mgLeF//fzyc+8aTKub83f7z4XavdXvpnJ8Dv0Oh2ZDVIh671kDWv8fVO2g3icnwMYP4dRfkJIEVdpC1yng5KnfhqkcG8g7P+Z27ognQ37n4Oo34cp2iA1X55dfU2g/GcpWU8vDT8LuqRCyH+7fBTd/eGo4NBuT936nBkJ0SNbP2n0CrcYbLy9QtPPvfiT8+z6cXw8aK6jVHTp/A3ZOxsnHsUXwz2s5b/vdS+BUNudlxjgmGXQ6WNQPLm2G5xdBzW76ZUW5xpf0MckvH+Z0nuSXFzDaeSLBhqW4ugcajwSfhpCWAls+hQW9YewBdZLHhkPsTej4OZStri4Ca95Wnz2/IOu2ev4EVdrr39vn8M9paPnlJ0PDofDsh/r3Ng7639NSYfEAdYM7YiPERcDfr4KVDbT/pOTyAvnnx9UX3rmQ9TtH5sLe6VDlkQuJKRwfFx9oPwncA9SF7fhi+GMQjN4FnjVhwwS4sBH6z1MTB617D5a+qI4DmNaxyS8/Op15nTviyZDfOehdHwIHqGvLg3uw/Wt1zXnrBFhp4UYQlCoLfX5V27p+UN0Ma7TQdFTe+372Q3XtzVDUG8HiykuGwp5/K0ZCbAS8tBJSk9WN/uo3od8s4+SjTp+s6QdYOQZSEnMPNDKU9DHJsP8nQJP9+0W9xpf0MckvH+Z0nuSXlwzGOE90wjLF3dbpPnHR6YJ3577OqRU63aceOl1Ksv6zT1x0ujOrDZ++wsopP7Of0+nW/V/u37mwUaeb5KbTxUboPzv4u073pa9Ol5xouLQWREGOz89P63QrX8v6makeH51Op/vKX6c7Mk+nexCl00121+lO/a1fduu8SnvIQfXelI9Nhoz85MSczh3x5Mjrf/bmSfU/evdy7t9fM16nm9M1731MqaPT7Z1R9DQWVGHzUtjz79Y59Z3QI/rPLmzS6T5x1emibxQpyTl6nGMSd1tdS4P+yHsfxjomN47rdN/V0OliwrP//YtyjTfWMckrHzkx5fMkv7wY6TyRPhuWKiFavTqUzmOdGLBzzl6lue5d+KYS/PosHF2gImhjyy0/J5eptM5oBpsnQdJ9/bLrB8GzdtamO1XaQWIM3D5r8CTnKb/jc+OYqr5t8FL2ZaZ2fNJS4eSfkHwffJuoJ0FpyVC5jX6dstXA1Q9CD6r3pnxsHs1PTszp3BGWL7//2aR4CFoEbhXAxTf37STE5F1mZNg9Fb6pCL+0hD3TIDWlyEnP5nHyUpjz7/pB9UTXp6H+s8ptVDORsMPGzUeG43+AjSPU6pn//kr6mCTdh79ega7fgbNX9u8U5RpvjGOSXz5yYqrnSUHzYoTzRJpRWaK0NFg/AfyagVetnNeJvws7/wuNXs76+bMfQqVnVHOky1th7TvqothstMGTnavc8hPYD9z8wNkbIk7Dpk9Un4iBi9TyuIjsVc+l0i98cbdKJu05KcjxOboAPKqDf9Osn5vS8Yk4Db93gJQEsHVSbUM9a6ggSWsLDm5Z1y9VVh0TMM1jk1t+HmVO546wbPn9zx78TV0Xk+PBvapqBmFtm/O2Qg7A6RXwwrK899n0VfCup262rh+ALZNVE4vOXxo3L4U9/+Ii1DXpYVprla+M65Qx8vGwowtUOfdw8+CcGOOYbJgAfk1U/7WcFOUab4xjkl8+HmXK50lB8mKk80SCDUu07h24dRaGr895eUIMLO6v2p+3mZB1Wev39b9711OR8t7pxr1hyi0/Tw3T/+5VG5y8YH4PiLwCZSqXbBoLI7/jk/xAPbFo/V72ZaZ0fNyrqraiiTFw5h9YORpeXlfy6SguueXn4RsFczt3hGXL73+27gAIaKv67O39AZa/DMM3go191u1EnIElg9TMwlXa5b3PFuP0v5erox4srHlLtcN/nFmJHzcvpnL+FdcxuX4Q7pyHPjPz32dJH5PIKxC8E17dVfRtl6TiyocpnycFzYuRzhMJNizN2nfhwgYYtg5cfbIvT4yFhX31EbHWJu/t+T4FO79VHdRKeHp7IP/8PMz3KfWaEWw4eUHY0azrxKc/UXm4arckFSQ/Z/5RVaP1BuW/PWMeH2tb1VENoHwD9bc+8DPU7gOpSfAgKmvtRvxtdUzANI9NbvnpPk19Zm7njrB8+f3P2ruqH/cA8G0M31SAc2uyjjxz65x6SNPo5ZwfcOTH9yk16EVUSNbRAo2Rl0fTldf55+SlrkkPS01RHbedCticxpD5ODoPygWqbRSWoY+JtQNEBsPX/lnXXzYE/FvAsLVFu8aX9DEpSD4ymPp5Upi8PJquEjhPJNiwFDqdGvHn3Bp4eS2Urph9nYQYWNgHtHYwaEn2Jyk5CT8B9m4lf7NUkPw8KvykenUqp179msCu7yDutr469/I2sHOBsjk0jzGkwuTn6AKo3gVKeeS/XWMdn5zo0tQwt+XrqxFHgnfo2xrfuQjR1/VtS03p2OQmIz9gXueOeHI9/D+bfaG6DqUk6j+6dRbmdVcPNtpNLNo+w0+q9tsFuV4VRmHzki1d+Zx/fk1U37kbx/Q39ME71H59nnqclD+S1CLkIzEOTq9UQ6UWhaGPSZv/QMNH+hP+3Bw6fQXVO6v3RbnGl/QxKUg+wDzOk4LmJVu6SuY8kWDDUqx9RzW9GbRYPXmNTW9LZ++i2uYlxKgh9pIfwMBf1VPaxFi1TikPNeze+X9VW0rfxuof78o22DUFWrxuevmJvKKWV+0ADmVUO8YNE6DC06rKElRVddka8Pco6PCpal+49XNo/ErJ3wDml58Mdy/DtT0w+M/s2zCl47N5khqS19UXkuLg5HK4uhuGrFBP7RoOgQ0fqnadds6w7n0VaPg1Vt83pWOTX37M7dwRT4a8/mcjg1W78oC2al6YmBuqs6qNPVTtqL4fcUbdQFVpB83H6a9JVlr9DVHoETVc6dBV4FJeNe0JPQyVWqnrWOgh1f+s7vMF6zBrqLwU5Px7NC9lq6vhP1e9Ad2+V4NarHsP6vQFF2/j5CPD6RXqKXjdAdn3YQrHxNkr5w7Irr76B2kFucYb+5gUJB/mcp4UJC9GPE8k2LAUh9PHO577SMegnj9Bg8Fw87h+5IDpj1TLvnkCSldQT6MP/QYb/qOetpSpDJ2+gIYvGzz52eSXH62tmhxp/0+qzaGrD9TsAc88VL1ppYUXlsKa8apDla2jejLx8LwcJSW//GQ4tlCNpR3QNvs2TOn4xN+Gv0dDXLh6UuVVW13wMtLd6Sv1FGfpENWkKiB9Ur/MvJjQsckvP8G7zOvcEU+GvP5nY27CtX2w/2fVnNHJEyq0gBGb9E+Zz/wD9+/AiaXqJ4OrP7ydXkucfB/uXlRj64O67p76S80PkZqoRlJq/pq6CTNmXgpy/j2aF4A+v6kbp/k91PWqZg/o8o3x8pHh6AKo2T37IBs55cMYx6QgCnKNN/YxKQhzOU8KwojniUank7EZhRBCCCGEEMVP5tkQQgghhBBCGIQEG0IIIYQQQgiDkGBDCCGEEEIIYRASbAghhBBCCCEMQoINIYQQQgghhEFIsCGEEEIIIYQwCAk2hBBCCCGEEAYhwYYQQgghhBDCICTYEKYpeBdMclUzrT6Ov8fAHy8US5KMYk5X+PeD/Neb3QVOLDd8eh62fBjs/aFk9ymEEKbs3jVVdt088XjbObsGptWHyaULVgaYmoKW4Ve2w4+NIS21JFKl3DoH/6sJSfElt88nnAQbwrAOzYIvfSA1Rf9ZYhx86q5upB+WcXGKvAJ+TeGdC2Dvavg0HpkLPz8NX5SHr/zhl5aw63+G329xObcO4m9Bnb7Fs72gxTCrU/7rPfMe7PwOEqKLZ79CCFEQ8XdgzdswpTZ8Vhb+WxUW9IaQ/cZOWfFZ8xbU6glvn4G2H+a8TvhJWDwQvg2AzzxhaiAsfxnibpdkSh/PpomqLLHSFs/2vg+Ey9vyXsezBvg+BftmFM8+Rb6sjZ0AYeEqPQNJcXDjGPg1Vp+F7AMnLwg7DMkJYGOvPr+6C1z9oExl9d7Zy/DpO7oA1k+ALt9AhachNQkiTsOtM4bfd3E58AvUHwxWxfTs4NxaqN4l//W8akGZSnBiGTQZWTz7FkKI/Cwdoq7VvX+G0hXVzXXwdrgfaeyUFY/EOIi/DVXagYt3zuvE34F5PaBaZxiyQj2YiwqB8/9CcjxQtkSTXCTX9kHkVajZo3i2F34KHkRDxZb5r9vgRVj1BrQcD1q5FTY0+QsLw/KoCk7lVCCREWxc3QXVn4PgnRB6CCq1Sv98N1RM/z14F8zrBv93DRzc4NgiFRT0n61eo8PAvxn0+gmcy6nvpKXCxo/h2EJ1491gCKDLO33n/4XavaHhS/rPPGtmXefvMerpvXddOPgrpCRBYD/o8i1Y26bvOw32TFW1JHG3wL2KelpTu5d+OxFnYNPH6gJr6wgBbaHTV1DKXS1Pioc14+HsarBzghav5//3jb+j/o5dvsn6+SRX6DYVzq9Xy938oOcMcHRXF9gbR8GrDvSZqQ/uQAV/l7dBu0/U+4O/wf6f1N/b3gX8m8PzC/TrV+sCp/6SYEMIUTIeREHIXnh5rf6m0s0ffBtlXW+SK3T9n7rGX92tHnB1+DTrNTk6FDZ8qK55Gg1UaAGdv4bSFfTrHJkH+35UzaPc/KHpq1mvd6FHYM2bcPuCKjueebcAebinmkZd+FeVJxWfVuWJe4C+7AOY1129Dl2jLyczhOyHxBjo8YP+Zrl0RfWA72FXd6tyMeIUOJSGeoOg7cf670wNhGZjoPlr+u/83BJqdIVnJ+j/lt2nw8WNcGmLCoA6fgE1ntN/58JGWP8BxISBb2O1n/yc+gsC2ugfOAJs+0o98Gr6Kmz/Wv2t6g2E5/6rmu3umwG6NGg2WpWxDzu/TgVoWhsVeK17Tz3cTE1Wx67DZ1Cto1q38rNq29d2Q+U2+adVPBZpRiUMr1IrFWBkCN6lComKT+s/T34AoYezX1AflnxfXWx6z4Rh61RBsfEj/fK9P0DQIuj5IwzfoC4kZ9fknTYnTxXwRIXkvV7wDrh9XhVw/WapgGDH1/rlu/8Hx5eoG/zX9kOz12DFKHWhB1VAzusO5erCqO3w4l8qKFk+VL+NjR/DtT0waDEM+Vt99+bxvNMVsg9sHMGjevZlO/6rLtKjd4NHNfhrhKqab/W2SgM6dTF+NJ8u3lC2GoQdhX//D579EF4/rNJc4ems6/s0grAjkJKYdzqFEKI42Dqpn3Nr87/ubP1CPTUfvQfqDoA/h6vrOKgb0AV91IOd4f/CiI1gWwoW9lUBAKha221fqpvzcQeh3UTY9oVqagqqBmLxAChbA17dAW0mZC2TcrPyNVXbP2gJvLIJdDpY1E+lya8pjDui1huwQDUn9muafRtOXpCWAudWq+/nJOYGLOoPPg3V36DrFDi2AHb+N/80PmrHN+rB3Jg9ULUjrBipr0mKDoWlL6oa8dG71cO7zZPy32bIPijfIPvn94Lh0iZV5vSbpdK8qL/Kz7B10GEybP1c3TM87Pw6FSQBrH1X/X8M+xfG7IX2k9XxzWBtC+UC1cM/YXASbAjDq9gKQg6ofhuJsRB+QgUbFZ7W34xfPwipifqajZykJaubeZ+GUL6+erp0ZYd++f6fodV4qNUDylaHbt+rp/F5afOBqn7+PhB+aKRqMU6tUDUVD9PaqJoBz5pQrRM8+x84MFOtl5IIu6ao5VXaq6ZFDQarwu3wHPX9g7+pmpH2n6gbee96av2ru+DOJVVoHVsAHT9TT1m8akOvn1Vhkpeo6+BUNucmVA0GQ50+4FEFnn5LBVSBA1Qay1aHpqP1f/8MDzehig5VF+dqndRTIe966mnSw5zLqeYMcRF5p1MIIYqD1lrVaActhq/9YVZH2DxZNaF5VO1e0Giouga2/Ujd2B6YqZadWqGekPf4UV1vy1aHnj+p617GQ7BtX0KnL1SZUrqiem02Vn9dP7lcvw3PmlC9M7R4I+/0372sbop7/KBqUsoFQt/fIeYmnFujboJLeah1HUqr5sQZNegP82sMrd6Bv16BbyupIGnPNPUQK8Oh38HFB577TpU7NbupgGjfj9nLuPzUf0HV6LsHqKArKU49kALVN7NMJfW38qiqyr76BRiYJeo6OOfQTEyXll7e1lDlUcVWcPeiqnXyqKqaQLlXVbX2GWJuqCbQVdqr99GhqvWDV22Vtuqd1QPOhzmXg+jrhfs7iCKRZlTC8Cq2VG1IbxxVT/jdq6iLaYWn1ROe5AR101u6omrukxsbx6xNfpzLqXatoJo5xYWDz1P65VprVbjk9tQnYxuvbFZNnK7tUUHPyjFwdD68uEJ/E+9VRzV9yuDXRF1sY0JV86fk+zC/V9ZtpyapAAMg4qSq0fmifPY03AuGlAdq/YfT71hGFZJ5SXkA1vY5L/Oqrf/dKb39rlethz7zhJQESIhRQZlOBxfWQ/+5annAs6oPzbR66gJepT3U6Jb172DjoF6TH+SdTiGEKC61ekLVTqo5VehhuLhJ3Wj3+EE9ZMng2yTr9/yaqE7VoK7JkVfUACYPS0lQ1+SkePX6zzjV9DRDWor+IdadC+o6+3AzIL9H9vmo2+fBylp1UM6Qca2/faFg+c/QbiI0H6dqpEMPw+HZanCTYf+qdN0+r9Kj0ei/498svewKy7u8fdTD5YltKbBz0Ze/dy5kLbsg/78DpJdfdtk/d/MHO2f9eydP1YH84YdqTp6qGXGG8+tU3hzc1Pumr8La8XB5q3qAV7MHlKuTdT82DqrsFgYnwYYwPPcA9XQleCckROmb4rh4g6sPXD+gniQ92tb0UVY2j3ygId8+GQXlVUv9NBkJ14bDnM6qLWd+aQL98HmDl2V/SpNxIU2KV09W2k/O/n3ncqrQKwpH99yHFszy99Lk/pku/QlX2BFVkGZU2ds5w6s71bG5vFU1H9j+FYzcpr+gP7iXng6PoqVfCCGKwsZe9XsLaAut31dBwfavsgYbeUmKVzXkfX7LvqyUh/663mO6ai76sOIaOak4OJZRzZtq91Z97Wa2Sm9u/EvBvq/JoRxNS86+Xk7lr66QtSOPyq38ymlf+e3//L+qL2iGRkNV/40LG1T5tWuKqnlp+qp+nQf3oHSlx8uDKBBpRiVKRsVWqvbi6u6sI0VUaKHaZoYdgYoFuLHPjb2r6oge9lAbztQUuBFU+G2VTe//kPTQE4+IU1mf3oceUu2GXXzV+lo7VW3rHpD1x9VXre9dT43t7VYh+zq2pdQFz8oma/of3FNV7nkpV1c1Ycq46X8c59aqp4UPF6Raa1XD0fEz1e41KiRr1fWtMyqQzOjkLoQQxlC2RvZ5E0IPZX/vUU397l1PXV9Llc1+TbZ3VU/Onb3h3tXsy0tXVNvwqKaa7iQn5L7PbOmsrh7qPNzf4H6kak6bUfYUlbWtKksy/g5lq6va+odr90P2g62zum6DCqxiw/XLE2JUZ/jC8KimyvCH5fd3AFV+ZfSheRyJcarlwMPBBqjyt/EIGLgIWoxTnf0fduusvvWBMCgJNkTJqNRKXeTCTz4SbLSEw3NVE6K8OocXRLPRsHuq6hR++4KqQs1vDog1b8OOb1XaokLg+iH4e7R6Uv9wNXBqsnpyduucGnVj21eqFsTKStUAtHhdjZIVtFjVUtwIUm2DMzoSNh6pAoK/hquLcuQVuLRZNSNLS1WdFBsOgY0TVT+UiDNqmSafU9S7nno6FHLgsf50QPqToYeGvD2/Hvb/oianigqB43+oJ0keVfXrXNunghEhhCgJ9yNhbjc4vlT107h3FU7/rZpR1XjkZvPMSjW8+Z1Lqv9F2BFoMkotCxygrp1LXoBre9V2gnfBuvfV6Hug+jfsmqKug3cuqcDi2ELY+2P6NvqrmoHVb+jLhvwmOnUPgOpd1Xeu7VNl4oqRqqY/o3NzQZxfD3+NVK93LsGdi7AnfcSojO00fkU1l1r3nioTz61VtT/Nx+qbJFV6Bk4sVX+DiNOqGXFha26eGg6Rl1Xn+DsX1QSzGWVfXqq0U53EH9elzap59sOjiP37gfr83lVVHgfvUv1WMty7pvp5yEhUJUKaUYmSUbGVap/pUU09Mcr8/GlIilWdvTKGsC2q5q9DbIS6WGo0aujbmt3Uk5rcVG6jCo9Ds+BBpCp8fBvD0FWqejpDpdaqkJjTRQVGdfqqgihD24/UE6JdU9TFzd5VBQKt3lHLXbzVaCebJqrJp1KSVHvZKu31AUWHz9QTqT8GqlqTFuPyTjuoQqHBYDi5TDXTKqrIK+qnSjv9Z/auatSt7V+pTvDuAdB3ln5o4OQEVXi9+FfR9yuEEIVhW0r1d9g/Q83RkJasntI3Gqq/3mZoM0ENr7r2HdXRuu8s1ekYVN+zYf/C5k/USEqJceo6Xam1vr9Ao6Gqr+DeaWrYchtH1Xeh2Ri13M4JBi1VD61mtlI1Ce0nw7Iheeeh1wx1M7z4eVWeVGgBg/9UA5EUVNnqqs/Bxg9VcGRtC2UCVL+VegPVOi7lYfByNdLhL0+rDucNhmQdMrbleHXjvfh51Q+j7YeFr9lw81MjZ22YAAd+Vc3O2k2Ef8bm/b3A/qpMvHMx60Oswjq/LvvcULpUNSJVzA11PKu0h85f6Zef+lM1wXPzL/p+RYFpdLq8es8KITLn2RhUgCc1xhAbAT81Vf0rinrh3PsjXNkOL/5Z8O8c+l3VIr20smj7FEIIQ5nkCs8vUg+chOna+JEapbL7tKJ9PzUFvqsCg//KPtdKblKS4IeGahQw/2ZF268oFGlGJYS5c/ZSQy9GhxZ9Gy7l1bDBhWFloyZaEkIIIYqi1btq1MPCDsWb4cE9NRyxT8OCfyf6uirvJNAoMVKzIUR+TL1mQwghRFZSsyGEyZBgQwghhBBCCGEQ0oxKCCGEEEIIYRASbAghhBBCCCEMQoINIYQQQgghhEFIsCGEEEIIIYQwCAk2hBBCCCGEEAYhwYYQQgghhBDCICTYEEIIIYQQQhiEBBtCCCGEEEIIg5BgQwghhBBCCGEQ/w9l0pw5Mb4KkQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " gravity=9.80665,\n", + " date=(2023, 10, 14, 14),\n", + " latitude=39.3900032043457,\n", + " longitude=-8.2895383834838,\n", + " elevation=107,\n", + " datum=\"WGS84\",\n", + " timezone=\"Portugal\",\n", + ")\n", + "\n", + "env.set_atmospheric_model(\n", + " type=\"Reanalysis\",\n", + " file=\"../../data/weather/euroc_2023_all_windows.nc\",\n", + " dictionary=\"ECMWF\",\n", + ")\n", + "env.max_expected_height = 4000\n", + "env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motor" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Mariachi = SolidMotor(\n", + " thrust_source=\"../../data/rockets/camoes/thrust_source.csv\",\n", + " burn_time=3.72,\n", + " grain_number=6,\n", + " grain_density=1637,\n", + " grain_initial_inner_radius=0.015,\n", + " grain_outer_radius=0.045,\n", + " grain_initial_height=0.15,\n", + " nozzle_radius=0.034,\n", + " throat_radius=0.0135,\n", + " grain_separation=0.005,\n", + " grains_center_of_mass_position=-0.7566,\n", + " dry_inertia=(0, 0, 0),\n", + " center_of_dry_mass_position=0,\n", + " dry_mass=0,\n", + " nozzle_position=-1.3346,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nozzle Details\n", + "Nozzle Radius: 0.034 m\n", + "Nozzle Throat Radius: 0.0135 m\n", + "\n", + "Grain Details\n", + "Number of Grains: 6\n", + "Grain Spacing: 0.005 m\n", + "Grain Density: 1637 kg/m3\n", + "Grain Outer Radius: 0.045 m\n", + "Grain Inner Radius: 0.015 m\n", + "Grain Height: 0.15 m\n", + "Grain Volume: 0.001 m3\n", + "Grain Mass: 1.389 kg\n", + "\n", + "Motor Details\n", + "Total Burning Time: 3.72 s\n", + "Total Propellant Mass: 8.331 kg\n", + "Structural Mass Ratio: 0.000\n", + "Average Propellant Exhaust Velocity: 1320.764 m/s\n", + "Average Thrust: 2957.984 N\n", + "Maximum Thrust: 4145.791 N at 1.65 s after ignition.\n", + "Total Impulse: 11003.702 Ns\n", + "\n" + ] + }, + { + "data": { + "image/png": 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RnDEQEZFN5TX1OJZnwJErBhzJLcevuWU4nl+B0mrjDV/noVQg3FcLT3M1ekaFopOfJyL0OgR7a6DXeUCv9YBep274vvGxl0YFlUIBlbLxS6GAUtnUm2M2C6g1mVFjNKGm3oza+obvq4wmlFQZUVRlRFFVHYoq6xr/NKKwsg75FbW4UFKNwso6lNfW43heOY7nldtsc2ywN3qH+aB3qA96h/liYKQecaE+8GBQIpIFBiIiQkVtPQ5cLEX6+RIcvFSKI7kGZBVVXXf/Tn46xAZ7IzbIG7HBXogN9kb3IG9E+esQ5KWByVSPDRs2YPz4BKjV6ltun1KpgKdSBU+1qk2vr6qrx8XSGlwoqcaF0mrklFQhu7gKpwoqcKqgAhW1JvH7dc1e56lWYlAnP9wW7Y+EKD/cFuWPXqE+UCk59EbkbhiIiGRGEAScLaxEek4J0s+XID2nBEevGGC2MY8nUq9D/0hf9I/Qo3+EHv0i9OgR4n3TYGIyOajxbeSl8UCvUB/0CvVp8ZwgCLhUWoOTBeU4mV+BkwUVOJFfjsOXy1BRa8Le8yXYe75E3N9bo8Lt0f4Y3i0Iw7sFIrFLANTMR0QdHgMRkQzkFFdh65lCbDlTiG1nC5FfXttin2h/HRJjAjEkxh8DI/3QL8IXIT5aF7TWuRQKBaIDPBEd4ImkXqHidrNZwG9XK5BxqQwHL5Xi4MUyHL5chso6E3ZkFWFHVhGAhuG226L8ECkooDh1FffEhsDP89Z7xYjIuRiIiNxQUWUdtp0txNYzDV9nCyutnteolEiI8kNilwAkxgQgsUsAOvl5uqi10qRUKhAX5ou4MF88mhAFADCZBZwqqMCe7GLsOleEnVlFuFRWg30XSgEo8N3qDKiUCgzt7I/fxYXid3GhGNzJz2o+FBFJEwMRkZt59+dzmP2f41aXsquUCtwR7Y/RPYIxpmcwhsYEQOvRtvk4cqZSKtAn3Bd9wn3xZGIMBEFATkk1tv1WgP/7+QjOG71xtqgKe86XYM/5Ery08TSCvTVI6hmCiX3C8PDASF76TyRRkrl84o033oBCocDs2bPFbTU1NUhNTUVQUBB8fHwwadIk5OfnW73uwoULmDBhAry8vBAaGoq//vWvqK+vt9pnx44dGDx4MLRaLWJjY7F69WonVETkGl8eugxBAHqGeOPPw7riv9NuR/GiZOx95m4sGheHe7oHMwy1E4VCgS6BXpiS0Amzego48dfhOP/iaHz0UH880C8cvloPFFbW4cvDl/HIvw9h8dazrm4yEV2HJALRgQMH8NFHH6F///5W2+fMmYMffvgBa9euxc6dO5Gbm4sHH3xQfN5kMmHChAmoq6vD3r178dlnn2H16tVYsGCBuE92djYmTJiAkSNHIjMzE7Nnz8aMGTOwadMmp9VH5CyCIOBEfgUA4LvHb8c79/fFxD7h0Os4p8VZYgK98GRiDL57/HYULUrGrtQ78UC/cADAh3vPu7ZxRHRdLg9EFRUVePTRR/HPf/4TAQEB4vaysjJ88sknePvttzFq1CgkJCRg1apV2Lt3L/bt2wcA2Lx5M06cOIF///vfGDhwIMaNG4dFixZh+fLlqKurAwCsWLECXbt2xVtvvYXevXtj1qxZeOihh7B06VKX1EvkSFcMtSivrYdKqUCPYG9XN0f21ColhnULwvsP9AMA5BpqINizLDcROY3LA1FqaiomTJiAMWPGWG3PyMiA0Wi02h4XF4fOnTsjPT0dAJCeno5+/fohLCxM3Cc5ORkGgwHHjx8X97n22MnJyeIxiNzJ1cqGq8eCvTXQeLj815sa6XUN0zUtq2wTkfS4dFL1V199hUOHDuHAgQMtnsvLy4NGo4G/v7/V9rCwMOTl5Yn7NA9Dluctz91oH4PBgOrqanh6tryypra2FrW1TZclGwwGAIDRaITReONVeu1lOV57H1fK5FazM+strmi4jYWvVuXSny/PsTWNQoBCAQgCUFxZA42y4y9nILdzDMivZneo1562uywQXbx4EX/+85+RlpYGnU7nqmbYtHjxYixcuLDF9s2bN8PLy8sh75mWluaQ40qZ3Gp2Rr0HiwFACXNNJTZs2ODw97sZnuMmnkoFqkwK/LBpKzo55p8Rl5DbOQbkV3NHrreq6vor7l/LZYEoIyMDBQUFGDx4sLjNZDJh165deP/997Fp0ybU1dWhtLTUqpcoPz8f4eENExTDw8Oxf/9+q+NarkJrvs+1V6bl5+dDr9fb7B0CgHnz5mHu3LniY4PBgOjoaCQlJUGv17e9aBuMRiPS0tIwduzYdrnFQUcgt5qdWW/Fr1eAY78iOjQI48ff4dD3uhGe45YCf92BqrIaJAy9C4Oj/JzcwvYnt3MMyK9md6jXMsLTGi4LRKNHj8bRo0ettk2dOhVxcXF4/vnnER0dDbVaja1bt2LSpEkAgNOnT+PChQtITEwEACQmJuL1119HQUEBQkMbVphNS0uDXq9HfHy8uM+1/1NOS0sTj2GLVquFVtuyS1utVjvsL4Ujjy1VcqvZGfVW1TdM2NXrpPGz5Tlu4qvzAMqAKhPc6mcit3MMyK/mjlyvPe12WSDy9fVF3759rbZ5e3sjKChI3D59+nTMnTsXgYGB0Ov1ePrpp5GYmIihQ4cCAJKSkhAfH48pU6ZgyZIlyMvLw/z585GamioGmqeeegrvv/8+nnvuOUybNg3btm3D119/jfXr1zu3YCInKK9tWIPLMomXpEOvbTgn5TX1N9mTiFxB0v9qLl26FEqlEpMmTUJtbS2Sk5PxwQcfiM+rVCr8+OOPmDlzJhITE+Ht7Y2UlBS8+uqr4j5du3bF+vXrMWfOHCxbtgxRUVFYuXIlkpOTXVESkUMZGj9sfbWS/tWWJcs5MdQyEBFJkaT+1dyxY4fVY51Oh+XLl2P58uXXfU1MTMxNJ4+OGDEChw8fbo8mEkmapYeIgUh6LL125QxERJLEhUqI3AiHzKTLslq4gUNmRJLEQETkRjhkJl3ikFlNx13ThcidMRARuRH2EElX05AZV6omkiIGIiI3wh4i6WIPEZG0MRARuRFOqpYuTqomkjYGIiI3wiEz6WrqIWIgIpIiBiIiN8IhM+kSF2ZkDxGRJDEQEbkR9hBJl6+OCzMSSRkDEZGbqKs3o7beDIA9RFLEW3cQSRsDEZGbaD4Uw0AkPXr2EBFJGgMRkZuwBCJPtRIeKv5qS41vszlEgiC4uDVEdC3+q0nkJjihWtosPUSCAFTWcXFGIqlhICJyE00TqtUubgnZ4qlWQalo+J6X3hNJDwMRkZuwrIDsq1W5uCVki0KhEMMqL70nkh4GIiI3YblHFofMpMsSVtlDRCQ9DEREboJDZtLHHiIi6WIgInITTUNm7CGSKt7glUi6GIiI3IRlyIyrVEsXb99BJF0MRERugj1E0icuzsg5RESSw0BE5CYsvQ4MRNLlyx4iIsliICJyExwykz7evoNIuhiIiNwEh8ykr2lSNQMRkdQwEBG5iabL7hmIpMpybjhkRiQ9DEREboL3MpM+9hARSRcDEZGb4KRq6WMPEZF0MRARuQkOmUkfe4iIpIuBiMhNcMhM+thDRCRdDEREbsBoMqOm3gyAPURSptc23MuMPURE0sNAROQGmvc4sIdIunzZQ0QkWQxERG6gvLHHQeehhFrFX2upan4vM7NZcHFriKg5/stJ5AbEK8w4XCZpzc9PZZ3JhS0homsxEBG5AU6o7hh0Hkp4KBUAAEOt0cWtIaLmGIiI3IB4yT0DkaQpFApeek8kUQxERG7AwCGzDoOX3hNJEwMRkRuwTKpmD5H0iXe8Zw8RkaQwEBG5Ad62o+Pw1bKHiEiKGIiI3ACHzDoO9hARSRMDEZEb4JBZx8EeIiJpYiAicgMGDpl1GLx9B5E0MRARuQGxh4hDZpLnq1MBYA8RkdQwEBG5AU6q7jjYQ0QkTQxERG6AQ2Ydh7gwI1eqJpIUBiIiNyCuVM0hM8kTF2ZkDxGRpDAQEbkB3sus4xAvu+ccIiJJYSAicgNNPURqF7eEboaX3RNJEwMRkRvgpOqOgwszEkkTAxFRB1dvMqOqzgQA8NWqXNwauhn2EBFJEwMRUQdX0RiGAN66oyNgDxGRNDEQEXVwhpqGy7c1KiW0HuwhkjpLD1FlnQkms+Di1hCRBQMRUQdXXtvQQ8RL7juG5uepgsNmRJLBQETUwXFCdcei9VBBrVIA4DwiIilhICLq4CxDZgxEHYdey3lERFLDQETUwZU1fqj6eTIQdRSW9aK4OCORdDAQEXVwxVV1AIBATy7K2FGIl96zh4hIMhiIiDq44qqGIbMgb42LW0Kt1XT7Dt7glUgqGIiIOjhLIAr0YiDqKJp6iEw32ZOInIWBiKiDE4fMvDhk1lGwh4hIejgLk6iDMZkFFFTU4nJZDXLLavDp/osAGIg6EksPURnnEBFJBgMRkUQIgoDSamND0DHUILesFpcN1Q1/llUj19AQgvLKa2BrgeNwX53zG01tYukhWrDxNJbtOocof0908tMhyk/X8L1ehyh/HaL8PBHlrxOvSiMix2EgInISk1lAXkk1ckqqkFNS3fjV9P2F0mrxJq03o1Q0BKBIPy0i9Tr0DfdFcq8QB1dA7eXe+DCs2n8RJdVGFFU1fP2aa7ju/j5aVUM48tOhk58OnQM80TXQC10CvdA10AtRfjp4qDgDguhWMBARtRNBEFBUWYezRVXIKqxEVlEVzhVVIruoEqdyFSjevRn1rbh3VaCXGpH6hg8+8c/m3+t1CPPVQqVUOKEqcoSRscEoWpSM0mojLpXV4HJZDS6VVjd9X1aNS6UN35dUG1FRa8KpggqcKqiweTyVUoHO/p7oEtgQlCxflsAU7quFkn9fiG6IgYjIDmazgMtlNcgqqsTZxtDT/PvrrzysACDAQ6lAtL8nYgIsX15N3zf+T1+n5g1a5UChUCDAS4MALw36Reivu19VXX1jSGoKTTkl1ThfXIXs4iqcL65GncmM7MbH21HU4hhaDyW6BHgiNtgbPUK80SPYBz1DvNEj2BvR/p4MS0RgICKyyVBjxOmCSpwqKBf/Z36qoAJZRVWorTff8LWd/HSIDfZG9yAvdA/yRpSfBrknM/Hw+FHoHOjDnh2yi5fGAz1CfNAjxMfm82azgCvlNThfXC2GouyixrBUUoWLpTWorTfj9NVKnL5aCZy0fr3WQ4nuQV7oEeyNniE+jYGp4fsIvRYKBf++kjwwEJFsmc0CLpVVWwWeUwUVOF1QiVxDzXVf56FUoEugF7oHeVkFn9hgb3QN8oLnNT08RqMRG3IzEeWnYxiidqdUKtDJzxOd/DxxV9fAFs8bTWZcKq3BuaJKnC2qxG9XK3HmaiXOFFYiq6gStfVmnMivwIn8CgD5Vq/V6zwQH+aL+DAf9A71RXy4D+LDfNHZ39NJ1RE5DwMRuT1BEJBrqMGxK+U4lmf5MuBEfsUNJzGH+2oRF+pj9dUjxBud/T05gZU6DLVKia5BXuga5IXRsJ54X28y40JptRiQfrtaiTOFFThztRLZxQ1DwPtySrAvp8TqdV4aFXqFeENvVOCIZxb6RfghPtwX3YO8Gfqpw2IgIrdSXFXXLPgYcLTx+9Jq2wvgqVUKxAZ7twg+vUJ84Md7g5Gb81Ap0S3IG92CvJF8zXM1RhPOFFbiZH4FTuSX40R+OU7mV+D01Yb/SBy+bACgwM5NZ8TXeKqV6BPuiwERfugf6Yv+EXr0j9RzFXXqEBiIqEMSBAHZxVXIvGzA4ctlOHy5DJm5Blwusz3UpVIq0DPEG33DfdE3XI++4b7oE+6LbkFeULO3h6gFnVqFfhH6FhO+601mnCuuwpHLpfj+5wwIAVE4dbUSJ/PLUW004+DFMhy8WGb1mig/HfpH6hsCUoQeAyL16BnizZ5WkhQGIpI8o8mMk/kVVsEn83LZdVf57RLoKYaevuG+6Bvhi7hQH2g9ePUW0a3yUCnRM8QHXf21UOcA48f3h1qthsksIKuoEkdyDThyxYAjuQb8esWA88UNV8ZdKqvBhpMF4nG0HkrEh/lgYKQfBkf5YXAnPwyI1MNby48lcg3+zSNJqTGakJlrQMbFUhy+bEBmbhmO5ZXbvLJLo1Kib4QvBkbqMaiTHwZ18kO/CF+u6kvkAg29sD7oGeKDhwZEitvLqo04llfeFJJyDTiaZ0BFbcOw2+HLBqw60HD7GYUCiAv1waDGkDSmZzAGRPq5qiSSGQYichmTWcDpKwYcuFiK/RdKsf9iKY7kGmwuXqjXeYjBZ2CkHwZF6dE71BcaD3a5E0mZn6cad3UNtLoCzmwWcL6kCr/mGpB52YBDl8tw6FIZcg01OJlfgZP5Ffjy8GUAQNbfRqFbkLermk8y4tJA9OGHH+LDDz/E+fPnAQB9+vTBggULMG7cOABATU0N/vKXv+Crr75CbW0tkpOT8cEHHyAsLEw8xoULFzBz5kxs374dPj4+SElJweLFi+Hh0VTajh07MHfuXBw/fhzR0dGYP38+Hn/8cWeWKnuCIOBCSTX2XyzFvvPF2PSrAo/u24JKG1d5hfpocFu0f2Ovjx4DI/3QNdCLi8cRuQmlUiFO5n6gX4S4Pc9Qg8OXy3Dochne2nEOJdVGnC+uZiAip3BpIIqKisIbb7yBHj16QBAEfPbZZ7jvvvtw+PBh9OnTB3PmzMH69euxdu1a+Pn5YdasWXjwwQexZ88eAIDJZMKECRMQHh6OvXv34sqVK3jsscegVqvx97//HQCQnZ2NCRMm4KmnnsIXX3yBrVu3YsaMGYiIiEBy8rXXVVB7Ka+pxy8XSpCeU9LQ+3OhBAUVdc32UAAwwVujwm3R/rgj2h93dG74ivb35GJwRDIUrtdhnF6Hcb3D8P2xPBy8WIYqY+vu70d0q1waiCZOnGj1+PXXX8eHH36Iffv2ISoqCp988gm+/PJLjBo1CgCwatUq9O7dG/v27cPQoUOxefNmnDhxAlu2bEFYWBgGDhyIRYsW4fnnn8crr7wCjUaDFStWoGvXrnjrrbcAAL1798bu3buxdOlSBqJ2IggCckqqsSe7GHvPl2DP+WIcvWJocUd2D6UC/SP1uK2THtqSHEybMAz9IgO4bgkRteCtafh4au0Nj4lulWTmEJlMJqxduxaVlZVITExERkYGjEYjxowZI+4TFxeHzp07Iz09HUOHDkV6ejr69etnNYSWnJyMmTNn4vjx4xg0aBDS09OtjmHZZ/bs2c4qze3U1Ztx+HIZ9p4vxp7zJdh7vhhXDLUt9usS6InEmEAMjfHHHZ0DMDBSD51a1bBy84Yc9AnzZRgiIpu8Gld8ZyAiZ3F5IDp69CgSExNRU1MDHx8frFu3DvHx8cjMzIRGo4G/v7/V/mFhYcjLywMA5OXlWYUhy/OW5260j8FgQHV1NTw9Wy5BX1tbi9rapg94g8EAoOEWDEaj7QX+2spyvPY+bnuqqK1Hek4pdmUXY092CQ5eKkPNNVd9eSgVGNRJjztj/DE0JgCJMf6I1OuuOZIZRqO5Q9TcnuRWLyC/muVWL+D4mnUeDf9ZKq+pk8zPVW7n2R3qtaftLg9EvXr1QmZmJsrKyvDNN98gJSUFO3fudGmbFi9ejIULF7bYvnnzZnh5eTnkPdPS0hxy3LaorAdOlgHHyhQ4XgZklQNmWPfk+HoIiNMDcfqGP2N9Aa2qBEAJcDEbmReBzJu8j5Rqdga51QvIr2a51Qs4ruaSAgUABTKOHMOGkmMOeY+2ktt57sj1VlVVtXpflwcijUaD2NhYAEBCQgIOHDiAZcuW4eGHH0ZdXR1KS0uteony8/MRHh4OAAgPD8f+/futjpefny8+Z/nTsq35Pnq93mbvEADMmzcPc+fOFR8bDAZER0cjKSkJer3e5mvaymg0Ii0tDWPHjoVa7Zr1c4oq67D7fAl+PleMXdklOGJj/k+Mvw7DugViWNdA3NnFHz2Dvds88VkKNTuT3OoF5Fez3OoFHF/z+trj2FFwEZ2798T4MbHtfvy2kNt5dod6LSM8reHyQHQts9mM2tpaJCQkQK1WY+vWrZg0aRIA4PTp07hw4QISExMBAImJiXj99ddRUFCA0NBQAA1JVq/XIz4+Xtxnw4YNVu+RlpYmHsMWrVYLrVbbYrtarXbYXwpHHvtahhojdmYVYeuZQmw7W4ijV8pb7BMb7I17ugXhnu6BGN4tCDGB7d8z5syapUBu9QLyq1lu9QKOq9m3cYHVWpMguZ+p3M5zR67Xnna7NBDNmzcP48aNQ+fOnVFeXo4vv/wSO3bswKZNm+Dn54fp06dj7ty5CAwMhF6vx9NPP43ExEQMHToUAJCUlIT4+HhMmTIFS5YsQV5eHubPn4/U1FQx0Dz11FN4//338dxzz2HatGnYtm0bvv76a6xfv96VpTtVbb0J6edLsPVMIbaeKcT+i6UwXdMF1DvMpzEABWF4tyBE+l07/4eIyHk4qZqczaWBqKCgAI899hiuXLkCPz8/9O/fH5s2bcLYsWMBAEuXLoVSqcSkSZOsFma0UKlU+PHHHzFz5kwkJibC29sbKSkpePXVV8V9unbtivXr12POnDlYtmwZoqKisHLlSre+5N5kFpB5uUwMQD9nF6HaaD0JunuQF8b0DMHoHsG4p1sQQn1b9ogREbmKl6YxEHEdInISlwaiTz755IbP63Q6LF++HMuXL7/uPjExMS2GxK41YsQIHD58uE1t7CjOFlYi7ber2HqmENvPFqK4ynpmfZivFqNjgzG6R8OXI4bAiIjaC3uIyNkkN4eIWqeyth47sorw06kCbDxVgKwi65n0vloP3NM9CGMaA1CfcF+u/kxEHYalh8jW7X2IHIGBqIMQBAEn8yuw8XQBfjpZgF3nilFnahoGU6sUuLNLIEb3CMaYHsG4LdofahVvfEpEHZM3h8zIyRiIJMxQY8TWM4XYeKoAG09fxYWSaqvnYwI8MS4uFL+LC8Wo2GD46ng6icg9cMiMnI2foBKTXVSFNZmXsfH0VezJLkZ9s6vBtB5K3NMtCL+LC8G4uFD0CvXhMBgRuSVOqiZnYyCSkGqjCQlLd6GkumlCdI9gb/wuLhS/iwvBiO5B8NLwlBGR+2MPETkbP10lZG92MUqqjQj0UuPV5F74XVwougd7u7pZREROxx4icjYGIgk5cLEUAJDUMwSpd3d1bWOIiFzI0kPEq8zIWXgZkoRYFk8M9NK4uCVERK7l3Tg9gENm5CwMRBJSb24IRB4qTpQmInmzDJnVmcyoN5lvsjfRrWMgkhDLFWUeSgYiIpI3SyAC0OLWQ0SOwEAkIQxEREQNdB5NH0+cWE3OwEAkIQxEREQNFApFs9t31Lu4NSQHDEQSUm+yBCKeFiIi8fYdnFhNTsBPXgnhpGoioibi4owcMiMnYCCSEA6ZERE18WIPETkRA5GEMBARETVhDxE5EwORhDTNIWIgIiJiDxE5EwORhDT1EPG0EBF5a3j7DnIefvJKCCdVExE14ZAZORMDkYRwDhERURMOmZEzMRBJCAMREVET9hCRMzEQSQgnVRMRNWEPETkTA5GEcFI1EVETSw8RJ1WTM/CTV0I4qZqIqIm3xgMAUM0hM3ICBiIJ4RwiIqImnuqGjygOmZEzMBBJCAMREVETcQ4Re4jICRiIJISBiIioiXiVGXuIyAkYiCSk6SoznhYiIvYQkTPxk1dCOKmaiKgJ1yEiZ2IgkhAOmRERNeE6RORMDEQSwkBERNTES91w2T17iMgZGIgkhAszEhE1YQ8RORM/eSWEt+4gImrCOUTkTAxEEsJJ1URETZr3EAmC4OLWkLtjIJIQziEiImpi6SECgJp6swtbQnLAQCQhDERERE0st+4AOI+IHM/Dnp1LS0uxbt06/Pzzz8jJyUFVVRVCQkIwaNAgJCcn484773RUO2WBk6qJiJp4qJTQqJSoM5lRVWdCkLerW0TurFWfvLm5uZgxYwYiIiLw2muvobq6GgMHDsTo0aMRFRWF7du3Y+zYsYiPj8eaNWsc3Wa3xUnVRETWLPOIquvZQ0SO1aoeokGDBiElJQUZGRmIj4+3uU91dTW+//57vPPOO7h48SKeffbZdm2oHHBSNRGRNU+1EqXVHDIjx2tVIDpx4gSCgoJuuI+npyceeeQRPPLIIygqKmqXxsmJ2SygccSMPURERI14g1dyllYNmd0sDN3q/gSYml1SykBERNSAN3glZ2n1pOpdu3a1ar/hw4e3uTFyZplQDXBSNRGRBXuIyFlaHYhGjBhx3ecUCoX4Z319/S03So6sAhHnEBERAWAPETlPqwNRSUmJze1VVVVYtmwZ3n33XXTr1q3dGiY3livMAA6ZERFZsIeInKXVgcjPz8/qsdlsxqeffoqFCxdCqVRi+fLlSElJafcGykXzHiKVgoGIiAhgDxE5j10LM1p89913+Nvf/oarV69i3rx5ePrpp6HVatu7bbJiueReqQCU7CEiIgLAHiJyHrtm7+7cuRNDhw7FlClT8OCDD+LcuXN49tlnGYbaAVepJiJqiT1E5Cyt7iEaP348tmzZgmnTpuH7779HeHi4I9slO2Ig4oRqIiIRe4jIWVodiDZu3AgPDw+sWbMGX3/99XX3Ky4ubpeGyQ1v7EpE1BJ7iMhZWh2IVq1a5ch2yB4DERFRS+whImdpdSDiFWSOxUBERNQSe4jIWVo1g1dodlsJcoymO91zUjURkQV7iMhZWvXp26dPH3z11Veoq6u74X5nzpzBzJkz8cYbb7RL4+TExEnVREQteDYGomr2EJGDtWrI7L333sPzzz+P//f//h/Gjh2L2267DZGRkdDpdCgpKcGJEyewe/duHD9+HLNmzcLMmTMd3W63Y1mHiENmRERNOGRGztKqQDR69GgcPHgQu3fvxpo1a/DFF18gJycH1dXVCA4OxqBBg/DYY4/h0UcfRUBAgKPb7JY4h4iIqCUOmZGz2LVS9d133427777bUW2RNQYiIqKW2ENEzsIZvBLBlaqJiFpiDxE5Cz99JYIrVRMRtcQeInIWBiKJaLrsnoGIiMiCPUTkLAxEEmHpIVIzEBERiSw9RDX1ZpjNXBOPHIeBSCKahsx4SoiILCw9RADXIiLHsvvTV6VSoaCgoMX2oqIiqFQqG6+g1uA6RERELXk2C0ScR0SOZHcgut5tPGpra6HRaG65QXLFy+6JiFpSKhXQeTR8VHEeETlSq9chevfddwEACoUCK1euhI+Pj/icyWTCrl27EBcX1/4tlAkTAxERkU1eGhVq6s3sISKHanUgWrp0KYCGHqIVK1ZYDY9pNBp06dIFK1asaP8WygR7iIiIbPNSq1AMI3uIyKFaHYiys7MBACNHjsR3333HW3S0M06qJiKyjWsRkTPY/em7fft2qzBkMpmQmZmJkpKSdm2Y3HAdIiIi23jHe3IGuwPR7Nmz8cknnwBoCEPDhw/H4MGDER0djR07drR3+2SDQ2ZERLZxcUZyBrsD0dq1azFgwAAAwA8//IDz58/j1KlTmDNnDl588cV2b6BcMBAREdnGITNyBrsDUVFREcLDwwEAGzZswO9//3v07NkT06ZNw9GjR9u9gXLRtA4R5xARETXHHiJyBrs/fcPCwnDixAmYTCZs3LgRY8eOBQBUVVVxYcZbwJu7EhHZxh4icga7A9HUqVPxhz/8AX379oVCocCYMWMAAL/88ovd6xAtXrwYt99+O3x9fREaGor7778fp0+fttqnpqYGqampCAoKgo+PDyZNmoT8/HyrfS5cuIAJEybAy8sLoaGh+Otf/4r6+nqrfXbs2IHBgwdDq9UiNjYWq1evtrd0h+KQGRGRbewhImewOxC98sorWLlyJZ588kns2bMHWq0WQMMtPV544QW7jrVz506kpqZi3759SEtLg9FoRFJSEiorK8V95syZgx9++AFr167Fzp07kZubiwcffFB83mQyYcKECairq8PevXvx2WefYfXq1ViwYIG4T3Z2NiZMmICRI0ciMzMTs2fPxowZM7Bp0yZ7y3cYBiIiItvYQ0TO0Op1iJp76KGHWmxLSUmx+zgbN260erx69WqEhoYiIyMDw4cPR1lZGT755BN8+eWXGDVqFABg1apV6N27N/bt24ehQ4di8+bNOHHiBLZs2YKwsDAMHDgQixYtwvPPP49XXnkFGo0GK1asQNeuXfHWW28BAHr37o3du3dj6dKlSE5ObsNPoP0xEBER2cYeInIGuwPRq6++esPnm/fM2KusrAwAEBgYCADIyMiA0WgUh+UAIC4uDp07d0Z6ejqGDh2K9PR09OvXD2FhYeI+ycnJmDlzJo4fP45BgwYhPT3d6hiWfWbPnm2zHbW1taitrRUfGwwGAIDRaITRaGxzfbZYjlfX+D8fBYR2fw+psdTn7nVayK1eQH41y61ewLk1axunp1bUtv+/wfaQ23l2h3rtabvdgWjdunUt3iw7OxseHh7o3r17mwOR2WzG7Nmzcdddd6Fv374AgLy8PGg0Gvj7+1vtGxYWhry8PHGf5mHI8rzluRvtYzAYUF1dDU9PT6vnFi9ejIULF7Zo4+bNm+Hl5dWm+m4m+3wOAAVyzp3Dhg1ZDnkPqUlLS3N1E5xKbvUC8qtZbvUCzqn5wkUAUOK37AvYsCHH4e93M3I7zx253qqqqlbva3cgOnz4cIttBoMBjz/+OB544AF7DydKTU3FsWPHsHv37jYfo73MmzcPc+fOFR8bDAZER0cjKSkJer2+Xd/LaDQiLS0NkVFRQO5l9OoZi/Fje7Tre0iNpeaxY8dCrVa7ujkOJ7d6AfnVLLd6AefWfH5vDv6VfRKBoREYP36gQ9/rRuR2nt2hXssIT2u0aQ7RtfR6PRYuXIiJEydiypQpdr9+1qxZ+PHHH7Fr1y5ERUWJ28PDw1FXV4fS0lKrXqL8/HxxLaTw8HDs37/f6niWq9Ca73PtlWn5+fnQ6/UteocAQKvVipPFm1Or1Q77S2FGw9whrdqjw/7Fs5cjf55SJLd6AfnVLLd6AefU7KvTAABq6s2S+PnK7Tx35HrtaXe7rQJYVlYmzgFqLUEQMGvWLKxbtw7btm1D165drZ5PSEiAWq3G1q1bxW2nT5/GhQsXkJiYCABITEzE0aNHUVBQIO6TlpYGvV6P+Ph4cZ/mx7DsYzmGFHBSNRGRbbzKjJzB7h6id9991+qxIAi4cuUKPv/8c4wbN86uY6WmpuLLL7/Ef/7zH/j6+opzfvz8/ODp6Qk/Pz9Mnz4dc+fORWBgIPR6PZ5++mkkJiZi6NChAICkpCTEx8djypQpWLJkCfLy8jB//nykpqaKvTxPPfUU3n//fTz33HOYNm0atm3bhq+//hrr16+3t3yHaQpEXKmaiKg5XmVGzmB3IFq6dKnVY6VSiZCQEKSkpGDevHl2HevDDz8EAIwYMcJq+6pVq/D444+L76dUKjFp0iTU1tYiOTkZH3zwgbivSqXCjz/+iJkzZyIxMRHe3t5ISUmxuhqua9euWL9+PebMmYNly5YhKioKK1eulMwl9wBXqiYiuh5LD1EFAxE5kN2BKDs7u93eXBCEm+6j0+mwfPlyLF++/Lr7xMTEYMOGDTc8zogRI2xOCJcKDpkREdnWLajh6t7TBRX477E8dA3yQkyAJ/S6jjmvhaTJrkBkNBrh6emJzMxM8dJ4ah/1JgYiIiJbugV5o3uQF7KKqnDfqgPidn9PNboEeCImwBMxgQ0hKSbAE10CGr4P8tZAoeC/qdQ6dgUitVqNzp07w2Rit2V7Yw8REdH1ffRQfyzfex45JdXIKa5CUZURpdVGZFYbkZlr+9JqL43KKiDFBHiia6AXugc3BKwAL42TqyAps3vI7MUXX8Tf/vY3fP755+KK0nTrTJxUTUR0XaN7hmB0zxDxcUVtfUM4KqlCTkk1zhdXNT5u2HbFUIuqOhNO5lfgZH6FzWMGeKrRPdgL3Rt7oLoHeYuPI/U6KPkfVFmxOxC9//77OHv2LCIjIxETEwNvb2+r5w8dOtRujZOTerMZACdVExG1ho/WA33CfdEn3Nfm8zVGEy6WVrcIS+eKKpFVVIW88lqUVBtx8GIZDl5suWSMzkOJrkFeiG0WkroF6FBQA5jNN5//Sh2P3YHo/vvvd0AziENmRETtR6dWoUeID3qE+Nh8vrK2HueKq5BV2BCQsooqcbbx+5ySatTUm6/Tu6TEM4fS0CPEG71CfNAzxBu9Qn3QM8QHvUK8OQzXgdkdiF5++WVHtEP2GIiIiJzHW+uBfhF69ItoeTsmo8mMCyXVyGrsTcoqbAhLp69W4OzVCtTUm3H0SjmOXilv8doQHw16BluHpN5hvuge5AUPFadESFmbb91RV1eHgoICmBuHeiw6d+58y42SIwYiIiJpUKuUDROvg62nhBiNRvywfgN6DxmO7NJanC6owG9XG4LS6YJK5BpqcLWiDlcr6rDnfMk1x1SgV4gP4sN8ER/mgz7hvogP80VssDc0HgxKUmB3IPrtt98wffp07N2712q7IAhQKBS8Aq2NmhZm5C8GEZFUqRRAbLA3ekf4Y3zvMKvnKmrr8dvVxpBUUIHTjWHpVEEFqupMOJZXjmN51r1KHkoFeoR4Iz7MF30aw1J8uC96hnhD66FyZmmyZ3cgmjp1Kjw8PPDjjz8iIiKCazy0E/YQERF1bD5aDwyO8sfgKH+r7WazgAul1TiRX44TeRU4nl/e8H1+OSpqm66E+xZXxNeolArEBnmhX4Qe/SP16B+hR78IX3QJ8OLVbw5idyDKzMxERkYG4uLiHNEe2eLCjERE7kmpVKBLoBe6BHpZ9SoJgoBLpTU4kV/eEJLyKsSgVFZT39jDVIlvjjQFJR+tCv3CG0JSv3Dfhj8j9PD35Krdt8ruQBQfH4/CwkJHtEXW2ENERCQvCoUC0QGeiA7wRHJcqLhdEARcMdTi6BVDw+TtPAOO5BpwIr8CFbUmpOeUID3Heo5S5wBPMSD1j9BjQKQePUN8oOJnSqu1KhAZDE2rgL755pt47rnn8Pe//x39+vWDWm2dSvX6ljP26ebEdYj4l5eISNYUCgUi/XSI9NNZBSWjyYzfrlbi6BUDjlxpCElHrhhwsbQGF0qqcaGkGutPFoj7e2lUGBChx+AoPwzq5IdBnfToE+7LuUnX0apA5O/vbzVXSBAEjB492mofTqq+NSZOqiYiohtQq5TiYpSTB3USt5dU1eFYXrkYkI5cKcevuWWoqmvZm6RWKdAnzBeDOvmJQWlApB4+2jZfdO42WvUT2L59u6PbIXscMiMiorYI8NJgWLcgDOsWJG4zmQX8drUChy+X4dClMhy+bMDhy2Uoabz3W2auAasOXAQAKBRAz2Dvxl4kP9wW7Y+EKD94ySwjtarce+65B6+++iqeffZZeHl5ObpNssRARERE7UWlVKB3mC96h/nifwZHAWgYyckpqcbhy2VWQSnXUCNO4P4qM1c8Rs8Qb0QoFDjndx5DuwRhYCc/eKrdd7it1flv4cKFeOqppxiIHISBiIiIHEmhaLra7YF+EeL2/PJaq5B04GIpckqq8dvVSvwGBXb+cApAw+dTvwhf3B7t3/DV2R99wnzdZqpHqwORIPBmdo7EQERERK4Q5qvF7+JC8btmE7gLymux73wR/m/7ARg8w3DwUhkKKuoah94M+HjfBQCAp1qJQZ38cHu0P+7o7I+hMQHoGujVIdcotGuEsCMW2FGI6xC5SdImIqKOK9RXi3FxIRDOAePHJ8DDwwMXS6tx4GIpDlxo6EU6eKkUhpp67D1fgr3NblUS6qPB0JgA8ev2aP8OMWnbrhb27NnzpqGouLj4lhokV+whIiIiqVIoFOgc4IXOAV6Y1D8SQMMK3GcKKxtC0sVS/JJTgkOXG3qS/ns8H/89ng8AUCqAvuF6JHYJwNDOARga44+eIT6SW3HbrkC0cOFC+Pn5OaotssZ1iIiIqCNRKhXoFeqDXqE++GNCw8TtGqMJmbkG7MspQfr5Euy7UIILJdWNywEY8FF6DgDA31ONeaNi8dyoWFeWYMWuQDR58mSEhobefEeyG3uIiIioo9OpVeJQ2ezhDdtyy2qwL6ek4etCQ1AqrTbipY2nkXJ7NMJ8ta5tdKNWByLOH3Ics9DwBTAQERGRe4n00+HB/hF4sH/DlW1GkxldXtuKXEMNDl4sxYT4sJscwTlaPYOXV5k5jrnZj5aTqomIyJ2pVUp0DfQEANTUS+fuFq3uITI3znGh9mdqHojYQ0RERG7Ocj+12nrpZAt2R0iAmYGIiIhkROvRED8YiMgKe4iIiEhOGIjIpuaBSMVAREREbo6BiGyyBCKVUsGr+YiIyO1ZAlGdiYGImjHxknsiIpIR9hCRTQxEREQkJ1oVAxHZwEBERERywh4isomrVBMRkZxoLIGIc4ioObGHiKtUExGRDHDIjGzikBkREckJh8zIJgYiIiKSEwYisolziIiISE6a7mUmnZu7MhBJAHuIiIhITthDRDZxUjUREckJAxHZxB4iIiKSE966g2yyjKAyEBERkRzwsnuyiZOqiYhITjhkRjZxyIyIiOREy5WqyRZOqiYiIjnRcMiMbGEPERERyQmHzMgmziEiIiI5YSAim9hDREREcsJARDYxEBERkZwwEJFNnFRNRERyYrmXWZ3JDEEQXNyaBvwElgDOISIiIjmx9BAB0lmtmoFIAjhkRkREcmIViOrZQ0SNGIiIiEhOtM2miNTWm26wp/MwEElAUyDi6SAiIvenVCrETgCprFbNT2AJEOcQqdhDRERE8iC1K80YiCTAJDQEIQ6ZERGRXDAQUQucQ0RERHIjtfuZMRBJAAMRERHJDXuIqAUzJ1UTEZHMMBBRCyZOqiYiIplhIKIWOGRGRERyIwYiXnZPFgxEREQkN1pxUjUXZqRGvJcZERHJjXiDV966gyy4UjUREckN5xBRC5xUTUREctM0h4hDZtTIEojUHDIjIiKZYA8RtcB7mRERkdwwEFELnENERERyw1t3UAu87J6IiOSGPUTUAgMRERHJDQMRtcB1iIiISG64UjW1wMvuiYhIbrScQ9Rk165dmDhxIiIjI6FQKPD9999bPS8IAhYsWICIiAh4enpizJgxOHPmjNU+xcXFePTRR6HX6+Hv74/p06ejoqLCap8jR45g2LBh0Ol0iI6OxpIlSxxdml0sKzBwUjUREckFh8yaqaysxIABA7B8+XKbzy9ZsgTvvvsuVqxYgV9++QXe3t5ITk5GTU2NuM+jjz6K48ePIy0tDT/++CN27dqFJ598UnzeYDAgKSkJMTExyMjIwP/+7//ilVdewccff+zw+lqLQ2ZERCQ3lkBUJ5FA5OHKNx83bhzGjRtn8zlBEPDOO+9g/vz5uO+++wAA//rXvxAWFobvv/8ekydPxsmTJ7Fx40YcOHAAt912GwDgvffew/jx4/GPf/wDkZGR+OKLL1BXV4dPP/0UGo0Gffr0QWZmJt5++22r4ORKnFRNRERyY7mXmVTmELk0EN1IdnY28vLyMGbMGHGbn58fhgwZgvT0dEyePBnp6enw9/cXwxAAjBkzBkqlEr/88gseeOABpKenY/jw4dBoNOI+ycnJePPNN1FSUoKAgIAW711bW4va2lrxscFgAAAYjUYYjcZ2rdNoNIqBCIK53Y8vRZYa5VArIL96AfnVLLd6AdYsB46u10PR8OFXXVfvsPew57iSDUR5eXkAgLCwMKvtYWFh4nN5eXkIDQ21et7DwwOBgYFW+3Tt2rXFMSzP2QpEixcvxsKFC1ts37x5M7y8vNpY0fWZhIaeoYP7f0HNb+1+eMlKS0tzdROcSm71AvKrWW71AqxZDhxV78l8AFDicl4BNmzY4JD3qKqqavW+kg1ErjRv3jzMnTtXfGwwGBAdHY2kpCTo9fp2fS+j0QjzgS0AgLvvTMSdXVoGNHdjNBqRlpaGsWPHQq1Wu7o5Die3egH51Sy3egHWLIeaHV1v1ZE84HQm9AGBGD9+SLsfH2ga4WkNyQai8PBwAEB+fj4iIiLE7fn5+Rg4cKC4T0FBgdXr6uvrUVxcLL4+PDwc+fn5VvtYHlv2uZZWq4VWq22xXa1WO+QvhWXITKdxzPGlylE/T6mSW72A/GqWW70Aa5YDR9XrrW04Zp1JcNjP057jSvY6765duyI8PBxbt24VtxkMBvzyyy9ITEwEACQmJqK0tBQZGRniPtu2bYPZbMaQIUPEfXbt2mU1jpiWloZevXrZHC5zBU6qJiIiudHwsvsmFRUVyMzMRGZmJoCGidSZmZm4cOECFAoFZs+ejddeew3//e9/cfToUTz22GOIjIzE/fffDwDo3bs3fve73+GJJ57A/v37sWfPHsyaNQuTJ09GZGQkAOB//ud/oNFoMH36dBw/fhxr1qzBsmXLrIbEXI0LMxIRkdxIbaVqlw6ZHTx4ECNHjhQfW0JKSkoKVq9ejeeeew6VlZV48sknUVpairvvvhsbN26ETqcTX/PFF19g1qxZGD16NJRKJSZNmoR3331XfN7Pzw+bN29GamoqEhISEBwcjAULFkjmknug+TpEku2wIyIialdSW6napYFoxIgREAThus8rFAq8+uqrePXVV6+7T2BgIL788ssbvk///v3x888/t7mdjsYhMyIikhuuVE0tMBAREZHcMBBRCwxEREQkNwxE1IKZk6qJiEhmxHuZSWRSNQORBJg4qZqIiGRGvJdZvfmG84mdhZ/ALmY2CxDQ0DPEITMiIpILSw8RABhNDESyV29u+kvAQERERHLRPBBJYR4RA5GLMRAREZEcaVTNA5HJhS1pwEDkYlaBiJOqiYhIJlRKBVSNHQFSWK2agcjF6s1Nfwk4qZqIiORESpfe8xPYxZr3EHHEjIiI5ERKt+9gIHIxSyDyUCqgUDARERGRfLCHiETNAxEREZGcMBCRqN7EQERERPLEQEQi9hAREZFcSen2HQxELiYGIl5yT0REMsMeIhI19RDxVBARkbzwKjMSmRrXIeKQGRERyU3zG7y6GgORi3EOERERyRWHzEjEQERERHIlBiIT72Ume5bL7lUMREREJDMaziEiC/YQERGRXHHIjEQMREREJFcMRCTiOkRERCRXDEQkYg8RERHJFQMRierFdYh4KoiISF546w4SsYeIiIjkiitVk4iBiIiI5IpDZiSyrEPEQERERHLDQEQik5kLMxIRkTyJ9zLjHCLikBkREckVe4hIxHWIiIhIrjSNn3219byXmew1XXbPQERERPIiDpmxh4iahsx4KoiISF44ZEYiziEiIiK5YiAiEQMRERHJlRiIeJUZcR0iIiKSK8tK1XXsISL2EBERkVxxyIxEDERERCRXHDIjEdchIiIiuWIPEYlMjesQ8dYdREQkNwxEJOI6REREJFfNF2YUBMGlbeGnsItxDhEREcmVpYcIAIwmBiJZYyAiIiK50jSbP+vqYTMGIhfjOkRERCRXliEzwPU3eGUgcjH2EBERkVyplArxoiJXX3rPQORiDERERCRnUrnSjIHIxbgOERERyZlUbt/BQORi7CEiIiI5k8pq1QxELlbfuDAj1yEiIiI54pAZAQBMjT1EXKmaiIjkiIGIAHDIjIiI5I2BiABwHSIiIpI3BiICwB4iIiKSN8tVZgxEMmcUJ1UzEBERkfxo2ENEANchIiIieeOQGQHgHCIiIpI3ccjMxHuZyVrTHCKeCiIikh/LDV7ZQyRznFRNRERyZhkyq6sXXNoOBiIXMzEQERGRjDXduoNDZrLGHiIiIpIzTqomAAxEREQkbwxEBKApEPFeZkREJEcMRASg2d3uuQ4RERHJEFeqJgBch4iIiOSNPUQEgOsQERGRvDEQEQBOqiYiInnTiCtVMxDJGgMRERHJGXuICIIgMBAREZGsNQUiLswoW+Zmq5QzEBERkRxZ7mVWZ+KtO2TLcsk9AKhVPBVERCQ/HDJzgeXLl6NLly7Q6XQYMmQI9u/f79L21DdLw+whIiIiOeKQmZOtWbMGc+fOxcsvv4xDhw5hwIABSE5ORkFBgcvaVG9mICIiInljD5GTvf3223jiiScwdepUxMfHY8WKFfDy8sKnn37qsjYxEBERkdxJZaVqD5e+u5PU1dUhIyMD8+bNE7cplUqMGTMG6enpLfavra1FbW2t+NhgMAAAjEYjjEZju7WrurYOAKCAAJOpHkajPEKR5WfYnj9LKZNbvYD8apZbvQBrlgNn1atCQxCqrTe3+3vZczxZBKLCwkKYTCaEhYVZbQ8LC8OpU6da7L948WIsXLiwxfbNmzfDy8ur3dpVXAtolQooAKSlpbXbcTsKudUst3oB+dUst3oB1iwHjq43pxLwUChgrK3Ghg0b2vXYVVVVrd5XFoHIXvPmzcPcuXPFxwaDAdHR0UhKSoJer2/X93r4XiPS0tIwduxYqNXqdj22VBmN8qpZbvUC8qtZbvUCrFkONTuz3pm/d8xxLSM8rSGLQBQcHAyVSoX8/Hyr7fn5+QgPD2+xv1arhVarbbFdrVY77C+FI48tVXKrWW71AvKrWW71AqxZDjpyvfa0WxaTqjUaDRISErB161Zxm9lsxtatW5GYmOjClhEREZEUyKKHCADmzp2LlJQU3HbbbbjjjjvwzjvvoLKyElOnTnV104iIiMjFZBOIHn74YVy9ehULFixAXl4eBg4ciI0bN7aYaE1ERETyI5tABACzZs3CrFmzXN0MIiIikhhZzCEiIiIiuhEGIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPVmtVN1WgiAAAAwGQ7sf22g0oqqqCgaDocPeTdhecqtZbvUC8qtZbvUCrFkONbtDvZbPbcvn+I0wELVCeXk5ACA6OtrFLSEiIiJ7lZeXw8/P74b7KITWxCaZM5vNyM3Nha+vLxQKRbse22AwIDo6GhcvXoRer2/XY0uV3GqWW72A/GqWW70Aa5ZDze5QryAIKC8vR2RkJJTKG88SYg9RKyiVSkRFRTn0PfR6fYf9C9dWcqtZbvUC8qtZbvUCrFkOOnq9N+sZsuCkaiIiIpI9BiIiIiKSPQYiF9NqtXj55Zeh1Wpd3RSnkVvNcqsXkF/NcqsXYM1yILd6OamaiIiIZI89RERERCR7DEREREQkewxEREREJHsMRERERCR7DEROsHz5cnTp0gU6nQ5DhgzB/v37b7j/2rVrERcXB51Oh379+mHDhg1Oamn7sKfe1atXQ6FQWH3pdDontvbW7dq1CxMnTkRkZCQUCgW+//77m75mx44dGDx4MLRaLWJjY7F69WqHt7O92Fvvjh07WpxjhUKBvLw85zT4Fi1evBi33347fH19ERoaivvvvx+nT5++6es68u9xW2ru6L/LH374Ifr37y8uQpiYmIiffvrphq/pyOfY3no7+vltDQYiB1uzZg3mzp2Ll19+GYcOHcKAAQOQnJyMgoICm/vv3bsXjzzyCKZPn47Dhw/j/vvvx/33349jx445ueVtY2+9QMMqqFeuXBG/cnJynNjiW1dZWYkBAwZg+fLlrdo/OzsbEyZMwMiRI5GZmYnZs2djxowZ2LRpk4Nb2j7srdfi9OnTVuc5NDTUQS1sXzt37kRqair27duHtLQ0GI1GJCUlobKy8rqv6ei/x22pGejYv8tRUVF44403kJGRgYMHD2LUqFG47777cPz4cZv7d/RzbG+9QMc+v60ikEPdcccdQmpqqvjYZDIJkZGRwuLFi23u/4c//EGYMGGC1bYhQ4YIf/rTnxzazvZib72rVq0S/Pz8nNQ6xwMgrFu37ob7PPfcc0KfPn2stj388MNCcnKyA1vmGK2pd/v27QIAoaSkxCltcrSCggIBgLBz587r7tPRf4+v1Zqa3e13WRAEISAgQFi5cqXN59ztHAvCjet1x/N7LfYQOVBdXR0yMjIwZswYcZtSqcSYMWOQnp5u8zXp6elW+wNAcnLydfeXkrbUCwAVFRWIiYlBdHT0Tf+H4g468jm+FQMHDkRERATGjh2LPXv2uLo5bVZWVgYACAwMvO4+7naOW1Mz4D6/yyaTCV999RUqKyuRmJhocx93OsetqRdwn/N7PQxEDlRYWAiTyYSwsDCr7WFhYdedP5GXl2fX/lLSlnp79eqFTz/9FP/5z3/w73//G2azGXfeeScuXbrkjCa7xPXOscFgQHV1tYta5TgRERFYsWIFvv32W3z77beIjo7GiBEjcOjQIVc3zW5msxmzZ8/GXXfdhb59+153v478e3yt1tbsDr/LR48ehY+PD7RaLZ566imsW7cO8fHxNvd1h3NsT73ucH5vhne7J5dKTEy0+h/JnXfeid69e+Ojjz7CokWLXNgyai+9evVCr169xMd33nknsrKysHTpUnz++ecubJn9UlNTcezYMezevdvVTXGa1tbsDr/LvXr1QmZmJsrKyvDNN98gJSUFO3fuvG5I6Ojsqdcdzu/NMBA5UHBwMFQqFfLz86225+fnIzw83OZrwsPD7dpfStpS77XUajUGDRqEs2fPOqKJknC9c6zX6+Hp6emiVjnXHXfc0eFCxaxZs/Djjz9i165diIqKuuG+Hfn3uDl7ar5WR/xd1mg0iI2NBQAkJCTgwIEDWLZsGT766KMW+7rDOban3mt1xPN7MxwycyCNRoOEhARs3bpV3GY2m7F169brjtMmJiZa7Q8AaWlpNxzXlYq21Hstk8mEo0ePIiIiwlHNdLmOfI7bS2ZmZoc5x4IgYNasWVi3bh22bduGrl273vQ1Hf0ct6Xma7nD77LZbEZtba3N5zr6ObblRvVeyx3ObwuuntXt7r766itBq9UKq1evFk6cOCE8+eSTgr+/v5CXlycIgiBMmTJFeOGFF8T99+zZI3h4eAj/+Mc/hJMnTwovv/yyoFarhaNHj7qqBLvYW+/ChQuFTZs2CVlZWUJGRoYwefJkQafTCcePH3dVCXYrLy8XDh8+LBw+fFgAILz99tvC4cOHhZycHEEQBOGFF14QpkyZIu5/7tw5wcvLS/jrX/8qnDx5Uli+fLmgUqmEjRs3uqoEu9hb79KlS4Xvv/9eOHPmjHD06FHhz3/+s6BUKoUtW7a4qgS7zJw5U/Dz8xN27NghXLlyRfyqqqoS93G33+O21NzRf5dfeOEFYefOnUJ2drZw5MgR4YUXXhAUCoWwefNmQRDc7xzbW29HP7+twUDkBO+9957QuXNnQaPRCHfccYewb98+8bl77rlHSElJsdr/66+/Fnr27CloNBqhT58+wvr1653c4ltjT72zZ88W9w0LCxPGjx8vHDp0yAWtbjvLZeXXflnqTElJEe65554Wrxk4cKCg0WiEbt26CatWrXJ6u9vK3nrffPNNoXv37oJOpxMCAwOFESNGCNu2bXNN49vAVq0ArM6Zu/0et6Xmjv67PG3aNCEmJkbQaDRCSEiIMHr0aDEcCIL7nWN76+3o57c1FIIgCM7rjyIiIiKSHs4hIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiog7n8ccfx/333++y958yZQr+/ve/t3r/wsJChIaG4tKlSw5sFRHdCq5UTUSSolAobvj8yy+/jDlz5kAQBPj7+zunUc38+uuvGDVqFHJycuDj4wMAyM7OxosvvogdO3aguLgYwcHBSEhIwJtvvom4uDgAwLPPPouSkhJ88sknTm8zEd0cAxERSUpeXp74/Zo1a7BgwQKcPn1a3Obj4yMGEVeYMWMGPDw8sGLFCgCA0WhE79690atXL7z00kuIiIjApUuX8NNPP+Hee+/F0KFDAQDHjx9HQkICcnNzERgY6LL2E5FtHDIjIkkJDw8Xv/z8/KBQKKy2+fj4tBgyGzFiBJ5++mnMnj0bAQEBCAsLwz//+U9UVlZi6tSp8PX1RWxsLH766Ser9zp27BjGjRsHHx8fhIWFYcqUKSgsLLxu20wmE7755htMnDhR3Hb8+HFkZWXhgw8+wNChQxETE4O77roLr732mhiGAKBPnz6IjIzEunXr2u+HRUTthoGIiNzCZ599huDgYOzfvx9PP/00Zs6cid///ve48847cejQISQlJWHKlCmoqqoCAJSWlmLUqFEYNGgQDh48iI0bNyI/Px9/+MMfrvseR44cQVlZGW677TZxW0hICJRKJb755huYTKYbtvGOO+7Azz//3D4FE1G7YiAiIrcwYMAAzJ8/Hz169MC8efOg0+kQHByMJ554Aj169MCCBQtQVFSEI0eOAADef/99DBo0CH//+98RFxeHQYMG4dNPP8X27dvx22+/2XyPnJwcqFQqhIaGits6deqEd999FwsWLEBAQABGjRqFRYsW4dy5cy1eHxkZiZycHMf8AIjoljAQEZFb6N+/v/i9SqVCUFAQ+vXrJ24LCwsDABQUFABomBy9fft2cU6Sj4+POAE6KyvL5ntUV1dDq9W2mPidmpqKvLw8fPHFF0hMTMTatWvRp08fpKWlWe3n6ekp9lARkbR4uLoBRETtQa1WWz1WKBRW2ywhxmw2AwAqKiowceJEvPnmmy2OFRERYfM9goODUVVVhbq6Omg0GqvnfH19MXHiREycOBGvvfYakpOT8dprr2Hs2LHiPsXFxQgJCWlbgUTkUAxERCRLgwcPxrfffosuXbrAw6N1/xQOHDgQAHDixAnxe1sUCgXi4uKwd+9eq+3Hjh3DiBEj2thiInIkDpkRkSylpqaiuLgYjzzyCA4cOICsrCxs2rQJU6dOve7k6JCQEAwePBi7d+8Wt2VmZuK+++7DN998gxMnTuDs2bP45JNP8Omnn+K+++4T96uqqkJGRgaSkpIcXhsR2Y+BiIhkKTIyEnv27IHJZEJSUhL69euH2bNnw9/fH0rl9f9pnDFjBr744gvxcVRUFLp06YKFCxdiyJAhGDx4MJYtW4aFCxfixRdfFPf7z3/+g86dO2PYsGEOrYuI2oYLMxIR2aG6uhq9evXCmjVrkJiY2OrXDR06FM888wz+53/+x4GtI6K2Yg8REZEdPD098a9//euGCzheq7CwEA8++CAeeeQRB7aMiG4Fe4iIiIhI9thDRERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREsvf/AdMEG7OtK4KcAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Mariachi.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rocket and Aerodynamic surfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison purposes, no parachute will be added to the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "CAMOES = Rocket(\n", + " radius=0.0715,\n", + " mass=22.8,\n", + " inertia=(16.2, 16.2, 0.066),\n", + " center_of_mass_without_motor=0,\n", + " power_off_drag=\"../../data/rockets/camoes/drag_coefficient_power_off.csv\",\n", + " power_on_drag=\"../../data/rockets/camoes/drag_coefficient_power_on.csv\",\n", + " coordinate_system_orientation=\"tail_to_nose\",\n", + ")\n", + "\n", + "CAMOES.set_rail_buttons(0.5, 0.2)\n", + "\n", + "CAMOES.add_motor(Mariachi, position=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "NoseCone = CAMOES.add_nose(\n", + " length=0.455,\n", + " kind=\"vonKarman\",\n", + " position=1.1884,\n", + ")\n", + "\n", + "FinSet = CAMOES.add_trapezoidal_fins(\n", + " n=4,\n", + " span=0.155,\n", + " root_chord=0.185,\n", + " tip_chord=0.15,\n", + " position=-1.0866,\n", + ")\n", + "\n", + "Tail = CAMOES.add_tail(\n", + " top_radius=0.0715, bottom_radius=0.037, length=0.048, position=-1.2866\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "def drogue_trigger(p, h, y):\n", + " return True if y[5] < 5 and y[2] > 300 else False\n", + "\n", + "\n", + "Drogue = CAMOES.add_parachute(\n", + " \"Drogue\", cd_s=0.33, sampling_rate=400, lag=1.5, trigger=drogue_trigger\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CAMOES.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Air Brakes - Active Control Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For privacy reasons, the controller function is not the one developed and used by the team. \n", + "\n", + "The controller function just describes the actuation with time of the algorithm developed by the team. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def controller_function(\n", + " time, sampling_rate, state, state_history, observed_variables, air_brakes\n", + "):\n", + " new_deployment_level = 0\n", + "\n", + " if time <= 11.4:\n", + " new_deployment_level = 1\n", + " else:\n", + " new_deployment_level = (\n", + " -0.002906 * np.power(time, 3)\n", + " + 0.1497 * np.power(time, 2)\n", + " + -2.563 * time\n", + " + 14.96\n", + " )\n", + "\n", + " if time > 19.6:\n", + " new_deployment_level = 0\n", + "\n", + " if time < 3.8:\n", + " new_deployment_level = 0\n", + "\n", + " air_brakes.deployment_level = new_deployment_level\n", + "\n", + " return time, air_brakes.deployment_level" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "air_brakes = CAMOES.add_air_brakes(\n", + " drag_coefficient_curve=[\n", + " # Mach 0.1\n", + " [0, 0.1, 0.7692],\n", + " [10 / 100, 0.1, 0.7663],\n", + " [20 / 100, 0.1, 0.7762],\n", + " [30 / 100, 0.1, 0.7847],\n", + " [40 / 100, 0.1, 0.7999],\n", + " [50 / 100, 0.1, 0.8246],\n", + " [60 / 100, 0.1, 0.8310],\n", + " [70 / 100, 0.1, 0.8499],\n", + " [80 / 100, 0.1, 0.8738],\n", + " [90 / 100, 0.1, 0.8945],\n", + " [100 / 100, 0.1, 1.1018],\n", + " # Mach 0.2\n", + " [0, 0.2, 0.7508],\n", + " [10 / 100, 0.2, 0.7513],\n", + " [20 / 100, 0.2, 0.7670],\n", + " [30 / 100, 0.2, 0.7782],\n", + " [40 / 100, 0.2, 0.7943],\n", + " [50 / 100, 0.2, 0.8063],\n", + " [60 / 100, 0.2, 0.8243],\n", + " [70 / 100, 0.2, 0.8493],\n", + " [80 / 100, 0.2, 0.8781],\n", + " [90 / 100, 0.2, 0.8925],\n", + " [100 / 100, 0.2, 0.9851],\n", + " # Mach 0.3\n", + " [0, 0.3, 0.7445],\n", + " [10 / 100, 0.3, 0.7571],\n", + " [20 / 100, 0.3, 0.7699],\n", + " [30 / 100, 0.3, 0.7828],\n", + " [40 / 100, 0.3, 0.7985],\n", + " [50 / 100, 0.3, 0.8114],\n", + " [60 / 100, 0.3, 0.8379],\n", + " [70 / 100, 0.3, 0.8634],\n", + " [80 / 100, 0.3, 0.8834],\n", + " [90 / 100, 0.3, 0.8947],\n", + " [100 / 100, 0.3, 0.9782],\n", + " # Mach 0.4\n", + " [0, 0.4, 0.7492],\n", + " [10 / 100, 0.4, 0.7566],\n", + " [20 / 100, 0.4, 0.7698],\n", + " [30 / 100, 0.4, 0.7840],\n", + " [40 / 100, 0.4, 0.7994],\n", + " [50 / 100, 0.4, 0.8136],\n", + " [60 / 100, 0.4, 0.8399],\n", + " [70 / 100, 0.4, 0.8665],\n", + " [80 / 100, 0.4, 0.8834],\n", + " [90 / 100, 0.4, 0.8969],\n", + " [100 / 100, 0.4, 0.9659],\n", + " # Mach 0.5\n", + " [0, 0.5, 0.7454],\n", + " [10 / 100, 0.5, 0.7537],\n", + " [20 / 100, 0.5, 0.7651],\n", + " [30 / 100, 0.5, 0.7811],\n", + " [40 / 100, 0.5, 0.7987],\n", + " [50 / 100, 0.5, 0.8163],\n", + " [60 / 100, 0.5, 0.8404],\n", + " [70 / 100, 0.5, 0.8631],\n", + " [80 / 100, 0.5, 0.8788],\n", + " [90 / 100, 0.5, 0.8951],\n", + " [100 / 100, 0.5, 0.9416],\n", + " # Mach 0.6\n", + " [0, 0.6, 0.7036],\n", + " [10 / 100, 0.6, 0.7237],\n", + " [20 / 100, 0.6, 0.7359],\n", + " [30 / 100, 0.6, 0.7496],\n", + " [40 / 100, 0.6, 0.7684],\n", + " [50 / 100, 0.6, 0.7899],\n", + " [60 / 100, 0.6, 0.8153],\n", + " [70 / 100, 0.6, 0.8406],\n", + " [80 / 100, 0.6, 0.8539],\n", + " [90 / 100, 0.6, 0.8712],\n", + " [100 / 100, 0.6, 0.9286],\n", + " # Mach 0.7\n", + " [0, 0.7, 0.6810],\n", + " [10 / 100, 0.7, 0.6948],\n", + " [20 / 100, 0.7, 0.7076],\n", + " [30 / 100, 0.7, 0.7233],\n", + " [40 / 100, 0.7, 0.7427],\n", + " [50 / 100, 0.7, 0.7629],\n", + " [60 / 100, 0.7, 0.7876],\n", + " [70 / 100, 0.7, 0.8073],\n", + " [80 / 100, 0.7, 0.8272],\n", + " [90 / 100, 0.7, 0.8453],\n", + " [100 / 100, 0.7, 0.8960],\n", + " # Mach 0.8\n", + " [0, 0.8, 0.6578],\n", + " [10 / 100, 0.8, 0.6739],\n", + " [20 / 100, 0.8, 0.6870],\n", + " [30 / 100, 0.8, 0.7036],\n", + " [40 / 100, 0.8, 0.7240],\n", + " [50 / 100, 0.8, 0.7457],\n", + " [60 / 100, 0.8, 0.7676],\n", + " [70 / 100, 0.8, 0.7880],\n", + " [80 / 100, 0.8, 0.8098],\n", + " [90 / 100, 0.8, 0.8278],\n", + " [100 / 100, 0.8, 0.8799],\n", + " # Mach 0.9\n", + " [0, 0.9, 0.6574],\n", + " [10 / 100, 0.9, 0.6634],\n", + " [20 / 100, 0.9, 0.6794],\n", + " [30 / 100, 0.9, 0.6958],\n", + " [40 / 100, 0.9, 0.7201],\n", + " [50 / 100, 0.9, 0.7395],\n", + " [60 / 100, 0.9, 0.7618],\n", + " [70 / 100, 0.9, 0.7844],\n", + " [80 / 100, 0.9, 0.8057],\n", + " [90 / 100, 0.9, 0.8278],\n", + " [100 / 100, 0.9, 0.8628],\n", + " # Mach 1.0\n", + " [0, 1.0, 0.8350],\n", + " [10 / 100, 1.0, 0.8241],\n", + " [20 / 100, 1.0, 0.8403],\n", + " [30 / 100, 1.0, 0.8593],\n", + " [40 / 100, 1.0, 0.8832],\n", + " [50 / 100, 1.0, 0.9070],\n", + " [60 / 100, 1.0, 0.9358],\n", + " [70 / 100, 1.0, 0.9639],\n", + " [80 / 100, 1.0, 0.9881],\n", + " [90 / 100, 1.0, 1.0093],\n", + " [100 / 100, 1.0, 1.0347],\n", + " # Mach 1.1\n", + " [0, 1.1, 0.8610],\n", + " [10 / 100, 1.1, 0.8447],\n", + " [20 / 100, 1.1, 0.8617],\n", + " [30 / 100, 1.1, 0.8800],\n", + " [40 / 100, 1.1, 0.9027],\n", + " [50 / 100, 1.1, 0.9244],\n", + " [60 / 100, 1.1, 0.9515],\n", + " [70 / 100, 1.1, 0.9810],\n", + " [80 / 100, 1.1, 1.0069],\n", + " [90 / 100, 1.1, 1.0253],\n", + " [100 / 100, 1.1, 1.0560],\n", + " ],\n", + " controller_function=controller_function,\n", + " sampling_rate=10,\n", + " reference_area=None,\n", + " clamp=True,\n", + " initial_observed_variables=(0, 0),\n", + " override_rocket_drag=True,\n", + " name=\"Air Brakes\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "air_brakes.all_info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flight Simulation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R:\\RocketPy\\rocketpy\\simulation\\flight.py:1201: UserWarning: time_overshoot has been set to False due to the presence of controllers or sensors. \n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "test_flight = Flight(\n", + " rocket=CAMOES, environment=env, inclination=84, heading=133, rail_length=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Apogee State\n", + "\n", + "Apogee Time: 23.034 s\n", + "Apogee Altitude: 3110.284 m (ASL) | 3003.284 m (AGL)\n", + "Apogee Freestream Speed: 16.887 m/s\n", + "Apogee X position: 399.259 m\n", + "Apogee Y position: -457.173 m\n", + "Apogee latitude: 39.3858907°\n", + "Apogee longitude: -8.2848916°\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_flight.prints.apogee_conditions()\n", + "test_flight.plots.trajectory_3d()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Airbrakes actuation analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "time_list, deployment_level_list = [], []\n", + "\n", + "obs_vars = test_flight.get_controller_observed_variables()\n", + "\n", + "for time, deployment_level in obs_vars:\n", + " time_list.append(time)\n", + " deployment_level_list.append(deployment_level)\n", + "\n", + "# Plot deployment level by time\n", + "plt.plot(time_list, deployment_level_list)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Deployment Level\")\n", + "plt.title(\"Deployment Level by Time\")\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy.simulation.flight_data_importer import FlightDataImporter" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'altitude', 'az']\n" + ] + } + ], + "source": [ + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"filtered_altitude_AGL\": \"altitude\",\n", + " \"filtered_acceleration\": \"az\",\n", + "}\n", + "\n", + "cots_altimeter_flight = FlightDataImporter(\n", + " name=\"COTS Altimeter Flight\",\n", + " paths=\"../../data/rockets/camoes/flight_data.