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 zranked(cls, pk, parent_dimension='_', metric='hits', period=None,
at=None, start=0, size=10, sort_dir=None, tzoffset=None):
period, ats, tzoffset = Period.get_days(period, at)
dt = ats or [Period.convert(cls.now(), tzoffset)]
return map(try_loads,
_ranked(cls.whale_driver(), pk, parent_dimension, metric,
period, dt, start, size, sort_dir=sort_dir))
def _store(redis, pk, dimension, metric, period, dt, count, method='set',
rank=False):
# Keep a list of graphs per pk
key = keyify(pk, dimension, Period.get(period).interval, metric)
# Store pk dimensions
dimension_key = keyify('dimensions', pk)
dimension_json = keyify(dimension)
if not dimension_json in _added_dimensions[dimension_key]:
redis.sadd(dimension_key, dimension_json)
_added_dimensions[dimension_key].append(dimension_json)
# Store dimensional subdimensions
if dimension != '_':
subdimension_key = keyify('subdimensions', pk, parent(dimension))
if not dimension_json in _added_subdimensions[subdimension_key]:
redis.sadd(subdimension_key, dimension_json)
_added_subdimensions[subdimension_key].append(dimension_json)
if method == 'set':
new_val = float(count)
redis.hset(key, dt, new_val)
elif method == 'incr':
new_val = redis.execute_command('HINCRBYFLOAT', key, dt, float(count))
if rank and (isinstance(try_loads(pk), list) or dimension != '_'):
if isinstance(pk, list) and dimension == '_':
tgt_pk = parent(pk)
tgt_dimension = dimension
else:
tgt_pk = pk
tgt_dimension = parent(dimension)
rank_key = keyify('rank', tgt_pk, tgt_dimension,
Period.get(period).interval, dt, metric)
redis.zadd(rank_key, dimension_json, new_val)
return new_val
def _ranked(redis, pk, parent_dimension, metric, period, ats, start=0, size=10,
sort_dir=None):
top, bot = parse_formula(metric)
rank_keyify = lambda ats, met: keyify('rank', pk, parent_dimension,
Period.get(period).interval, ats, met)
final_rank_key = rank_keyify(ats, metric)
def squash_ats(met):
if len(ats) > 1:
map(lambda at: redis.zremrangebyscore(rank_keyify(at, met), 0, 0), ats)
redis.zunionstore(rank_keyify(ats, met),
map(lambda at: rank_keyify(at, met), ats))
squash_ats(top)
if bot:
squash_ats(bot)
top_key, bot_key = rank_keyify(ats, top), rank_keyify(ats, bot)
redis.execute_command("eval", """
for key_i, key_n in ipairs(redis.call("zrange", KEYS[2], 0, -1)) do
local top_s = tonumber(redis.call("zscore", KEYS[1], key_n))
local bot_s = tonumber(redis.call("zscore", KEYS[2], key_n))
if top_s and bot_s and bot_s > 0 then
redis.call("zadd", KEYS[3], top_s/bot_s, key_n)
end
end
""", 3, top_key, bot_key, final_rank_key)
redis.zremrangebyscore(final_rank_key, 0, 0)
return redis.zrange(final_rank_key, start, start + size,
desc=not sort_dir or sort_dir.upper() in ['-', 'DESC', 'HIGH'])
def rank_subdimensions_scalar(cls, pk, dimension='_', metric='hits', period=None):
period = period or Period.default_size()
d_k = keyify(dimension)
total = cls.totals(pk, dimension, metric, periods=[period])[str(period)][d_k][metric]
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, metric, period=period)[sub][metric]
sub_total = sum(pps.values())
return {
'points': pps,
'total': sub_total,
'important': sub_total > 10 and (sub_total > (total / 10)) or False
}
for sub in map(maybe_dumps, cls.all_subdimensions(pk, dimension)):
ranked[sub] = info(sub)
del(ranked[dimension])
# Prune parents
for sub, info in ranked.items():
children = map(maybe_dumps, cls.get_subdimensions(pk, sub))
children_total = sum(map(lambda s: ranked[s]['total'], children))
if info['important'] and (info['total'] - children_total) < (total / 10):
info['important'] = False
return ranked
def total(cls,
pk,
metric,
dimension='_',
period=None,
at=None,
index=None,
tzoffset=None):
period, ats, tzoffset = Period.get_days(period, at, tzoffset=None)
top, bot = parse_formula(metric)
if not ats and not index:
index = -1
if isinstance(index, int):
pps = cls.plotpoints(pk,
dimension,
metric,
period=period,
points_type=list)
return pps[dimension][metric][index][1]
else:
if not bot:
pps = cls.plotpoints(pk, dimension, metric, period=period)
ppsm = pps[dimension][metric]
return sum([ppsm[dt] for dt in ats if dt in ppsm])
else:
top_pps = cls.plotpoints(pk, dimension, top, period=period)
bot_pps = cls.plotpoints(pk, dimension, bot, period=period)
top_ppsm = top_pps[dimension][top]
bot_ppsm = bot_pps[dimension][bot]
top_tot = sum([top_ppsm[dt] for dt in ats if dt in top_ppsm])
bot_tot = sum([bot_ppsm[dt] for dt in ats if dt in bot_ppsm])
return bot_tot and top_tot / bot_tot or 0
def rank_subdimensions_scalar(cls, pk, dimension='_', metric='hits',
period=None, recursive=True, prune_parents=True, points=False):
period = period or Period.default_size()
d_k = keyify(dimension)
total = cls.cached_totals(pk, dimension, metric, periods=[period])[period][d_k][metric]
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, metric, period=period)[sub][metric]
sub_total = sum(pps.values())
data = {
'points': pps,
'score': sub_total,
'important': sub_total > 10 and (sub_total > (total / 10)) or False,
'effect': total - sub_total,
'difference': total - sub_total,
'value': sub_total,
'count': sub_total,
'dimension': sub
}
if not points:
del data['points']
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
# Prune parents
if recursive and prune_parents:
for sub, info in ranked.items():
children = map(maybe_dumps, cls.get_subdimensions(pk, sub))
children_total = sum(map(lambda s: ranked[s]['score'], children))
if info['important'] and (info['score'] - children_total) < (total / 10):
info['important'] = False
return ranked
def __call__(self, *args, **kwargs):
from whale import Whale
