diff --git a/data analysis/repack_json.py b/data analysis/dep/2019/repack_json.py
similarity index 100%
rename from data analysis/repack_json.py
rename to data analysis/dep/2019/repack_json.py
diff --git a/data analysis/scoutflex2019.py b/data analysis/dep/2019/scoutflex2019.py
similarity index 97%
rename from data analysis/scoutflex2019.py
rename to data analysis/dep/2019/scoutflex2019.py
index 2309e7b8..d30a554e 100644
--- a/data analysis/scoutflex2019.py	
+++ b/data analysis/dep/2019/scoutflex2019.py	
@@ -1,61 +1,61 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Mar 20 12:21:31 2019
-
-@author: creek
-"""
-import firebase_admin
-from firebase_admin import credentials
-from firebase_admin import firestore
-import pprint
-from pylatex import Document, Section, Subsection, Command
-from pylatex.utils import italic, NoEscape
-import requests
-
-def generate_team_report(team):
-    doc = Document('basic')
-    matches = team.reference.collection(u'matches').get()
-    matchnums = []
-    for match in matches:
-        matchnums.append(match.id)
-    with doc.create(Section('Qualification matches scouted')):
-        for matchnum in matchnums:
-            doc.append(str(matchnum))
-    with doc.create(Section('Details')):
-        hab = "Hab 1"
-        balls = 42
-        hatches = 0
-        count = 0
-        for match in matches:
-            for analysis in match:
-                if analysis.key().startswith('Quant'):
-                    balls = balls + analysis['cargoBalls']
-                    hatches = hatches + analysis['hatchPanels']
-                    count = count + 1
-                if analysis.key().startswith('Qual'):
-                    strategy = analysis['StrategyType']
-                    strongObject = analysis['TeleopStrongObject']
-        if count > 0:
-            doc.append("Average balls: " + str(float(balls)/count))
-            doc.append("Average hatches: " + str(float(hatches)/count))
-            doc.append("Strategy Type: " + str(strategy))
-            doc.append("Strongest object in teleop: " + str(strongObject))
-
-
-    doc.preamble.append(Command('title', team.id))
-    doc.preamble.append(Command('author', 'Generated by Team 2022'))
-    doc.preamble.append(Command('date', NoEscape(r'\today')))
-    doc.append(NoEscape(r'\maketitle'))
-
-    doc.generate_pdf(filepath= str(team.id), clean_tex=False)
-
-credential = credentials.Certificate('keys/firebasekey.json')
-
-firebase_admin.initialize_app(credential)
-
-db = firestore.Client()
-teams_ref = db.collection(u'data').document(u'team-2022').collection(u'Central 2019')
-teams = teams_ref.get()
-
-for team in teams:
-    generate_team_report(team)
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 20 12:21:31 2019
+
+@author: creek
+"""
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import firestore
+import pprint
+from pylatex import Document, Section, Subsection, Command
+from pylatex.utils import italic, NoEscape
+import requests
+
+def generate_team_report(team):
+    doc = Document('basic')
+    matches = team.reference.collection(u'matches').get()
+    matchnums = []
+    for match in matches:
+        matchnums.append(match.id)
+    with doc.create(Section('Qualification matches scouted')):
+        for matchnum in matchnums:
+            doc.append(str(matchnum))
+    with doc.create(Section('Details')):
+        hab = "Hab 1"
+        balls = 42
+        hatches = 0
+        count = 0
+        for match in matches:
+            for analysis in match:
+                if analysis.key().startswith('Quant'):
+                    balls = balls + analysis['cargoBalls']
+                    hatches = hatches + analysis['hatchPanels']
+                    count = count + 1
+                if analysis.key().startswith('Qual'):
+                    strategy = analysis['StrategyType']
+                    strongObject = analysis['TeleopStrongObject']
+        if count > 0:
+            doc.append("Average balls: " + str(float(balls)/count))
+            doc.append("Average hatches: " + str(float(hatches)/count))
+            doc.append("Strategy Type: " + str(strategy))
+            doc.append("Strongest object in teleop: " + str(strongObject))
+
+
+    doc.preamble.append(Command('title', team.id))
+    doc.preamble.append(Command('author', 'Generated by Team 2022'))
+    doc.preamble.append(Command('date', NoEscape(r'\today')))
+    doc.append(NoEscape(r'\maketitle'))
+
+    doc.generate_pdf(filepath= str(team.id), clean_tex=False)
+
+credential = credentials.Certificate('keys/firebasekey.json')
+
+firebase_admin.initialize_app(credential)
+
+db = firestore.Client()
+teams_ref = db.collection(u'data').document(u'team-2022').collection(u'Central 2019')
+teams = teams_ref.get()
+
+for team in teams:
+    generate_team_report(team)
diff --git a/data analysis/superscript.py b/data analysis/dep/2019/superscript.py
similarity index 100%
rename from data analysis/superscript.py
rename to data analysis/dep/2019/superscript.py
diff --git a/data analysis/superscript_nishant_only.py b/data analysis/dep/2019/superscript_nishant_only.py
similarity index 100%
rename from data analysis/superscript_nishant_only.py
rename to data analysis/dep/2019/superscript_nishant_only.py
diff --git a/data analysis/tbarequest.py b/data analysis/dep/2019/tbarequest.py
similarity index 100%
rename from data analysis/tbarequest.py
rename to data analysis/dep/2019/tbarequest.py
diff --git a/data analysis/titanlearn.py b/data analysis/dep/2019/titanlearn.py
similarity index 97%
rename from data analysis/titanlearn.py
rename to data analysis/dep/2019/titanlearn.py
index 59920869..a3730003 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/dep/2019/titanlearn.py	
@@ -1,206 +1,206 @@
-#Titan Robotics Team 2022: ML Module
-#Written by Arthur Lu & Jacob Levine
-#Notes:
