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plot_heartrate.py
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plot_heartrate.py
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#!/usr/bin/env python
'''
plot_heartrate.py
# Sample usage of python-fitparse to parse an activity and
# print its data records.
'''
import sys
import os
############################################################
# plot_heartrate function def #
############################################################
from activity_tools import FindConfigFile
def plot_heartrate(FitFilePath, ConfigFile=None, OutStream=sys.stdout):
verbose = False
(FilePath, FitFileName) = os.path.split(FitFilePath)
if ConfigFile is None:
ConfigFile = FindConfigFile('', FilePath)
if (ConfigFile is None) or (not os.path.exists(ConfigFile)):
raise IOError('Configuration file not specified or found')
#
# Parse the configuration file
#
from ConfigParser import ConfigParser
config = ConfigParser()
config.read(ConfigFile)
print >> OutStream, 'reading config file ' + ConfigFile
WeightEntry = config.getfloat( 'user', 'weight' )
WeightToKg = config.getfloat( 'user', 'WeightToKg' )
weight = WeightEntry * WeightToKg
age = config.getfloat( 'user', 'age' )
sex = config.get( 'user', 'sex' )
EndurancePower = config.getfloat( 'power', 'EndurancePower' )
ThresholdPower = config.getfloat( 'power', 'ThresholdPower' )
EnduranceHR = config.getfloat( 'power', 'EnduranceHR' )
ThresholdHR = config.getfloat( 'power', 'ThresholdHR' )
HRTimeConstant = config.getfloat( 'power', 'HRTimeConstant' )
HRDriftRate = config.getfloat( 'power', 'HRDriftRate' )
print >> OutStream, 'WeightEntry : ', WeightEntry
print >> OutStream, 'WeightToKg : ', WeightToKg
print >> OutStream, 'weight : ', weight
print >> OutStream, 'age : ', age
print >> OutStream, 'sex : ', sex
print >> OutStream, 'EndurancePower: ', EndurancePower
print >> OutStream, 'ThresholdPower: ', ThresholdPower
print >> OutStream, 'EnduranceHR : ', EnduranceHR
print >> OutStream, 'ThresholdHR : ', ThresholdHR
print >> OutStream, 'HRTimeConstant : ', HRTimeConstant
print >> OutStream, 'HRDriftRate : ', HRDriftRate
from datetime import datetime
from fitparse import Activity
from activity_tools import extract_activity_signals
required_signals = [ 'heart_rate' ]
# get the signals
activity = Activity(FitFilePath)
signals = extract_activity_signals(activity)
if not all( s in signals.keys() for s in required_signals ):
msg = 'required signals not in file'
print >> OutStream, msg
print >> OutStream, 'Signals required:'
for s in required_signals:
print >> OutStream, ' ' + s
print >> OutStream, 'Signals contained:'
for s in signals.keys():
print >> OutStream, ' ' + s
raise IOError(msg)
time_signal = signals['time']
heartrate_signal = signals['heart_rate']
# plot the heart rate
import numpy as np
########################################################################
### Compute Calories ###
########################################################################
'''
Formula widely available. One site:
https://www.easycalculation.com/formulas/heart-rate-calorie-burn.html
For Male,
Calorie Burned = ( ( -55.0969
+ (0.6309 x HR)
+ (0.1988 x W )
+ (0.2017 x A ) )
/ 4.184) x 60 x T
For Female,
Calorie Burned = ( ( -20.4022
+ (0.4472 x HR)
+ (0.1263 x W )
+ (0.0740 x A ) )
/ 4.184) x 60 x T
Where,
HR = Heart Rate
W = Weight in kilograms
A = Age
T = Exercise duration time in hours
However, the formula is adapted to the user's power capacity:
: At EnduranceHR, CalPerMin is set to EndurancePower
(assuming efficiency of 1/4.184 so that calories burned
equal kJ expended).
: At ThresholdHR, CalPerMin is set to ThresholdPower.
: Between EnduranceHR and ThresholdHR, CalPerMin is
interpolated.
