Merge pull request #71 from titanscouting/submoduling

Merge submoduling into master-staged

.devcontainer/Dockerfile

@@ -1,2 +1,7 @@
-FROM python
-WORKDIR ~/
+FROM ubuntu:20.04
+WORKDIR /
+RUN apt-get -y update
+RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends tzdata
+RUN apt-get install -y python3 python3-dev git python3-pip python3-kivy python-is-python3 libgl1-mesa-dev build-essential
+RUN ln -s $(which pip3) /usr/bin/pip
+RUN pip install pymongo pandas numpy scipy scikit-learn matplotlib pylint kivy

.devcontainer/dev-dockerfile

@@ -0,0 +1,2 @@
+FROM titanscout2022/tra-analysis-base:latest
+WORKDIR /

.devcontainer/devcontainer.json

@@ -1,7 +1,7 @@
 {
     "name": "TRA Analysis Development Environment",
     "build": {
-		"dockerfile": "Dockerfile",
+		"dockerfile": "dev-dockerfile",
     },
     "settings": { 
 		"terminal.integrated.shell.linux": "/bin/bash",
@@ -24,5 +24,5 @@
 		"ms-python.python",
 		"waderyan.gitblame"
 	],
-	"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install pylint ; pip install tra-analysis"
+	"postCreateCommand": "/usr/bin/pip3 install -r /workspaces/red-alliance-analysis/data-analysis/requirements.txt && /usr/bin/pip3 install -r /workspaces/red-alliance-analysis/analysis-master/requirements.txt && /usr/bin/pip3 install --no-cache-dir pylint && pip3 install pytest"
 }

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,7 @@
+Fixes #
+
+## Proposed Changes
+
+  -
+  -
+  -

.gitignore

@@ -39,3 +39,6 @@ analysis-master/tra_analysis/.ipynb_checkpoints
 analysis-master/tra_analysis/metrics/__pycache__
 analysis-master/dist
 data-analysis/config/
+analysis-master/tra_analysis/equation/__pycache__/*
+analysis-master/tra_analysis/equation/parser/__pycache__/*
+analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__pycache__/*

analysis-master/requirements.txt

@@ -1,6 +1,6 @@
-numba
 numpy
 scipy
 scikit-learn
 six
 matplotlib
+pyparsing

analysis-master/setup.py

@@ -1,4 +1,5 @@
 import setuptools
+import tra_analysis
 
 requirements = []
 
@@ -8,11 +9,11 @@ with open("requirements.txt", 'r') as file:
 
 setuptools.setup(
 	name="tra_analysis",
-	version="2.1.0",
+	version=tra_analysis.__version__,
 	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="../README.md",
 	long_description_content_type="text/markdown",
 	url="https://github.com/titanscout2022/tr2022-strategy",
 	packages=setuptools.find_packages(),
@@ -21,6 +22,8 @@ setuptools.setup(
 	classifiers=[
 		"Programming Language :: Python :: 3",
 		"Operating System :: OS Independent",
+		"Topic :: Data Analysis"
 	],
 	python_requires='>=3.6',
+	keywords="data analysis tools"
 )

analysis-master/test_analysis.py

@@ -1,15 +1,44 @@
-from tra_analysis import analysis as an
-from tra_analysis import metrics
-from tra_analysis import fits
+import numpy as np
+import sklearn
+from sklearn import metrics
+
+from tra_analysis import Analysis as an
+from tra_analysis import Array
+from tra_analysis import ClassificationMetric
+from tra_analysis import CorrelationTest
+from tra_analysis import Fit
+from tra_analysis import KNN
+from tra_analysis import NaiveBayes
+from tra_analysis import RandomForest
+from tra_analysis import RegressionMetric
+from tra_analysis import Sort
+from tra_analysis import StatisticalTest
+from tra_analysis import SVM
+
+from tra_analysis.equation.parser import BNF
 
 def test_():
+
 	test_data_linear = [1, 3, 6, 7, 9]
+	test_data_linear2 = [2, 2, 5, 7, 13]
+	test_data_array = Array(test_data_linear)
+
 	x_data_circular = []
 	y_data_circular = []
+
 	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]
+
+	test_data_2D_pairs = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
+	test_data_2D_positive = np.array([[23, 51], [21, 32], [15, 25], [17, 31]])
+	test_output = np.array([1, 3, 4, 5])
+	test_labels_2D_pairs = np.array([1, 1, 2, 2])
+	validation_data_2D_pairs = np.array([[-0.8, -1], [0.8, 1.2]])
+	validation_labels_2D_pairs = np.array([1, 2])
+
 	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]]
@@ -21,15 +50,144 @@ def test_():
 	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))
-	assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
+
+	assert test_data_array.elementwise_mean() == 5.2
+	assert test_data_array.elementwise_median() == 6.0
+	assert test_data_array.elementwise_stdev() == 2.85657137141714
+	assert test_data_array.elementwise_variance() == 8.16
+	assert test_data_array.elementwise_npmin() == 1
+	assert test_data_array.elementwise_npmax() == 9
+	assert test_data_array.elementwise_stats() == (5.2, 6.0, 2.85657137141714, 8.16, 1, 9)
+
+	classif_metric = ClassificationMetric(test_data_linear2, test_data_linear)
+	assert classif_metric[0].all() == metrics.confusion_matrix(test_data_linear, test_data_linear2).all()
+	assert classif_metric[1] == metrics.classification_report(test_data_linear, test_data_linear2)
+
+	assert all(np.isclose(list(CorrelationTest.anova_oneway(test_data_linear, test_data_linear2).values()), [0.05825242718446602, 0.8153507906592907], rtol=1e-10))
+	assert all(np.isclose(list(CorrelationTest.pearson(test_data_linear, test_data_linear2).values()),  [0.9153061540753287, 0.02920895440940868], rtol=1e-10))
+	assert all(np.isclose(list(CorrelationTest.spearman(test_data_linear, test_data_linear2).values()), [0.9746794344808964, 0.004818230468198537], rtol=1e-10))
+	assert all(np.isclose(list(CorrelationTest.point_biserial(test_data_linear, test_data_linear2).values()), [0.9153061540753287, 0.02920895440940868], rtol=1e-10))
+	assert all(np.isclose(list(CorrelationTest.kendall(test_data_linear, test_data_linear2).values()), [0.9486832980505137, 0.022977401503206086], rtol=1e-10))
+	assert all(np.isclose(list(CorrelationTest.kendall_weighted(test_data_linear, test_data_linear2).values()), [0.9750538072369643, np.nan], rtol=1e-10, equal_nan=True))
+
+	assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
+
+	model, metric = KNN.knn_classifier(test_data_2D_pairs, test_labels_2D_pairs, 2)
+	assert isinstance(model, sklearn.neighbors.KNeighborsClassifier)
+	assert np.array([[0,0], [2,0]]).all() == metric[0].all()
+	assert '              precision    recall  f1-score   support\n\n           1       0.00      0.00      0.00       0.0\n           2       0.00      0.00      0.00       2.0\n\n    accuracy                           0.00       2.0\n   macro avg       0.00      0.00      0.00       2.0\nweighted avg       0.00      0.00      0.00       2.0\n' == metric[1]
+	model, metric = KNN.knn_regressor(test_data_2D_pairs, test_output, 2)
+	assert isinstance(model, sklearn.neighbors.KNeighborsRegressor)
+	assert (-25.0, 6.5, 2.5495097567963922) == metric
+
+	model, metric = NaiveBayes.gaussian(test_data_2D_pairs, test_labels_2D_pairs)
+	assert isinstance(model, sklearn.naive_bayes.GaussianNB)
+	assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
+	model, metric = NaiveBayes.multinomial(test_data_2D_positive, test_labels_2D_pairs)
+	assert isinstance(model, sklearn.naive_bayes.MultinomialNB)
+	assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
+	model, metric = NaiveBayes.bernoulli(test_data_2D_pairs, test_labels_2D_pairs)
+	assert isinstance(model, sklearn.naive_bayes.BernoulliNB)
+	assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
+	model, metric = NaiveBayes.complement(test_data_2D_positive, test_labels_2D_pairs)
+	assert isinstance(model, sklearn.naive_bayes.ComplementNB)
+	assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
+
+	model, metric = RandomForest.random_forest_classifier(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2)
+	assert isinstance(model, sklearn.ensemble.RandomForestClassifier)
+	assert metric[0].all() == np.array([[0, 0], [2, 0]]).all()
+	model, metric = RandomForest.random_forest_regressor(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2)
+	assert isinstance(model, sklearn.ensemble.RandomForestRegressor)
+	assert metric == (0.0, 1.0, 1.0)
+
+	assert RegressionMetric(test_data_linear, test_data_linear2)== (0.7705314009661837, 3.8, 1.9493588689617927)
+
+	assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.introsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.insertionsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.timsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.selectionsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.shellsort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted))
+	assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted))
+	
+	assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
+
+	svm(test_data_2D_pairs, test_labels_2D_pairs, validation_data_2D_pairs, validation_labels_2D_pairs)
+	test_equation()
+
+def svm(data, labels, test_data, test_labels):
+
+	lin_kernel = SVM.PrebuiltKernel.Linear()
+	#ply_kernel = SVM.PrebuiltKernel.Polynomial(3, 0)
+	rbf_kernel = SVM.PrebuiltKernel.RBF('scale')
+	sig_kernel = SVM.PrebuiltKernel.Sigmoid(0)
+
+	lin_kernel = SVM.fit(lin_kernel, data, labels)
+	#ply_kernel = SVM.fit(ply_kernel, data, labels)
+	rbf_kernel = SVM.fit(rbf_kernel, data, labels)
+	sig_kernel = SVM.fit(sig_kernel, data, labels)
+
+	for i in range(len(test_data)):
+
+		assert lin_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
+
+	#for i in range(len(test_data)):
+
+	#	assert ply_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
+
+	for i in range(len(test_data)):
+
+		assert rbf_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
+
+	for i in range(len(test_data)):
+
+		assert sig_kernel.predict([test_data[i]]).tolist() == [test_labels[i]]
+
+def test_equation():
+
+	parser = BNF()
+
+	assert parser.eval("9") == 9.0
+	assert parser.eval("-9") == -9.0
+	assert parser.eval("--9") == 9.0
+	assert parser.eval("-E") == -2.718281828459045
+	assert parser.eval("9 + 3 + 6") == 18.0
+	assert parser.eval("9 + 3 / 11") == 9.272727272727273
+	assert parser.eval("(9 + 3)") == 12.0
+	assert parser.eval("(9+3) / 11") == 1.0909090909090908
+	assert parser.eval("9 - 12 - 6") == -9.0
+	assert parser.eval("9 - (12 - 6)") == 3.0
+	assert parser.eval("2*3.14159") == 6.28318
+	assert parser.eval("3.1415926535*3.1415926535 / 10") == 0.9869604400525172
+	assert parser.eval("PI * PI / 10") == 0.9869604401089358
+	assert parser.eval("PI*PI/10") == 0.9869604401089358
+	assert parser.eval("PI^2") == 9.869604401089358
+	assert parser.eval("round(PI^2)") == 10
+	assert parser.eval("6.02E23 * 8.048") == 4.844896e+24
+	assert parser.eval("e / 3") == 0.9060939428196817
+	assert parser.eval("sin(PI/2)") == 1.0
+	assert parser.eval("10+sin(PI/4)^2") == 10.5
+	assert parser.eval("trunc(E)") == 2
+	assert parser.eval("trunc(-E)") == -2
+	assert parser.eval("round(E)") == 3
+	assert parser.eval("round(-E)") == -3
+	assert parser.eval("E^PI") == 23.140692632779263
+	assert parser.eval("exp(0)") == 1.0
+	assert parser.eval("exp(1)") == 2.718281828459045
+	assert parser.eval("2^3^2") == 512.0
+	assert parser.eval("(2^3)^2") == 64.0
+	assert parser.eval("2^3+2") == 10.0
+	assert parser.eval("2^3+5") == 13.0
+	assert parser.eval("2^9") == 512.0
+	assert parser.eval("sgn(-2)") == -1
+	assert parser.eval("sgn(0)") == 0
+	assert parser.eval("sgn(0.1)") == 1
+	assert parser.eval("sgn(cos(PI/4))") == 1
+	assert parser.eval("sgn(cos(PI/2))") == 0
+	assert parser.eval("sgn(cos(PI*3/4))") == -1
+	assert parser.eval("+(sgn(cos(PI/4)))") == 1
+	assert parser.eval("-(sgn(cos(PI/4)))") == -1

analysis-master/tra_analysis/.ipynb_checkpoints/equation-checkpoint.ipynb

@@ -1,35 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string = \"3+4+5\"\n",
-    "re.sub(\"\\d+[+]{1}\\d+\", string, sum([int(i) for i in re.split(\"[+]{1}\", re.search(\"\\d+[+]{1}\\d+\", string).group())]))"
-   ]
-  }
- ],
- "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

