Plot coconutBattery.app output

coconut_plot.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@file coconut_plot.py -- plot Macbook battery data
@author Tim van Werkhoven
@date 20121223
@copyright Copyright (c) 2012 Tim van Werkhoven <timvanwerkhoven@gmail.com>

This scripts plots data exported from [CoconutBattery.app][1] as CSV. This 
can be useful to graphically investigate your battery performance.

Besides plotting the data, the script also fits a straight line to the 
number of cycles and capacity as function of time, and calculates the 
correlation between capacicty and cycles.

Because you might have started logging data later, adding the age in months 
with --age will add a zero-point to the data. This adds a tuple (age=0, 
capacity=max, cycles=0) to the dataset.

  [1]: http://www.coconut-flavour.com/coconutbattery/

This file is licensed under the Creative Commons Attribution-Share Alike
license versions 3.0 or higher, see
http://creativecommons.org/licenses/by-sa/3.0/
"""

#############################################################################
### PREAMBLE
#############################################################################

# Libs
import numpy as np
import scipy as sp
import scipy.stats
import pylab as plt
from matplotlib.dates import datestr2num, epoch2num
import time

import os, sys
import argparse

AUTHOR = "Tim van Werkhoven (timvanwerkhoven@gmail.com)"
DATE = "20121223"

def main():
	# Parse & check options
	(parser, args) = parsopts()

	# Load data: skip one row (header), use comma as delimiter, convert 
	# first column to data, convert '-' to 0 in last column
	cocodat = np.loadtxt(args.datafile, skiprows=1, delimiter=',', dtype=int, converters={0: datestr2num, 2: lambda c: c.strip('-') or -1})

	# If the age of the battery is given, add zero-point data to dataset
	if (args.age):
		# Date in months, average month is 365.25/12 days
		secpmon = (24*3600*365.25/12)
		zerotime = int(0.5+epoch2num(time.time()-args.age*secpmon))
		cocodat = np.vstack([np.r_[zerotime, args.capacity, 0], cocodat])

	# Plot capacity as function of time
	# =======================================================================

	# Calc regression between time and capacity
	slope, icept, rval, pval, stderr = scipy.stats.linregress(cocodat[:,0], cocodat[:,1])
	print "Plotting Battery capacity vs time (%.2g%%/day)" % (slope*100./args.capacity)

	fig = plt.figure(10); fig.clf();
	ax1 = fig.add_subplot(111)
	ax1.set_title("Battery capacity vs time (%.2g%%/day)" % (slope*100./args.capacity))
	# Plot data
	ax1.plot_date(cocodat[:,0], cocodat[:,1], 'o')
	ax1.set_ylabel("Capacity [mAh]")
	# Plot fit
	ax1.plot_date(cocodat[:,0], cocodat[:,0]*slope + icept, '--')
	# Add second y-axis for percentage data
	ax2 = ax1.twinx()
	ax2.set_yticks(np.linspace(0,1, len(ax1.get_yticks())))
	ax2.set_yticklabels(["%.0f" % x for x in ax1.get_yticks()*100./args.capacity])
	ax2.set_ylabel("Capacity [%]")
	fig.autofmt_xdate()
	fig.savefig("%s_capacity_vs_time.pdf" % (args.plotname))

	# Plot cycles as function of time
	# =======================================================================

	# Select data
	cocodat2 = cocodat[cocodat[:,2] >= 0]

	# Calc regression between time and cycles
	slope, icept, rval, pval, stderr = scipy.stats.linregress(cocodat2[:,0], cocodat2[:,2])
	print "Plotting Charging cycles vs time (%.2g/day)" % (slope)

	fig = plt.figure(20); fig.clf();
	ax1 = fig.add_subplot(111)
	ax1.set_title("Charging cycles vs time (%.2g/day)" % (slope))
	# Plot data
	ax1.plot_date(cocodat2[:,0], cocodat2[:,2], 'o')
	# Plot fit
	ax1.plot_date(cocodat2[:,0], cocodat2[:,0]*slope + icept, '--')
	# Format axes
	ax1.set_ylabel("Cycles [N]")
	fig.autofmt_xdate()
	fig.savefig("%s_cycles_vs_time.pdf" % (args.plotname))

	# Plot capacity vs cycles
	# =======================================================================

	# Calc regression between cycles and capacity
	slope, icept, rval, pval, stderr = scipy.stats.linregress(cocodat2[:,2], cocodat2[:,1])
	print "Charging cycles vs capacity (R=%.2g)" % (rval)

	fig = plt.figure(30); fig.clf();
	ax1 = fig.add_subplot(111)
	ax1.set_title("Charging cycles vs capacity (R^2=%.2f)" % (rval**2.0))
	# Plot data
	ax1.plot(cocodat2[:,2], cocodat2[:,1], 'o')
	# Plot fit
	fitx = np.r_[cocodat2[:,2].min(), cocodat2[:,2].max()]
	ax1.plot(fitx, fitx*slope + icept, '--')
	# Format axes
	ax1.set_ylabel("Capacity [mAh]")
	ax1.set_xlabel("Cycles [N]")
	# Add second y-axis for percentage data
	ax2 = ax1.twinx()
	ax2.set_yticks(np.linspace(0,1, len(ax1.get_yticks())))
	ax2.set_yticklabels(["%.0f" % x for x in ax1.get_yticks()*100./args.capacity])
	ax2.set_ylabel("Capacity [%]")
	fig.savefig("%s_capacity_vs_cycles.pdf" % (args.plotname))


def parsopts():
	### Parse program options and return results
	parser = argparse.ArgumentParser(description='Plot Coconut battery data.', epilog='Comments & bugreports to %s' % (AUTHOR), prog='coconut_plot')
	
	parser.add_argument('datafile',
				help='Coconut datafile to process')
	parser.add_argument('--capacity', metavar='C', default=5770, type=int,
				help='Design capacity of battery (5770 mAh)')
	parser.add_argument('--plotname', metavar='name', default='coconut',
				help='Names for plot (None)')
	parser.add_argument('--age', type=float, help='Battery age in months')

	g4 = parser.add_argument_group("Miscellaneous options")
	g4.add_argument('-v', dest='debug', action='append_const', const=1,
				help='increase verbosity')
	g4.add_argument('-q', dest='debug', action='append_const', const=-1,
				help='decrease verbosity')
	
	args = parser.parse_args()

 	# Check & fix some options
	checkopts(parser, args)
	
	# Return results
	return (parser, args)

# Check command-line options
def checkopts(parser, args):
	args.verb = 0
	if (args.debug):
		args.verb = sum(args.debug)
	
	if (args.verb > 1):
		# Print some debug output
		print "Running program %s for tgt %s" % (sys.argv[0], args.tgtname)
		print args
	
	# In case of errors
	if (False):
		parser.print_usage()
		exit(-1)


# Run main program, must be at end
if __name__ == "__main__":
	sys.exit(main())

# EOF

sample_data.csv