コード例 #1
0
 def test_get_progress(self):
     """
     Test getMovementProgress function behaves as expected
     """
     start_point = {'x': 0, 'y': 0, 'z': 0}
     end_point = {'x': 2, 'y': 2, 'z': 2}
     current_point = {'x': 1, 'y': 1, 'z': 1}
     progress = getMovementProgress(current_point, start_point, end_point)
     self.assertTrue(util.almost_equal(progress, 0.5, rtol=RTOL_PROGRESS))
     current_point = {
         'x': .998,
         'y': .999,
         'z': .999
     }  # slightly off the line
     progress = getMovementProgress(current_point, start_point, end_point)
     self.assertTrue(util.almost_equal(progress, 0.5, rtol=RTOL_PROGRESS))
     current_point = {'x': 3, 'y': 3, 'z': 3}  # away from the line
     progress = getMovementProgress(current_point, start_point, end_point)
     self.assertIsNone(progress)
     current_point = {'x': 1, 'y': 1, 'z': 3}  # away from the line
     progress = getMovementProgress(current_point, start_point, end_point)
     self.assertIsNone(progress)
     current_point = {'x': -1, 'y': 0, 'z': 0}  # away from the line
     progress = getMovementProgress(current_point, start_point, end_point)
     self.assertIsNone(progress)
コード例 #2
0
def getMovementProgress(current_pos, start_pos, end_pos):
    """
    Compute the position on the path between start and end positions of a stage movement (such as LOADING to IMAGING)
    If it’s too far from the line between the start and end positions, then it’s considered out of the path.
    :param current_pos: (dict str->float) Current position of the stage
    :param start_pos: (dict str->float) A position to start the movement from
    :param end_pos: (dict str->float) A position to end the movement to
    :return:(0<=float<=1, or None) Ratio of the progress, None if it's far away from of the path
    """
    def get_distance(start, end):
        # Calculate the euclidean distance between two 3D points
        axes = start.keys() & end.keys()  # only the axes found on both points
        sp = numpy.array([start[a] for a in axes])
        ep = numpy.array([end[a] for a in axes])
        return scipy.spatial.distance.euclidean(ep, sp)

    # Get distance for current point in respect to start and end
    from_start = get_distance(start_pos, current_pos)
    to_end = get_distance(current_pos, end_pos)
    total_length = get_distance(start_pos, end_pos)
    if total_length == 0:  # same value
        return 1
    # Check if current position is on the line from start to end position
    # That would happen if start_to_current +  current_to_start = total_distance from start to end
    if util.almost_equal((from_start + to_end),
                         total_length,
                         rtol=RTOL_PROGRESS):
        return min(from_start / total_length,
                   1.0)  # Clip in case from_start slightly > total_length
    else:
        return None
コード例 #3
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ファイル: settings.py プロジェクト: lanery/odemis
    def _onUpdateTriggerDelayMD(self, evt):
        """
        Callback method for trigger delay ctrl GUI element.
        Overwrites the triggerDelay value in the MD after a new value was requested via the GUI.
        """
        evt.Skip()
        cur_timeRange = self.streak_unit.timeRange.value
        requested_triggerDelay = self.ctrl_triggerDelay.GetValue()
        # get a copy of  MD
        trigger2delay_MD = self.streak_delay.getMetadata()[model.MD_TIME_RANGE_TO_DELAY]

        # check if key already exists (prevent creating new key due to floating point issues)
        key = util.find_closest(cur_timeRange, trigger2delay_MD.keys())
        if util.almost_equal(key, cur_timeRange):
            # Replace the current delay value with the requested for an already existing timeRange in the dict.
            # This avoid duplication of keys, which are only different because of floating point issues.
            trigger2delay_MD[key] = requested_triggerDelay
        else:
            trigger2delay_MD[cur_timeRange] = requested_triggerDelay
            logging.warning("A new entry %s was added to MD_TIME_RANGE_TO_DELAY, "
                            "which is not in the device .timeRange choices.", cur_timeRange)

        # check the number of keys in the dict is same as choices for VA
        if len(trigger2delay_MD.keys()) != len(self.streak_unit.timeRange.choices):
            logging.warning("MD_TIME_RANGE_TO_DELAY has %d entries, while the device .timeRange has %d choices.",
                            len(trigger2delay_MD.keys()), len(self.streak_unit.timeRange.choices))

        self.streak_delay.updateMetadata({model.MD_TIME_RANGE_TO_DELAY: trigger2delay_MD})
        # Note: updateMetadata should here never raise an exception as the UnitFloatCtrl already
        # catches errors regarding type and out-of-range inputs

        # update txt displayed in GUI
        self._onUpdateTriggerDelayGUI("Calibration not saved yet", odemis.gui.FG_COLOUR_WARNING)
コード例 #4
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ファイル: test.py プロジェクト: delmic/odemis
def assert_pos_almost_equal(actual, expected, *args, **kwargs):
    """
    Asserts that two stage positions have almost equal coordinates.
    """
    if set(expected.keys()) != set(actual.keys()):
        raise AssertionError("Dimensions of position do not match: %s != %s" % (actual.keys(), expected.keys()))

    for k in expected.keys():
        if not util.almost_equal(actual[k], expected[k], *args, **kwargs):
            raise AssertionError("Position %s != %s" % (actual, expected))
コード例 #5
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ファイル: test.py プロジェクト: thomasaarholt/odemis
def assert_pos_almost_equal(actual, expected, *args, **kwargs):
    """
    Asserts that two stage positions have almost equal coordinates.
    """
    if set(expected.keys()) != set(actual.keys()):
        raise AssertionError("Dimensions of position do not match: %s != %s" %
                             (actual.keys(), expected.keys()))

    for k in expected.keys():
        if not util.almost_equal(actual[k], expected[k], *args, **kwargs):
            raise AssertionError("Position %s != %s" % (actual, expected))
コード例 #6
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ファイル: util_test.py プロジェクト: delmic/odemis
 def test_simple(self):
     in_exp = {(0., 0): True,
               (-5, -5.): True,
               (1., 1. - 1e-9): True,
               (1., 1. - 1e-3): False,
               (1., 1. + 1e-3): False,
               (-5e-8, -5e-8 + 1e-19): True,
               (5e18, 5e18 + 1): True,
               }
     for i, eo in in_exp.items():
         o = util.almost_equal(*i)
         self.assertEqual(o, eo, "Failed to get correct output for %s" % (i,))
コード例 #7
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ファイル: util.py プロジェクト: pieleric/odemis
 def cb_set(value, ctrl=value_ctrl, unit=unit):
     for i in range(ctrl.GetCount()):
         if ((isinstance(value, float) and util.almost_equal(ctrl.GetClientData(i), value)) or
                 ctrl.GetClientData(i) == value):
             logging.debug("Setting ComboBox value to %s", ctrl.Items[i])
             ctrl.SetSelection(i)
             break
     else:
         logging.warning("No existing label found for value %s", value)
         # entering value as free text
         txt = value_to_str(value, unit)
         ctrl.SetValue(txt)
コード例 #8
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ファイル: util.py プロジェクト: lanery/odemis
 def cb_set(value, ctrl=value_ctrl, unit=unit):
     for i in range(ctrl.GetCount()):
         if ((isinstance(value, float) and util.almost_equal(ctrl.GetClientData(i), value)) or
                 ctrl.GetClientData(i) == value):
             logging.debug("Setting ComboBox value to %s", ctrl.Items[i])
             ctrl.SetSelection(i)
             break
     else:
         logging.warning("No existing label found for value %s", value)
         # entering value as free text
         txt = value_to_str(value, unit)
         ctrl.SetValue(txt)
コード例 #9
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ファイル: spectrum_volt.py プロジェクト: lazem/odemis
def acquire_volts(volts, detector):
    """
    vots (list of floats > 0): voltage in kV
    detector (str): role of the spectrometer to use
    returns (list of DataArray): all the spectra, in order
    """
    ebeam = model.getComponent(role="e-beam")
    sed = model.getComponent(role="se-detector")
    spmt = model.getComponent(role=detector)
    hw_settings = save_hw_settings(ebeam)

    # Go to spot mode (ie, res = 1x1)
    if ebeam.resolution.value != (1, 1):
        ebeam.resolution.value = (1, 1)
        ebeam.translation.value = (0, 0)  # at the center of the FoV
    else:
        logging.info("Leaving the e-beam in spot mode at %s",
                     ebeam.translation.value)

    ebeam.dwellTime.value = 0.1

    try:
        # Activate the e-beam
        sed.data.subscribe(discard_data)

        das = []
        for vstr in volts:
            v = float(vstr) * 1000
            ebeam.accelVoltage.value = v
            if not util.almost_equal(ebeam.accelVoltage.value, v):
                logging.warning(
                    "Voltage requested at %g kV, but e-beam set at %g kV",
                    v / 1000, ebeam.accelVoltage.value / 1000)
            else:
                logging.info("Acquiring at %g kV", v / 1000)

            # Acquire one spectrum
            spec = spmt.data.get()
            # Add dimensions to make it a spectrum (X, first dim -> C, 5th dim)
            spec.shape = (spec.shape[-1], 1, 1, 1, 1)

            # Add some useful metadata
            spec.metadata[model.MD_DESCRIPTION] = "Spectrum at %g kV" % (v /
                                                                         1000)
            spec.metadata[model.MD_EBEAM_VOLTAGE] = v
            # TODO: store the spot position in MD_POS
            das.append(spec)

    finally:
        sed.data.unsubscribe(discard_data)  # Just to be sure
        resume_hw_settings(ebeam, hw_settings)

    return das
コード例 #10
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def assert_pos_almost_equal(actual, expected, match_all=True, *args, **kwargs):
    """
    Asserts that two stage positions have almost equal coordinates.
    :param match_all: (bool) if False, only the expected keys are checked, and actual can have more keys
    """
    if match_all and set(expected.keys()) != set(actual.keys()):
        raise AssertionError("Dimensions of position do not match: %s != %s" %
                             (list(actual.keys()), list(expected.keys())))

    for k in expected.keys():
        if not util.almost_equal(actual[k], expected[k], *args, **kwargs):
            raise AssertionError("Position %s != %s" % (actual, expected))
コード例 #11
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ファイル: actuator.py プロジェクト: pieleric/odemis-old
 def _updatePosition(self):
     """
     update the position VA
     """
     # if it is an unsupported position report the nearest supported one
     real_pos = self._position[self._axis]
     nearest = util.find_closest(real_pos, self._positions.keys())
     if not util.almost_equal(real_pos, nearest):
         logging.warning("Reporting axis %s @ %s (known position), while physical axis %s @ %s",
                         self._axis, nearest, self._caxis, real_pos)
     pos = {self._axis: nearest}
     logging.debug("reporting position %s", pos)
     self.position._set_value(pos, force_write=True)
コード例 #12
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 def _updatePosition(self):
     """
     update the position VA
     """
     # if it is an unsupported position report the nearest supported one
     real_pos = self._position[self._axis]
     nearest = util.find_closest(real_pos, self._positions.keys())
     if not util.almost_equal(real_pos, nearest):
         logging.warning("Reporting axis %s @ %s (known position), while physical axis %s @ %s",
                         self._axis, nearest, self._caxis, real_pos)
     pos = {self._axis: nearest}
     logging.debug("reporting position %s", pos)
     self.position._set_value(pos, force_write=True)
コード例 #13
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ファイル: tileacq.py プロジェクト: effting/odemis
 def _check_fov(self, das, sfov):
     """
     Checks the fov based on the data arrays.
     das: list of DataArryas
     sfov: previous estimate for the fov
     """
     afovs = [self._get_fov(d) for d in das]
     asfov = (min(f[1] for f in afovs), min(f[0] for f in afovs))
     if not all(util.almost_equal(e, a) for e, a in zip(sfov, asfov)):
         logging.warning("Unexpected min FoV = %s, instead of %s", asfov,
                         sfov)
         sfov = asfov
     return sfov
コード例 #14
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ファイル: tileacq.py プロジェクト: delmic/odemis
 def _check_fov(self, das, sfov):
     """
     Checks the fov based on the data arrays.
     das: list of DataArryas
     sfov: previous estimate for the fov
     """
     afovs = [self._get_fov(d) for d in das]
     asfov = (min(f[1] for f in afovs),
              min(f[0] for f in afovs))
     if not all(util.almost_equal(e, a) for e, a in zip(sfov, asfov)):
         logging.warning("Unexpected min FoV = %s, instead of %s", asfov, sfov)
         sfov = asfov
     return sfov
コード例 #15
0
ファイル: calibration.py プロジェクト: amuskens/odemis
def get_time_range_to_trigger_delay(data, timeRange_choices,
                                    triggerDelay_range):
    """
    Reads the time range and trigger delay values from a csv object.
    Checks values for validity.
    :parameter data: (csv.reader object) calibration file
    :parameter timeRange_choices: (frozenset) choices possible for timeRange VA
    :parameter triggerDelay_range: (tuple) range possible for trigger delay values
    :return: (dict) new dictionary containing the loaded time range to trigger delay info
    """
    new_dict = {}

    for timeRange, delay in data:

        try:
            timeRange = float(timeRange)
            delay = float(delay)
        except ValueError:
            raise ValueError(
                "Trigger delay %s and/or time range %s is not of type float. "
                "Please check calibration file for trigger delay." %
                (delay, timeRange))

