def test_get_column_mappings(self):
"""We produce appropriate data structure for mapping"""
expected = dict(
sorted(
[
(u"example_9", u"mapped_9"),
(u"example_8", u"mapped_8"),
(u"example_7", u"mapped_7"),
(u"example_6", u"mapped_6"),
(u"example_5", u"mapped_5"),
(u"example_4", u"mapped_4"),
(u"example_3", u"mapped_3"),
(u"example_2", u"mapped_2"),
(u"example_1", u"mapped_1"),
(u"example_0", u"mapped_0"),
]
)
)
org = Organization.objects.create()
raw = []
mapped = []
for x in range(10):
raw.append(seed_models.Column.objects.create(column_name="example_{0}".format(x), organization=org))
mapped.append(seed_models.Column.objects.create(column_name="mapped_{0}".format(x), organization=org))
for x in range(10):
column_mapping = seed_models.ColumnMapping.objects.create(super_organization=org)
column_mapping.column_raw.add(raw[x])
column_mapping.column_mapped.add(mapped[x])
test_mapping, _ = seed_models.get_column_mappings(org)
self.assertDictEqual(test_mapping, expected)
def test_get_column_mappings(self):
"""We produce appropriate data structure for mapping"""
expected = dict(
sorted([(u'example_9', u'mapped_9'), (u'example_8', u'mapped_8'),
(u'example_7', u'mapped_7'), (u'example_6', u'mapped_6'),
(u'example_5', u'mapped_5'), (u'example_4', u'mapped_4'),
(u'example_3', u'mapped_3'), (u'example_2', u'mapped_2'),
(u'example_1', u'mapped_1'), (u'example_0', u'mapped_0')]))
org = Organization.objects.create()
raw = []
mapped = []
for x in range(10):
raw.append(
seed_models.Column.objects.create(
column_name='example_{0}'.format(x), organization=org))
mapped.append(
seed_models.Column.objects.create(
column_name='mapped_{0}'.format(x), organization=org))
for x in range(10):
column_mapping = seed_models.ColumnMapping.objects.create(
super_organization=org, )
column_mapping.column_raw.add(raw[x])
column_mapping.column_mapped.add(mapped[x])
test_mapping, _ = seed_models.get_column_mappings(org)
self.assertDictEqual(test_mapping, expected)
def test_get_column_mappings(self):
"""We produce appropriate data structure for mapping"""
expected = dict(sorted([
(u'example_9', u'mapped_9'),
(u'example_8', u'mapped_8'),
(u'example_7', u'mapped_7'),
(u'example_6', u'mapped_6'),
(u'example_5', u'mapped_5'),
(u'example_4', u'mapped_4'),
(u'example_3', u'mapped_3'),
(u'example_2', u'mapped_2'),
(u'example_1', u'mapped_1'),
(u'example_0', u'mapped_0')
]))
org = Organization.objects.create()
for x in range(10):
seed_models.ColumnMapping.objects.create(
super_organization=org,
source_type=seed_models.ASSESSED_RAW,
column_raw='example_{0}'.format(x),
column_mapped='mapped_{0}'.format(x)
)
test_mapping = seed_models.get_column_mappings(org)
self.assertDictEqual(test_mapping, expected)
def map_row_chunk(chunk, file_pk, source_type, prog_key, increment, *args,
**kwargs):
"""Does the work of matching a mapping to a source type and saving
:param chunk: list of dict of str. One row's worth of parse data.
:param file_pk: int, the PK for an ImportFile obj.
:param source_type: int, represented by either ASSESSED_RAW, or
PORTFOLIO_RAW.
:param cleaner: (optional), the cleaner class you want to send
to mapper.map_row. (e.g. turn numbers into floats.).
:param raw_ids: (optional kwarg), the list of ids in chunk order.
"""
import_file = ImportFile.objects.get(pk=file_pk)
save_type = PORTFOLIO_BS
if source_type == ASSESSED_RAW:
save_type = ASSESSED_BS
concats = []
org = Organization.objects.get(
pk=import_file.import_record.super_organization.pk)
mapping, concats = get_column_mappings(org)
map_cleaner = _build_cleaner(org)
# For those column mapping which are not db columns, we
# need to let MCM know that we apply our mapping function to those.
apply_columns = []
mappable_columns = get_mappable_columns()
for item in mapping:
if mapping[item] not in mappable_columns:
apply_columns.append(item)
apply_func = apply_data_func(mappable_columns)
for row in chunk:
model = mapper.map_row(row,
mapping,
BuildingSnapshot,
cleaner=map_cleaner,
concat=concats,
apply_columns=apply_columns,
apply_func=apply_func,
*args,
**kwargs)
model.import_file = import_file
model.source_type = save_type
model.clean()
model.super_organization = import_file.import_record.super_organization
model.save()
if model:
# Make sure that we've saved all of the extra_data column names
save_column_names(model, mapping=mapping)
increment_cache(prog_key, increment)
def map_row_chunk(
chunk, file_pk, source_type, prog_key, increment, *args, **kwargs
):
"""Does the work of matching a mapping to a source type and saving
:param chunk: list of dict of str. One row's worth of parse data.