analysis-master/tra_analysis/Analysis.py

@@ -0,0 +1,635 @@
+# Titan Robotics Team 2022: Analysis Module
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import Analysis'
+#    this should be included in the local directory or environment variable
+#    this module has been optimized for multhreaded computing
+#    current benchmark of optimization: 1.33 times faster
+# setup:
+
+__version__ = "3.0.1"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+	3.0.1:
+		- removed numba dependency and calls
+	3.0.0:
+		- exported several submodules to their own files while preserving backwards compatibility:
+			- Array
+			- ClassificationMetric
+			- CorrelationTest
+			- KNN
+			- NaiveBayes
+			- RandomForest
+			- RegressionMetric
+			- Sort
+			- StatisticalTest
+			- SVM
+		- note: above listed submodules will not be supported in the future
+		- future changes to all submodules will be held in their respective changelogs
+		- future changes altering the parent package will be held in the __changelog__ of the parent package (in __init__.py)
+		- changed reference to module name to Analysis
+	2.3.1:
+		- fixed bugs in Array class
+	2.3.0:
+		- overhauled Array class
+	2.2.3:
+		- fixed spelling of RandomForest
+		- made n_neighbors required for KNN
+		- made n_classifiers required for SVM
+	2.2.2:
+		- fixed 2.2.1 changelog entry
+		- changed regression to return dictionary
+	2.2.1:
+		- changed all references to parent package analysis to tra_analysis
+	2.2.0:
+		- added Sort class
+		- added several array sorting functions to Sort class including:
+			- quick sort
+			- merge sort
+			- intro(spective) sort
+			- heap sort
+			- insertion sort
+			- tim sort
+			- selection sort
+			- bubble sort
+			- cycle sort
+			- cocktail sort
+		- tested all sorting algorithms with both lists and numpy arrays
+		- depreciated sort function from Array class
+		- added warnings as an import
+	2.1.4:
+		- added sort and search functions to Array class
+	2.1.3:
+		- changed output of basic_stats and histo_analysis to libraries
+		- fixed __all__
+	2.1.2:
+		- renamed ArrayTest class to Array
+	2.1.1:
+		- added add, mul, neg, and inv functions to ArrayTest class
+		- added normalize function to ArrayTest class
+		- added dot and cross functions to ArrayTest class
+	2.1.0:
+		- added ArrayTest class
+		- added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class
+		- added elementwise_stats to ArrayTest which encapsulates elementwise statistics
+		- appended to __all__ to reflect changes
+	2.0.6:
+		- renamed func functions in regression to lin, log, exp, and sig 
+	2.0.5:
+		- moved random_forrest_regressor and random_forrest_classifier to RandomForrest class
+		- renamed Metrics to Metric
+		- renamed RegressionMetrics to RegressionMetric
+		- renamed ClassificationMetrics to ClassificationMetric
+		- renamed CorrelationTests to CorrelationTest
+		- renamed StatisticalTests to StatisticalTest
+		- reflected rafactoring to all mentions of above classes/functions
+	2.0.4:
+		- fixed __all__ to reflected the correct functions and classes
+		- fixed CorrelationTests and StatisticalTests class functions to require self invocation
+		- added missing math import
+		- fixed KNN class functions to require self invocation
+		- fixed Metrics class functions to require self invocation
+		- various spelling fixes in CorrelationTests and StatisticalTests
+	2.0.3:
+		- bug fixes with CorrelationTests and StatisticalTests
+		- moved glicko2 and trueskill to the metrics subpackage
+		- moved elo to a new metrics subpackage
+	2.0.2:
+		- fixed docs
+	2.0.1:
+		- fixed docs
+	2.0.0:
+		- cleaned up wild card imports with scipy and sklearn
+		- added CorrelationTests class
+		- added StatisticalTests class
+		- added several correlation tests to CorrelationTests
+		- added several statistical tests to StatisticalTests
+	1.13.9:
+		- moved elo, glicko2, trueskill functions under class Metrics
+	1.13.8:
+		- moved Glicko2 to a seperate package
+	1.13.7:
+		- fixed bug with trueskill
+	1.13.6:
+		- cleaned up imports
+	1.13.5:
+		- cleaned up package
+	1.13.4:
+		- small fixes to regression to improve performance
+	1.13.3:
+		- filtered nans from regression
+	1.13.2:
+		- removed torch requirement, and moved Regression back to regression.py
+	1.13.1:
+		- bug fix with linear regression not returning a proper value
+		- cleaned up regression
+		- fixed bug with polynomial regressions
+	1.13.0:
+		- fixed all regressions to now properly work
+	1.12.6:
+		- fixed bg with a division by zero in histo_analysis
+	1.12.5:
+		- fixed numba issues by removing numba  from elo, glicko2 and trueskill
+	1.12.4:
+		- renamed gliko to glicko
+	1.12.3:
+		- removed depreciated code
+	1.12.2:
+		- removed team first time trueskill instantiation in favor of integration in superscript.py
+	1.12.1:
+		- improved readibility of regression outputs by stripping tensor data
+		- used map with lambda to acheive the improved readibility
+		- lost numba jit support with regression, and generated_jit hangs at execution
+		- TODO: reimplement correct numba integration in regression
+	1.12.0:
+		- temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
+	1.11.010:
+		- alphabeticaly ordered import lists
+	1.11.9:
+		- bug fixes
+	1.11.8:
+		- bug fixes
+	1.11.7:
+		- bug fixes
+	1.11.6:
+		- tested min and max
+		- bug fixes 
+	1.11.5:
+		- added min and max in basic_stats
+	1.11.4:
+		- bug fixes
+	1.11.3:
+		- bug fixes
+	1.11.2:
+		- consolidated metrics
+		- fixed __all__
+	1.11.1:
+		- added test/train split to RandomForestClassifier and RandomForestRegressor
+	1.11.0:
+		- added RandomForestClassifier and RandomForestRegressor
+		- note: untested
+	1.10.0:
+		- added numba.jit to remaining functions
+	1.9.2:
+		- kernelized PCA and KNN
+	1.9.1:
+		- fixed bugs with SVM and NaiveBayes
+	1.9.0:
+		- added SVM class, subclasses, and functions
+		- note: untested
+	1.8.0:
+		- added NaiveBayes classification engine
+		- note: untested
+	1.7.0:
+		- added knn()
+		- added confusion matrix to decisiontree()
+	1.6.2:
+		- changed layout of __changelog to be vscode friendly
+	1.6.1:
+		- added additional hyperparameters to decisiontree()
+	1.6.0:
+		- fixed __version__
+		- fixed __all__ order
+		- added decisiontree()
+	1.5.3:
+		- added pca
+	1.5.2:
+		- reduced import list
+		- added kmeans clustering engine
+	1.5.1:
+		- simplified regression by using .to(device)
+	1.5.0:
+		- added polynomial regression to regression(); untested
+	1.4.0:
+		- added trueskill()
+	1.3.2:
+		- renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
+	1.3.1:
+		- changed glicko2() to return tuple instead of array
+	1.3.0:
+		- added glicko2_engine class and glicko()
+		- verified glicko2() accuracy
+	1.2.3:
+		- fixed elo()
+	1.2.2:
+		- added elo()
+		- elo() has bugs to be fixed
+	1.2.1:
+		- readded regrression import
+	1.2.0:
+		- integrated regression.py as regression class
+		- removed regression import
+		- fixed metadata for regression class
+		- fixed metadata for analysis class
+	1.1.1:
+		- regression_engine() bug fixes, now actaully regresses
+	1.1.0:
+		- added regression_engine()
+		- added all regressions except polynomial
+	1.0.7:
+		- updated _init_device()
+	1.0.6:
+		- removed useless try statements
+	1.0.5:
+		- removed impossible outcomes
+	1.0.4:
+		- added performance metrics (r^2, mse, rms)
+	1.0.3:
+		- resolved nopython mode for mean, median, stdev, variance
+	1.0.2:
+		- snapped (removed) majority of uneeded imports
+		- forced object mode (bad) on all jit
+		- TODO: stop numba complaining about not being able to compile in nopython mode
+	1.0.1:
+		- removed from sklearn import * to resolve uneeded wildcard imports
+	1.0.0:
+		- removed c_entities,nc_entities,obstacles,objectives from __all__
+		- applied numba.jit to all functions
+		- depreciated and removed stdev_z_split
+		- cleaned up histo_analysis to include numpy and numba.jit optimizations
+		- depreciated and removed all regression functions in favor of future pytorch optimizer
+		- depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
+		- optimized z_normalize using sklearn.preprocessing.normalize
+		- TODO: implement kernel/function based pytorch regression optimizer
+	0.9.0:
+		- refactored
+		- numpyed everything
+		- removed stats in favor of numpy functions
+	0.8.5:
+		- minor fixes
+	0.8.4:
+		- removed a few unused dependencies
+	0.8.3:
+		- added p_value function
+	0.8.2:
+		- updated __all__ correctly to contain changes made in v 0.8.0 and v 0.8.1
+	0.8.1:
+		- refactors
+		- bugfixes
+	0.8.0:
+		- depreciated histo_analysis_old
+		- depreciated debug
+		- altered basic_analysis to take array data instead of filepath
+		- refactor
+		- optimization
+	0.7.2:
+		- bug fixes
+	0.7.1:
+		- bug fixes
+	0.7.0:
+		- added tanh_regression (logistical regression)
+		- bug fixes
+	0.6.5:
+		- added z_normalize function to normalize dataset
+		- bug fixes
+	0.6.4:
+		- bug fixes
+	0.6.3:
+		- bug fixes
+	0.6.2:
+		- bug fixes
+	0.6.1:
+		- corrected __all__ to contain all of the functions
+	0.6.0:
+		- added calc_overfit, which calculates two measures of overfit, error and performance
+		- added calculating overfit to optimize_regression
+	0.5.0:
+		- added optimize_regression function, which is a sample function to find the optimal regressions
+		- optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
+		- planned addition: overfit detection in the optimize_regression function
+	0.4.2:
+		- added __changelog__
+		- updated debug function with log and exponential regressions
+	0.4.1:
+		- added log regressions
+		- added exponential regressions
+		- added log_regression and exp_regression to __all__
+	0.3.8:
+		- added debug function to further consolidate functions
+	0.3.7:
+		- added builtin benchmark function
+		- added builtin random (linear) data generation function
+		- added device initialization (_init_device)
+	0.3.6:
+		- reorganized the imports list to be in alphabetical order
+		- added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
+	0.3.5:
+		- major bug fixes
+		- updated historical analysis
+		- depreciated old historical analysis
+	0.3.4:
+		- added __version__, __author__, __all__
+		- added polynomial regression
+		- added root mean squared function
+		- added r squared function
+	0.3.3:
+		- bug fixes
+		- added c_entities
+	0.3.2:
+		- bug fixes
+		- added nc_entities, obstacles, objectives
+		- consolidated statistics.py to analysis.py
+	0.3.1:
+		- compiled 1d, column, and row basic stats into basic stats function
+	0.3.0:
+		- added historical analysis function
+	0.2.x:
+		- added z score test
+	0.1.x:
+		- major bug fixes
+	0.0.x:
+		- added loading csv
+		- added 1d, column, row basic stats
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+	'load_csv',
+	'basic_stats',
+	'z_score',
+	'z_normalize',
+	'histo_analysis',
+	'regression',
+	'Metric',
+	'RegressionMetric',
+	'ClassificationMetric',
+	'kmeans',
+	'pca',
+	'decisiontree',
+	'KNN',
+	'NaiveBayes',
+	'SVM',
+	'RandomForrest',
+	'CorrelationTest',
+	'StatisticalTest',
+	'Array',
+	# all statistics functions left out due to integration in other functions
+]
+
+# now back to your regularly scheduled programming:
+
+# imports (now in alphabetical order! v 0.3.006):
+
+import csv
+from tra_analysis.metrics import elo as Elo
+from tra_analysis.metrics import glicko2 as Glicko2
+import math
+import numpy as np
+import scipy
+from scipy import optimize, stats
+import sklearn
+from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
+from tra_analysis.metrics import trueskill as Trueskill
+import warnings
+
+# import submodules
+
+from .Array import Array
+from .ClassificationMetric import ClassificationMetric
+from .CorrelationTest_obj import CorrelationTest
+from .KNN_obj import KNN
+from .NaiveBayes_obj import NaiveBayes
+from .RandomForest_obj import RandomForest
+from .RegressionMetric import RegressionMetric
+from .Sort_obj import Sort
+from .StatisticalTest_obj import StatisticalTest
+from . import SVM
+
+class error(ValueError):
+	pass
+
+def load_csv(filepath):
+	with open(filepath, newline='') as csvfile:
+		file_array = np.array(list(csv.reader(csvfile)))
+		csvfile.close()
+	return file_array
+
+# expects 1d array
+def basic_stats(data):
+
+	data_t = np.array(data).astype(float)
+
+	_mean = mean(data_t)
+	_median = median(data_t)
+	_stdev = stdev(data_t)
+	_variance = variance(data_t)
+	_min = npmin(data_t)
+	_max = npmax(data_t)
+
+	return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
+
+# returns z score with inputs of point, mean and standard deviation of spread
+def z_score(point, mean, stdev):
+	score = (point - mean) / stdev
+	
+	return score
+
+# expects 2d array, normalizes across all axes
+def z_normalize(array, *args):
+
+	array = np.array(array)
+	for arg in args:
+		array = sklearn.preprocessing.normalize(array, axis = arg)
+
+	return array
+
+# expects 2d array of [x,y]
+def histo_analysis(hist_data):
+
+	if len(hist_data[0]) > 2:
+
+		hist_data = np.array(hist_data)
+		derivative = np.array(len(hist_data) - 1, dtype = float)
+		t = np.diff(hist_data)
+		derivative = t[1] / t[0]
+		np.sort(derivative)
+
+		return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]}
+
+	else:
+
+		return None
+
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
+
+	X = np.array(inputs)
+	y = np.array(outputs)
+
+	regressions = {}
+
+	if 'lin' in args: # formula: ax + b
+
+		try:
+
+			def lin(x, a, b):
+
+				return a * x + b
+
+			popt, pcov = scipy.optimize.curve_fit(lin, X, y)
+
+			coeffs = popt.flatten().tolist()
+			regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1]))
+
+		except Exception as e:
+
+			pass
+
+	if 'log' in args: # formula: a log (b(x + c)) + d
+
+		try:
+
+			def log(x, a, b, c, d):
+
+				return a * np.log(b*(x + c)) + d
+
+			popt, pcov = scipy.optimize.curve_fit(log, X, y)
+
+			coeffs = popt.flatten().tolist()
+			regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+
+		except Exception as e:
+			
+			pass
+
+	if 'exp' in args: # formula: a e ^ (b(x + c)) + d
+
+		try:        
+
+			def exp(x, a, b, c, d):
+
+				return a * np.exp(b*(x + c)) + d
+
+			popt, pcov = scipy.optimize.curve_fit(exp, X, y)
+
+			coeffs = popt.flatten().tolist()
+			regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+
+		except Exception as e:
+
+			pass
+
+	if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
+		
+		inputs = np.array([inputs])
+		outputs = np.array([outputs])
+
+		plys = {}
+		limit = len(outputs[0])
+
+		for i in range(2, limit):
+
+			model = sklearn.preprocessing.PolynomialFeatures(degree = i)
+			model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression())
+			model = model.fit(np.rot90(inputs), np.rot90(outputs))
+
+			params = model.steps[1][1].intercept_.tolist()
+			params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
+			params = params.flatten().tolist()
+
+			temp = ""
+			counter = 0
+			for param in params:
+				temp += "(" + str(param) + "*x^" + str(counter) + ")"
+				counter += 1
+			plys["x^" + str(i)] = (temp)
+
+		regressions["ply"] = (plys)
+
+	if 'sig' in args: # formula: a tanh (b(x + c)) + d
+
+		try:        
+
+			def sig(x, a, b, c, d):
+
+				return a * np.tanh(b*(x + c)) + d
+
+			popt, pcov = scipy.optimize.curve_fit(sig, X, y)
+
+			coeffs = popt.flatten().tolist()
+			regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+
+		except Exception as e:
+		   
+			pass
+
+	return regressions
+
+class Metric:
+
+	def elo(self, starting_score, opposing_score, observed, N, K):
+
+		return Elo.calculate(starting_score, opposing_score, observed, N, K)
+
+	def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+
+		player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
+
+		player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
+
+		return (player.rating, player.rd, player.vol)
+
+	def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+
+		team_ratings = []
+
+		for team in teams_data:
+			team_temp = ()
+			for player in team:
+				player = Trueskill.Rating(player[0], player[1])
+				team_temp = team_temp + (player,)
+			team_ratings.append(team_temp)
+
+		return Trueskill.rate(team_ratings, ranks=observations)
+
+def mean(data):
+
+	return np.mean(data)
+
+def median(data):
+
+	return np.median(data)
+
+def stdev(data):
+
+	return np.std(data)
+
+def variance(data):
+
+	return np.var(data)
+
+def npmin(data):
+
+	return np.amin(data)
+
+def npmax(data):
+
+	return np.amax(data)
+
+def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
+
+	kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
+	kernel.fit(data)
+	predictions = kernel.predict(data)
+	centers = kernel.cluster_centers_
+
+	return centers, predictions
+
+def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
+
+	kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
+
+	return kernel.fit_transform(data)
+
+def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
+	model = model.fit(data_train,labels_train)
+	predictions = model.predict(data_test)
+	metrics = ClassificationMetric(predictions, labels_test)
+
+	return model, metrics

