docs

Signed-off-by: Dev Singh <dev@devksingh.com>

.devcontainer/Dockerfile

@@ -0,0 +1,2 @@
+FROM python
+WORKDIR ~/

.devcontainer/devcontainer.json

@@ -0,0 +1,27 @@
+{
+    "name": "TRA Analysis Development Environment",
+    "build": {
+		"dockerfile": "Dockerfile",
+    },
+    "settings": { 
+		"terminal.integrated.shell.linux": "/bin/bash",
+		"python.pythonPath": "/usr/local/bin/python",
+		"python.linting.enabled": true,
+		"python.linting.pylintEnabled": true,
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
+	},
+	"extensions": [
+		"mhutchie.git-graph",
+		"donjayamanne.jupyter",
+	],
+	"postCreateCommand": "pip install -r analysis-master/requirements.txt"
+}

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/publish-analysis.yml

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+# This workflows will upload a Python Package using Twine when a release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+name: Upload Analysis Package
+
+on:
+  release:
+    types: [published, edited]
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    env:
+      working-directory: ./analysis-master/
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      working-directory: ${{env.working-directory}}
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel twine
+    - name: Build package
+      working-directory: ${{env.working-directory}}
+      run: |
+        python setup.py sdist bdist_wheel
+    - name: Publish package to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_TOKEN }}
+        packages_dir: analysis-master/dist/

.github/workflows/ut-analysis.yml

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Analysis Unit Tests
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8]
+
+    env:
+      working-directory: ./analysis-master/
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      working-directory: ${{ env.working-directory }}
+    - name: Test with pytest
+      run: |
+        pytest
+      working-directory: ${{ env.working-directory }}

.github/workflows/ut-superscript.yml

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Superscript Unit Tests
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8]
+
+    env:
+      working-directory: ./data-analysis/
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      working-directory: ${{ env.working-directory }}
+    - name: Test with pytest
+      run: |
+        pytest
+      working-directory: ${{ env.working-directory }}

.gitignore

@@ -1,2 +1,39 @@
-
 benchmark_data.csv
+data-analysis/keys/keytemp.json
+data-analysis/__pycache__/analysis.cpython-37.pyc
+apps/android/source/app/src/main/res/drawable-v24/uuh.png
+apps/android/source/app/src/main/java/com/example/titanscouting/tits.java
+
+data-analysis/analysis.cp37-win_amd64.pyd
+data-analysis/analysis/analysis.c
+data-analysis/analysis/analysis.cp37-win_amd64.pyd
+data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp
+data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.lib
+data-analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj
+data-analysis/test.ipynb
+data-analysis/.ipynb_checkpoints/test-checkpoint.ipynb
+.vscode/settings.json
+.vscode
+data-analysis/arthur_pull.ipynb
+data-analysis/keys.txt
+data-analysis/check_for_new_matches.ipynb
+data-analysis/test.ipynb
+data-analysis/visualize_pit.ipynb
+data-analysis/config/keys.config
+analysis-master/analysis/__pycache__/
+analysis-master/analysis/metrics/__pycache__/
+data-analysis/__pycache__/
+analysis-master/analysis.egg-info/
+analysis-master/build/
+analysis-master/metrics/
+data-analysis/config-pop.json
+data-analysis/__pycache__/
+analysis-master/__pycache__/
+analysis-master/.pytest_cache/
+data-analysis/.pytest_cache/
+analysis-master/tra_analysis.egg-info
+analysis-master/tra_analysis/__pycache__
+analysis-master/tra_analysis/.ipynb_checkpoints
+.pytest_cache
+analysis-master/tra_analysis/metrics/__pycache__
+analysis-master/dist

CONTRIBUTING.md

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+# Contributing Guidelines
+
+This project accept contributions via GitHub pull requests.
+This document outlines some of the
+conventions on development workflow, commit message formatting, contact points,
+and other resources to make it easier to get your contribution accepted.
+
+## Certificate of Origin
+
+By contributing to this project, you agree to the [Developer Certificate of
+Origin (DCO)](https://developercertificate.org/). This document was created by the Linux Kernel community and is a
+simple statement that you, as a contributor, have the legal right to make the
+contribution.
+
+In order to show your agreement with the DCO you should include at the end of the commit message,
+the following line: `Signed-off-by: John Doe <john.doe@example.com>`, using your real name.
+
+This can be done easily using the [`-s`](https://github.com/git/git/blob/b2c150d3aa82f6583b9aadfecc5f8fa1c74aca09/Documentation/git-commit.txt#L154-L161) flag on the `git commit`.
+
+Visual Studio code also has a flag to enable signoff on commits 
+
+If you find yourself pushed a few commits without `Signed-off-by`, you can still add it afterwards. Read this for help: [fix-DCO.md](https://github.com/src-d/guide/blob/master/developer-community/fix-DCO.md).
+
+## Support Channels
+
+The official support channel, for both users and contributors, is:
+
+- GitHub issues: each repository has its own list of issues.
+
+*Before opening a new issue or submitting a new pull request, it's helpful to
+search the project - it's likely that another user has already reported the
+issue you're facing, or it's a known issue that we're already aware of.
+
+
+## How to Contribute
+In general, please use conventional approaches to development and contribution such as:
+* Create branches for additions or deletions, and or side projects
+* Do not commit to master!
+* Use Pull Requests (PRs) to indicate that an addition is ready to merge. 
+PRs are the main and exclusive way to contribute code to source{d} projects.
+In order for a PR to be accepted it needs to pass this list of requirements:
+
+- The contribution must be correctly explained with natural language and providing a minimum working example that reproduces it.
+- All PRs must be written idiomaticly:
+    - for Node: formatted according to [AirBnB standards](https://github.com/airbnb/javascript), and no warnings from `eslint` using the AirBnB style guide
+    - for other languages, similar constraints apply.
+- They should in general include tests, and those shall pass.
+    - In any case, all the PRs have to pass the personal evaluation of at least one of the [maintainers](MAINTAINERS) of the project.
+
+
+### Format of the commit message
+
+Every commit message should describe what was changed, under which context and, if applicable, the issue it relates to (mentioning a GitHub issue number when applicable):
+
+For small changes, or changes to a testing or personal branch, the commit message should be a short changelog entry
+
+For larger changes or for changes on branches that are more widely used, the commit message should simply reference an entry to some other changelog system. It is encouraged to use some sort of versioning system to log changes. Example commit messages:
+```
+superscript.py v 2.0.5.006
+```
+
+The format can be described more formally as follows:
+
+```
+<package> v <version number>
+```

LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2020, Titan Robotics FRC 2022
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MAINTAINERS

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+Arthur Lu <learthurgo@gmail.com>
+Jacob Levine <jacoblevine18@gmail.com>
+Dev Singh <dev@devksingh.com>

README.md

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+# red-alliance-analysis
+Titan Robotics 2022 Strategy Team Repository for Data Analysis Tools. Included with these tools are the backend data analysis engine formatted as a python package, associated binaries for the analysis package, and premade scripts that can be pulled directly from this repository and will integrate with other Red Alliance applications to quickly deploy FRC scouting tools.
+
+# Installing 
+`pip install tra_analysis`

analysis-master/build.sh

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+python setup.py sdist bdist_wheel || python3 setup.py sdist bdist_wheel

analysis-master/docker/Dockerfile

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+FROM python
+WORKDIR ~/
+COPY ./ ./
+RUN pip install -r requirements.txt
+CMD ["bash"]

analysis-master/docker/start-docker.sh

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+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/requirements.txt

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+numba
+numpy
+scipy
+scikit-learn
+six
+matplotlib

analysis-master/setup.py

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+import setuptools
+
+requirements = []
+
+with open("requirements.txt", 'r') as file:
+	for line in file:
+		requirements.append(line)
+
+setuptools.setup(
+	name="tra_analysis",
+	version="2.0.3",
+	author="The Titan Scouting Team",
+	author_email="titanscout2022@gmail.com",
+	description="Analysis package developed by Titan Scouting for The Red Alliance",
+	long_description="",
+	long_description_content_type="text/markdown",
+	url="https://github.com/titanscout2022/tr2022-strategy",
+	packages=setuptools.find_packages(),
+	install_requires=requirements,
+	license = "GNU General Public License v3.0",
+	classifiers=[
+		"Programming Language :: Python :: 3",
+		"Operating System :: OS Independent",
+	],
+	python_requires='>=3.6',
+)