from periods import Period
if len(args) and args[0] == Whale or issubclass(args[0], Whale):
args = args[1:]
clear_cache = kwargs.pop("unmemoize", False)
self.get_cache()
if "period" in kwargs:
p = Period.get(kwargs["period"])
kwargs["period"] = str(p)
ttl = int(p.interval) / 5
else:
ttl = 60
key_name = self.keyify(args, kwargs)
if clear_cache:
self.cache.delete(key_name)
try:
return json.loads(self.cache[key_name])
except KeyError:
value = self.func(Whale, *args, **kwargs)
self.cache[key_name] = json.dumps(value)
self.cache.expire(key_name, ttl)
return value
except TypeError:
# uncachable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(Whale, *args, **kwargs)
def totals(cls, pk, dimensions=None, metrics=None, periods=None, at=None):
if not periods:
periods = DEFAULT_PERIODS
if not isinstance(periods, list):
periods = [periods]
metrics = metrics or ['hits']
if not isinstance(metrics, list):
metrics = [metrics]
ratios = []
for metric in metrics:
if '/' in metric:
metrics.remove(metric)
ratios.append(metric)
metrics += metric.split('/')
d = {}
for p in periods:
period, ats, tzoffset = Period.get_days(p,at)
p_data = cls.plotpoints(pk, dimensions, metrics, period=p,at=at)
p_totals = dict()
for dim in p_data.keys():
p_totals[dim] = dict()
for met, vals in p_data[dim].items():
p_totals[dim][met] = sum([
v for k, v in vals.items()
if k in ats])
for rat in ratios:
top, bot = parse_formula(rat)
topt, bott = p_totals[dim][top], p_totals[dim][bot]
p_totals[dim][rat] = bott and topt / bott or 0
d[str(p)] = p_totals
return d
def total(cls, pk, metric, dimension='_', period=None, at=None, index=None,
tzoffset=None):
period, ats, tzoffset = Period.get_days(period, at, tzoffset=tzoffset)
top, bot = parse_formula(metric)
dimension = maybe_dumps(dimension)
if not ats and not index:
index = -1
if isinstance(index, int):
pps = cls.plotpoints(pk, dimension, metric, period=period, points_type=list)
return pps[dimension][metric][index][1]
else:
if not bot:
pps = cls.plotpoints(pk, dimension, metric, period=period,
tzoffset=tzoffset)
ppsm = pps[dimension][metric]
return sum([ppsm[dt] for dt in ats if dt in ppsm])
else:
top_pps = cls.plotpoints(pk, dimension, top, period=period,
tzoffset=tzoffset)
bot_pps = cls.plotpoints(pk, dimension, bot, period=period,
tzoffset=tzoffset)
top_ppsm = top_pps[dimension][top]
bot_ppsm = bot_pps[dimension][bot]
top_tot = sum([top_ppsm[dt] for dt in ats if dt in top_ppsm])
bot_tot = sum([bot_ppsm[dt] for dt in ats if dt in bot_ppsm])
return bot_tot and top_tot/bot_tot or 0
def totals(cls, pk, dimensions=None, metrics=None, periods=None, at=None):
if not periods:
periods = DEFAULT_PERIODS
if not isinstance(periods, list):
periods = [periods]
metrics = metrics or ['hits']
if not isinstance(metrics, list):
metrics = [metrics]
ratios = []
for metric in metrics:
if '/' in metric:
metrics.remove(metric)
ratios.append(metric)
metrics += metric.split('/')
d = {}
for p in periods:
period, ats, tzoffset = Period.get_days(p, at)
p_data = cls.plotpoints(pk, dimensions, metrics, period=p, at=at)
p_totals = dict()
for dim in p_data.keys():
p_totals[dim] = dict()
for met, vals in p_data[dim].items():
p_totals[dim][met] = sum(
[v for k, v in vals.items() if k in ats])
for rat in ratios:
top, bot = parse_formula(rat)
topt, bott = p_totals[dim][top], p_totals[dim][bot]
p_totals[dim][rat] = bott and topt / bott or 0
d[str(p)] = p_totals
return d
def rank_subdimensions_ratio(cls,
pk,
numerator,
denominator='hits',
dimension='_',
period=None,
recursive=True,
points=False):
top, bottom = numerator, denominator
p_obj, ats, tzoffset = Period.get_days(period)
p_s = str(p_obj)
d_k = keyify(dimension)
top_points = cls.totals(pk, dimension, top, periods=[p_s])
top_total = p_s in top_points and top_points[p_s][d_k][top] or 0
bottom_points = cls.totals(pk, dimension, bottom, periods=[p_s])
bottom_total = p_s in bottom_points and bottom_points[p_s][d_k][
bottom] or 0
ratio_total = bottom_total and float(top_total / bottom_total) or 0
ranked = dict()
def info(sub):
pps = cls.plotpoints(
pk, sub, [top, bottom, '%s/%s' % (top, bottom)],
period=period)[sub]
top_pps = pps[top]
bottom_pps = pps[bottom]
sub_top_sum = sum({k: v
for k, v in top_pps.items()
if k in ats}.values())
sub_bottom_sum = sum(
{k: v
for k, v in bottom_pps.items() if k in ats}.values())
ratio = sub_bottom_sum and float(sub_top_sum / sub_bottom_sum) or 0
difference = ratio_total and (ratio -
ratio_total) / ratio_total or 0
important = sub_bottom_sum > 5 and math.fabs(difference) > .1
data = {
'points': pps,
'score': ratio,
'difference': difference,
'effect': difference * sub_bottom_sum * ratio_total,
'value': sub_top_sum,
'count': sub_bottom_sum,
'important': important,
'dimension': sub
}
if not points:
del data['points']
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
return ranked
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 retrieve(self, pk, dimensions, metrics, period=None, dt=None):
nested = defaultdict(dict)
period = str(Period.get(period))
for dimension in map(maybe_dumps, iterate_dimensions(dimensions)):
for metric in map(maybe_dumps, metrics):
hash_key = keyify(pk, dimension, period, metric)
value_dict = self.hgetall(hash_key)
nested[dimension][metric] = dict([(k, float(v)) for k, v in value_dict.items()])
return dict(nested)
def render_divs(cls,
pk,
metric,
dimension='_',
period=None,
at=None,
tzoffset=None,
format=None,
hidden=False):
period, ats, tzoffset = Period.get_days(period, at, tzoffset=tzoffset)
top, bot = parse_formula(metric)
pps = cls.plotpoints(pk, dimension, metric, period=period)
ppsm = pps[dimension][metric]
if not format:
if bot:
format = 'pct'
else:
format = 'grouped'