analysis-master/tra_analysis/Array.py

@@ -0,0 +1,152 @@
+# Titan Robotics Team 2022: Array submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import Array'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.Array() here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+import numpy as np
+
+class Array(): # tests on nd arrays independent of basic_stats
+
+	def __init__(self, narray):
+
+		self.array = np.array(narray)
+
+	def __str__(self):
+
+		return str(self.array)
+	
+	def elementwise_mean(self, axis = 0): # expects arrays that are size normalized
+
+		return np.mean(self.array, axis = axis)
+
+	def elementwise_median(self, axis = 0):
+
+		return np.median(self.array, axis = axis)
+
+	def elementwise_stdev(self, axis = 0):
+
+		return np.std(self.array, axis = axis)
+
+	def elementwise_variance(self, axis = 0):
+
+		return np.var(self.array, axis = axis)
+
+	def elementwise_npmin(self, axis = 0):
+		return np.amin(self.array, axis = axis)
+
+
+	def elementwise_npmax(self, axis = 0):
+		return np.amax(self.array, axis = axis)
+
+	def elementwise_stats(self, axis = 0):
+
+		_mean = self.elementwise_mean(axis = axis)
+		_median = self.elementwise_median(axis = axis)
+		_stdev = self.elementwise_stdev(axis = axis)
+		_variance = self.elementwise_variance(axis = axis)
+		_min = self.elementwise_npmin(axis = axis)
+		_max = self.elementwise_npmax(axis = axis)
+
+		return _mean, _median, _stdev, _variance, _min, _max
+
+	def __getitem__(self, key):
+
+		return self.array[key]
+
+	def __setitem__(self, key, value):
+
+		self.array[key] == value
+
+	def normalize(self, array):
+
+		a = np.atleast_1d(np.linalg.norm(array))
+		a[a==0] = 1
+		return array / np.expand_dims(a, -1)
+
+	def __add__(self, other):
+
+		return self.array + other.array
+
+	def __sub__(self, other):
+
+		return self.array - other.array
+
+	def __neg__(self):
+		
+		return -self.array
+
+	def __abs__(self):
+
+		return abs(self.array)
+
+	def __invert__(self):
+
+		return 1/self.array
+
+	def __mul__(self, other):
+
+		return self.array.dot(other.array)
+
+	def __rmul__(self, other):
+
+		return self.array.dot(other.array)
+
+	def cross(self, other):
+
+		return np.cross(self.array, other.array)
+
+	def sort(self, array): # depreciated
+		warnings.warn("Array.sort has been depreciated in favor of Sort")
+		array_length = len(array)
+		if array_length <= 1:
+			return array
+		middle_index = int(array_length / 2)
+		left = array[0:middle_index]
+		right = array[middle_index:]
+		left = self.sort(left)
+		right = self.sort(right)
+		return self.__merge(left, right)
+
+
+	def __merge(self, left, right):
+		sorted_list = []
+		left = left[:]
+		right = right[:]
+		while len(left) > 0 or len(right) > 0:
+			if len(left) > 0 and len(right) > 0:
+				if left[0] <= right[0]:
+					sorted_list.append(left.pop(0))
+				else:
+					sorted_list.append(right.pop(0))
+			elif len(left) > 0:
+				sorted_list.append(left.pop(0))
+			elif len(right) > 0:
+				sorted_list.append(right.pop(0))
+		return sorted_list
+
+	def search(self, arr, x):
+		return self.__search(arr, 0, len(arr) - 1, x)
+
+	def __search(self, arr, low, high, x): 
+		if high >= low: 
+			mid = (high + low) // 2
+			if arr[mid] == x: 
+				return mid 
+			elif arr[mid] > x: 
+				return binary_search(arr, low, mid - 1, x) 
+			else: 
+				return binary_search(arr, mid + 1, high, x) 
+		else:
+			return -1

analysis-master/tra_analysis/ClassificationMetric.py

@@ -0,0 +1,36 @@
+# Titan Robotics Team 2022: ClassificationMetric submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import ClassificationMetric'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.ClassificationMetric() here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+]
+
+import sklearn
+from sklearn import metrics
+
+class ClassificationMetric():
+
+	def __new__(cls, predictions, targets):
+
+		return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
+
+	def cm(self, predictions, targets):
+
+		return sklearn.metrics.confusion_matrix(targets, predictions)
+
+	def cr(self, predictions, targets):
+
+		return sklearn.metrics.classification_report(targets, predictions)

analysis-master/tra_analysis/CorrelationTest.py

@@ -0,0 +1,58 @@
+# Titan Robotics Team 2022: CorrelationTest submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import CorrelationTest'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.CorrelationTest() here
+		- removed classness
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+]
+
+import scipy
+from scipy import stats
+
+def anova_oneway(*args): #expects arrays of samples
+
+	results = scipy.stats.f_oneway(*args)
+	return {"f-value": results[0], "p-value": results[1]}
+
+def pearson(x, y):
+
+	results = scipy.stats.pearsonr(x, y)
+	return {"r-value": results[0], "p-value": results[1]}
+
+def spearman(a, b = None, axis = 0, nan_policy = 'propagate'):
+
+	results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
+	return {"r-value": results[0], "p-value": results[1]}
+
+def point_biserial(x, y):
+
+	results = scipy.stats.pointbiserialr(x, y)
+	return {"r-value": results[0], "p-value": results[1]}
+
+def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
+
+	results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
+	return {"tau": results[0], "p-value": results[1]}
+
+def kendall_weighted(x, y, rank = True, weigher = None, additive = True):
+
+	results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
+	return {"tau": results[0], "p-value": results[1]}
+
+def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
+
+	results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
+	return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value

analysis-master/tra_analysis/CorrelationTest_obj.py

@@ -0,0 +1,41 @@
+# Only included for backwards compatibility! Do not update, CorrelationTest is preferred and supported.
+
+import scipy
+from scipy import stats
+
+class CorrelationTest:
+
+	def anova_oneway(self, *args): #expects arrays of samples
+
+		results = scipy.stats.f_oneway(*args)
+		return {"f-value": results[0], "p-value": results[1]}
+
+	def pearson(self, x, y):
+
+		results = scipy.stats.pearsonr(x, y)
+		return {"r-value": results[0], "p-value": results[1]}
+
+	def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
+
+		results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
+		return {"r-value": results[0], "p-value": results[1]}
+
+	def point_biserial(self, x,y):
+
+		results = scipy.stats.pointbiserialr(x, y)
+		return {"r-value": results[0], "p-value": results[1]}
+
+	def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
+
+		results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
+		return {"tau": results[0], "p-value": results[1]}
+
+	def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
+
+		results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
+		return {"tau": results[0], "p-value": results[1]}
+
+	def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
+
+		results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
+		return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value

analysis-master/tra_analysis/Fit.py

@@ -4,10 +4,12 @@
 #   this module is cuda-optimized (as appropriate) and vectorized (except for one small part)
 # setup:
 
-__version__ = "0.0.1"
+__version__ = "0.0.2"
 
 # changelog should be viewed using print(analysis.fits.__changelog__)
 __changelog__ = """changelog:
+	0.0.2:
+		- renamed module to Fit
 	0.0.1:
 		- initial release, add circle fitting with LSC
 """

analysis-master/tra_analysis/KNN.py

@@ -0,0 +1,45 @@
+# Titan Robotics Team 2022: KNN submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import KNN'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.KNN() here
+		- removed classness
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+	"James Pan <zpan@imsa.edu>"
+)
+
+__all__ = [
+	'knn_classifier',
+	'knn_regressor'
+]
+
+import sklearn
+from sklearn import model_selection, neighbors
+from . import ClassificationMetric, RegressionMetric
+
+def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+	model.fit(data_train, labels_train)
+	predictions = model.predict(data_test)
+
+	return model, ClassificationMetric(predictions, labels_test)
+
+def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+
+	data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+	model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+	model.fit(data_train, outputs_train)
+	predictions = model.predict(data_test)
+
+	return model, RegressionMetric.RegressionMetric(predictions, outputs_test)

analysis-master/tra_analysis/KNN_obj.py

@@ -0,0 +1,25 @@
+# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
+
+import sklearn
+from sklearn import model_selection, neighbors
+from . import ClassificationMetric, RegressionMetric
+
+class KNN:
+
+	def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		model = sklearn.neighbors.KNeighborsClassifier()
+		model.fit(data_train, labels_train)
+		predictions = model.predict(data_test)
+
+		return model, ClassificationMetric(predictions, labels_test)
+
+	def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+
+		data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+		model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+		model.fit(data_train, outputs_train)
+		predictions = model.predict(data_test)
+
+		return model, RegressionMetric(predictions, outputs_test)

analysis-master/tra_analysis/NaiveBayes.py

@@ -0,0 +1,63 @@
+# Titan Robotics Team 2022: NaiveBayes submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import NaiveBayes'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.NaiveBayes() here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+	'gaussian',
+	'multinomial'
+	'bernoulli',
+	'complement'
+]
+
+import sklearn
+from sklearn import model_selection, naive_bayes
+from . import ClassificationMetric, RegressionMetric
+
+def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
+	model.fit(data_train, labels_train)
+	predictions = model.predict(data_test)
+
+	return model, ClassificationMetric(predictions, labels_test)
+
+def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
+	model.fit(data_train, labels_train)
+	predictions = model.predict(data_test)
+
+	return model, ClassificationMetric(predictions, labels_test)
+
+def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
+	model.fit(data_train, labels_train)
+	predictions = model.predict(data_test)
+
+	return model, ClassificationMetric(predictions, labels_test)
+
+def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
+	model.fit(data_train, labels_train)
+	predictions = model.predict(data_test)
+
+	return model, ClassificationMetric(predictions, labels_test)

analysis-master/tra_analysis/NaiveBayes_obj.py

@@ -0,0 +1,43 @@
+# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
+
+import sklearn
+from sklearn import model_selection, naive_bayes
+from . import ClassificationMetric, RegressionMetric
+
+class NaiveBayes:
+
+	def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
+		model.fit(data_train, labels_train)
+		predictions = model.predict(data_test)
+
+		return model, ClassificationMetric(predictions, labels_test)
+
+	def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
+		model.fit(data_train, labels_train)
+		predictions = model.predict(data_test)
+
+		return model, ClassificationMetric(predictions, labels_test)
+
+	def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
+		model.fit(data_train, labels_train)
+		predictions = model.predict(data_test)
+
+		return model, ClassificationMetric(predictions, labels_test)
+
+	def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
+		model.fit(data_train, labels_train)
+		predictions = model.predict(data_test)
+
+		return model, ClassificationMetric(predictions, labels_test)

analysis-master/tra_analysis/RandomForest.py

@@ -0,0 +1,42 @@
+# Titan Robotics Team 2022: RandomForest submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import RandomForest'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.RandomFores() here
+		- removed classness
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+]
+
+import sklearn
+from sklearn import ensemble, model_selection
+from . import ClassificationMetric, RegressionMetric
+
+def random_forest_classifier(data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
+
+	data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+	kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
+	kernel.fit(data_train, labels_train)
+	predictions = kernel.predict(data_test)
+
+	return kernel, ClassificationMetric(predictions, labels_test)
+
+def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
+
+	data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+	kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
+	kernel.fit(data_train, outputs_train)
+	predictions = kernel.predict(data_test)
+
+	return kernel, RegressionMetric.RegressionMetric(predictions, outputs_test)

analysis-master/tra_analysis/RandomForest_obj.py

@@ -0,0 +1,25 @@
+# Only included for backwards compatibility! Do not update, RandomForest is preferred and supported.
+
+import sklearn
+from sklearn import ensemble, model_selection
+from . import ClassificationMetric, RegressionMetric
+
+class RandomForest:
+
+	def random_forest_classifier(self, data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
+
+		data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+		kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
+		kernel.fit(data_train, labels_train)
+		predictions = kernel.predict(data_test)
+
+		return kernel, ClassificationMetric(predictions, labels_test)
+
+	def random_forest_regressor(self, data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
+
+		data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+		kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
+		kernel.fit(data_train, outputs_train)
+		predictions = kernel.predict(data_test)
+
+		return kernel, RegressionMetric(predictions, outputs_test)

analysis-master/tra_analysis/RegressionMetric.py

@@ -0,0 +1,42 @@
+# Titan Robotics Team 2022: RegressionMetric submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import RegressionMetric'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.RegressionMetric() here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+	'RegressionMetric'
+]
+
+import numpy as np
+import sklearn
+from sklearn import metrics
+
+class RegressionMetric():
+
+	def __new__(cls, predictions, targets):
+
+		return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
+
+	def r_squared(self, predictions, targets):  # assumes equal size inputs
+
+		return sklearn.metrics.r2_score(targets, predictions)
+
+	def mse(self, predictions, targets):
+
+		return sklearn.metrics.mean_squared_error(targets, predictions)
+
+	def rms(self, predictions, targets):
+
+		return np.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))

analysis-master/tra_analysis/SVM.py

@@ -0,0 +1,80 @@
+# Titan Robotics Team 2022: SVM submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import SVM'
+# setup:
+
+__version__ = "1.0.1"
+
+__changelog__ = """changelog:
+	1.0.1:
+		- removed unessasary self calls
+		- removed classness
+	1.0.0:
+		- ported analysis.SVM() here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+]
+
+import sklearn
+from sklearn import svm
+from . import ClassificationMetric, RegressionMetric
+
+class CustomKernel:
+
+	def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
+
+		return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+class StandardKernel:
+
+	def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
+
+		return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
+
+class PrebuiltKernel:
+
+	class Linear:
+
+		def __new__(cls):
+
+			return sklearn.svm.SVC(kernel = 'linear')
+
+	class Polynomial:
+
+		def __new__(cls, power, r_bias):
+
+			return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
+
+	class RBF:
+
+		def __new__(cls, gamma):
+
+			return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
+
+	class Sigmoid:
+
+		def __new__(cls, r_bias):
+
+			return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
+
+def fit(kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
+
+	return kernel.fit(train_data, train_outputs)
+
+def eval_classification(kernel, test_data, test_outputs):
+
+	predictions = kernel.predict(test_data)
+
+	return ClassificationMetric(predictions, test_outputs)
+
+def eval_regression(kernel, test_data, test_outputs):
+
+	predictions = kernel.predict(test_data)
+
+	return RegressionMetric(predictions, test_outputs)

