Merge branch 'master' into devksingh4-patch-1

.devcontainer/devcontainer.json

@@ -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 tra-analysis"
+	"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install tra-analysis"
 }

.gitignore

@@ -37,4 +37,5 @@ analysis-master/tra_analysis/__pycache__
 analysis-master/tra_analysis/.ipynb_checkpoints
 .pytest_cache
 analysis-master/tra_analysis/metrics/__pycache__
-analysis-master/dist
+analysis-master/dist
+data-analysis/config/

README.md

@@ -11,9 +11,9 @@ after installing python, or with a package manager on linux. Refer to the [pip i
 ### Standard Platforms
 For the latest version of tra-analysis, run `pip install tra-analysis` or `pip install tra_analysis`. The requirements for tra-analysis should be automatically installed.
 ### Exotic Platforms (Android)
-[Termux](https://termux.com/) is recommended for a linux environemnt on Android. Consult the [documentation](https://titanscouting.github.io/analysis/installation#exotic-platforms-android) for advice on installing the prerequisites. After installing the prerequisites, the package should be installed normally with `pip install tra-analysis` or `pip install tra_analysis`. 
+[Termux](https://termux.com/) is recommended for a linux environemnt on Android. Consult the [documentation](https://titanscouting.github.io/analysis/general/installation#exotic-platforms-android) for advice on installing the prerequisites. After installing the prerequisites, the package should be installed normally with `pip install tra-analysis` or `pip install tra_analysis`. 
 ## Use
-tra-analysis operates like any other python package. Consult the [documentation](https://titanscouting.github.io/analysis/) for more information.
+tra-analysis operates like any other python package. Consult the [documentation](https://titanscouting.github.io/analysis/tra_analysis/) for more information.
 # Supported Platforms
 Although any modern 64 bit platform should be supported, the following platforms have been tested to be working:
 * AMD64 (Tested on Zen, Zen+, and Zen 2)

analysis-master/setup.py

@@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
 
 setuptools.setup(
 	name="tra_analysis",
-	version="2.0.3",
+	version="2.1.0",
 	author="The Titan Scouting Team",
 	author_email="titanscout2022@gmail.com",
 	description="Analysis package developed by Titan Scouting for The Red Alliance",

analysis-master/tra_analysis/analysis.py

@@ -7,12 +7,23 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "2.2.1"
+__version__ = "2.3.1"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+	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
+		- 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:
@@ -424,7 +435,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 	X = np.array(inputs)
 	y = np.array(outputs)
 
-	regressions = []
+	regressions = {}
 
 	if 'lin' in args: # formula: ax + b
 
@@ -437,7 +448,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 			popt, pcov = scipy.optimize.curve_fit(lin, X, y)
 
 			coeffs = popt.flatten().tolist()
-			regressions.append(str(coeffs[0]) + "*x+" + str(coeffs[1]))
+			regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1]))
 
 		except Exception as e:
 
@@ -454,7 +465,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 			popt, pcov = scipy.optimize.curve_fit(log, X, y)
 
 			coeffs = popt.flatten().tolist()
-			regressions.append(str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+			regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e:
 			
@@ -471,7 +482,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 			popt, pcov = scipy.optimize.curve_fit(exp, X, y)
 
 			coeffs = popt.flatten().tolist()
-			regressions.append(str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+			regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e:
 
@@ -482,7 +493,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 		inputs = np.array([inputs])
 		outputs = np.array([outputs])
 
-		plys = []
+		plys = {}
 		limit = len(outputs[0])
 
 		for i in range(2, limit):
@@ -500,9 +511,9 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 			for param in params:
 				temp += "(" + str(param) + "*x^" + str(counter) + ")"
 				counter += 1
-			plys.append(temp)
+			plys["x^" + str(i)] = (temp)
 
-		regressions.append(plys)
+		regressions["ply"] = (plys)
 
 	if 'sig' in args: # formula: a tanh (b(x + c)) + d
 
@@ -515,7 +526,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 			popt, pcov = scipy.optimize.curve_fit(sig, X, y)
 
 			coeffs = popt.flatten().tolist()
-			regressions.append(str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
+			regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e:
 		   
