def wait_for_gui_process(self, retry_count=20, retry_duration_s=1):
'''
.. versionchanged:: 2.7.2
Do not execute `refresh_gui()` while waiting for response from
`hub_execute()`.
'''
start = datetime.now()
for i in xrange(retry_count):
try:
hub_execute(self.name, 'ping', timeout_s=5, silent=True)
except Exception:
logger.debug('[wait_for_gui_process] failed (%d of %d)',
i + 1,
retry_count,
exc_info=True)
else:
logger.info('[wait_for_gui_process] success (%d of %d)', i + 1,
retry_count)
self.alive_timestamp = datetime.now()
return
for j in xrange(10):
time.sleep(retry_duration_s / 10.)
refresh_gui()
raise IOError('Timed out after %ss waiting for GUI process to connect '
'to hub.' % si_format(
(datetime.now() - start).total_seconds()))
def on_step_run(self):
'''
Handler called whenever a step is executed.
Plugins that handle this signal must emit the on_step_complete signal
once they have completed the step. The protocol controller will wait
until all plugins have completed the current step before proceeding.
.. versionchanged:: 2.2.2
Emit ``on_step_complete`` signal within thread-safe function, since
signal callbacks may use GTK.
'''
app = get_app()
if (app.realtime_mode or app.running) and self.gui_process is not None:
step_options = self.get_step_options()
if not step_options['video_enabled']:
command = 'disable_video'
else:
command = 'enable_video'
hub_execute(self.name, command)
# Call as thread-safe function, since signal callbacks may use GTK.
gtk_threadsafe(emit_signal)('on_step_complete', [self.name, None])
def get_ui_json_settings(self):
'''
Get current video settings from DMF device UI plugin.
Returns
-------
(dict) : DMF device UI plugin settings in JSON-compatible format
(i.e., only basic Python data types).
'''
video_settings = {}
# Try to request video configuration.
try:
video_config = hub_execute(self.name, 'get_video_config',
wait_func=lambda *args: refresh_gui(),
timeout_s=2)
except IOError:
logger.warning('Timed out waiting for device window size and '
'position request.')
else:
if video_config is not None:
video_settings['video_config'] = video_config.to_json()
else:
video_settings['video_config'] = ''
# Try to request allocation to save in app options.
try:
data = hub_execute(self.name, 'get_corners', wait_func=lambda
*args: refresh_gui(), timeout_s=2)
except IOError:
logger.warning('Timed out waiting for device window size and '
'position request.')
else:
if data:
# Get window allocation settings (i.e., width, height, x, y).
# Replace `df_..._corners` with CSV string named `..._corners`
# (no `df_` prefix).
for k in ('df_canvas_corners', 'df_frame_corners'):
if k in data:
data['allocation'][k[3:]] = data.pop(k).to_csv()
video_settings.update(data['allocation'])
# Try to request surface alphas.
try:
surface_alphas = hub_execute(self.name, 'get_surface_alphas',
wait_func=lambda *args: refresh_gui(),
timeout_s=2)
except IOError:
logger.warning('Timed out waiting for surface alphas.')
else:
if surface_alphas is not None:
video_settings['surface_alphas'] = surface_alphas.to_json()
else:
video_settings['surface_alphas'] = ''
return video_settings
def _wait_for_gui():
self.wait_for_gui_process()
# Get current video settings from UI.
app_values = self.get_app_values()
# Convert JSON settings to 0MQ plugin API Python types.
ui_settings = self.json_settings_as_python(app_values)
self.set_ui_settings(ui_settings, default_corners=True)
self.gui_heartbeat_id = gobject.timeout_add(1000, keep_alive)
# Refresh list of electrode and route commands.
hub_execute('microdrop.command_plugin', 'get_commands')
def wait_for_gui_process(self, retry_count=20, retry_duration_s=1):
start = datetime.now()
for i in xrange(retry_count):
try:
hub_execute(self.name, 'ping', wait_func=lambda *args:
refresh_gui(), timeout_s=5, silent=True)
except:
logger.debug('[wait_for_gui_process] failed (%d of %d)', i + 1,
retry_count, exc_info=True)
else:
logger.info('[wait_for_gui_process] success (%d of %d)', i + 1,
retry_count)
self.alive_timestamp = datetime.now()
return
for j in xrange(10):
time.sleep(retry_duration_s / 10.)
refresh_gui()
raise IOError('Timed out after %ss waiting for GUI process to connect '
'to hub.' % si_format((datetime.now() -
start).total_seconds()))
def on_step_run(self):
'''
Handler called whenever a step is executed.