analysis-master/test_analysis.py

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+from tra_analysis import analysis as an
+from tra_analysis import metrics
+
+def test_():
+	test_data_linear = [1, 3, 6, 7, 9]
+	y_data_ccu = [1, 3, 7, 14, 21]
+	y_data_ccd = [1, 5, 7, 8.5, 8.66]
+	test_data_scrambled = [-32, 34, 19, 72, -65, -11, -43, 6, 85, -17, -98, -26, 12, 20, 9, -92, -40, 98, -78, 17, -20, 49, 93, -27, -24, -66, 40, 84, 1, -64, -68, -25, -42, -46, -76, 43, -3, 30, -14, -34, -55, -13, 41, -30, 0, -61, 48, 23, 60, 87, 80, 77, 53, 73, 79, 24, -52, 82, 8, -44, 65, 47, -77, 94, 7, 37, -79, 36, -94, 91, 59, 10, 97, -38, -67, 83, 54, 31, -95, -63, 16, -45, 21, -12, 66, -48, -18, -96, -90, -21, -83, -74, 39, 64, 69, -97, 13, 55, 27, -39]
+	test_data_sorted = [-98, -97, -96, -95, -94, -92, -90, -83, -79, -78, -77, -76, -74, -68, -67, -66, -65, -64, -63, -61, -55, -52, -48, -46, -45, -44, -43, -42, -40, -39, -38, -34, -32, -30, -27, -26, -25, -24, -21, -20, -18, -17, -14, -13, -12, -11, -3, 0, 1, 6, 7, 8, 9, 10, 12, 13, 16, 17, 19, 20, 21, 23, 24, 27, 30, 31, 34, 36, 37, 39, 40, 41, 43, 47, 48, 49, 53, 54, 55, 59, 60, 64, 65, 66, 69, 72, 73, 77, 79, 80, 82, 83, 84, 85, 87, 91, 93, 94, 97, 98]
+	assert an.basic_stats(test_data_linear) == {"mean": 5.2, "median": 6.0, "standard-deviation": 2.85657137141714, "variance": 8.16, "minimum": 1.0, "maximum": 9.0}
+	assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665
+	assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
+	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
+	assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
+	assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
+	#assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
+	assert all(a == b for a, b in zip(an.Sort().quicksort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().mergesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().introsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().heapsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().insertionsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().timsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().selectionsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().shellsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().bubblesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().cyclesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(an.Sort().cocktailsort(test_data_scrambled), test_data_sorted))

analysis-master/tra_analysis/equation.ipynb

@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from decimal import Decimal\n",
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add(string):\n",
+    "    while(len(re.findall(\"[+]{1}[-]?\", string)) != 0):\n",
+    "        string = re.sub(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split(\"[+]{1}\", re.search(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sub(string):\n",
+    "    while(len(re.findall(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string)) != 0):\n",
+    "        g = re.search(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string).group()\n",
+    "        if(re.search(\"[-]{1,2}\", g).group() == \"-\"):\n",
+    "            r = re.sub(\"[-]{1}\", \"+-\", g, 1)\n",
+    "            string = re.sub(g, r, string, 1)\n",
+    "        elif(re.search(\"[-]{1,2}\", g).group() == \"--\"):\n",
+    "            r = re.sub(\"[-]{2}\", \"+\", g, 1)\n",
+    "            string = re.sub(g, r, string, 1)\n",
+    "        else:\n",
+    "            pass\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mul(string):\n",
+    "    while(len(re.findall(\"[*]{1}[-]?\", string)) != 0):\n",
+    "        string = re.sub(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split(\"[*]{1}\", re.search(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def div(string):\n",
+    "    while(len(re.findall(\"[/]{1}[-]?\", string)) != 0):\n",
+    "        string = re.sub(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split(\"[/]{1}\", re.search(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exp(string):\n",
+    "    while(len(re.findall(\"[\\^]{1}[-]?\", string)) != 0):\n",
+    "        string = re.sub(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split(\"[\\^]{1}\", re.search(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate(string):\n",
+    "    string = exp(string)\n",
+    "    string = div(string)\n",
+    "    string = mul(string)\n",
+    "    string = sub(string)\n",
+    "    print(string)\n",
+    "    string = add(string)\n",
+    "    return string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "SyntaxError",
+     "evalue": "unexpected EOF while parsing (<ipython-input-13-f9fb4aededd9>, line 1)",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-13-f9fb4aededd9>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    def parentheses(string):\u001b[0m\n\u001b[1;37m                            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
+     ]
+    }
+   ],
+   "source": [
+    "def parentheses(string):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "-158456325028528675187087900672.000000+0.8\n"
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "'-158456325028528675187087900672.000000'"
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ],
+   "source": [
+    "string = \"8^32*4/-2+0.8\"\n",
+    "evaluate(string)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.6-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

analysis-master/tra_analysis/metrics/elo.py

@@ -0,0 +1,7 @@
+import numpy as np
+
+def calculate(starting_score, opposing_score, observed, N, K):
+
+		expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+
+		return starting_score + K*(np.sum(observed) - np.sum(expected))

analysis-master/tra_analysis/metrics/glicko2.py

@@ -0,0 +1,99 @@
+import math
+
+class Glicko2:
+	_tau = 0.5
+
+	def getRating(self):
+		return (self.__rating * 173.7178) + 1500 
+
+	def setRating(self, rating):
+		self.__rating = (rating - 1500) / 173.7178
+
+	rating = property(getRating, setRating)
+
+	def getRd(self):
+		return self.__rd * 173.7178
+
+	def setRd(self, rd):
+		self.__rd = rd / 173.7178
+
+	rd = property(getRd, setRd)
+		
+	def __init__(self, rating = 1500, rd = 350, vol = 0.06):
+
+		self.setRating(rating)
+		self.setRd(rd)
+		self.vol = vol
+			
+	def _preRatingRD(self):
+
+		self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
+		
+	def update_player(self, rating_list, RD_list, outcome_list):
+
+		rating_list = [(x - 1500) / 173.7178 for x in rating_list]
+		RD_list = [x / 173.7178 for x in RD_list]
+
+		v = self._v(rating_list, RD_list)
+		self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
+		self._preRatingRD()
+		
+		self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
+		
+		tempSum = 0
+		for i in range(len(rating_list)):
+			tempSum += self._g(RD_list[i]) * \
+						(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+		self.__rating += math.pow(self.__rd, 2) * tempSum
+		
+		
+	def _newVol(self, rating_list, RD_list, outcome_list, v):
+
+		i = 0
+		delta = self._delta(rating_list, RD_list, outcome_list, v)
+		a = math.log(math.pow(self.vol, 2))
+		tau = self._tau
+		x0 = a
+		x1 = 0
+		
+		while x0 != x1:
+			# New iteration, so x(i) becomes x(i-1)
+			x0 = x1
+			d = math.pow(self.__rating, 2) + v + math.exp(x0)
+			h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
+			/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
+			h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
+			(math.pow(self.__rating, 2) + v) \
+			/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
+			* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
+			x1 = x0 - (h1 / h2)
+
+		return math.exp(x1 / 2)
+		
+	def _delta(self, rating_list, RD_list, outcome_list, v):
+
+		tempSum = 0
+		for i in range(len(rating_list)):
+			tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+		return v * tempSum
+		
+	def _v(self, rating_list, RD_list):
+
+		tempSum = 0
+		for i in range(len(rating_list)):
+			tempE = self._E(rating_list[i], RD_list[i])
+			tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
+		return 1 / tempSum
+		
+	def _E(self, p2rating, p2RD):
+
+		return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
+									(self.__rating - p2rating)))
+		
+	def _g(self, RD):
+
+		return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
+		
+	def did_not_compete(self):
+
+		self._preRatingRD()