def fmt(v):
import locale
if v == 'None':
v = None
f = format
if f == 'int':
f = lambda s: int(float(s or 0))
elif f == 'float':
v = v or 0.0
f = float
elif f == 'grouped':
v = v or 0
f = lambda s: locale.format('%d', int(float(s)), True)
elif f in ['pct', 'percent', '%', 'ratio']:
v = min(101, v and float(v) * 100 or 0)
f = lambda s: '%.2f%%' % s
elif f in ['cash', 'money', 'usd', '$', 'dollars', 'cents']:
v = v and float(v) or 0.0
if f == 'cents':
v = v / 100.0
f = locale.currency
if not f:
return v
return callable(f) and f(v) or v
hidden = hidden and 'style="display: none"' or ''
rep = lambda s: s.format(
pk=pk, metric=metric, dimension=dimension, hidden=hidden)
table = rep(
'<table {hidden} data-hw-pk="{pk}" data-hw-name="{{name}}" \
data-hw-dimension="{dimension}" data-metric="{metric}">'
) + '\n'.join([
'<tr><td>%s</td><td>%s</td></tr>' %
(at.replace(' 00:00:00', ''), fmt(count))
for at, count in ppsm.items()
]) + '</table>'
return table
def update_count_to(cls, pk, dimensions='_', metrics=None, period=False,
at=False, rank=False):
period = Period.get(period)
at = at or cls.now()
dt = period.flatten_str(at)
pipe = cls.whale_driver().pipeline(transaction=False)
for (metric, i) in metrics.iteritems():
_store(pipe, pk, dimensions, metric, period, dt, i,
rank=rank)
pipe.execute()
def _store(redis,
pk,
dimension,
metric,
period,
dt,
count,
method='set',
rank=False):
# Keep a list of graphs per pk
key = keyify(pk, dimension, Period.get(period).interval, metric)
# Store pk dimensions
dimension_key = keyify('dimensions', pk)
dimension_json = keyify(dimension)
if not dimension_json in _added_dimensions[dimension_key]:
redis.sadd(dimension_key, dimension_json)
_added_dimensions[dimension_key].append(dimension_json)
# Store dimensional subdimensions
if dimension != '_':
subdimension_key = keyify('subdimensions', pk, parent(dimension))
if not dimension_json in _added_subdimensions[subdimension_key]:
redis.sadd(subdimension_key, dimension_json)
_added_subdimensions[subdimension_key].append(dimension_json)
if method == 'set':
new_val = float(count)
redis.hset(key, dt, new_val)
elif method == 'incr':
new_val = redis.execute_command('HINCRBYFLOAT', key, dt, float(count))
if rank and (isinstance(try_loads(pk), list) or dimension != '_'):
if isinstance(pk, list) and dimension == '_':
tgt_pk = parent(pk)
tgt_dimension = dimension
else:
tgt_pk = pk
tgt_dimension = parent(dimension)
rank_key = keyify('rank', tgt_pk, tgt_dimension,
Period.get(period).interval, dt, metric)
redis.zadd(rank_key, dimension_json, new_val)
return new_val
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
def _retrieve(redis, pk, dimensions, metrics, period=None, dt=None):
nested = defaultdict(dict)
interval = Period.get(period).interval
for dimension in iterate_dimensions(dimensions)+['_']:
for metric in metrics:
if ':' in metric:
metric_name = metric.split(':')[0]
else: metric_name = metric
hash_key = keyify(pk, dimension, interval, metric_name)
value_dict = redis.hgetall(hash_key)
nested[maybe_dumps(dimension)][maybe_dumps(metric)] = dict([
(k, float(v)) for k, v in value_dict.items()])
return dict(nested)
def zranked(cls,
pk,
parent_dimension='_',
metric='hits',
period=None,
at=None,
start=0,
size=10,
sort_dir=None,
tzoffset=None):
period, ats, tzoffset = Period.get_days(period, at)
dt = ats or [Period.convert(cls.now(), tzoffset)]
return map(
try_loads,
_ranked(cls.whale_driver(),
pk,
parent_dimension,
metric,
period,
dt,
start,
size,
sort_dir=sort_dir))
def update_count_to(cls,
pk,
dimensions='_',
metrics=None,
period=False,
at=False,
rank=False):
period = Period.get(period)
at = at or cls.now()
dt = period.flatten_str(at)
pipe = cls.whale_driver().pipeline(transaction=False)
for (metric, i) in metrics.iteritems():
_store(pipe, pk, dimensions, metric, period, dt, i, rank=rank)
pipe.execute()
def table_graph():
from periods import Period
params = {
'tzoffset': g('tzoffset', 0.0),
'period': g('period', str(Period.get(None))),
}
debug = g('debug', False)
table = g('table', '')
height = g('height', '300px')
delay = g('delay', 5000)
hwurl = req.GET.get('hwurl', '/' or req.url.split('table_graph.js')[0])
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/d3.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/nvd3.js'></script>\");"%hwurl
return_string = '''
appended=false;\n
function jqinit() {{\n
if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n
if(!appended) {{\n
appended = true;\n
{include_string}\n
}}\n
setTimeout(jqinit, 250);\n
}} else {{\n
$(function() {{\n
init_graphs =function() {{
$.hailwhale('{hwurl}').graph_tables('{table}', {options});\n
}}
setTimeout(init_graphs, {delay});
if(ui_loaded_funcs)
ui_loaded_funcs.init_graphs = init_graphs;
}});\n
}}
}}
jqinit();\n
'''.format(include_string=include_string,
table=table,
delay=delay,
hwurl=hwurl,
options=util.maybe_dumps(params))
return return_string
def reasons_for(cls, pk, formula="value/hits", known_data=None, period=None, recursive=True):
metric, denomenator = parse_formula(formula)
period = Period.get(period)
pk_base, decision, option = pk
base = "_"
best = worst = None
ranks = cls.cached_rank(pk, formula=formula, dimension=base, period=period, recursive=recursive, points=False)
overall = cls.cached_rank(
[pk_base, decision], formula=formula, dimension=base, period=period, recursive=recursive, points=False
)
parent_score = overall[base]["score"]
parent_count = overall[base]["count"]
ranks[base]["effect"] = ranks[base]["count"] * ranks[base]["difference"]
def delta(info):
diff = info["score"] - parent_score
info["value_diff"] = info["value"] - overall[base]["value"]
info["difference"] += diff