-#   this should be imported as a python module using 'import titanlearn'
-#   this should be included in the local directory or environment variable
-#   this module has not been optimized for multhreaded computing
-#   this module learns from its mistakes far faster than 2022's captains
-#setup:
-
-__version__ = "1.0.0.001"
-
-#changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-1.0.0.xxx:
-    -added generation of ANNS, basic SGD training"""
-__author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>, "
-    "Jacob Levine <jlevine@ttic.edu>,"
-    )
-__all__ = [
-    'linear_nn',
-    'train_sgd_minibatch',
-    'train_sgd_simple'
-    ]
-#imports
-import torch
-import warnings
-from collections import OrderedDict
-from sklearn import metrics, datasets
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-import time
-
-#enable CUDA if possible
-device = torch.device("cpu")
-
-#linear_nn: creates a fully connected network given params
-def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
-    if act_fn.lower()=="tanh":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
-
-    elif act_fn.lower()=="sigmoid":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
-
-    elif act_fn.lower()=="relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
-
-    elif act_fn.lower()=="leaky relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
-    else:
-        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
-        return None
-
-    if end.lower()=="softmax":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
-    elif end.lower()=="none":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
-    elif end.lower()=="sigmoid":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
-    else:
-        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
-        return None
-
-    return torch.nn.Sequential(k)
-
-#train_sgd_simple: trains network using SGD
-def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss()
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else:
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
-    for i in range(iters):
-        if i%testevery==0:
-            with torch.no_grad():
-                output = model(data)
-                if evalType == "ap":
-                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
-                if evalType == "regression":
-                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
-                losses.append(ap)
-                print(str(i)+": "+str(ap))
-                plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
-                if dev != None:
-                    output = model(dev)
-                    print(evalType)
-                    if evalType == "ap":
-
-                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ap)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
-                    elif evalType == "regression":
-                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ev)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
-
-
-                if graphsaveloc != None:
-                    plt.savefig(graphsaveloc+".pdf")
-        with torch.enable_grad():
-            optimizer.zero_grad()
-            output = model(data)
-            loss = loss_fn(output, ground)
-            print(loss.item())
-            loss.backward()
-            optimizer.step()
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
-
-#train_sgd_minibatch: same as above, but with minibatches
-def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss()
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else:
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
-    itercount=0
-    for i in range(epoch):
-        print("EPOCH "+str(i)+" OF "+str(epoch-1))
-        batches=math.ceil(data.size()[0].item()/batchsize)
-        for j in range(batches):
-            batchdata=[]
-            batchground=[]
-            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
-                batchdata.append(data[k])
-                batchground.append(ground[k])
-            batchdata=torch.stack(batchdata)
-            batchground=torch.stack(batchground)
-            if itercount%testevery==0:
-                with torch.no_grad():
-                    output = model(data)
-                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
-                    losses.append(ap)
-                    print(str(i)+": "+str(ap))
-                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
-                    if dev != None:
-                        output = model(dev)
-                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ap)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
-                    if graphsaveloc != None:
-                        plt.savefig(graphsaveloc+".pdf")
-            with torch.enable_grad():
-                optimizer.zero_grad()
-                output = model(batchdata)
-                loss = loss_fn(output, ground)
-                loss.backward()
-                optimizer.step()
-            itercount +=1
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
-
-def retyuoipufdyu():
-
-    data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
-    ground = datasets.fetch_california_housing()['target']
-    ground = torch.tensor(ground).to(torch.float)
-    model = linear_nn(8, 100, 1, 20, act_fn = "relu")
-    print(model)
-    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
-
-start = time.time()
-retyuoipufdyu()
-end = time.time()
-print(end-start)
+#Titan Robotics Team 2022: ML Module
+#Written by Arthur Lu & Jacob Levine
+#Notes:
+#   this should be imported as a python module using 'import titanlearn'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+#   this module learns from its mistakes far faster than 2022's captains
