def df_heartrate(query):
"""
# Input: querystring to retrieve the full signal
# Output: PD dataframe with 5 minute discretised intervals with all the features
"""
time1 = time.time()
HRrsh = pd.DataFrame()
HRframe = pd.read_gbq(query, globe.LRSid,private_key=globe.LRSkey) # Populating the dataframe
if len(HRframe)>0:
HRdf = HRframe[['timestamp','resultResponse','actorId']]
HRrsh = core.emailToId(HRdf,'actorId')
HRrsh.set_index(['timestamp','actorId'], inplace=True)
HRrsh.resultResponse = HRrsh.resultResponse.astype(int)
HRrsh = HRrsh.groupby([pd.TimeGrouper('5Min',level=0),
HRrsh.index.get_level_values('actorId')]).agg({'resultResponse': {
'hr_mean': np.mean, # Mean of the signal
'hr_max': np.max, # Maximum
'hr_min': np.min, # Minimum
'hr_std': np.std,
'hr_avc': avg_change }})['resultResponse']
time2 = time.time()
print '3 ----- Heartrate feature generation took %0.1f s' % ((time2-time1))
else:
print '3 ----- No Heartrate values found in this time-window'
return HRrsh
def df_heartrate(query):
"""
# Input: querystring to retrieve the full signal
# Output: PD dataframe with 5 minute discretised intervals with all the features
"""
time1 = time.time()
HRrsh = pd.DataFrame()
HRframe = pd.read_gbq(query, globe.LRSid,
private_key=globe.LRSkey) # Populating the dataframe
if len(HRframe) > 0:
HRdf = HRframe[['timestamp', 'resultResponse', 'actorId']]
HRrsh = core.emailToId(HRdf, 'actorId')
HRrsh.set_index(['timestamp', 'actorId'], inplace=True)
HRrsh.resultResponse = HRrsh.resultResponse.astype(int)
HRrsh = HRrsh.groupby([
pd.TimeGrouper('5Min', level=0),
HRrsh.index.get_level_values('actorId')
]).agg({
'resultResponse': {
'hr_mean': np.mean, # Mean of the signal
'hr_max': np.max, # Maximum
'hr_min': np.min, # Minimum
'hr_std': np.std,
'hr_avc': avg_change
}
})['resultResponse']
time2 = time.time()
print '3 ----- Heartrate feature generation took %0.1f s' % (
(time2 - time1))
else:
print '3 ----- No Heartrate values found in this time-window'
return HRrsh
def df_ratings(query):
time1 = time.time()
# Populating the dataframe
RTframe = pd.read_gbq(query, globe.LRSid,private_key=globe.LRSkey)
RTrsh = pd.DataFrame()
if len(RTframe)>0:
# Filtering the results
RTdf = RTframe[RTframe['objectId']!='dashboard']
RTdf = RTdf[['timestamp','objectId','resultResponse','actorId']]
#Rename the columns
RTdf.rename(columns={'objectId':'Indicators'}, inplace=True)
RTdf.rename(columns={'resultResponse':'value'}, inplace=True)
#Drop the entries which are exactely identical
RTdf = RTdf.drop_duplicates()
#Drop the duplicate ratings (not indentical), take the last
RTdf = RTdf.set_index(['timestamp','actorId','Indicators']).sort_index()
RTdf = RTdf.groupby(level=RTdf.index.names).last().reset_index()
RTrsh = RTdf.pivot_table(values='value',
index=['timestamp','actorId'],
columns=['Indicators'],aggfunc=lambda x: x.iloc[0])
RTrsh.reset_index(inplace=True)
# Fix: the index will shift -1 hr. E.g. 9:00 -> 8:00
# Indicating the rating done at 9:00 for the 8:xx activities
RTrsh.timestamp = RTrsh.timestamp - pd.offsets.Hour(1)
# Retstrict to the arlearn value
RTrsh = core.emailToId(RTrsh,'actorId')
RTrsh['timeframe'] = RTrsh.timestamp.map(lambda x: x.strftime('%H')).astype(int)
RTrsh.set_index(['timestamp','actorId'], inplace=True)
RTrsh = RTrsh.dropna()
RTrsh['MainActivity'] = activityToId(RTrsh['MainActivity'])
# 1. First check for missing values and fill them backward
# 2. Then check again and fill them forward (workaround for latest missing)
# 3. Then cast to int
RTrsh = RTrsh.fillna(method='bfill').fillna(method='pad').astype(int)
