example.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

#
# Copyright (C) 2014 University of Dundee & Open Microscopy Environment.
# All rights reserved.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

import numpy

import features

import logging
features.OmeroTablesFeatureStore.log.setLevel(logging.DEBUG)

# Note the OMERO client variable must exist (for instance run this script
# from inside `bin/omero shell --login`)
session = client.getSession()

# 10 features within this featureset
featureset_name = 'Test Featureset'
feature_names = ['x%04d' % n for n in xrange(10)]

manager = features.OmeroTablesFeatureStore.FeatureTableManager(session)

# Create a new featureset (name must be unique within this group)
manager.create(featureset_name, feature_names)

# Retrieve an existing featureset
fs = manager.get(featureset_name)

# Store some features associated with an image. This will automatically create
# an annotation on the image linking it to the underlying table file
imageid = 3889L

values = numpy.random.rand(len(feature_names))
fs.store_by_image(imageid, values)

# Retrieve the features
r = fs.fetch_by_image(imageid)
# If multiple matching rows are found this will throw an exception, pass
# last=True to return just the last matching row
r = fs.fetch_by_image(imageid, last=True)

# Feature metadata (currently just Image/Roi IDs):
print '\n'.join('%s=%s' % kv for kv in zip(r.infonames, r.infovalues))

# Feature names and values
print '\n'.join('%s=%s' % kv for kv in zip(r.names, r.values))

# Store some features for z single Z/C/T plane by creating a ROI
z, c, t = 0, 0, 0
roiid = features.utils.create_roi_for_plane(session, imageid, z, c, t)
values = numpy.random.rand(len(feature_names))
fs.store_by_roi(roiid, values)

# Retrieve raw data as a tuple
rs = fs.fetch_by_object('Image', imageid)
# Same, using a query
rs = fs.filter_raw('ImageID==%d' % imageid)

# Convert to numpy arrays
ids = numpy.vstack(r[:2] for r in rs)
arr = numpy.vstack(r[2] for r in rs)


# Delete the entire featureset and annotations (may be very slow)
fs.delete()

# Close all tables
manager.close()