def _process_and_display_trends(self, query, objects_expected):
from qtools.lib.nstats import moving_average_by_interval
from numpy import histogram
import numpy as np
group_by_plate = self.form_result['group_by_plate']
if objects_expected:
results = execute_built_query(query, group_by_plate, True, self.form_result['metric'][1])
else:
results = execute_built_query(query, group_by_plate, False)
epoch_results = [(float(stat), time.mktime(dt.timetuple()), id, name) for stat, dt, id, name in results if stat is not None]
exclude_func = create_exclude_function(self.form_result)
filtered_results = [tup for tup in epoch_results if exclude_func(tup[0])]
c.yaxis_title = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))
c.mean_value = 'N/A'
c.std_value = 'N/A'
c.metric_name = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))
c.table_data =[]
if group_by_plate:
moving_average = 10
epoch_url_results = [(stat, stamp*1000, url(controller='plate', action='view', id=id), name) for stat, stamp, id, name in filtered_results]
else:
moving_average = 50
epoch_url_results = [(stat, stamp*1000, url(controller='well', action='view', id=id), name) for stat, stamp, id, name in filtered_results]
if epoch_url_results:
c.stats = {}
stats, time_points, urls, names = zip(*epoch_url_results)
c.stats = h.literal(json.dumps(zip(time_points, stats)))
c.table_data = zip( names, stats, time_points )
#update if we can.
c.mean_value = str( np.mean( stats ) )
c.std_value = str( np.std( stats ) )
INTERVAL = 24*60*60*1000
ma = moving_average_by_interval(stats, time_points, INTERVAL, 10)
c.moving_avg = h.literal(json.dumps(ma))
c.urls = h.literal(json.dumps(urls))
c.names = h.literal(json.dumps(names))
# get histogram of points by day
#raise Exception, len(ma)
density, edges = histogram(time_points, bins=len(ma), range=(ma[0][0], ma[-1][0]))
c.densities = h.literal(json.dumps(zip([m[0] for m in ma], [int(d) for d in density])))
c.max_density_axis = 3*max(density)
else:
c.stats = {}
c.moving_avg = {}
c.urls = {}
c.names = {}
c.densities = {}
c.max_density_axis = 3
return render('/trend/results.html')
def test_moving_average_by_interval(self):
interval_width = 100
array = [1, 4, 5, 2, 2, 6]
interval_array = [90, 100, 150, 200, 300, 500]
avgs = moving_average_by_interval(array, interval_array, interval_width, 2)
bins, vals = zip(*avgs)
assert bins == (0.0, 100.0, 200.0, 300.0, 400.0, 500.0)
assert vals == (1.0, 2.75, 3.25, 2.0, 2.0, 4.0)
