Ulm-IQO /
qudi
| Total Complexity | 211 |
| Total Lines | 1981 |
| Duplicated Lines | 6.71 % |
| Changes | 3 | ||
| Bugs | 0 | Features | 0 |
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like MagnetLogic often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | # -*- coding: utf-8 -*- |
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| 32 | class MagnetLogic(GenericLogic): |
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| 33 | """ A general magnet logic to control an magnetic stage with an arbitrary |
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| 34 | set of axis. |
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| 35 | |||
| 36 | DISCLAIMER: |
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| 37 | =========== |
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| 38 | |||
| 39 | The current status of the magnet logic is highly experimental and not well |
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| 40 | tested. The implementation has some considerable imperfections. The state of |
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| 41 | this module is considered to be UNSTABLE. |
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| 42 | |||
| 43 | This module has two major issues: |
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| 44 | - a lack of proper documentation of all the methods |
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| 45 | - usage of tasks is not implemented and therefore direct connection to |
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| 46 | all the modules is used (I tried to compress as good as possible all |
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| 47 | the part, where access to other modules occurs so that a later |
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| 48 | replacement would be easier and one does not have to search throughout |
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| 49 | the whole file.) |
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| 50 | |||
| 51 | However, the 'high-level state maschine' for the alignment should be rather |
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| 52 | general and very powerful to use. The different state were divided in |
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| 53 | several consecutive methods, where each method can be implemented |
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| 54 | separately and can be extended for custom needs. (I have drawn a diagram, |
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| 55 | which is much more telling then the documentation I can write down here.) |
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| 56 | |||
| 57 | I am currently working on that and will from time to time improve the status |
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| 58 | of this module. So if you want to use it, be aware that there might appear |
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| 59 | drastic changes. |
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| 60 | |||
| 61 | --- |
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| 62 | Alexander Stark |
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| 63 | """ |
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| 64 | |||
| 65 | |||
| 66 | _modclass = 'MagnetLogic' |
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| 67 | _modtype = 'logic' |
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| 68 | |||
| 69 | ## declare connectors |
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| 70 | _in = {'magnetstage': 'MagnetInterface', |
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| 71 | 'optimizerlogic': 'OptimizerLogic', |
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| 72 | 'counterlogic': 'CounterLogic', |
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| 73 | 'odmrlogic': 'ODMRLogic', |
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| 74 | 'savelogic': 'SaveLogic', |
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| 75 | 'scannerlogic':'ScannerLogic', |
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| 76 | 'traceanalysis':'TraceAnalysisLogic', |
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| 77 | 'gatedcounterlogic': 'GatedCounterLogic', |
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| 78 | 'sequencegeneratorlogic': 'SequenceGeneratorLogic'} |
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| 79 | _out = {'magnetlogic': 'MagnetLogic'} |
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| 80 | |||
| 81 | # General Signals, used everywhere: |
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| 82 | sigIdleStateChanged = QtCore.Signal(bool) |
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| 83 | sigPosChanged = QtCore.Signal(dict) |
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| 84 | sigVelChanged = QtCore.Signal(dict) |
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| 85 | |||
| 86 | sigMeasurementStarted = QtCore.Signal() |
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| 87 | sigMeasurementContinued = QtCore.Signal() |
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| 88 | sigMeasurementStopped = QtCore.Signal() |
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| 89 | sigMeasurementFinished = QtCore.Signal() |
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| 90 | |||
| 91 | # Signals for making the move_abs, move_rel and abort independent: |
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| 92 | sigMoveAbs = QtCore.Signal(dict) |
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| 93 | sigMoveRel = QtCore.Signal(dict) |
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| 94 | sigAbort = QtCore.Signal() |
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| 95 | |||
| 96 | # Alignment Signals, remember do not touch or connect from outer logic or |
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| 97 | # GUI to the leading underscore signals! |
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| 98 | _sigStepwiseAlignmentNext = QtCore.Signal() |
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| 99 | _sigContinuousAlignmentNext = QtCore.Signal() |
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| 100 | _sigInitializeMeasPos = QtCore.Signal(bool) # signal to go to the initial measurement position |
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| 101 | sigPosReached = QtCore.Signal() |
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| 102 | |||
| 103 | # signals if new data are writen to the data arrays (during measurement): |
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| 104 | sig1DMatrixChanged = QtCore.Signal() |
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| 105 | sig2DMatrixChanged = QtCore.Signal() |
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| 106 | sig3DMatrixChanged = QtCore.Signal() |
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| 107 | |||
| 108 | # signals if the axis for the alignment are changed/renewed (before a measurement): |