: Below EnduranceHR, CalPerMin follows the formula, but
it is scaled onto [EnduranceHR, EndurancePower].
: Above ThresholdHR, CalPerMin follows the formula, but
it is scaled onto [ThresholdHR, ThresholdPower].
'''
hr_sig = signals['heart_rate']
t_sig = signals['time']
dt_sig = np.append( np.array([1.0]),
t_sig[1:] - t_sig[0:-1] )
nPts = t_sig.size
calories = np.zeros(nPts)
EnduranceBurn = EndurancePower*3600/1e3/60 # Cal/min
print >> OutStream, 'EnduranceBurn = %5.2f cal/min' % EnduranceBurn
ThresholdBurn = ThresholdPower*3600/1e3/60 # Cal/min
print >> OutStream, 'ThresholdBurn = %5.2f cal/min' % ThresholdBurn
if sex == 'male':
# calibration at endurance
EnduranceCoef = EnduranceBurn \
/ ( -55.0969 \
+ 0.6309*EnduranceHR \
+ 0.1988*weight \
+ 0.2017*age) \
* 4.184
# calibration at threshold
ThresholdCoef = ThresholdBurn \
/ ( -55.0969 \
+ 0.6309*EnduranceHR \
+ 0.1988*weight \
+ 0.2017*age) \
* 4.184
else: # female
# calibration at endurance
EnduranceCoef = EnduranceBurn \
/ ( -20.4022 \
+ 0.4472*EnduranceHR \
+ 0.1263*weight \
+ 0.0740*age) \
* 4.184
# calibration at threshold
ThresholdCoef = ThresholdBurn \
/ ( -20.4022 \
+ 0.4472*EnduranceHR \
+ 0.1263*weight \
+ 0.0740*age) \
* 4.184
for i, dt, HR in zip( range(nPts), dt_sig, hr_sig ):
# calories per minute
if HR >= EnduranceHR and HR <= ThresholdHR:
CalPerMin = EnduranceBurn \
+ (HR-EnduranceHR) \
* (ThresholdBurn-EnduranceBurn) \
/ (ThresholdHR-EnduranceHR)
else:
coef = EnduranceCoef if (HR < EnduranceHR) else ThresholdCoef
if sex == 'male':
CalPerMin = ( -55.0969 \
+ 0.6309*HR \
+ 0.1988*weight \
+ 0.2017*age) \
/ 4.184 \
* coef
else:
CalPerMin = ( -20.4022 \
+ 0.4472*HR \
+ 0.1263*weight \
+ 0.0740*age) \
/ 4.184 \
* coef
calories[i] = dt * CalPerMin / 60
running_calories = np.cumsum( calories )
print >> OutStream, 'total calories = %i' % running_calories[nPts-1]
########################################################################
### Zone Histogram ###
########################################################################
# heart-rate zones from "Cyclist's Training Bible" 5th ed. by Joe Friel, p50
FTHR = ThresholdHR
hZones = { 1 : ([ 0 , 0.82*FTHR ], ' 1' ),
2 : ([ 0.82*FTHR, 0.89*FTHR ], ' 2' ),
3 : ([ 0.89*FTHR, 0.94*FTHR ], ' 3' ),
4 : ([ 0.94*FTHR, 1.00*FTHR ], ' 4' ),
5 : ([ 1.00*FTHR, 1.03*FTHR ], '5a' ),
6 : ([ 1.03*FTHR, 1.06*FTHR ], '5b' ),
7 : ([ 1.07*FTHR, 1.15*FTHR ], '5c' )}
h_zone_bounds = [ 0.4*FTHR, # 1 lo
hZones[2][0][0], # 2 lo
hZones[3][0][0], # 3 lo
hZones[4][0][0], # 4 lo
hZones[5][0][0], # 5a lo
hZones[6][0][0], # 5b lo
hZones[7][0][0], # 5c lo
hZones[7][0][1] ] # 5c hi
h_zone_labels = [ hZones[k][1] for k in range(1,8) ]
ZoneCounts, ZoneBins = np.histogram( hr_sig, bins=h_zone_bounds )
# formatted print of histogram
SampleRate = 1.0
print >> OutStream, 'Heart-Rate Zone Histogram:'
for i in range(7):
dur = ZoneCounts[i]/SampleRate
pct = dur / sum( ZoneCounts/SampleRate ) * 100