# Calculate the Flow-Score - see function flowPoints for explaination
RTrsh['Flow'] = RTrsh.apply(flowPoints, axis=1)
#Create 5 minutes intervals
RTrsh = RTrsh.unstack().fillna(-1).resample('5Min').fillna(method='pad').stack().replace(-1,np.NaN)
# The correlation between Flow and Productivity
#flowProdCorr = RTrsh[['Productivity','Flow']].corr().iloc[0]['Flow']
time2 = time.time()
print '1 ----- Ratings values read from BigQuery in %0.3f s' % ((time2-time1))
else:
print '1 ----- No ratings found in this time-window'
return RTrsh
def df_weather(query,start_date,end_date):
# Populating the dataframe
time1 = time.time()
# Check the CSV file
WTcsv = pd.DataFrame()
if os.path.exists(globe.weatherFile):
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')
WTdf = pd.read_csv(globe.weatherFile, parse_dates=['timestamp'], date_parser=dateparse)
mask = (WTdf['timestamp'] > start_date) & (WTdf['timestamp'] <= end_date)
WTdf = WTdf.loc[mask]
if len(WTdf)>0:
WTdf['weather'] = WTdf['weather'].replace({"u'":"'","'":'"'}, regex=True)
WTdf['lat'], WTdf['lng'], WTdf['weatherId'], WTdf['pressure'],\
WTdf['temp'], WTdf['humidity'] = zip(*WTdf['weather'].map(jsonToDF))
WTcsv = WTdf.drop(['weather'], axis=1)
WTcsv.set_index(['timestamp','actorId'], inplace=True)
WTcsv = WTcsv.unstack().resample('5min').fillna(method='bfill').fillna(method='pad').stack()
time2 = time.time()
print '5.1 ----- Weather generation (from CSV) took %0.1f s' % ((time2-time1))
# Check the Bigquery
WTframe = pd.read_gbq(query, globe.PRSid,private_key=globe.PRSkey)
WTgbq = pd.DataFrame()
if len(WTframe)>0:
WTdf = WTframe[['date','status','user']]
WTgbq = core.emailToId(WTdf,'user')
WTgbq['status'] = WTgbq['status'].replace({"u'":"'","'":'"'}, regex=True)
WTgbq['lat'], WTgbq['lng'], WTgbq['weatherId'], WTgbq['pressure'],\
WTgbq['temp'],WTgbq['humidity'] = zip(*WTgbq['status'].map(jsonToDF))
WTgbq.rename(columns={'date':'timestamp'}, inplace=True)
WTgbq.rename(columns={'user':'******'}, inplace=True)
WTgbq = WTgbq.drop(['status'], axis=1)
WTgbq.set_index(['timestamp','actorId'], inplace=True)
WTgbq = WTgbq.unstack().resample('5min').stack()
time2 = time.time()
print '5.2 ----- Weather generation (from BigQuery) took %0.1f s' % ((time2-time1))
WTrsh = pd.DataFrame()
if len(WTcsv)>0 and len(WTgbq)>0:
WTrsh = pd.concat([WTcsv,WTgbq])
WTrsh = WTrsh[~WTrsh.index.duplicated(keep='last')]
elif len(WTcsv)>0:
WTrsh = WTcsv
else:
WTrsh = WTgbq
return WTrsh
def df_steps(query,allUsers=False):
# Populating the dataframe
time1 = time.time()
SCframe = pd.read_gbq(query, globe.LRSid,private_key=globe.LRSkey)
SCrsh = pd.DataFrame()
if len(SCframe)>0:
# Filtering the results
SCdf = SCframe[['timestamp','resultResponse','actorId']]
SCrsh = core.emailToId(SCdf,'actorId')
#Rename columns
SCrsh.rename(columns={'resultResponse':'Steps'}, inplace=True)
SCrsh.Steps = SCrsh.Steps.astype(int)
SCrsh.set_index(['timestamp','actorId'], inplace=True)
SCrsh = SCrsh.groupby([pd.TimeGrouper('5Min',level=0), SCrsh.index.get_level_values('actorId')])['Steps'].sum()
time2 = time.time()
print '2 ----- Steps values read from BigQuery in %0.3f s' % ((time2-time1))
return SCrsh
def df_activities(query):
# Populating the dataframe
time1 = time.time()
ACframe = pd.read_gbq(query, globe.LRSid, private_key=globe.LRSkey)
ACrsh = pd.DataFrame()
CArsh = pd.DataFrame()
if len(ACframe)>0:
