def get_list(self):
# Just get closed per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
mclosed = Closed(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = mclosed.filters
mclosed.filters = mfilter
closed = mclosed.get_agg()
mclosed.filters = mfilter_orig
checkListArray(closed)
return closed
def get_list(self):
# Just get sent per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
msent = EmailsSent(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = msent.filters
msent.filters = mfilter
sent = msent.get_agg()
msent.filters = mfilter_orig
checkListArray(sent)
return sent
def get_list(self):
# Just get sent per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
msent = EmailsSent(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = msent.filters
msent.filters = mfilter
sent = msent.get_agg()
msent.filters = mfilter_orig
checkListArray(sent)
return sent
def get_list(self):
# Just get commits per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
mcommits = Commits(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = mcommits.filters
mcommits.filters = mfilter
commits = mcommits.get_agg()
mcommits.filters = mfilter_orig
checkListArray(commits)
return commits
def get_list(self):
# Just get commits per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
mcommits = Commits(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = mcommits.filters
mcommits.filters = mfilter
commits = mcommits.get_agg()
mcommits.filters = mfilter_orig
checkListArray(commits)
return commits
def get_list(self):
# Just get closed per project
startdate = self.filters.startdate
enddate = self.filters.enddate
type_analysis = ['project', None]
period = None
evol = False
mclosed = Closed(self.db, self.filters)
mfilter = MetricFilters(period, startdate, enddate, type_analysis)
mfilter_orig = mclosed.filters
mclosed.filters = mfilter
closed = mclosed.get_agg()
mclosed.filters = mfilter_orig
checkListArray(closed)
return closed
def _get_agg_all(self):
# All items data is returned together
# Show review and upload time for all pending reviews
# and just for reviews pending for reviewers
reviewers_pending = True
startdate = self.filters.startdate
enddate = self.filters.enddate
identities_db = self.db.identities_db
type_analysis = self.filters.type_analysis
bots = []
# First, we need to group by the filter field the data
all_items = self.db.get_all_items(self.filters.type_analysis)
group_field = self.db.get_group_field(all_items)
id_field = group_field.split('.')[1] # remove table name
time_to = {"review_time_pending_days_median":[],
"review_time_pending_days_avg":[],
"review_time_pending_ReviewsWaitingForReviewer_days_median":[],
"review_time_pending_ReviewsWaitingForReviewer_days_avg":[],
"review_time_pending_upload_days_median":[],
"review_time_pending_upload_days_avg":[],
"review_time_pending_upload_ReviewsWaitingForReviewer_days_median":[],
"review_time_pending_upload_ReviewsWaitingForReviewer_days_avg":[]
}
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db, bots)
ttr_data = self.db.ExecuteQuery(q)
checkListArray(ttr_data)
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db, bots, reviewers_pending)
ttr_reviewers_data = self.db.ExecuteQuery(q)
checkListArray(ttr_reviewers_data)
q = self.db.GetTimeToReviewPendingQuerySQL (self.filters, identities_db, bots, False, True)
ttr_upload_data = self.db.ExecuteQuery(q)
checkListArray(ttr_upload_data)
