Merge pull request #26 from titanscout2022/master

Merge master into master-staged

.devcontainer/devcontainer.json

@@ -23,5 +23,5 @@
 		"mhutchie.git-graph",
 		"donjayamanne.jupyter",
 	],
-	"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
+	"postCreateCommand": "pip install -r analysis-master/requirements.txt"
 }

.gitignore

@@ -21,11 +21,13 @@ data-analysis/test.ipynb
 data-analysis/visualize_pit.ipynb
 data-analysis/config/keys.config
 analysis-master/analysis/__pycache__/
+analysis-master/analysis/metrics/__pycache__/
 data-analysis/__pycache__/
 analysis-master/analysis.egg-info/
 analysis-master/build/
 analysis-master/metrics/
 data-analysis/config-pop.json
+data-analysis/__pycache__/
 analysis-master/__pycache__/
 analysis-master/.pytest_cache/
 data-analysis/.pytest_cache/

analysis-master/analysis/analysis.py

@@ -12,6 +12,7 @@ __version__ = "1.2.1.003"
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
 	1.2.1.003:
+		- changed output of basic_stats and histo_analysis to libraries
 		- fixed __all__
 	1.2.1.002:
 		- renamed ArrayTest class to Array
@@ -360,7 +361,7 @@ def basic_stats(data):
 	_min = npmin(data_t)
 	_max = npmax(data_t)
 
-	return _mean, _median, _stdev, _variance, _min, _max
+	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
 @jit(forceobj=True)
@@ -383,7 +384,7 @@ def z_normalize(array, *args):
 # expects 2d array of [x,y]
 def histo_analysis(hist_data):
 
-	if(len(hist_data[0]) > 2):
+	if len(hist_data[0]) > 2:
 
 		hist_data = np.array(hist_data)
 		derivative = np.array(len(hist_data) - 1, dtype = float)
@@ -391,7 +392,7 @@ def histo_analysis(hist_data):
 		derivative = t[1] / t[0]
 		np.sort(derivative)
 
-		return basic_stats(derivative)[0], basic_stats(derivative)[3]
+		return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]}
 
 	else:
 

analysis-master/test_analysis.py

@@ -1,4 +1,5 @@
 from analysis import analysis as an
+from analysis import metrics
 
 def test_():
 	test_data_linear = [1, 3, 6, 7, 9]
@@ -6,12 +7,12 @@ def test_():
 	y_data_ccd = [1, 5, 7, 8.5, 8.66]
 	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], 0).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
+	assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
 	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
-	assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
+	#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
 	assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
 	assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
-	assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(an.metrics.trueskill.Rating(mu=21.346, sigma=7.875), an.metrics.trueskill.Rating(mu=20.415, sigma=7.808), an.metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (an.metrics.trueskill.Rating(mu=28.654, sigma=7.875), an.metrics.trueskill.Rating(mu=28.654, sigma=7.875), an.metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
+	#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))]

data-analysis/config.json

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

data-analysis/config/competition.config

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

data-analysis/config/keys.config

@@ -1,2 +0,0 @@
-mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true
-UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5

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/data.py

@@ -8,7 +8,7 @@ def pull_new_tba_matches(apikey, competition, cutoff):
 	x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
 	out = []
 	for i in x.json():
-		if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
+		if i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm":
 			out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
 	return out
 
@@ -34,17 +34,6 @@ def get_team_metrics_data(apikey, competition, team_num):
 	mdata = db.team_metrics
 	return mdata.find_one({"competition" : competition, "team": team_num})
 
-def unkeyify_2l(layered_dict):
-	out = {}
-	for i in layered_dict.keys():
-		add = []
-		sortkey = []
-		for j in layered_dict[i].keys():
-			add.append([j,layered_dict[i][j]])
-		add.sort(key = lambda x: x[0])
-		out[i] = list(map(lambda x: x[1], add))
-	return out
-
 def get_match_data_formatted(apikey, competition):
 	client = pymongo.MongoClient(apikey)
 	db = client.data_scouting
@@ -58,6 +47,19 @@ def get_match_data_formatted(apikey, competition):
 			pass
 	return out
 
+def get_metrics_data_formatted(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_scouting
+	mdata = db.teamlist
+	x=mdata.find_one({"competition":competition})
+	out = {}
+	for i in x:
+		try:
+			out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
+		except:
+			pass
+	return out
+
 def get_pit_data_formatted(apikey, competition):
 	client = pymongo.MongoClient(apikey)
 	db = client.data_scouting
@@ -71,6 +73,20 @@ def get_pit_data_formatted(apikey, competition):
 			pass
 	return out
 
