def test_getRowId(self):
tid = self.create_table_with_data()
ftc = FeatureTableConnection(client=self.cli, tableName=self.tableName)
ftc.openTable(tid)
self.assertEqual(ftc.getRowId(1), 0)
self.assertEqual(ftc.getRowId(3), 2)
self.assertEqual(ftc.getRowId(6), 3)
self.assertEqual(ftc.getRowId(8), 1)
self.assertIsNone(ftc.getRowId(1000))
class FeatureTable(object):
def __init__(self, client, tableName):
self.tc = FeatureTableConnection(client=client, tableName=tableName)
self.versiontag = None
def close(self):
self.tc.close(False)
@property
def conn(self):
return self.tc.conn
def createTable(self, featureNames, version):
"""
Initialise an OMERO.table for storing features
@param featureNames Either a mapping of feature names to feature sizes,
or a list of single value feature names which can be parsed using
parseFeatureName
"""
# Unparsed list or dict?
if hasattr(featureNames, 'keys'):
features = featureNames
else:
features = featureSizes(featureNames)
colNames = sorted(features.keys())
desc = [(name, features[name]) for name in colNames]
self.tc.createNewTable('id', desc)
self.versiontag = getVersionAnnotation(self.conn, version)
if not self.versiontag:
self.versiontag = createVersionAnnotation(self.conn, version)
addTagTo(self.conn, self.versiontag, 'OriginalFile', self.tc.tableId)
# TODO: There's a bug or race condition somewhere which means this
# annotation may be lost. Closing and reopening the table seems to
# avoid this.
tid = self.tc.tableId
self.close()
self.openTable(tid, version)
def openTable(self, tableId, version=None):
try:
vertag = getVersion(self.conn, 'OriginalFile', tableId)
if not vertag:
raise WndcharmStorageError(
'Table id %d has no version tag' % tableId)
if version is not None:
assertVersionMatch(version, vertag, 'table:%d' % tableId)
self.tc.openTable(tableId)
self.versiontag = vertag
return True
except TableConnectionError as e:
print "No table found: %s" % e
return False
def isTableCompatible(self, features):
"""
Check whether an existing table is compatible with this set of features,
that is whether suitable columns exist
@return true if this set of features can be stored in this table
"""
cols = self.tc.getHeaders()
colMap = dict([(c.name, c) for c in cols])
featSizes = featureSizes(features.names)
for (ft, sz) in featSizes.iteritems():
if (ft not in colMap or colMap[ft].size != sz):
print '%s [%d] is incompatible' % (ft, sz)
return False
return True
def tableContainsId(self, id):
"""
Check whether this ID is already present in the table
"""
return self.tc.getRowId(id) is not None
def saveFeatures(self, id, features):
"""
Save the features to a table
@param features an object with field names holding a list of single
value feature names, and values holding a list of doubles corresponding
to names
"""
cols = self.tc.getHeaders()
colMap = dict([(c.name, c) for c in cols])
cols[0].values = [id]
for (name, value) in izip(features.names, features.values):
ft, idx = parseFeatureName(name)
col = colMap[ft]
if not col.values:
col.values = [[float('nan')] * col.size]
col.values[0][idx] = value
self.tc.addData(cols)
def loadFeatures(self, id):
"""
Load features for an object from a table
@return a (names, values) tuple where names is a list of single value
features and value are the corresponding feature values
"""
r = self.tc.getRowId(id)
# Skip the first id column
colNumbers = range(1, len(self.tc.getHeaders()))
cols = self.tc.readArray(colNumbers, r, r + 1)
names = []
values = []
for col in cols:
names.extend([createFeatureName(col.name, x)
for x in xrange(col.size)])
values.extend(col.values[0])
return (names, values)
def bulkLoadFeatures(self):
"""
Load features for all objects in a table
@return a (names, values, ids) tuple where names is a list of single
value features, values is a list of lists of the corresponding feature
values and ids is a list of object IDs.
In other words values[i] is the list of feature values corresponding to
object with ID given by ids[i].
"""
colNumbers = range(len(self.tc.getHeaders()))
nr = self.tc.getNumberOfRows()
cols = self.tc.readArray(colNumbers, 0, nr, CHUNK_SIZE)
names = []
ids = cols[0].values
for col in cols[1:]:
names.extend([createFeatureName(col.name, x) for x in xrange(col.size)])
values = map(lambda *args: list(chain.from_iterable(args)),
*[c.values for c in cols[1:]])
return (names, values, ids)