def _save_raw_data_chunk(chunk, file_pk, prog_key, increment, *args, **kwargs):
"""Save the raw data to the database."""
import_file = ImportFile.objects.get(pk=file_pk)
# Save our "column headers" and sample rows for F/E.
source_type = get_source_type(import_file)
for c in chunk:
raw_bs = BuildingSnapshot()
raw_bs.import_file = import_file
raw_bs.extra_data = c
raw_bs.source_type = source_type
# We require a save to get our PK
# We save here to set our initial source PKs.
raw_bs.save()
super_org = import_file.import_record.super_organization
raw_bs.super_organization = super_org
set_initial_sources(raw_bs)
raw_bs.save()
# Indicate progress
increment_cache(prog_key, increment)
def _save_raw_data_chunk(chunk, file_pk, prog_key, increment, *args, **kwargs):
"""Save the raw data to the database."""
import_file = ImportFile.objects.get(pk=file_pk)
# Save our "column headers" and sample rows for F/E.
source_type = get_source_type(import_file)
for c in chunk:
raw_bs = BuildingSnapshot()
raw_bs.import_file = import_file
raw_bs.extra_data = c
raw_bs.source_type = source_type
# We require a save to get our PK
# We save here to set our initial source PKs.
raw_bs.save()
super_org = import_file.import_record.super_organization
raw_bs.super_organization = super_org
set_initial_sources(raw_bs)
raw_bs.save()
# Indicate progress
increment_cache(prog_key, increment)
def create_models(data, import_file):
"""
Create a BuildingSnapshot, a CanonicalBuilding, and a Meter. Then, create
TimeSeries models for each meter reading in data.
:param data: dictionary of building data from a Green Button XML file
in the form returned by xml_importer.building_data
:param import_file: ImportFile referencing the original xml file; needed
for linking to BuildingSnapshot and for determining super_organization
:returns: the created CanonicalBuilding
"""
# cache data on import_file; this is a proof of concept and we
# only have two example files available so we hardcode the only
# heading present.
import_file.cached_first_row = ROW_DELIMITER.join(["address"])
import_file.cached_second_to_fifth_row = ROW_DELIMITER.join(
[data['address']]
)
import_file.save()
raw_bs = BuildingSnapshot()
raw_bs.import_file = import_file
# We require a save to get our PK
# We save here to set our initial source PKs.
raw_bs.save()
super_org = import_file.import_record.super_organization
raw_bs.super_organization = super_org
set_initial_sources(raw_bs)
raw_bs.address_line_1 = data['address']
raw_bs.source_type = GREEN_BUTTON_BS
raw_bs.save()
# create canonical building
cb = CanonicalBuilding.objects.create(canonical_snapshot=raw_bs)
raw_bs.canonical_building = cb
raw_bs.save()
# log building creation
AuditLog.objects.create(
organization=import_file.import_record.super_organization,
user=import_file.import_record.owner,
content_object=cb,
action="create_building",
action_note="Created building",
)
# create meter for this dataset (each dataset is a single energy type)
e_type = energy_type(data['service_category'])
e_type_string = next(
pair[1] for pair in seed.models.ENERGY_TYPES if pair[0] == e_type
)
m_name = "gb_{0}[{1}]".format(str(raw_bs.id), e_type_string)
m_energy_units = energy_units(data['meter']['uom'])
meter = Meter.objects.create(
name=m_name, energy_type=e_type, energy_units=m_energy_units
)
meter.building_snapshot.add(raw_bs)
meter.save()
# now time series data for the meter
for reading in data['interval']['readings']:
start_time = int(reading['start_time'])
duration = int(reading['duration'])
begin_time = datetime.fromtimestamp(start_time)
end_time = datetime.fromtimestamp(start_time + duration)
value = reading['value']
cost = reading['cost']
new_ts = TimeSeries.objects.create(
begin_time=begin_time,
end_time=end_time,
reading=value,
cost=cost
)
new_ts.meter = meter
new_ts.save()
return cb
def create_models(data, import_file):
"""
Create a BuildingSnapshot, a CanonicalBuilding, and a Meter. Then, create
TimeSeries models for each meter reading in data.
:param data: dictionary of building data from a Green Button XML file
in the form returned by xml_importer.building_data
:param import_file: ImportFile referencing the original xml file; needed
for linking to BuildingSnapshot and for determining super_organization
:returns: the created CanonicalBuilding
"""
# cache data on import_file; this is a proof of concept and we
# only have two example files available so we hardcode the only
# heading present.
import_file.cached_first_row = ROW_DELIMITER.join(["address"])
import_file.cached_second_to_fifth_row = ROW_DELIMITER.join(
[data['address']])
import_file.save()
raw_bs = BuildingSnapshot()
raw_bs.import_file = import_file
# We require a save to get our PK
# We save here to set our initial source PKs.
raw_bs.save()
super_org = import_file.import_record.super_organization
raw_bs.super_organization = super_org
raw_bs.address_line_1 = data['address']
raw_bs.source_type = GREEN_BUTTON_BS
raw_bs.save()
# create canonical building
cb = CanonicalBuilding.objects.create(canonical_snapshot=raw_bs)
raw_bs.canonical_building = cb
raw_bs.save()
# log building creation
AuditLog.objects.create(
organization=import_file.import_record.super_organization,
user=import_file.import_record.owner,
content_object=cb,
action="create_building",
action_note="Created building",
)
# create meter for this dataset (each dataset is a single energy type)
e_type = energy_type(data['service_category'])
e_type_string = next(pair[1] for pair in seed.models.ENERGY_TYPES
if pair[0] == e_type)
m_name = "gb_{0}[{1}]".format(str(raw_bs.id), e_type_string)
m_energy_units = energy_units(data['meter']['uom'])
meter = Meter.objects.create(name=m_name,
energy_type=e_type,
energy_units=m_energy_units)
meter.building_snapshot.add(raw_bs)
meter.save()
# now time series data for the meter
for reading in data['interval']['readings']:
start_time = int(reading['start_time'])
duration = int(reading['duration'])
begin_time = datetime.fromtimestamp(start_time,
tz=timezone.get_current_timezone())
end_time = datetime.fromtimestamp(start_time + duration,
tz=timezone.get_current_timezone())
value = reading['value']
cost = reading['cost']
new_ts = TimeSeries.objects.create(begin_time=begin_time,
end_time=end_time,
reading=value,
cost=cost)
new_ts.meter = meter
new_ts.save()
return cb
