def _generate_data_with_time(self, config, search_results_collection, search_configuration):
data_columns = [{'type':'string', 'name':'Time'}]
data_rows = []
data_dimensions_value = config['data_dimensions'][0]['value']
start_time, end_time = self._extract_time_bounds_from_search_configuration(search_configuration)
time_interval = int((end_time - start_time) / self.steps_backwards)
array_of_start_times = range(start_time, end_time, time_interval)
results_data_columns = []
for search_result in search_results_collection:
facets = [fg for fg in search_result['facet_groups'] if fg['name'] == data_dimensions_value['value']][0]['facets']
for f in facets:
if f['name'] not in results_data_columns:
results_data_columns.append(f['name'])
data_columns += [{'type':'number', 'name':'%s' % c } for c in results_data_columns]
number_of_empty_ranges = 0
for x in range(len(array_of_start_times)):
if x >= len(search_results_collection):
continue
search_result = search_results_collection[x]
start_time_pretty = get_pretty_date(array_of_start_times[x] + time_interval)
data_row = [start_time_pretty]
facets = [fg for fg in search_result['facet_groups'] if fg['name'] == data_dimensions_value['value']][0]['facets']
dynamic_data_rows = []
for c in results_data_columns:
candidate_facet = [f for f in facets if f['name'] == c]
if candidate_facet:
dynamic_data_rows.append(candidate_facet[0]['count'])
else:
dynamic_data_rows.append(0)
if not sum(dynamic_data_rows):
number_of_empty_ranges += 1
data_row += dynamic_data_rows
data_rows.append(data_row)
return data_columns, data_rows
def _pretty_date(self, time=False):
return get_pretty_date(time)
def _get_pretty_date(self, time_value, days_in_time_range):
if days_in_time_range:
formatted_time = datetime.date.fromtimestamp(time_value).strftime('%m/%d/%y')
else:
formatted_time = get_pretty_date(time_value)
return formatted_time