@@ -642,7 +653,7 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
 class KNN:
 
-	def knn_classifier(self, data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
+	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()
@@ -651,7 +662,7 @@ class KNN:
 
 		return model, ClassificationMetric(predictions, labels_test)
 
-	def knn_regressor(self, data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+	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)
@@ -754,9 +765,9 @@ class SVM:
 
 		return RegressionMetric(predictions, test_outputs)
 
-class RandomForrest:
+class RandomForest:
 
-	def random_forest_classifier(self, data, labels, test_size, n_estimators="warn", 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):
+	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)
@@ -765,7 +776,7 @@ class RandomForrest:
 
 		return kernel, ClassificationMetric(predictions, labels_test)
 
-	def random_forest_regressor(self, data, outputs, test_size, n_estimators="warn", 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):
+	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)
@@ -779,7 +790,7 @@ 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]}
+		return {"f-value": results[0], "p-value": results[1]}
 
 	def pearson(self, x, y):
 
@@ -978,81 +989,112 @@ class StatisticalTest:
 		return {"z-score": results[0], "p-value": results[1]}
 		
 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, *args): # expects arrays that are size normalized
+	def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized
+		if len(*args) == 0:
+			return np.mean(self.array, axis = axis)
+		else:
+			return np.mean([*args], axis = axis)
 
-		return np.mean([*args], axis = 0)
+	def elementwise_median(self, *args, axis = 0):
 
-	def elementwise_median(self, *args):
+		if len(*args) == 0:
+			return np.median(self.array, axis = axis)
+		else:
+			return np.median([*args], axis = axis)
 
-		return np.median([*args], axis = 0)
+	def elementwise_stdev(self, *args, axis = 0):
 
-	def elementwise_stdev(self, *args):
+		if len(*args) == 0:
+			return np.std(self.array, axis = axis)
+		else:
+			return np.std([*args], axis = axis)
 
-		return np.std([*args], axis = 0)
+	def elementwise_variance(self, *args, axis = 0):
 
-	def elementwise_variance(self, *args):
+		if len(*args) == 0:
+			return np.var(self.array, axis = axis)
+		else:
+			return np.var([*args], axis = axis)
 
-		return np.var([*args], axis = 0)
+	def elementwise_npmin(self, *args, axis = 0):
 
-	def elementwise_npmin(self, *args):
+		if len(*args) == 0:
+			return np.amin(self.array, axis = axis)
+		else:
+			return np.amin([*args], axis = axis)
 
-		return np.amin([*args], axis = 0)
+	def elementwise_npmax(self, *args, axis = 0):
 
-	def elementwise_npmax(self, *args):
+		if len(*args) == 0:
+			return np.amax(self.array, axis = axis)
+		else:
+			return np.amax([*args], axis = axis)
 
-		return np.amax([*args], axis = 0)
+	def elementwise_stats(self, *args, axis = 0):
 
-	def elementwise_stats(self, *args):
+		_mean = self.elementwise_mean(*args, axis = axis)
-
+		_median = self.elementwise_median(*args, axis = axis)
-		_mean = self.elementwise_mean(*args)
+		_stdev = self.elementwise_stdev(*args, axis = axis)
-		_median = self.elementwise_median(*args)
+		_variance = self.elementwise_variance(*args, axis = axis)
-		_stdev = self.elementwise_stdev(*args)
+		_min = self.elementwise_npmin(*args, axis = axis)
-		_variance = self.elementwise_variance(*args)
+		_max = self.elementwise_npmax(*args, axis = axis)
-		_min = self.elementwise_npmin(*args)
-		_max = self.elementwise_npmax(*args)
 
 		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, *args):
+	def __add__(self, other):
 
-		temp = np.array([])
+		return self.array + other.array
 
-		for a in args:
+	def __sub__(self, other):
-			temp += a
+
+		return self.array - other.array
+
+	def __neg__(self):
 		
-		return temp
+		return -self.array
 
-	def mul(self, *args):
+	def __abs__(self):
 
-		temp = np.array([])
+		return abs(self.array)
 
