def execute_process(self, key, **kwargs):
logging.info('Commencing caching of fundraiser totals data at: %s' %
self.CACHING_HOME)
end_time = TP.timestamp_from_obj(datetime.datetime.utcnow(), 1, 3)
""" DATA CONFIG """
""" set the metrics to plot """
lttdl = DL.LongTermTrendsLoader(db='db1025')
start_of_2011_fundraiser = '20111116000000'
countries = DL.CiviCRMLoader().get_ranked_donor_countries(
start_of_2011_fundraiser)
countries.append('Total')
""" Dictionary object storing lists of regexes - each expression must pass for a label to persist """
year_groups = dict()
for country in countries:
if cmp(country, 'Total') == 0:
year_groups['2011 Total'] = ['2011.*']
year_groups['2010 Total'] = ['2010.*']
else:
year_groups['2011 ' + country] = ['2011' + country]
year_groups['2010 ' + country] = ['2010' + country]
metrics = 'amount'
weights = ''
groups = year_groups
group_metrics = ['year', 'country']
metric_types = DL.LongTermTrendsLoader._MT_AMOUNT_
include_totals = False
include_others = False
hours_back = 0
time_unit = TP.DAY
""" END CONFIG """
""" For each metric use the LongTermTrendsLoader to generate the data to plot """
dr = DR.DataReporting()
times, counts = lttdl.run_fundrasing_totals(end_time, metric_name=metrics, metric_type=metric_types, groups=groups, group_metric=group_metrics, include_other=include_others, \
include_total=include_totals, hours_back=hours_back, weight_name=weights, time_unit=time_unit)
dict_param = dict()
for country in countries:
key_2011 = '2011 ' + country
key_2010 = '2010 ' + country
new_counts = dict()
new_counts[key_2010] = counts[key_2010]
new_counts[key_2011] = counts[key_2011]
new_times = dict()
new_times[key_2010] = times[key_2010]
new_times[key_2011] = times[key_2011]
dr._counts_ = new_counts
dr._times_ = new_times
empty_data = [0] * len(new_times[new_times.keys()[0]])
data = list()
data.append(dr.get_data_lists([''], empty_data))
dict_param[country] = Hlp.combine_data_lists(data)
self.clear_cached_data(key)
self.cache_data(dict_param, key)
logging.info('Caching complete.')
def execute_process(self, key, **kwargs):
logging.info('Commencing caching of long term trends data at: %s' %
self.CACHING_HOME)
end_time, start_time = TP.timestamps_for_interval(datetime.datetime.utcnow(), 1, \
hours=-self.VIEW_DURATION_HRS, resolution=1)
""" DATA CONFIG """
countries = DL.CiviCRMLoader().get_ranked_donor_countries(start_time)
countries = countries[1:6]
""" set the metrics to plot """
lttdl = DL.LongTermTrendsLoader(db='storage3')
""" Dictionary object storing lists of regexes - each expression must pass for a label to persist """
# country_groups = {'US': ['(US)'], 'CA': ['(CA)'], 'JP': ['(JP)'], 'IN': ['(IN)'], 'NL': ['(NL)']}
payment_groups = {'Credit Card': ['^cc$'], 'Paypal': ['^pp$']}
currency_groups = {
'USD': ['(USD)'],
'CAD': ['(CAD)'],
'JPY': ['(JPY)'],
'EUR': ['(EUR)']
}
lang_cntry_groups = {
'US': ['US..', '.{4}'],
'EN': ['[^U^S]en', '.{4}']
}
top_cntry_groups = dict()
for country in countries:
top_cntry_groups[country] = [country, '.{2}']
# To include click rate
# groups = [ lang_cntry_groups] metrics = ['click_rate'] metrics_index = [3]
# group_metrics = [DL.LongTermTrendsLoader._MT_RATE_] metric_types = ['country', 'language'] include_totals = [True] include_others = [True]
metrics = [
'impressions', 'views', 'donations', 'donations', 'amount',
'amount', 'diff_don', 'diff_don', 'donations', 'conversion_rate'
]
weights = ['', '', '', '', '', '', 'donations', 'donations', '', '']
metrics_index = [0, 1, 2, 2, 2, 4, 5, 5, 6, 6]
groups = [lang_cntry_groups, lang_cntry_groups, lang_cntry_groups, top_cntry_groups, lang_cntry_groups, currency_groups, \
lang_cntry_groups, lang_cntry_groups, payment_groups, payment_groups]
""" The metrics that are used to build a group string to be qualified via regex - the values of the list metrics are concatenated """
group_metrics = [['country', 'language'], ['country', 'language'], ['country', 'language'], \
['country', 'language'], ['country', 'language'], ['currency'], ['country', 'language'], \
['country', 'language'], ['payment_method'], ['payment_method']]
metric_types = [DL.LongTermTrendsLoader._MT_AMOUNT_, DL.LongTermTrendsLoader._MT_AMOUNT_, DL.LongTermTrendsLoader._MT_AMOUNT_, \
DL.LongTermTrendsLoader._MT_AMOUNT_, DL.LongTermTrendsLoader._MT_AMOUNT_, DL.LongTermTrendsLoader._MT_AMOUNT_, \
DL.LongTermTrendsLoader._MT_RATE_WEIGHTED_, DL.LongTermTrendsLoader._MT_RATE_WEIGHTED_, DL.LongTermTrendsLoader._MT_AMOUNT_, \
DL.LongTermTrendsLoader._MT_RATE_]
include_totals = [
True, True, True, False, True, True, False, False, False, True
]
include_others = [
True, True, True, False, True, True, True, True, True, False
]
hours_back = [0, 0, 0, 0, 0, 0, 24, 168, 0, 0]
time_unit = [
TP.HOUR, TP.HOUR, TP.HOUR, TP.HOUR, TP.HOUR, TP.HOUR, TP.HOUR,
TP.HOUR, TP.HOUR, TP.HOUR
]
data = list()
""" END CONFIG """
""" For each metric use the LongTermTrendsLoader to generate the data to plot """
for index in range(len(metrics)):
dr = DR.DataReporting()
times, counts = lttdl.run_query(start_time, end_time, metrics_index[index], metric_name=metrics[index], metric_type=metric_types[index], \
groups=groups[index], group_metric=group_metrics[index], include_other=include_others[index], \
include_total=include_totals[index], hours_back=hours_back[index], weight_name=weights[index], \
time_unit=time_unit[index])
times = TP.normalize_timestamps(times, False, time_unit[index])
dr._counts_ = counts
dr._times_ = times
empty_data = [0] * len(times[times.keys()[0]])
data.append(dr.get_data_lists([''], empty_data))
dict_param = Hlp.combine_data_lists(data)
dict_param['interval'] = self.VIEW_DURATION_HRS
dict_param['end_time'] = TP.timestamp_convert_format(end_time, 1, 2)
self.clear_cached_data(key)
self.cache_data(dict_param, key)
logging.info('Caching complete.')