'weekdays': [],
'aggregate_period': False
}
query_dict = build_query_dict(render_heat_map_dict)
if query_dict['date'] == 'loop_through_period':
# if we have the flag loop_through_period in the query dict, it means the period
# set for the query is multiple dates, therefore we want the query to return an
# average on a time interval, and not on a single date
period = render_heat_map_dict['period']
daterange = pd.date_range(period[0], period[1])
query_dict['date'] = period
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
for single_map, base_map, projection in base_maps:
# we process the query results
outgoing_flow, incoming_flow = process_query_results(
query_results, base_map)
print('Rendering {}...'.format(single_map))
if single_map == 'total':
if time_granularity == 'weekdays_vs_weekends':
file_name = ['NYC', '2018_diff_WD_WE']
else:
file_name = ['NYC', '2018']
else:
if time_granularity == 'weekdays_vs_weekends':
file_name = ['{}'.format(single_map), '2018_diff_WD_WE']
else:
def process_query_arg(render_animation_dict):
period = render_animation_dict['period']
query_dict = render_animation_dict['query_dict']
database = render_animation_dict['database']
specific_weekdays = query_dict['specific_weekdays']
date = query_dict['date']
aggregate_period = render_animation_dict['aggregate_period']
weekdays = render_animation_dict['weekdays']
query_results_dict = {}
if aggregate_period is False and query_dict['date'] == 'loop_through_period':
# in this case we want the result for each day of the period provided
# if we have the flag loop_through_period in the query dict, it means the period
# set for the query is multiple dates
daterange = pd.date_range(period[0], period[1])
# we run queries for each date in the daterange specified
for single_date in daterange:
date = pd.to_datetime(single_date)
if specific_weekdays == 'on_specific_weekdays':
# we check if the date of the daterange matches the weekday(s) we target
if date.dayofweek in weekdays:
single_date = date.date().strftime('%Y-%m-%d')
query_dict['date'] = single_date
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
else:
# if a date in the range is not among the weekdays we want, we skip it
continue
else:
single_date = date.date().strftime('%Y-%m-%d')
query_dict['date'] = single_date
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
elif aggregate_period is True and query_dict['date'] == 'loop_through_period':
# in this case, we want to aggregate the results (sum) per week
daterange = pd.date_range(period[0], period[1])
start_date = pd.to_datetime(period[0])
end_date = pd.to_datetime(period[1])
# let's build a list of all intervals we will want to aggregate the data for
all_aggr_init = []
start = start_date
end = end_date
# we add one list of dates per week to the list of all intervals
i = 0
for date in daterange:
# we handle separately the first date of the period
if i == 0:
curr_week = [start.date().strftime('%Y-%m-%d')]
if date != start_date and date != end_date:
start_week_number = start.isocalendar()[1]
date_week_number = date.isocalendar()[1]
if date_week_number == start_week_number:
curr_week.append(date.date().strftime('%Y-%m-%d'))
i += 1
else:
start = date
all_aggr_init.append(curr_week)
i = 0
# we handle separately the last date of the period
if curr_week not in all_aggr_init:
curr_week.append(end_date.date().strftime('%Y-%m-%d'))
all_aggr_init.append(curr_week)
else:
curr_week = [end_date.date().strftime('%Y-%m-%d')]
all_aggr_init.append(curr_week)
# now we keep only the first and last item of each interval
all_aggr = []
for interval in all_aggr_init:
interval_new = [interval[0], interval[-1]]
all_aggr.append(interval_new)
# we now query for each interval
for interval in all_aggr:
query_dict['date'] = interval
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date'][0]] = query_results
else:
# we have a single date to render for, so nothing to aggregate!
# just in case we check that there is no mismatch between the single day and the
# argument containing specific weekdays restrictions if any
if specific_weekdays == 'on_specific_weekdays':
# we check if the date of the daterange matches the weekday(s) we target
date = pd.to_datetime(query_dict['date'])
if date.dayofweek in weekdays:
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
else:
print("The date selected does not match the weekday(s) indicated. Please select either an interval ('time_granularity': 'period') or a valid weekday(s) list.")
else:
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
return query_results_dict