-		for a in args:
+	def __invert__(self):
-			temp *= a
-		
-		return temp
 
-	def neg(self, array):
+		return 1/self.array
-		
-		return -array
 
-	def inv(self, array):
+	def __mul__(self, other):
 
-		return 1/array
+		return self.array.dot(other.array)
 
-	def dot(self, a, b):
+	def __rmul__(self, other):
 
-		return np.dot(a, b)
+		return self.array.dot(other.array)
 
-	def cross(self, a, b):
+	def cross(self, other):
 
-		return np.cross(a, b)
+		return np.cross(self.array, other.array)
 
 	def sort(self, array): # depreciated
 		warnings.warn("Array.sort has been depreciated in favor of Sort")

data-analysis/config.json

@@ -1,6 +1,6 @@
 {
 	"team": "",
-	"competition": "",
+	"competition": "2020ilch",
 	"key":{
 		"database":"",
 		"tba":""

data-analysis/config/competition.config

@@ -1 +0,0 @@
-2020ilch

data-analysis/config/stats.config

@@ -1,14 +0,0 @@
-balls-blocked,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-collected,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-lower-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
-wheel-mechanism
-low-balls
-high-balls
-wheel-success
-strategic-focus
-climb-mechanism
-attitude

data-analysis/superscript.py

@@ -3,10 +3,12 @@
 # Notes:
 # setup:
 
-__version__ = "0.6.2"
+__version__ = "0.7.0"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+	0.7.0:
+		- finished implementing main function
 	0.6.2:
 		- integrated get_team_rankings.py as get_team_metrics() function
 		- integrated visualize_pit.py as graph_pit_histogram() function
@@ -120,6 +122,65 @@ import matplotlib.pyplot as plt
 import time
 import warnings
 
+def main():
+
+	warnings.filterwarnings("ignore")
+
+	while (True):
+
+		current_time = time.time()
+		print("[OK] time: " + str(current_time))
+
+		config = load_config("config.json")
+		competition = config["competition"]
+		match_tests = config["statistics"]["match"]
+		pit_tests = config["statistics"]["pit"]
+		metrics_tests = config["statistics"]["metric"]
+		print("[OK] configs loaded")
+
+		apikey = config["key"]["database"]
+		tbakey = config["key"]["tba"]
+		print("[OK] loaded keys")
+
+		previous_time = get_previous_time(apikey)
+		print("[OK] analysis backtimed to: " + str(previous_time))
+
+		print("[OK] loading data")
+		start = time.time()
+		match_data = load_match(apikey, competition)
+		pit_data = load_pit(apikey, competition)
+		print("[OK] loaded data in " + str(time.time() - start) + " seconds")
+
+		print("[OK] running tests")
+		start = time.time()
+		matchloop(apikey, competition, match_data, match_tests)
+		print("[OK] finished tests in " + str(time.time() - start) + " seconds")
+
+		print("[OK] running metrics")
+		start = time.time()
+		metricloop(tbakey, apikey, competition, previous_time, metrics_tests)
+		print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
+
+		print("[OK] running pit analysis")
+		start = time.time()
+		pitloop(apikey, competition, pit_data, pit_tests)
+		print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
+		
+		set_current_time(apikey, current_time)
+		print("[OK] finished all tests, looping")
+
+		clear()
+
+def clear(): 
+	
+	# for windows 
+	if name == 'nt': 
+		_ = system('cls') 
+  
+	# for mac and linux(here, os.name is 'posix') 
+	else: 
+		_ = system('clear')
+
 def load_config(file):
 
 	config_vector = {}
@@ -148,6 +209,10 @@ def get_previous_time(apikey):
 
 	return previous_time
 
+def set_current_time(apikey, current_time):
+
+	d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
+
 def load_match(apikey, competition):
 
 	return d.get_match_data_formatted(apikey, competition)
@@ -402,4 +467,6 @@ def graph_pit_histogram(apikey, competition, figsize=(80,15)):
 
 		i+=1
 
-	plt.show()
+	plt.show()
+
+main()