if math.fabs(diff) > 0 and info["count"] > 0:
info["effect"] += diff * info["count"]
info["significance"] = ((0.5 * info["effect"]) ** 2) / parent_count
else:
info["effect"] = 0
info["significance"] = 0
return info
known_dimensions = iterate_dimensions(known_data)
for dim, info in ranks.items():
ranks[dim] = info = delta(info)
if try_loads(dim) in known_dimensions and info["important"]:
best_score = best and ranks[best]["score"]
worst_score = worst and ranks[worst]["score"]
if info["score"] > best_score:
best = dim
if info["score"] < worst_score:
worst = dim
i = {
"good": best and ranks[best] or {},
"bad": worst and ranks[worst] or {},
#'ranks': ranks,
"base": ranks[base],
"parent": overall[base],
}
i["high"] = i["good"].get("difference", 0)
i["high_sig"] = i["good"].get("significance", 0) > 4
i["low"] = i["bad"].get("difference", 0)
i["low_sig"] = i["bad"].get("significance", 0) > 4
return i
def rank_subdimensions_ratio(cls, pk, numerator, denominator='hits',
dimension='_', period=None, recursive=True, points=False):
top, bottom = numerator, denominator
p_obj, ats, tzoffset = Period.get_days(period)
p_s = str(p_obj)
d_k = keyify(dimension)
top_points = cls.totals(pk, dimension, top,
periods=[p_s])
top_total = p_s in top_points and top_points[p_s][d_k][top] or 0
bottom_points = cls.totals(pk, dimension, bottom,
periods=[p_s])
bottom_total = p_s in bottom_points and bottom_points[p_s][d_k][bottom] or 0
ratio_total = bottom_total and float(top_total / bottom_total) or 0
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, [top, bottom, '%s/%s' % (top, bottom)], period=period)[sub]
top_pps = pps[top]
bottom_pps = pps[bottom]
sub_top_sum = sum({k: v for k, v in top_pps.items() if k in ats}.values())
sub_bottom_sum = sum({k: v for k, v in bottom_pps.items() if k in ats}.values())
ratio = sub_bottom_sum and float(sub_top_sum / sub_bottom_sum) or 0
difference = ratio_total and (ratio - ratio_total) / ratio_total or 0
important = sub_bottom_sum > 5 and math.fabs(difference) > .1
data = {
'points': pps,
'score': ratio,
'difference': difference,
'effect': difference * sub_bottom_sum * ratio_total,
'value': sub_top_sum,
'count': sub_bottom_sum,
'important': important,
'dimension': sub
}
if not points:
del data['points']
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
return ranked
def _retrieve(redis, pk, dimensions, metrics, period=None, dt=None):
nested = defaultdict(dict)
interval = Period.get(period).interval
for dimension in iterate_dimensions(dimensions) + ['_']:
for metric in metrics:
if ':' in metric:
metric_name = metric.split(':')[0]
else:
metric_name = metric
hash_key = keyify(pk, dimension, interval, metric_name)
value_dict = redis.hgetall(hash_key)
nested[maybe_dumps(dimension)][maybe_dumps(metric)] = dict([
(k, float(v)) for k, v in value_dict.items()
])
return dict(nested)
def rank_subdimensions_scalar(cls,
pk,
dimension='_',
metric='hits',
period=None,
recursive=True,
prune_parents=True,
points=False):
p_obj, ats, tzoffset = Period.get_days(period)
p_s = str(period)
d_k = keyify(dimension)
# sum of all values in metric
total = cls.totals(pk, dimension, metric,
periods=[p_s])[p_s][d_k][metric]
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, metric, period=period)[sub][metric]
sub_total = sum(pps.values())
data = {
'points': pps,
'score': sub_total,
'important': sub_total > 10 and (sub_total > (total / 10)),
'effect': total - sub_total,
'difference': total - sub_total,
'value': sub_total,
'count': sub_total,
'dimension': sub
}
if not points:
del data['points']
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
# Prune parents
if recursive and prune_parents:
for sub, info in ranked.items():
children = map(maybe_dumps, cls.get_subdimensions(pk, sub))
children_total = sum(
map(lambda s: ranked[s]['score'], children))
if info['important'] and (info['score'] -
children_total) < (total / 10):
info['important'] = False
return ranked
def table_graph():
from periods import Period
params = {
'tzoffset': g('tzoffset', 0.0),
'period': g('period', str(Period.get(None))),
}
debug = g('debug', False)
table = g('table', '')
height = g('height', '300px')
delay = g('delay', 5000)
hwurl = req.GET.get('hwurl', '/' or req.url.split('table_graph.js')[0])
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/d3.js'></script>\");"%hwurl
include_string += \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/nvd3.js'></script>\");"%hwurl
return_string = '''
appended=false;\n
function jqinit() {{\n
if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n
if(!appended) {{\n
appended = true;\n
{include_string}\n
}}\n
setTimeout(jqinit, 250);\n
}} else {{\n
$(function() {{\n
init_graphs =function() {{
$.hailwhale('{hwurl}').graph_tables('{table}', {options});\n
}}
setTimeout(init_graphs, {delay});
if(ui_loaded_funcs)
ui_loaded_funcs.init_graphs = init_graphs;
}});\n
}}
}}
jqinit();\n
'''.format( include_string=include_string, table=table, delay=delay,
hwurl=hwurl, options=util.maybe_dumps(params))
return return_string
def render_hw_plotpoint_table(cls, pk, metric, dimension='_', period=None, at=None,
tzoffset=None, format=None, hidden=False, graph_color=''):
period, ats, tzoffset = Period.get_days(period, tzoffset=tzoffset)
top, bot = parse_formula(metric)
pps = cls.plotpoints(pk, dimension, metric, period=period,
tzoffset=tzoffset)
ppsm = pps[dimension][metric]
if not format:
if bot:
format = 'pct'
else:
format = 'grouped'
def fmt(v):
import locale
if v == 'None':
v = None
f = format
if f == 'int':
f = lambda s: int(float(s or 0))
elif f == 'float':
v = v or 0.0
f = float
elif f == 'grouped':
v = v or 0
f = lambda s: locale.format('%d', int(float(s)), True)