analysis-master/tra_analysis/Sort.py

@@ -0,0 +1,411 @@
+# Titan Robotics Team 2022: Sort submodule
+# Written by Arthur Lu and James Pan
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import Sort'
+# setup:
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+		- ported analysis.Sort() here
+		- removed classness
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+	"James Pan <zpan@imsa.edu>"
+)
+
+__all__ = [
+]
+
+import numpy as np
+
+def quicksort(a):
+
+	def sort(array):
+
+		less = []
+		equal = []
+		greater = []
+
+		if len(array) > 1:
+			pivot = array[0]
+			for x in array:
+				if x < pivot:
+					less.append(x)
+				elif x == pivot:
+					equal.append(x)
+				elif x > pivot:
+					greater.append(x)
+			return sort(less)+equal+sort(greater) 
+		else:
+			return array
+
+	return np.array(sort(a))
+
+def mergesort(a):
+
+	def sort(array):
+
+		array = array
+
+		if len(array) >1: 
+			middle = len(array) // 2
+			L = array[:middle]
+			R = array[middle:]
+	
+			sort(L)
+			sort(R)
+	
+			i = j = k = 0
+
+			while i < len(L) and j < len(R): 
+				if L[i] < R[j]: 
+					array[k] = L[i] 
+					i+= 1
+				else: 
+					array[k] = R[j] 
+					j+= 1
+				k+= 1
+
+			while i < len(L): 
+				array[k] = L[i] 
+				i+= 1
+				k+= 1
+			
+			while j < len(R): 
+				array[k] = R[j] 
+				j+= 1
+				k+= 1
+
+			return array
+
+	return sort(a)
+
+def introsort(a):
+
+	def sort(array, start, end, maxdepth):
+
+		array = array
+
+		if end - start <= 1:
+			return
+		elif maxdepth == 0:
+			heapsort(array, start, end)
+		else:
+			p = partition(array, start, end)
+			sort(array, start, p + 1, maxdepth - 1)
+			sort(array, p + 1, end, maxdepth - 1)
+
+		return array
+
+	def partition(array, start, end):
+		pivot = array[start]
+		i = start - 1
+		j = end
+	
+		while True:
+			i = i + 1
+			while array[i] < pivot:
+				i = i + 1
+			j = j - 1
+			while array[j] > pivot:
+				j = j - 1
+	
+			if i >= j:
+				return j
+	
+			swap(array, i, j)
+
+	def swap(array, i, j):
+		array[i], array[j] = array[j], array[i]
+
+	def heapsort(array, start, end):
+		build_max_heap(array, start, end)
+		for i in range(end - 1, start, -1):
+			swap(array, start, i)
+			max_heapify(array, index=0, start=start, end=i)
+
+	def build_max_heap(array, start, end):
+		def parent(i):
+			return (i - 1)//2
+		length = end - start
+		index = parent(length - 1)
+		while index >= 0:
+			max_heapify(array, index, start, end)
+			index = index - 1
+
+	def max_heapify(array, index, start, end):
+		def left(i):
+			return 2*i + 1
+		def right(i):
+			return 2*i + 2
+	
+		size = end - start
+		l = left(index)
+		r = right(index)
+		if (l < size and array[start + l] > array[start + index]):
+			largest = l
+		else:
+			largest = index
+		if (r < size and array[start + r] > array[start + largest]):
+			largest = r
+		if largest != index:
+			swap(array, start + largest, start + index)
+			max_heapify(array, largest, start, end)
+
+	maxdepth = (len(a).bit_length() - 1)*2
+
+	return sort(a, 0, len(a), maxdepth)
+
+def heapsort(a):
+
+	def sort(array):
+		
+		array = array
+
+		n = len(array) 
+
+		for i in range(n//2 - 1, -1, -1): 
+			heapify(array, n, i) 
+
+		for i in range(n-1, 0, -1): 
+			array[i], array[0] = array[0], array[i]
+			heapify(array, i, 0) 
+
+		return array
+
+	def heapify(array, n, i):
+
+		array = array
+
+		largest = i
+		l = 2 * i + 1
+		r = 2 * i + 2
+
+		if l < n and array[i] < array[l]: 
+			largest = l 
+
+		if r < n and array[largest] < array[r]: 
+			largest = r 
+
+		if largest != i: 
+			array[i],array[largest] = array[largest],array[i]
+			heapify(array, n, largest)
+		
+		return array
+
+	return sort(a)
+
+def insertionsort(a):
+
+	def sort(array):
+
+		array = array
+
+		for i in range(1, len(array)): 
+	
+			key = array[i] 
+
+			j = i-1
+			while j >=0 and key < array[j] : 
+					array[j+1] = array[j] 
+					j -= 1
+			array[j+1] = key 
+
+		return array
+
+	return sort(a)
+
+def timsort(a, block = 32):
+
+	BLOCK = block
+
+	def sort(array, n):
+
+		array = array
+
+		for i in range(0, n, BLOCK):  
+			insertionsort(array, i, min((i+31), (n-1)))
+
+		size = BLOCK 
+		while size < n:
+
+			for left in range(0, n, 2*size):  
+	
+				mid = left + size - 1 
+				right = min((left + 2*size - 1), (n-1))  
+				merge(array, left, mid, right)  
+		
+			size = 2*size
+
+		return array
+
+	def insertionsort(array, left, right):
+
+		array = array 
+
+		for i in range(left + 1, right+1):  
+		
+			temp = array[i]  
+			j = i - 1 
+			while j >= left and array[j] > temp :  
+			
+				array[j+1] = array[j]  
+				j -= 1
+			
+			array[j+1] = temp
+
+		return array
+		
+
+	def merge(array, l, m, r): 
+	
+		len1, len2 =  m - l + 1, r - m  
+		left, right = [], []  
+		for i in range(0, len1):  
+			left.append(array[l + i])  
+		for i in range(0, len2):  
+			right.append(array[m + 1 + i])  
+		
+		i, j, k = 0, 0, l 
+
+		while i < len1 and j < len2:  
+		
+			if left[i] <= right[j]:  
+				array[k] = left[i]  
+				i += 1 
+			
+			else: 
+				array[k] = right[j]  
+				j += 1 
+			
+			k += 1
+
+		while i < len1:  
+		
+			array[k] = left[i]  
+			k += 1 
+			i += 1
+
+		while j < len2:  
+			array[k] = right[j]  
+			k += 1
+			j += 1
+
+	return sort(a, len(a))
+
+def selectionsort(a):
+	array = a
+	for i in range(len(array)): 
+		min_idx = i
+		for j in range(i+1, len(array)): 
+			if array[min_idx] > array[j]: 
+				min_idx = j         
+		array[i], array[min_idx] = array[min_idx], array[i]
+	return array
+
+def shellsort(a):
+	array = a
+	n = len(array)
+	gap = n//2
+
+	while gap > 0: 
+
+		for i in range(gap,n): 
+
+			temp = array[i] 
+			j = i 
+			while  j >= gap and array[j-gap] >temp: 
+				array[j] = array[j-gap] 
+				j -= gap 
+			array[j] = temp 
+		gap //= 2
+
+	return array
+
+def bubblesort(a):
+
+	def sort(array):
+		for i, num in enumerate(array):
+			try:
+				if array[i+1] < num:
+					array[i] = array[i+1]
+					array[i+1] = num
+					sort(array)
+			except IndexError:
+				pass
+		return array
+
+	return sort(a)
+
+def cyclesort(a):
+
+	def sort(array):
+
+		array = array
+		writes = 0
+
+		for cycleStart in range(0, len(array) - 1): 
+			item = array[cycleStart] 
+
+			pos = cycleStart 
+			for i in range(cycleStart + 1, len(array)): 
+				if array[i] < item: 
+					pos += 1
+
+			if pos == cycleStart: 
+				continue
+
+			while item == array[pos]: 
+				pos += 1
+				array[pos], item = item, array[pos] 
+				writes += 1
+
+			while pos != cycleStart: 
+
+				pos = cycleStart 
+				for i in range(cycleStart + 1, len(array)): 
+					if array[i] < item: 
+						pos += 1
+
+				while item == array[pos]: 
+					pos += 1
+				array[pos], item = item, array[pos] 
+				writes += 1
+			
+		return array
+	
+	return sort(a)
+
+def cocktailsort(a):
+
+	def sort(array):
+
+		array = array
+
+		n = len(array) 
+		swapped = True
+		start = 0
+		end = n-1
+		while (swapped == True): 
+			swapped = False
+			for i in range (start, end): 
+				if (array[i] > array[i + 1]) : 
+					array[i], array[i + 1]= array[i + 1], array[i] 
+					swapped = True
+			if (swapped == False): 
+				break
+			swapped = False
+			end = end-1
+			for i in range(end-1, start-1, -1): 
+				if (array[i] > array[i + 1]): 
+					array[i], array[i + 1] = array[i + 1], array[i] 
+					swapped = True
+			start = start + 1
+
+		return array
+
+	return sort(a)

analysis-master/tra_analysis/Sort_obj.py

@@ -0,0 +1,391 @@
+# Only included for backwards compatibility! Do not update, Sort is preferred and supported.
+
+class Sort: # if you haven't used a sort, then you've never lived
+
+	def quicksort(self, a):
+
+		def sort(array):
+
+			less = []
+			equal = []
+			greater = []
+
+			if len(array) > 1:
+				pivot = array[0]
+				for x in array:
+					if x < pivot:
+						less.append(x)
+					elif x == pivot:
+						equal.append(x)
+					elif x > pivot:
+						greater.append(x)
+				return sort(less)+equal+sort(greater) 
+			else:
+				return array
+
+		return np.array(sort(a))
+
+	def mergesort(self, a):
+
+		def sort(array):
+
+			array = array
+
+			if len(array) >1: 
+				middle = len(array) // 2
+				L = array[:middle]
+				R = array[middle:]
+		
+				sort(L)
+				sort(R)
+		
+				i = j = k = 0
+
+				while i < len(L) and j < len(R): 
+					if L[i] < R[j]: 
+						array[k] = L[i] 
+						i+= 1
+					else: 
+						array[k] = R[j] 
+						j+= 1
+					k+= 1
+
+				while i < len(L): 
+					array[k] = L[i] 
+					i+= 1
+					k+= 1
+				
+				while j < len(R): 
+					array[k] = R[j] 
+					j+= 1
+					k+= 1
+
+				return array
+
+		return sort(a)
+
+	def introsort(self, a):
+
+		def sort(array, start, end, maxdepth):
+
+			array = array
+
+			if end - start <= 1:
+				return
+			elif maxdepth == 0:
+				heapsort(array, start, end)
+			else:
+				p = partition(array, start, end)
+				sort(array, start, p + 1, maxdepth - 1)
+				sort(array, p + 1, end, maxdepth - 1)
+
+			return array
+
+		def partition(array, start, end):
+			pivot = array[start]
+			i = start - 1
+			j = end
+		
+			while True:
+				i = i + 1
+				while array[i] < pivot:
+					i = i + 1
+				j = j - 1
+				while array[j] > pivot:
+					j = j - 1
+		
+				if i >= j:
+					return j
+		
+				swap(array, i, j)
+
+		def swap(array, i, j):
+			array[i], array[j] = array[j], array[i]
+
+		def heapsort(array, start, end):
+			build_max_heap(array, start, end)
+			for i in range(end - 1, start, -1):
+				swap(array, start, i)
+				max_heapify(array, index=0, start=start, end=i)
+
+		def build_max_heap(array, start, end):
+			def parent(i):
+				return (i - 1)//2
+			length = end - start
+			index = parent(length - 1)
+			while index >= 0:
+				max_heapify(array, index, start, end)
+				index = index - 1
+
+		def max_heapify(array, index, start, end):
+			def left(i):
+				return 2*i + 1
+			def right(i):
+				return 2*i + 2
+		
+			size = end - start
+			l = left(index)
+			r = right(index)
+			if (l < size and array[start + l] > array[start + index]):
+				largest = l
+			else:
+				largest = index
+			if (r < size and array[start + r] > array[start + largest]):
+				largest = r
+			if largest != index:
+				swap(array, start + largest, start + index)
+				max_heapify(array, largest, start, end)
+
+		maxdepth = (len(a).bit_length() - 1)*2
+
+		return sort(a, 0, len(a), maxdepth)
+
+	def heapsort(self, a):
+
+		def sort(array):
+			
+			array = array
+
+			n = len(array) 
+ 
+			for i in range(n//2 - 1, -1, -1): 
+				heapify(array, n, i) 
+
+			for i in range(n-1, 0, -1): 
+				array[i], array[0] = array[0], array[i]
+				heapify(array, i, 0) 
+
+			return array
+
+		def heapify(array, n, i):
+
+			array = array
+
+			largest = i
+			l = 2 * i + 1
+			r = 2 * i + 2
+
+			if l < n and array[i] < array[l]: 
+				largest = l 
+
+			if r < n and array[largest] < array[r]: 
+				largest = r 
+
+			if largest != i: 
+				array[i],array[largest] = array[largest],array[i]
+				heapify(array, n, largest)
+			
+			return array
+
+		return sort(a)
+
+	def insertionsort(self, a):
+
+		def sort(array):
+
+			array = array
+
+			for i in range(1, len(array)): 
+		
+				key = array[i] 
+
+				j = i-1
+				while j >=0 and key < array[j] : 
+						array[j+1] = array[j] 
+						j -= 1
+				array[j+1] = key 
+
+			return array
+
+		return sort(a)
+
+	def timsort(self, a, block = 32):
+
+		BLOCK = block
+
+		def sort(array, n):
+
+			array = array
+
+			for i in range(0, n, BLOCK):  
+				insertionsort(array, i, min((i+31), (n-1)))
+
+			size = BLOCK 
+			while size < n:
+
+				for left in range(0, n, 2*size):  
+	 
+					mid = left + size - 1 
+					right = min((left + 2*size - 1), (n-1))  
+					merge(array, left, mid, right)  
+		  
+				size = 2*size
+
+			return array
+
+		def insertionsort(array, left, right):
+
+			array = array 
+   
+			for i in range(left + 1, right+1):  
+			
+				temp = array[i]  
+				j = i - 1 
+				while j >= left and array[j] > temp :  
+				
+					array[j+1] = array[j]  
+					j -= 1
+				
+				array[j+1] = temp
+
+			return array
+			
+ 
+		def merge(array, l, m, r): 
+		
+			len1, len2 =  m - l + 1, r - m  
+			left, right = [], []  
+			for i in range(0, len1):  
+				left.append(array[l + i])  
+			for i in range(0, len2):  
+				right.append(array[m + 1 + i])  
+			
+			i, j, k = 0, 0, l 
+
+			while i < len1 and j < len2:  
+			
+				if left[i] <= right[j]:  
+					array[k] = left[i]  
+					i += 1 
+				
+				else: 
+					array[k] = right[j]  
+					j += 1 
+				
+				k += 1
+
+			while i < len1:  
+			
+				array[k] = left[i]  
+				k += 1 
+				i += 1
+
+			while j < len2:  
+				array[k] = right[j]  
+				k += 1
+				j += 1
+
+		return sort(a, len(a))
+	
+	def selectionsort(self, a):
+		array = a
+		for i in range(len(array)): 
+			min_idx = i
+			for j in range(i+1, len(array)): 
+				if array[min_idx] > array[j]: 
+					min_idx = j         
+			array[i], array[min_idx] = array[min_idx], array[i]
+		return array
+
+	def shellsort(self, a):
+		array = a
+		n = len(array)
+		gap = n//2
+
+		while gap > 0: 
+	
+			for i in range(gap,n): 
+
+				temp = array[i] 
+				j = i 
+				while  j >= gap and array[j-gap] >temp: 
+					array[j] = array[j-gap] 
+					j -= gap 
+				array[j] = temp 
+			gap //= 2
+
+		return array
+
+	def bubblesort(self, a):
+
+		def sort(array):
+			for i, num in enumerate(array):
+				try:
+					if array[i+1] < num:
+						array[i] = array[i+1]
+						array[i+1] = num
+						sort(array)
+				except IndexError:
+					pass
+			return array
+
+		return sort(a)
+
+	def cyclesort(self, a):
+
+		def sort(array):
+
+			array = array
+			writes = 0
+
+			for cycleStart in range(0, len(array) - 1): 
+				item = array[cycleStart] 
+
+				pos = cycleStart 
+				for i in range(cycleStart + 1, len(array)): 
+					if array[i] < item: 
+						pos += 1
+
+				if pos == cycleStart: 
+					continue
+
+				while item == array[pos]: 
+					pos += 1
+					array[pos], item = item, array[pos] 
+					writes += 1
+
+				while pos != cycleStart: 
+
+					pos = cycleStart 
+					for i in range(cycleStart + 1, len(array)): 
+						if array[i] < item: 
+							pos += 1
+
+					while item == array[pos]: 
+						pos += 1
+					array[pos], item = item, array[pos] 
+					writes += 1
+				
+			return array
+		
+		return sort(a)
+
+	def cocktailsort(self, a):
+
+		def sort(array):
+
+			array = array
+
+			n = len(array) 
+			swapped = True
+			start = 0
+			end = n-1
+			while (swapped == True): 
+				swapped = False
+				for i in range (start, end): 
+					if (array[i] > array[i + 1]) : 
+						array[i], array[i + 1]= array[i + 1], array[i] 
+						swapped = True
+				if (swapped == False): 
+					break
+				swapped = False
+				end = end-1
+				for i in range(end-1, start-1, -1): 
+					if (array[i] > array[i + 1]): 
+						array[i], array[i + 1] = array[i + 1], array[i] 
+						swapped = True
+				start = start + 1
+
+			return array
+
+		return sort(a)