data-analysis/superscript_old.py

@@ -1,378 +0,0 @@
-# Titan Robotics Team 2022: Superscript Script
-# Written by Arthur Lu & Jacob Levine
-# Notes:
-# setup:
-
-__version__ = "0.0.5.002"
-
-# changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-	0.0.5.002:
-		- made changes due to refactoring of analysis
-	0.0.5.001:
-		- text fixes
-		- removed matplotlib requirement
-	0.0.5.000:
-		- improved user interface
-	0.0.4.002:
-		- removed unessasary code
-	0.0.4.001:
-		- fixed bug where X range for regression was determined before sanitization
-		- better sanitized data
-	0.0.4.000:
-		- fixed spelling issue in __changelog__
-		- addressed nan bug in regression
-		- fixed errors on line 335 with metrics calling incorrect key "glicko2"
-		- fixed errors in metrics computing 
-	0.0.3.000:
-		- added analysis to pit data
-	0.0.2.001:
-		- minor stability patches
-		- implemented db syncing for timestamps
-		- fixed bugs
-	0.0.2.000:
-		- finalized testing and small fixes
-	0.0.1.004:
-		- finished metrics implement, trueskill is bugged
-	0.0.1.003:
-		- working
-	0.0.1.002:
-		- started implement of metrics
-	0.0.1.001:
-		- cleaned up imports
-	0.0.1.000:
-		- tested working, can push to database
-	0.0.0.009:
-		- tested working
-		- prints out stats for the time being, will push to database later
-	0.0.0.008:
-		- added data import
-		- removed tba import
-		- finished main method
-	0.0.0.007:
-		- added load_config
-		- optimized simpleloop for readibility
-		- added __all__ entries
-		- added simplestats engine
-		- pending testing
-	0.0.0.006:
-		- fixes
-	0.0.0.005:
-		- imported pickle
-		- created custom database object
-	0.0.0.004:
-		- fixed simpleloop to actually return a vector
-	0.0.0.003:
-		- added metricsloop which is unfinished
-	0.0.0.002:
-		- added simpleloop which is untested until data is provided
-	0.0.0.001:
-		- created script
-		- added analysis, numba, numpy imports
-"""
-
-__author__ = (
-	"Arthur Lu <learthurgo@gmail.com>",
-	"Jacob Levine <jlevine@imsa.edu>",
-)
-
-__all__ = [
-	"main", 
-	"load_config",
-	"simpleloop",
-	"simplestats",
-	"metricsloop"
-]
-
-# imports:
-
-from tra_analysis import analysis as an
-import data as d
-import numpy as np
-from os import system, name
-from pathlib import Path
-import time
-import warnings
-
-def main():
-	warnings.filterwarnings("ignore")
-	while(True):
-
-		current_time = time.time()
-		print("[OK] time: " + str(current_time))
-
-		start = time.time()
-		config = load_config(Path("config/stats.config"))
-		competition = an.load_csv(Path("config/competition.config"))[0][0]
-		print("[OK] configs loaded")
-
-		apikey = an.load_csv(Path("config/keys.config"))[0][0]
-		tbakey = an.load_csv(Path("config/keys.config"))[1][0]
-		print("[OK] loaded keys")
-
-		previous_time = d.get_analysis_flags(apikey, "latest_update")
-
-		if(previous_time == None):
-
-			d.set_analysis_flags(apikey, "latest_update", 0)
-			previous_time = 0
-
-		else:
-
-			previous_time = previous_time["latest_update"]
-
-		print("[OK] analysis backtimed to: " + str(previous_time))
-
-		print("[OK] loading data")
-		start = time.time()
-		data = d.get_match_data_formatted(apikey, competition)
-		pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
-		print("[OK] loaded data in " + str(time.time() - start) + " seconds")
-
-		print("[OK] running tests")
-		start = time.time()
-		results = simpleloop(data, config)
-		print("[OK] finished tests in " + str(time.time() - start) + " seconds")
-
-		print("[OK] running metrics")
-		start = time.time()
-		metricsloop(tbakey, apikey, competition, previous_time)
-		print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
-
-		print("[OK] running pit analysis")
-		start = time.time()
-		pit = pitloop(pit_data, config)
-		print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
-
-		d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
-		
-		print("[OK] pushing to database")
-		start = time.time()
-		push_to_database(apikey, competition, results, pit)
-		print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
-
-		clear()
-
-def clear(): 
-	
-	# for windows 
-	if name == 'nt': 
-		_ = system('cls') 
-  
-	# for mac and linux(here, os.name is 'posix') 
-	else: 
-		_ = system('clear')
-
-def load_config(file):
-	config_vector = {}
-	file = an.load_csv(file)
-	for line in file:
-		config_vector[line[0]] = line[1:]
-
-	return config_vector
-
-def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
-
-	return_vector = {}
-	for team in data:
-		variable_vector = {}
-		for variable in data[team]:
-			test_vector = {}
-			variable_data = data[team][variable]
-			if(variable in tests):
-				for test in tests[variable]:
-					test_vector[test] = simplestats(variable_data, test)