Plugins that handle this signal must emit the on_step_complete signal
once they have completed the step. The protocol controller will wait
until all plugins have completed the current step before proceeding.
'''
app = get_app()
if (app.realtime_mode or app.running) and self.gui_process is not None:
step_options = self.get_step_options()
if not step_options['video_enabled']:
hub_execute(self.name, 'disable_video',
wait_func=lambda *args: refresh_gui(), timeout_s=5,
silent=True)
else:
hub_execute(self.name, 'enable_video',
wait_func=lambda *args: refresh_gui(), timeout_s=5,
silent=True)
emit_signal('on_step_complete', [self.name, None])
def set_ui_settings(self, ui_settings, default_corners=False):
'''
Set DMF device UI settings from settings dictionary.
Args
----
ui_settings (dict) : DMF device UI plugin settings in format
returned by `json_settings_as_python` method.
.. versionchanged:: 2.7.2
Do not execute `refresh_gui()` while waiting for response from
`hub_execute()`.
'''
if self.alive_timestamp is None or self.gui_process is None:
# Repeat until GUI process has started.
raise IOError('GUI process not ready.')
if 'video_config' in ui_settings:
hub_execute(self.name,
'set_video_config',
video_config=ui_settings['video_config'],
timeout_s=5)
if 'surface_alphas' in ui_settings:
hub_execute(self.name,
'set_surface_alphas',
surface_alphas=ui_settings['surface_alphas'],
timeout_s=5)
if all((k in ui_settings)
for k in ('df_canvas_corners', 'df_frame_corners')):
if default_corners:
hub_execute(self.name,
'set_default_corners',
canvas=ui_settings['df_canvas_corners'],
frame=ui_settings['df_frame_corners'],
timeout_s=5)
else:
hub_execute(self.name,
'set_corners',
df_canvas_corners=ui_settings['df_canvas_corners'],
df_frame_corners=ui_settings['df_frame_corners'],
timeout_s=5)
def set_ui_settings(self, ui_settings, default_corners=False):
'''
Set DMF device UI settings from settings dictionary.
Args
----
ui_settings (dict) : DMF device UI plugin settings in format
returned by `json_settings_as_python` method.
'''
if self.alive_timestamp is None or self.gui_process is None:
# Repeat until GUI process has started.
raise IOError('GUI process not ready.')
if 'video_config' in ui_settings:
hub_execute(self.name, 'set_video_config',
video_config=ui_settings['video_config'],
wait_func=lambda *args: refresh_gui(), timeout_s=5)
if 'surface_alphas' in ui_settings:
hub_execute(self.name, 'set_surface_alphas',
surface_alphas=ui_settings['surface_alphas'],
wait_func=lambda *args: refresh_gui(), timeout_s=5)
if all((k in ui_settings) for k in ('df_canvas_corners',
'df_frame_corners')):
if default_corners:
hub_execute(self.name, 'set_default_corners',
canvas=ui_settings['df_canvas_corners'],
frame=ui_settings['df_frame_corners'],
wait_func=lambda *args: refresh_gui(), timeout_s=5)
else:
hub_execute(self.name, 'set_corners',
df_canvas_corners=ui_settings['df_canvas_corners'],
df_frame_corners=ui_settings['df_frame_corners'],
wait_func=lambda *args: refresh_gui(), timeout_s=5)
def get_ui_json_settings(self):
'''
Get current video settings from DMF device UI plugin.
Returns
-------
(dict) : DMF device UI plugin settings in JSON-compatible format
(i.e., only basic Python data types).
.. versionchanged:: 2.7.2
Do not execute `refresh_gui()` while waiting for response from
`hub_execute()`.
'''
video_settings = {}
# Try to request video configuration.
try:
video_config = hub_execute(self.name,
'get_video_config',
timeout_s=2)
except IOError:
logger.warning('Timed out waiting for device window size and '
'position request.')
else:
if video_config is not None:
video_settings['video_config'] = video_config.to_json()
else:
video_settings['video_config'] = ''
# Try to request allocation to save in app options.
try:
data = hub_execute(self.name, 'get_corners', timeout_s=2)
except IOError:
logger.warning('Timed out waiting for device window size and '
'position request.')
else:
if data:
# Get window allocation settings (i.e., width, height, x, y).
# Replace `df_..._corners` with CSV string named `..._corners`
# (no `df_` prefix).
for k in ('df_canvas_corners', 'df_frame_corners'):
if k in data:
data['allocation'][k[3:]] = data.pop(k).to_csv()
video_settings.update(data['allocation'])
# Try to request surface alphas.
try:
surface_alphas = hub_execute(self.name,
'get_surface_alphas',
timeout_s=2)
except IOError:
logger.warning('Timed out waiting for surface alphas.')
else:
if surface_alphas is not None:
video_settings['surface_alphas'] = surface_alphas.to_json()
else:
video_settings['surface_alphas'] = ''
return video_settings
def check_dstat_status(self):
'''
1. Check to see if acquisition is finished.
2. If (1), emit `on_step_complete` signal.
'''
try:
completed_timestamp = hub_execute('dstat-interface',
'acquisition_complete',
experiment_id=
self.dstat_experiment_id,
timeout_s=5.)
if completed_timestamp is not None:
# ## Acquisition is complete ##
app = get_app()
# Increment the number of completed DStat experiments for
# current step.
step_i = app.protocol.current_step_number
count_i = 1 + self.dstat_experiment_count_by_step.get(step_i,
0)
self.dstat_experiment_count_by_step[step_i] = count_i
# ### Save results data and plot ###
output_directory = (path(app.experiment_log.get_log_path())
.abspath())
output_namebase = str(app.protocol.current_step_number)
step_label = self.get_step_label()
if step_label is not None:
# Replace characters that are not allowed in a filename
# with underscore.
output_namebase = re.sub(r'[:/\\\?{}]', '_', step_label)
# Save results to a text file in the experiment log directory.
output_txt_path = get_unique_path(output_directory
.joinpath(output_namebase +
'.txt'))
logger.info('Save results to: %s', output_txt_path)
dstat_params = hub_execute('dstat-interface', 'get_params')
hub_execute('dstat-interface', 'save_text',
save_data_path=output_txt_path)
data_i = hub_execute('dstat-interface', 'get_experiment_data',
experiment_id=self.dstat_experiment_id)
metadata_i = self.get_step_metadata()
# Compute (approximate) `utc_timestamp` for each DStat
# measurement.
max_time_s = data_i.time_s.max()
metadata_i['utc_timestamp'] = (completed_timestamp -
data_i.time_s
.map(lambda t:
timedelta(seconds=
max_time_s - t)))
# Step label from step label plugin.
metadata_i['step_label'] = step_label
# Compute UTC start time from local experiment start time.
metadata_i['experiment_start'] = \
(dt.datetime.fromtimestamp(app.experiment_log.start_time())
+ (dt.datetime.utcnow() - dt.datetime.now()))
# Compute UTC start time from local experiment start time.
metadata_i['experiment_length_min'] = \
(completed_timestamp -
metadata_i['experiment_start']).total_seconds() / 60.
# Record synchronous detection parameters from DStat (if set).
if dstat_params['sync_true']:
metadata_i['target_hz'] = float(dstat_params['sync_freq'])
else:
metadata_i['target_hz'] = None
metadata_i['sample_frequency_hz'] = float(dstat_params['adc_rate_hz'])
# Cast metadata `unicode` fields as `str` to enable HDF export.
for k, v in metadata_i.iteritems():
if isinstance(v, types.StringTypes):
metadata_i[k] = str(v)
data_md_i = data_i.copy()
for i, (k, v) in enumerate(metadata_i.iteritems()):
try:
data_md_i.insert(i, k, v)
except Exception, e:
logger.info('Skipping metadata field %s: %s.\n%s', k,
v, e)
# Set order for known columns. Unknown columns are ordered
# last, alphabetically.
column_order = ['instrument_id', 'experiment_id',
'experiment_uuid', 'experiment_start',
'experiment_length_min', 'utc_timestamp',
'device_id', 'batch_id', 'sample_id',
'step_label', 'step_number', 'attempt_number',
'temperature_celsius', 'relative_humidity',
'target_hz', 'sample_frequency_hz', 'time_s',
'current_amps']
column_index = dict([(k, i) for i, k in
enumerate(column_order)])
ordered_columns = sorted(data_md_i.columns, key=lambda k:
(column_index
.get(k, len(column_order)), k))
data_md_i = data_md_i[ordered_columns]
namebase_i = ('e[{}]-d[{}]-s[{}]'
.format(metadata_i['experiment_uuid'][:8],
metadata_i.get('device_id'),
metadata_i.get('sample_id')))
if self.dstat_experiment_data is None:
self.dstat_experiment_data = data_md_i
else:
combined = pd.concat([self.dstat_experiment_data,
data_md_i])
self.dstat_experiment_data = combined.reset_index(drop=
True)