analysis-master/tra_analysis/metrics/trueskill.py

@@ -0,0 +1,907 @@
+from __future__ import absolute_import
+
+from itertools import chain
+import math
+
+from six import iteritems
+from six.moves import map, range, zip
+from six import iterkeys
+
+import copy
+try:
+	from numbers import Number
+except ImportError:
+	Number = (int, long, float, complex)
+
+inf = float('inf')
+
+class Gaussian(object):
+	#: Precision, the inverse of the variance.
+	pi = 0
+	#: Precision adjusted mean, the precision multiplied by the mean.
+	tau = 0
+
+	def __init__(self, mu=None, sigma=None, pi=0, tau=0):
+		if mu is not None:
+			if sigma is None:
+				raise TypeError('sigma argument is needed')
+			elif sigma == 0:
+				raise ValueError('sigma**2 should be greater than 0')
+			pi = sigma ** -2
+			tau = pi * mu
+		self.pi = pi
+		self.tau = tau
+
+	@property
+	def mu(self):
+		return self.pi and self.tau / self.pi
+
+	@property
+	def sigma(self):
+		return math.sqrt(1 / self.pi) if self.pi else inf
+
+	def __mul__(self, other):
+		pi, tau = self.pi + other.pi, self.tau + other.tau
+		return Gaussian(pi=pi, tau=tau)
+
+	def __truediv__(self, other):
+		pi, tau = self.pi - other.pi, self.tau - other.tau
+		return Gaussian(pi=pi, tau=tau)
+
+	__div__ = __truediv__  # for Python 2
+
+	def __eq__(self, other):
+		return self.pi == other.pi and self.tau == other.tau
+
+	def __lt__(self, other):
+		return self.mu < other.mu
+
+	def __le__(self, other):
+		return self.mu <= other.mu
+
+	def __gt__(self, other):
+		return self.mu > other.mu
+
+	def __ge__(self, other):
+		return self.mu >= other.mu
+
+	def __repr__(self):
+		return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
+
+	def _repr_latex_(self):
+		latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
+		return '$%s$' % latex
+
+class Matrix(list):
+	def __init__(self, src, height=None, width=None):
+		if callable(src):
+			f, src = src, {}
+			size = [height, width]
+			if not height:
+				def set_height(height):
+					size[0] = height
+				size[0] = set_height
+			if not width:
+				def set_width(width):
+					size[1] = width
+				size[1] = set_width
+			try:
+				for (r, c), val in f(*size):
+					src[r, c] = val
+			except TypeError:
+				raise TypeError('A callable src must return an interable '
+								'which generates a tuple containing '
+								'coordinate and value')
+			height, width = tuple(size)
+			if height is None or width is None:
+				raise TypeError('A callable src must call set_height and '
+								'set_width if the size is non-deterministic')
+		if isinstance(src, list):
+			is_number = lambda x: isinstance(x, Number)
+			unique_col_sizes = set(map(len, src))
+			everything_are_number = filter(is_number, sum(src, []))
+			if len(unique_col_sizes) != 1 or not everything_are_number:
+				raise ValueError('src must be a rectangular array of numbers')
+			two_dimensional_array = src
+		elif isinstance(src, dict):
+			if not height or not width:
+				w = h = 0
+				for r, c in iterkeys(src):
+					if not height:
+						h = max(h, r + 1)
+					if not width:
+						w = max(w, c + 1)
+				if not height:
+					height = h
+				if not width:
+					width = w
+			two_dimensional_array = []
+			for r in range(height):
+				row = []
+				two_dimensional_array.append(row)
+				for c in range(width):
+					row.append(src.get((r, c), 0))
+		else:
+			raise TypeError('src must be a list or dict or callable')
+		super(Matrix, self).__init__(two_dimensional_array)
+
+	@property
+	def height(self):
+		return len(self)
+
+	@property
+	def width(self):
+		return len(self[0])
+
+	def transpose(self):
+		height, width = self.height, self.width
+		src = {}
+		for c in range(width):
+			for r in range(height):
+				src[c, r] = self[r][c]
+		return type(self)(src, height=width, width=height)
+
+	def minor(self, row_n, col_n):
+		height, width = self.height, self.width
+		if not (0 <= row_n < height):
+			raise ValueError('row_n should be between 0 and %d' % height)
+		elif not (0 <= col_n < width):
+			raise ValueError('col_n should be between 0 and %d' % width)
+		two_dimensional_array = []
+		for r in range(height):
+			if r == row_n:
+				continue
+			row = []
+			two_dimensional_array.append(row)
+			for c in range(width):
+				if c == col_n:
+					continue
+				row.append(self[r][c])
+		return type(self)(two_dimensional_array)
+
+	def determinant(self):
+		height, width = self.height, self.width
+		if height != width:
+			raise ValueError('Only square matrix can calculate a determinant')
+		tmp, rv = copy.deepcopy(self), 1.
+		for c in range(width - 1, 0, -1):
+			pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
+			pivot = tmp[r][c]
+			if not pivot:
+				return 0.
+			tmp[r], tmp[c] = tmp[c], tmp[r]
+			if r != c:
+				rv = -rv
+			rv *= pivot
+			fact = -1. / pivot
+			for r in range(c):
+				f = fact * tmp[r][c]
+				for x in range(c):
+					tmp[r][x] += f * tmp[c][x]
+		return rv * tmp[0][0]
+
+	def adjugate(self):
+		height, width = self.height, self.width
+		if height != width:
+			raise ValueError('Only square matrix can be adjugated')
+		if height == 2:
+			a, b = self[0][0], self[0][1]
+			c, d = self[1][0], self[1][1]
+			return type(self)([[d, -b], [-c, a]])
+		src = {}
+		for r in range(height):
+			for c in range(width):
+				sign = -1 if (r + c) % 2 else 1
+				src[r, c] = self.minor(r, c).determinant() * sign
+		return type(self)(src, height, width)
+
+	def inverse(self):
+		if self.height == self.width == 1:
+			return type(self)([[1. / self[0][0]]])
+		return (1. / self.determinant()) * self.adjugate()
+
+	def __add__(self, other):
+		height, width = self.height, self.width
+		if (height, width) != (other.height, other.width):
+			raise ValueError('Must be same size')
+		src = {}
+		for r in range(height):
+			for c in range(width):
+				src[r, c] = self[r][c] + other[r][c]
+		return type(self)(src, height, width)
+
+	def __mul__(self, other):
+		if self.width != other.height:
+			raise ValueError('Bad size')
+		height, width = self.height, other.width
+		src = {}
+		for r in range(height):
+			for c in range(width):
+				src[r, c] = sum(self[r][x] * other[x][c]
+								for x in range(self.width))
+		return type(self)(src, height, width)
+
+	def __rmul__(self, other):
+		if not isinstance(other, Number):
+			raise TypeError('The operand should be a number')
+		height, width = self.height, self.width
+		src = {}
+		for r in range(height):
+			for c in range(width):
+				src[r, c] = other * self[r][c]
+		return type(self)(src, height, width)
+
+	def __repr__(self):
+		return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
+
+	def _repr_latex_(self):
+		rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
+		latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
+		return '$%s$' % latex
+
+def _gen_erfcinv(erfc, math=math):
+	def erfcinv(y):
+		"""The inverse function of erfc."""
+		if y >= 2:
+			return -100.
+		elif y <= 0:
+			return 100.
+		zero_point = y < 1
+		if not zero_point:
+			y = 2 - y
+		t = math.sqrt(-2 * math.log(y / 2.))
+		x = -0.70711 * \
+			((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
+		for i in range(2):
+			err = erfc(x) - y
+			x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
+		return x if zero_point else -x
+	return erfcinv
+
+def _gen_ppf(erfc, math=math):
+	erfcinv = _gen_erfcinv(erfc, math)
+	def ppf(x, mu=0, sigma=1):
+		return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
+	return ppf
+
+def erfc(x):
+	z = abs(x)
+	t = 1. / (1. + z / 2.)
+	r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
+		0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
+			0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
+				-0.82215223 + t * 0.17087277
+			)))
+		)))
+	)))
+	return 2. - r if x < 0 else r
+
+def cdf(x, mu=0, sigma=1):
+	return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
+
+
+def pdf(x, mu=0, sigma=1):
+	return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
+			math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
+
+ppf = _gen_ppf(erfc)
+
+def choose_backend(backend):
+	if backend is None:  # fallback
+		return cdf, pdf, ppf
+	elif backend == 'mpmath':
+		try:
+			import mpmath
+		except ImportError:
+			raise ImportError('Install "mpmath" to use this backend')
+		return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
+	elif backend == 'scipy':
+		try:
+			from scipy.stats import norm
+		except ImportError:
+			raise ImportError('Install "scipy" to use this backend')
+		return norm.cdf, norm.pdf, norm.ppf
+	raise ValueError('%r backend is not defined' % backend)
+
+def available_backends():
+	backends = [None]
+	for backend in ['mpmath', 'scipy']:
+		try:
+			__import__(backend)
+		except ImportError:
+			continue
+		backends.append(backend)
+	return backends
+
+class Node(object):
+
+	pass
+
+class Variable(Node, Gaussian):
+
+	def __init__(self):
+		self.messages = {}
+		super(Variable, self).__init__()
+
+	def set(self, val):
+		delta = self.delta(val)
+		self.pi, self.tau = val.pi, val.tau
+		return delta
+