hh = dur // 3600
mm = (dur % 3600) // 60
ss = (dur % 3600) % 60
print >> OutStream, ' Zone %2s: %2i:%02i:%02i (%2i%%)' \
% ( h_zone_labels[i], hh, mm, ss, pct)
dur = sum(ZoneCounts)/SampleRate
hh = dur // 3600
mm = (dur % 3600) // 60
ss = (dur % 3600) % 60
print >> OutStream, ' total: %2i:%02i:%02i' % (hh, mm, ss)
########################################################################
### Power & TSS Estimation ###
########################################################################
from endurance_summary import BackwardMovingAverage
# see
# https://docs.scipy.org/doc/scipy/reference/signal.html
from scipy import signal
poles = 3
cutoff = 0.10 # Hz
Wn = cutoff / (SampleRate/2)
'''
# construct and apply a differentiating, lowpass filter
NumB, DenB = signal.butter(poles, Wn, btype='lowpass',
output='ba', analog=True)
NumF = signal.convolve( NumB, [1,0]) # add differentiator
bDLP, aDLP = signal.bilinear( NumF,DenB, fs=SampleRate )
hr_dot = signal.lfilter(bDLP, aDLP, hr_sig)
'''
# apply a phaseless lowpass filter, then differentiate.
# for some reason, running the Butterworth analog filter
# through bilinear() gives a better result. Otherwise,
# set analog=False to get coefficients directly.
PadLen = int(SampleRate/cutoff) # one period of cutoff
NumB, DenB = signal.butter(poles, Wn, btype='lowpass',
output='ba', analog=True)
bLPF, aLPF = signal.bilinear( NumB,DenB, fs=SampleRate )
hr_lpf = signal.filtfilt(bLPF, aLPF, hr_sig, padlen=PadLen)
hr_dot = np.gradient(hr_lpf, 1/SampleRate)
FTP = ThresholdPower
FTHR = ThresholdHR
tau = HRTimeConstant
PwHRTable = np.array( [
[ 0 , 0.50*FTHR ], # Active resting HR
[ 0.55*FTP, 0.70*FTHR ], # Recovery
[ 0.70*FTP, 0.82*FTHR ], # Aerobic threshold
[ 1.00*FTP, FTHR ], # Functional threshold
[ 1.20*FTP, 1.03*FTHR ], # Aerobic capacity
[ 1.50*FTP, 1.06*FTHR ]]) # Max HR
# loop through the time series building running power and TSS.
# Notice this is necessary because HRd[i] depends on TSS[i-1].
sPower = np.zeros(nPts)
TSS = np.zeros(nPts)
HRd = np.zeros(nPts) # fatigue drift
HRp = np.zeros(nPts) # power target
p30 = np.zeros(nPts) # 30-sec boxcar average
w = int(30*SampleRate) # window for boxcar
NPower = np.zeros(nPts) # normalized power
for i in range(1,nPts):
HRd[i] = HRDriftRate*TSS[i-1]
HRp[i] = hr_sig[i] + tau*hr_dot[i] - HRd[i]
sPower[i] = np.interp( HRp[i], PwHRTable[:,1], PwHRTable[:,0] )
if i < w:
p30[i] = np.average(sPower[:i+1]) # include i
else:
p30[i] = np.average(sPower[i-w:i+1]) # include i
NPower[i] = np.average( p30[:i]**4 )**(0.25)
TSS[i] = t_sig[i]/36*(NPower[i]/FTP)**2
print >> OutStream, 'estimated NP = %6i W' % NPower[-1]
print >> OutStream, 'estimated work = %6i kJ' % \
( np.cumsum(sPower)[-1] / 1e3 / SampleRate )
print >> OutStream, 'estimated TSS = %6i TSS' % TSS[-1]
if 'power' in signals.keys():
mPower = signals['power']
print >> OutStream, 'measured work = %6i kJ' % \