-			else:
-				pass      
-			variable_vector[variable] = test_vector
-		return_vector[team] = variable_vector
-
-	return return_vector
-
-def simplestats(data, test):
-
-	data = np.array(data)
-	data = data[np.isfinite(data)]
-	ranges = list(range(len(data)))
-
-	if(test == "basic_stats"):
-		return an.basic_stats(data)
-
-	if(test == "historical_analysis"):
-		return an.histo_analysis([ranges, data])
-
-	if(test == "regression_linear"):
-		return an.regression(ranges, data, ['lin'])
-
-	if(test == "regression_logarithmic"):
-		return an.regression(ranges, data, ['log'])
-
-	if(test == "regression_exponential"):
-		return an.regression(ranges, data, ['exp'])
-
-	if(test == "regression_polynomial"):
-		return an.regression(ranges, data, ['ply'])
-
-	if(test == "regression_sigmoidal"):
-		return an.regression(ranges, data, ['sig'])
-
-def push_to_database(apikey, competition, results, pit):
-
-	for team in results:
-
-		d.push_team_tests_data(apikey, competition, team, results[team])
-
-	for variable in pit:
-
-		d.push_team_pit_data(apikey, competition, variable, pit[variable])
-
-def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
-
-	elo_N = 400
-	elo_K = 24
-
-	matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
-
-	red = {}
-	blu = {}
-
-	for match in matches:
-
-		red = load_metrics(apikey, competition, match, "red")
-		blu = load_metrics(apikey, competition, match, "blue")
- 
-		elo_red_total = 0
-		elo_blu_total = 0
-
-		gl2_red_score_total = 0
-		gl2_blu_score_total = 0
-
-		gl2_red_rd_total = 0
-		gl2_blu_rd_total = 0
-
-		gl2_red_vol_total = 0
-		gl2_blu_vol_total = 0
-
-		for team in red:
-
-			elo_red_total += red[team]["elo"]["score"]
-
-			gl2_red_score_total += red[team]["gl2"]["score"]
-			gl2_red_rd_total += red[team]["gl2"]["rd"]
-			gl2_red_vol_total += red[team]["gl2"]["vol"]
-
-		for team in blu:
-
-			elo_blu_total += blu[team]["elo"]["score"]
-
-			gl2_blu_score_total += blu[team]["gl2"]["score"]
-			gl2_blu_rd_total += blu[team]["gl2"]["rd"]
-			gl2_blu_vol_total += blu[team]["gl2"]["vol"]
-
-		red_elo = {"score": elo_red_total / len(red)}
-		blu_elo = {"score": elo_blu_total / len(blu)}
-
-		red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
-		blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
-
-
-		if(match["winner"] == "red"):
-
-			observations = {"red": 1, "blu": 0}
-
-		elif(match["winner"] == "blue"):
-
-			observations = {"red": 0, "blu": 1}
-
-		else:
-
-			observations = {"red": 0.5, "blu": 0.5}
-
-		red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
-		blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
-
-		new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
-		new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
-
-		red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
-		blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
-
-		for team in red:
-
-			red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
-
-			red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
-			red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
-			red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
-
-		for team in blu:
-
-			blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
-
-			blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
-			blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
-			blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
-
-		temp_vector = {}
-		temp_vector.update(red)
-		temp_vector.update(blu)
-
-		for team in temp_vector:
-
-			d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
-
-def load_metrics(apikey, competition, match, group_name):
-
-	group = {}
-
-	for team in match[group_name]:
-
-		db_data = d.get_team_metrics_data(apikey, competition, team)
-
-		if d.get_team_metrics_data(apikey, competition, team) == None:
-
-			elo = {"score": 1500}
-			gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
-			ts = {"mu": 25, "sigma": 25/3}
-
-			#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
-
-			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
-
-		else:
-
-			metrics = db_data["metrics"]
-
-			elo = metrics["elo"]
-			gl2 = metrics["gl2"]
-			ts = metrics["ts"]
-
-			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
-
-	return group
-
-def pitloop(pit, tests):
-
-	return_vector = {}
-	for team in pit:
-		for variable in pit[team]:
-			if(variable in tests):
-				if(not variable in return_vector):
-					return_vector[variable] = []
-				return_vector[variable].append(pit[team][variable])
-
-	return return_vector
-
-main()
-
-"""
-Metrics Defaults:
-
-elo starting score = 1500
-elo N = 400
-elo K = 24
-
-gl2 starting score = 1500
-gl2 starting rd = 350
-gl2 starting vol = 0.06
-"""

