From 2436928e7ab1b71bb31ff1b88158f7c9c4fd9231 Mon Sep 17 00:00:00 2001
From: Jose Gracia <gracia@hlrs.de>
Date: Thu, 22 Feb 2024 11:32:20 +0100
Subject: [PATCH] Refactor contructors for class Power

---
 monitoring/db/scripts/get_detailed_power.py | 41 ++++++++++-----------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/monitoring/db/scripts/get_detailed_power.py b/monitoring/db/scripts/get_detailed_power.py
index f56ce3f..898f60f 100755
--- a/monitoring/db/scripts/get_detailed_power.py
+++ b/monitoring/db/scripts/get_detailed_power.py
@@ -32,13 +32,6 @@ def parse_jobid(s):
 
 
 class Power:
-	def __init__(self, nodes):
-		self.nodes = nodes
-		self.epochs = None
-		self.first_ts = None
-		self.last_ts = None
-		self.warnings = ""
-
 	@classmethod
 	def from_list(cls, data):
 		"""
@@ -48,7 +41,7 @@ class Power:
 		 - data is sorted by timestamp ascending
 		"""
 		df = pd.DataFrame(data, columns=['time', 'node', 'power'])
-		power = cls.from_pandas(df, columns={})
+		power = cls(df, columns={})
 
 		return power
 
@@ -57,25 +50,31 @@ class Power:
 		df = db.db_to_pf(jobid, interval, hawk_ai)
 		if df.empty:
 			raise RuntimeError
-		power = cls.from_pandas(df, {'time': 'time', 'name': 'node', 'avg': 'power'})
+		power = cls(df, {'time': 'time', 'name': 'node', 'avg': 'power'})
 
 		return power
 
-	@classmethod
-	def from_pandas(cls, dataframe, columns):
-		dataframe.rename(columns=columns, inplace=True)
+	def __init__(self, dataframe, columns={}):
+		if columns:
+			dataframe.rename(columns=columns, inplace=True)
+		_required_cols = {'time', 'node', 'power'}
+		if not _required_cols.issubset(set(dataframe.columns)):
+			raise RuntimeError
+		if not dataframe['time'].is_monotonic_increasing:
+			raise RuntimeError
+
 		by_node = dataframe.groupby('node')
 		nodes = list(by_node.groups.keys())
+		epochs = dataframe.groupby('time')
+		times = list(epochs.groups.keys())
 
-		power = cls(nodes)
-		power.epochs = dataframe.groupby('time')
-		times = list(power.epochs.groups.keys())
-		power.first_ts, power.last_ts = times[0], times[-1]
-		power.dataframe = dataframe
-		power.by_node = power.dataframe.groupby('node')
-		power.energy = power.summarize_energy()
-
-		return power
+		self.dataframe = dataframe
+		self.nodes = nodes
+		self.epochs = epochs 
+		self.by_node = by_node
+		self.first_ts, self.last_ts = times[0], times[-1]
+		self.warnings = ""    # add check for warning, i.e. data gaps due to missing nodes
+		self.energy = self.summarize_energy()
 
 	def to_file(self, jobid, header=""):
 		"""Dumps power data to file. Returns filename is succesfull and None if unsucessfull."""