        # check delay in range allowed
        if not triggerDelay_range[0] <= delay <= triggerDelay_range[1]:
            raise ValueError(
                "Trigger delay %s corresponding to time range %s is not in range %s. "
                "Please check the calibration file for the trigger delay." %
                (delay, timeRange, triggerDelay_range))

        # check timeRange is in possible choices for timeRange on HW
        choice = find_closest(timeRange, timeRange_choices)
        if not almost_equal(timeRange, choice):
            raise ValueError(
                "Time range % s found in calibration file is not a possible choice "
                "for the time range of the streak unit. "
                "Please modify csv file so it fits the possible choices for the "
                "time range of the streak unit. "
                "Values in file must be of format timeRange:triggerDelay (per line)."
                % timeRange)

        new_dict[timeRange] = delay

    # check all time ranges are there
    if len(new_dict) == len(timeRange_choices):
        return new_dict
    else:
        raise ValueError(
            "The total number of %s time ranges in the loaded calibration file does not "
            "match the requested number of %s time ranges." %
            (len(new_dict), len(timeRange_choices)))
コード例 #16
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ファイル: spectrum_volt.py プロジェクト: delmic/odemis
def acquire_volts(volts, detector):
    """
    vots (list of floats > 0): voltage in kV
    detector (str): role of the spectrometer to use
    returns (list of DataArray): all the spectra, in order
    """
    ebeam = model.getComponent(role="e-beam")
    sed = model.getComponent(role="se-detector")
    spmt = model.getComponent(role=detector)
    hw_settings = save_hw_settings(ebeam)

    # Go to spot mode (ie, res = 1x1)
    if ebeam.resolution.value != (1, 1):
        ebeam.resolution.value = (1, 1)
        ebeam.translation.value = (0, 0) # at the center of the FoV
    else:
        logging.info("Leaving the e-beam in spot mode at %s", ebeam.translation.value)

    ebeam.dwellTime.value = 0.1

    try:
        # Activate the e-beam
        sed.data.subscribe(discard_data)

        das = []
        for vstr in volts:
            v = float(vstr) * 1000
            ebeam.accelVoltage.value = v
            if not util.almost_equal(ebeam.accelVoltage.value, v):
                logging.warning("Voltage requested at %g kV, but e-beam set at %g kV",
                                v / 1000, ebeam.accelVoltage.value / 1000)
            else:
                logging.info("Acquiring at %g kV", v / 1000)

            # Acquire one spectrum
            spec = spmt.data.get()
            # Add dimensions to make it a spectrum (X, first dim -> C, 5th dim)
            spec.shape = (spec.shape[-1], 1, 1, 1, 1)

            # Add some useful metadata
            spec.metadata[model.MD_DESCRIPTION] = "Spectrum at %g kV" % (v / 1000)
            spec.metadata[model.MD_EBEAM_VOLTAGE] = v
            # TODO: store the spot position in MD_POS
            das.append(spec)

    finally:
        sed.data.unsubscribe(discard_data)  # Just to be sure
        resume_hw_settings(ebeam, hw_settings)

    return das
コード例 #17
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 def test_simple(self):
     in_exp = {
         (0., 0): True,
         (-5, -5.): True,
         (1., 1. - 1e-9): True,
         (1., 1. - 1e-3): False,
         (1., 1. + 1e-3): False,
         (-5e-8, -5e-8 + 1e-19): True,
         (5e18, 5e18 + 1): True,
     }
     for i, eo in in_exp.items():
         o = util.almost_equal(*i)
         self.assertEqual(o, eo,
                          "Failed to get correct output for %s" % (i, ))
コード例 #18
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ファイル: calibration.py プロジェクト: effting/odemis
def read_trigger_delay_csv(filename, time_choices, trigger_delay_range):
    """
    Read the MD_TIME_RANGE_TO_DELAY from a CSV file, and check its validity based on the hardware
    filename (str): the path to file
    time_choices (set): choices possible for timeRange VA
    trigger_delay_range (float, float): min/max value of the trigger delay
    return (dict float -> float): new dictionary containing the loaded time range to trigger delay info
    raise ValueError: if the data of the CSV file cannot be parsed or doesn't fit the hardware
    raise IOError: if the file doesn't exist
    """
    tr2d = {}
    with open(filename, 'r', newline='') as csvfile:
        calibFile = csv.reader(csvfile, delimiter=':')
        for time_range, delay in calibFile:
            try:
                time_range = float(time_range)
                delay = float(delay)
            except ValueError:
                raise ValueError(
                    "Trigger delay %s and/or time range %s is not of type float. "
                    "Please check calibration file for trigger delay." %
                    (delay, time_range))

            # check delay in range allowed
            if not trigger_delay_range[0] <= delay <= trigger_delay_range[1]:
                raise ValueError(
                    "Trigger delay %s corresponding to time range %s is not in range %s. "
                    "Please check the calibration file for the trigger delay."
                    % (delay, time_range, trigger_delay_range))

            # check timeRange is in possible choices for timeRange on HW
            time_range_hw = find_closest(time_range, time_choices)
            if not almost_equal(time_range, time_range_hw):
                raise ValueError(
                    "Time range % s found in calibration file is not a possible choice "
                    "for the time range of the streak unit. "
                    "Please modify CSV file so it fits the possible choices for the "
                    "time range of the streak unit. "
                    "Values in file must be of format timeRange:triggerDelay (per line)."
                    % time_range)
            tr2d[time_range_hw] = delay

    # check all time ranges are there
    if len(tr2d) != len(time_choices):
        raise ValueError(
            "The total number of %s time ranges in the loaded calibration file does not "
            "match the requested number of %s time ranges." %
            (len(tr2d), len(time_choices)))
    return tr2d
コード例 #19
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ファイル: util.py プロジェクト: thomasaarholt/odemis
 def cb_set(value, ctrl=value_ctrl, u=unit, acc=accuracy):
     for i in range(ctrl.Count):
         d = ctrl.GetClientData(i)
         if (d == value or
             (all(isinstance(v, float) for v in (value, d)) and
              util.almost_equal(d, value))
            ):
             logging.debug("Setting combobox value to %s", ctrl.Items[i])
             ctrl.SetSelection(i)
             break
     else:
         logging.debug("No existing label found for value %s in combobox ctrl %d",
                       value, id(ctrl))
         # entering value as free text
         txt = readable_str(value, u, sig=acc)
         ctrl.SetValue(txt)
コード例 #20
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ファイル: calibration.py プロジェクト: delmic/odemis
def get_time_range_to_trigger_delay(data, timeRange_choices, triggerDelay_range):
    """
    Reads the time range and trigger delay values from a csv object.
    Checks values for validity.
    :parameter data: (csv.reader object) calibration file
    :parameter timeRange_choices: (frozenset) choices possible for timeRange VA
    :parameter triggerDelay_range: (tuple) range possible for trigger delay values
    :return: (dict) new dictionary containing the loaded time range to trigger delay info
    """
    new_dict = {}

    for timeRange, delay in data:

        try:
            timeRange = float(timeRange)
            delay = float(delay)
        except ValueError:
            raise ValueError("Trigger delay %s and/or time range %s is not of type float. "
                             "Please check calibration file for trigger delay." % (delay, timeRange))

        # check delay in range allowed
        if not triggerDelay_range[0] <= delay <= triggerDelay_range[1]:
            raise ValueError("Trigger delay %s corresponding to time range %s is not in range (0, 1). "
                             "Please check the calibration file for the trigger delay." % (delay, timeRange))

        # check timeRange is in possible choices for timeRange on HW
        choice = find_closest(timeRange, timeRange_choices)
        if not almost_equal(timeRange, choice):
            raise ValueError("Time range % s found in calibration file is not a possible choice "
                             "for the time range of the streak unit. "
                             "Please modify csv file so it fits the possible choices for the "
                             "time range of the streak unit. "
                             "Values in file must be of format timeRange:triggerDelay (per line)."
                             % timeRange)

        new_dict[timeRange] = delay

    # check all time ranges are there
    if len(new_dict) == len(timeRange_choices):
        return new_dict
    else:
        raise ValueError("The total number of %s time ranges in the loaded calibration file does not "
                         "match the requested number of %s time ranges."
                         % (len(new_dict), len(timeRange_choices)))
コード例 #21
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ファイル: util.py プロジェクト: lanery/odemis
 def cb_set(value, va=va, ctrl=value_ctrl, u=unit, acc=accuracy):
     # Re-read the value from the VA because it'll be called via
     # CallAfter(), and if the value is changed multiple times, it might
     # not be in chronological order.
     value = va.value
     for i in range(ctrl.GetCount()):
         d = ctrl.GetClientData(i)
         if (d == value or
             (all(isinstance(v, float) for v in (value, d)) and
              util.almost_equal(d, value))
            ):
             logging.debug("Setting combobox value to %s", ctrl.Items[i])
             ctrl.SetSelection(i)
             break
     else:
         logging.debug("No existing label found for value %s in combobox ctrl %d",
                       value, id(ctrl))
         # entering value as free text
         txt = value_to_str(value, u, acc)
         ctrl.SetValue(txt)
コード例 #22
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ファイル: util.py プロジェクト: pieleric/odemis
 def cb_set(value, va=va, ctrl=value_ctrl, u=unit, acc=accuracy):
     # Re-read the value from the VA because it'll be called via
     # CallAfter(), and if the value is changed multiple times, it might
     # not be in chronological order.
     value = va.value
     for i in range(ctrl.GetCount()):
         d = ctrl.GetClientData(i)
         if (d == value or
             (all(isinstance(v, float) for v in (value, d)) and
              util.almost_equal(d, value))
            ):
             logging.debug("Setting combobox value to %s", ctrl.Items[i])
             ctrl.SetSelection(i)
             break
     else:
         logging.debug("No existing label found for value %s in combobox ctrl %d",
                       value, id(ctrl))
         # entering value as free text
         txt = value_to_str(value, u, acc)
         ctrl.SetValue(txt)
コード例 #23
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def assert_pos_not_almost_equal(actual,
                                expected,
                                match_all=True,
                                *args,
                                **kwargs):
    """
    Asserts that two stage positions do not have almost equal coordinates. This means at least one of the axes has a
    different value.
    :param match_all: (bool) if False, only the expected keys are checked, and actual can have more keys
    """
    if match_all and set(expected.keys()) != set(actual.keys()):
        raise AssertionError("Dimensions of position do not match: %s != %s" %
                             (list(actual.keys()), list(expected.keys())))

    # Check that at least one of the axes not equal
    for k in expected.keys():
        if not util.almost_equal(actual[k], expected[k], *args, **kwargs):
            return
    # Otherwise coordinates are almost equal
    raise AssertionError("Position %s == %s" % (actual, expected))
コード例 #24
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    def _setTimeRange(self, value):
        """
        Updates the timeRange VA.
        :parameter value: (float) value to be set
        :return: (float) current time range
        """
        logging.debug("Reporting time range %s for streak unit.", value)
        self._metadata[model.MD_STREAK_TIMERANGE] = value

        # set corresponding trigger delay
        tr2d = self.parent._delaybox._metadata.get(model.MD_TIME_RANGE_TO_DELAY)
        if tr2d:
            key = util.find_closest(value, tr2d.keys())
            if util.almost_equal(key, value):
                self.parent._delaybox.triggerDelay.value = tr2d[key]
            else:
                logging.warning("Time range %s is not a key in MD for time range to "
                                "trigger delay calibration" % value)

        return value
コード例 #25
0
ファイル: simstreakcam.py プロジェクト: pieleric/odemis
    def _setTimeRange(self, value):
        """
        Updates the timeRange VA.
        :parameter value: (float) value to be set
        :return: (float) current time range
        """
        logging.debug("Reporting time range %s for streak unit.", value)
        self._metadata[model.MD_STREAK_TIMERANGE] = value

