Remove dependece on numpy

Note, this changes the standard deviation from population (old, numpy) to sample (new, pandas) and thus numerical values.

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
 
@@ -32,13 +32,6 @@ def parse_jobid(s):
 
 
 class Power:
-	def __init__(self, nodes):
-		self.nodes = nodes
-		self.epochs = OrderedDict()
-		self.first_ts = None
-		self.last_ts = None
-		self.warnings = ""
-
 	@classmethod
 	def from_list(cls, data):
 		"""
@@ -46,51 +39,43 @@ class Power:
 
 		Assumptions:
 		 - data is sorted by timestamp ascending
-		 - for each timestamp, there is the same set of nodes and in the same order
 		"""
+		df = pd.DataFrame(data, columns=['time', 'node', 'power'])
+		power = cls(df, columns={})
 
-		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
-		
 		return power
 
 	@classmethod
 	def from_db(cls, db, jobid, interval, hawk_ai):
-		all_list = db.db_to_list(jobid, interval, hawk_ai)
+		df = db.db_to_pf(jobid, interval, hawk_ai)
-		if not all_list:
+		if df.empty:
+			raise RuntimeError
+		power = cls(df, {'time': 'time', 'name': 'node', 'avg': 'power'})
+
+		return power
+
+	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
 
-		power = cls.from_list(all_list)
+		by_node = dataframe.groupby('node')
+		nodes = list(by_node.groups.keys())
+		epochs = dataframe.groupby('time')
+		times = list(epochs.groups.keys())
+
+		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()
 
-		return power 
- 
 	def to_file(self, jobid, header=""):
 		"""Dumps power data to file. Returns filename is succesfull and None if unsucessfull."""
 		fname = self.filename(jobid)
@@ -99,6 +84,7 @@ class Power:
 			return None
 
 		header += self.warnings
+		header += self.energy
 		try:
 			with open(fname, "w+") as f:
 				f.write(header + self.header())
@@ -108,17 +94,11 @@ class Power:
 			fname = None
 
 		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"
-		hd += "timestamp,RESERVED,head_node_power,avg_node_power,median_node_power,min_node_power,max_node_power,std_dev_node_power"
+		hd += "timestamp,RESERVED,head_node_power,avg_node_power,median_node_power,min_node_power,max_node_power,std_dev_sample_node_power"
 		# add node names here instead
 		hd += "," + ",".join(self.nodes)
 		hd += "\n"
@@ -126,31 +106,59 @@ class Power:
 
 	def body(self):
 		_body = ""
-		for epoch in self.epochs.items():
+		for epoch in self.epochs:
-			_body += self.pretty_print(self.summarize_epoch(epoch))
+			_body += self.pretty_print(*self.summarize_epoch(epoch))
 		return _body
 	
-	def summarize_time(self, ts):
+	def _summarize_time(self, ts):
-		return ts, ""
+		return Power.to_csv(ts, "")
 
 	@staticmethod
-	def summarize_values(val):
+	def _summarize_values(df):
-		values = np.asarray(val)
+		values = df['power']
-		head = values[0]
+		head = values.iloc[0]
 		min, max = values.min(), values.max()
 		avg, stddev = values.mean(), values.std()
-		median = np.median(values)
+		median = values.median()
-		return head, avg, median, min, max, stddev
+		return Power.to_csv(head, avg, median, min, max, stddev)
 
 	def summarize_epoch(self, epoch):
 		ts, values = epoch
-		return self.summarize_time(ts) \
+		return self._summarize_time(ts), \
-				+ self.summarize_values(values) \
+				self._summarize_values(values), \
-				+ tuple(values)
+				self._all_values(values)
+
+	def _all_values(self, values):
+		# reindex frame to get all nodes; introduces gaps
+		values = values[['node', 'power']].set_index('node').reindex(self.nodes)
+		# hack to_csv() to transpose array
+		csv = values.to_csv(header=False, index=False, line_terminator=',', na_rep=' ')
+		csv = csv[:-1]  # strip line terminator ',' from end of string
+		return csv
+
+	def energy_total(self):
+		energy = None
+		if hasattr(self, "by_node"):
+			energy = self.by_node.apply(self._energy_node).sum()
+		return energy
 	
 	@staticmethod
-	def pretty_print(args):
+	def _energy_node(group):
-		return ",".join(str(a) for a in args) + '\n'
+		"""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['power']
+		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 to_csv(*args):
+		return ",".join(str(a) for a in args)
+	
+	@staticmethod
+	def pretty_print(*args):
+		return Power.to_csv(*args) + '\n'
 
 	def filename(self, jobid):
 		fname = "detailed_power_{jobid}.hawk-pbs5.{first}-{last}.csv".format(
@@ -235,7 +243,7 @@ class App:
 		if warnings:
 			print(warnings)
 
-		header = f"# {config.datetime}: {config.cmd}\n"
+		header = f"# {self.config.datetime}: {self.config.cmd}\n"
 		if warnings:
 			header +=  f"{warnings}\n"
 		header += "#\n"
@@ -252,7 +260,8 @@ 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