def read_humon_file(file_name):
    humon_columns = [
    'POSIX Time [seconds]',
    'Time [seconds]',
    'smo2 [%]',
    'hgb [g/dL]',
    'zone [-]',
    'heartRate [bpm]',
    'altitude [meters]',
    'speed [meters/sec]',
    'latitude [degrees]',
    'longitude [degrees]',
    'distance [meters]',
    'wrk_date',
    'wrk_act',
    'wrk_type',
    'wrk_score'
    ]
    
    hr_ut2 = 137
    hr_ut1 = 145
    hr_at = 158
    hr_tr = 164
    hr_an = 178
    hr_max = 185

#	This section can be ignored.  I needed to do some stuff to deal with bugs
#	in the way that Humon exported data.  The key thing is it identified where the
#	real data started.
    f=open(file_name,'r')
    raw_data=f.readlines()
    
    found_header = False
    raw_data_index = 0
    
    while not found_header:
        if "Workout UUID" in raw_data[raw_data_index]:
            workout_UUID = raw_data[raw_data_index][14:50]
        elif "GMT" in raw_data[raw_data_index]:
            workout_date_time = raw_data[raw_data_index][4:24]
        elif "Activity" in raw_data[raw_data_index]:
            workout_activity = raw_data[raw_data_index][15:len(raw_data[raw_data_index])-1]
        elif "Workout Type" in raw_data[raw_data_index]:
            workout_type = raw_data[raw_data_index][14:len(raw_data[raw_data_index])-1]
        elif "Effort" in raw_data[raw_data_index]:
            workout_effort = raw_data[raw_data_index][8:9]
        elif "Score" in raw_data[raw_data_index]:
            workout_score = raw_data[raw_data_index][7:9]
        elif "Threshold" in raw_data[raw_data_index]:
            workout_threshold = raw_data[raw_data_index][11:14]
        elif "POSIX" in raw_data[raw_data_index]:
            found_header = True
        raw_data_index = raw_data_index + 1
    workout_name = file_name
#	End of weirdness

    df = pd.read_csv(file_name, skiprows=raw_data_index-1)

#	Instead of having data columns for each of the Humon zones, 
#	I added a single field for zone_clr (zone color) and populated it by testing
#	for the recorded zone number
    df['zone_clr']='grey'
    df.loc[df['zone [-]']==2,'zone_clr']='blue'
    df.loc[df['zone [-]']==3,'zone_clr']='green'
    df.loc[df['zone [-]']==4,'zone_clr']='orange'
    df.loc[df['zone [-]']==5,'zone_clr']='red'
#	In the Humon data, the HR can either be present or not present.  I start by 
#	setting the zone to <UT2 (grey), and then use the hr zone boundaries to 
# 	fill a hr_clr (heart rate color) field in the data frame
    df['hr_clr'] = 'grey'
    if "heartRate" in raw_data[raw_data_index-1]:
        print("found HR")
        df.loc[df['heartRate [bpm]']>hr_ut2,'hr_clr'] = 'yellow'
        df.loc[df['heartRate [bpm]']>hr_ut1,'hr_clr'] = 'green'
        df.loc[df['heartRate [bpm]']>hr_at,'hr_clr'] = 'blue'
        df.loc[df['heartRate [bpm]']>hr_tr,'hr_clr'] = 'purple'
        df.loc[df['heartRate [bpm]']>hr_an,'hr_clr'] = 'red'

    return df;
    
    workout = './Documents/Humon_new_data/Humon-2019-06-09_18_53_59_UTC.csv'

#  This is the plotting routine.  At this point, all workouts are in a data frame called
#	df_all.  In this routine, I pick out a specific workout based on it's filename and then 
#	move it into a temporary data frame (df) to work on.           
df = df_all[df_all['wrk_name']==workout] 

#	This is setting up all the axes and labels
fig = plt.figure() 
ax1=fig.add_axes([0.1,2.1,1.4,1.0]) 
plt.grid() 
plt.xlim(0,max(df['Time [seconds]']))
plt.ylim(min(df['smo2 [%]'])-10,max(df['smo2 [%]'])+10)
plt.ylabel("smo2 [%]")
ax2=fig.add_axes([0.1,1.1,1.4,1.0]) 
plt.grid() 
plt.xlim(0,max(df['Time [seconds]']))
plt.ylim(min(df["hgb [g/dL]"])-1,max(df["hgb [g/dL]"])+1)
plt.ylabel("hgb [g/dL]")
ax3=fig.add_axes([0.1,0.1,1.4,1.0]) 
plt.grid() 
plt.xlim(0,max(df['Time [seconds]']))
plt.ylim(min(df['heartRate [bpm]']),max(df['heartRate [bpm]'])+10)
plt.ylabel("HR (bpm)")
x_values = pd.to_numeric(df['Time [seconds]'])

# 	smO2 plot.  I create a field in the dataframe called "tempval" which gets filled and 
#	and plotted by masking all values that are do not match the target fill color
df.loc[:,'tempval'] = df['smo2 [%]']
df.loc[df.zone_clr!='green','tempval']=0
ax1.fill_between(x_values,df.tempval,color='green')
df.loc[:,'tempval'] = df['smo2 [%]']
df.loc[df.zone_clr!='orange','tempval']=0
ax1.fill_between(x_values,df.tempval,color='orange')
df.loc[:,'tempval'] = df['smo2 [%]']
df.loc[df.zone_clr!='red','tempval']=0
ax1.fill_between(x_values,df.tempval,color='red')
df.loc[:,'tempval'] = df['smo2 [%]']
df.loc[df.zone_clr!='blue','tempval']=0
ax1.fill_between(x_values,df.tempval,color='blue')

# hgb plot.  This uses the same field (zone_clr) to color the hgb plot in the same way, 
#	using the tempval field
df.loc[:,'tempval'] = df["hgb [g/dL]"]
df.loc[df.zone_clr!='green','tempval']=0
ax2.fill_between(x_values,df.tempval,color='green')
df.loc[:,'tempval'] = df["hgb [g/dL]"]
df.loc[df.zone_clr!='orange','tempval']=0
ax2.fill_between(x_values,df.tempval,color='orange')
df.loc[:,'tempval'] = df["hgb [g/dL]"]
df.loc[df.zone_clr!='red','tempval']=0
ax2.fill_between(x_values,df.tempval,color='red')
df.loc[:,'tempval'] = df["hgb [g/dL]"]
df.loc[df.zone_clr!='blue','tempval']=0
ax2.fill_between(x_values,df.tempval,color='blue')

# hr plot.	This plot uses the same method, but the hr_clr field as the masking variable
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='grey','tempval']=0
ax3.fill_between(x_values,df.tempval,color='grey')
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='yellow','tempval']=0
ax3.fill_between(x_values,df.tempval,color='yellow')
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='green','tempval']=0
ax3.fill_between(x_values,df.tempval,color='green')
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='blue','tempval']=0
ax3.fill_between(x_values,df.tempval,color='blue')
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='purple','tempval']=0
ax3.fill_between(x_values,df.tempval,color='purple')
df.loc[:,'tempval'] = df['heartRate [bpm]']
df.loc[df.hr_clr!='red','tempval']=0
ax3.fill_between(x_values,df.tempval,color='red')