data-analysis/tasks.py

@@ -1,188 +0,0 @@
-import json
-import superscript as su
-import threading
-
-__author__ = (
-	"Arthur Lu <learthurgo@gmail.com>",
-)
-
-class Tasker():
-
-	match_ = False
-	metric_ = False
-	pit_ = False
-
-	match_enable = True
-	metric_enable = True
-	pit_enable = True
-
-	config = {}
-
-	def __init__(self):
-
-		self.config = su.load_config("config.json")
-
-	def match(self):
-
-		self.match_ = True
-
-		apikey = self.config["key"]["database"]
-		competition = self.config["competition"]
-		tests = self.config["statistics"]["match"]
-
-		data = su.load_match(apikey, competition)
-		su.matchloop(apikey, competition, data, tests)
-
-		self.match_ = False
-
-		if self.match_enable == True and self.match_ == False:
-			
-			task = threading.Thread(name = "match", target = match)
-			task.start()
-
-	def metric():
-
-		self.metric_ = True
-
-		apikey = self.config["key"]["database"]
-		tbakey = self.config["key"]["tba"]
-		competition = self.config["competition"]
-		metric = self.config["statistics"]["metric"]
-
-		timestamp = su.get_previous_time(apikey)
-
-		su.metricloop(tbakey, apikey, competition, timestamp, metric)
-
-		self.metric_ = False
-
-		if self.metric_enable == True and self.metric_ == False:
-			
-			task = threading.Thread(name = "match", target = metric)
-			task.start()
-
-	def pit():
-
-		self.pit_ = True
-
-		apikey = self.config["key"]["database"]
-		competition = self.config["competition"]
-		tests = self.config["statistics"]["pit"]
-
-		data = su.load_pit(apikey, competition)
-		su.pitloop(apikey, competition, data, tests)
-
-		self.pit_ = False
-
-		if self.pit_enable == True and self.pit_ == False:
-			
-			task = threading.Thread(name = "pit", target = pit)
-			task.start()
-
-	def start_match():
-		task = threading.Thread(name = "match", target = match)
-		task.start()
-
-	def start_metric():
-		task = threading.Thread(name = "match", target = metric)
-		task.start()
-
-	def start_pit():
-		task = threading.Thread(name = "pit", target = pit)
-		task.start()
-
-	def stop_match():
-		self.match_enable = False
-
-	def stop_metric():
-		self.metric_enable = False
-
-	def stop_pit():
-		self.pit_enable = False
-
-	def get_match():
-		return self.match_
-
-	def get_metric():
-		return self.metric_
-
-	def get_pit():
-		return self.pit_
-
-	def get_match_enable():
-		return self.match_enable
-
-	def get_metric_enable():
-		return self.metric_enable
-
-	def get_pit_enable():
-		return self.pit_enable
-"""
-def main():
-
-	init()
-	start_match()
-	start_metric()
-	start_pit()
-
-	exit = False
-	while(not exit):
-
-		i = input("> ")
-		cmds = i.split(" ")
-		cmds = [x for x in cmds if x != ""]
-		l = len(cmds)
-
-		if(l == 0):
-			pass
-		else:
-			if(cmds[0] == "exit"):
-				if(l == 1):
-					exit = True
-				else:
-					print("exit command expected no arguments but encountered " + str(l - 1))
-			if(cmds[0] == "status"):
-				if(l == 1):
-					print("status command expected 1 argument but encountered none\ntype status help for usage")
-				elif(l > 2):
-					print("status command expected 1 argument but encountered " + str(l - 1))
-				elif(cmds[1] == "threads"):
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					print("running threads:")
-					for thread in threads:
-						print("    " + thread)
-				elif(cmds[1] == "flags"):
-					print("current flags:")
-					print("    match running: " + match_)
-					print("    metric running: " + metric_)
-					print("    pit running: " + pit_)
-					print("    match enable: " + match_enable)
-					print("    metric enable: " + metric_enable)
-					print("    pit enable: " + pit_enable)
-				elif(cmds[1] == "config"):
-					print("current config:")
-					print(json.dumps(config))
-				elif(cmds[1] == "all"):
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					print("running threads:")
-					for thread in threads:
-						print("    " + thread)
-					print("current flags:")
-					print("    match running: " + match_)
-					print("    metric running: " + metric_)
-					print("    pit running: " + pit_)
-					print("    match enable: " + match_enable)
-					print("    metric enable: " + metric_enable)
-					print("    pit enable: " + pit_enable)
-				elif(cmds[1] == "help"):
-					print("usage: status [arg]\nDisplays the status of the tra data analysis threads.\nArguments:\n    threads - prints the stuatus ofcurrently running threads\n    flags - prints the status of control and indicator flags\n    config - prints the current configuration information\n    all - prints all statuses\n    <name_of_thread> - prints the status of a specific thread")
-				else:
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					if(cmds[1] in threads):
-						print(cmds[1] + " is running")
-				
-if(__name__ == "__main__"):
-	main()
-"""

