def test_returning_columns_with_profile(self):
col1 = Column.objects.create(
column_name='New Column',
table_name='PropertyState',
organization=self.fake_org,
is_extra_data=True,
)
col2 = Column.objects.create(
column_name='Second Column',
table_name='PropertyState',
organization=self.fake_org,
is_extra_data=True,
)
new_list_setting = ColumnListSetting.objects.create(
name='example list setting')
ColumnListSettingColumn.objects.create(
column=col1,
column_list_setting=new_list_setting,
order=1,
pinned=False)
ColumnListSettingColumn.objects.create(
column=col2,
column_list_setting=new_list_setting,
order=2,
pinned=True)
# do not set up a profile and return the columns, should be all columns
ids, name_mappings, objs = ColumnListSetting.return_columns(
self.fake_org, new_list_setting.id)
# not the most robust tests, but they are least check for non-zero results
self.assertIsInstance(ids[0], int)
self.assertIsInstance(list(name_mappings.keys())[0], basestring)
self.assertIsInstance(list(name_mappings.values())[0], basestring)
def test_returning_columns_no_profile(self):
# do not set up a profile and return the columns, should be all columns
ids, name_mappings, objs = ColumnListSetting.return_columns(self.fake_org, None)
# not the most robust tests, but they are least check for non-zero results
self.assertIsInstance(ids[0], int)
self.assertIsInstance(list(name_mappings.keys())[0], basestring)
self.assertIsInstance(list(name_mappings.values())[0], basestring)
def export(self, request):
"""
Download a csv of the TaxLot and Properties
.. code-block::
{
"ids": [1,2,3],
"columns": ["tax_jurisdiction_tax_lot_id", "address_line_1", "property_view_id"]
}
---
parameter_strategy: replace
parameters:
- name: cycle
description: cycle
required: true
paramType: query
- name: inventory_type
description: properties or taxlots (as defined by the inventory list page)
required: true
paramType: query
- name: ids
description: list of property/taxlot ids to export (not property/taxlot views)
required: true
paramType: body
- name: columns
description: list of columns to export
required: true
paramType: body
- name: filename
description: name of the file to create
required: false
paramType: body
- name: profile_id
description: Either an id of a list settings profile, or undefined
paramType: body
"""
cycle_pk = request.query_params.get('cycle_id', None)
if not cycle_pk:
return JsonResponse({
'status':
'error',
'message':
'Must pass in cycle_id as query parameter'
})
org_id = request.query_params['organization_id']
if 'profile_id' not in request.data:
profile_id = None
else:
if request.data['profile_id'] == 'None' or request.data[
'profile_id'] == '':
profile_id = None
else:
profile_id = request.data['profile_id']
# get the class to operate on and the relationships
view_klass_str = request.query_params.get('inventory_type',
'properties')
view_klass = INVENTORY_MODELS[view_klass_str]
# Set the first column to be the ID
column_name_mappings = OrderedDict([('id', 'ID')])
column_ids, add_column_name_mappings, columns_from_database = ColumnListSetting.return_columns(
org_id, profile_id, view_klass_str)
column_name_mappings.update(add_column_name_mappings)
select_related = ['state', 'cycle']
ids = request.data.get('ids', [])
filter_str = {'cycle': cycle_pk}
if hasattr(view_klass, 'property'):
select_related.append('property')
prefetch_related = ['labels']
filter_str = {'property__organization_id': org_id}
if ids:
filter_str['property__id__in'] = ids
# always export the labels
column_name_mappings['property_labels'] = 'Property Labels'
elif hasattr(view_klass, 'taxlot'):
select_related.append('taxlot')
prefetch_related = ['labels']
filter_str = {'taxlot__organization_id': org_id}
if ids:
filter_str['taxlot__id__in'] = ids
# always export the labels
column_name_mappings['taxlot_labels'] = 'Tax Lot Labels'
model_views = view_klass.objects.select_related(
*select_related).prefetch_related(*prefetch_related).filter(
**filter_str).order_by('id')
# get the data in a dict which includes the related data
data = TaxLotProperty.get_related(model_views, column_ids,
columns_from_database)
# add labels
for i, record in enumerate(model_views):
label_string = []
if hasattr(record, 'property'):
for label in list(record.labels.all().order_by('name')):
label_string.append(label.name)
data[i]['property_labels'] = ','.join(label_string)
elif hasattr(record, 'taxlot'):
for label in list(record.labels.all().order_by('name')):
label_string.append(label.name)