elif f in ['pct', 'percent', '%', 'ratio']:
v = min(101, v and float(v)*100 or 0)
f = lambda s: '%.2f%%'%s
elif f in ['cash', 'money', 'usd', '$', 'dollars', 'cents']:
v = v and float(v) or 0.0
if f == 'cents':
v = v/100.0
f = locale.currency
if not f:
return v
return callable(f) and f(v) or v
hidden = hidden and 'style="display: none"' or ''
rep = lambda s: s.format(pk=pk, metric=metric, dimension=dimension,
hidden=hidden, color=graph_color)
table = rep('<table {hidden} data-hw-pk="{pk}" data-hw-name="{{name}}" \
data-hw-dimension="{dimension}" data-metric="{metric}" \
data-hw-color="{color}">')+'\n'.join([
'<tr><td>%s</td><td>%s</td></tr>'%(at.replace(' 00:00:00', ''), fmt(count) )
for at, count in ppsm.items()])+'</table>'
return table
def reasons_for(cls, pk, formula='value/hits', known_data=None, period=None, recursive=True):
metric, denomenator = parse_formula(formula)
period = Period.get(period)
pk_base, decision, option = pk
base = '_'
best = worst = None
ranks = cls.cached_rank(pk, formula=formula, dimension=base,
period=period, recursive=recursive, points=False)
overall = cls.cached_rank([pk_base, decision], formula=formula, dimension=base,
period=period, recursive=recursive, points=False)
parent_score = overall[base]['score']
parent_count = overall[base]['count']
ranks[base]['effect'] = ranks[base]['count'] * ranks[base]['difference']
def delta(info):
diff = info['score'] - parent_score
info['value_diff'] = info['value'] - overall[base]['value']
info['difference'] += diff
if math.fabs(diff) > 0 and info['count'] > 0:
info['effect'] += diff * info['count']
info['significance'] = ((.5 * info['effect']) ** 2) / parent_count
else:
info['effect'] = 0
info['significance'] = 0
return info
known_dimensions = iterate_dimensions(known_data)
for dim, info in ranks.items():
ranks[dim] = info = delta(info)
if try_loads(dim) in known_dimensions and info['important']:
best_score = best and ranks[best]['score']
worst_score = worst and ranks[worst]['score']
if info['score'] > best_score:
best = dim
if info['score'] < worst_score:
worst = dim
i = {'good': best and ranks[best] or {},
'bad': worst and ranks[worst] or {},
#'ranks': ranks,
'base': ranks[base],
'parent': overall[base]}
i['high'] = i['good'].get('difference', 0)
i['high_sig'] = i['good'].get('significance', 0) > 4
i['low'] = i['bad'].get('difference', 0)
i['low_sig'] = i['bad'].get('significance', 0) > 4
return i
def rank_subdimensions_ratio(
cls, pk, numerator, denominator="hits", dimension="_", period=None, recursive=True, points=False
):
top, bottom = numerator, denominator
period = period or Period.default_size()
d_k = keyify(dimension)
top_total = cls.totals(pk, dimension, top, periods=[period])[str(period)][d_k][top]
bottom_total = cls.totals(pk, dimension, bottom, periods=[period])[str(period)][d_k][bottom]
ratio_total = bottom_total and float(top_total / bottom_total) or 0
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, [top, bottom, "%s/%s" % (top, bottom)], period=period)[sub]
top_pps = pps[top]
bottom_pps = pps[bottom]
sub_top_sum = sum(top_pps.values())
sub_bottom_sum = sum(bottom_pps.values())
ratio = sub_bottom_sum and float(sub_top_sum / sub_bottom_sum) or 0
difference = ratio_total and (ratio - ratio_total) / ratio_total or 0
important = sub_bottom_sum > 5 and math.fabs(difference) > 0.1
data = {
"points": pps,
"score": ratio,
"difference": difference,
"effect": difference * sub_bottom_sum * ratio_total,
"value": sub_top_sum,
"count": sub_bottom_sum,
"important": important,
"dimension": sub,
}
if not points:
del data["points"]
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
return ranked
def _ranked(redis,
pk,
parent_dimension,
metric,
period,
ats,
start=0,
size=10,
sort_dir=None):
top, bot = parse_formula(metric)
rank_keyify = lambda ats, met: keyify('rank', pk, parent_dimension,
Period.get(period).interval, ats, met
)
final_rank_key = rank_keyify(ats, metric)
def squash_ats(met):
if len(ats) > 1:
map(lambda at: redis.zremrangebyscore(rank_keyify(at, met), 0, 0),
ats)
redis.zunionstore(rank_keyify(ats, met),
map(lambda at: rank_keyify(at, met), ats))
squash_ats(top)
if bot:
squash_ats(bot)
top_key, bot_key = rank_keyify(ats, top), rank_keyify(ats, bot)
redis.execute_command(
"eval", """
for key_i, key_n in ipairs(redis.call("zrange", KEYS[2], 0, -1)) do
local top_s = tonumber(redis.call("zscore", KEYS[1], key_n))
local bot_s = tonumber(redis.call("zscore", KEYS[2], key_n))
if top_s and bot_s and bot_s > 0 then
redis.call("zadd", KEYS[3], top_s/bot_s, key_n)
end
end
""", 3, top_key, bot_key, final_rank_key)
redis.zremrangebyscore(final_rank_key, 0, 0)
return redis.zrange(final_rank_key,
start,
start + size,
desc=not sort_dir
or sort_dir.upper() in ['-', 'DESC', 'HIGH'])
def totals(cls, pk, dimensions=None, metrics=None, periods=None):
if not periods:
periods = DEFAULT_PERIODS
if not isinstance(periods, list):
periods = [periods]
metrics = metrics or ['hits']
if not isinstance(metrics, list):
metrics = [metrics]
d = {}
for p in periods:
p_data = cls.plotpoints(pk, dimensions, metrics, period=str(p))
p_totals = dict()
for dim, mets in p_data.items():
p_totals[dim] = dict()
for met, vals in mets.items():
p_totals[dim][met] = sum([
v for k, v in vals.items()
if Period.get(p).flatten(k)])
d[str(p)] = p_totals
d['alltime'] = cls.whale_driver().retrieve(
pk, dimensions, metrics, period='all')
return d
def rank_subdimensions_ratio(cls, pk, numerator, denominator='hits', dimension='_', period=None):
top, bottom = numerator, denominator
period = period or Period.default_size()
d_k = keyify(dimension)
top_total = cls.totals(pk, dimension, top, periods=[period])[str(period)][d_k][top]