# This query is really slow.
q = self.db.GetTimeToReviewPendingQuerySQL (self.filters, identities_db, bots, reviewers_pending, True)
ttr_reviewers_upload_data = self.db.ExecuteQuery(q)
checkListArray(ttr_reviewers_upload_data)
all_items_ids = []
for items in [ttr_data,ttr_reviewers_data,ttr_upload_data,ttr_reviewers_upload_data]:
# Get the list of items and add them to the global list
all_items_ids = list(Set(items[id_field] + all_items_ids))
time_to['name'] = all_items_ids
# Review time
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_data[id_field])):
if ttr_data[id_field][i] == item:
data_item_revtime.append(ttr_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_median = float("nan")
ttr_avg = float("nan")
else:
ttr_median = median(removeDecimals(data_item_revtime))
ttr_avg = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_days_median"].append(ttr_median)
time_to["review_time_pending_days_avg"].append(ttr_avg)
# Review time for reviewers
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_reviewers_data[id_field])):
if ttr_reviewers_data[id_field][i] == item:
data_item_revtime.append(ttr_reviewers_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_reviewers_median = float("nan")
ttr_reviewers_avg = float("nan")
else:
ttr_reviewers_median = median(removeDecimals(data_item_revtime))
ttr_reviewers_avg = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_ReviewsWaitingForReviewer_days_median"].append(ttr_reviewers_median)
time_to["review_time_pending_ReviewsWaitingForReviewer_days_avg"].append(ttr_reviewers_avg)
# Upload time
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_upload_data[id_field])):
if ttr_upload_data[id_field][i] == item:
data_item_revtime.append(ttr_upload_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_median_upload = float("nan")
ttr_avg_upload = float("nan")
else:
ttr_median_upload = median(removeDecimals(data_item_revtime))
ttr_avg_upload = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_upload_days_median"].append(ttr_median_upload)
time_to["review_time_pending_upload_days_avg"].append(ttr_median_upload)
# Upload time for reviewers
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_reviewers_upload_data[id_field])):
if ttr_reviewers_upload_data[id_field][i] == item:
data_item_revtime.append(ttr_reviewers_upload_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_reviewers_median_upload = float("nan")
ttr_reviewers_avg_upload = float("nan")
else:
ttr_reviewers_median_upload = median(removeDecimals(data_item_revtime))
ttr_reviewers_avg_upload = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_upload_ReviewsWaitingForReviewer_days_median"].append(ttr_reviewers_median_upload)
time_to["review_time_pending_upload_ReviewsWaitingForReviewer_days_avg"].append(ttr_reviewers_avg_upload)
# In SCR the item field name must be url for repository
if self.filters.type_analysis[0] == 'repository':
time_to['url'] = time_to.pop('name')
return time_to
def _get_ts_all():
""" Get the metrics for all items at the same time """
all_items = self.db.get_all_items(self.filters.type_analysis)
group_field = self.db.get_group_field(all_items)
id_field = group_field.split('.')[1] # remove table name
metrics = ["review_time_pending_reviews",
"review_time_pending_days_acc_median",
"review_time_pending_upload_reviews",
"review_time_pending_upload_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_reviews",
"review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_upload_ReviewsWaitingForReviewer_reviews"]
metrics = ["review_time_pending_reviews",
"review_time_pending_days_acc_median",
"review_time_pending_upload_reviews",
"review_time_pending_upload_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_reviews",
"review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_upload_ReviewsWaitingForReviewer_reviews"]
acc_pending_time_median_month = {"month":[],"name":[]} # Used to store each month all items data
acc_pending_time_median = {"month":[]} # Used to store the final format
for metric in metrics:
acc_pending_time_median_month[metric] = []
acc_pending_time_median[metric] = []
# months = 2 # to debug
for i in range(0, months+1):
# Complete the skeletom of the data dict
acc_pending_time_median_month["month"].append(start_month+i)
acc_pending_time_median["month"].append(start_month+i)
