verena/sw-k-means
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compare: verena:9e5f1ff1783ab6c6ff8ac038a7e17e8bbd259850
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reduced_kmeans.py
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@ -861,9 +861,6 @@ class Clusters():
red_data_1 = self.get_red_dataset(p1_new) red_data_1 = self.get_red_dataset(p1_new)
predict1=self.clusterlist[self.labellist[i1]][n].predict(red_data_1) predict1=self.clusterlist[self.labellist[i1]][n].predict(red_data_1)
if ICME_data:
tmask = (self.data.time>=tf[0])&(self.data.time<=tf[1])
else:
tmask = (self.data.time[~self.data.icme]>=tf[0])&(self.data.time[~self.data.icme]<=tf[1]) tmask = (self.data.time[~self.data.icme]>=tf[0])&(self.data.time[~self.data.icme]<=tf[1])
x_limits = [min(self.plotdata[:,self.getIndex(x_key)][tmask]),max(self.plotdata[:,self.getIndex(x_key)][tmask])] x_limits = [min(self.plotdata[:,self.getIndex(x_key)][tmask]),max(self.plotdata[:,self.getIndex(x_key)][tmask])]
@ -894,9 +891,6 @@ class Clusters():
lmask2 = predict2 ==k lmask2 = predict2 ==k
for l in range(self.nClusters): for l in range(self.nClusters):
lmask1 = predict1 ==l lmask1 = predict1 ==l
if ICME_data:
a,b,c = np.histogram2d(self.plotdata_ICME[:,self.getIndex(x_key)][lmask1&tmask&lmask2],self.plotdata_ICME[:,self.getIndex(y_key)][lmask1&tmask&lmask2], bins=75, range=[x_limits,y_limits])
else:
a,b,c = np.histogram2d(self.plotdata[:,self.getIndex(x_key)][lmask1&tmask&lmask2],self.plotdata[:,self.getIndex(y_key)][lmask1&tmask&lmask2], bins=75, range=[x_limits,y_limits]) a,b,c = np.histogram2d(self.plotdata[:,self.getIndex(x_key)][lmask1&tmask&lmask2],self.plotdata[:,self.getIndex(y_key)][lmask1&tmask&lmask2], bins=75, range=[x_limits,y_limits])
cs.append(c) cs.append(c)
bs.append(b) bs.append(b)
@ -1291,7 +1285,7 @@ class Clusters():
if key=="dsw": if key=="dsw":
range=np.array([0,60]) range=np.array([0,60])
if key=="tsw": if key=="tsw":
range=np.array([0,1])#np.multiply(np.array([0,1]),1e6) range=np.multiply(np.array([0,1]),1e6)
if key=="B": if key=="B":
range=np.array([0,20]) range=np.array([0,20])
if key=="dO7_6": if key=="dO7_6":