+#setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.0.xxx:
+    -added generation of ANNS, basic SGD training"""
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+__all__ = [
+    'linear_nn',
+    'train_sgd_minibatch',
+    'train_sgd_simple'
+    ]
+#imports
+import torch
+import warnings
+from collections import OrderedDict
+from sklearn import metrics, datasets
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import time
+
+#enable CUDA if possible
+device = torch.device("cpu")
+
+#linear_nn: creates a fully connected network given params
+def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
+    if act_fn.lower()=="tanh":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
+
+    elif act_fn.lower()=="sigmoid":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
+
+    elif act_fn.lower()=="relu":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
+
+    elif act_fn.lower()=="leaky relu":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
+    else:
+        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
+        return None
+
+    if end.lower()=="softmax":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
+    elif end.lower()=="none":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
+    elif end.lower()=="sigmoid":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
+    else:
+        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
+        return None
+
+    return torch.nn.Sequential(k)
+
+#train_sgd_simple: trains network using SGD
+def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+    model=net.to(device)
+    data=data.to(device)
+    ground=ground.to(device)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
+    for i in range(iters):
+        if i%testevery==0:
+            with torch.no_grad():
+                output = model(data)
+                if evalType == "ap":
+                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
+                if evalType == "regression":
+                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
+                losses.append(ap)
+                print(str(i)+": "+str(ap))
+                plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
+                if dev != None:
+                    output = model(dev)
+                    print(evalType)
+                    if evalType == "ap":
+
+                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    elif evalType == "regression":
+                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ev)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
+
+
+                if graphsaveloc != None:
+                    plt.savefig(graphsaveloc+".pdf")
+        with torch.enable_grad():
+            optimizer.zero_grad()
+            output = model(data)
+            loss = loss_fn(output, ground)
+            print(loss.item())
+            loss.backward()
+            optimizer.step()
+    if modelsaveloc != None:
+        torch.save(model, modelsaveloc)
+    plt.show()
+    return model
+
+#train_sgd_minibatch: same as above, but with minibatches
+def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+    model=net.to(device)
+    data=data.to(device)
+    ground=ground.to(device)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
+    itercount=0
+    for i in range(epoch):
+        print("EPOCH "+str(i)+" OF "+str(epoch-1))
+        batches=math.ceil(data.size()[0].item()/batchsize)
+        for j in range(batches):
+            batchdata=[]
+            batchground=[]
+            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
+                batchdata.append(data[k])
+                batchground.append(ground[k])
+            batchdata=torch.stack(batchdata)
+            batchground=torch.stack(batchground)
+            if itercount%testevery==0:
+                with torch.no_grad():
+                    output = model(data)
+                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
+                    losses.append(ap)
+                    print(str(i)+": "+str(ap))
+                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
+                    if dev != None:
+                        output = model(dev)
+                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    if graphsaveloc != None:
+                        plt.savefig(graphsaveloc+".pdf")
+            with torch.enable_grad():
+                optimizer.zero_grad()
+                output = model(batchdata)
+                loss = loss_fn(output, ground)
+                loss.backward()
+                optimizer.step()
+            itercount +=1
+    if modelsaveloc != None:
+        torch.save(model, modelsaveloc)
+    plt.show()
+    return model
+
+def retyuoipufdyu():
+
+    data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
+    ground = datasets.fetch_california_housing()['target']
+    ground = torch.tensor(ground).to(torch.float)
+    model = linear_nn(8, 100, 1, 20, act_fn = "relu")
+    print(model)
+    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
+
+start = time.time()
+retyuoipufdyu()
+end = time.time()
+print(end-start)
diff --git a/data analysis/visualization.py b/data analysis/dep/2019/visualization.py
similarity index 96%
rename from data analysis/visualization.py
rename to data analysis/dep/2019/visualization.py
index cffcde7e..21e86beb 100644
--- a/data analysis/visualization.py	
+++ b/data analysis/dep/2019/visualization.py	
@@ -1,130 +1,130 @@
-#Titan Robotics Team 2022: Visualization Module
-#Written by Arthur Lu & Jacob Levine
-#Notes:
-#   this should be imported as a python module using 'import visualization'
-#   this should be included in the local directory or environment variable
-#   this module has not been optimized for multhreaded computing
-#Number of easter eggs: Jake is Jewish and does not observe easter.