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| 109 | sig1DAxisChanged = QtCore.Signal() |
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| 110 | sig2DAxisChanged = QtCore.Signal() |
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| 111 | sig3DAxisChanged = QtCore.Signal() |
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| 112 | |||
| 113 | # signal for ODMR alignment |
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| 114 | sigODMRLowFreqChanged = QtCore.Signal() |
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| 115 | sigODMRHighFreqChanged = QtCore.Signal() |
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| 116 | |||
| 117 | sigTest = QtCore.Signal() |
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| 118 | |||
| 119 | def __init__(self, config, **kwargs): |
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| 120 | super().__init__(config=config, **kwargs) |
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| 121 | |||
| 122 | self._stop_measure = False |
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| 123 | |||
| 124 | def on_activate(self, e): |
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| 125 | """ Definition and initialisation of the GUI. |
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| 126 | |||
| 127 | @param object e: Fysom.event object from Fysom class. |
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| 128 | An object created by the state machine module Fysom, |
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| 129 | which is connected to a specific event (have a look in |
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| 130 | the Base Class). This object contains the passed event, |
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| 131 | the state before the event happened and the destination |
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| 132 | of the state which should be reached after the event |
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| 133 | had happened. |
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| 134 | """ |
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| 135 | self._magnet_device = self.get_in_connector('magnetstage') |
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| 136 | self._save_logic = self.get_in_connector('savelogic') |
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| 137 | |||
| 138 | self.log.info('The following configuration was found.') |
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| 139 | # checking for the right configuration |
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| 140 | config = self.getConfiguration() |
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| 141 | for key in config.keys(): |
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| 142 | self.log.info('{0}: {1}'.format(key,config[key])) |
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| 143 | |||
| 144 | #FIXME: THAT IS JUST A TEMPORARY SOLUTION! Implement the access on the |
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| 145 | # needed methods via the TaskRunner! |
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| 146 | self._optimizer_logic = self.get_in_connector('optimizerlogic') |
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| 147 | self._confocal_logic = self.get_in_connector('scannerlogic') |
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| 148 | self._counter_logic = self.get_in_connector('counterlogic') |
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| 149 | self._odmr_logic = self.get_in_connector('odmrlogic') |
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| 150 | |||
| 151 | self._gc_logic = self.get_in_connector('gatedcounterlogic') |
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| 152 | self._ta_logic = self.get_in_connector('traceanalysis') |
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| 153 | self._odmr_logic = self.get_in_connector('odmrlogic') |
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| 154 | |||
| 155 | self._seq_gen_logic = self.get_in_connector('sequencegeneratorlogic') |
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| 156 | |||
| 157 | # EXPERIMENTAL: |
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| 158 | # connect now directly signals to the interface methods, so that |
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| 159 | # the logic object will be not blocks and can react on changes or abort |
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| 160 | self.sigMoveAbs.connect(self._magnet_device.move_abs) |
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| 161 | self.sigMoveRel.connect(self._magnet_device.move_rel) |
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| 162 | self.sigAbort.connect(self._magnet_device.abort) |
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| 163 | |||
| 164 | # signal connect for alignment: |
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| 165 | |||
| 166 | self._sigInitializeMeasPos.connect(self._move_to_curr_pathway_index) |
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| 167 | self._sigStepwiseAlignmentNext.connect(self._stepwise_loop_body, |
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| 168 | QtCore.Qt.QueuedConnection) |
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| 169 | |||
| 170 | self.pathway_modes = ['spiral-in', 'spiral-out', 'snake-wise', 'diagonal-snake-wise'] |
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| 171 | |||
| 172 | if 'curr_2d_pathway_mode' in self._statusVariables: |
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| 173 | self.curr_2d_pathway_mode = self._statusVariables['curr_2d_pathway_mode'] |
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| 174 | else: |
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| 175 | self.curr_2d_pathway_mode = 'snake-wise' # choose that as default |
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| 176 | |||
| 177 | if '_checktime' in self._statusVariables: |
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| 178 | self._checktime = self._statusVariables['_checktime'] |
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| 179 | else: |
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| 180 | self._checktime = 2.5 # in seconds |
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| 181 | |||
| 182 | self.sigTest.connect(self._do_premeasurement_proc) |
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| 183 | |||
| 184 | if '_1D_axis0_data' in self._statusVariables: |
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| 185 | self._1D_axis0_data = self._statusVariables['_1D_axis0_data'] |
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| 186 | else: |
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| 187 | self._1D_axis0_data = np.zeros(2) |
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| 188 | |||
| 189 | if '_2D_axis0_data' in self._statusVariables: |
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| 190 | self._2D_axis0_data = self._statusVariables['_2D_axis0_data'] |
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| 191 | else: |
