def get_group_name(urlString, req_type):
try:
url2 = urlString[urlString.find('profile/'):]
url3 = url2[url2.find('/') + 1:]
url4 = url3[url3.find('/') + 1:]
return url4
except:
gc.connect_to_database()
gc.create_session()
url = req_type['filter']
url2 = url[url.find('profile/'):]
url3 = url2[url2.find('/') + 1:]
group_guid = url3[:url3.find('/')]
group_name = gc.groups.name_from_guid(group_guid)
return group_name
def connect_to_collab():
"""
Helper function to streamline connection to the GCcollab database
"""
engine, conn = gccollab.connect_to_database()
session, Base = gccollab.create_session()
return engine, conn, session, Base
def main(testing=False):
# Get JSON object from stdin
if testing == False:
req_obj = read_in()
else:
req_obj = json.loads(testing)
# Pick apart request object
req_type = req_obj['reqType']
metric = req_obj['metric']
metric2 = req_obj['metric2']
start_time = req_obj['time']['startDate']
end_time = req_obj['time']['endDate']
all_time = req_obj['time']['allTime']
#https://gcconnex.gc.ca/newsfeed/
# https://gcconnex.gc.ca/groups/profile/24345174/endata-visualizationfr
# https://gcconnex.gc.ca/groups/profile/272967/enblueprint-2020-objectif-2020fr
# Start parsing the request. This is where it gets interesting / messy!
# Type
if req_type['category'] == 1: # Groups
url = req_type['filter']
if metric == 1: # Pageviews
ga = gcga()
ga.set_platform('gccollab')
group_name = get_group_name(url, req_type)
# Request a dataframe containing pageviews and corresponding dates
ret = ga.pageviews([url, 'NOToffset'],
intervals=True,
start_date=start_time,
end_date=end_time)
ret['group_name'] = group_name
print(json.dumps(ret))
elif metric == 2: # Top content
# Establish database connection
gc.connect_to_database()
gc.create_session()
# Figure out group guid from url
url2 = url[url.find('profile/'):]
url3 = url2[url2.find('/') + 1:]
group_guid = url3[:url3.find('/')]
# Get the guids of all content within group
guid_list = gc.content.get_top_content(group_guid)
# Get the clean group name from the guid
group_name = gc.groups.name_from_guid(group_guid)
# Build regex string from guids
regex_str = ''
l = guid_list.values.astype(str).tolist()
for i in range(len(l)):
regex_str = regex_str + ('/' + l[i][0] + '/|'
) # ADD FIX TO GCCONNEX
# Remove trailing pipe
regex_str = regex_str[:-1]
# Send google analytics request with regex string
ga = gcga()
ga.set_platform('gccollab')
ret = ga.content_views(regex_str)
# Format results and print to stdout
ret['group_name'] = group_name
print(json.dumps(ret))
elif metric == 3: # Number of members
# Establish database connection
gc.connect_to_database()
gc.create_session()
# Determine group guid
group_guid = get_group_guid(url)
# Query the database
group_members = gc.groups.get_group_members(group_guid,
cleaned=False)
group_name = gc.groups.name_from_guid(group_guid)
# Get mungin'
# Convert timjes to datetime objects
group_members['time_created'] = group_members[
'time_created'].apply(lambda x: pd.to_datetime(x))
group_members.set_index('time_created', inplace=True)
group_members = group_members[
group_members.index > pd.to_datetime('2000-01-01')]
# Daily
group_members_daily = group_members['user_name'].groupby(
pd.TimeGrouper(freq='D')).count().cumsum()
group_members_daily = group_members_daily.reset_index()
# Only keep current time selection
group_members_daily = group_members_daily[
group_members_daily['time_created'] >= pd.to_datetime(
start_time)]
group_members_daily = group_members_daily[
group_members_daily['time_created'] <= pd.to_datetime(
end_time)]
# If the requested start date predates the oldest time on the dataframe, pad with 0s
if min(group_members_daily['time_created']) > pd.to_datetime(
start_time):
ix = pd.DatetimeIndex(start=pd.to_datetime(start_time),
end=max(
group_members_daily['time_created']),
freq='D')
group_members_daily = group_members_daily.set_index(
'time_created').reindex(ix, fill_value=0).reset_index()
group_members_daily.rename(columns={'index': 'time_created'},
inplace=True)
group_members_daily['time_created'] = group_members_daily[
'time_created'].apply(lambda x: x.strftime('%Y%m%d'))
# Monthly
group_members_monthly = group_members['user_name'].groupby(
pd.TimeGrouper(freq='M')).count().cumsum()
group_members_monthly = group_members_monthly.reset_index()
# Only keep current time selection
group_members_monthly = group_members_monthly[
group_members_monthly['time_created'] >= pd.to_datetime(
start_time)]
group_members_monthly = group_members_monthly[
group_members_monthly['time_created'] <= pd.to_datetime(
end_time)]
# (monthly) If the requested start date predates the oldest time on the dataframe, pad with 0s
if min(group_members_monthly['time_created']) > pd.to_datetime(
start_time):
ix = pd.DatetimeIndex(
start=pd.to_datetime(start_time),