acc_pending_time_median_month["name"].append([])
for metric in metrics:
acc_pending_time_median_month[metric].append([])
acc_pending_time_median[metric].append([])
for i in range(0, months+1):
# First get all data from SQL
newtime = self.db.ExecuteQuery(get_sql(start_month+i))
uploadtime = self.db.ExecuteQuery(get_sql(start_month+i, False, True))
newtime_rev = self.db.ExecuteQuery(get_sql(start_month+i, True))
# This is the slow query
uploadtime_rev = self.db.ExecuteQuery(get_sql(start_month+i, True, True))
# Build a common list for all items
all_items_month_ids = []
# for data_sql in [newtime, uploadtime, newtime_rev, uploadtime_rev]:
for data_sql in [newtime, uploadtime, newtime_rev, uploadtime_rev]:
checkListArray(data_sql)
all_items_month_ids = list(Set(data_sql[id_field]+all_items_month_ids))
acc_pending_time_median_month["name"][i] = all_items_month_ids
# Now add the data in a common dict for all metrics in this month
# review time
for item in all_items_month_ids:
data_item = []
for j in range(0, len(newtime[id_field])):
if newtime[id_field][j] == item:
data_item.append(newtime['newtime'][j])
values = get_values_median(data_item)
# print start_month+i, "newtime", item, values
nreviews = len(data_item)
acc_pending_time_median_month['review_time_pending_reviews'][i].append(nreviews)
acc_pending_time_median_month['review_time_pending_days_acc_median'][i].append(values)
# upload time
for item in all_items_month_ids:
data_item = []
for j in range(0, len(uploadtime[id_field])):
if uploadtime[id_field][j] == item:
data_item.append(uploadtime['uploadtime'][j])
values = get_values_median(data_item)
# print start_month+i, "upload", item, values
nreviews = len(data_item)
acc_pending_time_median_month['review_time_pending_upload_reviews'][i].append(nreviews)
acc_pending_time_median_month['review_time_pending_upload_days_acc_median'][i].append(values)
# review time reviewers
for item in all_items_month_ids:
# Now just for reviews waiting for Reviewer
data_item = []
for j in range(0, len(newtime_rev[id_field])):
if newtime_rev[id_field][j] == item:
data_item.append(newtime_rev['newtime'][j])
values = get_values_median(data_item)
nreviews = len(data_item)
acc_pending_time_median_month['review_time_pending_ReviewsWaitingForReviewer_reviews'][i].append(nreviews)
acc_pending_time_median_month['review_time_pending_ReviewsWaitingForReviewer_days_acc_median'][i].append(values)
# upload time reviewers
for item in all_items_month_ids:
data_item = []
for j in range(0, len(uploadtime_rev[id_field])):
if uploadtime_rev[id_field][j] == item:
data_item.append(uploadtime_rev['uploadtime'][j])
values = get_values_median(data_item)
nreviews = len(data_item)
acc_pending_time_median_month['review_time_pending_upload_ReviewsWaitingForReviewer_reviews'][i].append(nreviews)
acc_pending_time_median_month['review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median'][i].append(values)
# Now we need to consolidate all names in a single list
all_items = []
for lnames in acc_pending_time_median_month['name']:
all_items = list(Set(lnames+all_items))
# And now time to create the final version that should be completePeriod
for item in all_items:
# Add the ts for the item to the final dict
for i in range(0, months+1):
mitems = acc_pending_time_median_month['name'][i]
found_item = False
for k in range(0, len(mitems)):
if mitems[k] == item:
# Found the item, get all metrics for this month
found_item = True
for metric in metrics:
item_metric_month_value = acc_pending_time_median_month[metric][i][k]
acc_pending_time_median[metric][i].append(item_metric_month_value)
if not found_item:
for metric in metrics:
# 0 reviews, 0 review time
acc_pending_time_median[metric][i].append(0)
# Now we need to completePeriods to add time series fields
# All the time series are already complete because the way they are built
# but we miss some time series fields
ts_fields = ['unixtime','date','month','id']
ts_aux = {}
ts_aux['month'] = acc_pending_time_median['month']
ts_aux = completePeriodIds(ts_aux, self.filters.period,
self.filters.startdate, self.filters.enddate)
for field in ts_fields:
acc_pending_time_median[field] = ts_aux[field]
# After completing the time series, add the name/url series
if self.filters.type_analysis[0] != "repository":
acc_pending_time_median["name"] = all_items
else:
