def delete_experiment(host, port, user, password, experiment_name):
"""
Return list of names of experiments that exist on the given TM server.
"""
# NOTE: the experiment name is only given at initialization time...
client = TmClient(host, port, user, password, experiment_name)
return client.delete_experiment()
def list_experiment_names(host, port, user, password):
"""
Return list of names of experiments that exist on the given TM server.
"""
# it's not possible to use the generic client as a client for a
# specific experiment, as the experiment name is only given at
# initialization time...
client = TmClient(host, port, user, password)
return [exp['name'] for exp in client.get_experiments()]
def run_workflow(case, host, port, user, password, experiment_name,
server_log=None, interval=30):
# we really need an API client here, as the command-line
# version does not print enough information to deduce the
# workflow progress and termination status
wkf_client = TmClient(host, port, user, password, experiment_name)
if server_log:
with open(server_log) as logfile:
logfile.seek(0, os.SEEK_END)
server_log_start = logfile.tell()
try:
wkf_client.submit_workflow()
response = wkf_client.get_workflow_status(depth=1)
while not response['done']:
sleep(interval)
# as of 2018-04-27, TM generates a 6 levels deep (!!)
# hierarchy of tasks for the ingestion workflow -- be sure
# we capture all of it (for better reporting and for
# debugging purposes).
response = wkf_client.get_workflow_status(depth=8)
logging.debug(
"client.get_workflow_status(%s) => %r",
experiment_name, response)
percent_done = compute_workflow_progress(response)
logging.info(
"%s: Workflow %.2f%% done.",
experiment_name, percent_done)
if response['failed']:
case.fail('Workflow failed (%.2f%% done)' % percent_done)
else:
case.success("Workflow successfully completed!")
except Exception as err:
case.error(str(err))
finally:
# abuse test stderr to report server-side logs
if server_log:
with open(server_log) as logfile:
logfile.seek(0, os.SEEK_END)
server_log_end = logfile.tell()
logfile.seek(server_log_start, os.SEEK_SET)
case.stderr = logfile.read(server_log_end - server_log_start)
def client(root_dir, experiment_info):
host = 'localhost'
port = 8002
username = '******'
password = '******'
_docker_up(root_dir)
try:
client = TmClient(host, port, username, password, experiment_info.name)
yield client
except requests.ConnectionError:
_docker_down(root_dir)
raise OSError(
'Client could not connect to host "{0}" on port {1}.'.format(
host, port))
_docker_down(root_dir)
import shutil
import yaml
# Variables to be set before using the script
folderPath="/PATH/TO/TARGET/FOLDER" # Where all the data for the test case will be saved
host="app.tissuemaps.org"
port = "80"
experimentName="NameOfExperiment"
username="******"
password="******"
folderContainingOriginalImages = ["/PATH/TO/FOLDER/CONTAINING/ORIGINAL/IMAGES"] # List of all acquisitions
estimatedRuntime = 7200 # In seconds. After that amount, the test will run into a timeout
# Create tmclient instance
client = TmClient(host, port, username, password, experimentName)
# print json.dumps(mapobject_download,sort_keys=True, indent = 4, separators=(',', ': '))
# Write experiment_description file
exp_description = dict(
microscope_type = "cellvoyager",
workflow_type = "canonical",
plate_format = 384,
plate_acquisition_mode = "basic"
)
with open(os.path.join(folderPath,'experiment_description.yaml'),'w') as outfile:
yaml.dump(exp_description, outfile, default_flow_style=False, explicit_start=True)
# Get plate name
plateDownload = client.get_plates()
from tmclient.api import TmClient
import shutil
import yaml
# Variables to be set before using the script
folderPath = "/PATH/TO/TARGET/FOLDER" # Where all the data for the test case will be saved
host = "app.tissuemaps.org"
port = "80"
experimentName = "NameOfExperiment"
username = "******"
password = "******"
folderContainingOriginalImages = "/PATH/TO/FOLDER/CONTAINING/ORIGINAL/IMAGES"
estimatedRuntime = 7200 # In seconds. After that amount, the test will run into a timeout
# Create tmclient instance
client = TmClient(host, port, username, password, experimentName)
# print json.dumps(mapobject_download,sort_keys=True, indent = 4, separators=(',', ': '))
# Write experiment_description file
exp_description = dict(microscope_type="cellvoyager",
workflow_type="canonical",
plate_format=384,
plate_acquisition_mode="basic")
with open(os.path.join(folderPath, 'experiment_description.yaml'),
'w') as outfile:
yaml.dump(exp_description,
outfile,
default_flow_style=False,
explicit_start=True)
def __init__(self, host, port, user, password, experiment_name):
self._client = TmClient(host, port, user, password, experiment_name)
if client_opt == 'api':
return ApiTmClient(host, port, user, password, experiment_name)
elif client_opt == 'cli':
return CommandLineTmClient(host, port, user, password, experiment_name)
else:
raise RuntimeError(
"Invalid choice for -c/--client: {}"
.format(client_opt))
def run_workflow(case, host, port, user, password, experiment_name,
server_log=None, interval=30):
# we really need an API client here, as the command-line
# version does not print enough information to deduce the
# workflow progress and termination status
wkf_client = TmClient(host, port, user, password, experiment_name)
if server_log:
with open(server_log) as logfile:
logfile.seek(0, os.SEEK_END)
server_log_start = logfile.tell()
try:
wkf_client.submit_workflow()
response = wkf_client.get_workflow_status(depth=1)
while not response['done']:
sleep(interval)
# as of 2018-04-27, TM generates a 6 levels deep (!!)
# hierarchy of tasks for the ingestion workflow -- be sure
# we capture all of it (for better reporting and for
# debugging purposes).
response = wkf_client.get_workflow_status(depth=8)
logging.debug(