bottom_total = cls.totals(pk, dimension, bottom, periods=[period])[str(period)][d_k][bottom]
ratio_total = bottom_total and float(top_total / bottom_total) or 0
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, [top, bottom], period=period)[sub]
ratio_points = cls.ratio_plotpoints(pk, top, bottom, sub, period=period)[sub]
top_pps = pps[top]
bottom_pps = pps[bottom]
sub_top_sum = sum(top_pps.values())
sub_bottom_sum = sum(bottom_pps.values())
ratio = sub_bottom_sum and float(sub_top_sum / sub_bottom_sum) or 0
difference = (ratio - ratio_total) / ratio_total
important = sub_bottom_sum > 5 and (difference > .1 or -difference > .1)
return {
'points': pps,
'ratio_points': ratio_points,
'difference': difference,
'effect': difference * sub_bottom_sum,
'important': important
}
for sub in map(maybe_dumps, cls.all_subdimensions(pk, dimension)):
ranked[sub] = info(sub)
del(ranked[dimension])
return ranked
def rank_subdimensions_scalar(
cls, pk, dimension="_", metric="hits", period=None, recursive=True, prune_parents=True, points=False
):
period = period or Period.default_size()
d_k = keyify(dimension)
total = cls.totals(pk, dimension, metric, periods=[period])[period][d_k][metric]
ranked = dict()
def info(sub):
pps = cls.plotpoints(pk, sub, metric, period=period)[sub][metric]
sub_total = sum(pps.values())
data = {
"points": pps,
"score": sub_total,
"important": sub_total > 10 and (sub_total > (total / 10)) or False,
"effect": total - sub_total,
"difference": total - sub_total,
"value": sub_total,
"count": sub_total,
"dimension": sub,
}
if not points:
del data["points"]
return data
_subs = recursive and cls.all_subdimensions or cls.get_subdimensions
for sub in map(maybe_dumps, _subs(pk, dimension)):
ranked[sub] = info(sub)
# Prune parents
if recursive and prune_parents:
for sub, info in ranked.items():
children = map(maybe_dumps, cls.get_subdimensions(pk, sub))
children_total = sum(map(lambda s: ranked[s]["score"], children))
if info["important"] and (info["score"] - children_total) < (total / 10):
info["important"] = False
return ranked
def reasons_for(cls,
pk,
formula='value/hits',
known_data=None,
period=None,
recursive=True):
metric, denomenator = parse_formula(formula)
period = Period.get(period)
pk_base, decision, option = pk
base = '_'
best = worst = None
ranks = cls.cached_rank(pk,
formula=formula,
dimension=base,
period=period,
recursive=recursive,
points=False)
overall = cls.cached_rank([pk_base, decision],
formula=formula,
dimension=base,
period=period,
recursive=recursive,
points=False)
parent_score = overall[base]['score']
parent_count = overall[base]['count']
ranks[base][
'effect'] = ranks[base]['count'] * ranks[base]['difference']
def delta(info):
diff = info['score'] - parent_score
info['value_diff'] = info['value'] - overall[base]['value']
info['difference'] += diff
if math.fabs(diff) > 0 and info['count'] > 0:
info['effect'] += diff * info['count']
info['significance'] = (
(.5 * info['effect'])**2) / parent_count
else:
info['effect'] = 0
info['significance'] = 0
return info
known_dimensions = iterate_dimensions(known_data)
for dim, info in ranks.items():
ranks[dim] = info = delta(info)
if try_loads(dim) in known_dimensions and info['important']:
best_score = best and ranks[best]['score']
worst_score = worst and ranks[worst]['score']
if info['score'] > best_score:
best = dim
if info['score'] < worst_score:
worst = dim
i = {
'good': best and ranks[best] or {},
'bad': worst and ranks[worst] or {},
#'ranks': ranks,
'base': ranks[base],
'parent': overall[base]
}
i['high'] = i['good'].get('difference', 0)
i['high_sig'] = i['good'].get('significance', 0) > 4
i['low'] = i['bad'].get('difference', 0)
i['low_sig'] = i['bad'].get('significance', 0) > 4
return i
def scalar_plotpoints(cls,
pk,
dimensions=None,
metrics=None,
at=None,
depth=0,
period=None,
flot_time=False,
points_type=OrderedDict):
metrics = metrics or ['hits']
at = at or times.now()
if isinstance(metrics, basestring):
metrics = [metrics]
p_obj, ats, tzoffset = Period.get_days(period, at)
p_s = str(p_obj)
dts = list(p_obj.datetimes_strs(end=Period.parse_dt_str(at)))
sparse = _retrieve(cls.whale_driver(),
pk,
dimensions,
metrics,
period=p_obj)
nonsparse = defaultdict(dict)
if flot_time:
points_type = list
for dim, mets in sparse.items():
for met, points in mets.items():
nonsparse[dim][met] = []
use_method = False
met_name = met
if ':' in met:
met_name, use_method = met.split(':')
const_value = False
if met_name in TIME_MATRIX:
const_value = float(p_obj.getUnits()[0] /
TIME_MATRIX[met_name])
# Try to parse static metrics too
elif met_name == '_count':
const_value = len(dts)
try:
const_value = float(met_name)
except:
pass
last_value = total = 0
for dt in dts:
dt_obj = Period.parse_dt_str(dt)
if met_name == '_days_in_month':
from calendar import monthrange
const_value = monthrange(dt_obj.year, dt_obj.month)[1]
if flot_time:
dt_t = to_flot_time(dt_obj)
else:
dt_t = dt
if const_value:
value = const_value
else:
value = points[dt] if dt in points else 0
if use_method == 'count' or not use_method:
value = value
elif use_method in ['+', 'sum', 'add', 'cumulative']:
total += value
value = total
elif use_method in ['_', 'set', 'last', 'level']:
if not last_value:
last_value = value
if not value:
value = last_value
last_value = value
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,
at=at,
depth=depth - 1,
period=period,
flot_time=flot_time,
points_type=points_type).items())
return nonsparse
def yesterday(cls, pk, metric, dimension='_'):
return cls.total(pk, metric, dimension, Period.all_sizes()[1],
at=cls.now()-timedelta(days=1))
def today(cls, pk, metric, dimension='_'):