+	def delta(self, other):
+		pi_delta = abs(self.pi - other.pi)
+		if pi_delta == inf:
+			return 0.
+		return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
+
+	def update_message(self, factor, pi=0, tau=0, message=None):
+		message = message or Gaussian(pi=pi, tau=tau)
+		old_message, self[factor] = self[factor], message
+		return self.set(self / old_message * message)
+
+	def update_value(self, factor, pi=0, tau=0, value=None):
+		value = value or Gaussian(pi=pi, tau=tau)
+		old_message = self[factor]
+		self[factor] = value * old_message / self
+		return self.set(value)
+
+	def __getitem__(self, factor):
+		return self.messages[factor]
+
+	def __setitem__(self, factor, message):
+		self.messages[factor] = message
+
+	def __repr__(self):
+		args = (type(self).__name__, super(Variable, self).__repr__(),
+				len(self.messages), '' if len(self.messages) == 1 else 's')
+		return '<%s %s with %d connection%s>' % args
+
+
+class Factor(Node):
+
+	def __init__(self, variables):
+		self.vars = variables
+		for var in variables:
+			var[self] = Gaussian()
+
+	def down(self):
+		return 0
+
+	def up(self):
+		return 0
+
+	@property
+	def var(self):
+		assert len(self.vars) == 1
+		return self.vars[0]
+
+	def __repr__(self):
+		args = (type(self).__name__, len(self.vars),
+				'' if len(self.vars) == 1 else 's')
+		return '<%s with %d connection%s>' % args
+
+
+class PriorFactor(Factor):
+
+	def __init__(self, var, val, dynamic=0):
+		super(PriorFactor, self).__init__([var])
+		self.val = val
+		self.dynamic = dynamic
+
+	def down(self):
+		sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
+		value = Gaussian(self.val.mu, sigma)
+		return self.var.update_value(self, value=value)
+
+
+class LikelihoodFactor(Factor):
+
+	def __init__(self, mean_var, value_var, variance):
+		super(LikelihoodFactor, self).__init__([mean_var, value_var])
+		self.mean = mean_var
+		self.value = value_var
+		self.variance = variance
+
+	def calc_a(self, var):
+		return 1. / (1. + self.variance * var.pi)
+
+	def down(self):
+		# update value.
+		msg = self.mean / self.mean[self]
+		a = self.calc_a(msg)
+		return self.value.update_message(self, a * msg.pi, a * msg.tau)
+
+	def up(self):
+		# update mean.
+		msg = self.value / self.value[self]
+		a = self.calc_a(msg)
+		return self.mean.update_message(self, a * msg.pi, a * msg.tau)
+
+
+class SumFactor(Factor):
+
+	def __init__(self, sum_var, term_vars, coeffs):
+		super(SumFactor, self).__init__([sum_var] + term_vars)
+		self.sum = sum_var
+		self.terms = term_vars
+		self.coeffs = coeffs
+
+	def down(self):
+		vals = self.terms
+		msgs = [var[self] for var in vals]
+		return self.update(self.sum, vals, msgs, self.coeffs)
+
+	def up(self, index=0):
+		coeff = self.coeffs[index]
+		coeffs = []
+		for x, c in enumerate(self.coeffs):
+			try:
+				if x == index:
+					coeffs.append(1. / coeff)
+				else:
+					coeffs.append(-c / coeff)
+			except ZeroDivisionError:
+				coeffs.append(0.)
+		vals = self.terms[:]
+		vals[index] = self.sum
+		msgs = [var[self] for var in vals]
+		return self.update(self.terms[index], vals, msgs, coeffs)
+
+	def update(self, var, vals, msgs, coeffs):
+		pi_inv = 0
+		mu = 0
+		for val, msg, coeff in zip(vals, msgs, coeffs):
+			div = val / msg
+			mu += coeff * div.mu
+			if pi_inv == inf:
+				continue
+			try:
+				# numpy.float64 handles floating-point error by different way.
+				# For example, it can just warn RuntimeWarning on n/0 problem
+				# instead of throwing ZeroDivisionError.  So div.pi, the
+				# denominator has to be a built-in float.
+				pi_inv += coeff ** 2 / float(div.pi)
+			except ZeroDivisionError:
+				pi_inv = inf
+		pi = 1. / pi_inv
+		tau = pi * mu
+		return var.update_message(self, pi, tau)
+
+
+class TruncateFactor(Factor):
+
+	def __init__(self, var, v_func, w_func, draw_margin):
+		super(TruncateFactor, self).__init__([var])
+		self.v_func = v_func
+		self.w_func = w_func
+		self.draw_margin = draw_margin
+
+	def up(self):
+		val = self.var
+		msg = self.var[self]
+		div = val / msg
+		sqrt_pi = math.sqrt(div.pi)
+		args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
+		v = self.v_func(*args)
+		w = self.w_func(*args)
+		denom = (1. - w)
+		pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
+		return val.update_value(self, pi, tau)
+
+#: Default initial mean of ratings.
+MU = 25.
+#: Default initial standard deviation of ratings.
+SIGMA = MU / 3
+#: Default distance that guarantees about 76% chance of winning.
+BETA = SIGMA / 2
+#: Default dynamic factor.
+TAU = SIGMA / 100
+#: Default draw probability of the game.
+DRAW_PROBABILITY = .10
+#: A basis to check reliability of the result.
+DELTA = 0.0001
+
+
+def calc_draw_probability(draw_margin, size, env=None):
+	if env is None:
+		env = global_env()
+	return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
+
+
+def calc_draw_margin(draw_probability, size, env=None):
+	if env is None:
+		env = global_env()
+	return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
+
+
+def _team_sizes(rating_groups):
+	team_sizes = [0]
+	for group in rating_groups:
+		team_sizes.append(len(group) + team_sizes[-1])
+	del team_sizes[0]
+	return team_sizes
+
+
+def _floating_point_error(env):
+	if env.backend == 'mpmath':
+		msg = 'Set "mpmath.mp.dps" to higher'
+	else:
+		msg = 'Cannot calculate correctly, set backend to "mpmath"'
+	return FloatingPointError(msg)
+
+
+class Rating(Gaussian):
+	def __init__(self, mu=None, sigma=None):
+		if isinstance(mu, tuple):
+			mu, sigma = mu
+		elif isinstance(mu, Gaussian):
+			mu, sigma = mu.mu, mu.sigma
+		if mu is None:
+			mu = global_env().mu
+		if sigma is None:
+			sigma = global_env().sigma
+		super(Rating, self).__init__(mu, sigma)
+
+	def __int__(self):
+		return int(self.mu)
+
+	def __long__(self):
+		return long(self.mu)
+
+	def __float__(self):
+		return float(self.mu)
+
+	def __iter__(self):
+		return iter((self.mu, self.sigma))
+
+	def __repr__(self):
+		c = type(self)
+		args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
+		return '%s(mu=%.3f, sigma=%.3f)' % args
+
+
+class TrueSkill(object):
+	def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+				 draw_probability=DRAW_PROBABILITY, backend=None):
+		self.mu = mu
+		self.sigma = sigma
+		self.beta = beta
+		self.tau = tau
+		self.draw_probability = draw_probability
+		self.backend = backend
+		if isinstance(backend, tuple):
+			self.cdf, self.pdf, self.ppf = backend
+		else:
+			self.cdf, self.pdf, self.ppf = choose_backend(backend)
+
+	def create_rating(self, mu=None, sigma=None):
+		if mu is None:
+			mu = self.mu
+		if sigma is None:
+			sigma = self.sigma
+		return Rating(mu, sigma)
+
+	def v_win(self, diff, draw_margin):
+		x = diff - draw_margin
+		denom = self.cdf(x)
+		return (self.pdf(x) / denom) if denom else -x
+
+	def v_draw(self, diff, draw_margin):
+		abs_diff = abs(diff)
+		a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+		denom = self.cdf(a) - self.cdf(b)
+		numer = self.pdf(b) - self.pdf(a)
+		return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
+
+	def w_win(self, diff, draw_margin):
+		x = diff - draw_margin
+		v = self.v_win(diff, draw_margin)
+		w = v * (v + x)
+		if 0 < w < 1:
+			return w
+		raise _floating_point_error(self)
+
+	def w_draw(self, diff, draw_margin):
+		abs_diff = abs(diff)
+		a, b = draw_margin - abs_diff, -draw_margin - abs_diff
+		denom = self.cdf(a) - self.cdf(b)
+		if not denom:
+			raise _floating_point_error(self)
+		v = self.v_draw(abs_diff, draw_margin)
+		return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
+
+	def validate_rating_groups(self, rating_groups):
+		# check group sizes
+		if len(rating_groups) < 2:
+			raise ValueError('Need multiple rating groups')
+		elif not all(rating_groups):
+			raise ValueError('Each group must contain multiple ratings')
+		# check group types
+		group_types = set(map(type, rating_groups))
+		if len(group_types) != 1:
+			raise TypeError('All groups should be same type')
+		elif group_types.pop() is Rating:
+			raise TypeError('Rating cannot be a rating group')
+		# normalize rating_groups
+		if isinstance(rating_groups[0], dict):
+			dict_rating_groups = rating_groups
+			rating_groups = []
+			keys = []
+			for dict_rating_group in dict_rating_groups:
+				rating_group, key_group = [], []
+				for key, rating in iteritems(dict_rating_group):
+					rating_group.append(rating)
+					key_group.append(key)
+				rating_groups.append(tuple(rating_group))
+				keys.append(tuple(key_group))
+		else:
+			rating_groups = list(rating_groups)
+			keys = None
+		return rating_groups, keys
+
+	def validate_weights(self, weights, rating_groups, keys=None):
+		if weights is None:
+			weights = [(1,) * len(g) for g in rating_groups]
+		elif isinstance(weights, dict):
+			weights_dict, weights = weights, []
+			for x, group in enumerate(rating_groups):
+				w = []
+				weights.append(w)
+				for y, rating in enumerate(group):
+					if keys is not None:
+						y = keys[x][y]
+					w.append(weights_dict.get((x, y), 1))
+		return weights
