added TBA requests module

data analysis/tbarequest.py

@@ -0,0 +1,94 @@
+#Titan Robotics Team 2022: TBA Requests 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: none yet
+#setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.0.xxx:
+    -added common requests and JSON processing"""
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+__all__ = [
+    'process_json_ret',
+    'req_all_events',
+    'req_event_matches',
+    'req_event_insights',
+    'req_event_elim_alli'
+    'req_team_events',
+    'req_team_matches'
+    ]
+#imports
+import requests
+
+#as this code is public, i'm not putting 2022's API key in here. just add it as a var in your script and go
+#requests a list of events that a team went to
+def req_team_events(team,year,apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/team/frc'+str(team)+'/events/'+str(year),headers=headers)
+    return r
+
+#gets every match that a team played in
+def req_team_matches(team,year,apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/team/frc'+str(team)+'/matches/'+str(year), headers=headers)
+    return r
+
+#gets all events in a certain year
+def req_all_events(year, apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/events/'+str(year), headers=headers)
+    return r
+
+#gets all matches for an event
+def req_event_matches(event_key,apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/matches', headers=headers)
+    return r
+
+#gets elimination alliances from a event
+def req_event_elim_alli(event_key, apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/alliances', headers=headers)
+    return r
+
+#gets TBA's insights from an event
+def req_event_insights(event_key, apikey):
+    headers={'X-TBA-Auth-Key':apikey}
+    r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/insights', headers=headers)
+    return r
+
+#processes the json return. right now, it's slow and not great. will throw an exception if it doesn't get a good status code
+def process_json_ret(req):
+    if req.status_code == 200:
+        keys=[]
+        for i in req.json():
+            for j in i.keys():
+                read=False
+                for k in keys:
+                    if k==j:
+                        read=True
+                        break
+                if not read:
+                    keys.append(j)
+        out=[]
+        out.append(keys)
+        for i in req.json():
+            buf=[]
+            for j in keys:
+                try:
+                    buf.append(i[j])
+                except:
+                    buf.append("")
+            out.append(buf)
+        return out
+    else:
+        raise ValueError('Status code is: '+req.status_code+', not 200')

data analysis/visualization.py

@@ -34,7 +34,6 @@ import matplotlib.pyplot as plt
 from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
 from sklearn.preprocessing import StandardScaler
 from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
-import statistics
 
 #bar of x,y
 def bar_graph(x,y):
@@ -125,7 +124,6 @@ def kmeans(data, num_clusters):
 #Spectral Clustering- Seems to work really well
 def spectral(data, num_clusters):
     td_norm=StandardScaler().fit_transform(data)
-    db = SpectralClustering(n_clusters=num_clusters, eigen_solver='arpack',
-        affinity="nearest_neighbors").fit(td)
+    db = SpectralClustering(n_clusters=num_clusters).fit(td)
     y=db.labels_.astype(np.int)
     return y