return cls.total(pk, metric, dimension, Period.all_sizes()[1],
at=cls.now())
def scalar_plotpoints(cls, pk, dimensions=None, metrics=None, at=None,
depth=0, period=None, flot_time=False, points_type=OrderedDict):
metrics = metrics or ['hits']
at = at or times.now()
if isinstance(metrics, basestring):
metrics = [metrics]
p_obj, ats, tzoffset = Period.get_days(period,at)
p_s = str(p_obj)
dts = list(p_obj.datetimes_strs(end=Period.parse_dt_str(at)))
sparse = _retrieve(cls.whale_driver(), pk, dimensions, metrics, period=p_obj)
nonsparse = defaultdict(dict)
if flot_time:
points_type = list
for dim, mets in sparse.items():
for met, points in mets.items():
nonsparse[dim][met] = []
use_method = False
met_name = met
if ':' in met:
met_name, use_method = met.split(':')
const_value = False
if met_name in TIME_MATRIX:
const_value = float(p_obj.getUnits()[0] /
TIME_MATRIX[met_name])
# Try to parse static metrics too
elif met_name == '_count':
const_value = len(dts)
try:
const_value = float(met_name)
except:
pass
last_value = total = 0
for dt in dts:
dt_obj = Period.parse_dt_str(dt)
if met_name == '_days_in_month':
from calendar import monthrange
const_value = monthrange(dt_obj.year, dt_obj.month)[1]
if flot_time:
dt_t = to_flot_time(dt_obj)
else:
dt_t = dt
if const_value:
value = const_value
else:
value = points[dt] if dt in points else 0
if use_method == 'count' or not use_method:
value = value
elif use_method in ['+', 'sum', 'add', 'cumulative']:
total += value
value = total
elif use_method in ['_', 'set', 'last', 'level']:
if not last_value:
last_value = value
if not value:
value = last_value
last_value = value
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, at=at, depth=depth - 1, period=period,
flot_time=flot_time, points_type=points_type).items())
return nonsparse
"""
query_str = """
{"{{dataset_id_field}}": "{{dataset.dataset_id}}",
"{{form_meta_timeend}}": {
"$gte": "{{period.start}}",
"$lte": "{{period.end}}"
}
}
"""
aggregate_str = """
{"$group": {"_id": 0, "total": {"$sum": "$value.{{num_using_fp}}"}}}
"""
dataset_id = "5791793ac29b4d77b20cf1a04d8e7161"
dataset = Dataset.find_one(dataset_id)
period = Period.month_period(2013, 3)
if dataset:
fields = Observation.encoding(dataset)
fields["dataset"] = dataset
fields['dataset_id_field'] = fields[DATASET_ID]
fields['period'] = Period.month_period(2013, 3)
mapper = Code(Template(mapper_str).render(fields))
reducer = Code(Template(reducer_str).render(fields))
query = json.loads(Template(query_str).render(fields))
query['%(form_meta_timeend)s' % fields]['$gte'] = period.start
query['%(form_meta_timeend)s' % fields]['$lte'] = period.end
aggregate = json.loads(Template(aggregate_str).render(fields))
results = db.observations.map_reduce(mapper, reducer, 'myresults_fp', query=query)
if results.count():
value = results.aggregate(aggregate)
def add_period(self, start, end):
periods = self.periods
new = Period(start, end)
if new not in periods:
periods.append(new)
def yesterday(cls, pk, metric, dimension='_'):
return cls.total(pk,
metric,
dimension,
Period.all_sizes()[1],
at=cls.now() - timedelta(days=1))
def graph():
from periods import Period
params = {
'pk': g('pk', '_', False),
'dimension': g('dimension', '_', False),
'metric': g('metric', 'hits', False),
'depth': g('depth', 0),
'tzoffset': g('tzoffset', 0.0),
'period': g('period', str(Period.get(None))),
'area': g('area', ''),
}
pk = params['pk']
dimension = params['dimension']
metric = params['metric']
period = Period.get(params['period'])
debug = g('debug', False)
parent_div = g('parent_div', 'hailwhale_graphs')
table = g('table', False)
height = g('height', '300px')
params['title'] = g('title', '')
if not params['title']:
pkname = g('pk', '')
dimname = util.try_loads(g('dimension', 'Overall'))
dimname = isinstance(dimname, list) and dimname[-1] or dimname
params['title'] = '%s [%s]' % (util.maybe_dumps(pkname),
util.maybe_dumps(dimname))
if isinstance(table, basestring):
table = table.lower() == 'true'
hwurl = req.GET.get('hwurl', req.url.split('graph.js')[0])
params['autoupdate'] = g('live', True)
params['interval'] = g('interval', 6000)
graph_id = hashlib.md5(str(params)).hexdigest()
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl
if table:
try:
columns = int(g('table', 6, int))
except:
columns = 6
pps = Whale.plotpoints(pk,
dimension,
metric,
period=period,
depth=params['depth'])
dates = [p for p in Period.get(period).datetimes_strs()
][(-1 * columns - 1):]
table_str = '''
$('#{id} .table').html('<table style="width: 100%"> <tr> <th></th> <th></th> {columns} </tr>
'''.strip().format(id=graph_id,
columns=' '.join([
'<th>%s</th>' % date.replace('00:00:00 ', '')
for date in dates
]))
dimensions = pps.keys()
if '_' in dimensions:
dimensions.remove('_')
dimensions = ['_'] + dimensions
for dimension_counter, dimension in enumerate(dimensions):
checked = 'off'
if dimension_counter < 10:
checked = 'on'
if dimension == '_':
if params['depth']:
continue
dimension_name = '<b>Overall</b>'
else:
dimension_name = dimension.capitalize()
table_str += '''
<tr> <td><input id="" style="display: none" type="checkbox" value="{checked}" name="checkbox-{pk}-{dimension}"></td> <td>{dimension_name}</td> {columns} </tr>
'''.format(pk=pk,
dimension=dimension,
checked=checked,
dimension_name=dimension_name,
columns=' '.join([
"<td>%s</td>" %
int(pps[dimension][metric][date])
for date in dates
])).strip()
table_str += '''</table>');'''
else:
table_str = ''
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.min.js'></script>\");"%hwurl
return_string = '''
appended=false;\n
document.write('<div id="{id}"><div class="graph" style="height: {height}"></div><div class="table"></div></div>');\n