analysis-master/tra_analysis/StatisticalTest.py

@@ -0,0 +1,222 @@
+# Titan Robotics Team 2022: StatisticalTest submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import StatisticalTest'
+# setup:
+
+__version__ = "1.0.1"
+
+__changelog__ = """changelog:
+	1.0.1:
+		- fixed typo in __all__
+	1.0.0:
+		- ported analysis.StatisticalTest() here
+		- removed classness
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = [
+	'ttest_onesample',
+	'ttest_independent',
+	'ttest_statistic',
+	'ttest_related',
+	'ks_fitness',
+	'chisquare',
+	'powerdivergence'
+	'ks_twosample',
+	'es_twosample',
+	'mw_rank',
+	'mw_tiecorrection',
+	'rankdata',
+	'wilcoxon_ranksum',
+	'wilcoxon_signedrank',
+	'kw_htest',
+	'friedman_chisquare',
+	'bm_wtest',
+	'combine_pvalues',
+	'jb_fitness',
+	'ab_equality',
+	'bartlett_variance',
+	'levene_variance',
+	'sw_normality',
+	'shapiro',
+	'ad_onesample',
+	'ad_ksample',
+	'binomial',
+	'fk_variance',
+	'mood_mediantest',
+	'mood_equalscale',
+	'skewtest',
+	'kurtosistest',
+	'normaltest'
+]
+
+import scipy
+from scipy import stats
+
+def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
+
+	results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def ttest_independent(a, b, equal = True, nan_policy = 'propagate'):
+
+	results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def ttest_statistic(o1, o2, equal = True):
+
+	results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def ttest_related(a, b, axis = 0, nan_policy='propagate'):
+
+	results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def ks_fitness(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
+
+	results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
+	return {"ks-value": results[0], "p-value": results[1]}
+
+def chisquare(f_obs, f_exp = None, ddof = None, axis = 0):
+
+	results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
+	return {"chisquared-value": results[0], "p-value": results[1]}
+
+def powerdivergence(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
+
+	results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
+	return {"powerdivergence-value": results[0], "p-value": results[1]}
+
+def ks_twosample(x, y, alternative = 'two_sided', mode = 'auto'):
+	
+	results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
+	return {"ks-value": results[0], "p-value": results[1]}
+
+def es_twosample(x, y, t = (0.4, 0.8)):
+
+	results = scipy.stats.epps_singleton_2samp(x, y, t = t)
+	return {"es-value": results[0], "p-value": results[1]}
+
+def mw_rank(x, y, use_continuity = True, alternative = None):
+
+	results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
+	return {"u-value": results[0], "p-value": results[1]}
+
+def mw_tiecorrection(rank_values):
+
+	results = scipy.stats.tiecorrect(rank_values)
+	return {"correction-factor": results}
+
+def rankdata(a, method = 'average'):
+
+	results = scipy.stats.rankdata(a, method = method)
+	return results
+
+def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
+
+	results = scipy.stats.ranksums(a, b)
+	return {"u-value": results[0], "p-value": results[1]}
+
+def wilcoxon_signedrank(x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
+
+	results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def kw_htest(*args, nan_policy = 'propagate'):
+
+	results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
+	return {"h-value": results[0], "p-value": results[1]}
+
+def friedman_chisquare(*args):
+
+	results = scipy.stats.friedmanchisquare(*args)
+	return {"chisquared-value": results[0], "p-value": results[1]}
+
+def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
+
+	results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
+	return {"w-value": results[0], "p-value": results[1]}
+
+def combine_pvalues(pvalues, method = 'fisher', weights = None):
+
+	results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
+	return {"combined-statistic": results[0], "p-value": results[1]}
+
+def jb_fitness(x):
+
+	results = scipy.stats.jarque_bera(x)
+	return {"jb-value": results[0], "p-value": results[1]}
+
+def ab_equality(x, y):
+
+	results = scipy.stats.ansari(x, y)
+	return {"ab-value": results[0], "p-value": results[1]}
+
+def bartlett_variance(*args):
+
+	results = scipy.stats.bartlett(*args)
+	return {"t-value": results[0], "p-value": results[1]}
+
+def levene_variance(*args, center = 'median', proportiontocut = 0.05):
+
+	results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
+	return {"w-value": results[0], "p-value": results[1]}
+
+def sw_normality(x):
+
+	results = scipy.stats.shapiro(x)
+	return {"w-value": results[0], "p-value": results[1]}
+
+def shapiro(x):
+
+	return "destroyed by facts and logic"
+
+def ad_onesample(x, dist = 'norm'):
+
+	results = scipy.stats.anderson(x, dist = dist)
+	return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
+
+def ad_ksample(samples, midrank = True):
+
+	results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
+	return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
+
+def binomial(x, n = None, p = 0.5, alternative = 'two-sided'):
+
+	results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
+	return {"p-value": results}
+
+def fk_variance(*args, center = 'median', proportiontocut = 0.05):
+
+	results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
+	return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
+
+def mood_mediantest(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
+
+	results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
+	return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
+
+def mood_equalscale(x, y, axis = 0):
+
+	results = scipy.stats.mood(x, y, axis = axis)
+	return {"z-score": results[0], "p-value": results[1]}
+
+def skewtest(a, axis = 0, nan_policy = 'propogate'):
+
+	results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
+	return {"z-score": results[0], "p-value": results[1]}
+
+def kurtosistest(a, axis = 0, nan_policy = 'propogate'):
+
+	results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
+	return {"z-score": results[0], "p-value": results[1]}
+
+def normaltest(a, axis = 0, nan_policy = 'propogate'):
+
+	results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
+	return {"z-score": results[0], "p-value": results[1]}

analysis-master/tra_analysis/StatisticalTest_obj.py

@@ -0,0 +1,170 @@
+# Only included for backwards compatibility! Do not update, StatisticalTest is preferred and supported.
+
+import scipy
+from scipy import stats
+
+class StatisticalTest:
+
+	def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):
+
+		results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
+
+		results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def ttest_statistic(self, o1, o2, equal = True):
+
+		results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def ttest_related(self, a, b, axis = 0, nan_policy='propagate'):
+
+		results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
+
+		results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
+		return {"ks-value": results[0], "p-value": results[1]}
+
+	def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0):
+
+		results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
+		return {"chisquared-value": results[0], "p-value": results[1]}
+
+	def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
+
+		results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
+		return {"powerdivergence-value": results[0], "p-value": results[1]}
+
+	def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'):
+		
+		results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
+		return {"ks-value": results[0], "p-value": results[1]}
+
+	def es_twosample(self, x, y, t = (0.4, 0.8)):
+
+		results = scipy.stats.epps_singleton_2samp(x, y, t = t)
+		return {"es-value": results[0], "p-value": results[1]}
+
+	def mw_rank(self, x, y, use_continuity = True, alternative = None):
+
+		results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
+		return {"u-value": results[0], "p-value": results[1]}
+
+	def mw_tiecorrection(self, rank_values):
+
+		results = scipy.stats.tiecorrect(rank_values)
+		return {"correction-factor": results}
+
+	def rankdata(self, a, method = 'average'):
+
+		results = scipy.stats.rankdata(a, method = method)
+		return results
+
+	def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
+
+		results = scipy.stats.ranksums(a, b)
+		return {"u-value": results[0], "p-value": results[1]}
+
+	def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
+
+		results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def kw_htest(self, *args, nan_policy = 'propagate'):
+
+		results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
+		return {"h-value": results[0], "p-value": results[1]}
+
+	def friedman_chisquare(self, *args):
+
+		results = scipy.stats.friedmanchisquare(*args)
+		return {"chisquared-value": results[0], "p-value": results[1]}
+
+	def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
+
+		results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
+		return {"w-value": results[0], "p-value": results[1]}
+
+	def combine_pvalues(self, pvalues, method = 'fisher', weights = None):
+
+		results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
+		return {"combined-statistic": results[0], "p-value": results[1]}
+
+	def jb_fitness(self, x):
+
+		results = scipy.stats.jarque_bera(x)
+		return {"jb-value": results[0], "p-value": results[1]}
+
+	def ab_equality(self, x, y):
+
+		results = scipy.stats.ansari(x, y)
+		return {"ab-value": results[0], "p-value": results[1]}
+
+	def bartlett_variance(self, *args):
+
+		results = scipy.stats.bartlett(*args)
+		return {"t-value": results[0], "p-value": results[1]}
+
+	def levene_variance(self, *args, center = 'median', proportiontocut = 0.05):
+
+		results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
+		return {"w-value": results[0], "p-value": results[1]}
+
+	def sw_normality(self, x):
+
+		results = scipy.stats.shapiro(x)
+		return {"w-value": results[0], "p-value": results[1]}
+
+	def shapiro(self, x):
+
+		return "destroyed by facts and logic"
+
+	def ad_onesample(self, x, dist = 'norm'):
+
+		results = scipy.stats.anderson(x, dist = dist)
+		return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
+	
+	def ad_ksample(self, samples, midrank = True):
+
+		results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
+		return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
+
+	def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'):
+
+		results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
+		return {"p-value": results}
+
+	def fk_variance(self, *args, center = 'median', proportiontocut = 0.05):
+
+		results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
+		return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
+
+	def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
+
+		results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
+		return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
+
+	def mood_equalscale(self, x, y, axis = 0):
+
+		results = scipy.stats.mood(x, y, axis = axis)
+		return {"z-score": results[0], "p-value": results[1]}
+
+	def skewtest(self, a, axis = 0, nan_policy = 'propogate'):
+
+		results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
+		return {"z-score": results[0], "p-value": results[1]}
+
+	def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'):
+
+		results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
+		return {"z-score": results[0], "p-value": results[1]}
+
+	def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
+
+		results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
+		return {"z-score": results[0], "p-value": results[1]}

analysis-master/tra_analysis/__init__.py

@@ -0,0 +1,45 @@
+# Titan Robotics Team 2022: tra_analysis package
+# Written by Arthur Lu, Jacob Levine, Dev Singh, and James Pan
+# Notes:
+#    this should be imported as a python package using 'import tra_analysis'
+#    this should be included in the local directory or environment variable
+#    this module has been optimized for multhreaded computing
+#    current benchmark of optimization: 1.33 times faster
+# setup:
+
+__version__ = "2.1.0-alpha.3"
+
+# changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+	2.1.0-alpha.3:
+		- fixed indentation in meta data
+	2.1.0-alpha.2:
+		- updated SVM import
+	2.1.0-alpha.1:
+		- moved multiple submodules under analysis to their own modules/files
+		- added header, __version__, __changelog__, __author__, __all__ (unpopulated)
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+	"Jacob Levine <jlevine@imsa.edu>",
+	"Dev Singh <dev@devksingh.com>",
+	"James Pan <zpan@imsa.edu>"
+)
+
+__all__ = [
+]
+
+from . import Analysis as Analysis
+from .Array import Array
+from .ClassificationMetric import ClassificationMetric
+from . import CorrelationTest
+from .equation import Expression
+from . import Fit
+from . import KNN
+from . import NaiveBayes
+from . import RandomForest
+from .RegressionMetric import RegressionMetric
+from . import Sort
+from . import StatisticalTest
+from . import SVM

analysis-master/tra_analysis/equation.ipynb

@@ -1,162 +0,0 @@
-{
- "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/equation/Expression.py