+def get_pit_variable_data(apikey, competition):
+	client = pymongo.MongoClient(apikey)
+	db = client.data_processing
+	mdata = db.team_pit
+	out = {}
+	return mdata.find()
+
+def get_pit_variable_formatted(apikey, competition):
+	temp = get_pit_variable_data(apikey, competition)
+	out = {}
+	for i in temp:
+		out[i["variable"]] = i["data"]
+	return out
+
 def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
 	client = pymongo.MongoClient(apikey)
 	db = client[dbname]
@@ -99,4 +115,15 @@ def set_analysis_flags(apikey, flag, data):
 	client = pymongo.MongoClient(apikey)
 	db = client.data_processing
 	mdata = db.flags
-	return mdata.replace_one({flag:{"$exists":True}}, data, True)
+	return mdata.replace_one({flag:{"$exists":True}}, data, True)
+
+def unkeyify_2l(layered_dict):
+	out = {}
+	for i in layered_dict.keys():
+		add = []
+		sortkey = []
+		for j in layered_dict[i].keys():
+			add.append([j,layered_dict[i][j]])
+		add.sort(key = lambda x: x[0])
+		out[i] = list(map(lambda x: x[1], add))
+	return out

data-analysis/get_team_rankings.py

@@ -1,59 +0,0 @@
-import data as d
-from analysis import analysis as an
-import pymongo
-import operator
-
-def load_config(file):
-	config_vector = {}
-	file = an.load_csv(file)
-	for line in file[1:]:
-		config_vector[line[0]] = line[1:]
-
-	return (file[0][0], config_vector)
-
-def get_metrics_processed_formatted(apikey, competition):
-	client = pymongo.MongoClient(apikey)
-	db = client.data_scouting
-	mdata = db.teamlist
-	x=mdata.find_one({"competition":competition})
-	out = {}
-	for i in x:
-		try:
-			out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
-		except:
-			pass
-	return out
-
-def main():
-
-	apikey = an.load_csv("keys.txt")[0][0]
-	tbakey = an.load_csv("keys.txt")[1][0]
-
-	competition, config = load_config("config.csv")
-
-	metrics = get_metrics_processed_formatted(apikey, competition)
-
-	elo = {}
-	gl2 = {}
-
-	for team in metrics:
-
-		elo[team] = metrics[team]["metrics"]["elo"]["score"]
-		gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
-
-	elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
-	gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
-
-	for team in elo:
-
-		print("teams sorted by elo:")
-		print("" + str(team) + " | " + str(elo[team]))
-
-	print("*"*25)
-
-	for team in gl2:
-
-		print("teams sorted by glicko2:")
-		print("" + str(team) + " | " + str(gl2[team]))
-
-main()

data-analysis/superscript.py

@@ -3,10 +3,27 @@
 # Notes:
 # setup:
 
-__version__ = "0.0.5.002"
+__version__ = "0.0.6.002"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+	0.0.6.002:
+		- integrated get_team_rankings.py as get_team_metrics() function
+		- integrated visualize_pit.py as graph_pit_histogram() function
+	0.0.6.001:
+		- bug fixes with analysis.Metric() calls
+		- modified metric functions to use config.json defined default values
+	0.0.6.000:
+		- removed main function
+		- changed load_config function
+		- added save_config function
+		- added load_match function
+		- renamed simpleloop to matchloop
+		- moved simplestats function inside matchloop
+		- renamed load_metrics to load_metric
+		- renamed metricsloop to metricloop
+		- split push to database functions amon push_match, push_metric, push_pit
+		- moved
 	0.0.5.002:
 		- made changes due to refactoring of analysis
 	0.0.5.001:
@@ -77,101 +94,92 @@ __author__ = (
 )
 
 __all__ = [
-	"main", 
 	"load_config",
-	"simpleloop",
+	"save_config",
-	"simplestats",
+	"get_previous_time",
-	"metricsloop"
+	"load_match",
+	"matchloop",
+	"load_metric",
+	"metricloop",
+	"load_pit",
+	"pitloop",
+	"push_match",
+	"push_metric",
+	"push_pit",
 ]
 
 # imports:
 
 from analysis import analysis as an
 import data as d
+import json
 import numpy as np
 from os import system, name
 from pathlib import Path
+import matplotlib.pyplot as plt
 import time
 import warnings
 