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [\n", + " (test_flight.altitude, \"RocketPy\"),\n", + " (cots_altimeter_flight.altitude, \"COTS Altimeter\"),\n", + " ],\n", + " title=\"Altitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Altitude (m)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'latitude', 'longitude']\n" + ] + } + ], + "source": [ + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"latitude\": \"latitude\",\n", + " \"longitude\": \"longitude\",\n", + "}\n", + "\n", + "cots_gnss_flight = FlightDataImporter(\n", + " name=\"COTS GNSS Flight\",\n", + " paths=\"../../data/rockets/camoes/gnss_flight.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [(test_flight.latitude, \"RocketPy\"), (cots_gnss_flight.latitude, \"Actual - GNSS\")],\n", + " title=\"Latitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Latitude (deg)\",\n", + ")\n", + "Function.compare_plots(\n", + " [\n", + " (test_flight.longitude, \"RocketPy\"),\n", + " (cots_gnss_flight.longitude, \"Actual - GNSS\"),\n", + " ],\n", + " title=\"Longitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Longitude (deg)\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "RocketPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/genesis_flight_sim.ipynb b/docs/examples/genesis_flight_sim.ipynb new file mode 100644 index 000000000..fcad36f92 --- /dev/null +++ b/docs/examples/genesis_flight_sim.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GENESIS - Faraday Rocketry UPV - 2023\n", + "\n", + "Launched at EuRoC'23, having a total success in their flight. The trajectory was nominal through the whole flight, which allowed the team to win the award for 3km flight. \n", + "Permission to use flight data given by Nacho Coello, 2024.\n", + "\n", + "These results were extracted out of the flight card:\n", + "\n", + "1. Team number: `11`\n", + "2. Launch date: `October 12th, 2023. Around 14hrs local time`\n", + "3. Simulated apogee: `3044 m` \n", + "4. Official recorded apogee: `2916 m`\n", + "\n", + "The relative error of altitude apogee is only `4.4%`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Function, Environment, Flight, Rocket, SolidMotor\n", + "from rocketpy.motors import CylindricalTank, SolidMotor\n", + "from rocketpy.motors.tank import MassFlowRateBasedTank\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gravity Details\n", + "\n", + "Acceleration of gravity at surface level: 9.8066 m/s²\n", + "Acceleration of gravity at 4.000 km (ASL): 9.8066 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Date: 2023-10-12 13:00:00 UTC | 2023-10-12 14:00:00 Portugal\n", + "Launch Site Latitude: 39.38895°\n", + "Launch Site Longitude: -8.28837°\n", + "Reference Datum: WGS84\n", + "Launch Site UTM coordinates: 44461.73 W 4373301.99 N\n", + "Launch Site UTM zone: 30S\n", + "Launch Site Surface Elevation: 160.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: Reanalysis\n", + "Reanalysis Maximum Height: 4.000 km\n", + "Reanalysis Time Period: from 2023-10-12 08:00:00 to 2023-10-15 18:00:00 utc\n", + "Reanalysis Hour Interval: 1 hrs\n", + "Reanalysis Latitude Range: From 40.0° to 39.0°\n", + "Reanalysis Longitude Range: From -9.0° to -7.0°\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 193.47 m/s\n", + "Surface Wind Direction: 13.47°\n", + "Surface Wind Heading: 2.95°\n", + "Surface Pressure: 999.67 hPa\n", + "Surface Temperature: 303.08 K\n", + "Surface Air Density: 1.149 kg/m³\n", + "Surface Speed of Sound: 349.00 m/s\n", + "\n", + "\n", + "Earth Model Details\n", + "\n", + "Earth Radius at Launch site: 6369.57 km\n", + "Semi-major Axis: 6378.14 km\n", + "Semi-minor Axis: 6356.75 km\n", + "Flattening: 0.0034\n", + "\n", + "\n", + "Atmospheric Model Plots\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " gravity=9.80665,\n", + " date=(2023, 10, 12, 14),\n", + " latitude=39.38895,\n", + " longitude=-8.28837,\n", + " elevation=160,\n", + " datum=\"WGS84\",\n", + " timezone=\"Portugal\",\n", + ")\n", + "\n", + "env.set_atmospheric_model(\n", + " type=\"Reanalysis\",\n", + " file=\"../../data/weather/euroc_2023_all_windows.nc\",\n", + " dictionary=\"ECMWF\",\n", + ")\n", + "env.max_expected_height = 4000\n", + "env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motor" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ProL995 = SolidMotor(\n", + " thrust_source=\"../../data/motors/cesaroni/Cesaroni_3618L995-P.eng\",\n", + " burn_time=3.8,\n", + " grain_number=3,\n", + " grain_density=1815,\n", + " grain_initial_inner_radius=0.015,\n", + " grain_outer_radius=0.033,\n", + " grain_initial_height=0.12,\n", + " nozzle_radius=0.042354,\n", + " throat_radius=0.00925,\n", + " grain_separation=0.005,\n", + " grains_center_of_mass_position=0.243,\n", + " dry_inertia=(0, 0, 0),\n", + " center_of_dry_mass_position=0.895,\n", + " dry_mass=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nozzle Details\n", + "Nozzle Radius: 0.042354 m\n", + "Nozzle Throat Radius: 0.00925 m\n", + "\n", + "Grain Details\n", + "Number of Grains: 3\n", + "Grain Spacing: 0.005 m\n", + "Grain Density: 1815 kg/m3\n", + "Grain Outer Radius: 0.033 m\n", + "Grain Inner Radius: 0.015 m\n", + "Grain Height: 0.12 m\n", + "Grain Volume: 0.000 m3\n", + "Grain Mass: 0.591 kg\n", + "\n", + "Motor Details\n", + "Total Burning Time: 3.8 s\n", + "Total Propellant Mass: 1.774 kg\n", + "Structural Mass Ratio: 0.000\n", + "Average Propellant Exhaust Velocity: 2039.968 m/s\n", + "Average Thrust: 952.100 N\n", + "Maximum Thrust: 1280.0 N at 1.2 s after ignition.\n", + "Total Impulse: 3617.980 Ns\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ProL995.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rocket and Aerodynamic surfaces" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "GENESIS = Rocket(\n", + " radius=0.047,\n", + " mass=9.214,\n", + " inertia=(7.5046, 7.5046, 0.042241),\n", + " center_of_mass_without_motor=0.895,\n", + " power_off_drag=\"../../data/rockets/genesis/drag_coefficient_power_off.csv\",\n", + " power_on_drag=\"../../data/rockets/genesis/drag_coefficient_power_on.csv\",\n", + " coordinate_system_orientation=\"tail_to_nose\",\n", + ")\n", + "\n", + "GENESIS.set_rail_buttons(0.98, 0.095)\n", + "\n", + "GENESIS.add_motor(ProL995, position=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "NoseCone = GENESIS.add_nose(length=0.27, kind=\"vonKarman\", position=2.372)\n", + "\n", + "FinSet = GENESIS.add_trapezoidal_fins(\n", + " n=4,\n", + " span=0.105,\n", + " root_chord=0.2,\n", + " tip_chord=0.11,\n", + " position=0.25,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "Drogue = GENESIS.add_parachute(\n", + " name=\"Drogue\",\n", + " cd_s=0.285005285533666,\n", + " trigger=\"apogee\",\n", + " sampling_rate=105,\n", + " lag=1,\n", + " noise=(0, 8.3, 0.5),\n", + ")\n", + "\n", + "Main = GENESIS.add_parachute(\n", + " name=\"Main\",\n", + " cd_s=1.1,\n", + " trigger=870,\n", + " sampling_rate=105,\n", + " lag=1,\n", + " noise=(0, 8.3, 0.5),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "GENESIS.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flight Simulation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "test_flight = Flight(\n", + " rocket=GENESIS, environment=env, inclination=84, heading=133, rail_length=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Apogee State\n", + "\n", + "Apogee Time: 24.165 s\n", + "Apogee Altitude: 3204.100 m (ASL) | 3044.100 m (AGL)\n", + "Apogee Freestream Speed: 27.443 m/s\n", + "Apogee X position: 401.587 m\n", + "Apogee Y position: -590.700 m\n", + "Apogee latitude: 39.3836364°\n", + "Apogee longitude: -8.2836963°\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_flight.prints.apogee_conditions()\n", + "test_flight.plots.trajectory_3d()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'altitude', 'az']\n" + ] + } + ], + "source": [ + "from rocketpy.simulation.flight_data_importer import FlightDataImporter\n", + "\n", + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"filtered_altitude_AGL\": \"altitude\",\n", + " \"filtered_acceleration\": \"az\",\n", + "}\n", + "\n", + "cots_altimeter_flight = FlightDataImporter(\n", + " name=\"COTS Altimeter Flight\",\n", + " paths=\"../../data/rockets/genesis/flight_data_faraday.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [\n", + " (test_flight.altitude, \"RocketPy\"),\n", + " (cots_altimeter_flight.altitude, \"COTS Altimeter\"),\n", + " ],\n", + " title=\"Altitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Altitude (m)\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "RocketPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 64c672a95..bf183ed6e 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -3,7 +3,7 @@ Flights simulated with RocketPy RocketPy has been used to simulate many flights, from small amateur rockets to large professional ones. -This section contains some of the most interesting +This section contains some of the most interesting results obtained with RocketPy. The following plot shows the comparison between the simulated and measured apogee of some rockets. @@ -22,6 +22,8 @@ apogee of some rockets. "NDRT (2020)": (1296.77, 1316.75), "Prometheus (2022)": (4190.05, 3898.37), "Cavour (2023)": (2818.90, 2789), + "Genesis (2023)": (3076.45, 2916), + "Camoes (2023)": (3003.28, 3015), "Juno III (2023)": (3026.05, 3213), "Halcyon (2023)": (3212.775, 3450), } @@ -60,14 +62,14 @@ apogee of some rockets. In the next sections you will find the simulations of the rockets listed above. .. note:: - + If you want to see your rocket here, please contact the maintainers! \ We would love to include your rocket in the examples. .. toctree:: :maxdepth: 1 :caption: Contents: - + bella_lui_flight_sim.ipynb ndrt_2020_flight_sim.ipynb valetudo_flight_sim.ipynb @@ -76,4 +78,6 @@ In the next sections you will find the simulations of the rockets listed above. prometheus_2022_flight_sim.ipynb halcyon_flight_sim.ipynb cavour_flight_sim.ipynb + genesis_flight_sim.ipynb + camoes_flight_sim.ipynb diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index 3886f72c4..2f94a0b16 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -1115,6 +1115,100 @@ " type=\"impact\",\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom exports using callback functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have shown, so far, how to perform to use the `MonteCarlo` class and visualize its results. By default, some variables exported to the output files, such as *apogee* and *x_impact*. The `export_list` argument provides a simplified way for the user to export additional variables listed in the documentation, such as *inclination* and *heading*. \n", + "\n", + "There are applications in which you might need to extract more information in the results than the `export_list` argument can handle. To that end, the `MonteCarlo` class has a `data_collector` argument which allows you customize further the output of the simulation.\n", + "\n", + "To exemplify its use, we show how to export the *date* of the environment used in the simulation together with the *average reynolds number* along with the default variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the `stochastic_env`, `stochastic_rocket` and `stochastic_flight` objects previously defined, and only change the `MonteCarlo` object. First, we need to define our customized data collector." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "# Defining custom callback functions\n", + "def get_average_reynolds_number(flight):\n", + " reynold_number_list = flight.reynolds_number(flight.time)\n", + " average_reynolds_number = np.mean(reynold_number_list)\n", + " return average_reynolds_number\n", + "\n", + "\n", + "def get_date(flight):\n", + " return flight.env.date\n", + "\n", + "\n", + "custom_data_collector = {\n", + " \"average_reynolds_number\": get_average_reynolds_number,\n", + " \"date\": get_date,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `data_collector` must be a dictionary whose keys are the names of the variables we want to export and the values are callback functions (python callables) that compute these variable values. Notice how we can compute complex expressions in this function and just export the result. For instance, the *get_average_reynolds_number* calls the `flight.reynolds_number` method for each value in `flight.time` list and computes the average value using numpy's `mean`. The *date* variable is straightforward.\n", + "\n", + "After we define the data collector, we pass it as an argument to the `MonteCarlo` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion = MonteCarlo(\n", + " filename=\"monte_carlo_analysis_outputs/monte_carlo_class_example_customized\",\n", + " environment=stochastic_env,\n", + " rocket=stochastic_rocket,\n", + " flight=stochastic_flight,\n", + " export_list=[\"apogee\", \"apogee_time\", \"x_impact\"],\n", + " data_collector=custom_data_collector,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion.simulate(number_of_simulations=10, append=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion.prints.all()" + ] } ], "metadata": { @@ -1134,7 +1228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/requirements.in b/docs/requirements.in index ea736a11a..b5138ee0c 100644 --- a/docs/requirements.in +++ b/docs/requirements.in @@ -6,3 +6,4 @@ ipykernel==6.29.4 pandas==2.2.2 lxml_html_clean==0.1.1 sphinx-copybutton==0.5.2 +sphinx-tabs==3.4.7 diff --git a/docs/requirements.txt b/docs/requirements.txt index a5d9c279d..c56c20cae 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -43,6 +43,7 @@ docutils==0.20.1 # nbsphinx # pydata-sphinx-theme # sphinx + # sphinx-tabs entrypoints==0.4 # via nbconvert executing==2.0.1 @@ -159,6 +160,7 @@ pygments==2.18.0 # nbconvert # pydata-sphinx-theme # sphinx + # sphinx-tabs python-dateutil==2.9.0.post0 # via # jupyter-client @@ -195,10 +197,13 @@ sphinx==7.3.7 # pydata-sphinx-theme # sphinx-copybutton # sphinx-design + # sphinx-tabs sphinx-copybutton==0.5.2 # via -r requirements.in sphinx-design==0.5.0 # via -r requirements.in +sphinx-tabs==3.4.7 + # via -r requirements.in sphinxcontrib-applehelp==1.0.8 # via sphinx sphinxcontrib-devhelp==1.0.6 diff --git a/docs/user/environment/1-atm-models/ensemble.rst b/docs/user/environment/1-atm-models/ensemble.rst index c1d19722c..97c247f68 100644 --- a/docs/user/environment/1-atm-models/ensemble.rst +++ b/docs/user/environment/1-atm-models/ensemble.rst @@ -24,7 +24,7 @@ Global Ensemble Forecast System (GEFS) The ``GEFS`` model is a global ensemble forecast model ... -.. jupyter-execute:: +.. code-block:: python from datetime import date, timedelta from rocketpy import Environment @@ -45,7 +45,7 @@ The ``GEFS`` model is a global ensemble forecast model ... .. forecasts are kept away stored. .. Changing this is as easy as the following line of code. -.. jupyter-execute:: +.. code-block:: python env_gefs.select_ensemble_member(3) # selects the 3rd forecast member @@ -56,7 +56,7 @@ A very useful thing is to compare the forecast of all ensemble members, as well as confirming how many are there. All of this can be done by calling the :meth:`rocketpy.Environment.all_info` method. -.. jupyter-execute:: +.. code-block:: python env_gefs.all_info() diff --git a/docs/user/installation.rst b/docs/user/installation.rst index b8f0d73b7..dabf967c8 100644 --- a/docs/user/installation.rst +++ b/docs/user/installation.rst @@ -19,7 +19,7 @@ If you want to choose a specific version to guarantee compatibility, you may ins .. code-block:: shell - pip install rocketpy==1.6.2 + pip install rocketpy==1.7.1 Optional Installation Method: ``conda`` diff --git a/docs/user/motors/motors.rst b/docs/user/motors/motors.rst index a6bc6ef4b..b690cf9e8 100644 --- a/docs/user/motors/motors.rst +++ b/docs/user/motors/motors.rst @@ -1,3 +1,5 @@ +.. _motors_usage: + Motors Usage ============ @@ -12,13 +14,13 @@ Motors Usage :caption: Solid Motors Solid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Hybrid Motors Hybrid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Liquid Motors @@ -30,7 +32,7 @@ Motors Usage :caption: Generic Motors Generic Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Tanks and Fluid diff --git a/pyproject.toml b/pyproject.toml index 244de58d5..e17c280ee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "rocketpy" -version = "1.6.2" +version = "1.7.1" description="Advanced 6-DOF trajectory simulation for High-Power Rocketry." dynamic = ["dependencies"] readme = "README.md" diff --git a/rocketpy/environment/environment.py b/rocketpy/environment/environment.py index 5cef60b21..dbabeee78 100644 --- a/rocketpy/environment/environment.py +++ b/rocketpy/environment/environment.py @@ -688,7 +688,9 @@ def set_date(self, date, timezone="UTC"): "Ensemble", ]: self.set_atmospheric_model( - self.atmospheric_model_file, self.atmospheric_model_dict + type=self.atmospheric_model_type, + file=self.atmospheric_model_file, + dictionary=self.atmospheric_model_dict, ) def set_location(self, latitude, longitude): @@ -726,7 +728,9 @@ def set_location(self, latitude, longitude): "Ensemble", ]: self.set_atmospheric_model( - self.atmospheric_model_file, self.atmospheric_model_dict + type=self.atmospheric_model_type, + file=self.atmospheric_model_file, + dictionary=self.atmospheric_model_dict, ) def set_gravity_model(self, gravity=None): @@ -1746,6 +1750,8 @@ def process_forecast_reanalysis( # Read weather file if isinstance(file, str): data = netCDF4.Dataset(file) + if dictionary["time"] not in data.variables.keys(): + dictionary = self.__weather_model_map.get("ECMWF_v0") else: data = file @@ -1884,9 +1890,9 @@ def process_forecast_reanalysis( temper, wind_u, wind_v, - wind_speed, wind_heading, wind_direction, + wind_speed, ) # Save atmospheric data self.__set_pressure_function(data_array[:, (1, 0)]) @@ -1910,7 +1916,12 @@ def process_forecast_reanalysis( # Compute info data self.atmospheric_model_init_date = get_initial_date_from_time_array(time_array) self.atmospheric_model_end_date = get_final_date_from_time_array(time_array) - self.atmospheric_model_interval = get_interval_date_from_time_array(time_array) + if self.atmospheric_model_init_date != self.atmospheric_model_end_date: + self.atmospheric_model_interval = get_interval_date_from_time_array( + time_array + ) + else: + self.atmospheric_model_interval = 0 self.atmospheric_model_init_lat = lat_list[0] self.atmospheric_model_end_lat = lat_list[-1] self.atmospheric_model_init_lon = lon_list[0] diff --git a/rocketpy/environment/weather_model_mapping.py b/rocketpy/environment/weather_model_mapping.py index 7aed6d5e1..59c6e2fd9 100644 --- a/rocketpy/environment/weather_model_mapping.py +++ b/rocketpy/environment/weather_model_mapping.py @@ -27,7 +27,7 @@ class WeatherModelMapping: "u_wind": "ugrdprs", "v_wind": "vgrdprs", } - ECMWF = { + ECMWF_v0 = { "time": "time", "latitude": "latitude", "longitude": "longitude", @@ -39,6 +39,18 @@ class WeatherModelMapping: "u_wind": "u", "v_wind": "v", } + ECMWF = { + "time": "valid_time", + "latitude": "latitude", + "longitude": "longitude", + "level": "pressure_level", + "temperature": "t", + "surface_geopotential_height": None, + "geopotential_height": None, + "geopotential": "z", + "u_wind": "u", + "v_wind": "v", + } NOAA = { "time": "time", "latitude": "lat", @@ -108,6 +120,7 @@ def __init__(self): self.all_dictionaries = { "GFS": self.GFS, "NAM": self.NAM, + "ECMWF_v0": self.ECMWF_v0, "ECMWF": self.ECMWF, "NOAA": self.NOAA, "RAP": self.RAP, diff --git a/rocketpy/mathutils/function.py b/rocketpy/mathutils/function.py index 314e6415b..ca6005cf3 100644 --- a/rocketpy/mathutils/function.py +++ b/rocketpy/mathutils/function.py @@ -1498,6 +1498,7 @@ def plot_1d( # pylint: disable=too-many-statements force_points=False, return_object=False, equal_axis=False, + *, filename=None, ): """Plot 1-Dimensional Function, from a lower limit to an upper limit, @@ -1597,6 +1598,7 @@ def plot_2d( # pylint: disable=too-many-statements disp_type="surface", alpha=0.6, cmap="viridis", + *, filename=None, ): """Plot 2-Dimensional Function, from a lower limit to an upper limit, @@ -1729,6 +1731,7 @@ def compare_plots( # pylint: disable=too-many-statements force_points=False, return_object=False, show=True, + *, filename=None, ): """Plots N 1-Dimensional Functions in the same plot, from a lower diff --git a/rocketpy/motors/hybrid_motor.py b/rocketpy/motors/hybrid_motor.py index b3527c604..163cdd811 100644 --- a/rocketpy/motors/hybrid_motor.py +++ b/rocketpy/motors/hybrid_motor.py @@ -601,7 +601,7 @@ def add_tank(self, tank, position): ) reset_funcified_methods(self) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws a representation of the HybridMotor. Parameters @@ -616,4 +616,4 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/liquid_motor.py b/rocketpy/motors/liquid_motor.py index cc7bf5027..d9a3220ae 100644 --- a/rocketpy/motors/liquid_motor.py +++ b/rocketpy/motors/liquid_motor.py @@ -463,7 +463,7 @@ def add_tank(self, tank, position): self.positioned_tanks.append({"tank": tank, "position": position}) reset_funcified_methods(self) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the LiquidMotor. Parameters @@ -478,4 +478,4 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/motor.py b/rocketpy/motors/motor.py index 558c9f9a2..93571b879 100644 --- a/rocketpy/motors/motor.py +++ b/rocketpy/motors/motor.py @@ -1083,7 +1083,7 @@ def get_attr_value(obj, attr_name, multiplier=1): # Write last line file.write(f"{self.thrust.source[-1, 0]:.4f} {0:.3f}\n") - def info(self, filename=None): + def info(self, *, filename=None): """Prints out a summary of the data and graphs available about the Motor. diff --git a/rocketpy/motors/solid_motor.py b/rocketpy/motors/solid_motor.py index 09a930522..f0862e26a 100644 --- a/rocketpy/motors/solid_motor.py +++ b/rocketpy/motors/solid_motor.py @@ -727,7 +727,7 @@ def propellant_I_13(self): def propellant_I_23(self): return 0 - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the SolidMotor. Parameters @@ -742,4 +742,4 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 418124cf0..982380891 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -23,7 +23,7 @@ class Tank(ABC): Name of the tank. Tank.geometry : TankGeometry Geometry of the tank. - Tank.flux_time : float, tuple of float, optional + Tank.flux_time : float, tuple of float Tank flux time in seconds. Tank.liquid : Fluid Liquid inside the tank as a Fluid object. @@ -84,13 +84,14 @@ def __init__(self, name, geometry, flux_time, liquid, gas, discretize=100): Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied.If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. gas : Fluid Gas inside the tank as a Fluid object. liquid : Fluid @@ -335,7 +336,7 @@ def center_of_mass(self): return center_of_mass - @funcify_method("Time (s)", "Inertia tensor of liquid (kg*m²)") + @funcify_method("Time (s)", "Liquid Inertia (kg*m²)") def liquid_inertia(self): """ Returns the inertia tensor of the liquid portion of the tank @@ -361,7 +362,7 @@ def liquid_inertia(self): return self.liquid.density * Ix_volume - @funcify_method("Time (s)", "inertia tensor of gas (kg*m^2)") + @funcify_method("Time (s)", "Gas Inertia (kg*m^2)") def gas_inertia(self): """ Returns the inertia tensor of the gas portion of the tank @@ -387,7 +388,7 @@ def gas_inertia(self): return self.gas.density * inertia_volume - @funcify_method("Time (s)", "inertia tensor (kg*m^2)") + @funcify_method("Time (s)", "Fluid Inertia (kg*m^2)") def inertia(self): """ Returns the inertia tensor of the tank's fluids as a function of @@ -476,7 +477,7 @@ def underfill_height_exception(param_name, param): elif (height < bottom_tolerance).any(): underfill_height_exception(name, height) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws the tank geometry. Parameters @@ -491,7 +492,18 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) + + def info(self): + """Prints out a summary of the tank properties.""" + self.prints.all() + + def all_info(self): + """Prints out detailed information and plots of the tank + properties. + """ + self.prints.all() + self.plots.all() class MassFlowRateBasedTank(Tank): @@ -527,14 +539,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. - If a float is given, the flux time is assumed to be between 0 and - the given float, in seconds. If a tuple of float is given, the flux - time is assumed to be between the first and second elements of the - tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -586,28 +598,28 @@ def __init__( self.liquid_mass_flow_rate_in = Function( liquid_mass_flow_rate_in, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Liquid Mass Flow Rate In (kg/s)", interpolation="linear", extrapolation="zero", ) self.gas_mass_flow_rate_in = Function( gas_mass_flow_rate_in, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Gas Mass Flow Rate In (kg/s)", interpolation="linear", extrapolation="zero", ) self.liquid_mass_flow_rate_out = Function( liquid_mass_flow_rate_out, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Liquid Mass Flow Rate Out (kg/s)", interpolation="linear", extrapolation="zero", ) self.gas_mass_flow_rate_out = Function( gas_mass_flow_rate_out, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Gas Mass Flow Rate Out (kg/s)", interpolation="linear", extrapolation="zero", ) @@ -620,7 +632,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -633,7 +645,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time by integrating @@ -644,7 +656,9 @@ def liquid_mass(self): Function Mass of the liquid as a function of time. """ - liquid_flow = self.net_liquid_flow_rate.integral_function() + liquid_flow = self.net_liquid_flow_rate.integral_function( + datapoints=self.discretize + ) liquid_mass = self.initial_liquid_mass + liquid_flow if (liquid_mass < 0).any(): raise ValueError( @@ -657,7 +671,7 @@ def liquid_mass(self): ) return liquid_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time by integrating @@ -668,7 +682,7 @@ def gas_mass(self): Function Mass of the gas as a function of time. """ - gas_flow = self.net_gas_flow_rate.integral_function() + gas_flow = self.net_gas_flow_rate.integral_function(datapoints=self.discretize) gas_mass = self.initial_gas_mass + gas_flow if (gas_mass < -1e-6).any(): # -1e-6 is to avoid numerical errors raise ValueError( @@ -682,7 +696,7 @@ def gas_mass(self): return gas_mass - @funcify_method("Time (s)", "liquid mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Liquid Mass Flow Rate (kg/s)", extrapolation="zero") def net_liquid_flow_rate(self): """ Returns the net mass flow rate of liquid as a function of time. @@ -696,7 +710,7 @@ def net_liquid_flow_rate(self): """ return self.liquid_mass_flow_rate_in - self.liquid_mass_flow_rate_out - @funcify_method("Time (s)", "gas mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Gas Mass Flow Rate (kg/s)", extrapolation="zero") def net_gas_flow_rate(self): """ Returns the net mass flow rate of gas as a function of time. @@ -710,7 +724,7 @@ def net_gas_flow_rate(self): """ return self.gas_mass_flow_rate_in - self.gas_mass_flow_rate_out - @funcify_method("Time (s)", "mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)", extrapolation="zero") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time. @@ -724,7 +738,7 @@ def net_mass_flow_rate(self): """ return self.net_liquid_flow_rate + self.net_gas_flow_rate - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -738,7 +752,7 @@ def fluid_volume(self): """ return self.liquid_volume + self.gas_volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -750,7 +764,7 @@ def liquid_volume(self): """ return self.liquid_mass / self.liquid.density - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. @@ -762,7 +776,7 @@ def gas_volume(self): """ return self.gas_mass / self.gas.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -799,7 +813,7 @@ def liquid_height(self): return liquid_height - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Gas Height (m)") def gas_height(self): """ Returns the gas level as a function of time. This @@ -864,13 +878,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -902,7 +917,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -915,7 +930,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time by @@ -928,7 +943,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -944,7 +959,7 @@ def fluid_volume(self): self.gas_volume ) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. The @@ -958,7 +973,7 @@ def liquid_volume(self): """ return -(self.ullage - self.geometry.total_volume) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. From the @@ -971,7 +986,7 @@ def gas_volume(self): """ return self.ullage - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -983,7 +998,7 @@ def gas_mass(self): """ return self.gas_volume * self.gas.density - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -995,7 +1010,7 @@ def liquid_mass(self): """ return self.liquid_volume * self.liquid.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -1009,7 +1024,7 @@ def liquid_height(self): """ return self.geometry.inverse_volume.compose(self.liquid_volume) - @funcify_method("Time (s)", "Height (m)", "linear") + @funcify_method("Time (s)", "Gas Height (m)", "linear") def gas_height(self): """ Returns the gas level as a function of time. This height is measured @@ -1059,13 +1074,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -1096,7 +1112,7 @@ def __init__( self._check_height_bounds() self._check_volume_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -1112,7 +1128,7 @@ def fluid_mass(self): sum_mass.set_discrete_based_on_model(self.liquid_level) return sum_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time by @@ -1125,7 +1141,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -1148,7 +1164,7 @@ def fluid_volume(self): ) return volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -1160,7 +1176,7 @@ def liquid_volume(self): """ return self.geometry.volume.compose(self.liquid_height) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. The gas volume @@ -1178,7 +1194,7 @@ def gas_volume(self): func -= self.liquid_volume return func - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This height is @@ -1191,7 +1207,7 @@ def liquid_height(self): """ return self.liquid_level - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -1203,7 +1219,7 @@ def gas_mass(self): """ return self.gas_volume * self.gas.density - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -1215,7 +1231,7 @@ def liquid_mass(self): """ return self.liquid_volume * self.liquid.density - @funcify_method("Time (s)", "Height (m)", "linear") + @funcify_method("Time (s)", "Gas Height (m)", "linear") def gas_height(self): """ Returns the gas level as a function of time. This @@ -1268,13 +1284,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -1311,7 +1328,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as @@ -1324,7 +1341,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time @@ -1337,7 +1354,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -1349,7 +1366,7 @@ def liquid_mass(self): """ return self.liquid_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -1361,7 +1378,7 @@ def gas_mass(self): """ return self.gas_mass - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -1389,7 +1406,7 @@ def fluid_volume(self): return fluid_volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. @@ -1401,7 +1418,7 @@ def gas_volume(self): """ return self.gas_mass / self.gas.density - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -1413,7 +1430,7 @@ def liquid_volume(self): """ return self.liquid_mass / self.liquid.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -1448,7 +1465,7 @@ def liquid_height(self): return liquid_height - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Gas Height (m)") def gas_height(self): """ Returns the gas level as a function of time. This diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index a0b7d4c4b..d501d1352 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -25,7 +25,7 @@ def __init__(self, aero_surface): self.aero_surface = aero_surface @abstractmethod - def draw(self, filename=None): + def draw(self, *, filename=None): pass def lift(self): @@ -54,7 +54,7 @@ class _NoseConePlots(_AeroSurfacePlots): """Class that contains all nosecone plots. This class inherits from the _AeroSurfacePlots class.""" - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the nosecone shape along with some important information, including the center line and the center of pressure position. @@ -140,7 +140,7 @@ class _FinsPlots(_AeroSurfacePlots): _AeroSurfacePlots class.""" @abstractmethod - def draw(self, filename=None): + def draw(self, *, filename=None): pass def airfoil(self): @@ -201,7 +201,7 @@ class _TrapezoidalFinsPlots(_FinsPlots): """Class that contains all trapezoidal fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. @@ -330,7 +330,7 @@ class _EllipticalFinsPlots(_FinsPlots): """Class that contains all elliptical fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information. These being: the center line and the center of pressure position. @@ -409,7 +409,7 @@ class _FreeFormFinsPlots(_FinsPlots): """Class that contains all free form fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information. These being: the center line and the center of pressure position. @@ -483,7 +483,7 @@ def draw(self, filename=None): class _TailPlots(_AeroSurfacePlots): """Class that contains all tail plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): # This will de done in the future pass @@ -498,7 +498,7 @@ def drag_coefficient_curve(self): else: return self.aero_surface.drag_coefficient.plot() - def draw(self, filename=None): + def draw(self, *, filename=None): raise NotImplementedError def all(self): @@ -514,12 +514,12 @@ def all(self): class _GenericSurfacePlots(_AeroSurfacePlots): """Class that contains all generic surface plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): pass class _LinearGenericSurfacePlots(_AeroSurfacePlots): """Class that contains all linear generic surface plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): pass diff --git a/rocketpy/plots/compare/compare_flights.py b/rocketpy/plots/compare/compare_flights.py index 2689ac482..521b2cf6b 100644 --- a/rocketpy/plots/compare/compare_flights.py +++ b/rocketpy/plots/compare/compare_flights.py @@ -6,6 +6,8 @@ from ..plot_helpers import show_or_save_fig, show_or_save_plot from .compare import Compare +# TODO: needs to refactor this class to use the show_or_save_plot + class CompareFlights(Compare): # pylint: disable=too-many-public-methods """A class to compare the results of multiple flights. diff --git a/rocketpy/plots/environment_analysis_plots.py b/rocketpy/plots/environment_analysis_plots.py index b5b63642c..010ae5c06 100644 --- a/rocketpy/plots/environment_analysis_plots.py +++ b/rocketpy/plots/environment_analysis_plots.py @@ -74,7 +74,7 @@ def __beaufort_wind_scale(self, units, max_wind_speed=None): # Surface level plots - def wind_gust_distribution(self, filename=None): + def wind_gust_distribution(self, *, filename=None): """Get all values of wind gust speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution, however, the result is not always a perfect fit, and sometimes it may @@ -124,7 +124,9 @@ def wind_gust_distribution(self, filename=None): plt.legend() show_or_save_plot(filename) - def surface10m_wind_speed_distribution(self, wind_speed_limit=False, filename=None): + def surface10m_wind_speed_distribution( + self, wind_speed_limit=False, *, filename=None + ): """Get all values of sustained surface wind speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution. The wind speed limit is plotted as a vertical line. @@ -190,6 +192,7 @@ def surface10m_wind_speed_distribution(self, wind_speed_limit=False, filename=No def average_surface_temperature_evolution( self, + *, filename=None, ): # pylint: disable=too-many-statements """Plots average temperature progression throughout the day, including @@ -265,7 +268,7 @@ def average_surface_temperature_evolution( show_or_save_plot(filename) def average_surface10m_wind_speed_evolution( - self, wind_speed_limit=False, filename=None + self, wind_speed_limit=False, *, filename=None ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including sigma contours. @@ -366,6 +369,7 @@ def average_surface10m_wind_speed_evolution( def average_surface100m_wind_speed_evolution( self, + *, filename=None, ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including @@ -449,7 +453,7 @@ def average_surface100m_wind_speed_evolution( # Average profiles plots (pressure level data) - def average_wind_speed_profile(self, clear_range_limits=False, filename=None): + def average_wind_speed_profile(self, clear_range_limits=False, *, filename=None): """Average wind speed for all datetimes available. The plot also includes sigma contours. @@ -554,7 +558,9 @@ def average_wind_speed_profile(self, clear_range_limits=False, filename=None): ) show_or_save_plot(filename) - def average_wind_velocity_xy_profile(self, clear_range_limits=False, filename=None): + def average_wind_velocity_xy_profile( + self, clear_range_limits=False, *, filename=None + ): """Average wind X and wind Y for all datetimes available. The X component is the wind speed in the direction of East, and the Y component is the wind speed in the direction of North. @@ -621,7 +627,7 @@ def average_wind_velocity_xy_profile(self, clear_range_limits=False, filename=No plt.grid() show_or_save_plot(filename) - def average_wind_heading_profile(self, clear_range_limits=False, filename=None): + def average_wind_heading_profile(self, clear_range_limits=False, *, filename=None): """Average wind heading for all datetimes available. Parameters @@ -679,7 +685,7 @@ def average_wind_heading_profile(self, clear_range_limits=False, filename=None): plt.legend() show_or_save_plot(filename) - def average_pressure_profile(self, clear_range_limits=False, filename=None): + def average_pressure_profile(self, clear_range_limits=False, *, filename=None): """Average pressure profile for all datetimes available. The plot also includes sigma contours. @@ -772,7 +778,7 @@ def average_pressure_profile(self, clear_range_limits=False, filename=None): ) show_or_save_plot(filename) - def average_temperature_profile(self, clear_range_limits=False, filename=None): + def average_temperature_profile(self, clear_range_limits=False, *, filename=None): """Average temperature profile for all datetimes available. The plot also includes sigma contours. @@ -918,7 +924,7 @@ def plot_wind_rose( ax.yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0)) return ax - def average_wind_rose_specific_hour(self, hour, fig=None, filename=None): + def average_wind_rose_specific_hour(self, hour, fig=None, *, filename=None): """Plot a specific hour of the average windrose Parameters @@ -952,7 +958,7 @@ def average_wind_rose_specific_hour(self, hour, fig=None, filename=None): show_or_save_plot(filename) def average_wind_rose_grid( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Plot wind roses for all hours of a day, in a grid like plot. @@ -1088,7 +1094,7 @@ def animate_average_wind_rose(self, figsize=(5, 5), filename="wind_rose.gif"): # More plots and animations def wind_gust_distribution_grid( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Plots shown in the animation of how the wind gust distribution varies throughout the day. @@ -1264,7 +1270,7 @@ def update(frame): return HTML(animation.to_jshtml()) def surface_wind_speed_distribution_grid( - self, wind_speed_limit=False, filename=None + self, wind_speed_limit=False, *, filename=None ): # pylint: disable=too-many-statements """Plots shown in the animation of how the sustained surface wind speed distribution varies throughout the day. The plots are histograms of the @@ -1493,7 +1499,7 @@ def update(frame): return HTML(animation.to_jshtml()) def wind_speed_profile_grid( - self, clear_range_limits=False, filename=None + self, clear_range_limits=False, *, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind profile over the average day. Each subplot represents a different hour of the day. @@ -1592,7 +1598,7 @@ def wind_speed_profile_grid( show_or_save_plot(filename) def wind_heading_profile_grid( - self, clear_range_limits=False, filename=None + self, clear_range_limits=False, *, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind heading profile over the average day. Each subplot represents a different hour of the day. diff --git a/rocketpy/plots/environment_plots.py b/rocketpy/plots/environment_plots.py index f15d066af..4b8a91e15 100644 --- a/rocketpy/plots/environment_plots.py +++ b/rocketpy/plots/environment_plots.py @@ -173,7 +173,7 @@ def __pressure_temperature(self, ax): return ax - def gravity_model(self, filename=None): + def gravity_model(self, *, filename=None): """Plots the gravity model graph that represents the gravitational acceleration as a function of height. @@ -204,7 +204,7 @@ def gravity_model(self, filename=None): show_or_save_plot(filename) - def atmospheric_model(self, filename=None): + def atmospheric_model(self, *, filename=None): """Plots all atmospheric model graphs available. This includes wind speed and wind direction, density and speed of sound, wind u and wind v, and pressure and temperature. @@ -245,7 +245,7 @@ def atmospheric_model(self, filename=None): show_or_save_plot(filename) # pylint: disable=too-many-statements - def ensemble_member_comparison(self, filename=None): + def ensemble_member_comparison(self, *, filename=None): """Plots ensemble member comparisons. It requires that the environment model has been set as Ensemble. @@ -352,7 +352,7 @@ def ensemble_member_comparison(self, filename=None): # Clean up self.environment.select_ensemble_member(current_member) - def info(self, filename=None): + def info(self, *, filename=None): """Plots a summary of the atmospheric model, including wind speed and wind direction, density and speed of sound. This is important for the Environment.info() method. diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index edc30b956..9ece84cdd 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -54,7 +54,7 @@ def first_event_time_index(self): else: return -1 - def trajectory_3d(self, filename=None): # pylint: disable=too-many-statements + def trajectory_3d(self, *, filename=None): # pylint: disable=too-many-statements """Plot a 3D graph of the trajectory Parameters @@ -134,7 +134,7 @@ def trajectory_3d(self, filename=None): # pylint: disable=too-many-statements show_or_save_plot(filename) def linear_kinematics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out all Kinematics graphs available about the Flight @@ -215,7 +215,7 @@ def linear_kinematics_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def attitude_data(self, filename=None): # pylint: disable=too-many-statements + def attitude_data(self, *, filename=None): # pylint: disable=too-many-statements """Prints out all Angular position graphs available about the Flight Parameters @@ -273,7 +273,7 @@ def attitude_data(self, filename=None): # pylint: disable=too-many-statements plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def flight_path_angle_data(self, filename=None): + def flight_path_angle_data(self, *, filename=None): """Prints out Flight path and Rocket Attitude angle graphs available about the Flight @@ -324,7 +324,7 @@ def flight_path_angle_data(self, filename=None): show_or_save_plot(filename) def angular_kinematics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out all Angular velocity and acceleration graphs available about the Flight @@ -399,7 +399,9 @@ def angular_kinematics_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def rail_buttons_forces(self, filename=None): # pylint: disable=too-many-statements + def rail_buttons_forces( + self, *, filename=None + ): # pylint: disable=too-many-statements """Prints out all Rail Buttons Forces graphs available about the Flight. Parameters @@ -490,7 +492,9 @@ def rail_buttons_forces(self, filename=None): # pylint: disable=too-many-statem plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def