def test_line_in_error(self):
importer = MappingImporter()
data = csv_fixture("wrong-objecttype-mapping").read()
try:
importer.import_from_string(data)
except ValueError, E:
errors = E.args[0]
h.assert_equal(len(errors), 1)
h.assert_equal(errors[0]["line"], 2)
return
def test_nested_classifier_columns(self):
data = csv_fixture("nested-mapping").read()
importer = MappingImporter()
mapping = importer.import_from_string(data)
to_fields = mapping["to"]["fields"]
h.assert_equal(len(to_fields), 2)
h.assert_equal(to_fields[0]["column"], u"paid_to")
h.assert_equal(to_fields[0]["name"], "label")
h.assert_equal(to_fields[1]["column"], u"paid_to_identifier")
h.assert_equal(to_fields[1]["name"], "identifier")
def test_missing_columns(self):
data = "Uninteresting,Columns\n" "Uninteresting,Values"
importer = MappingImporter()
try:
importer.import_from_string(data)
except AssertionError, E:
h.assert_true('The Metadata document must have the columns "Original Field",' in str(E))
h.assert_true("The column(s)" in str(E))
h.assert_true("are missing." in str(E))
return
def _test_mapping(self, dir):
mapping_csv = h.fixture_file("csv_import/%s/mapping.csv" % dir)
mapping_json = h.fixture_file("csv_import/%s/mapping.json" % dir)
csv = mapping_csv.read()
expected_mapping_data = json.load(mapping_json)
importer = MappingImporter()
observed_mapping_data = importer.import_from_string(csv)
assert observed_mapping_data == expected_mapping_data
def command(self):
super(MappingConvertCommand, self).command()
self._check_args_length(1)
from openspending.lib import json
from openspending.etl.mappingimporter import MappingImporter
mapping_url = self.args[0]
importer = MappingImporter()
mapping = importer.import_from_url(mapping_url)
print json.dumps(mapping, indent=2)
def test_wrong_objecttype(self):
importer = MappingImporter()
data = csv_fixture("wrong-objecttype-mapping").read()
try:
importer.import_from_string(data)
except ValueError, E:
errors = E.args[0]
h.assert_equal(len(errors), 1)
h.assert_equal(errors[0]["line"], 2)
h.assert_equal(
errors[0]["message"],
(u'Value in column "ObjectType" is "entit". ' u'Allowed values: "classifier", "entity", ' u'"value"'),
)
return
def test_empty_mapping(self):
data = csv_fixture("simple-mapping").read()
importer = MappingImporter()
mapping = importer.import_from_string(data)
h.assert_equal(sorted(mapping.keys()), [u"amount", u"currency", u"from", u"time", u"to"])
h.assert_equal(
mapping["amount"],
{
"column": u"amount",
"datatype": u"float",
"default_value": u"x",
"description": u"z",
"label": u"y",
"type": u"value",
},
)
h.assert_equal(
mapping["currency"],
{
"column": u"currency",
"datatype": u"string",
"default_value": u"GBP",
"description": u"z",
"label": u"y",
"type": u"value",
},
)
h.assert_equal(
mapping["from"],
{
"description": u"z",
"fields": [
{
"column": u"paid_by",
"constant": "",
"datatype": u"string",
"default_value": u"x",
"name": "label",
}
],
"label": u"y",
"type": u"entity",
},
)
h.assert_equal(
mapping["to"],
{
"description": u"z",
"fields": [
{
"column": u"paid_to",
"constant": "",
"datatype": u"string",
"default_value": u"x",
"name": "label",
}
],
"label": u"y",
"type": u"entity",
},
)
h.assert_equal(
mapping["time"],
{
"column": u"date",
"datatype": u"date",
"default_value": u"x",
"description": u"z",
"label": u"y",
"type": u"value",
},
)