+
+	def factor_graph_builders(self, rating_groups, ranks, weights):
+		flatten_ratings = sum(map(tuple, rating_groups), ())
+		flatten_weights = sum(map(tuple, weights), ())
+		size = len(flatten_ratings)
+		group_size = len(rating_groups)
+		# create variables
+		rating_vars = [Variable() for x in range(size)]
+		perf_vars = [Variable() for x in range(size)]
+		team_perf_vars = [Variable() for x in range(group_size)]
+		team_diff_vars = [Variable() for x in range(group_size - 1)]
+		team_sizes = _team_sizes(rating_groups)
+		# layer builders
+		def build_rating_layer():
+			for rating_var, rating in zip(rating_vars, flatten_ratings):
+				yield PriorFactor(rating_var, rating, self.tau)
+		def build_perf_layer():
+			for rating_var, perf_var in zip(rating_vars, perf_vars):
+				yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
+		def build_team_perf_layer():
+			for team, team_perf_var in enumerate(team_perf_vars):
+				if team > 0:
+					start = team_sizes[team - 1]
+				else:
+					start = 0
+				end = team_sizes[team]
+				child_perf_vars = perf_vars[start:end]
+				coeffs = flatten_weights[start:end]
+				yield SumFactor(team_perf_var, child_perf_vars, coeffs)
+		def build_team_diff_layer():
+			for team, team_diff_var in enumerate(team_diff_vars):
+				yield SumFactor(team_diff_var,
+								team_perf_vars[team:team + 2], [+1, -1])
+		def build_trunc_layer():
+			for x, team_diff_var in enumerate(team_diff_vars):
+				if callable(self.draw_probability):
+					# dynamic draw probability
+					team_perf1, team_perf2 = team_perf_vars[x:x + 2]
+					args = (Rating(team_perf1), Rating(team_perf2), self)
+					draw_probability = self.draw_probability(*args)
+				else:
+					# static draw probability
+					draw_probability = self.draw_probability
+				size = sum(map(len, rating_groups[x:x + 2]))
+				draw_margin = calc_draw_margin(draw_probability, size, self)
+				if ranks[x] == ranks[x + 1]:  # is a tie?
+					v_func, w_func = self.v_draw, self.w_draw
+				else:
+					v_func, w_func = self.v_win, self.w_win
+				yield TruncateFactor(team_diff_var,
+									 v_func, w_func, draw_margin)
+		# build layers
+		return (build_rating_layer, build_perf_layer, build_team_perf_layer,
+				build_team_diff_layer, build_trunc_layer)
+
+	def run_schedule(self, build_rating_layer, build_perf_layer,
+					 build_team_perf_layer, build_team_diff_layer,
+					 build_trunc_layer, min_delta=DELTA):
+		if min_delta <= 0:
+			raise ValueError('min_delta must be greater than 0')
+		layers = []
+		def build(builders):
+			layers_built = [list(build()) for build in builders]
+			layers.extend(layers_built)
+			return layers_built
+		# gray arrows
+		layers_built = build([build_rating_layer,
+							  build_perf_layer,
+							  build_team_perf_layer])
+		rating_layer, perf_layer, team_perf_layer = layers_built
+		for f in chain(*layers_built):
+			f.down()
+		# arrow #1, #2, #3
+		team_diff_layer, trunc_layer = build([build_team_diff_layer,
+											  build_trunc_layer])
+		team_diff_len = len(team_diff_layer)
+		for x in range(10):
+			if team_diff_len == 1:
+				# only two teams
+				team_diff_layer[0].down()
+				delta = trunc_layer[0].up()
+			else:
+				# multiple teams
+				delta = 0
+				for x in range(team_diff_len - 1):
+					team_diff_layer[x].down()
+					delta = max(delta, trunc_layer[x].up())
+					team_diff_layer[x].up(1)  # up to right variable
+				for x in range(team_diff_len - 1, 0, -1):
+					team_diff_layer[x].down()
+					delta = max(delta, trunc_layer[x].up())
+					team_diff_layer[x].up(0)  # up to left variable
+			# repeat until to small update
+			if delta <= min_delta:
+				break
+		# up both ends
+		team_diff_layer[0].up(0)
+		team_diff_layer[team_diff_len - 1].up(1)
+		# up the remainder of the black arrows
+		for f in team_perf_layer:
+			for x in range(len(f.vars) - 1):
+				f.up(x)
+		for f in perf_layer:
+			f.up()
+		return layers
+
+	def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
+		rating_groups, keys = self.validate_rating_groups(rating_groups)
+		weights = self.validate_weights(weights, rating_groups, keys)
+		group_size = len(rating_groups)
+		if ranks is None:
+			ranks = range(group_size)
+		elif len(ranks) != group_size:
+			raise ValueError('Wrong ranks')
+		# sort rating groups by rank
+		by_rank = lambda x: x[1][1]
+		sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
+						 key=by_rank)
+		sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
+		for x, (g, r, w) in sorting:
+			sorted_rating_groups.append(g)
+			sorted_ranks.append(r)
+			# make weights to be greater than 0
+			sorted_weights.append(max(min_delta, w_) for w_ in w)
+		# build factor graph
+		args = (sorted_rating_groups, sorted_ranks, sorted_weights)
+		builders = self.factor_graph_builders(*args)
+		args = builders + (min_delta,)
+		layers = self.run_schedule(*args)
+		# make result
+		rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
+		transformed_groups = []
+		for start, end in zip([0] + team_sizes[:-1], team_sizes):
+			group = []
+			for f in rating_layer[start:end]:
+				group.append(Rating(float(f.var.mu), float(f.var.sigma)))
+			transformed_groups.append(tuple(group))
+		by_hint = lambda x: x[0]
+		unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
+						   key=by_hint)
+		if keys is None:
+			return [g for x, g in unsorting]
+		# restore the structure with input dictionary keys
+		return [dict(zip(keys[x], g)) for x, g in unsorting]
+
+	def quality(self, rating_groups, weights=None):
+		rating_groups, keys = self.validate_rating_groups(rating_groups)
+		weights = self.validate_weights(weights, rating_groups, keys)
+		flatten_ratings = sum(map(tuple, rating_groups), ())
+		flatten_weights = sum(map(tuple, weights), ())
+		length = len(flatten_ratings)
+		# a vector of all of the skill means
+		mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
+		# a matrix whose diagonal values are the variances (sigma ** 2) of each
+		# of the players.
+		def variance_matrix(height, width):
+			variances = (r.sigma ** 2 for r in flatten_ratings)
+			for x, variance in enumerate(variances):
+				yield (x, x), variance
+		variance_matrix = Matrix(variance_matrix, length, length)
+		# the player-team assignment and comparison matrix
+		def rotated_a_matrix(set_height, set_width):
+			t = 0
+			for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
+												rating_groups[1:])):
+				for x in range(t, t + len(cur)):
+					yield (r, x), flatten_weights[x]
+					t += 1
+				x += 1
+				for x in range(x, x + len(_next)):
+					yield (r, x), -flatten_weights[x]
+			set_height(r + 1)
+			set_width(x + 1)
+		rotated_a_matrix = Matrix(rotated_a_matrix)
+		a_matrix = rotated_a_matrix.transpose()
+		# match quality further derivation
+		_ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
+		_atsa = rotated_a_matrix * variance_matrix * a_matrix
+		start = mean_matrix.transpose() * a_matrix
+		middle = _ata + _atsa
+		end = rotated_a_matrix * mean_matrix
+		# make result
+		e_arg = (-0.5 * start * middle.inverse() * end).determinant()
+		s_arg = _ata.determinant() / middle.determinant()
+		return math.exp(e_arg) * math.sqrt(s_arg)
+
+	def expose(self, rating):
+		k = self.mu / self.sigma
+		return rating.mu - k * rating.sigma
+
+	def make_as_global(self):
+		return setup(env=self)
+
+	def __repr__(self):
+		c = type(self)
+		if callable(self.draw_probability):
+			f = self.draw_probability
+			draw_probability = '.'.join([f.__module__, f.__name__])
+		else:
+			draw_probability = '%.1f%%' % (self.draw_probability * 100)
+		if self.backend is None:
+			backend = ''
+		elif isinstance(self.backend, tuple):
+			backend = ', backend=...'
+		else:
+			backend = ', backend=%r' % self.backend
+		args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
+				self.beta, self.tau, draw_probability, backend)
+		return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
+				'draw_probability=%s%s)' % args)
+
+
+def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
+	if env is None:
+		env = global_env()
+	ranks = [0, 0 if drawn else 1]
+	teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
+	return teams[0][0], teams[1][0]
+
+
+def quality_1vs1(rating1, rating2, env=None):
+	if env is None:
+		env = global_env()
+	return env.quality([(rating1,), (rating2,)])
+
+
+def global_env():
+	try:
+		global_env.__trueskill__
+	except AttributeError:
+		# setup the default environment
+		setup()
+	return global_env.__trueskill__
+
+
+def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
+		  draw_probability=DRAW_PROBABILITY, backend=None, env=None):
+	if env is None:
+		env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
+	global_env.__trueskill__ = env
+	return env
+
+
+def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
+	return global_env().rate(rating_groups, ranks, weights, min_delta)
+
+
+def quality(rating_groups, weights=None):
+	return global_env().quality(rating_groups, weights)
+
+
+def expose(rating):
+	return global_env().expose(rating)