aerodynamic_forces(self, filename=None): # pylint: disable=too-many-statements + def aerodynamic_forces( + self, *, filename=None + ): # pylint: disable=too-many-statements """Prints out all Forces and Moments graphs available about the Flight Parameters @@ -578,7 +582,7 @@ def aerodynamic_forces(self, filename=None): # pylint: disable=too-many-stateme plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def energy_data(self, filename=None): # pylint: disable=too-many-statements + def energy_data(self, *, filename=None): # pylint: disable=too-many-statements """Prints out all Energy components graphs available about the Flight Parameters @@ -698,7 +702,7 @@ def energy_data(self, filename=None): # pylint: disable=too-many-statements show_or_save_plot(filename) def fluid_mechanics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out a summary of the Fluid Mechanics graphs available about the Flight @@ -798,7 +802,7 @@ def fluid_mechanics_data( show_or_save_plot(filename) def stability_and_control_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out Rocket Stability and Control parameters graphs available about the Flight @@ -887,7 +891,7 @@ def stability_and_control_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def pressure_rocket_altitude(self, filename=None): + def pressure_rocket_altitude(self, *, filename=None): """Plots out pressure at rocket's altitude. Parameters diff --git a/rocketpy/plots/hybrid_motor_plots.py b/rocketpy/plots/hybrid_motor_plots.py index eb13c4d69..61dc0940e 100644 --- a/rocketpy/plots/hybrid_motor_plots.py +++ b/rocketpy/plots/hybrid_motor_plots.py @@ -14,7 +14,7 @@ class _HybridMotorPlots(_MotorPlots): """ - def grain_inner_radius(self, lower_limit=None, upper_limit=None, filename=None): + def grain_inner_radius(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots grain_inner_radius of the hybrid_motor as a function of time. Parameters @@ -40,7 +40,7 @@ def grain_inner_radius(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def grain_height(self, lower_limit=None, upper_limit=None, filename=None): + def grain_height(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots grain_height of the hybrid_motor as a function of time. Parameters @@ -66,7 +66,7 @@ def grain_height(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def burn_rate(self, lower_limit=None, upper_limit=None, filename=None): + def burn_rate(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots burn_rate of the hybrid_motor as a function of time. Parameters @@ -92,7 +92,7 @@ def burn_rate(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def burn_area(self, lower_limit=None, upper_limit=None, filename=None): + def burn_area(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots burn_area of the hybrid_motor as a function of time. Parameters @@ -118,7 +118,7 @@ def burn_area(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def Kn(self, lower_limit=None, upper_limit=None, filename=None): + def Kn(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots Kn of the hybrid_motor as a function of time. Parameters @@ -144,7 +144,7 @@ def Kn(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the HybridMotor. Parameters diff --git a/rocketpy/plots/liquid_motor_plots.py b/rocketpy/plots/liquid_motor_plots.py index 822364fc2..08a385802 100644 --- a/rocketpy/plots/liquid_motor_plots.py +++ b/rocketpy/plots/liquid_motor_plots.py @@ -14,7 +14,7 @@ class _LiquidMotorPlots(_MotorPlots): """ - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the LiquidMotor. Parameters diff --git a/rocketpy/plots/motor_plots.py b/rocketpy/plots/motor_plots.py index e7ddb7efa..8ed7e3fd6 100644 --- a/rocketpy/plots/motor_plots.py +++ b/rocketpy/plots/motor_plots.py @@ -29,7 +29,7 @@ def __init__(self, motor): """ self.motor = motor - def thrust(self, lower_limit=None, upper_limit=None, filename=None): + def thrust(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots thrust of the motor as a function of time. Parameters @@ -52,7 +52,7 @@ def thrust(self, lower_limit=None, upper_limit=None, filename=None): """ self.motor.thrust.plot(lower=lower_limit, upper=upper_limit, filename=filename) - def total_mass(self, lower_limit=None, upper_limit=None, filename=None): + def total_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots total_mass of the motor as a function of time. Parameters @@ -77,7 +77,7 @@ def total_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def propellant_mass(self, lower_limit=None, upper_limit=None, filename=None): + def propellant_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots propellant_mass of the motor as a function of time. Parameters @@ -102,7 +102,7 @@ def propellant_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def center_of_mass(self, lower_limit=None, upper_limit=None, filename=None): + def center_of_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots center_of_mass of the motor as a function of time. Parameters @@ -127,7 +127,7 @@ def center_of_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def mass_flow_rate(self, lower_limit=None, upper_limit=None, filename=None): + def mass_flow_rate(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots mass_flow_rate of the motor as a function of time. Parameters @@ -152,7 +152,7 @@ def mass_flow_rate(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def exhaust_velocity(self, lower_limit=None, upper_limit=None, filename=None): + def exhaust_velocity(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots exhaust_velocity of the motor as a function of time. Parameters @@ -178,7 +178,7 @@ def exhaust_velocity(self, lower_limit=None, upper_limit=None, filename=None): ) def inertia_tensor( - self, lower_limit=None, upper_limit=None, show_products=False, filename=None + self, lower_limit=None, upper_limit=None, show_products=False, *, filename=None ): """Plots all inertia tensors (I_11, I_22, I_33, I_12, I_13, I_23) of the motor as a function of time in a single chart. diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 9689a257b..f305fcb99 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -54,7 +54,7 @@ def reduced_mass(self): self.rocket.reduced_mass() - def static_margin(self, filename=None): + def static_margin(self, *, filename=None): """Plots static margin of the rocket as a function of time. Parameters @@ -123,7 +123,7 @@ def power_off_drag(self): self.rocket.power_off_drag() # pylint: disable=too-many-statements - def drag_curves(self, filename=None): + def drag_curves(self, *, filename=None): """Plots power off and on drag curves of the rocket as a function of time. Parameters @@ -179,7 +179,7 @@ def thrust_to_weight(self): lower=0, upper=self.rocket.motor.burn_out_time ) - def draw(self, vis_args=None, plane="xz", filename=None): + def draw(self, vis_args=None, plane="xz", *, filename=None): """Draws the rocket in a matplotlib figure. Parameters diff --git a/rocketpy/plots/solid_motor_plots.py b/rocketpy/plots/solid_motor_plots.py index 202220102..cda7bb718 100644 --- a/rocketpy/plots/solid_motor_plots.py +++ b/rocketpy/plots/solid_motor_plots.py @@ -113,7 +113,7 @@ def Kn(self, lower_limit=None, upper_limit=None): self.motor.Kn.plot(lower=lower_limit, upper=upper_limit) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the SolidMotor. Parameters diff --git a/rocketpy/plots/tank_geometry_plots.py b/rocketpy/plots/tank_geometry_plots.py index d9bf141bf..8c6ca9afa 100644 --- a/rocketpy/plots/tank_geometry_plots.py +++ b/rocketpy/plots/tank_geometry_plots.py @@ -40,6 +40,6 @@ def all(self): ------- None """ - self.radius() - self.area() - self.volume() + self.tank_geometry.radius.plot(equal_axis=True) + self.tank_geometry.area() + self.tank_geometry.volume() diff --git a/rocketpy/plots/tank_plots.py b/rocketpy/plots/tank_plots.py index a4bc0dd2a..9c6ebb78a 100644 --- a/rocketpy/plots/tank_plots.py +++ b/rocketpy/plots/tank_plots.py @@ -2,6 +2,8 @@ import numpy as np from matplotlib.patches import Polygon +from rocketpy.mathutils.function import Function + from .plot_helpers import show_or_save_plot @@ -30,6 +32,7 @@ def __init__(self, tank): self.tank = tank self.name = tank.name + self.flux_time = tank.flux_time self.geometry = tank.geometry def _generate_tank(self, translate=(0, 0), csys=1): @@ -69,7 +72,7 @@ def _generate_tank(self, translate=(0, 0), csys=1): # Don't set any plot config here. Use the draw methods for that return tank - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws the tank geometry. Parameters @@ -101,6 +104,82 @@ def draw(self, filename=None): ax.set_ylim(-1.5 * y_max, 1.5 * y_max) show_or_save_plot(filename) + def fluid_volume(self, filename=None): + """Plots both the liquid and gas fluid volumes. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [self.tank.liquid_volume, self.tank.gas_volume], + *self.flux_time, + title="Fluid Volume (m^3) x Time (s)", + xlabel="Time (s)", + ylabel="Volume (m^3)", + show=False, + return_object=True, + ) + ax.legend(["Liquid", "Gas"]) + show_or_save_plot(filename) + + def fluid_height(self, filename=None): + """Plots both the liquid and gas fluid height. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [self.tank.liquid_height, self.tank.gas_height], + *self.flux_time, + title="Fluid Height (m) x Time (s)", + xlabel="Time (s)", + ylabel="Height (m)", + show=False, + return_object=True, + ) + ax.legend(["Liquid", "Gas"]) + show_or_save_plot(filename) + + def fluid_center_of_mass(self, filename=None): + """Plots the gas, liquid and combined center of mass. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [ + self.tank.liquid_center_of_mass, + self.tank.gas_center_of_mass, + self.tank.center_of_mass, + ], + *self.flux_time, + title="Fluid Center of Mass (m) x Time (s)", + xlabel="Time (s)", + ylabel="Center of Mass (m)", + show=False, + return_object=True, + ) + # Change style of lines + ax.lines[0].set_linestyle("--") + ax.lines[1].set_linestyle("-.") + ax.legend(["Liquid", "Gas", "Total"]) + show_or_save_plot(filename) + def all(self): """Prints out all graphs available about the Tank. It simply calls all the other plotter methods in this class. @@ -109,3 +188,10 @@ def all(self): ------- None """ + self.draw() + self.tank.fluid_mass.plot(*self.flux_time) + self.tank.net_mass_flow_rate.plot(*self.flux_time) + self.fluid_height() + self.fluid_volume() + self.fluid_center_of_mass() + self.tank.inertia.plot(*self.flux_time) diff --git a/rocketpy/prints/fluid_prints.py b/rocketpy/prints/fluid_prints.py index a90aac229..7b2d7cac8 100644 --- a/rocketpy/prints/fluid_prints.py +++ b/rocketpy/prints/fluid_prints.py @@ -32,3 +32,5 @@ def all(self): ------- None """ + print(f"Name: {self.fluid.name}") + print(f"Density: {self.fluid.density:.4f} kg/m^3") diff --git a/rocketpy/prints/monte_carlo_prints.py b/rocketpy/prints/monte_carlo_prints.py index 6249626ce..dc7cc1265 100644 --- a/rocketpy/prints/monte_carlo_prints.py +++ b/rocketpy/prints/monte_carlo_prints.py @@ -24,4 +24,7 @@ def all(self): print(f"{'Parameter':>25} {'Mean':>15} {'Std. Dev.':>15}") print("-" * 60) for key, value in self.monte_carlo.processed_results.items(): - print(f"{key:>25} {value[0]:>15.3f} {value[1]:>15.3f}") + try: + print(f"{key:>25} {value[0]:>15.3f} {value[1]:>15.3f}") + except TypeError: + print(f"{key:>25} {str(value[0]):>15} {str(value[1]):>15}") diff --git a/rocketpy/prints/tank_prints.py b/rocketpy/prints/tank_prints.py index 41732c6cf..7c61aa67f 100644 --- a/rocketpy/prints/tank_prints.py +++ b/rocketpy/prints/tank_prints.py @@ -25,6 +25,37 @@ def __init__( """ self.tank = tank + def fluid_parameters(self): + """Prints out the fluid parameters of the Tank. + + Returns + ------- + None + """ + print(f"Tank '{self.tank.name}' Fluid Parameters:") + print("\nLiquid Fluid") + self.tank.liquid.prints.all() + print("\nGas Fluid") + self.tank.gas.prints.all() + + def mass_flux(self): + """Prints out the mass flux of the Tank. + + Returns + ------- + None + """ + initial_time, final_time = self.tank.flux_time + print(f"\nTank '{self.tank.name}' Mass Flux Data:") + print(f"\nInitial Quantities at t = {initial_time:.2f} s:") + print(f"Initial Fluid Mass: {self.tank.fluid_mass(initial_time):.3e} kg") + print(f"Initial Liquid Volume: {self.tank.liquid_volume(initial_time):.3e} m^3") + print(f"Initial Liquid Level: {self.tank.liquid_height(initial_time):.3e} m") + print(f"\nFinal Quantities at t = {final_time:.2f} s:") + print(f"Final Fluid Mass: {self.tank.fluid_mass(final_time):.3e} kg") + print(f"Final Liquid Volume: {self.tank.liquid_volume(final_time):.3e} m^3") + print(f"Final Liquid Level: {self.tank.liquid_height(final_time):.3e} m") + def all(self): """Prints out all data available about the Tank. @@ -32,3 +63,7 @@ def all(self): ------- None """ + print(f"\nTank '{self.tank.name}' Data:\n") + self.tank.geometry.prints.all() + self.fluid_parameters() + self.mass_flux() diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index c7973bc69..b2af4f55f 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -426,7 +426,7 @@ def compute_forces_and_moments( M3 = M3_forcing - M3_damping return R1, R2, R3, M1, M2, M3 - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. @@ -442,4 +442,4 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index e9fbe4324..c8f3a2062 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -486,7 +486,7 @@ def evaluate_center_of_pressure(self): self.cp = (self.cpx, self.cpy, self.cpz) return self.cp - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the nosecone shape along with some important information, including the center of pressure position. @@ -502,7 +502,7 @@ def draw(self, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) def info(self): """Prints and plots summarized information of the nose cone. diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 58067a03f..aacf5b4a6 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1839,7 +1839,7 @@ def add_thrust_eccentricity(self, x, y): self.thrust_eccentricity_x = y return self - def draw(self, vis_args=None, plane="xz", filename=None): + def draw(self, vis_args=None, plane="xz", *, filename=None): """Draws the rocket in a matplotlib figure. Parameters @@ -1872,7 +1872,7 @@ def draw(self, vis_args=None, plane="xz", filename=None): eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff and webp (these are the formats supported by matplotlib). """ - self.plots.draw(vis_args, plane, filename) + self.plots.draw(vis_args, plane, filename=filename) def info(self): """Prints out a summary of the data and graphs available about diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 4ed051a4f..926576831 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -48,6 +48,9 @@ class MonteCarlo: The stochastic flight object to be iterated over. export_list : list The list of variables to export at each simulation. + data_collector : dict + A dictionary whose keys are the names of the additional + exported variables and the values are callback functions. inputs_log : list List of dictionaries with the inputs used in each simulation. outputs_log : list @@ -80,7 +83,13 @@ class MonteCarlo: """ def __init__( - self, filename, environment, rocket, flight, export_list=None + self, + filename, + environment, + rocket, + flight, + export_list=None, + data_collector=None, ): # pylint: disable=too-many-statements """ Initialize a MonteCarlo object. @@ -104,6 +113,18 @@ def __init__( `out_of_rail_stability_margin`, `out_of_rail_time`, `out_of_rail_velocity`, `max_mach_number`, `frontal_surface_wind`, `lateral_surface_wind`. Default is None. + data_collector : dict, optional + A dictionary whose keys are the names of the exported variables + and the values are callback functions. The keys (variable names) must not + overwrite the default names on 'export_list'. The callback functions receive + a Flight object and returns a value of that variable. For instance + + .. code-block:: python + + custom_data_collector = { + "max_acceleration": lambda flight: max(flight.acceleration(flight.time)), + "date": lambda flight: flight.env.date, + } Returns ------- @@ -132,6 +153,8 @@ def __init__( self._last_print_len = 0 # used to print on the same line self.export_list = self.__check_export_list(export_list) + self._check_data_collector(data_collector) + self.data_collector = data_collector try: self.import_inputs() @@ -359,6 +382,17 @@ def __export_flight_data( for export_item in self.export_list } + if self.data_collector is not None: + additional_exports = {} + for key, callback in self.data_collector.items(): + try: + additional_exports[key] = callback(flight) + except Exception as e: + raise ValueError( + f"An error was encountered running 'data_collector' callback {key}. " + ) from e + results = results | additional_exports + input_file.write(json.dumps(inputs_dict, cls=RocketPyEncoder) + "\n") output_file.write(json.dumps(results, cls=RocketPyEncoder) + "\n") @@ -466,6 +500,37 @@ def __check_export_list(self, export_list): return export_list + def _check_data_collector(self, data_collector): + """Check if data collector provided is a valid + + Parameters + ---------- + data_collector : dict + A dictionary whose keys are the names of the exported variables + and the values are callback functions that receive a Flight object + and returns a value of that variable + """ + + if data_collector is not None: + + if not isinstance(data_collector, dict): + raise ValueError( + "Invalid 'data_collector' argument! " + "It must be a dict of callback functions." + ) + + for key, callback in data_collector.items(): + if key in self.export_list: + raise ValueError( + "Invalid 'data_collector' key! " + f"Variable names overwrites 'export_list' key '{key}'." + ) + if not callable(callback): + raise ValueError( + f"Invalid value in 'data_collector' for key '{key}'! " + "Values must be python callables (callback functions)." + ) + def __reprint(self, msg, end="\n", flush=False): """ Prints a message on the same line as the previous one and replaces the @@ -654,9 +719,12 @@ def set_processed_results(self): """ self.processed_results = {} for result, values in self.results.items(): - mean = np.mean(values) - stdev = np.std(values) - self.processed_results[result] = (mean, stdev) + try: + mean = np.mean(values) + stdev = np.std(values) + self.processed_results[result] = (mean, stdev) + except TypeError: + self.processed_results[result] = (None, None) # Import methods diff --git a/rocketpy/stochastic/stochastic_rocket.py b/rocketpy/stochastic/stochastic_rocket.py index d9333dfc7..01e6c66a5 100644 --- a/rocketpy/stochastic/stochastic_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -442,8 +442,7 @@ def get_surface_position(self_object, _): for surface in surfaces: if surface.component == validated_object.obj: return surface.position - else: - raise AssertionError(error_msg) + raise AssertionError(error_msg) return get_surface_position diff --git a/rocketpy/utilities.py b/rocketpy/utilities.py index fb728dbb6..f6e68d162 100644 --- a/rocketpy/utilities.py +++ b/rocketpy/utilities.py @@ -208,6 +208,7 @@ def fin_flutter_analysis( flight, see_prints=True, see_graphs=True, + *, filename=None, ): """Calculate and plot the Fin Flutter velocity using the pressure profile @@ -320,7 +321,7 @@ def _flutter_safety_factor(flight, flutter_mach): return safety_factor -def _flutter_plots(flight, flutter_mach, safety_factor, filename=None): +def _flutter_plots(flight, flutter_mach, safety_factor, *, filename=None): """Plot the Fin Flutter Mach Number and the Safety Factor for the flutter. Parameters diff --git a/tests/acceptance/test_ndrt_2020_rocket.py b/tests/acceptance/test_ndrt_2020_rocket.py index f189bdac2..7874ee164 100644 --- a/tests/acceptance/test_ndrt_2020_rocket.py +++ b/tests/acceptance/test_ndrt_2020_rocket.py @@ -1,11 +1,19 @@ import numpy as np import pandas as pd +import pytest from scipy.signal import savgol_filter from rocketpy import Environment, Flight, Rocket, SolidMotor -def test_ndrt_2020_rocket_data_asserts_acceptance(): +@pytest.mark.parametrize( + "env_file", + [ + "data/weather/ndrt_2020_weather_data_ERA5.nc", + "data/weather/ndrt_2020_weather_data_ERA5_new.nc", + ], +) +def test_ndrt_2020_rocket_data_asserts_acceptance(env_file): # Notre Dame Rocket Team 2020 Flight # Launched at 19045-18879 Avery Rd, Three Oaks, MI 49128 # Permission to use flight data given by Brooke Mumma, 2020 @@ -73,7 +81,7 @@ def test_ndrt_2020_rocket_data_asserts_acceptance(): ) env.set_atmospheric_model( type="Reanalysis", - file="data/weather/ndrt_2020_weather_data_ERA5.nc", + file=env_file, dictionary="ECMWF", ) env.max_expected_height = 2000 diff --git a/tests/conftest.py b/tests/conftest.py index a23f5fd89..31f53484a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -9,6 +9,7 @@ "tests.fixtures.motor.hybrid_fixtures", "tests.fixtures.motor.solid_motor_fixtures", "tests.fixtures.motor.tanks_fixtures", + "tests.fixtures.motor.tank_geometry_fixtures", "tests.fixtures.motor.generic_motor_fixtures", "tests.fixtures.parachutes.parachute_fixtures", "tests.fixtures.rockets.rocket_fixtures", diff --git a/tests/fixtures/hybrid/__init__.py b/tests/fixtures/hybrid/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tests/fixtures/hybrid/hybrid_fixtures.py b/tests/fixtures/hybrid/hybrid_fixtures.py deleted file mode 100644 index 0f5a872fb..000000000 --- a/tests/fixtures/hybrid/hybrid_fixtures.py +++ /dev/null @@ -1,276 +0,0 @@ -import numpy as np -import pytest - -from rocketpy import ( - CylindricalTank, - Fluid, - Function, - HybridMotor, - LevelBasedTank, - LiquidMotor, - MassBasedTank, - SphericalTank, - UllageBasedTank, -) - - -@pytest.fixture -def pressurant_fluid(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 30MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=300) - - -@pytest.fixture -def fuel_pressurant(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 2MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=25) - - -@pytest.fixture -def oxidizer_pressurant(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 3MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=35) - - -@pytest.fixture -def fuel_fluid(): - """An example of propane as fuel fluid at - 273.15K and 2MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="Propane", density=500) - - -@pytest.fixture -def oxidizer_fluid(): - """An example