verena/sw-k-means
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base: verena:9e5f1ff1783ab6c6ff8ac038a7e17e8bbd259850
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verena:commented
verena:main
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compare: verena:f3d71442024f45ef1c7cf8222069960b00d6c85f
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verena:commented
verena:main

2 commits
9e5f1ff178 ... f3d7144202

Author SHA1 Message Date
STeichmann
f3d7144202 Merge branch 'commented' of ssh://forge.bexus.org:22/verena/sw-k-means into commented 2023-07-27 21:45:18 +02:00
STeichmann
ff6555c1df fixed the ICME method 2023-07-27 21:45:03 +02:00
1 changed files with 9 additions and 3 deletions
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reduced_kmeans.py
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@ -861,7 +861,10 @@ 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)
tmask = (self.data.time[~self.data.icme]>=tf[0])&(self.data.time[~self.data.icme]<=tf[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])
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])]
y_limits = [min(self.plotdata[:,self.getIndex(y_key)][tmask]),max(self.plotdata[:,self.getIndex(y_key)][tmask])] y_limits = [min(self.plotdata[:,self.getIndex(y_key)][tmask]),max(self.plotdata[:,self.getIndex(y_key)][tmask])]
@ -891,7 +894,10 @@ 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
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]) 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])
cs.append(c) cs.append(c)
bs.append(b) bs.append(b)
shared_hists.append(a) shared_hists.append(a)
@ -1285,7 +1291,7 @@ 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.multiply(np.array([0,1]),1e6) range=np.array([0,1])#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":