:param file_pk: int, the PK for an ImportFile obj.
:param source_type: int, represented by either ASSESSED_RAW, or
PORTFOLIO_RAW.
:param cleaner: (optional), the cleaner class you want to send
to mapper.map_row. (e.g. turn numbers into floats.).
:param raw_ids: (optional kwarg), the list of ids in chunk order.
"""
import_file = ImportFile.objects.get(pk=file_pk)
save_type = PORTFOLIO_BS
mapping = espm_mapping.MAP
map_cleaner = PORTFOLIO_CLEANER
# Default to PM so we don't unnecessarily query for mapping
if source_type == ASSESSED_RAW:
org = Organization.objects.filter(
users=import_file.import_record.owner
)[0]
mapping = get_column_mappings(org)
save_type = ASSESSED_BS
map_cleaner = ASSESSED_CLEANER
# Pull out any columns meant to be concatenated together.
mapping, concats = filter_concat_configs(mapping)
for row in chunk:
model = mapper.map_row(
row,
mapping,
BuildingSnapshot,
cleaner=map_cleaner,
concat=concats,
*args,
**kwargs
)
model.import_file = import_file
model.source_type = save_type
model.clean()
model.super_organization = import_file.import_record.super_organization
model.save()
increment_cache(prog_key, increment)
def test_get_column_mappings(self):
"""We produce appropriate data structure for mapping"""
expected = dict(sorted([
(u'example_9', u'mapped_9'),
(u'example_8', u'mapped_8'),
(u'example_7', u'mapped_7'),
(u'example_6', u'mapped_6'),
(u'example_5', u'mapped_5'),
(u'example_4', u'mapped_4'),
(u'example_3', u'mapped_3'),
(u'example_2', u'mapped_2'),
(u'example_1', u'mapped_1'),
(u'example_0', u'mapped_0')
]))
org = Organization.objects.create()
raw = []
mapped = []
for x in range(10):
raw.append(seed_models.Column.objects.create(
column_name='example_{0}'.format(x), organization=org
))
mapped.append(seed_models.Column.objects.create(
column_name='mapped_{0}'.format(x), organization=org
))
for x in range(10):
column_mapping = seed_models.ColumnMapping.objects.create(
super_organization=org,
)
column_mapping.column_raw.add(raw[x])
column_mapping.column_mapped.add(mapped[x])
test_mapping, _ = seed_models.get_column_mappings(org)
self.assertDictEqual(test_mapping, expected)
def map_row_chunk(chunk, file_pk, source_type, prog_key, increment, *args, **kwargs):
"""Does the work of matching a mapping to a source type and saving
:param chunk: list of dict of str. One row's worth of parse data.
:param file_pk: int, the PK for an ImportFile obj.
:param source_type: int, represented by either ASSESSED_RAW, or
PORTFOLIO_RAW.
:param prog_key: string, key of the progress key
:param increment: double, value by which to increment progress key
:param cleaner: (optional), the cleaner class you want to send
to mapper.map_row. (e.g. turn numbers into floats.).
:param raw_ids: (optional kwarg), the list of ids in chunk order.
"""
import_file = ImportFile.objects.get(pk=file_pk)
save_type = PORTFOLIO_BS
if source_type == ASSESSED_RAW:
save_type = ASSESSED_BS
concats = []
org = Organization.objects.get(
pk=import_file.import_record.super_organization.pk
)
mapping, concats = get_column_mappings(org)
map_cleaner = _build_cleaner(org)
# For those column mapping which are not db columns, we
# need to let MCM know that we apply our mapping function to those.
apply_columns = []
mappable_columns = get_mappable_columns()
for item in mapping:
if mapping[item] not in mappable_columns:
apply_columns.append(item)
apply_func = apply_data_func(mappable_columns)
for row in chunk:
model = mapper.map_row(
row,
mapping,
BuildingSnapshot,
cleaner=map_cleaner,
concat=concats,
apply_columns=apply_columns,
apply_func=apply_func,
*args,
**kwargs
)
model.import_file = import_file
model.source_type = save_type
model.clean()
model.super_organization = import_file.import_record.super_organization
model.save()
if model:
# Make sure that we've saved all of the extra_data column names
save_column_names(model, mapping=mapping)
increment_cache(prog_key, increment)