        # set corresponding trigger delay
        tr2d = self.parent._delaybox._metadata.get(model.MD_TIME_RANGE_TO_DELAY)
        if tr2d:
            key = util.find_closest(value, tr2d.keys())
            if util.almost_equal(key, value):
                self.parent._delaybox.triggerDelay.value = tr2d[key]
            else:
                logging.warning("Time range %s is not a key in MD for time range to "
                                "trigger delay calibration" % value)

        return value
コード例 #26
0
ファイル: util.py プロジェクト: pieleric/odemis
def hfw_choices(comp, va, conf):
    """ Return a set of HFW choices

    If the VA has predefined choices, return those. Otherwise calculate the choices using the range
    of the VA.
    """
    try:
        choices = va.choices
    except (NotApplicableError, AttributeError):
        # Pick every x2, x5, x10, starting from the min value
        factors = (2, 5, 10)
        mn, mx = va.range
        choices = {mn}
        cur_val = va.value

        # starting point (might be even less than mn)
        base = 10 ** int(math.log10(mn) - 1)

        while base < mx and max(choices) < mx:
            for f in factors:
                v = base * f
                if mn < v < mx:
                    if util.almost_equal(v, cur_val):
                        # To avoid having twice (almost) the same value shown in
                        # the choices when the current value is among them, but
                        # slightly modified due to rounding.
                        choices.add(cur_val)
                    else:
                        choices.add(v)
                elif v >= mx:
                    break

            base *= 10

        choices.add(mx)

        # We don't add the current value, as it's a range, so anyway any other
        # value can also happen so the GUI must be able to handle well any other
        # value.

    return choices
コード例 #27
0
ファイル: util.py プロジェクト: lanery/odemis
def hfw_choices(comp, va, conf):
    """ Return a set of HFW choices

    If the VA has predefined choices, return those. Otherwise calculate the choices using the range
    of the VA.
    """
    try:
        choices = va.choices
    except AttributeError:
        # Pick every x2, x5, x10, starting from the min value
        factors = (2, 5, 10)
        mn, mx = va.range
        choices = {mn}
        cur_val = va.value

        # starting point (might be even less than mn)
        base = 10 ** int(math.log10(mn) - 1)

        while base < mx and max(choices) < mx:
            for f in factors:
                v = base * f
                if mn < v < mx:
                    if util.almost_equal(v, cur_val):
                        # To avoid having twice (almost) the same value shown in
                        # the choices when the current value is among them, but
                        # slightly modified due to rounding.
                        choices.add(cur_val)
                    else:
                        choices.add(v)
                elif v >= mx:
                    break

            base *= 10

        choices.add(mx)

        # We don't add the current value, as it's a range, so anyway any other
        # value can also happen so the GUI must be able to handle well any other
        # value.

    return choices
コード例 #28
0
ファイル: move.py プロジェクト: Mahmood-B/odemis
def getMovementProgress(current_pos, start_pos, end_pos):
    """
    Compute the position on the path between start and end positions of a stage movement (such as LOADING to IMAGING)
    If it’s too far from the line between the start and end positions, then it’s considered out of the path.
    :param current_pos: (dict str->float) Current position of the stage
    :param start_pos: (dict str->float) A position to start the movement from
    :param end_pos: (dict str->float) A position to end the movement to
    :return:(0<=float<=1, or None) Ratio of the progress, None if it's far away from of the path
    """
    # Get distance for current point in respect to start and end
    from_start = _getDistance(start_pos, current_pos)
    to_end = _getDistance(current_pos, end_pos)
    total_length = _getDistance(start_pos, end_pos)
    if total_length == 0:  # same value
        return 1
    # Check if current position is on the line from start to end position
    # That would happen if start_to_current +  current_to_start = total_distance from start to end
    if util.almost_equal((from_start + to_end), total_length, rtol=RTOL_PROGRESS):
        return min(from_start / total_length, 1.0)  # Clip in case from_start slightly > total_length
    else:
        return None
コード例 #29
0
ファイル: move.py プロジェクト: lanery/odemis
def getMovementProgress(current_pos, start_pos, end_pos):
    """
    Compute the position on the path between start and end positions of a stage movement (such as LOADING to IMAGING)
    If it’s too far from the line between the start and end positions, then it’s considered out of the path.
    :param current_pos: (dict str->float) Current position of the stage
    :param start_pos: (dict str->float) A position to start the movement from
    :param end_pos: (dict str->float) A position to end the movement to
    :return:(0<=float<=1, or None) Ratio of the progress, None if it's far away from of the path
    """
    def get_distance(start, end):
        # Calculate the euclidean distance between two 3D points
        sp = numpy.array([start['x'], start['y'], start['z']])
        ep = numpy.array([end['x'], end['y'], end['z']])
        return scipy.spatial.distance.euclidean(ep, sp)

    def check_axes(pos):
        if not {'x', 'y', 'z'}.issubset(set(pos.keys())):
            raise ValueError(
                "Missing x,y,z axes in {} for correct distance measurement.".
                format(pos))

    # Check we have the x,y,z axes in all points
    check_axes(current_pos)
    check_axes(start_pos)
    check_axes(end_pos)
    # Get distance for current point in respect to start and end
    from_start = get_distance(start_pos, current_pos)
    to_end = get_distance(current_pos, end_pos)
    total_length = get_distance(start_pos, end_pos)
    # Check if current position is on the line from start to end position
    # That would happen if start_to_current +  current_to_start = total_distance from start to end
    if util.almost_equal((from_start + to_end),
                         total_length,
                         rtol=RTOL_PROGRESS):
        return min(from_start / total_length,
                   1.0)  # Clip in case from_start slightly > total_length
    else:
        return None
コード例 #30
0
ファイル: secom_cl.py プロジェクト: ihebdelmic/odemis
    def _get_center_pxs(self, rep, sub_shape, datatl, pxs):
        """
        Computes the center and pixel size of the entire data based on the
        top-left data acquired.
        rep (int, int): number of pixels (tiles) in X, Y
        sub_shape (int, int): number of sub-pixels in a pixel
        datatl (DataArray): first data array acquired
        pxs (float, float): the pixel in m of a pixel
        return:
            center (tuple of floats): position in m of the whole data
            pxs (tuple of floats): pixel size in m of the sub-pixels
        """
        # Compute center of area, based on the position of the first point (the
        # position of the other points can be wrong due to drift correction)
        center_tl = datatl.metadata[model.MD_POS]
        dpxs = datatl.metadata[model.MD_PIXEL_SIZE]
        tl = (center_tl[0] - (dpxs[0] * (datatl.shape[-1] - 1)) / 2,
              center_tl[1] + (dpxs[1] * (datatl.shape[-2] - 1)) / 2)
        logging.debug("Computed center of top-left pixel at at %s", tl)

        # Note: we don't rely on the MD_PIXEL_SIZE, because if the e-beam was in
        # spot mode (res 1x1), the scale is not always correct, which gives an
        # incorrect metadata.
        sub_pxs = pxs[0] / sub_shape[0], pxs[1] / sub_shape[1]
        trep = rep[0] * sub_shape[0], rep[1] * sub_shape[1]
        center = (tl[0] + (sub_pxs[0] * (trep[0] - 1)) / 2,
                  tl[1] - (sub_pxs[1] * (trep[1] - 1)) / 2)
        logging.debug("Computed data width to be %s x %s, with center at %s",
                      pxs[0] * rep[0], pxs[1] * rep[1], center)

        if numpy.prod(datatl.shape) > 1:
            # pxs and dpxs ought to be identical
            if not util.almost_equal(sub_pxs[0], dpxs[0]):
                logging.warning("Expected pixel size of %s, but data has %s",
                                sub_pxs, dpxs)

        return center, sub_pxs
コード例 #31
0
ファイル: secom_cl.py プロジェクト: delmic/odemis
    def _get_center_pxs(self, rep, sub_shape, datatl, pxs):
        """
        Computes the center and pixel size of the entire data based on the
        top-left data acquired.
        rep (int, int): number of pixels (tiles) in X, Y
        sub_shape (int, int): number of sub-pixels in a pixel
        datatl (DataArray): first data array acquired
        pxs (float, float): the pixel in m of a pixel
        return:
            center (tuple of floats): position in m of the whole data
            pxs (tuple of floats): pixel size in m of the sub-pixels
        """
        # Compute center of area, based on the position of the first point (the
        # position of the other points can be wrong due to drift correction)
        center_tl = datatl.metadata[model.MD_POS]
        dpxs = datatl.metadata[model.MD_PIXEL_SIZE]
        tl = (center_tl[0] - (dpxs[0] * (datatl.shape[-1] - 1)) / 2,
              center_tl[1] + (dpxs[1] * (datatl.shape[-2] - 1)) / 2)
        logging.debug("Computed center of top-left pixel at at %s", tl)

        # Note: we don't rely on the MD_PIXEL_SIZE, because if the e-beam was in
        # spot mode (res 1x1), the scale is not always correct, which gives an
        # incorrect metadata.
        sub_pxs = pxs[0] / sub_shape[0], pxs[1] / sub_shape[1]
        trep = rep[0] * sub_shape[0], rep[1] * sub_shape[1]
        center = (tl[0] + (sub_pxs[0] * (trep[0] - 1)) / 2,
                  tl[1] - (sub_pxs[1] * (trep[1] - 1)) / 2)
        logging.debug("Computed data width to be %s x %s, with center at %s",
                      pxs[0] * rep[0], pxs[1] * rep[1], center)

        if numpy.prod(datatl.shape) > 1:
            # pxs and dpxs ought to be identical
            if not util.almost_equal(sub_pxs[0], dpxs[0]):
                logging.warning("Expected pixel size of %s, but data has %s",
                                sub_pxs, dpxs)

        return center, sub_pxs
コード例 #32
0
ファイル: _static.py プロジェクト: delmic/odemis
    def __init__(self, name, data, *args, **kwargs):
        """
        name (string)
        data (model.DataArray(Shadow) of shape (YX) or list of such DataArray(Shadow)).
         The metadata MD_POS, MD_AR_POLE and MD_POL_MODE should be provided
        """
        if not isinstance(data, collections.Iterable):
            data = [data]  # from now it's just a list of DataArray

        # TODO: support DAS, as a "delayed loading" by only calling .getData()
        # when the projection for the particular data needs to be computed (or
        # .raw needs to be accessed?)
        # Ensure all the data is a DataArray, as we don't handle (yet) DAS
        data = [d.getData() if isinstance(d, model.DataArrayShadow) else d for d in data]

        # find positions of each acquisition
        # (float, float, str or None)) -> DataArray: position on SEM + polarization -> data
        self._pos = {}
        sempositions = set()
        polpositions = set()
        for d in data:
            try:
                sempos_cur = d.metadata[MD_POS]

                # When reading data: floating point error (slightly different keys for same ebeam pos)
                # -> check if there is already a position specified, which is very close by
                # (and therefore the same ebeam pos) and replace with that ebeam position
                # (e.g. all polarization positions for the same ebeam positions will have exactly the same ebeam pos)
                for sempos in sempositions:
                    if almost_equal(sempos_cur[0], sempos[0]) and almost_equal(sempos_cur[1], sempos[1]):
                        sempos_cur = sempos
                        break
                self._pos[sempos_cur + (d.metadata.get(MD_POL_MODE, None),)] = img.ensure2DImage(d)
                sempositions.add(sempos_cur)
                if MD_POL_MODE in d.metadata:
                    polpositions.add(d.metadata.get(MD_POL_MODE))
            except KeyError:
                logging.info("Skipping DataArray without known position")

        # Cached conversion of the CCD image to polar representation
        # TODO: automatically fill it in a background thread
        self._polar = {}  # dict tuple (float, float, str or None) -> DataArray

        # SEM position VA
        # SEM position displayed, (None, None) == no point selected (x, y)
        self.point = model.VAEnumerated((None, None),
                     choices=frozenset([(None, None)] + list(sempositions)))

        if self._pos:
            # Pick one point, e.g., top-left
            bbtl = (min(x for x, y in sempositions if x is not None),
                    min(y for x, y in sempositions if y is not None))

            # top-left point is the closest from the bounding-box top-left
            def dis_bbtl(v):
                try:
                    return math.hypot(bbtl[0] - v[0], bbtl[1] - v[1])
                except TypeError:
                    return float("inf")  # for None, None
            self.point.value = min(sempositions, key=dis_bbtl)

