spacing

CreateBash.py

@@ -2,9 +2,6 @@
 work_list = list(range(9,57,4))
 
 
-
-
-
 with open(output_name, "w") as f:
     for w in work_list:
         #f.write('python RotateLearning.py prepare-datasets-flt -W {}\n'.format(w))

DATASET.py

@@ -154,7 +154,6 @@ def generateDS(self, output, mask, w = WindowSize, choosy = False, ratio = 1.0,
         return [X,Y, IDX]
 
 
-
     def generateDSwithFilter(self, dstype, output, mask, w = WindowSize, choosy = False, ratio = 1.0):
         # When choosy = TRUE : it only picks the fault locations and labels are based on fault angels
         # ratio coresponds to randomly selecting all possible locations
@@ -208,8 +207,6 @@ def generateDSwithFilter(self, dstype, output, mask, w = WindowSize, choosy = Fa
         return [X,Y, IDX]
 
 
-
-
     def shrinkMask(self, maskName = 'train', number = 9):
         # Shrink mask into 1/9 and return 9 masks:
 
@@ -243,8 +240,6 @@ def shrinkMask(self, maskName = 'train', number = 9):
         return m
 
 
-
-
     def evaluate(self, _pmap, expand=0, mask = 'all', etype = 'our'):
         pmap = np.array(_pmap)
         labels = self.expandBy(width=expand, epsilon=0.9 ,type='normal', set=False)
@@ -264,26 +259,21 @@ def evaluate(self, _pmap, expand=0, mask = 'all', etype = 'our'):
             pmap[np.where(self.MASK == 0)] = 0
 
 
-
         if etype == 'our':
             IDX_pos = labels > 0
             differror = np.square(labels - pmap)
             differror[~IDX_pos] = 0
             pos_score = differror.sum() / IDX_pos.sum()
 
-
-
             IDX_neg = labels <= 0
             differror = np.square(labels - pmap)
             differror[~IDX_neg] = 0
             neg_score = differror.sum() / max(1, (pmap[IDX_neg] >0 ).sum())
 
             IDXa = np.where(pmap > 0)
 
-
             return [pos_score, neg_score]
-
-
+
         else:
             EPS = np.finfo(float).eps
 

Demo.py

@@ -13,25 +13,18 @@
 dir3 = './Results/TrainOnRandomSelection_w45_fault/Pmap_exist_quest.npz'
 dir4 = './Results/TrainOnRandomSelection_w45_fault/PMAP_exist.npz'
 
-
-
 dir5 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Australia.hdf5Australia_strip.mat.npz'
 dir6 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Australia.hdf5QUEST_strip.mat.npz'
 dir7 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Mixed.hdf5Australia_strip.mat.npz'
 dir8 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Mixed.hdf5QUEST_strip.mat.npz'
 dir9 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Quest.hdf5Australia_strip.mat.npz'
 dir10 = './Results/NewTrainingRandom_strip_mixed/Pmamp_Fault_Quest.hdf5QUEST_strip.mat.npz'
 
-
 dir11 = './Results/First3Layers/Pmamp_Fault_Australia.hdf5Australia_strip.mat.npz'
 
 
-
 jdir = './applet.json'
 # Load and run application:
 p = PmapViewer(dir=jdir)
 p.run()
 
-
-
-#TODO: Prepare a demo video on training phase

LogParser.py

@@ -6,8 +6,8 @@
 model_quest = {}
 model_mixed = {}
 
-
 models = [model_australia, model_quest, model_mixed]
+
 for m in models:
     m['Quest'] = {}
     m['Australia'] = {}
@@ -21,9 +21,6 @@
         m[mp]['All_n'] = []
 
 
-
-
-
 with open("log.txt", "r") as f:
     for it in range(72):