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added TBA requests module
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jlevine18
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data analysis/tbarequest.py
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94
data analysis/tbarequest.py
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#Titan Robotics Team 2022: TBA Requests Module
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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# this should be imported as a python module using 'import visualization'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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#Number of easter eggs: none yet
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#setup:
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__version__ = "1.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.xxx:
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-added common requests and JSON processing"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'process_json_ret',
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'req_all_events',
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'req_event_matches',
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'req_event_insights',
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'req_event_elim_alli'
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'req_team_events',
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'req_team_matches'
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]
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#imports
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import requests
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#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
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#requests a list of events that a team went to
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def req_team_events(team,year,apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/team/frc'+str(team)+'/events/'+str(year),headers=headers)
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return r
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#gets every match that a team played in
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def req_team_matches(team,year,apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/team/frc'+str(team)+'/matches/'+str(year), headers=headers)
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return r
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#gets all events in a certain year
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def req_all_events(year, apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/events/'+str(year), headers=headers)
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return r
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#gets all matches for an event
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def req_event_matches(event_key,apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/matches', headers=headers)
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return r
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#gets elimination alliances from a event
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def req_event_elim_alli(event_key, apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/alliances', headers=headers)
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return r
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#gets TBA's insights from an event
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def req_event_insights(event_key, apikey):
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headers={'X-TBA-Auth-Key':apikey}
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r=requests.get('https://www.thebluealliance.com/api/v3/event/'+str(event_key)+'/insights', headers=headers)
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return r
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#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
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def process_json_ret(req):
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if req.status_code == 200:
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keys=[]
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for i in req.json():
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for j in i.keys():
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read=False
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for k in keys:
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if k==j:
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read=True
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break
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if not read:
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keys.append(j)
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out=[]
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out.append(keys)
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for i in req.json():
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buf=[]
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for j in keys:
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try:
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buf.append(i[j])
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except:
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buf.append("")
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out.append(buf)
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return out
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else:
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raise ValueError('Status code is: '+req.status_code+', not 200')
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@ -1,131 +1,129 @@
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#Titan Robotics Team 2022: Visualization Module
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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# this should be imported as a python module using 'import visualization'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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#Number of easter eggs: Jake is Jewish and does not observe easter.
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#setup:
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__version__ = "1.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.xxx:
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-added basic plotting, clustering, and regression comparisons"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'affinity_prop',
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'bar_graph',
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'dbscan',
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'kmeans',
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'line_plot',
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'pca_comp',
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'regression_comp',
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'scatter_plot',
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'spectral',
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'vis_2d'
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]
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#imports
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
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from sklearn.preprocessing import StandardScaler
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from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
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import statistics
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#bar of x,y
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def bar_graph(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.bar(x,y)
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plt.show()
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#scatter of x,y
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def scatter_plot(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.scatter(x,y)
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plt.show()
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#line of x,y
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def line_plot(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.scatter(x,y)
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plt.show()
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#plot data + regression fit
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def regression_comp(x,y,reg):
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x=np.asarray(x)
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y=np.asarray(y)
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regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000))
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regy=[]
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for i in regx:
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regy.append(eval(reg[0].replace("z",str(i))))
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regy=np.asarray(regy)
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plt.scatter(x,y)
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plt.plot(regx,regy,color="orange",linewidth=3)
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plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
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u"R\u00b2="+str(round(reg[2],5)),
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horizontalalignment='center', verticalalignment='center')
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plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
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"MSE="+str(round(reg[1],5)),
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horizontalalignment='center', verticalalignment='center')
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plt.show()
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#PCA to compress down to 2d
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def pca_comp(big_multidim):
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pca=PCA(n_components=2)
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td_norm=StandardScaler().fit_transform(big_multidim)
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td_pca=pca.fit_transform(td_norm)
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return td_pca
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#one-stop visualization of multidim datasets
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def vis_2d(big_multidim):
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td_pca=pca_comp(big_multidim)
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plt.scatter(td_pca[:,0], td_pca[:,1])
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def cluster_vis(data, cluster_assign):
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pca=PCA(n_components=2)
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td_norm=StandardScaler().fit_transform(data)
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td_pca=pca.fit_transform(td_norm)
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colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
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'#f781bf', '#a65628', '#984ea3',
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'#999999', '#e41a1c', '#dede00']),
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int(max(clu) + 1))))
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colors = np.append(colors, ["#000000"])
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plt.figure(figsize=(8, 8))
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plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
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plt.show()
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#affinity prop- slow, but ok if you don't have any idea how many you want
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def affinity_prop(data, damping=.77, preference=-70):
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td_norm=StandardScaler().fit_transform(data)
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db = AffinityPropagation(damping=damping,preference=preference).fit(td)
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y=db.predict(td_norm)
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return y
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#DBSCAN- slightly faster but can label your dataset as all outliers
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def dbscan(data, eps=.3):
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td_norm=StandardScaler().fit_transform(data)
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db = DBSCAN(eps=eps).fit(td)
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y=db.labels_.astype(np.int)
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return y
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#K-means clustering- the classic
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def kmeans(data, num_clusters):
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td_norm=StandardScaler().fit_transform(data)
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db = KMeans(n_clusters=num_clusters).fit(td)
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y=db.labels_.astype(np.int)
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return y
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#Spectral Clustering- Seems to work really well
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def spectral(data, num_clusters):
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td_norm=StandardScaler().fit_transform(data)
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db = SpectralClustering(n_clusters=num_clusters, eigen_solver='arpack',
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affinity="nearest_neighbors").fit(td)
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y=db.labels_.astype(np.int)
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return y
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#Titan Robotics Team 2022: Visualization Module
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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# this should be imported as a python module using 'import visualization'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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#Number of easter eggs: Jake is Jewish and does not observe easter.
