def scalar_plotpoints(
cls, pk, dimensions=None, metrics=None, depth=0, period=None, flot_time=False, points_type=dict
):
metrics = metrics or ["hits"]
if isinstance(metrics, basestring):
metrics = [metrics]
period = Period.get(period)
sparse = cls.whale_driver().retrieve(pk, dimensions, metrics, period=period)
nonsparse = defaultdict(dict)
if flot_time:
points_type = list
for dim, mets in sparse.items():
for met, points in mets.items():
dts = period.datetimes_strs()
nonsparse[dim][met] = []
for dt in dts:
if flot_time:
dt_t = to_flot_time(Period.parse_dt_str(dt))
else:
dt_t = dt
value = points[dt] if dt in points else 0
nonsparse[dim][met].append([dt_t, float(value)])
nonsparse[dim][met] = points_type(nonsparse[dim][met])
if depth > 0:
for sub in cls.get_subdimensions(pk, dimensions):
nonsparse = dict(
nonsparse.items()
+ cls.plotpoints(
pk, sub, metrics, depth=depth - 1, period=period, flot_time=flot_time, points_type=points_type
).items()
)
return nonsparse
def plotpoints(cls, pk, dimensions=None, metrics=None,
period=None, overall=True):
metrics = metrics or ['hits',]
period = period or Period.default_size()
sparse = cls.whale_driver().retrieve(pk,dimensions,metrics,
period=period, overall=overall)
nonsparse = defaultdict(dict)
for dimensions, metrics in sparse.items():
for metric, points in metrics.items():
dts = Period(*period.split('x')).datetimes_strs()
nonsparse[dimensions][metric] = []
for dt in dts:
flot_time = to_flot_time(Period.parse_dt_str(dt))
value = points[dt] if dt in points else 0
nonsparse[dimensions][metric].append([flot_time,
float(value)])
return nonsparse
def plotpoints(self, categories=None, dimensions=None, metrics=None,
period=None, depth=0):
categories = categories or ''
dimensions = dimensions or json.dumps(list(list()))
# Convert categories to a list, if it's not
if type(categories) in [str,unicode]: categories = [categories,]
metrics = metrics or ['hits',]
period = period or Period.default_size()
sparse = self.driver().retrieve(categories,dimensions,metrics,
period=period, depth=depth)
nonsparse = defaultdict(dict)
for dimensions, metrics in sparse.items():
for metric, points in metrics.items():
dts = Period(*period.split('x')).datetimes_strs()
nonsparse[dimensions][metric] = []
for dt in dts:
flot_time = to_flot_time(Period.parse_dt_str(dt))
value = points[dt] if dt in points else 0
nonsparse[dimensions][metric].append([flot_time,
float(value)])
return nonsparse
def plotpoints(cls, pk, dimensions=None, metrics=None,
depth=0, period=None, flot_time=False, points_type=dict):
metrics = metrics or ['hits']
if isinstance(metrics, basestring):
metrics = [metrics]
period = Period.get(period)
dts = period.datetimes_strs()
nonsparse = defaultdict(defaultdict)
# Hardwire time-based metrics for lulz
time_metrics = {'second': 1, 'minute': 60, 'hour': 3600, 'day': 3600*24, 'week': 3600*24*7}
#for t_m, factor in time_metrics.items():
# if t_m in metrics:
# metrics.remove(t_m)
# for dimension in dimensions:
# nonsparse[dimension][t_m] = list()
# for dt in dts:
# if flot_time:
# dt = to_flot_time(Period.parse_dt_str(dt))
# nonsparse[dimension][t_m].append([dt, period.interval / factor])
# nonsparse[dimension][t_m] = points_type(nonsparse[dimension][t_m])
# Pull the plotpoints that exist from Redis
sparse = cls.whale_driver().retrieve(pk, dimensions, metrics, period=period)
for dimensions, metrics in sparse.items():
for metric, points in metrics.items():
#if metric in time_metrics: continue
nonsparse[dimensions][metric] = []
for dt in dts:
if flot_time:
dt = to_flot_time(Period.parse_dt_str(dt))
value = points[dt] if dt in points else 0
nonsparse[dimensions][metric].append([dt, float(value)])
nonsparse[dimensions][metric] = points_type(nonsparse[dimensions][metric])
if depth > 0:
for sub in cls.get_subdimensions(pk, dimensions):
nonsparse = dict(nonsparse.items() +
cls.plotpoints(pk, sub, metrics, depth=depth - 1, period=period,
flot_time=flot_time, points_type=points_type).items())
return nonsparse