function jqinit() {{\n
if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n
if(!appended) {{\n
appended = true;\n
{include_string}\n
}}\n
setTimeout(jqinit, 250);\n
}} else {{\n
$(function() {{\n
$.hailwhale('{hwurl}').add_graph('{id} .graph', {options});\n
{table_str}
}});\n
}}
}}
jqinit();\n
'''.format(parent_div=parent_div,
include_string=include_string,
hwurl=hwurl,
table_str=table_str,
height=height,
id=graph_id,
options=util.maybe_dumps(params))
return return_string
(indicator_def['type'], indicator_def['description'],
value, numerator, denominator))
print (indicator_def['type'], indicator_def['name'],
value, numerator, denominator)
filename = "%(name)s_mvp_indicator_%(start)s_to_%(end)s.csv" % {
'name': name,
'start': period.start.strftime('%Y-%m-%d'),
'end': period.end.strftime('%Y-%m-%d')}
filename = os.path.join(REPORTS_DIR, filename)
with open(filename, 'wb') as f:
csv_writer = csv.writer(
f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
csv_writer.writerows(RESULTS)
print "\nSuccessfully generated %s." % filename
if __name__ == '__main__':
arguments = sys.argv[1:]
if len(arguments) < 3:
print (u"Expected site_name YEAR MONTH\n"
u"Eg.\n\t python report.py ruhiira 2013 03")
else:
name = arguments[0]
year = int(arguments[1])
month = int(arguments[2])
period = Period.month_period(year, month)
indicator_name = None
if arguments.__len__() > 3:
indicator_name = arguments[3]
_generate_indicator_export(name, period, indicator_name)
def today(cls, pk, metric, dimension='_'):
return cls.total(pk,
metric,
dimension,
Period.all_sizes()[1],
at=cls.now())
def graph():
from periods import Period
params = {'pk': g('pk', '_', False),
'dimension': g('dimension', '_', False),
'metric': g('metric', 'hits', False),
'depth': g('depth', 0),
'tzoffset': g('tzoffset', 0.0),
'period': g('period', str(Period.get(None))),
'area': g('area', ''),
}
pk = params['pk']
dimension = params['dimension']
metric = params['metric']
period = Period.get(params['period'])
debug = g('debug', False)
parent_div = g('parent_div', 'hailwhale_graphs')
table = g('table', False)
height = g('height', '300px')
params['title'] = g('title', '')
if not params['title']:
pkname = g('pk', '')
dimname = util.try_loads(g('dimension', 'Overall'))
dimname = isinstance(dimname, list) and dimname[-1] or dimname
params['title'] = '%s [%s]' % (util.maybe_dumps(pkname), util.maybe_dumps(dimname))
if isinstance(table, basestring):
table = table.lower() == 'true'
hwurl = req.GET.get('hwurl', req.url.split('graph.js')[0])
params['autoupdate'] = g('live', True)
params['interval'] = g('interval', 6000)
graph_id = hashlib.md5(str(params)).hexdigest()
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl
if table:
try:
columns = int(g('table', 6, int))
except:
columns = 6
pps = Whale.plotpoints(pk, dimension, metric, period=period,
depth=params['depth'])
dates = [p for p in
Period.get(period).datetimes_strs()][(-1*columns - 1):]
table_str = '''
$('#{id} .table').html('<table style="width: 100%"> <tr> <th></th> <th></th> {columns} </tr>
'''.strip().format(id=graph_id,columns=' '.join([
'<th>%s</th>'%date.replace('00:00:00 ', '') for date in dates]))
dimensions = pps.keys()
if '_' in dimensions:
dimensions.remove('_')
dimensions = ['_'] + dimensions
for dimension_counter, dimension in enumerate(dimensions):
checked = 'off'
if dimension_counter < 10:
checked = 'on'
if dimension == '_':
if params['depth']:
continue
dimension_name = '<b>Overall</b>'
else:
dimension_name = dimension.capitalize()
table_str += '''
<tr> <td><input id="" style="display: none" type="checkbox" value="{checked}" name="checkbox-{pk}-{dimension}"></td> <td>{dimension_name}</td> {columns} </tr>
'''.format(pk=pk, dimension=dimension, checked=checked,
dimension_name=dimension_name,
columns=' '.join([
"<td>%s</td>"%int(pps[dimension][metric][date]) for date in dates])).strip()
table_str += '''</table>');'''
else:
table_str = ''
include_string = \
"document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.min.js'></script>\");"%hwurl
return_string = '''
appended=false;\n
document.write('<div id="{id}"><div class="graph" style="height: {height}"></div><div class="table"></div></div>');\n
function jqinit() {{\n
if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n
if(!appended) {{\n
appended = true;\n
{include_string}\n
}}\n
setTimeout(jqinit, 250);\n
}} else {{\n
$(function() {{\n
$.hailwhale('{hwurl}').add_graph('{id} .graph', {options});\n
{table_str}
}});\n
}}
}}
jqinit();\n
'''.format(parent_div=parent_div, include_string=include_string,
hwurl=hwurl, table_str=table_str, height=height,
id=graph_id,
options=util.maybe_dumps(params))
return return_string
"""
query_str = """
{"{{dataset_id_field}}": "{{dataset.dataset_id}}",
"{{form_meta_timeend}}": {
"$gte": "{{period.start}}",
"$lte": "{{period.end}}"
}
}
"""
aggregate_str = """
{"$group": {"_id": 0, "total": {"$sum": "$value.{{num_using_fp}}"}}}
"""
dataset_id = "5791793ac29b4d77b20cf1a04d8e7161"
dataset = Dataset.find_one(dataset_id)
period = Period.month_period(2013, 3)
if dataset:
fields = Observation.encoding(dataset)
fields["dataset"] = dataset
fields['dataset_id_field'] = fields[DATASET_ID]
fields['period'] = Period.month_period(2013, 3)
mapper = Code(Template(mapper_str).render(fields))
reducer = Code(Template(reducer_str).render(fields))
query = json.loads(Template(query_str).render(fields))
query['%(form_meta_timeend)s' % fields]['$gte'] = period.start
query['%(form_meta_timeend)s' % fields]['$lte'] = period.end
aggregate = json.loads(Template(aggregate_str).render(fields))
results = db.observations.map_reduce(mapper,
reducer,
'myresults_fp',