analysis-master/tra_analysis/regression.py

@@ -0,0 +1,220 @@
+# Titan Robotics Team 2022: CUDA-based Regressions Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
+#   this module is cuda-optimized and vectorized (except for one small part)
+# setup:
+
+__version__ = "1.0.0.004"
+
+# changelog should be viewed using print(analysis.regression.__changelog__)
+__changelog__ = """
+	1.0.0.004:
+		- bug fixes
+		- fixed changelog
+	1.0.0.003:
+		- bug fixes
+	1.0.0.002:
+		-Added more parameters to log, exponential, polynomial
+		-Added SigmoidalRegKernelArthur, because Arthur apparently needs
+		to train the scaling and shifting of sigmoids
+	1.0.0.001:
+		-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+		-already vectorized (except for polynomial generation) and CUDA-optimized
+"""
+
+__author__ = (
+	"Jacob Levine <jlevine@imsa.edu>",
+	"Arthur Lu <learthurgo@gmail.com>"
+)
+
+__all__ = [
+	'factorial',
+	'take_all_pwrs',
+	'num_poly_terms',
+	'set_device',
+	'LinearRegKernel',
+	'SigmoidalRegKernel',
+	'LogRegKernel',
+	'PolyRegKernel',
+	'ExpRegKernel',
+	'SigmoidalRegKernelArthur',
+	'SGDTrain',
+	'CustomTrain'
+]
+
+import torch
+
+global device
+
+device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
+
+#todo: document completely
+
+def set_device(self, new_device):
+	device=new_device
+
+class LinearRegKernel():
+	parameters= []
+	weights=None
+	bias=None
+	def __init__(self, num_vars):
+		self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+		self.bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.bias]
+	def forward(self,mtx):
+		long_bias=self.bias.repeat([1,mtx.size()[1]])
+		return torch.matmul(self.weights,mtx)+long_bias
+
+class SigmoidalRegKernel():
+	parameters= []
+	weights=None
+	bias=None
+	sigmoid=torch.nn.Sigmoid()
+	def __init__(self, num_vars):
+		self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+		self.bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.bias]
+	def forward(self,mtx):
+		long_bias=self.bias.repeat([1,mtx.size()[1]])
+		return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
+
+class SigmoidalRegKernelArthur():
+	parameters= []
+	weights=None
+	in_bias=None
+	scal_mult=None
+	out_bias=None
+	sigmoid=torch.nn.Sigmoid()
+	def __init__(self, num_vars):
+		self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+		self.in_bias=torch.rand(1, requires_grad=True, device=device)
+		self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+		self.out_bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+	def forward(self,mtx):
+		long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+		long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+		return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class LogRegKernel():
+	parameters= []
+	weights=None
+	in_bias=None
+	scal_mult=None
+	out_bias=None
+	def __init__(self, num_vars):
+		self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+		self.in_bias=torch.rand(1, requires_grad=True, device=device)
+		self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+		self.out_bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+	def forward(self,mtx):
+		long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+		long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+		return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class ExpRegKernel():
+	parameters= []
+	weights=None
+	in_bias=None
+	scal_mult=None
+	out_bias=None
+	def __init__(self, num_vars):
+		self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+		self.in_bias=torch.rand(1, requires_grad=True, device=device)
+		self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+		self.out_bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+	def forward(self,mtx):
+		long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+		long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+		return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class PolyRegKernel():
+	parameters= []
+	weights=None
+	bias=None
+	power=None
+	def __init__(self, num_vars, power):
+		self.power=power
+		num_terms=self.num_poly_terms(num_vars, power)
+		self.weights=torch.rand(num_terms, requires_grad=True, device=device)
+		self.bias=torch.rand(1, requires_grad=True, device=device)
+		self.parameters=[self.weights,self.bias]
+	def num_poly_terms(self,num_vars, power):
+		if power == 0:
+			return 0
+		return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
+	def factorial(self,n):
+		if n==0:
+			return 1
+		else:
+			return n*self.factorial(n-1)
+	def take_all_pwrs(self, vec, pwr):
+		#todo: vectorize (kinda)
+		combins=torch.combinations(vec, r=pwr, with_replacement=True)
+		out=torch.ones(combins.size()[0]).to(device).to(torch.float)
+		for i in torch.t(combins).to(device).to(torch.float):
+			out *= i
+		if pwr == 1:
+			return out
+		else:
+			return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
+	def forward(self,mtx):
+		#TODO: Vectorize the last part
+		cols=[]
+		for i in torch.t(mtx):
+			cols.append(self.take_all_pwrs(i,self.power))
+		new_mtx=torch.t(torch.stack(cols))
+		long_bias=self.bias.repeat([1,mtx.size()[1]])
+		return torch.matmul(self.weights,new_mtx)+long_bias
+
+def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
+	optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
+	data_cuda=data.to(device)
+	ground_cuda=ground.to(device)
+	if (return_losses):
+		losses=[]
+		for i in range(iterations):
+			with torch.set_grad_enabled(True):
+				optim.zero_grad()
+				pred=kernel.forward(data_cuda)
+				ls=loss(pred,ground_cuda)
+				losses.append(ls.item())
+				ls.backward()
+				optim.step()
+		return [kernel,losses]
+	else:
+		for i in range(iterations):
+			with torch.set_grad_enabled(True):
+				optim.zero_grad()
+				pred=kernel.forward(data_cuda)
+				ls=loss(pred,ground_cuda)
+				ls.backward()
+				optim.step()
+		return kernel
+
+def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
+	data_cuda=data.to(device)
+	ground_cuda=ground.to(device)
+	if (return_losses):
+		losses=[]
+		for i in range(iterations):
+			with torch.set_grad_enabled(True):
+				optim.zero_grad()
+				pred=kernel.forward(data)
+				ls=loss(pred,ground)
+				losses.append(ls.item())
+				ls.backward()
+				optim.step()
+		return [kernel,losses]
+	else:
+		for i in range(iterations):
+			with torch.set_grad_enabled(True):
+				optim.zero_grad()
+				pred=kernel.forward(data_cuda)
+				ls=loss(pred,ground_cuda)
+				ls.backward()
+				optim.step()
+		return kernel

analysis-master/tra_analysis/titanlearn.py

@@ -0,0 +1,122 @@
+# Titan Robotics Team 2022: ML Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import titanlearn'
+#    this should be included in the local directory or environment variable
+#    this module is optimized for multhreaded computing
+#    this module learns from its mistakes far faster than 2022's captains
+# setup:
+
+__version__ = "2.0.1.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+	2.0.1.001:
+		- removed matplotlib import
+		- removed graphloss()
+	2.0.1.000:
+		- added net, dataset, dataloader, and stdtrain template definitions
+		- added graphloss function
+	2.0.0.001:
+		- added clear functions
+	2.0.0.000:
+		- complete rewrite planned
+		- depreciated 1.0.0.xxx versions
+		- added simple training loop
+	1.0.0.xxx:
+		-added generation of ANNS, basic SGD training
+"""
+
+__author__ = (
+	"Arthur Lu <arthurlu@ttic.edu>,"
+	"Jacob Levine <jlevine@ttic.edu>,"
+	)
+
+__all__ = [
+	'clear',
+	'net',
+	'dataset',
+	'dataloader',
+	'train',
+	'stdtrainer',
+	]
+
+import torch
+from os import system, name
+import numpy as np
+
+def clear(): 
+	if name == 'nt': 
+		_ = system('cls') 
+	else: 
+		_ = system('clear') 
+
+class net(torch.nn.Module): #template for standard neural net
+	def __init__(self):
+		super(Net, self).__init__()
+		
+	def forward(self, input):
+		pass
+
+class dataset(torch.utils.data.Dataset): #template for standard dataset
+	
+	def __init__(self):
+		super(torch.utils.data.Dataset).__init__()
+		
+	def __getitem__(self, index):
+		pass
+	
+	def __len__(self):
+		pass
+
+def dataloader(dataset, batch_size, num_workers, shuffle = True):
+
+	return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+
+def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
+
+	dataset_len = trainloader.dataset.__len__()
+	iter_count = 0
+	running_loss = 0
+	running_loss_list = []
+
+	for epoch in range(epochs):  # loop over the dataset multiple times
+
+		for i, data in enumerate(trainloader, 0):
+
+			inputs = data[0].to(device)
+			labels = data[1].to(device)
+
+			optimizer.zero_grad()
+
+			outputs = net(inputs)
+			loss = criterion(outputs, labels.to(torch.float))
+			
+			loss.backward()
+			optimizer.step()
+
+			# monitoring steps below
+
+			iter_count += 1
+			running_loss += loss.item()
+			running_loss_list.append(running_loss)
+			clear()
+
+			print("training on: " + device)
+			print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
+			print("current batch loss: " + str(loss.item))
+			print("running loss: " + str(running_loss / iter_count))
+		
+	return net, running_loss_list
+	print("finished training")
+
+def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
+
+	device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+	net = net.to(device)
+	criterion = criterion.to(device)
+	optimizer = optimizer.to(device)
+	trainloader = dataloader
+	
+	return train(device, net, epochs, trainloader, optimizer, criterion)

analysis-master/tra_analysis/visualization.py

@@ -0,0 +1,58 @@
+# Titan Robotics Team 2022: Visualization Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import visualization'
+#    this should be included in the local directory or environment variable
+#    fancy
+# setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+	1.0.0.001:
+		- added graphhistogram function as a fragment of visualize_pit.py
+	1.0.0.000:
+		- created visualization.py
+		- added graphloss()
+		- added imports
+"""
+
+__author__ = (
+	"Arthur Lu <arthurlu@ttic.edu>,"
+	"Jacob Levine <jlevine@ttic.edu>,"
+	)
+
+__all__ = [
+	'graphloss',
+	]
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+def graphloss(losses):
+
+	x = range(0, len(losses))
+	plt.plot(x, losses)
+	plt.show()
+
+def graphhistogram(data, figsize, sharey = True): # expects library with key as variable and contents as occurances
+
+	fig, ax = plt.subplots(1, len(data), sharey=sharey, figsize=figsize)
+
+	i = 0
+
+	for variable in data:
+
+		ax[i].hist(data[variable])
+		ax[i].invert_xaxis()
+
+		ax[i].set_xlabel('Variable')
+		ax[i].set_ylabel('Frequency')
+		ax[i].set_title(variable)
+
+		plt.yticks(np.arange(len(data[variable])))
+
+		i+=1
+
+	plt.show()

data analysis/generate_data.py

@@ -1,16 +0,0 @@
-import random
-
-def generate(filename, x, y, low, high):
-
-    file = open(filename, "w")
-
-    for i in range (0, y, 1):
-
-        temp = ""
-        
-        for j in range (0, x - 1, 1):
-
-            temp = str(random.uniform(low, high)) +  ","  + temp
-
-        temp = temp + str(random.uniform(low, high))
-        file.write(temp + "\n")