( np.cumsum( mPower )[-1] / 1e3 / SampleRate )
mP30 = BackwardMovingAverage( mPower )
mNPower = np.average( mP30**4 )**(0.25)
mTSS = t_sig[-1]/36*(mNPower/FTP)**2
print >> OutStream, 'measured NP = %6i W' % mNPower
print >> OutStream, 'measured TSS = %6i TSS' % mTSS
###########################################################
### plotting ###
###########################################################
# power zones from "Cyclist's Training Bible", 5th ed., by Joe Friel, p51
pZones = { 1 : [ 0 , 0.55*FTP ],
2 : [ 0.55*FTP, 0.75*FTP ],
3 : [ 0.75*FTP, 0.90*FTP ],
4 : [ 0.90*FTP, 1.05*FTP ],
5 : [ 1.05*FTP, 1.20*FTP ],
6 : [ 1.20*FTP, 1.50*FTP ],
7 : [ 1.50*FTP, 2.50*FTP ]}
# heart-rate zones from "Cyclist's Training Bible" 5th ed. by Joe Friel, p50
hZones = { 1 : [ 0 , 0.82*FTHR ], # 1
2 : [ 0.82*FTHR, 0.89*FTHR ], # 2
3 : [ 0.89*FTHR, 0.94*FTHR ], # 3
4 : [ 0.94*FTHR, 1.00*FTHR ], # 4
5 : [ 1.00*FTHR, 1.03*FTHR ], # 5a
6 : [ 1.03*FTHR, 1.06*FTHR ], # 5b
7 : [ 1.07*FTHR, 1.15*FTHR ]} # 5c
# get zone bounds for plotting
p_zone_bounds = [ pZones[1][0],
pZones[2][0],
pZones[3][0],
pZones[4][0],
pZones[5][0],
pZones[6][0],
pZones[7][0],
pZones[7][1] ]
h_zone_bounds = [ 0.4*FTHR, # better plotting
hZones[2][0],
hZones[3][0],
hZones[4][0],
hZones[5][0],
hZones[6][0],
hZones[7][0],
hZones[7][1] ]
# power simulation plot
import matplotlib.pyplot as plt
import matplotlib.dates as md
from matplotlib.dates import date2num, DateFormatter
import datetime as dt
base = dt.datetime(2014, 1, 1, 0, 0, 0)
x = [base + dt.timedelta(seconds=t) for t in t_sig.astype('float')]
x = date2num(x) # Convert to matplotlib format
fig1, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
ax0.plot_date( x, hr_sig, 'r-', linewidth=2 );
ax0.plot_date( x, tau*hr_dot, 'b-', linewidth=2 );
ax0.plot_date( x, HRd, 'g-', linewidth=2 );
ax0.plot_date( x, HRp, 'k-', linewidth=2 );
ax0.set_yticks( h_zone_bounds, minor=False)
ax0.grid(True)
ax0.legend( ['HR', 'tau*HRdot', 'HRd', 'HRp' ], loc='upper left');
ax0.set_title('heart rate, BPM')
if 'power' in signals.keys():
mPower = signals['power']
ax1.plot_date( x, mPower, 'k-', linewidth=1);
ax1.plot_date( x, sPower, 'b-', linewidth=2 );
ax1.legend( ['measured power', 'simulated power' ], loc='upper left');
else:
ax1.plot_date( x, sPower, 'b-', linewidth=2 );
ax1.legend( ['simulated power' ], loc='upper left');
ax1.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
ax1.set_yticks( p_zone_bounds, minor=False)
ax1.grid(True)
ax1.set_title('power, watts')
fig1.autofmt_xdate()
fig1.suptitle('Pw:HR Transfer Function', fontsize=20)
fig1.tight_layout()
fig1.canvas.set_window_title(FitFilePath)
plt.show()
# plot heart rate and calories
import matplotlib.dates as md
from matplotlib.dates import date2num, DateFormatter
import datetime as dt
base = dt.datetime(2014, 1, 27, 0, 0, 0)
x = [base + dt.timedelta(seconds=t) for t in t_sig]
x = date2num(x) # Convert to matplotlib format
