codeaudit
diff --git a/‎data/signals.py‎
Lines changed: 1 addition & 1 deletion b/‎data/signals.py‎
Lines changed: 1 addition & 1 deletion
diff --git a/‎plasma/preprocessor/normalize.py‎
Lines changed: 11 additions & 0 deletions b/‎plasma/preprocessor/normalize.py‎
Lines changed: 11 additions & 0 deletions
diff --git a/‎plasma/primitives/data.py‎
Lines changed: 5 additions & 1 deletion b/‎plasma/primitives/data.py‎
Lines changed: 5 additions & 1 deletion
@@ -169,7 +169,7 @@ def fetch_nstx_data(signal_path,shot_num,c):
 
 li = Signal("internal inductance",["jpf/gs/bl-li<s","d3d/efsli"],[jet,d3d])
 lm = Signal("Locked mode amplitude",['jpf/da/c2-loca','d3d/dusbradial'],[jet,d3d])
-dens = Signal("Plasma density",['jpf/df/g1r-lid:003','d3d/dssdenest'],[jet,d3d])
+dens = Signal("Plasma density",['jpf/df/g1r-lid:003','d3d/dssdenest'],[jet,d3d],is_strictly_positive=True)
 energy = Signal("stored energy",['jpf/gs/bl-wmhd<s','d3d/efswmhd'],[jet,d3d])
 pin = Signal("Input Power (beam for d3d)",['jpf/gs/bl-ptot<s','d3d/bmspinj'],[jet,d3d]) #Total Beam Power
 
 
@@ -235,6 +235,7 @@ def incorporate_stats(self,stats):
 
 
     def apply(self,shot):
+        apply_positivity(shot)
         m = shot.machine
         assert self.means[m] is not None and self.stds[m] is not None, "self.means or self.stds not initialized"
         means = np.median(self.means[m],axis=0)
@@ -277,6 +278,7 @@ def load_stats(self):
 
 class VarNormalizer(MeanVarNormalizer):
     def apply(self,shot):
+        apply_positivity(shot)
         assert self.means is not None and self.stds is not None, "self.means or self.stds not initialized"
         m = shot.machine
         stds = np.median(self.stds[m],axis=0)
@@ -301,6 +303,7 @@ def __str__(self):
 class AveragingVarNormalizer(VarNormalizer):
 
     def apply(self,shot):
+        apply_positivity(shot)
         super(AveragingVarNormalizer,self).apply(shot)
         window_decay = self.conf['data']['window_decay']
         window_size = self.conf['data']['window_size']
@@ -366,6 +369,7 @@ def incorporate_stats(self,stats):
 
 
     def apply(self,shot):
+        apply_positivity(shot)
         assert(self.minimums is not None and self.maximums is not None) 
         m = shot.machine
         curr_range = (self.maximums[m] - self.minimums[m])
@@ -403,3 +407,10 @@ def load_stats(self):
 
 def get_individual_shot_file(prepath,shot_num,ext='.txt'):
     return prepath + str(shot_num) + ext 
+
+
+def apply_positivity(shot):
+    for (i,sig) in enumerate(shot.signals):
+        if sig.is_strictly_positive:
+            print ('Applying positivity constraint to {} signal'.format(sig.description))
+            shot.signals_dict[sig]=np.clip(shot.signals_dict[sig],0,np.inf)
@@ -15,7 +15,7 @@
 #           self.signals.append(Signal(signal_descriptions[i],signal_paths[i]))
 
 class Signal(object):
-    def __init__(self,description,paths,machines,tex_label=None,causal_shifts=None,is_ip=False,normalize=True,data_avail_tolerance=0):
+    def __init__(self,description,paths,machines,tex_label=None,causal_shifts=None,is_ip=False,normalize=True,data_avail_tolerance=0,is_strictly_positive=False):
         assert(len(paths) == len(machines))
         self.description = description
         self.paths = paths
@@ -27,6 +27,10 @@ def __init__(self,description,paths,machines,tex_label=None,causal_shifts=None,i
         self.num_channels = 1
         self.normalize = normalize
         self.data_avail_tolerance = data_avail_tolerance
+        self.is_strictly_positive=is_strictly_positive
+
+    def is_strictly_positive(self):
+        return self.is_strictly_positive
 
     def is_ip(self):
         return self.is_ip