def get_related_attrs_scan_count(cls, attr_names, queryset=None):
"""
Calculates the total count for a combination of related attributes
(``attr_names``).
Results might be optionally limited using ``queryset``.
Returns a ``len(attr_names)``-dimensional dict, one dimension
per attribute name. Each leaf is a tuple with the count of the
particular combination of attributes.
Example::
{
<Tag: thriller>: {
<Author: Stephen King>: 120
},
…
},
…
},
"""
objects = queryset or cls.objects
objects = objects.select_related(*attr_names)
# get a list (of dicts) with all object combinations and their counts
attr_combinations_values = objects.values(*attr_names).annotate(count=models.Count("pk")).order_by()
# a list of object lists, needed to determine possible object
# combinations
attrs_objects = [set(getattrs(objects, attr_name)) for attr_name in attr_names]
# needed to lookup counts for certain object combinations
counts_by_pk = values_to_hierarchical_dict(attr_combinations_values, attr_names)
# needed to calculate percentiles (that's why it's already float)
objects_count = float(len(objects))
# if object combination does not exist, this information is provided:
default_leaf = 0
# assemble a complete hierarchical dictionary of possible
# combinations of attributes, including their count and percentile
out = HierarchicalOrderedDict()
for combination in product(*attrs_objects):
count_dict = counts_by_pk.get_by_path((getattr(o, "pk", None) for o in combination), None)
if count_dict:
count = count_dict["count"]
else:
count = default_leaf
out.set_by_path(combination, count)
return out
def lookups(self, request, model_admin):
"""
Returns a list of tuples. The first element in each
tuple will appear in the URL query (the PK of the related model).
The second element is the human-readable name for the option that
will appear in the right sidebar on the admin page (``str(obj)``).
"""
scans = model_admin.resource_class._meta.model.objects.only(
"%s_id" % attr_name, attr_name
).distinct(
).select_related(
attr_name
)
components = set(getattrs(scans, attr_name))
return [(getattr(c, "pk", None), str(c)) for c in components]
def get_related_attr_scan_intervals(cls, attr_name, queryset=None):
"""
Creates a series of time deltas between scans, grouped by objects
found under ``attr_name``. Results might be optionally limited using
``queryset``.
Returns a dict with the related objects as keys and a list of tuples
as value each. The tuples are filled with the timestamp and the
``timedelta`` to the previous scan.
Example::
{
<Tag: thriller>: [
(datetime(…), timedelta(…)),
(datetime(…), timedelta(…)),
],
…
}
"""
objects = queryset or cls.objects
objects = objects.only("timestamp", attr_name).select_related(attr_name).order_by("timestamp")
# initialize with empty lists (avoids cumbersome checks below)
attr_objects = objects.only(attr_name).distinct()
all_series = {o: [] for o in getattrs(attr_objects, attr_name)}
for scan in objects:
timestamp = scan.timestamp
attr_object = getattr(scan, attr_name)
inner_series = all_series.get(attr_object)
if inner_series:
last_timestamp = inner_series[-1][0]
delta = timestamp - last_timestamp
else:
delta = timedelta()
inner_series.append((timestamp, delta))
return all_series