        # no need for init=True, as Stream.__init__ will update the image
        self.point.subscribe(self._onPoint)

        # polarization VA
        # check if any polarization analyzer data, (None) == no analyzer data (pol)
        if self._pos.keys()[0][-1]:
            # use first entry in acquisition to populate VA (acq could have 1 or 6 pol pos)
            self.polarization = model.VAEnumerated(self._pos.keys()[0][-1],
                                choices=polpositions)

        if self._pos.keys()[0][-1]:
            self.polarization.subscribe(self._onPolarization)

        if "acq_type" not in kwargs:
            kwargs["acq_type"] = model.MD_AT_AR
        super(StaticARStream, self).__init__(name, list(self._pos.values()), *args, **kwargs)
コード例 #33
0
ファイル: _weaver.py プロジェクト: delmic/odemis
    def getFullImage(self):
        """
        return (2D DataArray): same dtype as the tiles, with shape corresponding to the bounding box. 
        """

        tiles = self.tiles

        # Compute the bounding box of each tile and the global bounding box

        # Get a fixed pixel size by using the first one
        # TODO: use the mean, in case they are all slightly different due to
        # correction?
        pxs = tiles[0].metadata[model.MD_PIXEL_SIZE]

        tbbx_phy = []  # tuples of ltrb in physical coordinates
        for t in tiles:
            c = t.metadata[model.MD_POS]
            w = t.shape[-1], t.shape[-2]
            if not util.almost_equal(pxs[0], t.metadata[model.MD_PIXEL_SIZE][0], rtol=0.01):
                logging.warning("Tile @ %s has a unexpected pixel size (%g vs %g)",
                                c, t.metadata[model.MD_PIXEL_SIZE][0], pxs[0])
            bbx = (c[0] - (w[0] * pxs[0] / 2), c[1] - (w[1] * pxs[1] / 2),
                   c[0] + (w[0] * pxs[0] / 2), c[1] + (w[1] * pxs[1] / 2))

            tbbx_phy.append(bbx)

        gbbx_phy = (min(b[0] for b in tbbx_phy), min(b[1] for b in tbbx_phy),
                    max(b[2] for b in tbbx_phy), max(b[3] for b in tbbx_phy))

        # Compute the bounding-boxes in pixel coordinates
        tbbx_px = []

        # that's the origin (Y is max as Y is inverted)
        glt = gbbx_phy[0], gbbx_phy[3]
        for bp, t in zip(tbbx_phy, tiles):
            lt = (int(round((bp[0] - glt[0]) / pxs[0])),
                  int(round(-(bp[3] - glt[1]) / pxs[1])))
            w = t.shape[-1], t.shape[-2]
            bbx = (lt[0], lt[1],
                   lt[0] + w[0], lt[1] + w[1])
            tbbx_px.append(bbx)

        gbbx_px = (min(b[0] for b in tbbx_px), min(b[1] for b in tbbx_px),
                   max(b[2] for b in tbbx_px), max(b[3] for b in tbbx_px))

        assert gbbx_px[0] == gbbx_px[1] == 0

        if numpy.greater(gbbx_px[-2:], 4 * numpy.sum(tbbx_px[-2:])).any():
            # Overlap > 50% or missing tiles
            logging.warning("Global area much bigger than sum of tile areas")

        # Paste each tile
        logging.debug("Generating global image of size %dx%d px",
                      gbbx_px[-2], gbbx_px[-1])
        im = numpy.empty((gbbx_px[-1], gbbx_px[-2]), dtype=tiles[0].dtype)
        # Use minimum of the values in the tiles for background
        im[:] = numpy.amin(tiles)
        for b, t in zip(tbbx_px, tiles):
            im[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]] = t
            # TODO: border

        # Update metadata
        # TODO: check this is also correct based on lt + half shape * pxs
        c_phy = ((gbbx_phy[0] + gbbx_phy[2]) / 2,
                 (gbbx_phy[1] + gbbx_phy[3]) / 2)
        md = tiles[0].metadata.copy()
        md[model.MD_POS] = c_phy

        return model.DataArray(im, md)
コード例 #34
0
ファイル: _weaver.py プロジェクト: delmic/odemis
    def getFullImage(self):
        """
        return (2D DataArray): same dtype as the tiles, with shape corresponding to the bounding box. 
        """
        tiles = self.tiles

        # Compute the bounding box of each tile and the global bounding box

        # Get a fixed pixel size by using the first one
        # TODO: use the mean, in case they are all slightly different due to
        # correction?
        pxs = tiles[0].metadata[model.MD_PIXEL_SIZE]

        tbbx_phy = []  # tuples of ltrb in physical coordinates
        for t in tiles:
            c = t.metadata[model.MD_POS]
            w = t.shape[-1], t.shape[-2]
            if not util.almost_equal(pxs[0], t.metadata[model.MD_PIXEL_SIZE][0], rtol=0.01):
                logging.warning("Tile @ %s has a unexpected pixel size (%g vs %g)",
                                c, t.metadata[model.MD_PIXEL_SIZE][0], pxs[0])
            bbx = (c[0] - (w[0] * pxs[0] / 2), c[1] - (w[1] * pxs[1] / 2),
                   c[0] + (w[0] * pxs[0] / 2), c[1] + (w[1] * pxs[1] / 2))

            tbbx_phy.append(bbx)

        gbbx_phy = (min(b[0] for b in tbbx_phy), min(b[1] for b in tbbx_phy),
                    max(b[2] for b in tbbx_phy), max(b[3] for b in tbbx_phy))

        # Compute the bounding-boxes in pixel coordinates
        tbbx_px = []

        # that's the origin (Y is max as Y is inverted)
        glt = gbbx_phy[0], gbbx_phy[3]
        for bp, t in zip(tbbx_phy, tiles):
            lt = (int(round((bp[0] - glt[0]) / pxs[0])),
                  int(round(-(bp[3] - glt[1]) / pxs[1])))
            w = t.shape[-1], t.shape[-2]
            bbx = (lt[0], lt[1],
                   lt[0] + w[0], lt[1] + w[1])
            tbbx_px.append(bbx)

        gbbx_px = (min(b[0] for b in tbbx_px), min(b[1] for b in tbbx_px),
                   max(b[2] for b in tbbx_px), max(b[3] for b in tbbx_px))

        assert gbbx_px[0] == gbbx_px[1] == 0
        if numpy.greater(gbbx_px[-2:], 4 * numpy.sum(tbbx_px[-2:])).any():
            # Overlap > 50% or missing tiles
            logging.warning("Global area much bigger than sum of tile areas")

        # Weave tiles by using a smooth gradient. The part of the tile that does not overlap
        # with any previous tiles is inserted into the part of the
        # ovv image that is still empty. This part is determined by a mask, which indicates
        # the parts of the image that already contain image data (True) and the ones that are still
        # empty (False). For the overlapping parts, the tile is multiplied with weights corresponding
        # to a gradient that has its maximum at the center of the tile and
        # smoothly decreases toward the edges. The function for creating the weights is
        # a distance measure resembling the maximum-norm, i.e. equidistant points lie
        # on a rectangle (instead of a circle like for the euclidean norm). Additionally,
        # the x and y values generating this norm are raised to the power of 6 to
        # create a steeper gradient. The value 6 is quite arbitrary and was found to give
        # good results during experimentation.
        # The part of the overview image that overlaps with the new tile is multiplied with the
        # complementary weights (1 -  weights) and the weighted overlapping parts of the new tile and
        # the ovv image are added, so the resulting image contains a gradient in the overlapping regions
        # between all the tiles that have been inserted before and the newly inserted tile.

        # Paste each tile
        logging.debug("Generating global image of size %dx%d px",
                      gbbx_px[-2], gbbx_px[-1])
        im = numpy.empty((gbbx_px[-1], gbbx_px[-2]), dtype=tiles[0].dtype)
        # Use minimum of the values in the tiles for background
        im[:] = numpy.amin(tiles)

        # The mask is multiplied with the tile, thereby creating a tile with a gradient
        mask = numpy.zeros((gbbx_px[-1], gbbx_px[-2]), dtype=numpy.bool)

        for b, t in zip(tbbx_px, tiles):
            # Part of image overlapping with tile
            roi = im[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]]
            moi = mask[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]]

            # Insert image at positions that are still empty
            roi[~moi] = t[~moi]

            # Create gradient in overlapping region. Ratio between old image and new tile values determined by
            # distance to the center of the tile

            # Create weight matrix with decreasing values from its center that
            # has the same size as the tile.
            hh, hw = numpy.divide(roi.shape, 2)  # half-height, half-width
            # Deal with even/odd tile sizes
            sz = roi.shape
            if sz[1] % 2 == 0:
                x = numpy.arange(-hw, hw, 1)
            else:
                x = numpy.arange(-hw, hw + 1, 1)

            if sz[0] % 2 == 0:
                y = numpy.arange(-hh, hh, 1)
            else:
                y = numpy.arange(-hh, hh + 1, 1)

            xx, yy = numpy.meshgrid((x / hw) ** 6, (y / hh) ** 6)
            w = numpy.maximum(xx, yy)
            # Hardcoding a weight function is quite arbitrary and might result in
            # suboptimal solutions in some cases.
            # Alternatively, different weights might be used. One option would be to select
            # a fixed region on the sides of the image, e.g. 20% (expected overlap), and
            # only apply a (linear) gradient to these parts, while keeping the new tile for the
            # rest of the region. However, this approach does not solve the hardcoding problem
            # since the overlap region is still arbitrary. Future solutions might adaptively
            # select the this region.

            # Use weights to create gradient in overlapping region
            roi[moi] = (t * (1 - w))[moi] + (roi * w)[moi]

            # Update mask
            mask[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]] = True

        # Update metadata
        # TODO: check this is also correct based on lt + half shape * pxs
        c_phy = ((gbbx_phy[0] + gbbx_phy[2]) / 2,
                 (gbbx_phy[1] + gbbx_phy[3]) / 2)
        md = tiles[0].metadata.copy()
        md[model.MD_POS] = c_phy

        return model.DataArray(im, md)
コード例 #35
0
ファイル: delphi_man_calib.py プロジェクト: lanery/odemis
def man_calib(logpath, keep_loaded=False):
    escan = None
    detector = None
    ccd = None
    # find components by their role
    for c in model.getComponents():
        if c.role == "e-beam":
            escan = c
        elif c.role == "bs-detector":
            detector = c
        elif c.role == "ccd":
            ccd = c
        elif c.role == "sem-stage":
            sem_stage = c
        elif c.role == "align":
            opt_stage = c
        elif c.role == "ebeam-focus":
            ebeam_focus = c
        elif c.role == "overview-focus":
            navcam_focus = c
        elif c.role == "focus":
            focus = c
        elif c.role == "overview-ccd":
            overview_ccd = c
        elif c.role == "chamber":
            chamber = c
    if not all([escan, detector, ccd]):
        logging.error("Failed to find all the components")
        raise KeyError("Not all components found")

    hw_settings = aligndelphi.list_hw_settings(escan, ccd)

    try:
        # Get pressure values
        pressures = chamber.axes["pressure"].choices
        vacuum_pressure = min(pressures.keys())
        vented_pressure = max(pressures.keys())
        if overview_ccd:
            for p, pn in pressures.items():
                if pn == "overview":
                    overview_pressure = p
                    break
            else:
                raise IOError("Failed to find the overview pressure in %s" %
                              (pressures, ))

        calibconf = get_calib_conf()
        shid, sht = chamber.sampleHolder.value
        calib_values = calibconf.get_sh_calib(shid)
        if calib_values is None:
            first_hole = second_hole = offset = resa = resb = hfwa = scaleshift = (
                0, 0)
            scaling = iscale = iscale_xy = (1, 1)
            rotation = irot = ishear = 0
            hole_focus = aligndelphi.SEM_KNOWN_FOCUS
            opt_focus = aligndelphi.OPTICAL_KNOWN_FOCUS
            print_col(
                ANSI_RED,
                "Calibration values missing! All the steps will be performed anyway..."
            )
            force_calib = True
        else:
            first_hole, second_hole, hole_focus, opt_focus, offset, scaling, rotation, iscale, irot, iscale_xy, ishear, resa, resb, hfwa, scaleshift = calib_values

            force_calib = False
        print_col(
            ANSI_CYAN, "**Delphi Manual Calibration steps**\n"
            "1.Sample holder hole detection\n"
            "    Current values: 1st hole: " + str(first_hole) + "\n"
            "                    2st hole: " + str(second_hole) + "\n"
            "                    hole focus: " + str(hole_focus) + "\n"
            "2.SEM image calibration\n"
            "    Current values: resolution-a: " + str(resa) + "\n"
            "                    resolution-b: " + str(resb) + "\n"
            "                    hfw-a: " + str(hfwa) + "\n"
            "                    spot shift: " + str(scaleshift) + "\n"
            "3.Twin stage calibration\n"
            "    Current values: offset: " + str(offset) + "\n"
            "                    scaling: " + str(scaling) + "\n"
            "                    rotation: " + str(rotation) + "\n"
            "                    optical focus: " + str(opt_focus) + "\n"
            "4.Fine alignment\n"
            "    Current values: scale: " + str(iscale) + "\n"
            "                    rotation: " + str(irot) + "\n"
            "                    scale-xy: " + str(iscale_xy) + "\n"
            "                    shear: " + str(ishear))
        print_col(
            ANSI_YELLOW,
            "Note that you should not perform any stage move during the process.\n"
            "Instead, you may zoom in/out while focusing.")
        print_col(ANSI_BLACK, "Now initializing, please wait...")