of liquid oxygen as oxidizer fluid at - 100K and 3MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="O2", density=1000) - - -@pytest.fixture -def pressurant_tank(pressurant_fluid): - """An example of a pressurant cylindrical tank with spherical - caps. - - Parameters - ---------- - pressurant_fluid : rocketpy.Fluid - Pressurizing fluid. This is a pytest fixture. - - Returns - ------- - rocketpy.MassBasedTank - An object of the CylindricalTank class. - """ - geometry = CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) - pressurant_tank = MassBasedTank( - name="Pressure Tank", - geometry=geometry, - liquid_mass=0, - flux_time=(8, 20), - gas_mass="data/rockets/berkeley/pressurantMassFiltered.csv", - gas=pressurant_fluid, - liquid=pressurant_fluid, - ) - - return pressurant_tank - - -@pytest.fixture -def fuel_tank(fuel_fluid, fuel_pressurant): - """An example of a fuel cylindrical tank with spherical - caps. - - Parameters - ---------- - fuel_fluid : rocketpy.Fluid - Fuel fluid of the tank. This is a pytest fixture. - fuel_pressurant : rocketpy.Fluid - Pressurizing fluid of the fuel tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) - ullage = ( - -Function("data/rockets/berkeley/test124_Propane_Volume.csv") * 1e-3 - + geometry.total_volume - ) - fuel_tank = UllageBasedTank( - name="Propane Tank", - flux_time=(8, 20), - geometry=geometry, - liquid=fuel_fluid, - gas=fuel_pressurant, - ullage=ullage, - ) - - return fuel_tank - - -@pytest.fixture -def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): - """An example of a oxidizer cylindrical tank with spherical - caps. - - Parameters - ---------- - oxidizer_fluid : rocketpy.Fluid - Oxidizer fluid of the tank. This is a pytest fixture. - oxidizer_pressurant : rocketpy.Fluid - Pressurizing fluid of the oxidizer tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) - ullage = ( - -Function("data/rockets/berkeley/test124_Lox_Volume.csv") * 1e-3 - + geometry.total_volume - ) - oxidizer_tank = UllageBasedTank( - name="Lox Tank", - flux_time=(8, 20), - geometry=geometry, - liquid=oxidizer_fluid, - gas=oxidizer_pressurant, - ullage=ullage, - ) - - return oxidizer_tank - - -@pytest.fixture -def liquid_motor(pressurant_tank, fuel_tank, oxidizer_tank): - """An example of a liquid motor with pressurant, fuel and oxidizer tanks. - - Parameters - ---------- - pressurant_tank : rocketpy.MassBasedTank - Tank that contains pressurizing fluid. This is a pytest fixture. - fuel_tank : rocketpy.UllageBasedTank - Tank that contains the motor fuel. This is a pytest fixture. - oxidizer_tank : rocketpy.UllageBasedTank - Tank that contains the motor oxidizer. This is a pytest fixture. - - Returns - ------- - rocketpy.LiquidMotor - """ - liquid_motor = LiquidMotor( - thrust_source="data/rockets/berkeley/test124_Thrust_Curve.csv", - burn_time=(8, 20), - dry_mass=10, - dry_inertia=(5, 5, 0.2), - center_of_dry_mass_position=0, - nozzle_position=-1.364, - nozzle_radius=0.069 / 2, - ) - liquid_motor.add_tank(pressurant_tank, position=2.007) - liquid_motor.add_tank(fuel_tank, position=-1.048) - liquid_motor.add_tank(oxidizer_tank, position=0.711) - - return liquid_motor - - -@pytest.fixture -def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): - """An example of a oxidizer spherical tank. - - Parameters - ---------- - oxidizer_fluid : rocketpy.Fluid - Oxidizer fluid of the tank. This is a pytest fixture. - oxidizer_pressurant : rocketpy.Fluid - Pressurizing fluid of the oxidizer tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = SphericalTank(0.05) - liquid_level = Function(lambda t: 0.1 * np.exp(-t / 2) - 0.05) - oxidizer_tank = LevelBasedTank( - name="Lox Tank", - flux_time=10, - geometry=geometry, - liquid=oxidizer_fluid, - gas=oxidizer_pressurant, - liquid_height=liquid_level, - ) - - return oxidizer_tank - - -@pytest.fixture -def hybrid_motor(spherical_oxidizer_tank): - """An example of a hybrid motor with spherical oxidizer - tank and fuel grains. - - Parameters - ---------- - spherical_oxidizer_tank : rocketpy.LevelBasedTank - Example Tank that contains the motor oxidizer. This is a - pytest fixture. - - Returns - ------- - rocketpy.HybridMotor - """ - motor = HybridMotor( - thrust_source=lambda t: 2000 - 100 * t, - burn_time=10, - center_of_dry_mass_position=0, - dry_inertia=(4, 4, 0.1), - dry_mass=8, - grain_density=1700, - grain_number=4, - grain_initial_height=0.1, - grain_separation=0, - grain_initial_inner_radius=0.04, - grain_outer_radius=0.1, - nozzle_position=-0.4, - nozzle_radius=0.07, - grains_center_of_mass_position=-0.1, - ) - - motor.add_tank(spherical_oxidizer_tank, position=0.3) - - return motor diff --git a/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv b/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv new file mode 100644 index 000000000..22652a6ce --- /dev/null +++ b/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.4034,0.0,0.0,0.0 +-0.3697833333333333,0.00022435619637196954,-0.38148562747252746,3.2817704908519726e-05 +-0.33616666666666667,0.0007382946059424683,-0.36099299529814066,9.719213246759829e-05 +-0.30255,0.0013224978119496303,-0.3425968277449047,0.00015763404974113978 +-0.26893333333333336,0.0019070864787702323,-0.3251687335359676,0.00020622875048451338 +-0.23531666666666667,0.002491675145590835,-0.30803145186305303,0.0002442533372601051 +-0.2017,0.0030762638124114438,-0.29101919163369117,0.00027302907431388793 +-0.16808333333333333,0.003660852479232046,-0.27407206014303415,0.00029387722589183405 +-0.13446666666666668,0.004245441146052648,-0.2571631531039504,0.0003081190562399166 +-0.10085,0.004830029812873251,-0.24027859135913576,0.0003170758296041081 +-0.06723333333333337,0.005414618479693852,-0.22341048959783028,0.0003220688102303815 +-0.033616666666666684,0.005999207146514448,-0.20655403602428474,0.0003244192623647094 +0.0,0.006583795813335056,-0.1897061278394083,0.00032544845025306453 +0.03361666666666663,0.007168384480155651,-0.1728646743891429,0.0003264776381414197 +0.06723333333333331,0.00775297314697626,-0.156028215576324,0.0003288280902757475 +0.10085,0.008337561813796869,-0.13919570080300325,0.0003338210709020209 +0.13446666666666662,0.008922150480617464,-0.1223663548163549,0.00034277784426621244 +0.16808333333333325,0.009506739147438074,-0.10553959305102469,0.00035701967461429507 +0.2017,0.010091327814258668,-0.08871496639669146,0.00037786782619224097 +0.23531666666666662,0.010675916481079278,-0.07189212411203103,0.00040664356324602403 +0.26893333333333325,0.011260505147899872,-0.05507078829060723,0.000444668150021615 +0.30255,0.011845093814720481,-0.038250735887836625,0.0004932628507649892 +0.3361666666666666,0.012429297020727635,-0.021442820198694704,0.0005537047680385298 +0.36978333333333324,0.012943235430298142,-0.006612617441073541,0.0006180791955976091 +0.4034,0.013167591626670111,-8.233868449333761e-18,0.0006508969005061287 diff --git a/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv b/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv new file mode 100644 index 000000000..caf9862e7 --- /dev/null +++ b/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.4905,0.0,0.0,0.0 +-0.449625,0.0002827826025953255,-0.46411144141531335,6.113435649768482e-05 +-0.40875,0.0008480974805291653,-0.4405110759493672,0.0001657333255803535 +-0.36787499999999995,0.0014331773879828432,-0.41920154494382006,0.00025470343771418223 +-0.32699999999999996,0.0020182572954365085,-0.3983976063829787,0.00032607798469747707 +-0.28612499999999996,0.0026033372028901733,-0.3777583762886599,0.00038181202932474384 +-0.24524999999999997,0.003188417110343852,-0.3571931818181816,0.00042386063439048673 +-0.20437499999999997,0.003773497017797517,-0.33666758534850627,0.00045417886268920747 +-0.16349999999999998,0.004358576925251182,-0.3161656403940886,0.00047472177701541056 +-0.12262499999999998,0.004943656832704848,-0.2956789495114007,0.00048744444016359965 +-0.08174999999999999,0.005528736740158513,-0.27520266990291287,0.0004943019149282782 +-0.040874999999999995,0.006113816647612191,-0.2547338125548726,0.0004972492641039496 +5.551115123125783e-17,0.006698896555065856,-0.23427043269230766,0.0004982415504851175 +0.04087500000000005,0.007283976462519522,-0.21381121039056752,0.0004992338368662853 +0.08175000000000004,0.0078690563699732,-0.19335521828103658,0.0005021811860419568 +0.12262500000000004,0.008454136277426865,-0.17290178571428558,0.0005090386608066354 +0.16350000000000003,0.009039216184880532,-0.15245041567695955,0.0005217613239548245 +0.20437500000000003,0.009624296092334197,-0.13200073201338547,0.0005423042382810275 +0.24525000000000002,0.010209375999787874,-0.11155244479495245,0.000572622466579749 +0.286125,0.010794455907241539,-0.09110532695176517,0.000614671071645491 +0.327,0.011379535814695205,-0.07065919811320753,0.0006704051162727579 +0.367875,0.01196461572214887,-0.050213913189771295,0.0007417796632560529 +0.40875,0.01254969562960255,-0.029769354148844927,0.0008307497753898814 +0.449625,0.013115010507536386,-0.010007055749004045,0.0009353487444725499 +0.4905,0.013397793110131711,0.0,0.0009964831009702348 diff --git a/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv b/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv new file mode 100644 index 000000000..9d1cd0c62 --- /dev/null +++ b/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.05,0.0,0.0,0.0 +-0.04583333333333334,2.6513248185677844e-06,-0.047232142857142854,6.095088014828042e-09 +-0.04166666666666667,1.0302290723577582e-05,-0.044485294117647074,2.175980154667401e-08 +-0.037500000000000006,2.2498384888989377e-05,-0.04176136363636364,4.363855762203842e-08 +-0.03333333333333334,3.87850944887629e-05,-0.0390625,6.908594955810907e-08 +-0.02916666666666667,5.8707906696857845e-05,-0.036391129032258066,9.609572933369276e-08 +-0.025,8.181230868723432e-05,-0.03374999999999999,1.2322978996014713e-07 +-0.020833333333333336,0.00010764378763385171,-0.031142241379310345,1.4954714785231024e-07 +-0.01666666666666667,0.00013574783071067004,-0.028571428571428584,1.7453292519943379e-07 +-0.012500000000000004,0.0001656699250916491,-0.026041666666666678,1.9802733233611288e-07 +-0.008333333333333338,0.00019695555795074912,-0.0235576923076923,2.201546501132184e-07 +-0.004166666666666673,0.00022915021646192904,-0.02112500000000001,2.412522122688262e-07 +0.0,0.00026179938779914946,-0.018749999999999996,2.61799387799151e-07 +0.004166666666666666,0.0002944485591363695,-0.01644021739130436,2.8234656332947566e-07 +0.008333333333333331,0.00032664321764755015,-0.014204545454545458,3.034441254850839e-07 +0.012499999999999997,0.0003579288505066499,-0.012053571428571419,3.2557144326218885e-07 +0.016666666666666663,0.0003878509448876289,-0.009999999999999992,3.490658503988677e-07 +0.02083333333333333,0.0004159549879644476,-0.008059210526315778,3.740516277459912e-07 +0.024999999999999994,0.0004417864669110648,-0.00624999999999999,4.0036898563815394e-07 +0.02916666666666666,0.0004648908689014413,-0.004595588235294098,4.2750304626460787e-07 +0.033333333333333326,0.0004848136811095362,-0.0031249999999999993,4.54512826040191e-07 +0.03749999999999999,0.0005011003907093094,-0.0018750000000000101,4.799602179762609e-07 +0.04166666666666666,0.000513296484874721,-0.0008928571428571531,5.018389740516248e-07 +0.04583333333333332,0.000520947450779731,-0.0002403846153846192,5.17503687583471e-07 +0.05,0.0005235987755982989,6.229927327055379e-19,5.235987755982989e-07 diff --git a/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb b/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb new file mode 100644 index 000000000..63131969d --- /dev/null +++ b/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CadQuery Validation of Tank Geometry Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is an example of using CadQuery to compute properties, such as volume, center of mass and inertia of geometric shapes. Therefore, its main purpose is to validate the `rocketpy.TankGeometry` calculations for different fluid heights." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cadquery as cq\n", + "import numpy as np\n", + "import csv\n", + "from rocketpy import CylindricalTank, SphericalTank" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Adding spherical caps to the tank will not modify the total height of the tank 0.8068 m. Its cylindrical portion height will be reduced to 0.6579999999999999 m.\n", + "Warning: Adding spherical caps to the tank will not modify the total height of the tank 0.981 m. Its cylindrical portion height will be reduced to 0.846 m.\n" + ] + } + ], + "source": [ + "# Create fixtures geometries\n", + "geometry_map = {}\n", + "geometry_map[\"sphere\"] = {\"spherical_oxidizer_tank\": SphericalTank(0.05)}\n", + "geometry_map[\"cylinder\"] = {\n", + " \"cylindrical_oxidizer_tank\": CylindricalTank(0.0744, 0.8068, True),\n", + " \"cylindrical_pressurant_tank\": CylindricalTank(0.135 / 2, 0.981, True),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def export_expected_parameters(name, datapoints):\n", + " with open(f'{name}_expected.csv', mode='w') as output_file:\n", + " writer = csv.writer(output_file)\n", + " writer.writerow(['level_height', 'volume', 'center_of_mass', 'inertia'])\n", + " for data_row in datapoints:\n", + " writer.writerow([*data_row])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CadQuery of a Spherical Tank" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A spherical shape is easily defined in CadQuery by using the `sphere` function. Furthermore, different levels of fluid height can be defined by using the `cut` function, i.e., subtracting a cylinder from the desired height and above." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_sphere_parameters(radius, level_height):\n", + " \"\"\"Computes the volume, center of mass and inertia\n", + " (with respect to the origin) of a sphere 'filled' up\n", + " to a certain level height.\n", + "\n", + " Parameters\n", + " ----------\n", + " radius : float\n", + " The radius of the sphere.\n", + " level_height : float\n", + " The height of the liquid inside the sphere.\n", + "\n", + " Returns\n", + " -------\n", + " volume : float\n", + " The volume of the sphere.\n", + " center_of_mass : float\n", + " The center of mass of the sphere.\n", + " inertia : float\n", + " The inertia of the sphere with respect to the origin.\n", + " \"\"\"\n", + " sphere = cq.Workplane(\"XY\").sphere(radius)\n", + "\n", + " # Cut the sphere to the level height\n", + " drill_height = 10 * radius\n", + " sphere = sphere.cut(\n", + " cq.Workplane(\"XY\")\n", + " .cylinder(drill_height, radius)\n", + " .translate((0, 0, drill_height / 2 + level_height))\n", + " )\n", + "\n", + " # Uncomment to display the CAD\n", + " # display(sphere)\n", + "\n", + " primitive = sphere.val()\n", + "\n", + " volume = primitive.Volume()\n", + " center_of_mass = primitive.centerOfMass(primitive)\n", + " inertia_tensor = primitive.matrixOfInertia(primitive)\n", + "\n", + " return volume, center_of_mass.z, inertia_tensor[0][0] + volume * center_of_mass.z**2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "for name, sphere_geometry in geometry_map[\"sphere\"].items():\n", + " radius = sphere_geometry.total_height / 2\n", + " datapoints = []\n", + " for h in np.linspace(-radius, radius, 25):\n", + " datapoints.append([h, *evaluate_sphere_parameters(radius, h)])\n", + "\n", + " export_expected_parameters(name, datapoints)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CadQuery of a Cylindrical Tank with Caps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A cylindrical shape with caps is defined by using the `cylinder` function for the main body, the `sphere` function for the caps and uniting the three shapes into one. Similarly to the sphere case, the fluid height is defined by using the `cut` function, i.e., subtracting a cylinder from the desired height and above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_cylinder_with_caps(total_length, radius, level_height):\n", + " \"\"\"Computes the volume, center of mass and inertia (with respect\n", + " to the origin) of a cylinder with spherical caps 'filled' up to a\n", + " certain level height.\n", + "\n", + " Parameters\n", + " ----------\n", + " total_length : float\n", + " The total length of the cylinder (with caps).\n", + " radius : float\n", + " The radius of the cylinder.\n", + "\n", + " Returns\n", + " -------\n", + " volume : float\n", + " The volume of the cylinder with caps.\n", + " center_of_mass : float\n", + " The z-coordinate of the center of mass.\n", + " inertia : float\n", + " The inertia of the cylinder with caps with respect to the origin.\n", + " \"\"\"\n", + " cylinder_height = total_length - 2 * radius\n", + "\n", + " cylinder = cq.Workplane(\"XY\").cylinder(cylinder_height, radius)\n", + " top_cap = (\n", + " cq.Workplane(\"XY\").sphere(radius).translate((0, 0, total_length / 2 - radius))\n", + " )\n", + " bottom_cap = (\n", + " cq.Workplane(\"XY\").sphere(radius).translate((0, 0, -total_length / 2 + radius))\n", + " )\n", + "\n", + " solid = cylinder.union(top_cap).union(bottom_cap)\n", + "\n", + " # Remove solid above the level_height\n", + " drill_height = 10 * total_length\n", + " solid = solid.cut(\n", + " cq.Workplane(\"XY\")\n", + " .cylinder(drill_height, radius)\n", + " .translate((0, 0, drill_height / 2 + level_height))\n", + " )\n", + " # Uncomment to display the CAD\n", + " # display(solid)\n", + "\n", + " primitive = solid.val()\n", + "\n", + " volume = primitive.Volume()\n", + " center_of_mass = primitive.centerOfMass(primitive)\n", + " inertia_tensor = primitive.matrixOfInertia(primitive)\n", + "\n", + " return volume, center_of_mass.z, inertia_tensor[0][0] + volume * center_of_mass.z**2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for name, cylinder_geometry in geometry_map[\"cylinder\"].items():\n", + " radius = cylinder_geometry.radius(0)\n", + " length = cylinder_geometry.total_height\n", + " datapoints = []\n", + " for h in np.linspace(-length / 2, length / 2, 25):\n", + " datapoints.append([h, *evaluate_cylinder_with_caps(length, radius, h)])\n", + "\n", + " export_expected_parameters(name, datapoints)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/fixtures/motor/liquid_fixtures.py b/tests/fixtures/motor/liquid_fixtures.py index 7cd708a48..0711f56d6 100644 --- a/tests/fixtures/motor/liquid_fixtures.py +++ b/tests/fixtures/motor/liquid_fixtures.py @@ -68,6 +68,32 @@ def oxidizer_fluid(): return Fluid(name="O2", density=1000) +@pytest.fixture +def lox_fluid_seblm(): + """A liquid oxygen fixture whose density comes + from testing data. + + Returns + ------- + rocketpy.Fluid + An object of the Fluid class. + """ + return Fluid(name="O2", density=1141.7) + + +@pytest.fixture +def nitrogen_fluid_seblm(): + """A nitrogen gas fixture whose density comes + from testing data. + + Returns + ------- + rocketpy.Fluid + An object of the Fluid class. + """ + return Fluid(name="N2", density=51.75) + + @pytest.fixture def liquid_motor(pressurant_tank, fuel_tank, oxidizer_tank): """An example of a liquid motor with pressurant, fuel and oxidizer tanks. diff --git a/tests/fixtures/motor/tank_geometry_fixtures.py b/tests/fixtures/motor/tank_geometry_fixtures.py new file mode 100644 index 000000000..c664fec21 --- /dev/null +++ b/tests/fixtures/motor/tank_geometry_fixtures.py @@ -0,0 +1,41 @@ +import pytest + +from rocketpy import CylindricalTank, SphericalTank + + +@pytest.fixture +def pressurant_tank_geometry(): + """An example of a pressurant cylindrical tank with spherical + caps. + + Returns + ------- + rocketpy.CylindricalTank + An object of the CylindricalTank class. + """ + return CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) + + +@pytest.fixture +def propellant_tank_geometry(): + """An example of a cylindrical tank with spherical + caps. + + Returns + ------- + rocketpy.CylindricalTank + An object of the CylindricalTank class. + """ + return CylindricalTank(0.0744, 0.8068, spherical_caps=True) + + +@pytest.fixture +def spherical_oxidizer_geometry(): + """An example of a spherical tank. + + Returns + ------- + rocketpy.SphericalTank + An object of the SphericalTank class. + """ + return SphericalTank(0.05) diff --git a/tests/fixtures/motor/tanks_fixtures.py b/tests/fixtures/motor/tanks_fixtures.py index 7dc1904b5..40417c8a4 100644 --- a/tests/fixtures/motor/tanks_fixtures.py +++ b/tests/fixtures/motor/tanks_fixtures.py @@ -3,16 +3,334 @@ from rocketpy import ( CylindricalTank, + Fluid, Function, LevelBasedTank, MassBasedTank, - SphericalTank, + MassFlowRateBasedTank, + TankGeometry, UllageBasedTank, ) @pytest.fixture -def pressurant_tank(pressurant_fluid): +def sample_full_mass_flow_rate_tank(): + """An example of a full MassFlowRateBasedTank. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + full_tank = MassFlowRateBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + initial_liquid_mass=9, + initial_gas_mass=0.001, + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass_flow_rate_in=0, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + liquid_mass_flow_rate_out=0, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_mass_flow_rate_tank(): + """An example of an empty MassFlowRateBasedTank. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + empty_tank = MassFlowRateBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + initial_liquid_mass=0, + initial_gas_mass=0, + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass_flow_rate_in=0, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + liquid_mass_flow_rate_out=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_ullage_tank(): + """An example of a UllageBasedTank full of liquid. + + Returns + ------- + rocketpy.UllageBasedTank + An object of the UllageBasedTank class. + """ + full_tank = UllageBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + ullage=0, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_ullage_tank(): + """An example of a UllageBasedTank with no liquid. + + Returns + ------- + rocketpy.UllageBasedTank + An object of the UllageBasedTank class. + """ + empty_tank = UllageBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + ullage=0.01, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_level_tank(): + """An example of a LevelBasedTank full of liquid. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + full_tank = LevelBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_height=1 / (2 * np.pi), + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_level_tank(): + """An example of a LevelBasedTank with no liquid. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + empty_tank = LevelBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_height=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_mass_tank(): + """An example of a full MassBasedTank. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + full_tank = MassBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass=9, + gas_mass=0.001, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_mass_tank(): + """An example of an empty MassBasedTank. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + empty_tank = MassBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass=0, + gas_mass=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def real_mass_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a real cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + geometry = CylindricalTank(0.0744, 0.8698, True) + + lox_tank = MassBasedTank( + name="Real Tank", + geometry=geometry, + flux_time=(0, 15.583), + liquid_mass="./data/rockets/berkeley/Test135LoxMass.csv", + gas_mass="./data/rockets/berkeley/Test135GasMass.csv", + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=200, + ) + + return lox_tank + + +@pytest.fixture +def example_mass_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """Example data of a cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + geometry = TankGeometry({(0, 5): 1}) + + example_tank = MassBasedTank( + name="Example Tank", + geometry=geometry, + flux_time=(0, 10), + liquid_mass="./data/rockets/berkeley/ExampleTankLiquidMassData.csv", + gas_mass="./data/rockets/berkeley/ExampleTankGasMassData.csv", + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=None, + ) + + return example_tank + + +@pytest.fixture +def real_level_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a real cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + geometry = TankGeometry( + { + (0, 0.0559): lambda h: np.sqrt(0.0775**2 - (0.0775 - h) ** 2), + (0.0559, 0.7139): 0.0744, + (0.7139, 0.7698): lambda h: np.sqrt(0.0775**2 - (h - 0.6924) ** 2), + } + ) + + level_tank = LevelBasedTank( + name="Level Tank", + geometry=geometry, + flux_time=(0, 15.583), + gas=nitrogen_fluid_seblm, + liquid=lox_fluid_seblm, + liquid_height="./data/rockets/berkeley/loxUllage.csv", + discretize=None, + ) + + return level_tank + + +@pytest.fixture +def example_mass_flow_rate_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a example cylindrical tank whose flux + is given by mass flow rates. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + mass_flow_rate_tank = MassFlowRateBasedTank( + name="Test Tank", + geometry=TankGeometry({(0, 1): 1}), + flux_time=(0, 10), + initial_liquid_mass=5, + initial_gas_mass=0.1, + liquid_mass_flow_rate_in=0.1, + gas_mass_flow_rate_in=0.01, + liquid_mass_flow_rate_out=0.2, + gas_mass_flow_rate_out=0.02, + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=11, + ) + + return mass_flow_rate_tank + + +@pytest.fixture +def pressurant_tank(pressurant_fluid, pressurant_tank_geometry): """An example of a pressurant cylindrical tank with spherical caps. @@ -20,16 +338,17 @@ def pressurant_tank(pressurant_fluid): ---------- pressurant_fluid : rocketpy.Fluid Pressurizing fluid. This is a pytest fixture. + pressurant_tank_geometry : rocketpy.CylindricalTank + Geometry of the pressurant tank. This is a pytest fixture. Returns ------- rocketpy.MassBasedTank An object of the CylindricalTank class. """ - geometry = CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) pressurant_tank = MassBasedTank( name="Pressure Tank", - geometry=geometry, + geometry=pressurant_tank_geometry, liquid_mass=0, flux_time=(8, 20), gas_mass="data/rockets/berkeley/pressurantMassFiltered.csv", @@ -41,7 +360,7 @@ def pressurant_tank(pressurant_fluid): @pytest.fixture -def fuel_tank(fuel_fluid, fuel_pressurant): +def fuel_tank(fuel_fluid, fuel_pressurant, propellant_tank_geometry): """An example of a fuel cylindrical tank with spherical caps. @@ -52,20 +371,21 @@ def fuel_tank(fuel_fluid, fuel_pressurant): fuel_pressurant : rocketpy.Fluid Pressurizing fluid of the fuel tank. This is a pytest fixture. + propellant_tank_geometry : rocketpy.CylindricalTank + Geometry of the fuel tank. This is a pytest fixture. Returns ------- rocketpy.UllageBasedTank """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) ullage = ( -Function("data/rockets/berkeley/test124_Propane_Volume.csv") * 1e-3 - + geometry.total_volume + + propellant_tank_geometry.total_volume ) fuel_tank = UllageBasedTank( name="Propane Tank", flux_time=(8, 20), - geometry=geometry, + geometry=propellant_tank_geometry, liquid=fuel_fluid, gas=fuel_pressurant, ullage=ullage, @@ -75,7 +395,7 @@ def fuel_tank(fuel_fluid, fuel_pressurant): @pytest.fixture -def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): +def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant, propellant_tank_geometry): """An example of a oxidizer cylindrical tank with spherical caps. @@ -86,20 +406,21 @@ def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): oxidizer_pressurant : rocketpy.Fluid Pressurizing fluid of the oxidizer tank. This is a pytest fixture. + propellant_tank_geometry : rocketpy.CylindricalTank + Geometry of the oxidizer tank. This is a pytest fixture. Returns ------- rocketpy.UllageBasedTank """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) ullage = ( -Function("data/rockets/berkeley/test124_Lox_Volume.csv") * 1e-3 - + geometry.total_volume + + propellant_tank_geometry.total_volume ) oxidizer_tank = UllageBasedTank( name="Lox Tank", flux_time=(8, 20), - geometry=geometry, + geometry=propellant_tank_geometry, liquid=oxidizer_fluid, gas=oxidizer_pressurant, ullage=ullage, @@ -109,7 +430,9 @@ def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): @pytest.fixture -def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): +def spherical_oxidizer_tank( + oxidizer_fluid, oxidizer_pressurant, spherical_oxidizer_geometry +): """An example of a oxidizer spherical tank. Parameters @@ -122,14 +445,13 @@ def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): Returns ------- - rocketpy.UllageBasedTank + rocketpy.LevelBasedTank """ - geometry = SphericalTank(0.05) liquid_level = Function(lambda t: 0.1 * np.exp(-t / 2) - 0.05) oxidizer_tank = LevelBasedTank( name="Lox Tank", flux_time=10, - geometry=geometry, + geometry=spherical_oxidizer_geometry, liquid=oxidizer_fluid, gas=oxidizer_pressurant, liquid_height=liquid_level, diff --git a/tests/integration/test_monte_carlo.py b/tests/integration/test_monte_carlo.py index b5caddbc8..51d8bfae9 100644 --- a/tests/integration/test_monte_carlo.py +++ b/tests/integration/test_monte_carlo.py @@ -111,3 +111,58 @@ def test_monte_carlo_export_ellipses_to_kml(monte_carlo_calisto_pre_loaded): ) os.remove("monte_carlo_class_example.kml") + + +@pytest.mark.slow +def test_monte_carlo_callback(monte_carlo_calisto): + """Tests the data_collector argument of the MonteCarlo class. + + Parameters + ---------- + monte_carlo_calisto : MonteCarlo + The MonteCarlo object, this is a pytest fixture. + """ + + # define valid data collector + valid_data_collector = { + "name": lambda flight: flight.name, + "density_t0": lambda flight: flight.env.density(0), + } + + monte_carlo_calisto.data_collector = valid_data_collector + # NOTE: this is really slow, it runs 10 flight simulations + monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + + # tests if print works when we have None in summary + monte_carlo_calisto.info() + + ## tests if an error is raised for invalid data_collector definitions + # invalid type + def invalid_data_collector(flight): + return flight.name + + with pytest.raises(ValueError): + monte_carlo_calisto._check_data_collector(invalid_data_collector) + + # invalid key overwrite + invalid_data_collector = {"apogee": lambda flight: flight.apogee} + with pytest.raises(ValueError): + monte_carlo_calisto._check_data_collector(invalid_data_collector) + + # invalid callback definition + invalid_data_collector = {"name": "Calisto"} # callbacks must be callables! + with pytest.raises(ValueError): + monte_carlo_calisto._check_data_collector(invalid_data_collector) + + # invalid logic (division by zero) + invalid_data_collector = { + "density_t0": lambda flight: flight.env.density(0) / "0", + } + monte_carlo_calisto.data_collector = invalid_data_collector + # NOTE: this is really slow, it runs 10 flight simulations + with pytest.raises(ValueError): + monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + + os.remove("monte_carlo_test.errors.txt") + os.remove("monte_carlo_test.outputs.txt") + os.remove("monte_carlo_test.inputs.txt") diff --git a/tests/integration/test_tank.py b/tests/integration/test_tank.py new file mode 100644 index 000000000..f79d670ab --- /dev/null +++ b/tests/integration/test_tank.py @@ -0,0 +1,35 @@ +# pylint: disable=unused-argument + +from unittest.mock import patch + +import pytest + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize( + "fixture_name", + [ + "sample_full_mass_flow_rate_tank", + "sample_empty_mass_flow_rate_tank", + "sample_full_ullage_tank", + "sample_empty_ullage_tank", + "sample_full_level_tank", + "sample_empty_level_tank", + "sample_full_mass_tank", + "sample_empty_mass_tank", + "real_mass_based_tank_seblm", + "pressurant_tank", + "fuel_tank", + "oxidizer_tank", + "spherical_oxidizer_tank", + ], +) +def test_tank_all_info(mock_show, fixture_name, request): + tank = request.getfixturevalue(fixture_name) + + assert tank.info() is None + assert tank.all_info() is None + + assert (tank.gas_height <= tank.geometry.top).all + assert (tank.liquid_height <= tank.geometry.top).all + assert (tank.fluid_volume <= tank.geometry.total_volume).all diff --git a/tests/unit/test_tank.py b/tests/unit/test_tank.py index 3002bee43..a313caf20 100644 --- a/tests/unit/test_tank.py +++ b/tests/unit/test_tank.py @@ -1,959 +1,357 @@ -# TODO: This file must be refactored to improve readability and maintainability. -# pylint: disable=too-many-statements -import os from math import isclose +from pathlib import Path import numpy as np import pytest +import scipy.integrate as spi -from rocketpy import ( - Fluid, - Function, - LevelBasedTank, - MassBasedTank, - MassFlowRateBasedTank, - TankGeometry, -) - -pressurant_params = (0.135 / 2, 0.981) -fuel_params = (0.0744, 0.8068) -oxidizer_params = (0.0744, 0.8068) +BASE_PATH = Path("./data/rockets/berkeley/") -parametrize_fixtures = pytest.mark.parametrize( +@pytest.mark.parametrize( "params", [ - ("pressurant_tank", pressurant_params), - ("fuel_tank", fuel_params), - ("oxidizer_tank", oxidizer_params), + ( + "real_mass_based_tank_seblm", + BASE_PATH / "Test135LoxMass.csv", + BASE_PATH / "Test135GasMass.csv", + ), + ( + "example_mass_based_tank_seblm", + BASE_PATH / "ExampleTankLiquidMassData.csv", + BASE_PATH / "ExampleTankGasMassData.csv", + ), ], ) +def test_mass_based_tank_fluid_mass(params, request): + """Test the fluid_mass property of the MassBasedTank subclass of Tank + class. - -@parametrize_fixtures -def test_tank_bounds(params, request): - """Test basic geometric properties of the tanks.""" - tank, (expected_radius, expected_height) = params - tank = request.getfixturevalue(tank) - - expected_total_height = expected_height - - assert tank.geometry.radius(0) == pytest.approx(expected_radius, abs=1e-6) - assert tank.geometry.total_height == pytest.approx(expected_total_height, abs=1e-6) - - -@parametrize_fixtures -def test_tank_coordinates(params, request): - """Test basic coordinate values of the tanks.""" - tank, (_, height) = params - tank = request.getfixturevalue(tank) - - expected_bottom = -height / 2 - expected_top = height / 2 - - assert tank.geometry.bottom == pytest.approx(expected_bottom, abs=1e-6) - assert tank.geometry.top == pytest.approx(expected_top, abs=1e-6) - - -@parametrize_fixtures -def test_tank_total_volume(params, request): - """Test the total volume of the tanks comparing to the analytically - calculated values. + Parameters + ---------- + params : tuple + A tuple containing test parameters. + request : _pytest.fixtures.FixtureRequest + A pytest fixture request object. """ - tank, (radius, height) = params + tank, liq_path, gas_path = params tank = request.getfixturevalue(tank) - - expected_total_volume = ( - np.pi * radius**2 * (height - 2 * radius) + 4 / 3 * np.pi * radius**3 + expected_liquid_mass = np.loadtxt(liq_path, skiprows=1, delimiter=",") + expected_gas_mass = np.loadtxt(gas_path, skiprows=1, delimiter=",") + + assert np.allclose( + expected_liquid_mass[:, 1], + tank.liquid_mass(expected_liquid_mass[:, 0]), + rtol=1e-2, + atol=1e-4, + ) + assert np.allclose( + expected_gas_mass[:, 1], + tank.gas_mass(expected_gas_mass[:, 0]), + rtol=1e-1, + atol=1e-4, ) - assert tank.geometry.total_volume == pytest.approx(expected_total_volume, abs=1e-6) - - -@parametrize_fixtures -def test_tank_volume(params, request): - """Test the volume of the tanks at different heights comparing to the - analytically calculated values. - """ - tank, (radius, height) = params - tank = request.getfixturevalue(tank) - - total_height = height - bottom = -height / 2 - top = height / 2 - - expected_volume = tank_volume_function(radius, total_height, bottom) - - for h in np.linspace(bottom, top, 101): - assert tank.geometry.volume(h) == pytest.approx(expected_volume(h), abs=1e-6) - - -@parametrize_fixtures -def test_tank_centroid(params, request): - """Test the centroid of the tanks at different heights comparing to the - analytically calculated values. - """ - tank, (radius, height) = params - tank = request.getfixturevalue(tank) - - total_height = height - bottom = -height / 2 - - expected_centroid = tank_centroid_function(radius, total_height, bottom) - - for h, liquid_com in zip( - tank.liquid_height.y_array, tank.liquid_center_of_mass.y_array - ): - # Loss of accuracy to 1e-3 when liquid height is close to zero - assert liquid_com == pytest.approx(expected_centroid(h), abs=1e-3) +@pytest.mark.parametrize( + "params", + [ + ( + "real_mass_based_tank_seblm", + BASE_PATH / "Test135LoxMass.csv", + BASE_PATH / "Test135GasMass.csv", + ), + ( + "example_mass_based_tank_seblm", + BASE_PATH / "ExampleTankLiquidMassData.csv", + BASE_PATH / "ExampleTankGasMassData.csv", + ), + ], +) +def test_mass_based_tank_net_mass_flow_rate(params, request): + """Test the net_mass_flow_rate property of the MassBasedTank + subclass of Tank. -@parametrize_fixtures -def test_tank_inertia(params, request): - """Test the inertia of the tanks at different heights comparing to the - analytically calculated values. + Parameters + ---------- + params : tuple + A tuple containing test parameters. + request : _pytest.fixtures.FixtureRequest + A pytest fixture request object. """ - tank, (radius, height) = params + tank, liq_path, gas_path = params tank = request.getfixturevalue(tank) + expected_liquid_mass = np.loadtxt(liq_path, skiprows=1, delimiter=",") + expected_gas_mass = np.loadtxt(gas_path, skiprows=1, delimiter=",") - total_height = height - bottom = -height / 2 - - expected_inertia = tank_inertia_function(radius, total_height, bottom) - - for h in tank.liquid_height.y_array: - assert tank.geometry.Ix_volume(tank.geometry.bottom, h)(h) == pytest.approx( - expected_inertia(h)[0], abs=1e-5 - ) - - -def test_mass_based_tank(): - """Tests the MassBasedTank subclass of Tank regarding its mass and - net mass flow rate properties. The test is performed on both a real - tank and a simplified tank. - """ - lox = Fluid(name="LOx", density=1141.7) - n2 = Fluid( - name="Nitrogen Gas", - density=51.75, - ) # density value may be estimate - - def top_endcap(y): - """Calculate the top endcap based on hemisphere equation. - - Parameters: - y (float): The y-coordinate. - - Returns: - float: The result of the hemisphere equation for the top endcap. - """ - return np.sqrt(0.0775**2 - (y - 0.7924) ** 2) - - def bottom_endcap(y): - """Calculate the bottom endcap based on hemisphere equation. - - Parameters: - y (float): The y-coordinate. - - Returns: - float: The result of the hemisphere equation for the bottom endcap. - """ - return np.sqrt(0.0775**2 - (0.0775 - y) ** 2) - - # Generate tank geometry {radius: height, ...} - real_geometry = TankGeometry( - { - (0, 0.0559): bottom_endcap, - (0.0559, 0.8039): lambda y: 0.0744, - (0.8039, 0.8698): top_endcap, - } + # Noisy derivatives, assert integrals + initial_mass = expected_liquid_mass[0, 1] + expected_gas_mass[0, 1] + expected_mass_variation = ( + expected_liquid_mass[-1, 1] + expected_gas_mass[-1, 1] - initial_mass ) - - # Import liquid mass data - lox_masses = "./data/rockets/berkeley/Test135LoxMass.csv" - example_liquid_masses = "./data/rockets/berkeley/ExampleTankLiquidMassData.csv" - - # Import gas mass data - gas_masses = "./data/rockets/berkeley/Test135GasMass.csv" - example_gas_masses = "./data/rockets/berkeley/ExampleTankGasMassData.csv" - - # Generate tanks based on Berkeley SEB team's real tank geometries - real_tank_lox = MassBasedTank( - name="Real Tank", - geometry=real_geometry, - flux_time=(0, 10), - liquid_mass=lox_masses, - gas_mass=gas_masses, - liquid=lox, - gas=n2, + computed_final_mass = spi.simpson( + tank.net_mass_flow_rate.y_array, + x=tank.net_mass_flow_rate.x_array, ) - # Generate tank geometry {radius: height, ...} - example_geometry = TankGeometry({(0, 5): 1}) - - # Generate tanks based on simplified tank geometry - example_tank_lox = MassBasedTank( - name="Example Tank", - geometry=example_geometry, - flux_time=(0, 10), - liquid_mass=example_liquid_masses, - gas_mass=example_gas_masses, - liquid=lox, - gas=n2, - discretize=None, - ) - - # Assert volume bounds - # pylint: disable=comparison-with-callable - assert (real_tank_lox.gas_height <= real_tank_lox.geometry.top).all - assert (real_tank_lox.fluid_volume <= real_tank_lox.geometry.total_volume).all - assert (example_tank_lox.gas_height <= example_tank_lox.geometry.top).all - assert (example_tank_lox.fluid_volume <= example_tank_lox.geometry.total_volume).all - - initial_liquid_mass = 5 - initial_gas_mass = 0 - liquid_mass_flow_rate_in = 0.1 - gas_mass_flow_rate_in = 0.1 - liquid_mass_flow_rate_out = 0.2 - gas_mass_flow_rate_out = 0.05 - - def test(calculated, expected, t, real=False): - """Iterate over time range and test that calculated value is close to actual value""" - j = 0 - for i in np.arange(0, t, 0.1): - try: - print(calculated.get_value(i), expected(i)) - assert isclose(calculated.get_value(i), expected(i), rel_tol=5e-2) - except IndexError: - break - - if real: - j += 4 - else: - j += 1 - - def test_mass(): - """Test mass function of MassBasedTank subclass of Tank""" - - def example_expected(t): - return ( - initial_liquid_mass - + t * (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) - + initial_gas_mass - + t * (gas_mass_flow_rate_in - gas_mass_flow_rate_out) - ) - - example_calculated = example_tank_lox.fluid_mass - - lox_vals = Function(lox_masses).y_array - - def real_expected(t): - return lox_vals[t] - - real_calculated = real_tank_lox.fluid_mass - - test(example_calculated, example_expected, 5) - test(real_calculated, real_expected, 15.5, real=True) - - def test_net_mfr(): - """Test net_mass_flow_rate function of MassBasedTank subclass of Tank""" - - def example_expected(_): - return ( - liquid_mass_flow_rate_in - - liquid_mass_flow_rate_out - + gas_mass_flow_rate_in - - gas_mass_flow_rate_out - ) - - example_calculated = example_tank_lox.net_mass_flow_rate + assert isclose(expected_mass_variation, computed_final_mass, rel_tol=1e-2) - liquid_mfrs = Function(example_liquid_masses).y_array - gas_mfrs = Function(example_gas_masses).y_array - - def real_expected(t): - return (liquid_mfrs[t] + gas_mfrs[t]) / t - - real_calculated = real_tank_lox.net_mass_flow_rate - - test(example_calculated, example_expected, 10) - test(real_calculated, real_expected, 15.5, real=True) - - test_mass() - test_net_mfr() - - -def test_level_based_tank(): - """Test LevelBasedTank subclass of Tank class using Berkeley SEB team's - tank data of fluid level. - """ - lox = Fluid(name="LOx", density=1141.7) - n2 = Fluid(name="Nitrogen Gas", density=51.75) - - test_dir = "./data/rockets/berkeley/" - - def top_endcap(y): - return np.sqrt(0.0775**2 - (y - 0.692300000000001) ** 2) - - def bottom_endcap(y): - return np.sqrt(0.0775**2 - (0.0775 - y) ** 2) - - tank_geometry = TankGeometry( - { - (0, 0.0559): bottom_endcap, - (0.0559, 0.7139): lambda y: 0.0744, - (0.7139, 0.7698): top_endcap, - } - ) - - ullage_data = Function(os.path.abspath(test_dir + "loxUllage.csv")).get_source() - level_tank = LevelBasedTank( - name="LevelTank", - geometry=tank_geometry, - flux_time=(0, 10), - gas=n2, - liquid=lox, - liquid_height=ullage_data, - discretize=None, - ) - - mass_data = Function(test_dir + "loxMass.csv").get_source() - mass_flow_rate_data = Function(test_dir + "loxMFR.csv").get_source() - - def align_time_series(small_source, large_source): - assert isinstance(small_source, np.ndarray) and isinstance( - large_source, np.ndarray - ), "Must be np.ndarrays" - if small_source.shape[0] > large_source.shape[0]: - small_source, large_source = large_source, small_source - - result_larger_source = np.ndarray(small_source.shape) - result_smaller_source = np.ndarray(small_source.shape) - tolerance = 0.1 - curr_ind = 0 - for val in small_source: - time = val[0] - delta_time_vector = abs(time - large_source[:, 0]) - large_index = np.argmin(delta_time_vector) - delta_time = abs(time - large_source[large_index][0]) - - if delta_time < tolerance: - result_larger_source[curr_ind] = large_source[large_index] - result_smaller_source[curr_ind] = val - curr_ind += 1 - return result_larger_source, result_smaller_source - - assert np.allclose(level_tank.liquid_height, ullage_data) - - calculated_mass = level_tank.liquid_mass.set_discrete( - mass_data[0][0], mass_data[0][-1], len(mass_data[0]) - ) - calculated_mass, mass_data = align_time_series( - calculated_mass.get_source(), mass_data - ) - assert np.allclose(calculated_mass, mass_data, rtol=1, atol=2) - - calculated_mfr = level_tank.net_mass_flow_rate.set_discrete( - mass_flow_rate_data[0][0], - mass_flow_rate_data[0][-1], - len(mass_flow_rate_data[0]), - ) - calculated_mfr, _ = align_time_series( - calculated_mfr.get_source(), mass_flow_rate_data - ) +def test_level_based_tank_liquid_level(real_level_based_tank_seblm): + """Test the liquid_level property of LevelBasedTank + subclass of Tank. - -def test_mfr_tank_basic(): - """Test MassFlowRateTank subclass of Tank class regarding its properties, - such as net_mass_flow_rate, fluid_mass, center_of_mass and inertia. + Parameters + ---------- + real_level_based_tank_seblm : LevelBasedTank + The LevelBasedTank to be tested. """ + tank = real_level_based_tank_seblm + level_data = np.loadtxt(BASE_PATH / "loxUllage.csv", delimiter=",") - def test(t, a, tol=1e-4): - for i in np.arange(0, 10, 1): - print(t.get_value(i), a(i)) - assert isclose(t.get_value(i), a(i), abs_tol=tol) - - def test_nmfr(): - def nmfr(_): - return ( - liquid_mass_flow_rate_in - + gas_mass_flow_rate_in - - liquid_mass_flow_rate_out - - gas_mass_flow_rate_out - ) - - test(t.net_mass_flow_rate, nmfr) - - def test_mass(): - def m(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) + ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - lm = t.fluid_mass - test(lm, m) - - def test_liquid_height(): - def alv(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) / lox.density - - def alh(x): - return alv(x) / (np.pi) - - tlh = t.liquid_height - test(tlh, alh) - - def test_com(): - def liquid_mass(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) - - def liquid_volume(x): - return liquid_mass(x) / lox.density - - def liquid_height(x): - return liquid_volume(x) / (np.pi) - - def gas_mass(x): - return ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - def gas_volume(x): - return gas_mass(x) / n2.density - - def gas_height(x): - return gas_volume(x) / np.pi + liquid_height(x) - - def liquid_com(x): - return liquid_height(x) / 2 - - def gas_com(x): - return (gas_height(x) - liquid_height(x)) / 2 + liquid_height(x) - - def acom(x): - return (liquid_mass(x) * liquid_com(x) + gas_mass(x) * gas_com(x)) / ( - liquid_mass(x) + gas_mass(x) - ) - - tcom = t.center_of_mass - test(tcom, acom) - - def test_inertia(): - def liquid_mass(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) - - def liquid_volume(x): - return liquid_mass(x) / lox.density + assert np.allclose(level_data, tank.liquid_height.get_source()) - def liquid_height(x): - return liquid_volume(x) / (np.pi) - def gas_mass(x): - return ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - def gas_volume(x): - return gas_mass(x) / n2.density - - def gas_height(x): - return gas_volume(x) / np.pi + liquid_height(x) - - def liquid_com(x): - return liquid_height(x) / 2 - - def gas_com(x): - return (gas_height(x) - liquid_height(x)) / 2 + liquid_height(x) - - def acom(x): - return (liquid_mass(x) * liquid_com(x) + gas_mass(x) * gas_com(x)) / ( - liquid_mass(x) + gas_mass(x) - ) - - r = 1 - - def ixy_gas(x): - return ( - 1 / 4 * gas_mass(x) * r**2 - + 1 / 12 * gas_mass(x) * (gas_height(x) - liquid_height(x)) ** 2 - + gas_mass(x) * (gas_com(x) - acom(x)) ** 2 - ) - - def ixy_liq(x): - return ( - 1 / 4 * liquid_mass(x) * r**2 - + 1 / 12 * liquid_mass(x) * (liquid_height(x) - t.geometry.bottom) ** 2 - + liquid_mass(x) * (liquid_com(x) - acom(x)) ** 2 - ) - - def ixy(x): - return ixy_gas(x) + ixy_liq(x) - - test(t.gas_inertia, ixy_gas, tol=1e-3) - test(t.liquid_inertia, ixy_liq, tol=1e-3) - test(t.inertia, ixy, tol=1e-3) - - tank_radius_function = TankGeometry({(0, 5): 1}) - lox = Fluid( - name="LOx", - density=1141, - ) - n2 = Fluid( - name="Nitrogen Gas", - density=51.75, - ) # density value may be estimate - initial_liquid_mass = 5 - initial_gas_mass = 0.1 - liquid_mass_flow_rate_in = 0.1 - gas_mass_flow_rate_in = 0.01 - liquid_mass_flow_rate_out = 0.2 - gas_mass_flow_rate_out = 0.02 - - t = MassFlowRateBasedTank( - name="Test Tank", - geometry=tank_radius_function, - flux_time=(0, 10), - initial_liquid_mass=initial_liquid_mass, - initial_gas_mass=initial_gas_mass, - liquid_mass_flow_rate_in=Function(0.1).set_discrete(0, 10, 1000), - gas_mass_flow_rate_in=Function(0.01).set_discrete(0, 10, 1000), - liquid_mass_flow_rate_out=Function(0.2).set_discrete(0, 10, 1000), - gas_mass_flow_rate_out=Function(0.02).set_discrete(0, 10, 1000), - liquid=lox, - gas=n2, - discretize=None, - ) - - test_nmfr() - test_mass() - test_liquid_height() - test_com() - test_inertia() - - -# Auxiliary testing functions - - -def cylinder_volume(radius, height): - """Returns the volume of a cylinder with the given radius and height. +def test_level_based_tank_mass(real_level_based_tank_seblm): + """Test the mass property of LevelBasedTank subclass of Tank. Parameters ---------- - radius : float - The radius of the cylinder. - height : float - The height of the cylinder. - - Returns - ------- - float - The volume of the cylinder. + real_level_based_tank_seblm : LevelBasedTank + The LevelBasedTank to be tested. """ - return np.pi * radius**2 * height + tank = real_level_based_tank_seblm + mass_data = np.loadtxt(BASE_PATH / "loxMass.csv", delimiter=",") + # Soft tolerances for the whole curve + assert np.allclose(mass_data, tank.fluid_mass.get_source(), rtol=1e-1, atol=6e-1) -def lower_spherical_cap_volume(radius, height=None): - """Returns the volume of a spherical cap with the given radius and filled - height that is filled from its convex side. - - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The volume of the spherical cap. - """ - if height is None: - height = radius - return np.pi / 3 * height**2 * (3 * radius - height) + # Tighter tolerances for middle of the curve + assert np.allclose( + mass_data[100:401], tank.fluid_mass.get_source()[100:401], rtol=5e-2, atol=1e-1 + ) -def upper_spherical_cap_volume(radius, height=None): - """Returns the volume of a spherical cap with the given radius and filled - height that is filled from its concave side. +def test_mass_flow_rate_tank_mass_flow_rate(example_mass_flow_rate_based_tank_seblm): + """Test the mass_flow_rate property of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The volume of the spherical cap. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. """ - if height is None: - height = radius - return np.pi / 3 * height * (3 * radius**2 - height**2) + tank = example_mass_flow_rate_based_tank_seblm + expected_mass_flow_rate = 0.1 - 0.2 + 0.01 - 0.02 -def tank_volume_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the volume of a cylindrical tank - with spherical caps. + assert np.allclose(expected_mass_flow_rate, tank.net_mass_flow_rate.y_array) - Parameters - ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the volume of the tank for a given height. - """ - def tank_volume(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - if h < tank_radius: - return lower_spherical_cap_volume(tank_radius, h) - elif tank_radius <= h < tank_height - tank_radius: - return lower_spherical_cap_volume(tank_radius) + cylinder_volume( - tank_radius, h - tank_radius - ) - else: - return ( - lower_spherical_cap_volume(tank_radius) - + cylinder_volume(tank_radius, tank_height - 2 * tank_radius) - + upper_spherical_cap_volume( - tank_radius, h - (tank_height - tank_radius) - ) - ) - - return tank_volume - - -def cylinder_centroid(height): - """Returns the centroid of a cylinder with the given height. +def test_mass_flow_rate_tank_fluid_mass(example_mass_flow_rate_based_tank_seblm): + """Test the fluid_mass property of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - height : float - The height of the cylinder. - - Returns - ------- - float - The centroid of the cylinder. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. """ - return height / 2 + tank = example_mass_flow_rate_based_tank_seblm + expected_initial_liquid_mass = 5 + expected_initial_gas_mass = 0.1 + expected_initial_mass = expected_initial_liquid_mass + expected_initial_gas_mass + expected_liquid_mass_flow = 0.1 - 0.2 + expected_gas_mass_flow = 0.01 - 0.02 + expected_total_mass_flow = expected_liquid_mass_flow + expected_gas_mass_flow -def lower_spherical_cap_centroid(radius, height=None): - """Returns the centroid of a spherical cap with the given radius and filled - height that is filled from its convex side. + times = np.linspace(0, 10, 11) - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The centroid of the spherical cap. - """ - if height is None: - height = radius - return radius - (0.75 * (2 * radius - height) ** 2 / (3 * radius - height)) + assert np.allclose( + expected_initial_liquid_mass + expected_liquid_mass_flow * times, + tank.liquid_mass.y_array, + ) + assert np.allclose( + expected_initial_gas_mass + expected_gas_mass_flow * times, + tank.gas_mass.y_array, + ) + assert np.allclose( + expected_initial_mass + expected_total_mass_flow * times, + tank.fluid_mass.y_array, + ) -def upper_spherical_cap_centroid(radius, height=None): - """Returns the centroid of a spherical cap with the given radius and filled - height that is filled from its concave side. +def test_mass_flow_rate_tank_liquid_height( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the liquid height properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The centroid of the spherical cap. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ - if height is None: - height = radius - return 0.75 * (height**3 - 2 * height * radius**2) / (height**2 - 3 * radius**2) + tank = example_mass_flow_rate_based_tank_seblm + def expected_liquid_volume(t): + return (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density -def tank_centroid_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the centroid of a cylindrical tank - with spherical caps. + def expected_gas_volume(t): + return (0.1 + (0.01 - 0.02) * t) / nitrogen_fluid_seblm.density - Parameters - ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the centroid of the tank for a given height. - """ + times = np.linspace(0, 10, 11) - def tank_centroid(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - cylinder_height = tank_height - 2 * tank_radius - - if h < tank_radius: - centroid = lower_spherical_cap_centroid(tank_radius, h) - - elif tank_radius <= h < tank_height - tank_radius: - # Fluid height from cylinder base - base = tank_radius - height = h - base - - balance = lower_spherical_cap_volume( - tank_radius - ) * lower_spherical_cap_centroid(tank_radius) + cylinder_volume( - tank_radius, height - ) * ( - cylinder_centroid(height) + tank_radius - ) - volume = lower_spherical_cap_volume(tank_radius) + cylinder_volume( - tank_radius, height - ) - centroid = balance / volume - - else: - # Fluid height from upper cap base - base = tank_height - tank_radius - height = h - base - - balance = ( - lower_spherical_cap_volume(tank_radius) - * lower_spherical_cap_centroid(tank_radius) - + cylinder_volume(tank_radius, cylinder_height) - * (cylinder_centroid(cylinder_height) + tank_radius) - + upper_spherical_cap_volume(tank_radius, height) - * (upper_spherical_cap_centroid(tank_radius, height) + base) - ) - volume = ( - lower_spherical_cap_volume(tank_radius) - + cylinder_volume(tank_radius, cylinder_height) - + upper_spherical_cap_volume(tank_radius, height) - ) - centroid = balance / volume - - return centroid + zero_height - - return tank_centroid - - -def cylinder_inertia(radius, height, reference=0): - """Returns the inertia of a cylinder with the given radius and height. - - Parameters - ---------- - radius : float - The radius of the cylinder. - height : float - The height of the cylinder. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the cylinder in the x, y, and z directions. - """ - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = cylinder_volume(radius, height) * ( - radius**2 / 4 + height**2 / 12 + (height / 2 - reference) ** 2 + assert np.allclose(expected_liquid_volume(times), tank.liquid_volume.y_array) + assert np.allclose( + expected_liquid_volume(times) / tank.geometry.area(0), + tank.liquid_height.y_array, + ) + assert np.allclose( + expected_gas_volume(times), + tank.gas_volume.y_array, + ) + assert np.allclose( + (expected_gas_volume(times) + expected_liquid_volume(times)) + / tank.geometry.area(0), + tank.gas_height.y_array, ) - inertia_y = inertia_x - inertia_z = cylinder_volume(radius, height) * radius**2 / 2 - - return np.array([inertia_x, inertia_y, inertia_z]) -def lower_spherical_cap_inertia(radius, height=None, reference=0): - """Returns the inertia of a spherical cap with the given radius and filled - height that is filled from its convex side. +def test_mass_flow_rate_tank_center_of_mass( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the center of mass properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float - The height of the spherical cap. If not given, the radius is used. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the spherical cap in the x, y, and z directions. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ - if height is None: - height = radius + # TODO: improve code context and repetition + tank = example_mass_flow_rate_based_tank_seblm - centroid = lower_spherical_cap_centroid(radius, height) + def expected_liquid_center_of_mass(t): + liquid_height = (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density / np.pi - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = lower_spherical_cap_volume(radius, height) * ( - ( - np.pi - * height**2 - * ( - -9 * height**3 - + 45 * height**2 * radius - - 80 * height * radius**2 - + 60 * radius**3 - ) - / 60 - ) - - (radius - centroid) ** 2 - + (centroid - reference) ** 2 - ) - inertia_y = inertia_x - inertia_z = lower_spherical_cap_volume(radius, height) * ( - np.pi * height**3 * (3 * height**2 - 15 * height * radius + 20 * radius**2) / 30 - ) - return np.array([inertia_x, inertia_y, inertia_z]) + return liquid_height / 2 + def expected_gas_center_of_mass(t): + liquid_height = (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density / np.pi + gas_height = (0.1 + (0.01 - 0.02) * t) / nitrogen_fluid_seblm.density / np.pi -def upper_spherical_cap_inertia(radius, height=None, reference=0): - """Returns the inertia of a spherical cap with the given radius and filled - height that is filled from its concave side. + return gas_height / 2 + liquid_height - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float - The height of the spherical cap. If not given, the radius is used. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the spherical cap in the x, y, and z directions. - """ - if height is None: - height = radius + def expected_center_of_mass(t): + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t - centroid = upper_spherical_cap_centroid(radius, height) + return ( + liquid_mass * expected_liquid_center_of_mass(t) + + gas_mass * expected_gas_center_of_mass(t) + ) / (liquid_mass + gas_mass) - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = upper_spherical_cap_volume(radius, height) * ( - ( - ( - np.pi - * height - * (-9 * height**4 + 10 * height**2 * radius**2 + 15 * radius**4) - ) - / 60 - ) - - centroid**2 - + (centroid - reference) ** 2 + times = np.linspace(0, 10, 11) + + assert np.allclose( + expected_liquid_center_of_mass(times), + tank.liquid_center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, + ) + assert np.allclose( + expected_gas_center_of_mass(times), + tank.gas_center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, ) - inertia_y = inertia_x - inertia_z = upper_spherical_cap_volume(radius, height) * ( - np.pi - * height - * (3 * height**4 - 10 * height**2 * radius**2 + 15 * radius**4) - / 30 + assert np.allclose( + expected_center_of_mass(times), + tank.center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, ) - return np.array([inertia_x, inertia_y, inertia_z]) -def tank_inertia_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the inertia of a cylindrical tank - with spherical caps. The reference point is the tank centroid. +def test_mass_flow_rate_tank_inertia( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the inertia properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the inertia of the tank for a given height. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ + # TODO: improve code context and repetition + tank = example_mass_flow_rate_based_tank_seblm - def tank_inertia(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - center = tank_height / 2 - cylinder_height = tank_height - 2 * tank_radius - - if h < tank_radius: - inertia = lower_spherical_cap_inertia(tank_radius, h, center) - - elif tank_radius <= h < tank_height - tank_radius: - # Fluid height from cylinder base - base = tank_radius - height = h - base + def expected_center_of_mass(t): + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + gas_height = gas_mass / nitrogen_fluid_seblm.density / np.pi - lower_centroid = lower_spherical_cap_centroid(tank_radius) - cyl_centroid = cylinder_centroid(height) + base - lower_inertia = lower_spherical_cap_inertia(tank_radius, reference=center) - cyl_inertia = cylinder_inertia(tank_radius, height, reference=center - base) + return ( + liquid_mass * liquid_height / 2 + + gas_mass * (gas_height / 2 + liquid_height) + ) / (liquid_mass + gas_mass) - inertia = lower_inertia + cyl_inertia - - else: - # Fluid height from upper cap base - base = tank_height - tank_radius - height = h - base - - lower_centroid = lower_spherical_cap_centroid(tank_radius) - cyl_centroid = cylinder_centroid(cylinder_height) + tank_radius - upper_centroid = upper_spherical_cap_centroid(tank_radius, height) + base - - lower_inertia = lower_spherical_cap_inertia( - tank_radius, reference=lower_centroid - center - ) - cyl_inertia = cylinder_inertia( - tank_radius, cylinder_height, cyl_centroid - tank_radius - ) - upper_inertia = upper_spherical_cap_inertia( - tank_radius, height, upper_centroid - base - ) + def expected_liquid_inertia(t): + r = 1 + liquid_mass = 5 + (0.1 - 0.2) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + liquid_com = liquid_height / 2 + + return ( + 1 / 4 * liquid_mass * r**2 + + 1 / 12 * liquid_mass * liquid_height**2 + + liquid_mass * (liquid_com - expected_center_of_mass(t)) ** 2 + ) - inertia = lower_inertia + cyl_inertia + upper_inertia + def expected_gas_inertia(t): + r = 1 + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + gas_height = gas_mass / nitrogen_fluid_seblm.density / np.pi + gas_com = gas_height / 2 + liquid_height + + return ( + 1 / 4 * gas_mass * r**2 + + 1 / 12 * gas_mass * (gas_height - liquid_height) ** 2 + + gas_mass * (gas_com - expected_center_of_mass(t)) ** 2 + ) - return inertia + times = np.linspace(0, 10, 11) - return tank_inertia + assert np.allclose( + expected_liquid_inertia(times), + tank.liquid_inertia.y_array, + atol=1e-3, + rtol=1e-2, + ) + assert np.allclose( + expected_gas_inertia(times), tank.gas_inertia.y_array, atol=1e-3, rtol=1e-2 + ) + assert np.allclose( + expected_liquid_inertia(times) + expected_gas_inertia(times), + tank.inertia.y_array, + atol=1e-3, + rtol=1e-2, + ) diff --git a/tests/unit/test_tank_geometry.py b/tests/unit/test_tank_geometry.py new file mode 100644 index 000000000..54e3671c2 --- /dev/null +++ b/tests/unit/test_tank_geometry.py @@ -0,0 +1,126 @@ +from pathlib import Path + +import numpy as np +import pytest + +PRESSURANT_PARAMS = (0.135 / 2, 0.981) +PROPELLANT_PARAMS = (0.0744, 0.8068) +SPHERICAL_PARAMS = (0.05, 0.1) + +BASE_PATH = Path("tests/fixtures/motor/data/") + +parametrize_fixtures = pytest.mark.parametrize( + "params", + [ + ( + "pressurant_tank_geometry", + PRESSURANT_PARAMS, + BASE_PATH / "cylindrical_pressurant_tank_expected.csv", + ), + ( + "propellant_tank_geometry", + PROPELLANT_PARAMS, + BASE_PATH / "cylindrical_oxidizer_tank_expected.csv", + ), + ( + "spherical_oxidizer_geometry", + SPHERICAL_PARAMS, + BASE_PATH / "spherical_oxidizer_tank_expected.csv", + ), + ], +) + + +@parametrize_fixtures +def test_tank_bounds(params, request): + """Test basic geometric properties of the tanks.""" + geometry, (expected_radius, expected_height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_total_height = expected_height + + assert np.isclose(geometry.radius(0), expected_radius) + assert np.isclose(geometry.total_height, expected_total_height) + + +@parametrize_fixtures +def test_tank_coordinates(params, request): + """Test basic coordinate values of the tanks.""" + geometry, (_, height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_bottom = -height / 2 + expected_top = height / 2 + + assert np.isclose(geometry.bottom, expected_bottom) + assert np.isclose(geometry.top, expected_top) + + +@parametrize_fixtures +def test_tank_total_volume(params, request): + """Test the total volume of the tanks comparing to the analytically + calculated values. + """ + geometry, (radius, height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_total_volume = ( + np.pi * radius**2 * (height - 2 * radius) + 4 / 3 * np.pi * radius**3 + ) + + assert np.isclose(geometry.total_volume, expected_total_volume) + + +@parametrize_fixtures +def test_tank_volume(params, request): + """Test the volume of the tanks at different heights comparing to the + CAD generated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_volumes = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 1), unpack=True + ) + + assert np.allclose(expected_volumes, geometry.volume(heights)) + + +@parametrize_fixtures +def test_tank_centroid(params, request): + """Test the centroid of the tanks at different heights comparing to the + analytically calculated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_volumes, expected_centroids = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 1, 2), unpack=True + ) + + # For higher accuracy: geometry.volume_moment(geometry.bottom, h)(h) + assert np.allclose( + expected_centroids * expected_volumes, + geometry.volume_moment(geometry.bottom, geometry.top)(heights), + ) + + +@parametrize_fixtures +def test_tank_inertia(params, request): + """Test the inertia of the tanks at different heights comparing to the + analytically calculated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_inertia = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 3), unpack=True + ) + + # For higher accuracy: geometry.Ix_volume(geometry.bottom, h)(h) + assert np.allclose( + expected_inertia[1:], + geometry.Ix_volume(geometry.bottom, geometry.top)(heights[1:]), + rtol=1e-5, + atol=1e-9, + )