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#setup:
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__version__ = "1.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.xxx:
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-added basic plotting, clustering, and regression comparisons"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'affinity_prop',
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'bar_graph',
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'dbscan',
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'kmeans',
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'line_plot',
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'pca_comp',
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'regression_comp',
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'scatter_plot',
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'spectral',
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'vis_2d'
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]
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#imports
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA, KernelPCA, IncrementalPCA
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from sklearn.preprocessing import StandardScaler
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from sklearn.cluster import AffinityPropagation, DBSCAN, KMeans, SpectralClustering
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#bar of x,y
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def bar_graph(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.bar(x,y)
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plt.show()
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#scatter of x,y
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def scatter_plot(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.scatter(x,y)
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plt.show()
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#line of x,y
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def line_plot(x,y):
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x=np.asarray(x)
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y=np.asarray(y)
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plt.scatter(x,y)
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plt.show()
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#plot data + regression fit
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def regression_comp(x,y,reg):
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x=np.asarray(x)
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y=np.asarray(y)
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regx=np.arange(x.min(),x.max(),(x.max()-x.min())/1000))
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regy=[]
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for i in regx:
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regy.append(eval(reg[0].replace("z",str(i))))
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regy=np.asarray(regy)
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plt.scatter(x,y)
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plt.plot(regx,regy,color="orange",linewidth=3)
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plt.text(.85*max([x.max(),regx.max()]),.95*max([y.max(),regy.max()]),
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u"R\u00b2="+str(round(reg[2],5)),
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horizontalalignment='center', verticalalignment='center')
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plt.text(.85*max([x.max(),regx.max()]),.85*max([y.max(),regy.max()]),
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"MSE="+str(round(reg[1],5)),
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horizontalalignment='center', verticalalignment='center')
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plt.show()
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#PCA to compress down to 2d
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def pca_comp(big_multidim):
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pca=PCA(n_components=2)
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td_norm=StandardScaler().fit_transform(big_multidim)
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td_pca=pca.fit_transform(td_norm)
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return td_pca
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#one-stop visualization of multidim datasets
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def vis_2d(big_multidim):
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td_pca=pca_comp(big_multidim)
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plt.scatter(td_pca[:,0], td_pca[:,1])
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def cluster_vis(data, cluster_assign):
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pca=PCA(n_components=2)
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td_norm=StandardScaler().fit_transform(data)
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td_pca=pca.fit_transform(td_norm)
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colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
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'#f781bf', '#a65628', '#984ea3',
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'#999999', '#e41a1c', '#dede00']),
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int(max(clu) + 1))))
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colors = np.append(colors, ["#000000"])
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plt.figure(figsize=(8, 8))
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plt.scatter(td_norm[:, 0], td_norm[:, 1], s=10, color=colors[cluster_assign])
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plt.show()
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#affinity prop- slow, but ok if you don't have any idea how many you want
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def affinity_prop(data, damping=.77, preference=-70):
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td_norm=StandardScaler().fit_transform(data)
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db = AffinityPropagation(damping=damping,preference=preference).fit(td)
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y=db.predict(td_norm)
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return y
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#DBSCAN- slightly faster but can label your dataset as all outliers
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def dbscan(data, eps=.3):
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td_norm=StandardScaler().fit_transform(data)
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db = DBSCAN(eps=eps).fit(td)
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y=db.labels_.astype(np.int)
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return y
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#K-means clustering- the classic
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def kmeans(data, num_clusters):
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td_norm=StandardScaler().fit_transform(data)
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db = KMeans(n_clusters=num_clusters).fit(td)
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y=db.labels_.astype(np.int)
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return y
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#Spectral Clustering- Seems to work really well
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def spectral(data, num_clusters):
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td_norm=StandardScaler().fit_transform(data)
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db = SpectralClustering(n_clusters=num_clusters).fit(td)
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y=db.labels_.astype(np.int)
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return y
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