acc_pending_time_median["url"] = all_items
# And now we need to adjust the format from
# month:[M1, M2, M3], url:[URL1, URL2. ...], metric:[[URL1_M1,URL2_M1], [URL1_M2, URL2_M2],[URL1_M3...]...]
# to
# month:[M1, M2, M3], url:[URL1, URL2. ...], metric:[[URL1_M1, URL1_M2, URL1_M3],[URL2_M1...]...]
time_to = {}
for field in acc_pending_time_median:
if field not in metrics:
time_to[field] = acc_pending_time_median[field]
else:
# The new metric field will have an array per item with the time series
time_to[field] = []
for i in range(0,len(all_items)):
time_to[field].append([])
for metrics_month in acc_pending_time_median[field]:
for j in range(0,len(all_items)):
time_to[field][j].append(metrics_month[j])
return time_to
def _get_agg_all(self):
# All items data is returned together
# Show review and upload time for all pending reviews
# and just for reviews pending for reviewers
reviewers_pending = True
startdate = self.filters.startdate
enddate = self.filters.enddate
identities_db = self.db.identities_db
type_analysis = self.filters.type_analysis
bots = []
# First, we need to group by the filter field the data
all_items = self.db.get_all_items(self.filters.type_analysis)
group_field = self.db.get_group_field(all_items)
id_field = group_field.split('.')[1] # remove table name
time_to = {
"review_time_pending_days_median": [],
"review_time_pending_days_avg": [],
"review_time_pending_ReviewsWaitingForReviewer_days_median": [],
"review_time_pending_ReviewsWaitingForReviewer_days_avg": [],
"review_time_pending_upload_days_median": [],
"review_time_pending_upload_days_avg": [],
"review_time_pending_upload_ReviewsWaitingForReviewer_days_median":
[],
"review_time_pending_upload_ReviewsWaitingForReviewer_days_avg":
[]
}
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db,
bots)
ttr_data = self.db.ExecuteQuery(q)
checkListArray(ttr_data)
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db,
bots, reviewers_pending)
ttr_reviewers_data = self.db.ExecuteQuery(q)
checkListArray(ttr_reviewers_data)
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db,
bots, False, True)
ttr_upload_data = self.db.ExecuteQuery(q)
checkListArray(ttr_upload_data)
# This query is really slow.
q = self.db.GetTimeToReviewPendingQuerySQL(self.filters, identities_db,
bots, reviewers_pending,
True)
ttr_reviewers_upload_data = self.db.ExecuteQuery(q)
checkListArray(ttr_reviewers_upload_data)
all_items_ids = []
for items in [
ttr_data, ttr_reviewers_data, ttr_upload_data,
ttr_reviewers_upload_data
]:
# Get the list of items and add them to the global list
all_items_ids = list(Set(items[id_field] + all_items_ids))
time_to['name'] = all_items_ids
# Review time
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_data[id_field])):
if ttr_data[id_field][i] == item:
data_item_revtime.append(ttr_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_median = float("nan")
ttr_avg = float("nan")
else:
ttr_median = median(removeDecimals(data_item_revtime))
ttr_avg = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_days_median"].append(ttr_median)
time_to["review_time_pending_days_avg"].append(ttr_avg)
# Review time for reviewers
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_reviewers_data[id_field])):
if ttr_reviewers_data[id_field][i] == item:
data_item_revtime.append(ttr_reviewers_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_reviewers_median = float("nan")
ttr_reviewers_avg = float("nan")
else:
ttr_reviewers_median = median(
removeDecimals(data_item_revtime))
ttr_reviewers_avg = average(removeDecimals(data_item_revtime))
time_to[
"review_time_pending_ReviewsWaitingForReviewer_days_median"].append(
ttr_reviewers_median)
time_to[
"review_time_pending_ReviewsWaitingForReviewer_days_avg"].append(
ttr_reviewers_avg)
# Upload time
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_upload_data[id_field])):
if ttr_upload_data[id_field][i] == item:
data_item_revtime.append(ttr_upload_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_median_upload = float("nan")
ttr_avg_upload = float("nan")
else:
ttr_median_upload = median(removeDecimals(data_item_revtime))
ttr_avg_upload = average(removeDecimals(data_item_revtime))
time_to["review_time_pending_upload_days_median"].append(
ttr_median_upload)
time_to["review_time_pending_upload_days_avg"].append(
ttr_median_upload)
# Upload time for reviewers
for item in time_to['name']:
data_item_revtime = []
# Get all values for the item
for i in range(0, len(ttr_reviewers_upload_data[id_field])):
if ttr_reviewers_upload_data[id_field][i] == item:
data_item_revtime.append(
ttr_reviewers_upload_data['revtime'][i])
if (len(data_item_revtime) == 0):
ttr_reviewers_median_upload = float("nan")
ttr_reviewers_avg_upload = float("nan")
else:
ttr_reviewers_median_upload = median(
removeDecimals(data_item_revtime))
ttr_reviewers_avg_upload = average(
removeDecimals(data_item_revtime))
time_to[
"review_time_pending_upload_ReviewsWaitingForReviewer_days_median"].append(
ttr_reviewers_median_upload)
time_to[
"review_time_pending_upload_ReviewsWaitingForReviewer_days_avg"].append(
ttr_reviewers_avg_upload)
# In SCR the item field name must be url for repository
if self.filters.type_analysis[0] == 'repository':
time_to['url'] = time_to.pop('name')
return time_to
def _get_ts_all():
""" Get the metrics for all items at the same time """
all_items = self.db.get_all_items(self.filters.type_analysis)
group_field = self.db.get_group_field(all_items)
id_field = group_field.split('.')[1] # remove table name
metrics = [
"review_time_pending_reviews",
"review_time_pending_days_acc_median",
"review_time_pending_upload_reviews",
"review_time_pending_upload_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_reviews",
"review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_upload_ReviewsWaitingForReviewer_reviews"
]
metrics = [
"review_time_pending_reviews",
"review_time_pending_days_acc_median",
"review_time_pending_upload_reviews",
"review_time_pending_upload_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_ReviewsWaitingForReviewer_reviews",
"review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median",
"review_time_pending_upload_ReviewsWaitingForReviewer_reviews"
]
acc_pending_time_median_month = {
"month": [],
"name": []
} # Used to store each month all items data
acc_pending_time_median = {
"month": []
} # Used to store the final format
for metric in metrics:
acc_pending_time_median_month[metric] = []
acc_pending_time_median[metric] = []
# months = 2 # to debug
for i in range(0, months + 1):
# Complete the skeletom of the data dict
acc_pending_time_median_month["month"].append(start_month + i)
acc_pending_time_median["month"].append(start_month + i)
acc_pending_time_median_month["name"].append([])
for metric in metrics:
acc_pending_time_median_month[metric].append([])
acc_pending_time_median[metric].append([])
for i in range(0, months + 1):
# First get all data from SQL
newtime = self.db.ExecuteQuery(get_sql(start_month + i))
uploadtime = self.db.ExecuteQuery(
get_sql(start_month + i, False, True))
newtime_rev = self.db.ExecuteQuery(
get_sql(start_month + i, True))
# This is the slow query
uploadtime_rev = self.db.ExecuteQuery(
get_sql(start_month + i, True, True))
# Build a common list for all items
all_items_month_ids = []
# for data_sql in [newtime, uploadtime, newtime_rev, uploadtime_rev]:
for data_sql in [
newtime, uploadtime, newtime_rev, uploadtime_rev
]:
checkListArray(data_sql)
all_items_month_ids = list(
Set(data_sql[id_field] + all_items_month_ids))
acc_pending_time_median_month["name"][i] = all_items_month_ids
# Now add the data in a common dict for all metrics in this month
# review time
for item in all_items_month_ids:
data_item = []
for j in range(0, len(newtime[id_field])):
if newtime[id_field][j] == item:
data_item.append(newtime['newtime'][j])
values = get_values_median(data_item)