data analysis/repack_json.py

@@ -1,28 +0,0 @@
-import os
-import json
-import ordereddict
-import collections
-import unicodecsv
-
-content = open("realtimeDatabaseExport2018.json").read()
-
-dict_content = json.loads(content)
-list_of_new_data = []
-
-for datak, datav in dict_content.iteritems():
-    for teamk, teamv in datav["teams"].iteritems():
-        for matchk, matchv in teamv.iteritems():
-            for detailk, detailv in matchv.iteritems():
-                new_data = collections.OrderedDict(detailv)
-                new_data["uuid"] = detailk
-                new_data["match"] = matchk
-                new_data["team"] = teamk
-                
-                list_of_new_data.append(new_data)
-
-allkey = reduce(lambda x, y: x.union(y.keys()), list_of_new_data, set())
-output_file = open('realtimeDatabaseExport2018.csv', 'wb')
-dict_writer = unicodecsv.DictWriter(csvfile=output_file, fieldnames=allkey)
-dict_writer.writerow(dict((fn,fn) for fn in dict_writer.fieldnames))
-dict_writer.writerows(list_of_new_data)
-output_file.close()

data-analysis/config.json

@@ -0,0 +1,45 @@
+{
+	"team": "",
+	"competition": "",
+	"key":{
+		"database":"",
+		"tba":""
+	},
+	"statistics":{
+		"match":{
+			"balls-blocked":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-collected":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-lower-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-lower-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-started":["basic_stats","historical_analyss","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-upper-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
+			"balls-upper-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"]
+
+		},
+		"metric":{
+			"elo":{
+				"score":1500,
+				"N":400,
+				"K":24
+			},
+			"gl2":{
+				"score":1500,
+				"rd":250,
+				"vol":0.06
+			},
+			"ts":{
+				"mu":25,
+				"sigma":8.33
+			}
+		},
+		"pit":{
+			"wheel-mechanism":true,
+			"low-balls":true,
+			"high-balls":true,
+			"wheel-success":true,
+			"strategic-focus":true,
+			"climb-mechanism":true,
+			"attitude":true
+		}
+	}
+}

data-analysis/data.py

@@ -0,0 +1,129 @@
+import requests
+import pymongo
+import pandas as pd
+import time
+
+def pull_new_tba_matches(apikey, competition, cutoff):
+	api_key= apikey 
+	x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
+	out = []
+	for i in x.json():
+		if i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm":
+			out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
+	return out
+
+def get_team_match_data(apikey, competition, team_num):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.matchdata
+	out = {}
+	for i in mdata.find({"competition" : competition, "team_scouted": team_num}):
+		out[i['match']] = i['data']
+	return pd.DataFrame(out)
+
+def get_team_pit_data(apikey, competition, team_num):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.pitdata
+	out = {}
+	return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
+
+def get_team_metrics_data(apikey, competition, team_num):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_processing
+	mdata = db.team_metrics
+	return mdata.find_one({"competition" : competition, "team": team_num})
+
+def get_match_data_formatted(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.teamlist
+	x=mdata.find_one({"competition":competition})
+	out = {}
+	for i in x:
+		try:
+			out[int(i)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
+		except:
+			pass
+	return out
+
+def get_metrics_data_formatted(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.teamlist
+	x=mdata.find_one({"competition":competition})
+	out = {}
+	for i in x:
+		try:
+			out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
+		except:
+			pass
+	return out
+
+def get_pit_data_formatted(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.teamlist
+	x=mdata.find_one({"competition":competition})
+	out = {}
+	for i in x:
+		try:
+			out[int(i)] = get_team_pit_data(apikey, competition, int(i))
+		except:
+			pass
+	return out
+
+def get_pit_variable_data(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_processing
+	mdata = db.team_pit
+	out = {}
+	return mdata.find()
+
+def get_pit_variable_formatted(apikey, competition):
+	temp = get_pit_variable_data(apikey, competition)
+	out = {}
+	for i in temp:
+		out[i["variable"]] = i["data"]
+	return out
+
+def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
+	client = pymongo.MongoClient(apikey)
+	db = client[dbname]
+	mdata = db[colname]
+	mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
+
+def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
+	client = pymongo.MongoClient(apikey)
+	db = client[dbname]
+	mdata = db[colname]
+	mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
+
+def push_team_pit_data(apikey, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
+	client = pymongo.MongoClient(apikey)
+	db = client[dbname]
+	mdata = db[colname]
+	mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
+
+def get_analysis_flags(apikey, flag):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_processing
+	mdata = db.flags
+	return mdata.find_one({flag:{"$exists":True}})
+
+def set_analysis_flags(apikey, flag, data):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_processing
+	mdata = db.flags
+	return mdata.replace_one({flag:{"$exists":True}}, data, True)
+
+def unkeyify_2l(layered_dict):
+	out = {}
+	for i in layered_dict.keys():
+		add = []
+		sortkey = []
+		for j in layered_dict[i].keys():
+			add.append([j,layered_dict[i][j]])
+		add.sort(key = lambda x: x[0])
+		out[i] = list(map(lambda x: x[1], add))
+	return out