        # Default value for the stage offset
        position = (offset[0] * scaling[0], offset[1] * scaling[1])

        if keep_loaded and chamber.position.value[
                "pressure"] == vacuum_pressure:
            logging.info(
                "Skipped optical lens detection, will use previous value %s",
                position)
        else:
            # Move to the overview position first
            f = chamber.moveAbs({"pressure": overview_pressure})
            f.result()

            # Reference the (optical) stage
            f = opt_stage.reference({"x", "y"})
            f.result()

            f = focus.reference({"z"})
            f.result()

            # SEM stage to (0,0)
            f = sem_stage.moveAbs({"x": 0, "y": 0})
            f.result()

            # Calculate offset approximation
            try:
                f = aligndelphi.LensAlignment(overview_ccd, sem_stage, logpath)
                position = f.result()
            except IOError as ex:
                logging.warning(
                    "Failed to locate the optical lens (%s), will use previous value %s",
                    ex, position)

            # Just to check if move makes sense
            f = sem_stage.moveAbs({"x": position[0], "y": position[1]})
            f.result()

            # Move to SEM
            f = chamber.moveAbs({"pressure": vacuum_pressure})
            f.result()

        # Set basic e-beam settings
        escan.spotSize.value = 2.7
        escan.accelVoltage.value = 5300  # V
        # Without automatic blanker, the background subtraction doesn't work
        if (model.hasVA(escan, "blanker") and  # For simulator
                None in escan.blanker.choices and escan.blanker.value
                is not None):
            logging.warning("Blanker set back to automatic")
            escan.blanker.value = None

        # Detect the holes/markers of the sample holder
        while True:
            ans = "Y" if force_calib else None
            while ans not in YES_NO_CHARS:
                ans = input_col(
                    ANSI_MAGENTA,
                    "Do you want to execute the sample holder hole detection? [Y/n]"
                )
            if ans in YES_CHARS:
                # Move Phenom sample stage next to expected hole position
                sem_stage.moveAbsSync(aligndelphi.SHIFT_DETECTION)
                ebeam_focus.moveAbsSync({"z": hole_focus})
                # Set the FoV to almost 2mm
                escan.horizontalFoV.value = escan.horizontalFoV.range[1]
                input_col(
                    ANSI_BLUE,
                    "Please turn on the SEM stream and focus the SEM image. Then turn off the stream and press Enter..."
                )
                print_col(
                    ANSI_BLACK,
                    "Trying to detect the holes/markers, please wait...")
                try:
                    hole_detectionf = aligndelphi.HoleDetection(
                        detector,
                        escan,
                        sem_stage,
                        ebeam_focus,
                        manual=True,
                        logpath=logpath)
                    new_first_hole, new_second_hole, new_hole_focus = hole_detectionf.result(
                    )
                    print_col(
                        ANSI_CYAN, "Values computed: 1st hole: " +
                        str(new_first_hole) + "\n"
                        "                 2st hole: " + str(new_second_hole) +
                        "\n"
                        "                 hole focus: " + str(new_hole_focus))
                    ans = "Y" if force_calib else None
                    while ans not in YES_NO_CHARS:
                        ans = input_col(
                            ANSI_MAGENTA,
                            "Do you want to update the calibration file with these values? [Y/n]"
                        )
                    if ans in YES_CHARS:
                        first_hole, second_hole, hole_focus = new_first_hole, new_second_hole, new_hole_focus
                        calibconf.set_sh_calib(shid, first_hole, second_hole,
                                               hole_focus, opt_focus, offset,
                                               scaling, rotation, iscale, irot,
                                               iscale_xy, ishear, resa, resb,
                                               hfwa, scaleshift)
                        print_col(ANSI_BLACK, "Calibration file is updated.")
                    break
                except IOError:
                    print_col(ANSI_RED, "Sample holder hole detection failed.")
            else:
                break

        while True:
            ans = "Y" if force_calib else None
            while ans not in YES_NO_CHARS:
                ans = input_col(
                    ANSI_MAGENTA,
                    "Do you want to execute the SEM image calibration? [Y/n]")
            if ans in YES_CHARS:
                # Resetting shift parameters, to not take them into account during calib
                blank_md = dict.fromkeys(aligndelphi.MD_CALIB_SEM, (0, 0))
                escan.updateMetadata(blank_md)

                # We measure the shift in the area just behind the hole where there
                # are always some features plus the edge of the sample carrier. For
                # that reason we use the focus measured in the hole detection step
                sem_stage.moveAbsSync(aligndelphi.SHIFT_DETECTION)

                ebeam_focus.moveAbsSync({"z": hole_focus})
                try:
                    # Compute spot shift percentage
                    print_col(
                        ANSI_BLACK,
                        "Spot shift measurement in progress, please wait...")
                    f = aligndelphi.ScaleShiftFactor(detector, escan, logpath)
                    new_scaleshift = f.result()

                    # Compute resolution-related values.
                    print_col(ANSI_BLACK,
                              "Calculating resolution shift, please wait...")
                    resolution_shiftf = aligndelphi.ResolutionShiftFactor(
                        detector, escan, logpath)
                    new_resa, new_resb = resolution_shiftf.result()

                    # Compute HFW-related values
                    print_col(ANSI_BLACK,
                              "Calculating HFW shift, please wait...")
                    hfw_shiftf = aligndelphi.HFWShiftFactor(
                        detector, escan, logpath)
                    new_hfwa = hfw_shiftf.result()

                    print_col(
                        ANSI_CYAN, "Values computed: resolution-a: " +
                        str(new_resa) + "\n"
                        "                 resolution-b: " + str(new_resb) +
                        "\n"
                        "                 hfw-a: " + str(new_hfwa) + "\n"
                        "                 spot shift: " + str(new_scaleshift))
                    ans = "Y" if force_calib else None
                    while ans not in YES_NO_CHARS:
                        ans = input_col(
                            ANSI_MAGENTA,
                            "Do you want to update the calibration file with these values? [Y/n]"
                        )
                    if ans in YES_CHARS:
                        resa, resb, hfwa, scaleshift = new_resa, new_resb, new_hfwa, new_scaleshift
                        calibconf.set_sh_calib(shid, first_hole, second_hole,
                                               hole_focus, opt_focus, offset,
                                               scaling, rotation, iscale, irot,
                                               iscale_xy, ishear, resa, resb,
                                               hfwa, scaleshift)
                        print_col(ANSI_BLACK, "Calibration file is updated.")
                    break
                except IOError:
                    print_col(ANSI_RED, "SEM image calibration failed.")
            else:
                break

        # Update the SEM metadata to have the spots already at corrected place
        escan.updateMetadata({
            model.MD_RESOLUTION_SLOPE: resa,
            model.MD_RESOLUTION_INTERCEPT: resb,
            model.MD_HFW_SLOPE: hfwa,
            model.MD_SPOT_SHIFT: scaleshift
        })

        f = sem_stage.moveAbs({"x": position[0], "y": position[1]})
        f.result()

        f = opt_stage.moveAbs({"x": 0, "y": 0})
        f.result()

        if hole_focus is not None:
            good_focus = hole_focus - aligndelphi.GOOD_FOCUS_OFFSET
        else:
            good_focus = aligndelphi.SEM_KNOWN_FOCUS - aligndelphi.GOOD_FOCUS_OFFSET
        f = ebeam_focus.moveAbs({"z": good_focus})
        f.result()

        # Set min fov
        # We want to be as close as possible to the center when we are zoomed in
        escan.horizontalFoV.value = escan.horizontalFoV.range[0]
        pure_offset = None

        # Start with the best optical focus known so far
        f = focus.moveAbs({"z": opt_focus})
        f.result()

        while True:
            ans = "Y" if force_calib else None
            while ans not in YES_NO_CHARS:
                ans = input_col(
                    ANSI_MAGENTA,
                    "Do you want to execute the twin stage calibration? [Y/n]")
            if ans in YES_CHARS:
                # Configure CCD and e-beam to write CL spots
                ccd.binning.value = ccd.binning.clip((4, 4))
                ccd.resolution.value = ccd.resolution.range[1]
                ccd.exposureTime.value = 900e-03
                escan.scale.value = (1, 1)
                escan.resolution.value = (1, 1)
                escan.translation.value = (0, 0)
                if not escan.rotation.readonly:
                    escan.rotation.value = 0
                escan.shift.value = (0, 0)
                escan.dwellTime.value = 5e-06
                detector.data.subscribe(_discard_data)
                print_col(
                    ANSI_BLUE,
                    "Please turn on the Optical stream, set Power to 0 Watt "
                    "and focus the image so you have a clearly visible spot.\n"
                    "Use the up and down arrows or the mouse to move the "
                    "optical focus and right and left arrows to move the SEM focus. "
                    "Then turn off the stream and press Enter...")
                if not force_calib:
                    print_col(
                        ANSI_YELLOW,
                        "If you cannot see the whole source background (bright circle) "
                        "you may try to move to the already known offset position. \n"
                        "To do this press the R key at any moment and use I to go back "
                        "to the initial position.")
                    rollback_pos = (offset[0] * scaling[0],
                                    offset[1] * scaling[1])
                else:
                    rollback_pos = None
                ar = ArrowFocus(sem_stage, focus, ebeam_focus,
                                ccd.depthOfField.value, 10e-6)
                ar.focusByArrow(rollback_pos)

                # Did the user adjust the ebeam-focus? If so, let's use this,
                # as it's probably better than the focus for the hole.
                new_ebeam_focus = ebeam_focus.position.value.get('z')
                new_hole_focus = new_ebeam_focus + aligndelphi.GOOD_FOCUS_OFFSET
                if not util.almost_equal(
                        new_hole_focus, hole_focus, atol=10e-6):
                    print_col(
                        ANSI_CYAN,
                        "Updating e-beam focus: %s (ie, hole focus: %s)" %
                        (new_ebeam_focus, new_hole_focus))
                    good_focus = new_ebeam_focus
                    hole_focus = new_hole_focus

                detector.data.unsubscribe(_discard_data)
                print_col(ANSI_BLACK,
                          "Twin stage calibration starting, please wait...")
                try:
                    # TODO: the first point (at 0,0) isn't different from the next 4 points,
                    # excepted it might be a little harder to focus.
                    # => use the same code for all of them
                    align_offsetf = aligndelphi.AlignAndOffset(
                        ccd, detector, escan, sem_stage, opt_stage, focus,
                        logpath)
                    align_offset = align_offsetf.result()
                    new_opt_focus = focus.position.value.get('z')

                    # If the offset is large, it can prevent the SEM stage to follow
                    # the optical stage. If it's really large (eg > 1mm) it will
                    # even prevent from going to the calibration locations.
                    # So warn about this, as soon as we detect it. It could be
                    # caused either due to a mistake in the offset detection, or
                    # because the reference switch of the optical axis is not
                    # centered (enough) on the axis. In such case, a technician
                    # should open the sample holder and move the reference switch.
                    # Alternatively, we could try to be more clever and support
                    # the range of the tmcm axes to be defined per sample
                    # holder, and set some asymmetric range (to reflect the fact
                    # that 0 is not at the center).
                    for a, trans in zip(("x", "y"), align_offset):
                        # SEM pos = Opt pos + offset
                        rng_sem = sem_stage.axes[a].range
                        rng_opt = opt_stage.axes[a].range
                        if (rng_opt[0] + trans < rng_sem[0]
                                or rng_opt[1] + trans > rng_sem[1]):
                            logging.info(
                                "Stage align offset = %s, which could cause "
                                "moves on the SEM stage out of range (on axis %s)",
                                align_offset, a)
                            input_col(
                                ANSI_RED,
                                "Twin stage offset on axis %s is %g mm, which could cause moves out of range.\n"
                                "Check that the reference switch in the sample holder is properly at the center."
                                % (a, trans * 1e3))

                    def ask_user_to_focus(n):
                        detector.data.subscribe(_discard_data)
                        input_col(
                            ANSI_BLUE,
                            "About to calculate rotation and scaling (%d/4). "
                            % (n + 1, ) + "Please turn on the Optical stream, "
                            "set Power to 0 Watt and focus the image using the mouse "
                            "so you have a clearly visible spot. \n"
                            "If you do not see a spot nor the source background, "
                            "move the sem-stage from the command line by steps of 200um "
                            "in x and y until you can see the source background at the center. \n"
                            "Then turn off the stream and press Enter...")
                        # TODO: use ArrowFocus() too?
                        print_col(
                            ANSI_BLACK,
                            "Calculating rotation and scaling (%d/4), please wait..."
                            % (n + 1, ))
                        detector.data.unsubscribe(_discard_data)

                    f = aligndelphi.RotationAndScaling(
                        ccd,
                        detector,
                        escan,
                        sem_stage,
                        opt_stage,
                        focus,
                        align_offset,
                        manual=ask_user_to_focus,
                        logpath=logpath)
                    acc_offset, new_rotation, new_scaling = f.result()