data-analysis/test.py

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

data-analysis/tra-cli.py

@@ -1,33 +0,0 @@
-import argparse
-from tasks import Tasker
-import test
-import threading
-from multiprocessing import Process, Queue
-
-t = Tasker()
-
-task_map = {"match":None, "metric":None, "pit":None, "test":None}
-status_map = {"match":None, "metric":None, "pit":None}
-status_map.update(task_map)
-
-parser = argparse.ArgumentParser(prog = "TRA")
-subparsers = parser.add_subparsers(title = "command", metavar = "C", help = "//commandhelp//")
-
-parser_start = subparsers.add_parser("start", help = "//starthelp//")
-parser_start.add_argument("targets", metavar = "T", nargs = "*", choices = task_map.keys())
-parser_start.set_defaults(which = "start")
-
-parser_stop = subparsers.add_parser("stop", help = "//stophelp//")
-parser_stop.add_argument("targets", metavar = "T", nargs = "*", choices = task_map.keys())
-parser_stop.set_defaults(which = "stop")
-
-parser_status = subparsers.add_parser("status", help = "//stophelp//")
-parser_status.add_argument("targets", metavar = "T", nargs = "*", choices = status_map.keys())
-parser_status.set_defaults(which = "status")
-
-args = parser.parse_args()
-
-if(args.which == "start" and "test" in args.targets):
-	a = test.testcls()
-	tmain = Process(name = "main", target = a.main)
-	tmain.start()