data-analysis/requirements.txt

@@ -0,0 +1,4 @@
+requests
+pymongo
+pandas
+dnspython

data-analysis/superscript.py

@@ -0,0 +1,407 @@
+# Titan Robotics Team 2022: Superscript Script
+# Written by Arthur Lu, Jacob Levine, and Dev Singh
+# Notes:
+# setup:
+
+__version__ = "0.0.6.002"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+	0.0.6.003:
+		- rename analysis imports to tra_analysis for PyPI publishing
+	0.0.6.002:
+		- integrated get_team_rankings.py as get_team_metrics() function
+		- integrated visualize_pit.py as graph_pit_histogram() function
+	0.0.6.001:
+		- bug fixes with analysis.Metric() calls
+		- modified metric functions to use config.json defined default values
+	0.0.6.000:
+		- removed main function
+		- changed load_config function
+		- added save_config function
+		- added load_match function
+		- renamed simpleloop to matchloop
+		- moved simplestats function inside matchloop
+		- renamed load_metrics to load_metric
+		- renamed metricsloop to metricloop
+		- split push to database functions amon push_match, push_metric, push_pit
+		- moved
+	0.0.5.002:
+		- made changes due to refactoring of analysis
+	0.0.5.001:
+		- text fixes
+		- removed matplotlib requirement
+	0.0.5.000:
+		- improved user interface
+	0.0.4.002:
+		- removed unessasary code
+	0.0.4.001:
+		- fixed bug where X range for regression was determined before sanitization
+		- better sanitized data
+	0.0.4.000:
+		- fixed spelling issue in __changelog__
+		- addressed nan bug in regression
+		- fixed errors on line 335 with metrics calling incorrect key "glicko2"
+		- fixed errors in metrics computing 
+	0.0.3.000:
+		- added analysis to pit data
+	0.0.2.001:
+		- minor stability patches
+		- implemented db syncing for timestamps
+		- fixed bugs
+	0.0.2.000:
+		- finalized testing and small fixes
+	0.0.1.004:
+		- finished metrics implement, trueskill is bugged
+	0.0.1.003:
+		- working
+	0.0.1.002:
+		- started implement of metrics
+	0.0.1.001:
+		- cleaned up imports
+	0.0.1.000:
+		- tested working, can push to database
+	0.0.0.009:
+		- tested working
+		- prints out stats for the time being, will push to database later
+	0.0.0.008:
+		- added data import
+		- removed tba import
+		- finished main method
+	0.0.0.007:
+		- added load_config
+		- optimized simpleloop for readibility
+		- added __all__ entries
+		- added simplestats engine
+		- pending testing
+	0.0.0.006:
+		- fixes
+	0.0.0.005:
+		- imported pickle
+		- created custom database object
+	0.0.0.004:
+		- fixed simpleloop to actually return a vector
+	0.0.0.003:
+		- added metricsloop which is unfinished
+	0.0.0.002:
+		- added simpleloop which is untested until data is provided
+	0.0.0.001:
+		- created script
+		- added analysis, numba, numpy imports
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+	"Jacob Levine <jlevine@imsa.edu>",
+)
+
+__all__ = [
+	"load_config",
+	"save_config",
+	"get_previous_time",
+	"load_match",
+	"matchloop",
+	"load_metric",
+	"metricloop",
+	"load_pit",
+	"pitloop",
+	"push_match",
+	"push_metric",
+	"push_pit",
+]
+
+# imports:
+
+from tra_analysis import analysis as an
+import data as d
+import json
+import numpy as np
+from os import system, name
+from pathlib import Path
+import matplotlib.pyplot as plt
+import time
+import warnings
+
+def load_config(file):
+
+	config_vector = {}
+	with open(file) as f:
+		config_vector = json.load(f)
+
+	return config_vector
+
+def save_config(file, config_vector):
+
+	with open(file) as f:
+		json.dump(config_vector, f)
+
+def get_previous_time(apikey):
+
+	previous_time = d.get_analysis_flags(apikey, "latest_update")
+
+	if previous_time == None:
+
+		d.set_analysis_flags(apikey, "latest_update", 0)
+		previous_time = 0
+
+	else:
+
+		previous_time = previous_time["latest_update"]
+
+	return previous_time
+
+def load_match(apikey, competition):
+
+	return d.get_match_data_formatted(apikey, competition)
+
+def matchloop(apikey, competition, data, tests): # expects 3D array with [Team][Variable][Match]
+
+	def simplestats(data, test):
+
+		data = np.array(data)
+		data = data[np.isfinite(data)]
+		ranges = list(range(len(data)))
+
+		if test == "basic_stats":
+			return an.basic_stats(data)
+
+		if test == "historical_analysis":
+			return an.histo_analysis([ranges, data])
+
+		if test == "regression_linear":
+			return an.regression(ranges, data, ['lin'])
+
+		if test == "regression_logarithmic":
+			return an.regression(ranges, data, ['log'])
+
+		if test == "regression_exponential":
+			return an.regression(ranges, data, ['exp'])
+
+		if test == "regression_polynomial":
+			return an.regression(ranges, data, ['ply'])
+
+		if test == "regression_sigmoidal":
+			return an.regression(ranges, data, ['sig'])
+
+	return_vector = {}
+	for team in data:
+		variable_vector = {}
+		for variable in data[team]:
+			test_vector = {}
+			variable_data = data[team][variable]
+			if variable in tests:
+				for test in tests[variable]:
+					test_vector[test] = simplestats(variable_data, test)
+			else:
+				pass      
+			variable_vector[variable] = test_vector
+		return_vector[team] = variable_vector
+
+	push_match(apikey, competition, return_vector)
+
+def load_metric(apikey, competition, match, group_name, metrics):
+
+	group = {}
+
+	for team in match[group_name]:
+
+		db_data = d.get_team_metrics_data(apikey, competition, team)
+
+		if d.get_team_metrics_data(apikey, competition, team) == None:
+
+			elo = {"score": metrics["elo"]["score"]}
+			gl2 = {"score": metrics["gl2"]["score"], "rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
+			ts = {"mu": metrics["ts"]["mu"], "sigma": metrics["ts"]["sigma"]}
+
+			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
+
+		else:
+
+			metrics = db_data["metrics"]
+
+			elo = metrics["elo"]
+			gl2 = metrics["gl2"]
+			ts = metrics["ts"]
+
+			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
+
+	return group
+
+def metricloop(tbakey, apikey, competition, timestamp, metrics): # listener based metrics update
+
+	elo_N = metrics["elo"]["N"]
+	elo_K = metrics["elo"]["K"]
+
+	matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
+
+	red = {}
+	blu = {}
+
+	for match in matches:
+
+		red = load_metric(apikey, competition, match, "red", metrics)
+		blu = load_metric(apikey, competition, match, "blue", metrics)
+ 
+		elo_red_total = 0
+		elo_blu_total = 0
+
+		gl2_red_score_total = 0
+		gl2_blu_score_total = 0
+
+		gl2_red_rd_total = 0
+		gl2_blu_rd_total = 0
+
+		gl2_red_vol_total = 0
+		gl2_blu_vol_total = 0
+
+		for team in red:
+
+			elo_red_total += red[team]["elo"]["score"]
+
+			gl2_red_score_total += red[team]["gl2"]["score"]
+			gl2_red_rd_total += red[team]["gl2"]["rd"]
+			gl2_red_vol_total += red[team]["gl2"]["vol"]
+
+		for team in blu:
+
+			elo_blu_total += blu[team]["elo"]["score"]
+
+			gl2_blu_score_total += blu[team]["gl2"]["score"]
+			gl2_blu_rd_total += blu[team]["gl2"]["rd"]
+			gl2_blu_vol_total += blu[team]["gl2"]["vol"]
+
+		red_elo = {"score": elo_red_total / len(red)}
+		blu_elo = {"score": elo_blu_total / len(blu)}
+
+		red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
+		blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
+
+
+		if match["winner"] == "red":
+
+			observations = {"red": 1, "blu": 0}
+
+		elif match["winner"] == "blue":
+
+			observations = {"red": 0, "blu": 1}
+
+		else:
+
+			observations = {"red": 0.5, "blu": 0.5}
+
+		red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
+		blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
+
+		new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metric().glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
+		new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metric().glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
+
+		red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
+		blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
+
+		for team in red:
+
+			red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
+
+			red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
+			red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
+			red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
+
+		for team in blu:
+
+			blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
+
+			blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
+			blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
+			blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
+
+		temp_vector = {}
+		temp_vector.update(red)
+		temp_vector.update(blu)
+
+		push_metric(apikey, competition, temp_vector)
+
+def load_pit(apikey, competition):
+
+	return d.get_pit_data_formatted(apikey, competition)
+
+def pitloop(apikey, competition, pit, tests):
+
+	return_vector = {}
+	for team in pit:
+		for variable in pit[team]:
+			if variable in tests:
+				if not variable in return_vector:
+					return_vector[variable] = []
+				return_vector[variable].append(pit[team][variable])
+
+	push_pit(apikey, competition, return_vector)
+
+def push_match(apikey, competition, results):
+
+	for team in results:
+
+		d.push_team_tests_data(apikey, competition, team, results[team])
+
+def push_metric(apikey, competition, metric):
+
+	for team in metric:
+
+			d.push_team_metrics_data(apikey, competition, team, metric[team])
+
+def push_pit(apikey, competition, pit):
+
+	for variable in pit:
+
+		d.push_team_pit_data(apikey, competition, variable, pit[variable])
+
+def get_team_metrics(apikey, tbakey, competition):
+
+	metrics = d.get_metrics_data_formatted(apikey, competition)
+
+	elo = {}
+	gl2 = {}
+
+	for team in metrics:
+
+		elo[team] = metrics[team]["metrics"]["elo"]["score"]
+		gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
+
+	elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
+	gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
+
+	elo_ranked = []
+
+	for team in elo:
+
+		elo_ranked.append({"team": str(team), "elo": str(elo[team])})
+
+	gl2_ranked = []
+
+	for team in gl2:
+
+		gl2_ranked.append({"team": str(team), "gl2": str(gl2[team])})
+
+	return {"elo-ranks": elo_ranked, "glicko2-ranks": gl2_ranked}
+
+def graph_pit_histogram(apikey, competition, figsize=(80,15)):
+
+	pit = d.get_pit_variable_formatted(apikey, competition)
+
+	fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=figsize)
+
+	i = 0
+
+	for variable in pit:
+
+		ax[i].hist(pit[variable])
+		ax[i].invert_xaxis()
+
+		ax[i].set_xlabel('')
+		ax[i].set_ylabel('Frequency')
+		ax[i].set_title(variable)
+
+		plt.yticks(np.arange(len(pit[variable])))
+
+		i+=1
+
+	plt.show()

data-analysis/test.py

@@ -0,0 +1,55 @@
+import threading
+from multiprocessing import Process, Queue
+import time
+from os import system
+
+class testcls():
+
+	i = 0
+	j = 0
+
+	t1_en = True
+	t2_en = True
+
+	def main(self):
+		t1 = Process(name = "task1", target = self.task1)
+		t2 = Process(name = "task2", target = self.task2)
+		t1.start()
+		t2.start()
+		#print(self.i)
+		#print(self.j)
+		
+	def task1(self):
+		self.i += 1
+		time.sleep(1)
+		if(self.i < 10):
+			t1 = Process(name = "task1", target = self.task1)
+			t1.start()
+
+	def task2(self):
+		self.j -= 1
+		time.sleep(1)
+		if(self.j > -10):
+			t2 = t2 = Process(name = "task2", target = self.task2)
+			t2.start()
+"""
+if __name__ == "__main__":
+
+	tmain = threading.Thread(name = "main", target = main)
+	tmain.start()
+
+	t = 0
+	while(True):
+		system("clear")
+		for thread in threading.enumerate():
+			if thread.getName() != "MainThread":
+				print(thread.getName())
+		print(str(len(threading.enumerate())))
+		print(i)
+		print(j)
+		time.sleep(0.1)
+		t += 1
+		if(t == 100):
+			t1_en = False
+			t2_en = False
+"""

data-analysis/test_superscript.py

@@ -0,0 +1,2 @@
+def test_():
+	assert 1 == 1

data-analysis/tra.py

@@ -0,0 +1,91 @@
+import json
+import superscript as su
+import threading
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+match = False
+metric = False
+pit = False
+
+match_enable = True
+metric_enable = True
+pit_enable = True
+
+config = {}
+
+def main():
+
+	global match
+	global metric
+	global pit
+
+	global match_enable
+	global metric_enable
+	global pit_enable
+
+	global config
+	config = su.load_config("config.json")
+
+	while(True):
+
+		if match_enable == True and match == False:
+
+			def target():
+
+				apikey = config["key"]["database"]
+				competition = config["competition"]
+				tests = config["statistics"]["match"]
+
+				data = su.load_match(apikey, competition)
+				su.matchloop(apikey, competition, data, tests)
+
+				match = False
+				return
+
+			match = True
+			task = threading.Thread(name = "match", target=target)
+			task.start()
+
+		if metric_enable == True and metric == False:
+			
+			def target():
+
+				apikey = config["key"]["database"]
+				tbakey = config["key"]["tba"]
+				competition = config["competition"]
+				metric = config["statistics"]["metric"]
+
+				timestamp = su.get_previous_time(apikey)
+
+				su.metricloop(tbakey, apikey, competition, timestamp, metric)
+
+				metric = False
+				return
+
+			match = True
+			task = threading.Thread(name = "metric", target=target)
+			task.start()
+
+		if pit_enable == True and pit == False:
+
+			def target():
+
+				apikey = config["key"]["database"]
+				competition = config["competition"]
+				tests = config["statistics"]["pit"]
+
+				data = su.load_pit(apikey, competition)
+				su.pitloop(apikey, competition, data, tests)
+
+				pit = False
+				return
+
+			pit = True
+			task = threading.Thread(name = "pit", target=target)
+			task.start()
+			
+task = threading.Thread(name = "main", target=main)
+task.start()