                    # Offset is divided by scaling, since Convert Stage applies scaling
                    # also in the given offset
                    pure_offset = acc_offset
                    new_offset = ((acc_offset[0] / new_scaling[0]),
                                  (acc_offset[1] / new_scaling[1]))

                    print_col(
                        ANSI_CYAN,
                        "Values computed: offset: " + str(new_offset) + "\n"
                        "                 scaling: " + str(new_scaling) + "\n"
                        "                 rotation: " + str(new_rotation) +
                        "\n"
                        "                 optical focus: " +
                        str(new_opt_focus))
                    ans = "Y" if force_calib else None
                    while ans not in YES_NO_CHARS:
                        ans = input_col(
                            ANSI_MAGENTA,
                            "Do you want to update the calibration file with these values? [Y/n]"
                        )
                    if ans in YES_CHARS:
                        offset, scaling, rotation, opt_focus = new_offset, new_scaling, new_rotation, new_opt_focus
                        calibconf.set_sh_calib(shid, first_hole, second_hole,
                                               hole_focus, opt_focus, offset,
                                               scaling, rotation, iscale, irot,
                                               iscale_xy, ishear, resa, resb,
                                               hfwa, scaleshift)
                        print_col(ANSI_BLACK, "Calibration file is updated.")
                    break
                except IOError:
                    print_col(ANSI_RED, "Twin stage calibration failed.")
            else:
                break

        while True:
            ans = "Y" if force_calib else None
            while ans not in YES_NO_CHARS:
                ans = input_col(
                    ANSI_MAGENTA,
                    "Do you want to execute the fine alignment? [Y/n]")
            if ans in YES_CHARS:
                # Return to the center so fine alignment can be executed just after calibration
                f = opt_stage.moveAbs({"x": 0, "y": 0})
                f.result()
                if pure_offset is not None:
                    f = sem_stage.moveAbs({
                        "x": pure_offset[0],
                        "y": pure_offset[1]
                    })
                elif offset is not None:
                    f = sem_stage.moveAbs({
                        "x": offset[0] * scaling[0],
                        "y": offset[1] * scaling[1]
                    })
                else:
                    f = sem_stage.moveAbs({"x": position[0], "y": position[1]})

                fof = focus.moveAbs({"z": opt_focus})
                fef = ebeam_focus.moveAbs({"z": good_focus})
                f.result()
                fof.result()
                fef.result()

                # Run the optical fine alignment
                # TODO: reuse the exposure time
                # Configure e-beam to write CL spots
                escan.horizontalFoV.value = escan.horizontalFoV.range[0]
                escan.scale.value = (1, 1)
                escan.resolution.value = (1, 1)
                escan.translation.value = (0, 0)
                if not escan.rotation.readonly:
                    escan.rotation.value = 0
                escan.shift.value = (0, 0)
                escan.dwellTime.value = 5e-06
                detector.data.subscribe(_discard_data)
                print_col(
                    ANSI_BLUE,
                    "Please turn on the Optical stream, set Power to 0 Watt "
                    "and focus the image so you have a clearly visible spot.\n"
                    "Use the up and down arrows or the mouse to move the "
                    "optical focus and right and left arrows to move the SEM focus. "
                    "Then turn off the stream and press Enter...")
                ar = ArrowFocus(sem_stage, focus, ebeam_focus,
                                ccd.depthOfField.value, 10e-6)
                ar.focusByArrow()
                detector.data.unsubscribe(_discard_data)

                print_col(ANSI_BLACK,
                          "Fine alignment in progress, please wait...")

                # restore CCD settings (as the GUI/user might have changed them)
                ccd.binning.value = (1, 1)
                ccd.resolution.value = ccd.resolution.range[1]
                ccd.exposureTime.value = 900e-03
                # Center (roughly) the spot on the CCD
                f = spot.CenterSpot(ccd, sem_stage, escan, spot.ROUGH_MOVE,
                                    spot.STAGE_MOVE, detector.data)
                dist, vect = f.result()
                if dist is None:
                    logging.warning(
                        "Failed to find a spot, twin stage calibration might have failed"
                    )

                try:
                    escan.horizontalFoV.value = 80e-06
                    f = align.FindOverlay(
                        (4, 4),
                        0.5,  # s, dwell time
                        10e-06,  # m, maximum difference allowed
                        escan,
                        ccd,
                        detector,
                        skew=True,
                        bgsub=True)
                    trans_val, cor_md = f.result()
                    trans_md, skew_md = cor_md
                    new_iscale = trans_md[model.MD_PIXEL_SIZE_COR]
                    new_irot = -trans_md[model.MD_ROTATION_COR] % (2 * math.pi)
                    new_ishear = skew_md[model.MD_SHEAR_COR]
                    new_iscale_xy = skew_md[model.MD_PIXEL_SIZE_COR]
                    print_col(
                        ANSI_CYAN,
                        "Values computed: scale: " + str(new_iscale) + "\n"
                        "                 rotation: " + str(new_irot) + "\n"
                        "                 scale-xy: " + str(new_iscale_xy) +
                        "\n"
                        "                 shear: " + str(new_ishear))
                    ans = "Y" if force_calib else None
                    while ans not in YES_NO_CHARS:
                        ans = input_col(
                            ANSI_MAGENTA,
                            "Do you want to update the calibration file with these values? [Y/n]"
                        )
                    if ans in YES_CHARS:
                        iscale, irot, iscale_xy, ishear = new_iscale, new_irot, new_iscale_xy, new_ishear
                        calibconf.set_sh_calib(shid, first_hole, second_hole,
                                               hole_focus, opt_focus, offset,
                                               scaling, rotation, iscale, irot,
                                               iscale_xy, ishear, resa, resb,
                                               hfwa, scaleshift)
                        print_col(ANSI_BLACK, "Calibration file is updated.")
                    break
                except ValueError:
                    print_col(ANSI_RED, "Fine alignment failed.")
            else:
                break
    except Exception:
        logging.exception("Unexpected failure during calibration")
    finally:
        aligndelphi.restore_hw_settings(escan, ccd, hw_settings)

        # Store the final version of the calibration file in the log folder
        try:
            shutil.copy(calibconf.file_path, logpath)
        except Exception:
            logging.info("Failed to log calibration file", exc_info=True)

        if not keep_loaded:
            # Eject the sample holder
            print_col(
                ANSI_BLACK,
                "Calibration ended, now ejecting sample, please wait...")
            f = chamber.moveAbs({"pressure": vented_pressure})
            f.result()

        ans = input_col(ANSI_MAGENTA, "Press Enter to close")
コード例 #36
0
ファイル: _static.py プロジェクト: ihebdelmic/odemis
    def __init__(self, name, data, *args, **kwargs):
        """
        name (string)
        data (model.DataArray(Shadow) of shape (YX) or list of such DataArray(Shadow)).
         The metadata MD_POS, MD_AR_POLE and MD_POL_MODE should be provided
        """
        if not isinstance(data, collections.Iterable):
            data = [data]  # from now it's just a list of DataArray

        # TODO: support DAS, as a "delayed loading" by only calling .getData()
        # when the projection for the particular data needs to be computed (or
        # .raw needs to be accessed?)
        # Ensure all the data is a DataArray, as we don't handle (yet) DAS
        data = [
            d.getData() if isinstance(d, model.DataArrayShadow) else d
            for d in data
        ]

        # find positions of each acquisition
        # (float, float, str or None)) -> DataArray: position on SEM + polarization -> data
        self._pos = {}
        sempositions = set()
        polpositions = set()
        for d in data:
            try:
                sempos_cur = d.metadata[MD_POS]

                # When reading data: floating point error (slightly different keys for same ebeam pos)
                # -> check if there is already a position specified, which is very close by
                # (and therefore the same ebeam pos) and replace with that ebeam position
                # (e.g. all polarization positions for the same ebeam positions will have exactly the same ebeam pos)
                for sempos in sempositions:
                    if almost_equal(sempos_cur[0], sempos[0]) and almost_equal(
                            sempos_cur[1], sempos[1]):
                        sempos_cur = sempos
                        break
                self._pos[sempos_cur + (d.metadata.get(MD_POL_MODE, None),
                                        )] = img.ensure2DImage(d)
                sempositions.add(sempos_cur)
                if MD_POL_MODE in d.metadata:
                    polpositions.add(d.metadata.get(MD_POL_MODE))
            except KeyError:
                logging.info("Skipping DataArray without known position")

        # Cached conversion of the CCD image to polar representation
        # TODO: automatically fill it in a background thread
        self._polar = {}  # dict tuple (float, float, str or None) -> DataArray

        # SEM position VA
        # SEM position displayed, (None, None) == no point selected (x, y)
        self.point = model.VAEnumerated(
            (None, None),
            choices=frozenset([(None, None)] + list(sempositions)))

        if self._pos:
            # Pick one point, e.g., top-left
            bbtl = (min(x for x, y in sempositions if x is not None),
                    min(y for x, y in sempositions if y is not None))

            # top-left point is the closest from the bounding-box top-left
            def dis_bbtl(v):
                try:
                    return math.hypot(bbtl[0] - v[0], bbtl[1] - v[1])
                except TypeError:
                    return float("inf")  # for None, None

            self.point.value = min(sempositions, key=dis_bbtl)

        # no need for init=True, as Stream.__init__ will update the image
        self.point.subscribe(self._onPoint)

        # polarization VA
        # check if any polarization analyzer data, set([]) == no analyzer data (pol)
        if polpositions:
            # use first entry in acquisition to populate VA (acq could have 1 or 6 pol pos)
            self.polarization = model.VAEnumerated(list(polpositions)[0],
                                                   choices=polpositions)
            self.polarization.subscribe(self._onPolarization)

        if "acq_type" not in kwargs:
            kwargs["acq_type"] = model.MD_AT_AR
        super(StaticARStream, self).__init__(name, list(self._pos.values()),
                                             *args, **kwargs)
コード例 #37
0
ファイル: _static.py プロジェクト: lanery/odemis
    def __init__(self, name, data, *args, **kwargs):
        """
        :param name: (string)
        :param data: (model.DataArray(Shadow) of shape (YX) or list of such DataArray(Shadow)).
        The metadata MD_POS, MD_AR_POLE and MD_POL_MODE should be provided
        """
        if not isinstance(data, collections.Iterable):
            data = [data]  # from now it's just a list of DataArray

        # TODO: support DAS, as a "delayed loading" by only calling .getData()
        # when the projection for the particular data needs to be computed (or
        # .raw needs to be accessed?)
        # Ensure all the data is a DataArray, as we don't handle (yet) DAS
        data = [d.getData() if isinstance(d, model.DataArrayShadow) else d for d in data]

        # find positions of each acquisition
        # (float, float, str or None)) -> DataArray: position on SEM + polarization -> data
        self._pos = {}

        sempositions = set()
        polpositions = set()

        for d in data:
            try:
                sempos_cur = d.metadata[MD_POS]

                # When reading data: floating point error (slightly different keys for same ebeam pos)
                # -> check if there is already a position specified, which is very close by
                # (and therefore the same ebeam pos) and replace with that ebeam position
                # (e.g. all polarization positions for the same ebeam positions will have exactly the same ebeam pos)
                for sempos in sempositions:
                    if almost_equal(sempos_cur[0], sempos[0]) and almost_equal(sempos_cur[1], sempos[1]):
                        sempos_cur = sempos
                        break
                self._pos[sempos_cur + (d.metadata.get(MD_POL_MODE, None),)] = img.ensure2DImage(d)

                sempositions.add(sempos_cur)
                if MD_POL_MODE in d.metadata:
                    polpositions.add(d.metadata[MD_POL_MODE])

            except KeyError:
                logging.info("Skipping DataArray without known position")

        # SEM position VA
        # SEM position displayed, (None, None) == no point selected (x, y)
        self.point = model.VAEnumerated((None, None),
                                        choices=frozenset([(None, None)] + list(sempositions)))

        if self._pos:
            # Pick one point, e.g., top-left
            bbtl = (min(x for x, y in sempositions if x is not None),
                    min(y for x, y in sempositions if y is not None))

            # top-left point is the closest from the bounding-box top-left
            def dis_bbtl(v):
                try:
                    return math.hypot(bbtl[0] - v[0], bbtl[1] - v[1])
                except TypeError:
                    return float("inf")  # for None, None
            self.point.value = min(sempositions, key=dis_bbtl)

