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

@@ -1,131 +1,129 @@
-#Titan Robotics Team 2022: Visualization Module
+#Titan Robotics Team 2022: Visualization Module
-#Written by Arthur Lu & Jacob Levine
+#Written by Arthur Lu & Jacob Levine
-#Notes:
+#Notes:
-#   this should be imported as a python module using 'import visualization'
+#   this should be imported as a python module using 'import visualization'
-#   this should be included in the local directory or environment variable
+#   this should be included in the local directory or environment variable
-#   this module has not been optimized for multhreaded computing
+#   this module has not been optimized for multhreaded computing
-#Number of easter eggs: Jake is Jewish and does not observe easter.
+#Number of easter eggs: Jake is Jewish and does not observe easter.
-#setup:
+#setup:
-
+
-__version__ = "1.0.0.001"
+__version__ = "1.0.0.001"
-
+
-#changelog should be viewed using print(analysis.__changelog__)
+#changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
+__changelog__ = """changelog:
-1.0.0.xxx:
+1.0.0.xxx:
-    -added basic plotting, clustering, and regression comparisons"""
+    -added basic plotting, clustering, and regression comparisons"""
-__author__ = (
+__author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Arthur Lu <arthurlu@ttic.edu>, "
-    "Jacob Levine <jlevine@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
-    )
+    )
-__all__ = [
+__all__ = [
-    'affinity_prop',
+    'affinity_prop',
-    'bar_graph',
+    'bar_graph',
-    'dbscan',
+    'dbscan',
-    'kmeans',
+    'kmeans',
-    'line_plot',
+    'line_plot',
-    'pca_comp',
+    'pca_comp',
-    'regression_comp',
+    'regression_comp',
-    'scatter_plot',
+    'scatter_plot',
-    'spectral',
+    'spectral',
-    'vis_2d'
+    'vis_2d'
-    ]
+    ]
-#imports
+#imports
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
-from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
+from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
-from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import StandardScaler
-from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
+from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
-import statistics
+
-
+#bar of x,y
-#bar of x,y
+def bar_graph(x,y):
-def bar_graph(x,y):
+    x=np.asarray(x)
-    x=np.asarray(x)
+    y=np.asarray(y)
-    y=np.asarray(y)
+    plt.bar(x,y)
-    plt.bar(x,y)
+    plt.show()
-    plt.show()
+
-
+#scatter of x,y
-#scatter of x,y
+def scatter_plot(x,y):
-def scatter_plot(x,y):
+    x=np.asarray(x)
-    x=np.asarray(x)
+    y=np.asarray(y)
-    y=np.asarray(y)
+    plt.scatter(x,y)
-    plt.scatter(x,y)
+    plt.show()
-    plt.show()
+
-
+#line of x,y
-#line of x,y
+def line_plot(x,y):
-def line_plot(x,y):
+    x=np.asarray(x)
-    x=np.asarray(x)
+    y=np.asarray(y)
-    y=np.asarray(y)
+    plt.scatter(x,y)
-    plt.scatter(x,y)
+    plt.show()
-    plt.show()
+
-
+#plot data + regression fit
-#plot data + regression fit
+def regression_comp(x,y,reg):
-def regression_comp(x,y,reg):
+    x=np.asarray(x)
-    x=np.asarray(x)
+    y=np.asarray(y)
-    y=np.asarray(y)
+    regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000))
-    regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000))
+    regy=[]
-    regy=[]
+    for i in regx:
-    for i in regx:
+        regy.append(eval(reg[0].replace("z",str(i))))
-        regy.append(eval(reg[0].replace("z",str(i))))
+    regy=np.asarray(regy)
-    regy=np.asarray(regy)
+    plt.scatter(x,y)
-    plt.scatter(x,y)
+    plt.plot(regx,regy,color="orange",linewidth=3)
-    plt.plot(regx,regy,color="orange",linewidth=3)
+    plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
-    plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
+            u"R\u00b2="+str(round(reg[2],5)),
-            u"R\u00b2="+str(round(reg[2],5)),
+            horizontalalignment='center', verticalalignment='center')
-            horizontalalignment='center', verticalalignment='center')
+    plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
-    plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
