def load_stats_cache(self):
self.log.info('Loading previously calculated stats from cache')
stats_days = Files(self.log).jsonl_load(self.config.filepath_stats_days)
stats_weeks = Files(self.log).jsonl_load(self.config.filepath_stats_weeks)
stats_remaining = Files(self.log).jsonl_load(self.config.filepath_stats_remaining)
return stats_days, stats_weeks, stats_remaining
def main(self):
"""Guesstimate efforts to completion, add result to a log file"""
self.log.info('Guesstimating days to completion')
for scan_week in self.__stats_weeks:
current_week = self.__stats_weeks[scan_week]
break
date_current = self.time.get_current_date()
remaining = {
self.config.get_config_value('stats_metric'): self.remaining_work[self.config.get_config_value('stats_metric')]
, 'datetime': date_current
, 'types': self.remaining_work['types']
, 'assignees': self.remaining_work['assignees']
, 'days_to_completion': {}
}
self.log.info('Remaining number of story ' + self.config.get_config_value('stats_metric') + ': ' + str(remaining[self.config.get_config_value('stats_metric')]))
for week_stat in current_week['stats']:
avg_elements_per_day = float(current_week['stats'][week_stat]['avg']) / 5
if avg_elements_per_day > 0:
remaining_days = round(float(remaining[self.config.get_config_value('stats_metric')]) / float(avg_elements_per_day), 1)
else:
remaining_days = 0
self.log.info('Over period: ' + str(week_stat) + ', average ' + self.config.get_config_value('stats_metric') + ' per day was: ' + str(
avg_elements_per_day) + ' should completed in: ' + str(remaining_days) + ' days')
remaining['days_to_completion'][week_stat] = remaining_days
current_element_per_day = current_week[self.config.get_config_value('stats_metric')] / current_week['days']
remaining_days = round(remaining[self.config.get_config_value('stats_metric')] / current_element_per_day, 1)
remaining['days_to_completion']['current'] = remaining_days
self.log.info('This week, average ' + self.config.get_config_value('stats_metric') + ' per day is: ' + str(
current_element_per_day) + ' should completed in: ' + str(remaining_days) + ' days')
# Load previous data and add current day (if already there, replace)
daily_data = collections.OrderedDict()
dict_idx = date_current.strftime('%Y-%m-%d')
daily_data[dict_idx] = remaining
current_data = Files(self.log).jsonl_load(self.config.filepath_stats_remaining)
for current_day_data in current_data:
if date_current.strftime('%Y-%m-%d') != current_data[current_day_data]['datetime'].strftime('%Y-%m-%d'):
dict_idx = current_data[current_day_data]['datetime'].strftime('%Y-%m-%d')
daily_data[dict_idx] = current_data[current_day_data]
if os.path.isfile(self.config.filepath_stats_remaining):
os.remove(self.config.filepath_stats_remaining)
for currentdata in daily_data:
daily_obj = copy.deepcopy(daily_data[currentdata])
daily_obj['datetime'] = daily_data[currentdata]['datetime'].isoformat()
self.log.info('StatsRemaining.main(): Writing stats for date: ' + daily_obj['datetime'])
Files(self.log).jsonl_append(self.config.filepath_stats_remaining, daily_obj)
return daily_data
def load_jira_cache(self):
self.log.info('Load daily data and remaining work from cache')
# Import existing data (if any) into a Python object
daily_data = Files(self.log).jsonl_load(
self.config.filepath_data_completion)
remaining_work = Files(self.log).json_load(
self.config.filepath_data_remaining)
if remaining_work is None or daily_data is None:
self.log.error(
'Unable to load cached data, please run \'jav load\' first')
exit()
return daily_data, remaining_work
def refresh_jira_cache(self):
self.log.info('Load updated data from Jira into Cache')
date_start = self.time.get_current_date()
date_end = self.time.get_end_date()
self.log.info('Load.main(): Start Date: ' +
date_start.strftime('%Y-%m-%d'))
self.log.info('Load.main(): End Date: ' +
date_end.strftime('%Y-%m-%d'))
loader = ImportData(self.log, self.config)
# Import existing data (if any) into a Python object
previous_data = Files(self.log).jsonl_load(
self.config.filepath_data_completion)
# Refresh the cache by checking if additional days can be added
daily_data = loader.refresh_dailydata_cache(previous_data, date_start,
date_end)
# Write back the data cache to file after clearing any existing one
loader.write_dailydata_cache(daily_data)
# Call Jira to get Remaining work
remaining_work = loader.get_remaining_work()
return daily_data, remaining_work
def main(self):
"""This hacky function publishes chart to github pages"""
