Reimplement from_list in terms of pandas

monitoring/db/scripts/get_detailed_power.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 import argparse
-import numpy as np
+import pandas as pd
 from collections import OrderedDict
 import os.path
 
@@ -46,38 +46,9 @@ class Power:
 
 		Assumptions:
 		 - data is sorted by timestamp ascending
-		 - for each timestamp, there is the same set of nodes and in the same order
 		"""
-
-		idx_ts = 0; idx_node = 1; idx_value = 2
-		nodes = list(OrderedDict.fromkeys([line[idx_node] for line in data]))   # preserves order of nodes
-		power = Power(nodes)
-
-		values = {}
-		for l in data:
-			ts = l[idx_ts]
-			if ts not in values:
-				values[ts] = []
-			value = l[idx_value]
-			values[ts].append(value)
-
-		epochs = values.keys()
-		for epoch in epochs:
-			power.insert_epoch(epoch, values[epoch])
-
-		# check implicit assumptions: 1) ts/epochs are sorted
-		e = list(epochs)
-		k = list(values.keys())
-		if not e == k:
-			power.warnings += "# Warning: Unexpected unsorted timestamps.\n"
-
-		# check implicit assumptions: 2) each line has #nodes values
-		nnodes = len(nodes)
-		for epoch in epochs:
-			actual = len(values[epoch])
-			if actual != nnodes:
-				power.warnings += "# Warning: Unexpected number of nodes ({actual}/{expected})\n".format(actual=actual, expected=nnodes)
-				break
+		df = pd.DataFrame(data, columns=['time', 'node', 'power'])
+		power = cls.from_pandas(df, columns={})
 
 		return power
 
@@ -125,12 +96,6 @@ class Power:
 
 		return fname
 
-	def insert_epoch(self, ts, values):
-		self.epochs[ts] = values
-		if not self.first_ts:
-			self.first_ts = ts
-		self.last_ts = ts
-
 	def header(self):
 		hd  = "# all timestamp have unit miliseconds since unix epoch\n"
 		hd += "# all power values have unit Watt\n"