def _schema2doc_map(self):
_map = {
'integer':
dsl_field.Long(),
'number':
dsl_field.ScaledFloat(scaling_factor=100),
'string':
dsl_field.Text(analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
'keyword': dsl_field.Keyword(),
}),
'any':
dsl_field.Text(analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
'keyword': dsl_field.Keyword(),
}),
'boolean':
dsl_field.Boolean(),
'time':
dsl_field.Text(
fields={
'text': dsl_field.Text(),
'time': dsl_field.Date(
format=constance_config.TIME_FORMATS),
}),
'duration':
dsl_field.DateRange(),
'default':
dsl_field.Text(),
'date':
dsl_field.Text(
fields={
'text': dsl_field.Text(),
'date': dsl_field.Date(
format=constance_config.DATE_FORMATS),
}),
'datetime':
dsl_field.Text(
fields={
'text':
dsl_field.Text(),
'datetime':
dsl_field.Date(format=constance_config.DATE_FORMATS),
})
}
for key, val in _map.items():
_map[key] = CustomObject(properties={
'val': val,
'repr': dsl_field.Keyword(),
})
return _map
def test_scaled_float():
with pytest.raises(TypeError):
field.ScaledFloat()
f = field.ScaledFloat(123)
assert f.to_dict() == {'scaling_factor': 123, 'type': 'scaled_float'}
es_connections = Connections()
es_connections.configure(**settings.ELASTICSEARCH_DSL)
STATUS_CHOICES = [
('published', _('Published')),
('draft', _('Draft'))
]
OPENNESS_SCORE = {_type: os for _, _type, _, os in settings.SUPPORTED_CONTENT_TYPES}
signal_logger = logging.getLogger('signals')
_schema2doc_map = {
'integer': dsl_field.Float(),
'number': dsl_field.ScaledFloat(scaling_factor=100),
'string': dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
}
),
'any': dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
}
),
'boolean': dsl_field.Boolean(),
'date': dsl_field.Date(),
'datetime': dsl_field.Date(),
class ShpData(IndexedData):
_type = 'geo'
_schema2doc_map = {
'C':
dsl_field.Text(
analyzer=polish_analyzer,
fields={
'raw': dsl_field.Text(),
'keyword': dsl_field.Keyword(),
},
),
'D':
dsl_field.Date(),
'N':
dsl_field.ScaledFloat(scaling_factor=100),
'L':
dsl_field.Boolean(),
'@':
dsl_field.Date(),
'I':
dsl_field.Long(),
'+':
dsl_field.Long(),
'F':
dsl_field.Float(),
'O':
dsl_field.Double(),
}
_schema_to_api_field = {
'C': api_fields.String,
'D': api_fields.DateTime,
'N': api_fields.Number,
'L': api_fields.Boolean,
'@': api_fields.DateTime,
'I': api_fields.Number,
'+': api_fields.Number,
'F': api_fields.Number,
'O': api_fields.Number,
}
_schema_long_names = {
'C': 'string',
'D': 'datetime',
'N': 'number',
'L': 'boolean',
'@': 'datetime',
'I': 'integer',
'+': 'integer',
'F': 'number',
'O': 'number',
}
_source = None
_schema = None
_transformer = None
def __init__(self, resource, from_table_index=False):
super().__init__(resource)
self.from_table_index = from_table_index
@property
def has_geo_data(self):
return True
@property
def is_chartable(self):
fields = self.schema
return len(fields) > 1 and any(
(field.type in ('N', 'I', '+', 'F', 'O') for field in fields))
@property
def source(self):
if not self._source:
with ArchiveReader(self.resource.main_file.path) as extracted:
shp_path = next(
iter(f for f in extracted if f.endswith('.shp')))
self._source = shapefile.Reader(shp_path)
self._transformer = ShapeTransformer(extracted)
return self._source
def get_schema(self, **kwargs):
use_aliases = kwargs.get('use_aliases', False)
headers = self.reversed_headers_map
return {
'fields': [{
'name': headers[item.name] if use_aliases else item.name,
'type': self._schema_long_names[item.type],
'format': 'default'
} for item in self.schema]
}
@property
def schema(self):
if not self._schema:
self._schema = [
DBSchemaField(*_f) for _f in self.source.fields[1:]
]
return self._schema
def prepare_doc(self):
_fields = {
'shape':
dsl_field.GeoShape(),
'point':
dsl_field.GeoPoint(),
'shape_type':
dsl_field.Integer(),
'label':
dsl_field.Text(),
'resource':
dsl_field.Nested(
properties={
'id':
dsl_field.Integer(),
'title':
dsl_field.Text(analyzer=polish_analyzer,
fields={'raw': dsl_field.Keyword()})
}),
'updated_at':
dsl_field.Date(),
'row_no':
dsl_field.Long()
}
_map = {}
for idx, _f in enumerate(self.schema, 1):
if _f.type not in self._schema2doc_map:
continue
alias_name = _f.name
field_name = f'col{idx}'
_field = self._schema2doc_map[_f.type]
_map[field_name] = alias_name
_fields[field_name] = _field
_fields['Index'] = type('Index', (type, ), {'name': self.idx_name})
doc = type(self.idx_name, (Document, ), _fields)
doc._doc_type.mapping._meta['_meta'] = {'headers': _map}
return doc
def get_api_fields(self):
record_fields = {}
for f in self.schema:
field_name = self.reversed_headers_map[f.name]
field_cls = self._schema_to_api_field[f.type]
record_fields[field_name] = field_cls(is_tabular_data_field=True)
return record_fields
@staticmethod
def _get_row_id(row):
return str(
uuid.uuid5(uuid.NAMESPACE_DNS,
'+|+'.join(str(i)[:10000] for i in row)))
def _docs_iter(self, doc):
for row_no, sr in enumerate(self.source.shapeRecords(), 1):
geojson = self._transformer.transform(sr.shape)
v = {
'shape': geojson,
'updated_at': datetime.now(),
'row_no': row_no,
'resource': {
'id': self.resource.id,
'title': self.resource.title
},
}
for i, val in enumerate(sr.record, 1):
v[f'col{i}'] = val if val != b'' else None
v['shape_type'] = sr.shape.shapeType
v['point'] = median_point(geojson)
tds = self.resource.tabular_data_schema
if tds is not None and 'geo' in tds and 'label' in tds['geo']:
v['label'] = sr.record[tds['geo']['label'].get('col_name')]
d = doc(**v)
d.meta.id = self._get_row_id(sr.record)
yield d
def test_scaled_float():
with pytest.raises(TypeError):
field.ScaledFloat()
f = field.ScaledFloat(123)
assert f.to_dict() == {"scaling_factor": 123, "type": "scaled_float"}