data-analysis/tra.py

@@ -1,166 +0,0 @@
-import json
-import superscript as su
-import threading
-
-__author__ = (
-	"Arthur Lu <learthurgo@gmail.com>",
-)
-
-match_ = False
-metric_ = False
-pit_ = False
-
-match_enable = True
-metric_enable = True
-pit_enable = True
-
-config = {}
-
-def __init__(self):
-
-	global match_
-	global metric_
-	global pit_
-
-	global match_enable
-	global metric_enable
-	global pit_enable
-
-	config = su.load_config("config.json")
-
-def match(self):
-
-	match_ = True
-
-	apikey = config["key"]["database"]
-	competition = config["competition"]
-	tests = config["statistics"]["match"]
-
-	data = su.load_match(apikey, competition)
-	su.matchloop(apikey, competition, data, tests)
-
-	match_ = False
-
-	if match_enable == True and match_ == False:
-		
-		task = threading.Thread(name = "match", target = match)
-		task.start()
-
-def metric():
-
-	metric_ = True
-
-	apikey = config["key"]["database"]
-	tbakey = config["key"]["tba"]
-	competition = config["competition"]
-	metric = config["statistics"]["metric"]
-
-	timestamp = su.get_previous_time(apikey)
-
-	su.metricloop(tbakey, apikey, competition, timestamp, metric)
-
-	metric_ = False
-
-	if metric_enable == True and metric_ == False:
-		
-		task = threading.Thread(name = "match", target = metric)
-		task.start()
-
-def pit():
-
-	pit_ = True
-
-	apikey = config["key"]["database"]
-	competition = config["competition"]
-	tests = config["statistics"]["pit"]
-
-	data = su.load_pit(apikey, competition)
-	su.pitloop(apikey, competition, data, tests)
-
-	pit_ = False
-
-	if pit_enable == True and pit_ == False:
-		
-		task = threading.Thread(name = "pit", target = pit)
-		task.start()
-
-def start_match():
-	task = threading.Thread(name = "match", target = match)
-	task.start()
-
-def start_metric():
-	task = threading.Thread(name = "match", target = metric)
-	task.start()
-
-def start_pit():
-	task = threading.Thread(name = "pit", target = pit)
-	task.start()
-
-def main():
-
-	init()
-	start_match()
-	start_metric()
-	start_pit()
-
-	exit = False
-	while(not exit):
-
-		i = input("> ")
-		cmds = i.split(" ")
-		cmds = [x for x in cmds if x != ""]
-		l = len(cmds)
-
-		if(l == 0):
-			pass
-		else:
-			if(cmds[0] == "exit"):
-				if(l == 1):
-					exit = True
-				else:
-					print("exit command expected no arguments but encountered " + str(l - 1))
-			if(cmds[0] == "status"):
-				if(l == 1):
-					print("status command expected 1 argument but encountered none\ntype status help for usage")
-				elif(l > 2):
-					print("status command expected 1 argument but encountered " + str(l - 1))
-				elif(cmds[1] == "threads"):
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					print("running threads:")
-					for thread in threads:
-						print("    " + thread)
-				elif(cmds[1] == "flags"):
-					print("current flags:")
-					print("    match running: " + match_)
-					print("    metric running: " + metric_)
-					print("    pit running: " + pit_)
-					print("    match enable: " + match_enable)
-					print("    metric enable: " + metric_enable)
-					print("    pit enable: " + pit_enable)
-				elif(cmds[1] == "config"):
-					print("current config:")
-					print(json.dumps(config))
-				elif(cmds[1] == "all"):
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					print("running threads:")
-					for thread in threads:
-						print("    " + thread)
-					print("current flags:")
-					print("    match running: " + match_)
-					print("    metric running: " + metric_)
-					print("    pit running: " + pit_)
-					print("    match enable: " + match_enable)
-					print("    metric enable: " + metric_enable)
-					print("    pit enable: " + pit_enable)
-				elif(cmds[1] == "help"):
-					print("usage: status [arg]\nDisplays the status of the tra data analysis threads.\nArguments:\n    threads - prints the stuatus ofcurrently running threads\n    flags - prints the status of control and indicator flags\n    config - prints the current configuration information\n    all - prints all statuses\n    <name_of_thread> - prints the status of a specific thread")
-				else:
-					threads = threading.enumerate()
-					threads = [x.getName() for x in threads]
-					if(cmds[1] in threads):
-						print(cmds[1] + " is running")
-				
-if(__name__ == "__main__"):
-	main()