gdoc = requests.get(globe.googleDocCategories)
data = gdoc.content
GDdf = pd.read_csv(StringIO(data), error_bad_lines=False)
n_app = len(GDdf)
df = GDdf.stack()
dict_apps = dict()
for i in range(0,n_app):
if len(df[i][df[i]=='x'])>0:
dict_apps[df[i][0]] = df[i][df[i]=='x'].index.get_values()[0]
ACframe['origin'] = ACframe['origin'].astype(str)
ACdf = ACframe[['timestamp','objectId','resultDuration','actorId']]
ACdf = core.emailToId(ACdf,'actorId')
#Rename columns
ACdf.rename(columns={'objectId':'App'}, inplace=True)
ACdf['Cat'] = ACdf['App'].map(dict_apps)
ACrsh = ACdf.groupby(['timestamp', 'actorId', 'App'])['resultDuration'].sum().unstack()
CArsh = ACdf.groupby(['timestamp', 'actorId', 'Cat'])['resultDuration'].sum().unstack()
ACrsh = ACrsh.fillna(0)
CArsh = CArsh.fillna(0)
#check distributon
#df[df['objectId'].str.contains("MS Word")].ix[:,2:].notnull().stack().idxmax()[1]
#CArsh.sum().plot(kind='bar')
#dfAC,dfCA = activities.df_activities("SELECT * FROM [xAPIStatements.xapiTableNew] WHERE origin = 'rescuetime' AND timestamp > PARSE_UTC_USEC('2015-11-23 07:00:00') AND timestamp < PARSE_UTC_USEC('2015-12-09 20:00:00') ORDER by timestamp")
time2 = time.time()
print '4 ----- Activities processed in %0.1f s' % ((time2-time1))
else:
time2 = time.time()
print '4 ----- No activities found in this time window'
return ACrsh,CArsh
def df_ratings(query):
time1 = time.time()
# Populating the dataframe
RTframe = pd.read_gbq(query, globe.LRSid, private_key=globe.LRSkey)
RTrsh = pd.DataFrame()
if len(RTframe) > 0:
# Filtering the results
RTdf = RTframe[RTframe['objectId'] != 'dashboard']
RTdf = RTdf[['timestamp', 'objectId', 'resultResponse', 'actorId']]
#Rename the columns
RTdf.rename(columns={'objectId': 'Indicators'}, inplace=True)
RTdf.rename(columns={'resultResponse': 'value'}, inplace=True)
#Drop the entries which are exactely identical
RTdf = RTdf.drop_duplicates()
#Drop the duplicate ratings (not indentical), take the last
RTdf = RTdf.set_index(['timestamp', 'actorId',
'Indicators']).sort_index()
RTdf = RTdf.groupby(level=RTdf.index.names).last().reset_index()
RTrsh = RTdf.pivot_table(values='value',
index=['timestamp', 'actorId'],
columns=['Indicators'],
aggfunc=lambda x: x.iloc[0])
RTrsh.reset_index(inplace=True)
# Fix: the index will shift -1 hr. E.g. 9:00 -> 8:00
# Indicating the rating done at 9:00 for the 8:xx activities
RTrsh.timestamp = RTrsh.timestamp - pd.offsets.Hour(1)
# Retstrict to the arlearn value
RTrsh = core.emailToId(RTrsh, 'actorId')
RTrsh['timeframe'] = RTrsh.timestamp.map(
lambda x: x.strftime('%H')).astype(int)
RTrsh.set_index(['timestamp', 'actorId'], inplace=True)
RTrsh = RTrsh.dropna()
RTrsh['MainActivity'] = activityToId(RTrsh['MainActivity'])
# 1. First check for missing values and fill them backward
# 2. Then check again and fill them forward (workaround for latest missing)
# 3. Then cast to int
RTrsh = RTrsh.fillna(method='bfill').fillna(method='pad').astype(int)
# Calculate the Flow-Score - see function flowPoints for explaination
RTrsh['Flow'] = RTrsh.apply(flowPoints, axis=1)
#Create 5 minutes intervals
RTrsh = RTrsh.unstack().fillna(-1).resample('5Min').fillna(
method='pad').stack().replace(-1, np.NaN)
# The correlation between Flow and Productivity
#flowProdCorr = RTrsh[['Productivity','Flow']].corr().iloc[0]['Flow']
time2 = time.time()
print '1 ----- Ratings values read from BigQuery in %0.3f s' % (
(time2 - time1))
else:
print '1 ----- No ratings found in this time-window'
return RTrsh