@@ -0,0 +1,37 @@
+# Titan Robotics Team 2022: Expression submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis.Equation import Expression'
+# 	 TODO:
+#	 	 - add option to pick parser backend
+#		 - fix unit tests
+# setup:
+
+__version__ = "0.0.1-alpha"
+
+__changelog__ = """changelog:
+	0.0.1-alpha:
+		- used the HybridExpressionParser as backend for Expression
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = {
+	"Expression"
+}
+
+import re
+from .parser import BNF, RegexInplaceParser, HybridExpressionParser, Core, equation_base
+
+class Expression(HybridExpressionParser):
+
+	expression = None
+	core = None
+
+	def __init__(self,expression,argorder=[],*args,**kwargs):
+		self.core = Core()
+		equation_base.equation_extend(self.core)
+		self.core.recalculateFMatch()
+		super().__init__(self.core, expression, argorder=[],*args,**kwargs)

analysis-master/tra_analysis/equation/__init__.py

@@ -0,0 +1,22 @@
+# Titan Robotics Team 2022: Expression submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis import Equation'
+# setup:
+
+__version__ = "0.0.1-alpha"
+
+__changelog__ = """changelog:
+	0.0.1-alpha:
+		- made first prototype of Expression
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = {
+    "Expression"
+}
+
+from .Expression import Expression

analysis-master/tra_analysis/equation/parser/BNF.py

@@ -0,0 +1,97 @@
+from __future__ import division
+from pyparsing import (Literal, CaselessLiteral, Word, Combine, Group, Optional, ZeroOrMore, Forward, nums, alphas, oneOf)
+from . import py2
+import math
+import operator
+
+class BNF(object):
+
+	def pushFirst(self, strg, loc, toks):
+		self.exprStack.append(toks[0])
+
+	def pushUMinus(self, strg, loc, toks):
+		if toks and toks[0] == '-':
+			self.exprStack.append('unary -')
+
+	def __init__(self):
+		"""
+		expop   :: '^'
+		multop  :: '*' | '/'
+		addop   :: '+' | '-'
+		integer :: ['+' | '-'] '0'..'9'+
+		atom    :: PI | E | real | fn '(' expr ')' | '(' expr ')'
+		factor  :: atom [ expop factor ]*
+		term    :: factor [ multop factor ]*
+		expr    :: term [ addop term ]*
+		"""
+		point = Literal(".")
+		e = CaselessLiteral("E")
+		fnumber = Combine(Word("+-" + nums, nums) +
+						  Optional(point + Optional(Word(nums))) +
+						  Optional(e + Word("+-" + nums, nums)))
+		ident = Word(alphas, alphas + nums + "_$")
+		plus = Literal("+")
+		minus = Literal("-")
+		mult = Literal("*")
+		div = Literal("/")
+		lpar = Literal("(").suppress()
+		rpar = Literal(")").suppress()
+		addop = plus | minus
+		multop = mult | div
+		expop = Literal("^")
+		pi = CaselessLiteral("PI")
+		expr = Forward()
+		atom = ((Optional(oneOf("- +")) +
+				 (ident + lpar + expr + rpar | pi | e | fnumber).setParseAction(self.pushFirst))
+				| Optional(oneOf("- +")) + Group(lpar + expr + rpar)
+				).setParseAction(self.pushUMinus)
+		factor = Forward()
+		factor << atom + \
+			ZeroOrMore((expop + factor).setParseAction(self.pushFirst))
+		term = factor + \
+			ZeroOrMore((multop + factor).setParseAction(self.pushFirst))
+		expr << term + \
+			ZeroOrMore((addop + term).setParseAction(self.pushFirst))
+
+		self.bnf = expr
+
+		epsilon = 1e-12
+		
+		self.opn = {"+": operator.add,
+					"-": operator.sub,
+					"*": operator.mul,
+					"/": operator.truediv,
+					"^": operator.pow}
+		self.fn = {"sin": math.sin,
+				   "cos": math.cos,
+				   "tan": math.tan,
+				   "exp": math.exp,
+				   "abs": abs,
+				   "trunc": lambda a: int(a),
+				   "round": round,
+				   "sgn": lambda a: abs(a) > epsilon and py2.cmp(a, 0) or 0}
+
+	def evaluateStack(self, s):
+		op = s.pop()
+		if op == 'unary -':
+			return -self.evaluateStack(s)
+		if op in "+-*/^":
+			op2 = self.evaluateStack(s)
+			op1 = self.evaluateStack(s)
+			return self.opn[op](op1, op2)
+		elif op == "PI":
+			return math.pi
+		elif op == "E":
+			return math.e
+		elif op in self.fn:
+			return self.fn[op](self.evaluateStack(s))
+		elif op[0].isalpha():
+			return 0
+		else:
+			return float(op)
+
+	def eval(self, num_string, parseAll=True):
+		self.exprStack = []
+		results = self.bnf.parseString(num_string, parseAll)
+		val = self.evaluateStack(self.exprStack[:])
+		return val

analysis-master/tra_analysis/equation/parser/Hybrid.py

@@ -0,0 +1,521 @@
+from .Hybrid_Utils import Core, ExpressionFunction, ExpressionVariable, ExpressionValue
+import sys
+
+if sys.version_info >= (3,):
+	xrange = range
+	basestring = str
+
+class HybridExpressionParser(object):
+
+	def __init__(self,core,expression,argorder=[],*args,**kwargs):
+		super(HybridExpressionParser,self).__init__(*args,**kwargs)
+		if isinstance(expression,type(self)): # clone the object
+			self.core = core
+			self.__args = list(expression.__args)
+			self.__vars = dict(expression.__vars) # intenral array of preset variables
+			self.__argsused = set(expression.__argsused)
+			self.__expr = list(expression.__expr)
+			self.variables = {} # call variables
+		else:
+			self.__expression = expression
+			self.__args = argorder;
+			self.__vars = {} # intenral array of preset variables
+			self.__argsused = set()
+			self.__expr = [] # compiled equation tokens
+			self.variables = {} # call variables
+			self.__compile()
+			del self.__expression
+
+	def __getitem__(self, name):
+		if name in self.__argsused:
+			if name in self.__vars:
+				return self.__vars[name]
+			else:
+				return None
+		else:
+			raise KeyError(name)
+
+	def __setitem__(self,name,value):
+
+		if name in self.__argsused:
+			self.__vars[name] = value
+		else:
+			raise KeyError(name)
+
+	def __delitem__(self,name):
+
+		if name in self.__argsused:
+			if name in self.__vars:
+				del self.__vars[name]
+		else:
+			raise KeyError(name)
+
+	def __contains__(self, name):
+
+		return name in self.__argsused
+
+	def __call__(self,*args,**kwargs):
+
+		if len(self.__expr) == 0:
+			return None
+		self.variables = {}
+		self.variables.update(self.core.constants)
+		self.variables.update(self.__vars)
+		if len(args) > len(self.__args):
+			raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() takes at most {4:d} arguments ({5:d} given)".format(
+					type(self).__module__,type(self).__name__,repr(self),id(self),len(self.__args),len(args)))
+		for i in xrange(len(args)):
+			if i < len(self.__args):
+				if self.__args[i] in kwargs:
+					raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() got multiple values for keyword argument '{4:s}'".format(
+						type(self).__module__,type(self).__name__,repr(self),id(self),self.__args[i]))
+				self.variables[self.__args[i]] = args[i]
+		self.variables.update(kwargs)
+		for arg in self.__argsused:
+			if arg not in self.variables:
+				min_args = len(self.__argsused - (set(self.__vars.keys()) | set(self.core.constants.keys())))
+				raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() takes at least {4:d} arguments ({5:d} given) '{6:s}' not defined".format(
+					type(self).__module__,type(self).__name__,repr(self),id(self),min_args,len(args)+len(kwargs),arg))
+		expr = self.__expr[::-1]
+		args = []
+		while len(expr) > 0:
+			t = expr.pop()
+			r = t(args,self)
+			args.append(r)
+		if len(args) > 1:
+			return args
+		else:
+			return args[0]
+
+	def __next(self,__expect_op):
+		if __expect_op:
+			m = self.core.gematch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'CLOSE'
+			m = self.core.smatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				return ",",'SEP'
+			m = self.core.omatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'OP'
+		else:
+			m = self.core.gsmatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'OPEN'
+			m = self.core.vmatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groupdict(0)
+				if g['dec']:
+					if g["ivalue"]:
+						return complex(int(g["rsign"]+"1")*float(g["rvalue"])*10**int(g["rexpoent"]),int(g["isign"]+"1")*float(g["ivalue"])*10**int(g["iexpoent"])),'VALUE'
+					elif g["rexpoent"] or g["rvalue"].find('.')>=0:
+						return int(g["rsign"]+"1")*float(g["rvalue"])*10**int(g["rexpoent"]),'VALUE'
+					else:
+						return int(g["rsign"]+"1")*int(g["rvalue"]),'VALUE'
+				elif g["hex"]:
+					return int(g["hexsign"]+"1")*int(g["hexvalue"],16),'VALUE'
+				elif g["oct"]:
+					return int(g["octsign"]+"1")*int(g["octvalue"],8),'VALUE'
+				elif g["bin"]:
+					return int(g["binsign"]+"1")*int(g["binvalue"],2),'VALUE'
+				else:
+					raise NotImplemented("'{0:s}' Values Not Implemented Yet".format(m.string))
+			m = self.core.nmatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'NAME'
+			m = self.core.fmatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'FUNC'
+			m = self.core.umatch.match(self.__expression)
+			if m != None:
+				self.__expression = self.__expression[m.end():]
+				g = m.groups()
+				return g[0],'UNARY'
+			return None
+
+	def show(self):
+		"""Show RPN tokens
+
+		This will print out the internal token list (RPN) of the expression
+		one token perline.
+		"""
+		for expr in self.__expr:
+			print(expr)
+
+	def __str__(self):
+		"""str(fn)
+
+		Generates a Printable version of the Expression
+
+		Returns
+		-------
+		str
+			Latex String respresation of the Expression, suitable for rendering the equation
+		"""
+		expr = self.__expr[::-1]
+		if len(expr) == 0:
+			return ""
+		args = [];
+		while len(expr) > 0:
+			t = expr.pop()
+			r = t.toStr(args,self)
+			args.append(r)
+		if len(args) > 1:
+			return args
+		else:
+			return args[0]
+
+	def __repr__(self):
+		"""repr(fn)
+
+		Generates a String that correctrly respresents the equation
+
+		Returns
+		-------
+		str
+			Convert the Expression to a String that passed to the constructor, will constuct
+			an identical equation object (in terms of sequence of tokens, and token type/value)
+		"""
+		expr = self.__expr[::-1]
+		if len(expr) == 0:
+			return ""
+		args = [];
+		while len(expr) > 0:
+			t = expr.pop()
+			r = t.toRepr(args,self)
+			args.append(r)
+		if len(args) > 1:
+			return args
+		else:
+			return args[0]
+
+	def __iter__(self):
+		return iter(self.__argsused)
+
+	def __lt__(self, other):
+		if isinstance(other, Expression):
+			return repr(self) < repr(other)
+		else:
+			raise TypeError("{0:s} is not an {1:s} Object, and can't be compared to an Expression Object".format(repr(other), type(other)))
+
+	def __eq__(self, other):
+		if isinstance(other, Expression):
+			return repr(self) == repr(other)
+		else:
+			raise TypeError("{0:s} is not an {1:s} Object, and can't be compared to an Expression Object".format(repr(other), type(other)))
+
+	def __combine(self,other,op):
+		if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)):
+			return NotImplemented
+		else:
+			obj = type(self)(self)
+			if isinstance(other,(int,float,complex)):
+				obj.__expr.append(ExpressionValue(other))
+			else:
+				if isinstance(other,basestring):
+					try:
+						other = type(self)(other)
+					except:
+						raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other))
+				obj.__expr += other.__expr
+				obj.__argsused |= other.__argsused
+				for v in other.__args:
+					if v not in obj.__args:
+						obj.__args.append(v)
+				for k,v in other.__vars.items():
+					if k not in obj.__vars:
+						obj.__vars[k] = v
+					elif v != obj.__vars[k]:
+						raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k))
+			fn = self.core.ops[op]
+			obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False))
+		return obj
+
+	def __rcombine(self,other,op):
+		if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)):
+			return NotImplemented
+		else:
+			obj = type(self)(self)
+			if isinstance(other,(int,float,complex)):
+				obj.__expr.insert(0,ExpressionValue(other))
+			else:
+				if isinstance(other,basestring):
+					try:
+						other = type(self)(other)
+					except:
+						raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other))
+				obj.__expr = other.__expr + self.__expr
+				obj.__argsused = other.__argsused | self.__expr
+				__args = other.__args
+				for v in obj.__args:
+					if v not in __args:
+						__args.append(v)
+				obj.__args = __args
+				for k,v in other.__vars.items():
+					if k not in obj.__vars:
+						obj.__vars[k] = v
+					elif v != obj.__vars[k]:
+						raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k))
+			fn = self.core.ops[op]
+			obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False))
+		return obj
+
+	def __icombine(self,other,op):
+		if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)):
+			return NotImplemented
+		else:
+			obj = self
+			if isinstance(other,(int,float,complex)):
+				obj.__expr.append(ExpressionValue(other))
+			else:
+				if isinstance(other,basestring):
+					try:
+						other = type(self)(other)
+					except:
+						raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other))
+				obj.__expr += other.__expr
+				obj.__argsused |= other.__argsused
+				for v in other.__args:
+					if v not in obj.__args:
+						obj.__args.append(v)
+				for k,v in other.__vars.items():
+					if k not in obj.__vars:
+						obj.__vars[k] = v
+					elif v != obj.__vars[k]:
+						raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k))
+			fn = self.core.ops[op]
+			obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False))
+		return obj
+
+	def __apply(self,op):
+		fn = self.core.unary_ops[op]
+		obj = type(self)(self)
+		obj.__expr.append(ExpressionFunction(fn['func'],1,fn['str'],fn['latex'],op,False))
+		return obj
+
+	def __applycall(self,op):
+		fn = self.core.functions[op]
+		if 1 not in fn['args'] or '*' not in fn['args']:
+			raise RuntimeError("Can't Apply {0:s} function, dosen't accept only 1 argument".format(op))
+		obj = type(self)(self)
+		obj.__expr.append(ExpressionFunction(fn['func'],1,fn['str'],fn['latex'],op,False))
+		return obj
+
+	def __add__(self,other):
+		return self.__combine(other,'+')
+
+	def __sub__(self,other):
+		return self.__combine(other,'-')
+
+	def __mul__(self,other):
+		return self.__combine(other,'*')
+
+	def __div__(self,other):
+		return self.__combine(other,'/')
+
+	def __truediv__(self,other):
+		return self.__combine(other,'/')
+
+	def __pow__(self,other):
+		return self.__combine(other,'^')
+
+	def __mod__(self,other):
+		return self.__combine(other,'%')
+
+	def __and__(self,other):
+		return self.__combine(other,'&')
+
+	def __or__(self,other):
+		return self.__combine(other,'|')
+
+	def __xor__(self,other):
+		return self.__combine(other,'</>')
+
+	def __radd__(self,other):
+		return self.__rcombine(other,'+')
+
+	def __rsub__(self,other):
+		return self.__rcombine(other,'-')
+
+	def __rmul__(self,other):
+		return self.__rcombine(other,'*')
+
+	def __rdiv__(self,other):
+		return self.__rcombine(other,'/')
+
+	def __rtruediv__(self,other):
+		return self.__rcombine(other,'/')
+
+	def __rpow__(self,other):
+		return self.__rcombine(other,'^')
+
+	def __rmod__(self,other):
+		return self.__rcombine(other,'%')
+
+	def __rand__(self,other):
+		return self.__rcombine(other,'&')
+
+	def __ror__(self,other):
+		return self.__rcombine(other,'|')
+
+	def __rxor__(self,other):
+		return self.__rcombine(other,'</>')
+
+	def __iadd__(self,other):
+		return self.__icombine(other,'+')
+
+	def __isub__(self,other):
+		return self.__icombine(other,'-')
+
+	def __imul__(self,other):
+		return self.__icombine(other,'*')
+
+	def __idiv__(self,other):
+		return self.__icombine(other,'/')
+
+	def __itruediv__(self,other):
+		return self.__icombine(other,'/')
+
+	def __ipow__(self,other):
+		return self.__icombine(other,'^')
+
+	def __imod__(self,other):
+		return self.__icombine(other,'%')
+
+	def __iand__(self,other):
+		return self.__icombine(other,'&')
+
+	def __ior__(self,other):
+		return self.__icombine(other,'|')
+
+	def __ixor__(self,other):
+		return self.__icombine(other,'</>')
+
+	def __neg__(self):
+		return self.__apply('-')
+
+	def __invert__(self):
+		return self.__apply('!')
+
+	def __abs__(self):
+		return self.__applycall('abs')
+
+	def __getfunction(self,op):
+		if op[1] == 'FUNC':
+			fn = self.core.functions[op[0]]
+			fn['type'] = 'FUNC'
+		elif op[1] == 'UNARY':
+			fn = self.core.unary_ops[op[0]]
+			fn['type'] = 'UNARY'
+			fn['args'] = 1
+		elif op[1] == 'OP':
+			fn = self.core.ops[op[0]]
+			fn['type'] = 'OP'
+		return fn
+
+	def __compile(self):
+		self.__expr = []
+		stack = []
+		argc = []
+		__expect_op = False
+		v = self.__next(__expect_op)
+		while v != None:
+			if not __expect_op and v[1] == "OPEN":
+				stack.append(v)
+				__expect_op = False
+			elif __expect_op and v[1] == "CLOSE":
+				op = stack.pop()
+				while op[1] != "OPEN":
+					fs = self.__getfunction(op)
+					self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False))
+					op = stack.pop()
+				if len(stack) > 0 and stack[-1][0] in self.core.functions:
+					op = stack.pop()
+					fs = self.core.functions[op[0]]
+					args = argc.pop()
+					if fs['args'] != '+' and (args != fs['args'] and args not in fs['args']):
+						raise SyntaxError("Invalid number of arguments for {0:s} function".format(op[0]))
+					self.__expr.append(ExpressionFunction(fs['func'],args,fs['str'],fs['latex'],op[0],True))
+				__expect_op = True
+			elif __expect_op and v[0] == ",":
+				argc[-1] += 1
+				op = stack.pop()
+				while op[1] != "OPEN":
+					fs = self.__getfunction(op)
+					self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False))
+					op = stack.pop()
+				stack.append(op)
+				__expect_op = False
+			elif __expect_op and v[0] in self.core.ops:
+				fn = self.core.ops[v[0]]
+				if len(stack) == 0:
+					stack.append(v)
+					__expect_op = False
+					v = self.__next(__expect_op)
+					continue
+				op = stack.pop()
+				if op[0] == "(":
+					stack.append(op)
+					stack.append(v)
+					__expect_op = False
+					v = self.__next(__expect_op)
+					continue
+				fs = self.__getfunction(op)
+				while True:
+					if (fn['prec'] >= fs['prec']):
+						self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False))
+						if len(stack) == 0:
+							stack.append(v)
+							break
+						op = stack.pop()
+						if op[0] == "(":
+							stack.append(op)
+							stack.append(v)
+							break
+						fs = self.__getfunction(op)
+					else:
+						stack.append(op)
+						stack.append(v)
+						break
+				__expect_op = False
+			elif not __expect_op and v[0] in self.core.unary_ops:
+				fn = self.core.unary_ops[v[0]]
+				stack.append(v)
+				__expect_op = False
+			elif not __expect_op and v[0] in self.core.functions:
+				stack.append(v)
+				argc.append(1)
+				__expect_op = False
+			elif not __expect_op and v[1] == 'NAME':
+				self.__argsused.add(v[0])
+				if v[0] not in self.__args:
+					self.__args.append(v[0])
+				self.__expr.append(ExpressionVariable(v[0]))
+				__expect_op = True
+			elif not __expect_op and v[1] == 'VALUE':
+				self.__expr.append(ExpressionValue(v[0]))
+				__expect_op = True
+			else:
+				raise SyntaxError("Invalid Token \"{0:s}\" in Expression, Expected {1:s}".format(v,"Op" if __expect_op else "Value"))
+			v = self.__next(__expect_op)
+		if len(stack) > 0:
+			op = stack.pop()
+			while op != "(":
+				fs = self.__getfunction(op)
+				self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False))
+				if len(stack) > 0:
+					op = stack.pop()
+				else:
+					break

analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py

@@ -0,0 +1,237 @@
+import math
+import sys
+import re
+
+if sys.version_info >= (3,):
+	xrange = range
+	basestring = str
+
+class ExpressionObject(object):
+	def __init__(self,*args,**kwargs):
+		super(ExpressionObject,self).__init__(*args,**kwargs)
+
+	def toStr(self,args,expression):
+		return ""
+
+	def toRepr(self,args,expression):
+		return ""
+
+	def __call__(self,args,expression):
+		pass
+
+class ExpressionValue(ExpressionObject):
+	def __init__(self,value,*args,**kwargs):
+		super(ExpressionValue,self).__init__(*args,**kwargs)
+		self.value = value
+
+	def toStr(self,args,expression):
+		if (isinstance(self.value,complex)):
+			V = [self.value.real,self.value.imag]
+			E = [0,0]
+			B = [0,0]
+			out = ["",""]
+			for i in xrange(2):
+				if V[i] == 0:
+					E[i] = 0
+					B[i] = 0
+				else:
+					E[i] = int(math.floor(math.log10(abs(V[i]))))
+					B[i] = V[i]*10**-E[i]
+					if E[i] in [0,1,2,3] and str(V[i])[-2:] == ".0":
+						B[i] = int(V[i])
+						E[i] = 0
+					if E[i] in [-1,-2] and len(str(V[i])) <= 7:
+						B[i] = V[i]
+						E[i] = 0
+				if i == 1:
+					fmt = "{{0:+{0:s}}}"
+				else:
+					fmt = "{{0:-{0:s}}}"
+				if type(B[i]) == int:
+					out[i] += fmt.format('d').format(B[i])
+				else:
+					out[i] += fmt.format('.5f').format(B[i]).rstrip("0.")
+				if i == 1:
+					out[i] += "\\imath"
+				if E[i] != 0:
+					out[i] += "\\times10^{{{0:d}}}".format(E[i])
+			return "\\left(" + ''.join(out) + "\\right)"
+		elif (isinstance(self.value,float)):
+			V = self.value
+			E = 0
+			B = 0
+			out = ""
+			if V == 0:
+				E = 0
+				B = 0
+			else:
+				E = int(math.floor(math.log10(abs(V))))
+				B = V*10**-E
+				if E in [0,1,2,3] and str(V)[-2:] == ".0":
+					B = int(V)
+					E = 0
+				if E in [-1,-2] and len(str(V)) <= 7:
+					B = V
+					E = 0
+			if type(B) == int:
+				out += "{0:-d}".format(B)
+			else:
+				out += "{0:-.5f}".format(B).rstrip("0.")
+			if E != 0:
+				out += "\\times10^{{{0:d}}}".format(E)
+				return "\\left(" + out + "\\right)"
+			else:
+				return out
+		else:
+			return str(self.value)
+
+	def toRepr(self,args,expression):
+		return str(self.value)
+
+	def __call__(self,args,expression):
+		return self.value
+
+	def __repr__(self):
+			return "<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.value),id(self))
+
+class ExpressionFunction(ExpressionObject):
+	def __init__(self,function,nargs,form,display,id,isfunc,*args,**kwargs):
+		super(ExpressionFunction,self).__init__(*args,**kwargs)
+		self.function = function
+		self.nargs = nargs
+		self.form = form
+		self.display = display
+		self.id = id
+		self.isfunc = isfunc
+
+	def toStr(self,args,expression):
+		params = []
+		for i in xrange(self.nargs):
+			params.append(args.pop())
+		if self.isfunc:
+			return str(self.display.format(','.join(params[::-1])))
+		else:
+			return str(self.display.format(*params[::-1]))
+
+	def toRepr(self,args,expression):
+		params = []
+		for i in xrange(self.nargs):
+			params.append(args.pop())
+		if self.isfunc:
+			return str(self.form.format(','.join(params[::-1])))
+		else:
+			return str(self.form.format(*params[::-1]))
+
+	def __call__(self,args,expression):
+		params = []
+		for i in xrange(self.nargs):
+			params.append(args.pop())
+		return self.function(*params[::-1])
+
+	def __repr__(self):
+		return "<{0:s}.{1:s}({2:s},{3:d}) object at {4:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.id),self.nargs,id(self))
+
+class ExpressionVariable(ExpressionObject):
+	def __init__(self,name,*args,**kwargs):
+		super(ExpressionVariable,self).__init__(*args,**kwargs)
+		self.name = name
+
+	def toStr(self,args,expression):
+		return str(self.name)
+
+	def toRepr(self,args,expression):
+		return str(self.name)
+
+	def __call__(self,args,expression):
+		if self.name in expression.variables:
+			return expression.variables[self.name]
+		else:
+			return 0 # Default variables to return 0
+
+	def __repr__(self):
+		return "<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.name),id(self))
+
+class Core():
+
+	constants = {}
+	unary_ops = {}
+	ops = {}
+	functions = {}
+	smatch = re.compile(r"\s*,")
+	vmatch = re.compile(r"\s*"
+						"(?:"
+							"(?P<oct>"
+								"(?P<octsign>[+-]?)"
+								r"\s*0o"
+								"(?P<octvalue>[0-7]+)"
+							")|(?P<hex>"
+								"(?P<hexsign>[+-]?)"
+								r"\s*0x"
+								"(?P<hexvalue>[0-9a-fA-F]+)"
+							")|(?P<bin>"
+								"(?P<binsign>[+-]?)"
+								r"\s*0b"
+								"(?P<binvalue>[01]+)"
+							")|(?P<dec>"
+								"(?P<rsign>[+-]?)"
+								r"\s*"
+								r"(?P<rvalue>(?:\d+\.\d+|\d+\.|\.\d+|\d+))"
+								"(?:"
+									"[Ee]"
+									r"(?P<rexpoent>[+-]?\d+)"
+								")?"
+								"(?:"
+									r"\s*"
+									r"(?P<sep>(?(rvalue)\+|))?"
+									r"\s*"
+									"(?P<isign>(?(rvalue)(?(sep)[+-]?|[+-])|[+-]?)?)"
+									r"\s*"
+									r"(?P<ivalue>(?:\d+\.\d+|\d+\.|\.\d+|\d+))"
+									"(?:"
+										"[Ee]"
+										r"(?P<iexpoent>[+-]?\d+)"
+									")?"
+									"[ij]"
+								")?"
+							")"
+						")")
+	nmatch = re.compile(r"\s*([a-zA-Z_][a-zA-Z0-9_]*)")
+	gsmatch = re.compile(r'\s*(\()')
+	gematch = re.compile(r'\s*(\))')
+
+	def recalculateFMatch(self):
+		
+		fks = sorted(self.functions.keys(), key=len, reverse=True)
+		oks = sorted(self.ops.keys(), key=len, reverse=True)
+		uks = sorted(self.unary_ops.keys(), key=len, reverse=True)
+		self.fmatch = re.compile(r'\s*(' + '|'.join(map(re.escape,fks)) + ')')
+		self.omatch = re.compile(r'\s*(' + '|'.join(map(re.escape,oks)) + ')')
+		self.umatch = re.compile(r'\s*(' + '|'.join(map(re.escape,uks)) + ')')
+
+	def addFn(self,id,str,latex,args,func):
+		self.functions[id] = {
+			'str': str,
+			'latex': latex,
+			'args': args,
+			'func': func}
+
+	def addOp(self,id,str,latex,single,prec,func):
+		if single:
+			raise RuntimeError("Single Ops Not Yet Supported")
+		self.ops[id] = {
+			'str': str,
+			'latex': latex,
+			'args': 2,
+			'prec': prec,
+			'func': func}
+
+	def addUnaryOp(self,id,str,latex,func):
+		self.unary_ops[id] = {
+			'str': str,
+			'latex': latex,
+			'args': 1,
+			'prec': 0,
+			'func': func}
+	
+	def addConst(self,name,value):
+		self.constants[name] = value

analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__init__.py

@@ -0,0 +1,2 @@
+from . import equation_base as equation_base
+from .ExpressionCore import ExpressionValue, ExpressionFunction, ExpressionVariable, Core

