def run_as_program():
"""
Testing function to allow the script to be called outside of the OMERO
scripting environment. The connection details and image ID must be valid.
"""
import getpass
HOST = 'localhost'
PORT = 4064
USERNAME = raw_input("OMERO username: "******"OMERO password: "******"OMERO host (%s): " % HOST)
if h:
HOST = h
p = raw_input("OMERO port (%d): " % PORT)
if p:
PORT = p
conn = BlitzGateway(USERNAME, PASSWORD, host=HOST, port=PORT)
conn.connect()
conn.keepAlive()
params = create_script_defaults()
params[PARAM_DATATYPE] = 'Image'
params[PARAM_IDS] = [51]
# params[PARAM_DATATYPE] = 'Dataset'
# params[PARAM_IDS] = [2]
params[PARAM_UPLOAD_RESULTS] = True
params[PARAM_EMAIL_RESULTS] = True
params[PARAM_EMAIL] = ADMIN_EMAIL
count = run(conn, params)
if count >= 0:
print ("Processed %d image%s" %
(count, count != 1 and 's' or ''))
def run_as_script():
"""
The main entry point of the script, as called by the client via the
scripting service, passing the required parameters.
"""
params = create_script_defaults()
methods = []
for m in ["Li", "MaxEntropy", "Mean", "MinError(I)",
"Moments", "None", "Otsu", "Percentile", "RenyiEntropy",
"Triangle", "Yen"]:
methods.append(rstring(m))
dataTypes = [rstring('Dataset'), rstring('Image')]
client = scripts.client('Colocalisation_Analyser.py', """\
Perform colocalisation analysis on the image channels.
Analysis is done using the Confined Displacement Algorithm (CDA). Images are
displaced a random amount and the colocalisation metric computed. A
distribution is constructed for the metric using multiple displacements and
used to assess the significance of the native result.
Processes a stack image with multiple channels. Each frame is processed
separately. Extracts the channels (collating z-stacks) and performs:
- (1). Thresholding to create a mask for each channel
- (2). CDA analysis of channel 1 vs channel 2, optionally within the region
defined by channel 3
Results are appended to the image as a file attachment and/or e-mailed
to you. Messages use your e-mail address from your OMERO profile (or the
address specified).
See: http://www.sussex.ac.uk/gdsc/intranet/microscopy/omero/scripts/colocalisation""", # noqa
scripts.String(PARAM_DATATYPE, optional=False, grouping="1",
description="Choose Images via their 'Dataset' or directly by "
"'Image' IDs.",
values=dataTypes, default="Image"),
scripts.List(PARAM_IDS, optional=False, grouping="2",
description="List of Image IDs").ofType(rlong(0)),
scripts.String(PARAM_CHANNEL1, optional=False, grouping="3",
default=params[PARAM_CHANNEL1],
description="Select the first channel"),
scripts.String(PARAM_CHANNEL2, optional=False, grouping="4",
default=params[PARAM_CHANNEL2],
description="Select the second channel"),
scripts.String(PARAM_CHANNEL3, grouping="5",
default=params[PARAM_CHANNEL3],
description="Select the third channel"),
scripts.String(PARAM_METHOD, optional=False, grouping="6",
values=methods,
default=params[PARAM_METHOD],
description="Select the thresholding method"),
scripts.Int(PARAM_PERMUTATIONS, optional=False, grouping="7.1",
default=params[PARAM_PERMUTATIONS], min=1,
description="The number of permutations to calculate"),
scripts.Int(PARAM_MIN_SHIFT, optional=False, grouping="7.2",
default=params[PARAM_MIN_SHIFT], min=1,
description="The minimum shift for random displacements"),
scripts.Int(PARAM_MAX_SHIFT, optional=False, grouping="7.3",
default=params[PARAM_MAX_SHIFT], min=2,
description="The maximum shift for random displacements"),
scripts.Float(PARAM_SIGNIFICANCE, optional=False, grouping="7.4",
default=params[PARAM_SIGNIFICANCE], min=0, max=1,
description="The p-value for significance"),
scripts.Bool(PARAM_UPLOAD_RESULTS, grouping="8",
default=params[PARAM_UPLOAD_RESULTS],
description="Attach the results to each image"),
scripts.Bool(PARAM_EMAIL_RESULTS, grouping="9",
default=params[PARAM_EMAIL_RESULTS],
description="E-mail the results"),
scripts.String(PARAM_EMAIL, grouping="9.1", default=params[PARAM_EMAIL],
description="Specify e-mail address"),
version="1.0",
authors=["Alex Herbert", "GDSC"],
institutions=["University of Sussex"],
contact="*****@*****.**",
) # noqa
try:
conn = BlitzGateway(client_obj=client)
conn.keepAlive()