self.log.info('Publishing Website')
self.log.info('Git local path: ' +
self.config.get_config_value('git_localpath'))
self.config.set_config_value(
'git_localpath',
Files.prep_path(self.config.get_config_value('git_localpath')))
if not self.github.is_folder_git_repo():
self.log.info('Cloning Git Repository: ' +
self.config.get_config_value('git_repo'))
self.github.git_clone()
else:
self.log.info(
'Repository already available on local filesystem: ' +
self.config.get_config_value('git_repo'))
if self.github.git_pull() is not True:
self.log.info('Git Stash')
self.github.git_stash()
# Test if requested branch exists, if not, create
# Then switch to that branch
remote_branches = self.github.get_branches()
if not any(
self.config.get_config_value('git_branch') in s
for s in remote_branches):
# Init branch
self.log.warning(
'The remote repository does not contain branch: ' +
self.config.get_config_value('git_branch'))
self.github.git_checkout_create(
self.config.get_config_value('git_branch'))
self.github.git_pull_branch(
self.config.get_config_value('git_branch'), True)
self.github.git_push_branch(
self.config.get_config_value('git_branch'))
elif any('* ' + self.config.get_config_value('git_branch') in s
for s in remote_branches):
self.log.info('Local repository already selected branch: ' +
self.config.get_config_value('git_branch'))
else:
self.log.info('Switching to branch: ' +
self.config.get_config_value('git_branch'))
self.github.git_checkout_f(
self.config.get_config_value('git_branch'))
# Next do one last pull and copy content
self.github.git_stash()
# Then copy chart to requested directory
if not os.path.isfile(self.config.filepath_charts + 'index.html'):
self.log.error('Unable to locate source chart file at: ' +
self.config.filepath_charts + 'index.html')
exit()
dst_path = Files.prep_path(
self.config.get_config_value('git_localpath') +
self.config.get_config_value('git_pathdirectory'))
self.log.info('Preparing to copy chart file to: ' + dst_path)
shutil.copyfile(self.config.filepath_charts + 'index.html',
dst_path + 'index.html')
self.log.info('Chart file copied')
self.github.git_add('--all')
self.github.git_commit('Copied chart file - ' +
self.time.get_current_date().isoformat())
self.github.git_push()
self.log.info('Chart pushed to: ' +
self.config.get_config_value('git_pageurl'))
def main(self):
self.log.info('Calculate weekly stats throughout the captured period')
# First pass, calculate weekly values
for current_day in self.daily_data:
week_txt = self.daily_data[current_day]['datetime'].strftime(
'%Y.%W')
if week_txt not in self.weeks_data:
self.weeks_data[week_txt] = {
'values': [],
'datetime': self.daily_data[current_day]['datetime'],
'weektxt': week_txt,
'stats': {}
}
self.weeks_data[week_txt]['values'].append(
self.daily_data[current_day][self.config.get_config_value(
'stats_metric')])
self.weeks_data[week_txt]['days'] = len(
self.weeks_data[week_txt]['values'])
self.weeks_data[week_txt][self.config.get_config_value(
'stats_metric')] = sum(self.weeks_data[week_txt]['values'])
# Second pass, get min and max since 'beginning of time'
for week_txt in self.weeks_data:
del self.weeks_data[week_txt]['values']
week_found = False
for scan_week in self.weeks_data:
if week_found is True:
if 'all' not in self.weeks_data[week_txt]['stats']:
self.weeks_data[week_txt]['stats']['all'] = {
'values': []
}
self.weeks_data[week_txt]['stats']['all']['values'].append(
self.weeks_data[scan_week][
self.config.get_config_value('stats_metric')])
self.weeks_data[week_txt]['stats']['all']['avg'] = int(
numpy.mean(self.weeks_data[week_txt]['stats']['all']
['values']))
self.weeks_data[week_txt]['stats']['all']['max'] = max(
self.weeks_data[week_txt]['stats']['all']['values'])
self.weeks_data[week_txt]['stats']['all']['min'] = min(
self.weeks_data[week_txt]['stats']['all']['values'])
for week_idx in self.config.get_config_value(
'rolling_stats'):
if week_idx == 'all' or len(
self.weeks_data[week_txt]['stats']['all']
['values']) <= week_idx:
if week_idx not in self.weeks_data[week_txt][
'stats']:
self.weeks_data[week_txt]['stats'][
week_idx] = {
'values': []
}
self.weeks_data[week_txt]['stats'][week_idx][
'values'].append(self.weeks_data[scan_week][
self.config.get_config_value(
'stats_metric')])
self.weeks_data[week_txt]['stats'][week_idx][
'avg'] = int(
numpy.mean(self.weeks_data[week_txt]
['stats'][week_idx]['values']))
self.weeks_data[week_txt]['stats'][week_idx][
'max'] = max(self.weeks_data[week_txt]['stats']
[week_idx]['values'])