        # check if any polarization analyzer data, (None) == no analyzer data (pol)
        if polpositions:
            # Check that for every position, all the polarizations are available,
            # as the GUI expects all the combinations possible, and weird errors
            # will happen when one is missing.
            for pos in sempositions:
                for pol in polpositions:
                    if pos + (pol,) not in self._pos:
                        logging.warning("Polarization data is not complete: missing %s,%s/%s",
                                        pos[0], pos[1], pol)

            # use first entry in acquisition to populate VA (acq could have 1 or 6 pol pos)
            current_pol = util.sorted_according_to(polpositions, POL_POSITIONS)[0]
            self.polarization = model.VAEnumerated(current_pol, choices=polpositions)

            # Add a polarimetry VA containing the polarimetry image results.
            # Note: Polarimetry analysis are only possible if all 6 images per ebeam pos exist.
            # Also check if arpolarimetry package can be imported as might not be installed.
            if polpositions >= set(POL_POSITIONS) and arpolarimetry:
                self.polarimetry = model.VAEnumerated(MD_POL_S0, choices=set(POL_POSITIONS_RESULTS))

        if "acq_type" not in kwargs:
            kwargs["acq_type"] = model.MD_AT_AR

        super(StaticARStream, self).__init__(name, list(self._pos.values()), *args, **kwargs)
コード例 #38
0
ファイル: autofocus.py プロジェクト: pieleric/odemis
def _DoBinaryFocus(future, detector, emt, focus, dfbkg, good_focus, rng_focus):
    """
    Iteratively acquires an optical image, measures its focus level and adjusts
    the optical focus with respect to the focus level.
    future (model.ProgressiveFuture): Progressive future provided by the wrapper
    detector: model.DigitalCamera or model.Detector
    emt (None or model.Emitter): In case of a SED this is the scanner used
    focus (model.Actuator): The focus actuator (with a "z" axis)
    dfbkg (model.DataFlow): dataflow of se- or bs- detector
    good_focus (float): if provided, an already known good focus position to be
      taken into consideration while autofocusing
    rng_focus (tuple of floats): if provided, the search of the best focus position is limited
      within this range
    returns:
        (float): Focus position (m)
        (float): Focus level
    raises:
            CancelledError if cancelled
            IOError if procedure failed
    """
    # TODO: dfbkg is mis-named, as it's the dataflow to use to _activate_ the
    # emitter. It's necessary to acquire the background, as otherwise we assume
    # the emitter is always active, but during background acquisition, that
    # emitter is explicitly _disabled_.
    # => change emt to "scanner", and "dfbkg" to "emitter". Or pass a stream?
    # Note: the emt is almost not used, only to estimate completion time,
    # and read the depthOfField.

    # It does a dichotomy search on the focus level. In practice, it means it
    # will start going into the direction that increase the focus with big steps
    # until the focus decreases again. Then it'll bounce back and forth with
    # smaller and smaller steps.
    # The tricky parts are:
    # * it's hard to estimate the focus level (on an arbitrary image)
    # * two acquisitions at the same focus position can have (slightly) different
    #   focus levels (due to noise and sample degradation)
    # * if the focus actuator is not precise (eg, open loop), it's hard to
    #   even go back to the same focus position when wanted
    logging.debug("Starting binary autofocus on detector %s...", detector.name)

    try:
        # Big timeout, most important being that it's shorter than eternity
        timeout = 3 + 2 * estimateAcquisitionTime(detector, emt)

        # use the .depthOfField on detector or emitter as maximum stepsize
        avail_depths = (detector, emt)
        if model.hasVA(emt, "dwellTime"):
            # Hack in case of using the e-beam with a DigitalCamera detector.
            # All the digital cameras have a depthOfField, which is updated based
            # on the optical lens properties... but the depthOfField in this
            # case depends on the e-beam lens.
            # TODO: or better rely on which component the focuser affects? If it
            # affects (also) the emitter, use this one first? (but in the
            # current models the focusers affects nothing)
            avail_depths = (emt, detector)
        for c in avail_depths:
            if model.hasVA(c, "depthOfField"):
                dof = c.depthOfField.value
                break
        else:
            logging.debug("No depth of field info found")
            dof = 1e-6  # m, not too bad value
        logging.debug("Depth of field is %f", dof)
        min_step = dof / 2

        # adjust to rng_focus if provided
        rng = focus.axes["z"].range
        if rng_focus:
            rng = (max(rng[0], rng_focus[0]), min(rng[1], rng_focus[1]))

        max_step = (rng[1] - rng[0]) / 2
        if max_step <= 0:
            raise ValueError("Unexpected focus range %s" % (rng,))

        max_reached = False  # True once we've passed the maximum level (ie, start bouncing)
        # It's used to cache the focus level, to avoid reacquiring at the same
        # position. We do it only for the 'rough' max search because for the fine
        # search, the actuator and acquisition delta are likely to play a role
        focus_levels = {}  # focus pos (float) -> focus level (float)

        best_pos = focus.position.value['z']
        best_fm = 0
        last_pos = None

        # Pick measurement method based on the heuristics that SEM detectors
        # are typically just a point (ie, shape == data depth).
        # TODO: is this working as expected? Alternatively, we could check
        # MD_DET_TYPE.
        if len(detector.shape) > 1:
            logging.debug("Using Optical method to estimate focus")
            Measure = MeasureOpticalFocus
        else:
            logging.debug("Using SEM method to estimate focus")
            Measure = MeasureSEMFocus

        step_factor = 2 ** 7
        if good_focus is not None:
            current_pos = focus.position.value['z']
            image = AcquireNoBackground(detector, dfbkg, timeout)
            fm_current = Measure(image)
            logging.debug("Focus level at %f is %f", current_pos, fm_current)
            focus_levels[current_pos] = fm_current

            focus.moveAbsSync({"z": good_focus})
            good_focus = focus.position.value["z"]
            image = AcquireNoBackground(detector, dfbkg, timeout)
            fm_good = Measure(image)
            logging.debug("Focus level at %f is %f", good_focus, fm_good)
            focus_levels[good_focus] = fm_good
            last_pos = good_focus

            if fm_good < fm_current:
                # Move back to current position if good_pos is not that good
                # after all
                focus.moveAbsSync({"z": current_pos})
                # it also means we are pretty close
            step_factor = 2 ** 4

        if step_factor * min_step > max_step:
            # Large steps would be too big. We can reduce step_factor and/or
            # min_step. => let's take our time, and maybe find finer focus
            min_step = max_step / step_factor
            logging.debug("Reducing min step to %g", min_step)

        # TODO: to go a bit faster, we could use synchronised acquisition on
        # the detector (if it supports it)
        # TODO: we could estimate the quality of the autofocus by looking at the
        # standard deviation of the the focus levels (and the standard deviation
        # of the focus levels measured for the same focus position)
        logging.debug("Step factor used for autofocus: %g", step_factor)
        step_cntr = 1
        while step_factor >= 1 and step_cntr <= MAX_STEPS_NUMBER:
            # TODO: update the estimated time (based on how long it takes to
            # move + acquire, and how many steps are approximately left)

            # Start at the current focus position
            center = focus.position.value['z']
            # Don't redo the acquisition either if we've just done it, or if it
            # was already done and we are still doing a rough search
            if (not max_reached or last_pos == center) and center in focus_levels:
                fm_center = focus_levels[center]
            else:
                image = AcquireNoBackground(detector, dfbkg, timeout)
                fm_center = Measure(image)
                logging.debug("Focus level (center) at %f is %f", center, fm_center)
                focus_levels[center] = fm_center

            last_pos = center

            # Move to right position
            right = center + step_factor * min_step
            right = max(rng[0], min(right, rng[1]))  # clip
            if not max_reached and right in focus_levels:
                fm_right = focus_levels[right]
            else:
                focus.moveAbsSync({"z": right})
                right = focus.position.value["z"]
                last_pos = right
                image = AcquireNoBackground(detector, dfbkg, timeout)
                fm_right = Measure(image)
                logging.debug("Focus level (right) at %f is %f", right, fm_right)
                focus_levels[right] = fm_right

            # Move to left position
            left = center - step_factor * min_step
            left = max(rng[0], min(left, rng[1]))  # clip
            if not max_reached and left in focus_levels:
                fm_left = focus_levels[left]
            else:
                focus.moveAbsSync({"z": left})
                left = focus.position.value["z"]
                last_pos = left
                image = AcquireNoBackground(detector, dfbkg, timeout)
                fm_left = Measure(image)
                logging.debug("Focus level (left) at %f is %f", left, fm_left)
                focus_levels[left] = fm_left

            fm_range = (fm_left, fm_center, fm_right)
            if all(almost_equal(fm_left, fm, rtol=1e-6) for fm in fm_range[1:]):
                logging.debug("All focus levels identical, picking the middle one")
                # Most probably the images are all noise, or they are not affected
                # by the focus. In any case, the best is to not move the focus,
                # so let's "center" on it. That's better than the default behaviour
                # which would tend to pick "left" because that's the first one.
                i_max = 1
                best_pos, best_fm = center, fm_center
            else:
                pos_range = (left, center, right)
                best_fm = max(fm_range)
                i_max = fm_range.index(best_fm)
                best_pos = pos_range[i_max]

            if future._autofocus_state == CANCELLED:
                raise CancelledError()

            if left == right:
                logging.info("Seems to have reached minimum step size (at %g m)", 2 * step_factor * min_step)
                break

            # if best focus was found at the center
            if i_max == 1:
                step_factor /= 2
                if not max_reached:
                    logging.debug("Now zooming in on improved focus")
                max_reached = True
            elif (rng[0] > best_pos - step_factor * min_step or
                  rng[1] < best_pos + step_factor * min_step):
                step_factor /= 1.5
                logging.debug("Reducing step factor to %g because the focus (%g) is near range limit %s",
                              step_factor, best_pos, rng)
                if step_factor <= 8:
                    max_reached = True  # Force re-checking data

            focus.moveAbsSync({"z": best_pos})
            step_cntr += 1

        worst_fm = min(focus_levels.values())
        if step_cntr == MAX_STEPS_NUMBER:
            logging.info("Auto focus gave up after %d steps @ %g m", step_cntr, best_pos)
        elif (best_fm - worst_fm) < best_fm * 0.5:
            # We can be confident of the data if there is a "big" (50%) difference
            # between the focus levels.
            logging.info("Auto focus indecisive but picking level %g @ %g m (lowest = %g)",
                         best_fm, best_pos, worst_fm)
        else:
            logging.info("Auto focus found best level %g @ %g m", best_fm, best_pos)

        return best_pos, best_fm

    except CancelledError:
        # Go to the best position known so far
        focus.moveAbsSync({"z": best_pos})
    finally:
        with future._autofocus_lock:
            if future._autofocus_state == CANCELLED:
                raise CancelledError()
            future._autofocus_state = FINISHED
コード例 #39
0
    def getFullImage(self):
        """
        return (2D DataArray): same dtype as the tiles, with shape corresponding to the bounding box. 
        """

        tiles = self.tiles

        # Compute the bounding box of each tile and the global bounding box

        # Get a fixed pixel size by using the first one
        # TODO: use the mean, in case they are all slightly different due to
        # correction?
        pxs = tiles[0].metadata[model.MD_PIXEL_SIZE]

        tbbx_phy = []  # tuples of ltrb in physical coordinates
        for t in tiles:
            c = t.metadata[model.MD_POS]
            w = t.shape[-1], t.shape[-2]
            if not util.almost_equal(pxs[0], t.metadata[model.MD_PIXEL_SIZE][0], rtol=0.01):
                logging.warning("Tile @ %s has a unexpected pixel size (%g vs %g)",
                                c, t.metadata[model.MD_PIXEL_SIZE][0], pxs[0])
            bbx = (c[0] - (w[0] * pxs[0] / 2), c[1] - (w[1] * pxs[1] / 2),
                   c[0] + (w[0] * pxs[0] / 2), c[1] + (w[1] * pxs[1] / 2))

            tbbx_phy.append(bbx)

        gbbx_phy = (min(b[0] for b in tbbx_phy), min(b[1] for b in tbbx_phy),
                    max(b[2] for b in tbbx_phy), max(b[3] for b in tbbx_phy))