# Process the list of args above.
for key in client.getInputKeys():
if client.getInput(key):
params[key] = client.getInput(key, unwrap=True)
if params[PARAM_EMAIL_RESULTS] and not validate_email(conn, params):
client.setOutput("Message", rstring("No valid email address"))
return
# Call the main script - returns the number of images processed
count = run(conn, params)
if count >= 0:
client.setOutput("Message",
rstring("Processed %d image%s" %
(count, count != 1 and 's' or '')))
else:
client.setOutput("Message",
rstring("Errors found in the input parameters. "
"Check the Info file."))
finally:
client.closeSession()
def run_as_script():
"""
The main entry point of the script, as called by the client via the
scripting service, passing the required parameters.
"""
params = create_script_defaults()
methods = []
for m in ["Li", "MaxEntropy", "Mean", "MinError(I)",
"Moments", "None", "Otsu", "Percentile", "RenyiEntropy",
"Triangle", "Yen"]:
methods.append(rstring(m))
dataTypes = [rstring('Dataset'), rstring('Image')]
client = scripts.client('Correlation_Analyser.py', """\
Perform correlation analysis on the image channels.
Each time-frame in the image is analysed. Optionally the z-stack for the frame
can be combined into a single result or analysed as separate slices.
- Each channel is thresholded to produce a foreground region (mask)
- All-vs-all correlation is computed using the mask of each channel
- Analysis is performed using the union or intersect of the mask overlap
Results are appended to the image as a file attachment and/or e-mailed
to you. Messages use your e-mail address from your OMERO profile (or the
address specified).
See: http://www.sussex.ac.uk/gdsc/intranet/microscopy/omero/scripts/correlation""", # noqa
scripts.String(PARAM_DATATYPE, optional=False, grouping="1",
description="Choose Images via their 'Dataset' or directly by "
"'Image' IDs.",
values=dataTypes, default="Image"),
scripts.List(PARAM_IDS, optional=False, grouping="2",
description="List of Image IDs").ofType(rlong(0)),
scripts.String(PARAM_METHOD, grouping="3",
values=methods,
default=params[PARAM_METHOD],
description="Select the thresholding method"),
scripts.Bool(PARAM_INTERSECT, grouping="4",
default=params[PARAM_INTERSECT],
description="Use the intersect of the mask regions"),
scripts.Bool(PARAM_AGGREGATE_STACK, grouping="5",
default=params[PARAM_AGGREGATE_STACK],
description="Aggregate z-stack"),
scripts.Bool(PARAM_UPLOAD_RESULTS, grouping="6",
default=params[PARAM_UPLOAD_RESULTS],
description="Attach the results to each image"),
scripts.Bool(PARAM_EMAIL_RESULTS, grouping="7",
default=params[PARAM_EMAIL_RESULTS],
description="E-mail the results"),
scripts.String(PARAM_EMAIL, grouping="7.1", default=params[PARAM_EMAIL],
description="Specify e-mail address"),
version="1.0",
authors=["Alex Herbert", "GDSC"],
institutions=["University of Sussex"],
contact="*****@*****.**",
) # noqa
try:
conn = BlitzGateway(client_obj=client)
conn.keepAlive()
# Process the list of args above.
for key in client.getInputKeys():
if client.getInput(key):
params[key] = client.getInput(key, unwrap=True)
if params[PARAM_EMAIL_RESULTS] and not validate_email(conn, params):
client.setOutput("Message", rstring("No valid email address"))
return
# Call the main script - returns the number of images processed
count = run(conn, params)
if count >= 0:
client.setOutput("Message",
rstring("Processed %d image%s" %
(count, count != 1 and 's' or '')))
else:
client.setOutput("Message",
rstring("Errors found in the input parameters. "
"Check the Info file."))
finally:
client.closeSession()