# Append DStat experiment data to CSV file.
csv_output_path = self.data_dir().joinpath(namebase_i + '.csv')
# Only include header if the file does not exist or is empty.
include_header = not (csv_output_path.isfile() and
(csv_output_path.size > 0))
with csv_output_path.open('a') as output:
data_md_i.to_csv(output, index=False,
header=include_header)
df_dstat_summary = self.dstat_summary_frame(numeric=True)
# Write DStat summary table to CSV file.
csv_summary_path = self.data_dir().joinpath('dstat-summary'
'.csv')
with csv_summary_path.open('w') as output:
df_dstat_summary.to_csv(output)
# Turn light back on after photomultiplier tube (PMT)
# measurement.
self.dropbot_dx_remote.light_enabled = True
# notify step complete.
emit_signal('on_step_complete', [self.name, None])
self.dstat_timeout_id = None
return False
else:
def on_step_run(self):
'''
Handler called whenever a step is executed. Note that this signal is
only emitted in realtime mode or if a protocol is running.
Plugins that handle this signal must emit the `on_step_complete` signal
once they have completed the step. The protocol controller will wait
until all plugins have completed the current step before proceeding.
The `on_step_complete` signal is emitted with following signature:
emit_signal('on_step_complete', [plugin_name, return_value])
where `plugin_name` is the name of the emitting plugin, and
`return_value` can be one of:
- `None`: Step completed successfully.
- `'Repeat'`: Repeat the step.
- `'Fail'`: Unrecoverable error (stop the protocol).
'''
app = get_app()
logger.info('[DropBotDxAccessoriesPlugin] on_step_run(): step #%d',
app.protocol.current_step_number)
options = self.get_step_options()
app_values = self.get_app_values()
if self.connected():
self.dropbot_dx_remote.light_enabled = not options['dstat_enabled']
self.dropbot_dx_remote.magnet_engaged=options['magnet_engaged']
try:
if self.has_environment_data:
env = self.get_environment_state().to_dict()
logger.info('temp=%.1fC, Rel. humidity=%.1f%%' %
(env['temperature_celsius'],
100 * env['relative_humidity']))
app.experiment_log.add_data({"environment state": env},
self.name)
except ValueError:
self.has_environment_data = False
if options['dstat_enabled']:
# D-stat is enabled for step. Request acquisition.
try:
if 'dstat_params_file' in options:
# Load Dstat parameters.
hub_execute('dstat-interface', 'load_params',
params_path=options['dstat_params_file'])
if self.dstat_timeout_id is not None:
# Timer was already set, so cancel previous timer.
gobject.source_remove(self.dstat_timeout_id)
# Delay before D-stat measurement (e.g., to allow webcam
# light to turn off).
dstat_delay_s = app_values.get('dstat_delay_s', 0)
time.sleep(max(0, dstat_delay_s))
step_label = self.get_step_label()
# Send Microdrop step label (if available) to provide name
# for DStat experiment.
metadata = self.metadata.copy()
metadata['name'] = (step_label if step_label else
str(app.protocol.current_step_number +
1))
metadata['patient_id'] = metadata.get('sample_id', 'None')
# Get target path for DStat database directory.
dstat_database_path = (path(app.config['data_dir'])
.realpath().joinpath('dstat-db'))
self.dstat_experiment_id = \
hub_execute('dstat-interface', 'run_active_experiment',
metadata=metadata,
params={'db_path_entry':
str(dstat_database_path),
'db_enable_checkbutton': True})
self._dstat_spinner = itertools.cycle(r'-\|/')
print ''
# Check every 250ms to see if dstat acquisition has
# completed.
self.dstat_timeout_id = \
gobject.timeout_add(250, self.check_dstat_status)
except:
print "Exception in user code:"
print '-'*60
traceback.print_exc(file=sys.stdout)
print '-'*60
# An error occurred while initializing Analyst remote
# control.
emit_signal('on_step_complete', [self.name, 'Fail'])
else:
# D-State is not enabled, so step is complete.
emit_signal('on_step_complete', [self.name, None])
else:
# DropBox-DX device is not connected, but allow protocol to
# continue.
#
# N.B., A warning message is display once at the *start* of the
# protocol if no DropBot-DX connection has been established, but
# *not* on each step.
emit_signal('on_step_complete', [self.name, None])