        # Compute the bounding-boxes in pixel coordinates
        tbbx_px = []

        # that's the origin (Y is max as Y is inverted)
        glt = gbbx_phy[0], gbbx_phy[3]
        for bp, t in zip(tbbx_phy, tiles):
            lt = (int(round((bp[0] - glt[0]) / pxs[0])),
                  int(round(-(bp[3] - glt[1]) / pxs[1])))
            w = t.shape[-1], t.shape[-2]
            bbx = (lt[0], lt[1],
                   lt[0] + w[0], lt[1] + w[1])
            tbbx_px.append(bbx)

        gbbx_px = (min(b[0] for b in tbbx_px), min(b[1] for b in tbbx_px),
                   max(b[2] for b in tbbx_px), max(b[3] for b in tbbx_px))

        assert gbbx_px[0] == gbbx_px[1] == 0

        if numpy.greater(gbbx_px[-2:], 4 * numpy.sum(tbbx_px[-2:])).any():
            # Overlap > 50% or missing tiles
            logging.warning("Global area much bigger than sum of tile areas")

        # Paste each tile
        logging.debug("Generating global image of size %dx%d px",
                      gbbx_px[-2], gbbx_px[-1])
        im = numpy.empty((gbbx_px[-1], gbbx_px[-2]), dtype=tiles[0].dtype)
        # Use minimum of the values in the tiles for background
        im[:] = numpy.amin(tiles)
        for b, t in zip(tbbx_px, tiles):
            im[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]] = t
            # TODO: border

        # Update metadata
        # TODO: check this is also correct based on lt + half shape * pxs
        c_phy = ((gbbx_phy[0] + gbbx_phy[2]) / 2,
                 (gbbx_phy[1] + gbbx_phy[3]) / 2)
        md = tiles[0].metadata.copy()
        md[model.MD_POS] = c_phy
        md[model.MD_DIMS] = "YX"
        return model.DataArray(im, md)
コード例 #40
0
    def getFullImage(self):
        """
        return (2D DataArray): same dtype as the tiles, with shape corresponding to the bounding box. 
        """
        tiles = self.tiles

        # Compute the bounding box of each tile and the global bounding box

        # Get a fixed pixel size by using the first one
        # TODO: use the mean, in case they are all slightly different due to
        # correction?
        pxs = tiles[0].metadata[model.MD_PIXEL_SIZE]

        tbbx_phy = []  # tuples of ltrb in physical coordinates
        for t in tiles:
            c = t.metadata[model.MD_POS]
            w = t.shape[-1], t.shape[-2]
            if not util.almost_equal(pxs[0], t.metadata[model.MD_PIXEL_SIZE][0], rtol=0.01):
                logging.warning("Tile @ %s has a unexpected pixel size (%g vs %g)",
                                c, t.metadata[model.MD_PIXEL_SIZE][0], pxs[0])
            bbx = (c[0] - (w[0] * pxs[0] / 2), c[1] - (w[1] * pxs[1] / 2),
                   c[0] + (w[0] * pxs[0] / 2), c[1] + (w[1] * pxs[1] / 2))

            tbbx_phy.append(bbx)

        gbbx_phy = (min(b[0] for b in tbbx_phy), min(b[1] for b in tbbx_phy),
                    max(b[2] for b in tbbx_phy), max(b[3] for b in tbbx_phy))

        # Compute the bounding-boxes in pixel coordinates
        tbbx_px = []

        # that's the origin (Y is max as Y is inverted)
        glt = gbbx_phy[0], gbbx_phy[3]
        for bp, t in zip(tbbx_phy, tiles):
            lt = (int(round((bp[0] - glt[0]) / pxs[0])),
                  int(round(-(bp[3] - glt[1]) / pxs[1])))
            w = t.shape[-1], t.shape[-2]
            bbx = (lt[0], lt[1],
                   lt[0] + w[0], lt[1] + w[1])
            tbbx_px.append(bbx)

        gbbx_px = (min(b[0] for b in tbbx_px), min(b[1] for b in tbbx_px),
                   max(b[2] for b in tbbx_px), max(b[3] for b in tbbx_px))

        assert gbbx_px[0] == gbbx_px[1] == 0
        if numpy.greater(gbbx_px[-2:], 4 * numpy.sum(tbbx_px[-2:])).any():
            # Overlap > 50% or missing tiles
            logging.warning("Global area much bigger than sum of tile areas")

        # Weave tiles by using a smooth gradient. The part of the tile that does not overlap
        # with any previous tiles is inserted into the part of the
        # ovv image that is still empty. This part is determined by a mask, which indicates
        # the parts of the image that already contain image data (True) and the ones that are still
        # empty (False). For the overlapping parts, the tile is multiplied with weights corresponding
        # to a gradient that has its maximum at the center of the tile and
        # smoothly decreases toward the edges. The function for creating the weights is
        # a distance measure resembling the maximum-norm, i.e. equidistant points lie
        # on a rectangle (instead of a circle like for the euclidean norm). Additionally,
        # the x and y values generating this norm are raised to the power of 6 to
        # create a steeper gradient. The value 6 is quite arbitrary and was found to give
        # good results during experimentation.
        # The part of the overview image that overlaps with the new tile is multiplied with the
        # complementary weights (1 -  weights) and the weighted overlapping parts of the new tile and
        # the ovv image are added, so the resulting image contains a gradient in the overlapping regions
        # between all the tiles that have been inserted before and the newly inserted tile.

        # Paste each tile
        logging.debug("Generating global image of size %dx%d px",
                      gbbx_px[-2], gbbx_px[-1])
        im = numpy.empty((gbbx_px[-1], gbbx_px[-2]), dtype=tiles[0].dtype)
        # Use minimum of the values in the tiles for background
        im[:] = numpy.amin(tiles)

        # The mask is multiplied with the tile, thereby creating a tile with a gradient
        mask = numpy.zeros((gbbx_px[-1], gbbx_px[-2]), dtype=numpy.bool)

        for b, t in zip(tbbx_px, tiles):
            # Part of image overlapping with tile
            roi = im[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]]
            moi = mask[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]]

            # Insert image at positions that are still empty
            roi[~moi] = t[~moi]

            # Create gradient in overlapping region. Ratio between old image and new tile values determined by
            # distance to the center of the tile

            # Create weight matrix with decreasing values from its center that
            # has the same size as the tile.
            sz = numpy.array(roi.shape)
            hh, hw = sz / 2  # half-height, half-width
            x = numpy.linspace(-hw, hw, sz[1])
            y = numpy.linspace(-hh, hh, sz[0])
            xx, yy = numpy.meshgrid((x / hw) ** 6, (y / hh) ** 6)
            w = numpy.maximum(xx, yy)
            # Hardcoding a weight function is quite arbitrary and might result in
            # suboptimal solutions in some cases.
            # Alternatively, different weights might be used. One option would be to select
            # a fixed region on the sides of the image, e.g. 20% (expected overlap), and
            # only apply a (linear) gradient to these parts, while keeping the new tile for the
            # rest of the region. However, this approach does not solve the hardcoding problem
            # since the overlap region is still arbitrary. Future solutions might adaptively
            # select the this region.

            # Use weights to create gradient in overlapping region
            roi[moi] = (t * (1 - w))[moi] + (roi * w)[moi]

            # Update mask
            mask[b[1]:b[1] + t.shape[0], b[0]:b[0] + t.shape[1]] = True

        # Update metadata
        # TODO: check this is also correct based on lt + half shape * pxs
        c_phy = ((gbbx_phy[0] + gbbx_phy[2]) / 2,
                 (gbbx_phy[1] + gbbx_phy[3]) / 2)
        md = tiles[0].metadata.copy()
        md[model.MD_POS] = c_phy
        md[model.MD_DIMS] = "YX"
        return model.DataArray(im, md)
コード例 #41
0
ファイル: main.py プロジェクト: ktsitsikas/odemis
def move_abs(comp_name, moves, check_distance=True):
    """
    move (in absolute) the axis of the given component to the specified position
    comp_name (str): name of the component
    check_distance (bool): if the axis is in meters, check that the move is not
      too big.
    moves (dict str -> str): axis -> position (as text)
    """
    component = get_component(comp_name)

    act_mv = {} # axis -> value
    for axis_name, str_position in moves.items():
        try:
            if axis_name not in component.axes:
                raise ValueError("Actuator %s has no axis '%s'" % (comp_name, axis_name))
            ad = component.axes[axis_name]
        except (TypeError, AttributeError):
            raise ValueError("Component %s is not an actuator" % comp_name)

        # Allow the user to indicate the position via the user-friendly choice entry
        position = None
        if (hasattr(ad, "choices") and isinstance(ad.choices, dict)):
            for key, value in ad.choices.items():
                if value == str_position:
                    logging.info("Converting '%s' into %s", str_position, key)
                    position = key
                    # Even if it's a big distance, we don't complain as it's likely
                    # that all choices are safe
                    break

        if position is None:
            if ad.unit == "m":
                try:
                    position = float(str_position)
                except ValueError:
                    raise ValueError("Position '%s' cannot be converted to a number" % str_position)

                # compare to the current position, to see if the new position sounds reasonable
                cur_pos = component.position.value[axis_name]
                if check_distance and abs(cur_pos - position) > MAX_DISTANCE:
                    raise IOError("Distance of %f m is too big (> %f m), use '--big-distance' to allow the move." %
                                  (abs(cur_pos - position), MAX_DISTANCE))
            else:
                position = convert_to_object(str_position)

            # If only a couple of positions are possible, and asking for a float,
            # avoid the rounding error by looking for the closest possible
            if (isinstance(position, numbers.Real) and
                hasattr(ad, "choices") and
                isinstance(ad.choices, collections.Iterable) and
                position not in ad.choices):
                closest = util.find_closest(position, ad.choices)
                if util.almost_equal(closest, position, rtol=1e-3):
                    logging.debug("Adjusting value %.15g to %.15g", position, closest)
                    position = closest

        act_mv[axis_name] = position
        logging.info(u"Will move %s.%s to %s", comp_name, axis_name,
                     units.readable_str(position, ad.unit, sig=3))

    try:
        m = component.moveAbs(act_mv)
        try:
            m.result(120)
        except KeyboardInterrupt:
            logging.warning("Cancelling absolute move of component %s", comp_name)
            m.cancel()
            raise
    except Exception as exc:
        raise IOError("Failed to move component %s to %s: %s" %
                      (comp_name, act_mv, exc))
コード例 #42
0
ファイル: main.py プロジェクト: amuskens/odemis
def move_abs(comp_name, moves, check_distance=True):
    """
    move (in absolute) the axis of the given component to the specified position
    comp_name (str): name of the component
    check_distance (bool): if the axis is in meters, check that the move is not
      too big.
    moves (dict str -> str): axis -> position (as text)
    """
    component = get_component(comp_name)

    act_mv = {} # axis -> value
    for axis_name, str_position in moves.items():
        try:
            if axis_name not in component.axes:
                raise ValueError("Actuator %s has no axis '%s'" % (comp_name, axis_name))
            ad = component.axes[axis_name]
        except (TypeError, AttributeError):
            raise ValueError("Component %s is not an actuator" % comp_name)

        # Allow the user to indicate the position via the user-friendly choice entry
        position = None
        if hasattr(ad, "choices") and isinstance(ad.choices, dict):
            for key, value in ad.choices.items():
                if value == str_position:
                    logging.info("Converting '%s' into %s", str_position, key)
                    position = key
                    # Even if it's a big distance, we don't complain as it's likely
                    # that all choices are safe
                    break

        if position is None:
            if ad.unit == "m":
                try:
                    position = float(convert_to_object(str_position))
                except ValueError:
                    raise ValueError("Position '%s' cannot be converted to a number" % str_position)

                # compare to the current position, to see if the new position sounds reasonable
                cur_pos = component.position.value[axis_name]
                if check_distance and abs(cur_pos - position) > MAX_DISTANCE:
                    raise IOError("Distance of %f m is too big (> %f m), use '--big-distance' to allow the move." %
                                  (abs(cur_pos - position), MAX_DISTANCE))
            else:
                position = convert_to_object(str_position)

            # If only a couple of positions are possible, and asking for a float,
            # avoid the rounding error by looking for the closest possible
            if (isinstance(position, numbers.Real) and
                hasattr(ad, "choices") and
                isinstance(ad.choices, collections.Iterable) and
                position not in ad.choices):
                closest = util.find_closest(position, ad.choices)
                if util.almost_equal(closest, position, rtol=1e-3):
                    logging.debug("Adjusting value %.15g to %.15g", position, closest)
                    position = closest

        act_mv[axis_name] = position
        if isinstance(position, numbers.Real):
            pos_pretty = units.readable_str(position, ad.unit, sig=3)
        else:
            pos_pretty = "%s" % (position,)
        logging.info(u"Will move %s.%s to %s", comp_name, axis_name, pos_pretty)

    try:
        m = component.moveAbs(act_mv)
        try:
            m.result(120)
        except KeyboardInterrupt:
            logging.warning("Cancelling absolute move of component %s", comp_name)
            m.cancel()
            raise
    except Exception as exc:
        raise IOError("Failed to move component %s to %s: %s" %
                      (comp_name, act_mv, exc))