-#setup:
-
-__version__ = "1.0.0.001"
-
-#changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-1.0.0.xxx:
-    -added basic plotting, clustering, and regression comparisons"""
-__author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>, "
-    "Jacob Levine <jlevine@ttic.edu>,"
-    )
-__all__ = [
-    'affinity_prop',
-    'bar_graph',
-    'dbscan',
-    'kmeans',
-    'line_plot',
-    'pca_comp',
-    'regression_comp',
-    'scatter_plot',
-    'spectral',
-    'vis_2d'
-    ]
-#imports
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
-from sklearn.preprocessing import StandardScaler
-from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
-
-#bar of x,y
-def bar_graph(x,y):
-    x=np.asarray(x)
-    y=np.asarray(y)
-    plt.bar(x,y)
-    plt.show()
-
-#scatter of x,y
-def scatter_plot(x,y):
-    x=np.asarray(x)
-    y=np.asarray(y)
-    plt.scatter(x,y)
-    plt.show()
-
-#line of x,y
-def line_plot(x,y):
-    x=np.asarray(x)
-    y=np.asarray(y)
-    plt.scatter(x,y)
-    plt.show()
-
-#plot data + regression fit
-def regression_comp(x,y,reg):
-    x=np.asarray(x)
-    y=np.asarray(y)
-    regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000)
-    regy=[]
-    for i in regx:
-        regy.append(eval(reg[0].replace("z",str(i))))
-    regy=np.asarray(regy)
-    plt.scatter(x,y)
-    plt.plot(regx,regy,color="orange",linewidth=3)
-    plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
-            u"R\u00b2="+str(round(reg[2],5)),
-            horizontalalignment='center', verticalalignment='center')
-    plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
-            "MSE="+str(round(reg[1],5)),
-             horizontalalignment='center', verticalalignment='center')
-    plt.show()
-
-#PCA to compress down to 2d
-def pca_comp(big_multidim):
-    pca=PCA(n_components=2)
-    td_norm=StandardScaler().fit_transform(big_multidim)
-    td_pca=pca.fit_transform(td_norm)
-    return td_pca
-
-#one-stop visualization of multidim datasets
-def vis_2d(big_multidim):
-    td_pca=pca_comp(big_multidim)
-    plt.scatter(td_pca[:,0], td_pca[:,1])
-
-def cluster_vis(data, cluster_assign):
-    pca=PCA(n_components=2)
-    td_norm=StandardScaler().fit_transform(data)
-    td_pca=pca.fit_transform(td_norm)
-    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
-                                             '#f781bf', '#a65628', '#984ea3',
-                                             '#999999', '#e41a1c', '#dede00']),
-                                      int(max(clu) + 1))))
-    colors = np.append(colors, ["#000000"])
-    plt.figure(figsize=(8, 8))
-    plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
-    plt.show()
-
-#affinity prop- slow, but ok if you don't have any idea how many you want
-def affinity_prop(data, damping=.77, preference=-70):
-    td_norm=StandardScaler().fit_transform(data)
-    db = AffinityPropagation(damping=damping,preference=preference).fit(td)
-    y=db.predict(td_norm)
-    return y
-
-#DBSCAN- slightly faster but can label your dataset as all outliers
-def dbscan(data, eps=.3):
-    td_norm=StandardScaler().fit_transform(data)
-    db = DBSCAN(eps=eps).fit(td)
-    y=db.labels_.astype(np.int)
-    return y
-
-#K-means clustering- the classic
-def kmeans(data, num_clusters):
-    td_norm=StandardScaler().fit_transform(data)
-    db = KMeans(n_clusters=num_clusters).fit(td)
-    y=db.labels_.astype(np.int)
-    return y
-
-#Spectral Clustering- Seems to work really well
-def spectral(data, num_clusters):
-    td_norm=StandardScaler().fit_transform(data)
-    db = SpectralClustering(n_clusters=num_clusters).fit(td)
-    y=db.labels_.astype(np.int)
-    return y
+#Titan Robotics Team 2022: Visualization Module
+#Written by Arthur Lu & Jacob Levine
+#Notes:
+#   this should be imported as a python module using 'import visualization'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+#Number of easter eggs: Jake is Jewish and does not observe easter.