self.weeks_data[week_txt]['stats'][week_idx][
'min'] = min(self.weeks_data[week_txt]['stats']
[week_idx]['values'])
if scan_week == week_txt:
week_found = True
# Then write content to a JSONL file
if os.path.isfile(self.config.filepath_stats_weeks):
os.remove(self.config.filepath_stats_weeks)
for week_txt in self.weeks_data:
if 'all' in self.weeks_data[week_txt]['stats']:
del self.weeks_data[week_txt]['stats']['all']['values']
for week_idx in self.config.get_config_value('rolling_stats'):
if week_idx in self.weeks_data[week_txt]['stats']:
del self.weeks_data[week_txt]['stats'][week_idx]['values']
week_obj = copy.deepcopy(self.weeks_data[week_txt])
week_obj['datetime'] = self.weeks_data[week_txt][
'datetime'].isoformat()
Files(self.log).jsonl_append(self.config.filepath_stats_weeks,
week_obj)
return self.weeks_data
def main(self):
self.log.info('Calculate daily stats throughout the captured period')
for current_day in self.daily_data:
day_txt = self.daily_data[current_day]['datetime'].strftime('%Y%m%d')
self.days[day_txt] = {
'datetime': self.daily_data[current_day]['datetime']
, 'daytxt': self.daily_data[current_day]['datetime'].strftime('%A')
, 'points': self.daily_data[current_day]['points']
, 'tickets': self.daily_data[current_day]['tickets']
, 'anyday': {'all': {'values': []}}
, 'sameday': {'all': {'values': []}}
}
for week_idx in self.config.get_config_value('rolling_stats'):
if week_idx not in self.days[day_txt]['sameday']:
self.days[day_txt]['anyday'][week_idx] = {'values': []}
self.days[day_txt]['sameday'][week_idx] = {'values': []}
day_found = False
for scan_day in self.daily_data:
if day_found:
for stats_type in ['anyday', 'sameday']:
same_weekday = False
if stats_type == 'anyday':
self.days[day_txt][stats_type]['all']['values'].append(
self.daily_data[scan_day][self.config.get_config_value('stats_metric')])
elif self.daily_data[current_day]['datetime'].strftime('%A') == self.daily_data[scan_day][
'datetime'].strftime('%A') and stats_type == 'sameday':
same_weekday = True
self.days[day_txt][stats_type]['all']['values'].append(
self.daily_data[scan_day][self.config.get_config_value('stats_metric')])
if self.days[day_txt][stats_type]['all']['values']:
self.days[day_txt][stats_type]['all']['avg'] = int(
numpy.mean(self.days[day_txt][stats_type]['all']['values']))
self.days[day_txt][stats_type]['all']['min'] = min(
self.days[day_txt][stats_type]['all']['values'])
self.days[day_txt][stats_type]['all']['max'] = max(
self.days[day_txt][stats_type]['all']['values'])
for week_idx in self.config.get_config_value('rolling_stats'):
in_range = False
if stats_type == 'anyday' and len(
self.days[day_txt][stats_type]['all']['values']) <= week_idx * 5:
in_range = True
elif stats_type == 'sameday' and len(
self.days[day_txt][stats_type]['all']['values']) <= week_idx:
in_range = True
if (stats_type == 'anyday' and in_range is True) or (
in_range is True and same_weekday is True):
self.days[day_txt][stats_type][week_idx]['values'].append(
self.daily_data[scan_day][self.config.get_config_value('stats_metric')])
self.days[day_txt][stats_type][week_idx]['avg'] = int(
numpy.mean(self.days[day_txt][stats_type]['all']['values']))
self.days[day_txt][stats_type][week_idx]['min'] = min(
self.days[day_txt][stats_type]['all']['values'])
self.days[day_txt][stats_type][week_idx]['max'] = max(
self.days[day_txt][stats_type]['all']['values'])
if self.daily_data[current_day]['datetime'].date() == self.daily_data[scan_day]['datetime'].date():
day_found = True
# Then write content to a JSONL file
if os.path.isfile(self.config.filepath_stats_days):
os.remove(self.config.filepath_stats_days)
# Clear un-necessary array values, and write output to a JSONL file
for current_day in self.days:
del self.days[current_day]['anyday']['all']['values']
del self.days[current_day]['sameday']['all']['values']
for week_idx in self.config.get_config_value('rolling_stats'):
for stats_type in ['anyday', 'sameday']:
if stats_type in self.days[current_day] and week_idx in self.days[current_day][
stats_type] and 'values' in self.days[current_day][stats_type][week_idx]:
del self.days[current_day][stats_type][week_idx]['values']
day_obj = copy.deepcopy(self.days[current_day])
day_obj['datetime'] = self.days[current_day]['datetime'].isoformat()
Files(self.log).jsonl_append(self.config.filepath_stats_days, day_obj)
return self.days
def __init__(self, log, config):
self.log = log
self.config = config
self.__jira = Jira(self.log, self.config)
self.__files = Files(self.log)