data[i]['taxlot_labels'] = ','.join(label_string)
# force the data into the same order as the IDs
if ids:
order_dict = {obj_id: index for index, obj_id in enumerate(ids)}
data.sort(key=lambda x: order_dict[x['id']]
) # x is the property/taxlot object
export_type = request.data.get('export_type', 'csv')
filename = request.data.get('filename', f"ExportedData.{export_type}")
if export_type == "csv":
return self._csv_response(filename, data, column_name_mappings)
elif export_type == "geojson":
return self._json_response(filename, data, column_name_mappings)
elif export_type == "xlsx":
return self._spreadsheet_response(filename, data,
column_name_mappings)
def csv(self, request):
"""
Download a csv of the TaxLot and Properties
.. code-block::
{
"ids": [1,2,3],
"columns": ["tax_jurisdiction_tax_lot_id", "address_line_1", "property_view_id"]
}
---
parameter_strategy: replace
parameters:
- name: cycle
description: cycle
required: true
paramType: query
- name: inventory_type
description: properties or taxlots (as defined by the inventory list page)
required: true
paramType: query
- name: ids
description: list of property ids to export (not property views)
required: true
paramType: body
- name: columns
description: list of columns to export
required: true
paramType: body
- name: filename
description: name of the file to create
required: false
paramType: body
- name: profile_id
description: Either an id of a list settings profile, or undefined
paramType: body
"""
cycle_pk = request.query_params.get('cycle_id', None)
if not cycle_pk:
return JsonResponse({
'status':
'error',
'message':
'Must pass in cycle_id as query parameter'
})
org_id = request.query_params['organization_id']
if 'profile_id' not in request.data:
profile_id = None
else:
if request.data['profile_id'] == 'None':
profile_id = None
else:
profile_id = request.data['profile_id']
# get the class to operate on and the relationships
view_klass_str = request.query_params.get('inventory_type',
'properties')
view_klass = INVENTORY_MODELS[view_klass_str]
# Set the first column to be the ID
column_name_mappings = OrderedDict([('id', 'ID')])
column_ids, add_column_name_mappings, columns_from_database = ColumnListSetting.return_columns(
org_id, profile_id, view_klass_str)
column_name_mappings.update(add_column_name_mappings)
select_related = ['state', 'cycle']
ids = request.data.get('ids', [])
filter_str = {'cycle': cycle_pk}
if hasattr(view_klass, 'property'):
select_related.append('property')
filter_str = {'property__organization_id': org_id}
if ids:
filter_str['property__id__in'] = ids
# always export the labels
column_name_mappings['property_labels'] = 'Property Labels'
elif hasattr(view_klass, 'taxlot'):
select_related.append('taxlot')
filter_str = {'taxlot__organization_id': org_id}
if ids:
filter_str['taxlot__id__in'] = ids
# always export the labels
column_name_mappings['taxlot_labels'] = 'Tax Lot Labels'
model_views = view_klass.objects.select_related(
*select_related).filter(**filter_str).order_by('id')
filename = request.data.get('filename', "ExportedData.csv")
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="{}"'.format(
filename)
writer = csv.writer(response)
# get the data in a dict which includes the related data
data = TaxLotProperty.get_related(model_views, column_ids,
columns_from_database)
# force the data into the same order as the IDs
if ids:
order_dict = {obj_id: index for index, obj_id in enumerate(ids)}
data.sort(key=lambda x: order_dict[x['id']]
) # x is the property/taxlot object
# check the first item in the header and make sure that it isn't ID (it can be id, or iD).
# excel doesn't like the first item to be ID in a CSV
header = list(column_name_mappings.values())
if header[0] == 'ID':
header[0] = 'id'
writer.writerow(header)
# iterate over the results to preserve column order and write row.
for datum in data:
row = []
for column in column_name_mappings:
row_result = datum.get(column, None)
# Try grabbing the value out of the related field if not found yet.
if row_result is None and datum.get('related'):
row_result = datum['related'][0].get(column, None)
# Convert quantities (this is typically handled in the JSON Encoder, but that isn't here).
if isinstance(row_result, ureg.Quantity):
row_result = row_result.magnitude
elif isinstance(row_result, datetime.datetime):
row_result = row_result.strftime("%Y-%m-%d %H:%M:%S")
elif isinstance(row_result, datetime.date):
row_result = row_result.strftime("%Y-%m-%d")
row.append(row_result)
writer.writerow(row)
return response