-def 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)
+	with open(file) as f:
-	for line in file:
+		config_vector = json.load(f)
-		config_vector[line[0]] = line[1:]
 
 	return config_vector
 
-def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
+def save_config(file, config_vector):
+
+	with open(file) as f:
+		json.dump(config_vector, f)
+
+def get_previous_time(apikey):
+
+	previous_time = d.get_analysis_flags(apikey, "latest_update")
+
+	if previous_time == None:
+
+		d.set_analysis_flags(apikey, "latest_update", 0)
+		previous_time = 0
+
+	else:
+
+		previous_time = previous_time["latest_update"]
+
+	return previous_time
+
+def load_match(apikey, competition):
+
+	return d.get_match_data_formatted(apikey, competition)
+
+def matchloop(apikey, competition, data, tests): # expects 3D array with [Team][Variable][Match]
+
+	def simplestats(data, test):
+
+		data = np.array(data)
+		data = data[np.isfinite(data)]
+		ranges = list(range(len(data)))
+
+		if test == "basic_stats":
+			return an.basic_stats(data)
+
+		if test == "historical_analysis":
+			return an.histo_analysis([ranges, data])
+
+		if test == "regression_linear":
+			return an.regression(ranges, data, ['lin'])
+
+		if test == "regression_logarithmic":
+			return an.regression(ranges, data, ['log'])
+
+		if test == "regression_exponential":
+			return an.regression(ranges, data, ['exp'])
+
+		if test == "regression_polynomial":
+			return an.regression(ranges, data, ['ply'])
+
+		if test == "regression_sigmoidal":
+			return an.regression(ranges, data, ['sig'])
 
 	return_vector = {}
 	for team in data:
@@ -179,7 +187,7 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
 		for variable in data[team]:
 			test_vector = {}
 			variable_data = data[team][variable]
-			if(variable in tests):
+			if variable in tests:
 				for test in tests[variable]:
 					test_vector[test] = simplestats(variable_data, test)
 			else:
@@ -187,49 +195,40 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
 			variable_vector[variable] = test_vector
 		return_vector[team] = variable_vector
 
-	return return_vector
+	push_match(apikey, competition, return_vector)
 
-def simplestats(data, test):
+def load_metric(apikey, competition, match, group_name, metrics):
 
-	data = np.array(data)
+	group = {}
-	data = data[np.isfinite(data)]
-	ranges = list(range(len(data)))
 
-	if(test == "basic_stats"):
+	for team in match[group_name]:
-		return an.basic_stats(data)
 
-	if(test == "historical_analysis"):
+		db_data = d.get_team_metrics_data(apikey, competition, team)
-		return an.histo_analysis([ranges, data])
 
-	if(test == "regression_linear"):
+		if d.get_team_metrics_data(apikey, competition, team) == None:
-		return an.regression(ranges, data, ['lin'])
 
-	if(test == "regression_logarithmic"):
+			elo = {"score": metrics["elo"]["score"]}
-		return an.regression(ranges, data, ['log'])
+			gl2 = {"score": metrics["gl2"]["score"], "rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
+			ts = {"mu": metrics["ts"]["mu"], "sigma": metrics["ts"]["sigma"]}
 
-	if(test == "regression_exponential"):
+			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
-		return an.regression(ranges, data, ['exp'])
 
-	if(test == "regression_polynomial"):
+		else:
-		return an.regression(ranges, data, ['ply'])
 
-	if(test == "regression_sigmoidal"):
+			metrics = db_data["metrics"]
-		return an.regression(ranges, data, ['sig'])
 
-def push_to_database(apikey, competition, results, pit):
+			elo = metrics["elo"]
+			gl2 = metrics["gl2"]
+			ts = metrics["ts"]
 
-	for team in results:
+			group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
 
-		d.push_team_tests_data(apikey, competition, team, results[team])
+	return group
 
-	for variable in pit:
+def metricloop(tbakey, apikey, competition, timestamp, metrics): # listener based metrics update
 
-		d.push_team_pit_data(apikey, competition, variable, pit[variable])
+	elo_N = metrics["elo"]["N"]
-
+	elo_K = metrics["elo"]["K"]
-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)
 
@@ -238,8 +237,8 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 
 	for match in matches:
 
