Read into pandas dataframe to compute consumed energy

monitoring/db/scripts/get_detailed_power.py

@@ -89,6 +89,12 @@ class Power:
 
 		power = cls.from_list(all_list)
 
+		power.df = db.db_to_pf(jobid, interval, hawk_ai)
+		# power.df.to_pickle("pickle_df")
+		power.by_node = power.df.groupby('name')
+		power.by_epoch = power.df.groupby('time')
+		power.energy = power.summarize_energy()
+
 		return power 
  
 	def to_file(self, jobid, header=""):
@@ -99,6 +105,7 @@ class Power:
 			return None
 
 		header += self.warnings
+		header += self.energy
 		try:
 			with open(fname, "w+") as f:
 				f.write(header + self.header())
@@ -148,6 +155,22 @@ class Power:
 				+ self.summarize_values(values) \
 				+ tuple(values)
 
+	def energy_total(self):
+		energy = None
+		if hasattr(self, "by_node"):
+			energy = self.by_node.apply(self.energy_node).sum()
+		return energy
+	
+	@staticmethod
+	def energy_node(group):
+		"""Left-sided Riemann sum is enough, as time is lower bound of bucket"""
+		delta_t = group["time"].diff().shift(-1)/1000.  # in seconds
+		pow =  group["avg"]
+		return (delta_t * pow).iloc[:-1].sum()	
+
+	def summarize_energy(self):
+		return "# Total energy consumed by job: {energy:.0f} J\n".format(energy=self.energy_total())
+
 	@staticmethod
 	def pretty_print(args):
 		return ",".join(str(a) for a in args) + '\n'
@@ -252,7 +275,8 @@ class App:
 				print('Created file {fn}'.format(fn=fn))
 			if power.warnings:
 				print(power.warnings)
-
+			if power.energy:
+				print(power.energy)		
 
 if __name__ == "__main__":
 	import sys