analysis-master/tra_analysis/equation/parser/Hybrid_Utils/equation_base.py

@@ -0,0 +1,106 @@
+try:
+	import numpy as np
+	has_numpy = True
+except ImportError:
+	import math
+	has_numpy = False
+try:
+	import scipy.constants
+	has_scipy = True
+except ImportError:
+	has_scipy = False
+import operator as op
+from .similar import sim, nsim, gsim, lsim
+
+def equation_extend(core):
+	def product(*args):
+		if len(args) == 1 and has_numpy:
+			return np.prod(args[0])
+		else:
+			return reduce(op.mul,args,1)
+
+	def sumargs(*args):
+		if len(args) == 1:
+			return sum(args[0])
+		else:
+			return sum(args)
+
+	core.addOp('+',"({0:s} + {1:s})","\\left({0:s} + {1:s}\\right)",False,3,op.add)
+	core.addOp('-',"({0:s} - {1:s})","\\left({0:s} - {1:s}\\right)",False,3,op.sub)
+	core.addOp('*',"({0:s} * {1:s})","\\left({0:s} \\times {1:s}\\right)",False,2,op.mul)
+	core.addOp('/',"({0:s} / {1:s})","\\frac{{{0:s}}}{{{1:s}}}",False,2,op.truediv)
+	core.addOp('%',"({0:s} % {1:s})","\\left({0:s} \\bmod {1:s}\\right)",False,2,op.mod)
+	core.addOp('^',"({0:s} ^ {1:s})","{0:s}^{{{1:s}}}",False,1,op.pow)
+	core.addOp('**',"({0:s} ^ {1:s})","{0:s}^{{{1:s}}}",False,1,op.pow)
+	core.addOp('&',"({0:s} & {1:s})","\\left({0:s} \\land {1:s}\\right)",False,4,op.and_)
+	core.addOp('|',"({0:s} | {1:s})","\\left({0:s} \\lor {1:s}\\right)",False,4,op.or_)
+	core.addOp('</>',"({0:s} </> {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor)
+	core.addOp('&|',"({0:s} </> {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor)
+	core.addOp('|&',"({0:s} </> {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor)
+	core.addOp('==',"({0:s} == {1:s})","\\left({0:s} = {1:s}\\right)",False,5,op.eq)
+	core.addOp('=',"({0:s} == {1:s})","\\left({0:s} = {1:s}\\right)",False,5,op.eq)
+	core.addOp('~',"({0:s} ~ {1:s})","\\left({0:s} \\approx {1:s}\\right)",False,5,sim)
+	core.addOp('!~',"({0:s} !~ {1:s})","\\left({0:s} \\not\\approx {1:s}\\right)",False,5,nsim)
+	core.addOp('!=',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne)
+	core.addOp('<>',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne)
+	core.addOp('><',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne)
+	core.addOp('<',"({0:s} < {1:s})","\\left({0:s} < {1:s}\\right)",False,5,op.lt)
+	core.addOp('>',"({0:s} > {1:s})","\\left({0:s} > {1:s}\\right)",False,5,op.gt)
+	core.addOp('<=',"({0:s} <= {1:s})","\\left({0:s} \\leq {1:s}\\right)",False,5,op.le)
+	core.addOp('>=',"({0:s} >= {1:s})","\\left({0:s} \\geq {1:s}\\right)",False,5,op.ge)
+	core.addOp('=<',"({0:s} <= {1:s})","\\left({0:s} \\leq {1:s}\\right)",False,5,op.le)
+	core.addOp('=>',"({0:s} >= {1:s})","\\left({0:s} \\geq {1:s}\\right)",False,5,op.ge)
+	core.addOp('<~',"({0:s} <~ {1:s})","\\left({0:s} \lessapprox {1:s}\\right)",False,5,lsim)
+	core.addOp('>~',"({0:s} >~ {1:s})","\\left({0:s} \\gtrapprox {1:s}\\right)",False,5,gsim)
+	core.addOp('~<',"({0:s} <~ {1:s})","\\left({0:s} \lessapprox {1:s}\\right)",False,5,lsim)
+	core.addOp('~>',"({0:s} >~ {1:s})","\\left({0:s} \\gtrapprox {1:s}\\right)",False,5,gsim)
+	core.addUnaryOp('!',"(!{0:s})","\\neg{0:s}",op.not_)
+	core.addUnaryOp('-',"-{0:s}","-{0:s}",op.neg)
+	core.addFn('abs',"abs({0:s})","\\left|{0:s}\\right|",1,op.abs)
+	core.addFn('sum',"sum({0:s})","\\sum\\left({0:s}\\right)",'+',sumargs)
+	core.addFn('prod',"prod({0:s})","\\prod\\left({0:s}\\right)",'+',product)
+	if has_numpy:
+		core.addFn('floor',"floor({0:s})","\\lfloor {0:s} \\rfloor",1,np.floor)
+		core.addFn('ceil',"ceil({0:s})","\\lceil {0:s} \\rceil",1,np.ceil)
+		core.addFn('round',"round({0:s})","\\lfloor {0:s} \\rceil",1,np.round)
+		core.addFn('sin',"sin({0:s})","\\sin\\left({0:s}\\right)",1,np.sin)
+		core.addFn('cos',"cos({0:s})","\\cos\\left({0:s}\\right)",1,np.cos)
+		core.addFn('tan',"tan({0:s})","\\tan\\left({0:s}\\right)",1,np.tan)
+		core.addFn('re',"re({0:s})","\\Re\\left({0:s}\\right)",1,np.real)
+		core.addFn('im',"re({0:s})","\\Im\\left({0:s}\\right)",1,np.imag)
+		core.addFn('sqrt',"sqrt({0:s})","\\sqrt{{{0:s}}}",1,np.sqrt)
+		core.addConst("pi",np.pi)
+		core.addConst("e",np.e)
+		core.addConst("Inf",np.Inf)
+		core.addConst("NaN",np.NaN)
+	else:
+		core.addFn('floor',"floor({0:s})","\\lfloor {0:s} \\rfloor",1,math.floor)
+		core.addFn('ceil',"ceil({0:s})","\\lceil {0:s} \\rceil",1,math.ceil)
+		core.addFn('round',"round({0:s})","\\lfloor {0:s} \\rceil",1,round)
+		core.addFn('sin',"sin({0:s})","\\sin\\left({0:s}\\right)",1,math.sin)
+		core.addFn('cos',"cos({0:s})","\\cos\\left({0:s}\\right)",1,math.cos)
+		core.addFn('tan',"tan({0:s})","\\tan\\left({0:s}\\right)",1,math.tan)
+		core.addFn('re',"re({0:s})","\\Re\\left({0:s}\\right)",1,complex.real)
+		core.addFn('im',"re({0:s})","\\Im\\left({0:s}\\right)",1,complex.imag)
+		core.addFn('sqrt',"sqrt({0:s})","\\sqrt{{{0:s}}}",1,math.sqrt)
+		core.addConst("pi",math.pi)
+		core.addConst("e",math.e)
+		core.addConst("Inf",float("Inf"))
+		core.addConst("NaN",float("NaN"))
+	if has_scipy:
+		core.addConst("h",scipy.constants.h)
+		core.addConst("hbar",scipy.constants.hbar)
+		core.addConst("m_e",scipy.constants.m_e)
+		core.addConst("m_p",scipy.constants.m_p)
+		core.addConst("m_n",scipy.constants.m_n)
+		core.addConst("c",scipy.constants.c)
+		core.addConst("N_A",scipy.constants.N_A)
+		core.addConst("mu_0",scipy.constants.mu_0)
+		core.addConst("eps_0",scipy.constants.epsilon_0)
+		core.addConst("k",scipy.constants.k)
+		core.addConst("G",scipy.constants.G)
+		core.addConst("g",scipy.constants.g)
+		core.addConst("q",scipy.constants.e)
+		core.addConst("R",scipy.constants.R)
+		core.addConst("sigma",scipy.constants.e)
+		core.addConst("Rb",scipy.constants.Rydberg)

analysis-master/tra_analysis/equation/parser/Hybrid_Utils/similar.py

@@ -0,0 +1,49 @@
+_tol = 1e-5
+
+def sim(a,b):
+	if (a==b):
+		return True
+	elif a == 0 or b == 0:
+		return False
+	if (a<b):
+		return (1-a/b)<=_tol
+	else:
+		return (1-b/a)<=_tol
+
+def nsim(a,b):
+	if (a==b):
+		return False
+	elif a == 0 or b == 0:
+		return True
+	if (a<b):
+		return (1-a/b)>_tol
+	else:
+		return (1-b/a)>_tol
+	
+def gsim(a,b):
+	if a >= b:
+		return True
+	return (1-a/b)<=_tol
+
+def lsim(a,b):
+	if a <= b:
+		return True
+	return (1-b/a)<=_tol
+	
+def set_tol(value=1e-5):
+	r"""Set Error Tolerance
+	
+	Set the tolerance for detriming if two numbers are simliar, i.e
+	:math:`\left|\frac{a}{b}\right| = 1 \pm tolerance`
+	
+	Parameters
+	----------
+	value: float
+		The Value to set the tolerance to show be very small as it respresents the
+		percentage of acceptable error in detriming if two values are the same.
+	"""
+	global _tol
+	if isinstance(value,float):
+		_tol = value
+	else:
+		raise TypeError(type(value))

analysis-master/tra_analysis/equation/parser/RegexInplaceParser.py

@@ -0,0 +1,51 @@
+import re
+from decimal import Decimal
+from functools import reduce
+
+class RegexInplaceParser(object):
+
+	def __init__(self, string):
+
+		self.string = string
+
+	def add(self, string):
+		while(len(re.findall("[+]{1}[-]?", string)) != 0):
+			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)
+		return string
+
+	def sub(self, string):
+		while(len(re.findall("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string)) != 0):
+			g = re.search("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string).group()
+			if(re.search("[-]{1,2}", g).group() == "-"):
+				r = re.sub("[-]{1}", "+-", g, 1)
+				string = re.sub(g, r, string, 1)
+			elif(re.search("[-]{1,2}", g).group() == "--"):
+				r = re.sub("[-]{2}", "+", g, 1)
+				string = re.sub(g, r, string, 1)
+			else:
+				pass
+		return string
+
+	def mul(self, string):
+		while(len(re.findall("[*]{1}[-]?", string)) != 0):
+			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)
+		return string
+
+	def div(self, string):
+		while(len(re.findall("[/]{1}[-]?", string)) != 0):
+			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)
+		return string
+
+	def exp(self, string):
+		while(len(re.findall("[\^]{1}[-]?", string)) != 0):
+			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)
+		return string
+
+	def evaluate(self):
+		string = self.string
+		string = self.exp(string)
+		string = self.div(string)
+		string = self.mul(string)
+		string = self.sub(string)
+		string = self.add(string)
+		return string

analysis-master/tra_analysis/equation/parser/__init__.py

@@ -0,0 +1,34 @@
+# Titan Robotics Team 2022: Expression submodule
+# Written by Arthur Lu
+# Notes:
+#    this should be imported as a python module using 'from tra_analysis.Equation import parser'
+# setup:
+
+__version__ = "0.0.4-alpha"
+
+__changelog__ = """changelog:
+	0.0.4-alpha:
+		- moved individual parsers to their own files
+	0.0.3-alpha:
+		- readded old regex based parser as RegexInplaceParser
+	0.0.2-alpha:
+		- wrote BNF using pyparsing and uses a BNF metasyntax
+		- renamed this submodule parser
+	0.0.1-alpha:
+		- took items from equation.ipynb and ported here
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+__all__ = {
+	"BNF",
+	"RegexInplaceParser",
+	"HybridExpressionParser"
+}
+
+from .BNF import BNF as BNF
+from .RegexInplaceParser import RegexInplaceParser as RegexInplaceParser
+from .Hybrid import HybridExpressionParser
+from .Hybrid_Utils import equation_base, Core

analysis-master/tra_analysis/equation/parser/py2.py

@@ -0,0 +1,21 @@
+# Titan Robotics Team 2022: py2 module
+# Written by Arthur Lu
+# Notes:
+#    this module should only be used internally, contains old python 2.X functions that have been removed.
+# setup:
+
+from __future__ import division
+
+__version__ = "1.0.0"
+
+__changelog__ = """changelog:
+	1.0.0:
+	- added cmp function
+"""
+
+__author__ = (
+	"Arthur Lu <learthurgo@gmail.com>",
+)
+
+def cmp(a, b):
+    return (a > b) - (a < b) 

data-analysis/design.kv

@@ -0,0 +1,46 @@
+<HomeScreen>:
+    GridLayout:
+        cols: 1
+        GridLayout:
+            cols: 1
+            padding: 15, 15
+            spacing: 20, 20
+            Label:
+                text: "User Login"
+                font_size: "20sp"
+            TextInput:
+                id: username
+                hint_text: "Username"
+            TextInput:
+                id: password
+                password: True
+                hint_text: "Password"
+            RelativeLayout:
+                Button:
+                    text: "Login"
+                    on_press: root.login(root.ids.username.text, root.ids.password.text)
+                    size_hint: 0.3, 0.5
+                    pos_hint: {"center_x": 0.5, "center_y": 0.6}
+            Label:
+                id: login_wrong
+                text: ""
+        GridLayout:
+            cols: 2
+            size_hint: 0.2, 0.2
+            padding: 10, 10
+            spacing: 10, 0
+            Button:
+                text: "Forgot Password?"
+                background_color: 1, 1, 1, 0
+                opacity: 1 if self.state == "normal" else 0.5
+                color: 0.1, 0.7, 1, 1
+            Button:
+                text: "Sign Up"
+                on_press: root.sign_up()
+                background_color: 1, 1, 1 , 0
+                opacity: 1 if self.state == "normal" else 0.5
+                color: 0.1, 0.7, 1, 1
+
+<RootWidget>:
+    HomeScreen:
+        name: "home_screen"

data-analysis/main.py

@@ -0,0 +1,41 @@
+from kivy.app import App
+from kivy.lang import Builder
+from kivy.uix.screenmanager import ScreenManager , Screen
+from kivy.animation import Animation
+from hoverable import HoverBehavior
+from kivy.uix.image import Image
+from kivy.uix.behaviors import ButtonBehavior
+import json
+from datetime import datetime
+import glob
+from pathlib import Path
+import random
+
+import superscript as ss
+
+Builder.load_file('design.kv')
+
+class HomeScreen(Screen):
+    # def sign_up(self):
+    #     self.manager.transition.direction = "left"
+    #     self.manager.current = "sign_up_screen"
+    
+    # def login(self, uname, pword):
+    #     with open ("users.json") as file:
+    #         users = json.load(file)
+    #     if uname in users and users[uname]["password"] == pword:
+    #         self.manager.transition.direction = "left"
+    #         self.manager.current = "login_screen_success"
+    #     else:
+    #         self.ids.login_wrong.text = "Incorrect Username or Password"
+
+
+class RootWidget(ScreenManager):
+    pass
+
+class MainApp(App):
+    def build(self):
+        return RootWidget()
+
+if __name__ == "__main__":
+    MainApp().run()

data-analysis/requirements.txt

@@ -2,3 +2,4 @@ requests
 pymongo
 pandas
 tra-analysis
+kivy==2.0.0rc2