fig1, ax0 = plt.subplots()
ax0.plot_date( x, hr_sig, 'r-', linewidth=3 );
ax0.set_yticks( h_zone_bounds, minor=False)
ax0.grid(True)
ax0.set_title('heart rate, BPM')
ax0.set_title('Heart Rate Analysis')
fig1.autofmt_xdate()
fig1.tight_layout()
fig1.canvas.set_window_title(FitFilePath)
plt.show()
# plt.plot(t_sig/60, hr_sig, 'r.-')
# plt.title('Heart Rate and Calories')
# plt.ylabel('BPM')
# plt.subplot(2, 1, 2)
# plt.plot(t_sig/60, running_calories, 'b.-')
# plt.xlabel('time (min)')
# plt.ylabel('calories')
# heart rate histogram plot
fig2, ax2 = plt.subplots()
bar_width = 0.80 # 0.35
opacity = 0.4
#error_config = {'ecolor': '0.3'}
zone_ints = np.arange(7)+1
LogY = False
rects1 = ax2.bar(zone_ints+bar_width/2, ZoneCounts/SampleRate/60,
bar_width, alpha=opacity, color='r', log=LogY,
label='heart rate')
ax2.set_xlabel('Zone')
ax2.set_ylabel('minutes')
ax2.set_title('Heart Rate Zone Histogram')
ax2.set_xticks(zone_ints + bar_width / 2)
ax2.set_xticklabels(h_zone_labels)
ax2.legend()
fig2.tight_layout()
fig2.canvas.set_window_title(FitFilePath)
plt.show()
def ClosePlots():
plt.close('all')
return ClosePlots
# end plot_heartrate()
############################################################
# main program execution #
############################################################
'''
This technique allows the module to be imported without
executing it until one of its functions is called.
'''
if __name__ == '__main__':
import sys
if len(sys.argv) >= 2:
print 'command line args: ', sys.argv[1:]
fitfilepath = sys.argv[1]
plot_heartrate(fitfilepath, ConfigFile=None)
else:
raise IOError('Need a .FIT file')
#FitFilePath = r'S:\will\documents\OneDrive\bike\activities\will\\' \
# + r'2018-12-02-13-13-19.fit'
# SAMPLE OUTPUT:
# CWD: D:\Users\Owner\Documents\OneDrive\bike\activities\will
# PATH: D:\Users\Owner\Documents\OneDrive\bike\activities\will
# FILE: 2018-12-02-13-13-19.fit
#
# -------------------- Heart Rate --------------------
#
# reading config file D:\...\fitfiles\cyclingconfig_will.txt
# WeightEntry : 190.0
# WeightToKg : 0.45359237
# weight : 86.1825503
# age : 52.0
# sex : male
# EndurancePower: 175.0
# ThresholdPower: 250.0
# EnduranceHR : 140.0
# ThresholdHR : 170.0
# total calories = 645
# Heart-Rate Zone Histogram:
# Zone 1: 1:16:43 (92%)
# Zone 2: 0:03:46 ( 4%)
# Zone 3: 0:01:57 ( 2%)
# Zone 4: 0:00:11 ( 0%)
# Zone 5a: 0:00:00 ( 0%)
# Zone 5b: 0:00:00 ( 0%)
# Zone 5c: 0:00:00 ( 0%)
# total: 1:22:37
# -------------------- Endurance Laps --------------------
#
# reading config file D:\...\cyclingconfig_will.txt
# WeightEntry : 190.0
# WeightToKg : 0.45359237
# weight : 86.1825503
# age : 52.0
# EndurancePower: 175.0
# ThresholdPower: 250.0
# EnduranceHR : 140.0
# ThresholdHR : 170.0
# required signals not in file
# Signals required:
# power
# heart_rate
# Signals contained:
# distance
# temperature
# altitude
# heart_rate
# time
# metadata