-		red = load_metrics(apikey, competition, match, "red")
+		red = load_metric(apikey, competition, match, "red", metrics)
-		blu = load_metrics(apikey, competition, match, "blue")
+		blu = load_metric(apikey, competition, match, "blue", metrics)
  
 		elo_red_total = 0
 		elo_blu_total = 0
@@ -276,11 +275,11 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 		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"):
+		if match["winner"] == "red":
 
 			observations = {"red": 1, "blu": 0}
 
-		elif(match["winner"] == "blue"):
+		elif match["winner"] == "blue":
 
 			observations = {"red": 0, "blu": 1}
 
@@ -288,11 +287,11 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 
 			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"]
+		red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
-		blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
+		blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
 
-		new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
+		new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metric().glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
-		new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
+		new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metric().glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
 
 		red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
 		blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
@@ -317,62 +316,90 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 		temp_vector.update(red)
 		temp_vector.update(blu)
 
-		for team in temp_vector:
+		push_metric(apikey, competition, temp_vector)
 
-			d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
+def load_pit(apikey, competition):
 
-def load_metrics(apikey, competition, match, group_name):
+	return d.get_pit_data_formatted(apikey, competition)
 
-	group = {}
+def pitloop(apikey, competition, pit, tests):
-
-	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 variable in tests:
-				if(not variable in return_vector):
+				if not variable in return_vector:
 					return_vector[variable] = []
 				return_vector[variable].append(pit[team][variable])
 
-	return return_vector
+	push_pit(apikey, competition, return_vector)
 
-main()
+def push_match(apikey, competition, results):
 
-"""
+	for team in results:
-Metrics Defaults:
 
-elo starting score = 1500
+		d.push_team_tests_data(apikey, competition, team, results[team])
-elo N = 400
-elo K = 24
 
-gl2 starting score = 1500
+def push_metric(apikey, competition, metric):
-gl2 starting rd = 350
+
-gl2 starting vol = 0.06
+	for team in metric:
-"""
+
+			d.push_team_metrics_data(apikey, competition, team, metric[team])
+
+def push_pit(apikey, competition, pit):
+
+	for variable in pit:
+
+		d.push_team_pit_data(apikey, competition, variable, pit[variable])
+
+def get_team_metrics(apikey, tbakey, competition):
+
+	metrics = d.get_metrics_data_formatted(apikey, competition)
+
+	elo = {}
+	gl2 = {}
+
+	for team in metrics:
+
+		elo[team] = metrics[team]["metrics"]["elo"]["score"]
+		gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
+
+	elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
+	gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
+
+	elo_ranked = []
+
+	for team in elo:
+
+		elo_ranked.append({"team": str(team), "elo": str(elo[team])})
+
+	gl2_ranked = []
+
+	for team in gl2:
+
+		gl2_ranked.append({"team": str(team), "gl2": str(gl2[team])})
+
+	return {"elo-ranks": elo_ranked, "glicko2-ranks": gl2_ranked}
+
+def graph_pit_histogram(apikey, competition, figsize=(80,15)):
+
+	pit = d.get_pit_variable_formatted(apikey, competition)
+
+	fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=figsize)
+
+	i = 0
+
+	for variable in pit:
+
+		ax[i].hist(pit[variable])
+		ax[i].invert_xaxis()
+
+		ax[i].set_xlabel('')
+		ax[i].set_ylabel('Frequency')
+		ax[i].set_title(variable)
+
+		plt.yticks(np.arange(len(pit[variable])))
+
+		i+=1
+
+	plt.show()

data-analysis/tra.py

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

data-analysis/visualize_pit.py

@@ -1,59 +0,0 @@
-# To add a new cell, type '# %%'
-# To add a new markdown cell, type '# %% [markdown]'
-# %%
-import matplotlib.pyplot as plt
-import data as d
-import pymongo
-
-
-# %%
-def get_pit_variable_data(apikey, competition):
-	client = pymongo.MongoClient(apikey)
-	db = client.data_processing
-	mdata = db.team_pit
-	out = {}
-	return mdata.find()
-
-
-# %%
-def get_pit_variable_formatted(apikey, competition):
-	temp = get_pit_variable_data(apikey, competition)
-	out = {}
-	for i in temp:
-		out[i["variable"]] = i["data"]
-	return out
-
-
-# %%
-pit = get_pit_variable_formatted("mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true", "2020ilch")
-
-
-# %%
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-# %%
-fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=(80,15))
-
-i = 0
-
-for variable in pit:
-
-	ax[i].hist(pit[variable])
-	ax[i].invert_xaxis()
-
-	ax[i].set_xlabel('')
-	ax[i].set_ylabel('Frequency')
-	ax[i].set_title(variable)
-
-	plt.yticks(np.arange(len(pit[variable])))
-
-	i+=1
-
-plt.show()
-
-
-# %%
-
-