end=max(group_members_monthly['time_created']),
freq='M')
group_members_monthly = group_members_monthly.set_index(
'time_created').reindex(ix, fill_value=0).reset_index()
group_members_monthly.rename(columns={'index': 'time_created'},
inplace=True)
group_members_monthly['time_created'] = group_members_monthly[
'time_created'].apply(lambda x: x.strftime('%Y%m%d'))
send_obj = {
'monthly': {
'dates':
group_members_monthly['time_created'].values.tolist(),
'users':
group_members_monthly['user_name'].values.tolist()
},
'daily': {
'dates':
group_members_daily['time_created'].values.tolist(),
'users': group_members_daily['user_name'].values.tolist(),
},
'group_name': group_name
}
print(json.dumps(send_obj))
elif metric == 4: # Group members by department
gc.connect_to_database()
gc.create_session()
# Determine group guid
group_guid = get_group_guid(url)
members = gc.groups.get_group_members(group_guid)
# The index used below could be any column, not important
members = members.groupby('department').count().reset_index(
).set_index('time_created').sort_index(
ascending=False).reset_index()
send_obj = {
'departments': members['department'].values.tolist(),
'members': members['time_created'].values.tolist()
}
print(json.dumps(send_obj))
elif req_type['category'] == 2: # Users
if req_type['filter'] == 1: # All users
# This is for both 'Registered users' and 'Active users' queries.
# (The queries only differ by one line)
gc.connect_to_database()
gc.create_session()
users = gc.users.get_all()
users['time_created'] = users['time_created'].apply(
lambda x: pd.to_datetime(x))
# Get monthly totals
monthly = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='M')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
monthly['time_created'] = monthly['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
# Get daily totals
daily = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='D')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
daily['time_created'] = daily['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
send_obj = {
'monthly': {
'dates': monthly['time_created'].values.tolist(),
'users': monthly['email'].values.tolist()
},
'daily': {
'dates': daily['time_created'].values.tolist(),
'users': daily['email'].values.tolist()
}
}
print(json.dumps(send_obj))
elif req_type['filter'] == 2: # Users from a particular department
if metric2 == 1: # Number of users from particular department
gc.connect_to_database()
gc.create_session()
users = gc.users.department()
users['time_created'] = users['time_created'].apply(
lambda x: pd.to_datetime(x))
# Only keep users from indicated department
# Need to import that list of deps to parse the number from
dep_list = []
with open('en_dpts.json') as depts:
depts_dict = json.load(depts)
for dep in depts_dict.values():
dep_list.append(dep)
# Figure out the corresponding index
dep = dep_list[metric]
users = users[users['string'].str.contains(dep)]
# Get monthly totals
monthly = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='M')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
monthly['time_created'] = monthly['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
# Get daily totals
daily = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='D')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
daily['time_created'] = daily['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
send_obj = {
'monthly': {
'dates': monthly['time_created'].values.tolist(),
'users': monthly['email'].values.tolist()
},
'daily': {
'dates': daily['time_created'].values.tolist(),
'users': daily['email'].values.tolist()
}
}
print(json.dumps(send_obj))
elif metric2 == 2: # Users opted in to oppurtunities platform from particular department
gc.connect_to_database()
gc.create_session()
users = gc.micromissions.get_users()
dep_list = []
with open('en_dpts.json') as depts:
depts_dict = json.load(depts)
for dep in depts_dict.values():
dep_list.append(dep)
# Figure out the corresponding index
dep = dep_list[metric]
users = users[users['department'].str.contains(dep)]
users = users[users['opt-in'].str.contains('yes')]
# Get monthly totals
monthly = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='M')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
monthly['time_created'] = monthly['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
# Get daily totals
daily = users.set_index('time_created').groupby(
pd.TimeGrouper(freq='D')).count().cumsum().reset_index()[[
'time_created', 'email'
]]
daily['time_created'] = daily['time_created'].apply(
lambda x: x.strftime('%Y%m%d'))
send_obj = {
'monthly': {
'dates': monthly['time_created'].values.tolist(),
'users': monthly['email'].values.tolist()
},
'daily': {
'dates': daily['time_created'].values.tolist(),
'users': daily['email'].values.tolist()
}
}
print(json.dumps(send_obj))