+            "MSE="+str(round(reg[1],5)),
-            "MSE="+str(round(reg[1],5)),
+             horizontalalignment='center', verticalalignment='center')
-             horizontalalignment='center', verticalalignment='center')
+    plt.show()
-    plt.show()
+
-
+#PCA to compress down to 2d
-#PCA to compress down to 2d
+def pca_comp(big_multidim):
-def pca_comp(big_multidim):
+    pca=PCA(n_components=2)
-    pca=PCA(n_components=2)
+    td_norm=StandardScaler().fit_transform(big_multidim)
-    td_norm=StandardScaler().fit_transform(big_multidim)
+    td_pca=pca.fit_transform(td_norm)
-    td_pca=pca.fit_transform(td_norm)
+    return td_pca
-    return td_pca
+
-
+#one-stop visualization of multidim datasets
-#one-stop visualization of multidim datasets
+def vis_2d(big_multidim):
-def vis_2d(big_multidim):
+    td_pca=pca_comp(big_multidim)
-    td_pca=pca_comp(big_multidim)
+    plt.scatter(td_pca[:,0], td_pca[:,1])
-    plt.scatter(td_pca[:,0], td_pca[:,1])
+
-
+def cluster_vis(data, cluster_assign):
-def cluster_vis(data, cluster_assign):
+    pca=PCA(n_components=2)
-    pca=PCA(n_components=2)
+    td_norm=StandardScaler().fit_transform(data)
-    td_norm=StandardScaler().fit_transform(data)
+    td_pca=pca.fit_transform(td_norm)
-    td_pca=pca.fit_transform(td_norm)
+    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
-    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
+                                             '#f781bf', '#a65628', '#984ea3',
-                                             '#f781bf', '#a65628', '#984ea3',
+                                             '#999999', '#e41a1c', '#dede00']),
-                                             '#999999', '#e41a1c', '#dede00']),
+                                      int(max(clu) + 1))))
-                                      int(max(clu) + 1))))
+    colors = np.append(colors, ["#000000"])
-    colors = np.append(colors, ["#000000"])
+    plt.figure(figsize=(8, 8))
-    plt.figure(figsize=(8, 8))
+    plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
-    plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
+    plt.show()
-    plt.show()
+
-
+#affinity prop- slow, but ok if you don't have any idea how many you want
-#affinity prop- slow, but ok if you don't have any idea how many you want
+def affinity_prop(data, damping=.77, preference=-70):
-def affinity_prop(data, damping=.77, preference=-70):
+    td_norm=StandardScaler().fit_transform(data)
-    td_norm=StandardScaler().fit_transform(data)
+    db = AffinityPropagation(damping=damping,preference=preference).fit(td)
-    db = AffinityPropagation(damping=damping,preference=preference).fit(td)
+    y=db.predict(td_norm)
-    y=db.predict(td_norm)
+    return y
-    return y
+
-
+#DBSCAN- slightly faster but can label your dataset as all outliers
-#DBSCAN- slightly faster but can label your dataset as all outliers
+def dbscan(data, eps=.3):
-def dbscan(data, eps=.3):
+    td_norm=StandardScaler().fit_transform(data)
-    td_norm=StandardScaler().fit_transform(data)
+    db = DBSCAN(eps=eps).fit(td)
-    db = DBSCAN(eps=eps).fit(td)
+    y=db.labels_.astype(np.int)
-    y=db.labels_.astype(np.int)
+    return y
-    return y
+
-
+#K-means clustering- the classic
-#K-means clustering- the classic
+def kmeans(data, num_clusters):
-def kmeans(data, num_clusters):
+    td_norm=StandardScaler().fit_transform(data)
-    td_norm=StandardScaler().fit_transform(data)
+    db = KMeans(n_clusters=num_clusters).fit(td)
-    db = KMeans(n_clusters=num_clusters).fit(td)
+    y=db.labels_.astype(np.int)
-    y=db.labels_.astype(np.int)
+    return y
-    return y
+
-
+#Spectral Clustering- Seems to work really well
-#Spectral Clustering- Seems to work really well
+def spectral(data, num_clusters):
-def spectral(data, num_clusters):
+    td_norm=StandardScaler().fit_transform(data)
-    td_norm=StandardScaler().fit_transform(data)
+    db = SpectralClustering(n_clusters=num_clusters).fit(td)
-    db = SpectralClustering(n_clusters=num_clusters, eigen_solver='arpack',
+    y=db.labels_.astype(np.int)
-        affinity="nearest_neighbors").fit(td)
+    return y
-    y=db.labels_.astype(np.int)
-    return y