# print start_month+i, "newtime", item, values
nreviews = len(data_item)
acc_pending_time_median_month[
'review_time_pending_reviews'][i].append(nreviews)
acc_pending_time_median_month[
'review_time_pending_days_acc_median'][i].append(
values)
# upload time
for item in all_items_month_ids:
data_item = []
for j in range(0, len(uploadtime[id_field])):
if uploadtime[id_field][j] == item:
data_item.append(uploadtime['uploadtime'][j])
values = get_values_median(data_item)
# print start_month+i, "upload", item, values
nreviews = len(data_item)
acc_pending_time_median_month[
'review_time_pending_upload_reviews'][i].append(
nreviews)
acc_pending_time_median_month[
'review_time_pending_upload_days_acc_median'][
i].append(values)
# review time reviewers
for item in all_items_month_ids:
# Now just for reviews waiting for Reviewer
data_item = []
for j in range(0, len(newtime_rev[id_field])):
if newtime_rev[id_field][j] == item:
data_item.append(newtime_rev['newtime'][j])
values = get_values_median(data_item)
nreviews = len(data_item)
acc_pending_time_median_month[
'review_time_pending_ReviewsWaitingForReviewer_reviews'][
i].append(nreviews)
acc_pending_time_median_month[
'review_time_pending_ReviewsWaitingForReviewer_days_acc_median'][
i].append(values)
# upload time reviewers
for item in all_items_month_ids:
data_item = []
for j in range(0, len(uploadtime_rev[id_field])):
if uploadtime_rev[id_field][j] == item:
data_item.append(uploadtime_rev['uploadtime'][j])
values = get_values_median(data_item)
nreviews = len(data_item)
acc_pending_time_median_month[
'review_time_pending_upload_ReviewsWaitingForReviewer_reviews'][
i].append(nreviews)
acc_pending_time_median_month[
'review_time_pending_upload_ReviewsWaitingForReviewer_days_acc_median'][
i].append(values)
# Now we need to consolidate all names in a single list
all_items = []
for lnames in acc_pending_time_median_month['name']:
all_items = list(Set(lnames + all_items))
# And now time to create the final version that should be completePeriod
for item in all_items:
# Add the ts for the item to the final dict
for i in range(0, months + 1):
mitems = acc_pending_time_median_month['name'][i]
found_item = False
for k in range(0, len(mitems)):
if mitems[k] == item:
# Found the item, get all metrics for this month
found_item = True
for metric in metrics:
item_metric_month_value = acc_pending_time_median_month[
metric][i][k]
acc_pending_time_median[metric][i].append(
item_metric_month_value)
if not found_item:
for metric in metrics:
# 0 reviews, 0 review time
acc_pending_time_median[metric][i].append(0)
# Now we need to completePeriods to add time series fields
# All the time series are already complete because the way they are built
# but we miss some time series fields
ts_fields = ['unixtime', 'date', 'month', 'id']
ts_aux = {}
ts_aux['month'] = acc_pending_time_median['month']
ts_aux = completePeriodIds(ts_aux, self.filters.period,
self.filters.startdate,
self.filters.enddate)
for field in ts_fields:
acc_pending_time_median[field] = ts_aux[field]
# After completing the time series, add the name/url series
if self.filters.type_analysis[0] != "repository":
acc_pending_time_median["name"] = all_items
else:
acc_pending_time_median["url"] = all_items
# And now we need to adjust the format from
# month:[M1, M2, M3], url:[URL1, URL2. ...], metric:[[URL1_M1,URL2_M1], [URL1_M2, URL2_M2],[URL1_M3...]...]
# to
# month:[M1, M2, M3], url:[URL1, URL2. ...], metric:[[URL1_M1, URL1_M2, URL1_M3],[URL2_M1...]...]
time_to = {}
for field in acc_pending_time_median:
if field not in metrics:
time_to[field] = acc_pending_time_median[field]
else:
# The new metric field will have an array per item with the time series
time_to[field] = []
for i in range(0, len(all_items)):
time_to[field].append([])
for metrics_month in acc_pending_time_median[field]:
for j in range(0, len(all_items)):
time_to[field][j].append(metrics_month[j])
return time_to