示例#1
0
 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()))
示例#2
0
    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
示例#4
0
 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])
示例#7
0
    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)
示例#9
0
    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])