+#setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.0.xxx:
+    -added basic plotting, clustering, and regression comparisons"""
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+__all__ = [
+    'affinity_prop',
+    'bar_graph',
+    'dbscan',
+    'kmeans',
+    'line_plot',
+    'pca_comp',
+    'regression_comp',
+    'scatter_plot',
+    'spectral',
+    'vis_2d'
+    ]
+#imports
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
+
+#bar of x,y
+def bar_graph(x,y):
+    x=np.asarray(x)
+    y=np.asarray(y)
+    plt.bar(x,y)
+    plt.show()
+
+#scatter of x,y
+def scatter_plot(x,y):
+    x=np.asarray(x)
+    y=np.asarray(y)
+    plt.scatter(x,y)
+    plt.show()
+
+#line of x,y
+def line_plot(x,y):
+    x=np.asarray(x)
+    y=np.asarray(y)
+    plt.scatter(x,y)
+    plt.show()
+
+#plot data + regression fit
+def regression_comp(x,y,reg):
+    x=np.asarray(x)
+    y=np.asarray(y)
+    regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000)
+    regy=[]
+    for i in regx:
+        regy.append(eval(reg[0].replace("z",str(i))))
+    regy=np.asarray(regy)
+    plt.scatter(x,y)
+    plt.plot(regx,regy,color="orange",linewidth=3)
+    plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
+            u"R\u00b2="+str(round(reg[2],5)),
+            horizontalalignment='center', verticalalignment='center')
+    plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
+            "MSE="+str(round(reg[1],5)),
+             horizontalalignment='center', verticalalignment='center')
+    plt.show()
+
+#PCA to compress down to 2d
+def pca_comp(big_multidim):
+    pca=PCA(n_components=2)
+    td_norm=StandardScaler().fit_transform(big_multidim)
+    td_pca=pca.fit_transform(td_norm)
+    return td_pca
+
+#one-stop visualization of multidim datasets
+def vis_2d(big_multidim):
+    td_pca=pca_comp(big_multidim)
+    plt.scatter(td_pca[:,0], td_pca[:,1])
+
+def cluster_vis(data, cluster_assign):
+    pca=PCA(n_components=2)
+    td_norm=StandardScaler().fit_transform(data)
+    td_pca=pca.fit_transform(td_norm)
+    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
+                                             '#f781bf', '#a65628', '#984ea3',
+                                             '#999999', '#e41a1c', '#dede00']),
+                                      int(max(clu) + 1))))
+    colors = np.append(colors, ["#000000"])
+    plt.figure(figsize=(8, 8))
+    plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
+    plt.show()
+
+#affinity prop- slow, but ok if you don't have any idea how many you want
+def affinity_prop(data, damping=.77, preference=-70):
+    td_norm=StandardScaler().fit_transform(data)
+    db = AffinityPropagation(damping=damping,preference=preference).fit(td)
+    y=db.predict(td_norm)
+    return y
+
+#DBSCAN- slightly faster but can label your dataset as all outliers
+def dbscan(data, eps=.3):
+    td_norm=StandardScaler().fit_transform(data)
+    db = DBSCAN(eps=eps).fit(td)
+    y=db.labels_.astype(np.int)
+    return y
+
+#K-means clustering- the classic
+def kmeans(data, num_clusters):
+    td_norm=StandardScaler().fit_transform(data)
+    db = KMeans(n_clusters=num_clusters).fit(td)
+    y=db.labels_.astype(np.int)
+    return y
+
+#Spectral Clustering- Seems to work really well
+def spectral(data, num_clusters):
+    td_norm=StandardScaler().fit_transform(data)
+    db = SpectralClustering(n_clusters=num_clusters).fit(td)
+    y=db.labels_.astype(np.int)
+    return y