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| 192 | self._2D_axis0_data = np.zeros(2) |
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| 193 | |||
| 194 | if '_2D_axis1_data' in self._statusVariables: |
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| 195 | self._2D_axis1_data = self._statusVariables['_2D_axis1_data'] |
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| 196 | else: |
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| 197 | self._2D_axis1_data = np.zeros(2) |
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| 198 | |||
| 199 | if '_3D_axis0_data' in self._statusVariables: |
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| 200 | self._3D_axis0_data = self._statusVariables['_3D_axis0_data'] |
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| 201 | else: |
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| 202 | self._3D_axis0_data = np.zeros(2) |
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| 203 | |||
| 204 | if '_3D_axis1_data' in self._statusVariables: |
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| 205 | self._3D_axis1_data = self._statusVariables['_3D_axis1_data'] |
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| 206 | else: |
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| 207 | self._3D_axis1_data = np.zeros(2) |
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| 208 | |||
| 209 | if '_3D_axis2_data' in self._statusVariables: |
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| 210 | self._3D_axis2_data = self._statusVariables['_3D_axis2_data'] |
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| 211 | else: |
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| 212 | self._3D_axis2_data = np.zeros(2) |
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| 213 | |||
| 214 | if '_1D_add_data_matrix' in self._statusVariables: |
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| 215 | self._1D_add_data_matrix = self._statusVariables['_1D_add_data_matrix'] |
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| 216 | else: |
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| 217 | self._1D_add_data_matrix = np.zeros(shape=np.shape(self._1D_axis0_data), dtype=object) |
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| 218 | |||
| 219 | |||
| 220 | if '_2D_data_matrix' in self._statusVariables: |
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| 221 | self._2D_data_matrix = self._statusVariables['_2D_data_matrix'] |
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| 222 | else: |
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| 223 | self._2D_data_matrix = np.zeros((2, 2)) |
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| 224 | |||
| 225 | if '_2D_add_data_matrix' in self._statusVariables: |
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| 226 | self._2D_add_data_matrix = self._statusVariables['_2D_add_data_matrix'] |
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| 227 | else: |
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| 228 | self._2D_add_data_matrix = np.zeros(shape=np.shape(self._2D_data_matrix), dtype=object) |
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| 229 | |||
| 230 | if '_3D_data_matrix' in self._statusVariables: |
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| 231 | self._3D_data_matrix = self._statusVariables['_3D_data_matrix'] |
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| 232 | else: |
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| 233 | self._3D_data_matrix = np.zeros((2, 2, 2)) |
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| 234 | |||
| 235 | if '_3D_add_data_matrix' in self._statusVariables: |
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| 236 | self._3D_add_data_matrix = self._statusVariables['_3D_add_data_matrix'] |
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| 237 | else: |
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| 238 | self._3D_add_data_matrix = np.zeros(shape=np.shape(self._3D_data_matrix), dtype=object) |
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| 239 | |||
| 240 | if 'curr_alignment_method' in self._statusVariables: |
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| 241 | self.curr_alignment_method = self._statusVariables['curr_alignment_method'] |
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| 242 | else: |
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| 243 | self.curr_alignment_method = '2d_fluorescence' |
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| 244 | |||
| 245 | self.alignment_methods = ['2d_fluorescence', '2d_odmr', '2d_nuclear'] |
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| 246 | |||
| 247 | # Fluorescence alignment settings: |
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| 248 | if '_optimize_pos' in self._statusVariables: |
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| 249 | self._optimize_pos = self._statusVariables['_optimize_pos'] |
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| 250 | else: |
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| 251 | self._optimize_pos = False |
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| 252 | |||
| 253 | if 'fluorescence_integration_time' in self._statusVariables: |
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| 254 | self.fluorescence_integration_time = self._statusVariables['fluorescence_integration_time'] |
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| 255 | else: |
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| 256 | self.fluorescence_integration_time = 5 # integration time in s |
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| 257 | |||
| 258 | # ODMR alignment settings (ALL IN SI!!!): |
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| 259 | |||
| 260 | if 'odmr_2d_low_center_freq' in self._statusVariables: |
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| 261 | self.odmr_2d_low_center_freq = self._statusVariables['odmr_2d_low_center_freq'] |
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| 262 | else: |
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| 263 | self.odmr_2d_low_center_freq = 11028e6 |
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| 264 | |||
| 265 | if 'odmr_2d_low_step_freq' in self._statusVariables: |
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| 266 | self.odmr_2d_low_step_freq = self._statusVariables['odmr_2d_low_step_freq'] |
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| 267 | else: |
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| 268 | self.odmr_2d_low_step_freq = 0.15e6 |
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| 269 | |||
| 270 | if 'odmr_2d_low_range_freq' in self._statusVariables: |
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| 271 | self.odmr_2d_low_range_freq = self._statusVariables['odmr_2d_low_range_freq'] |
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| 272 | else: |
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| 273 | self.odmr_2d_low_range_freq = 25e6 |
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| 274 | |||
| 275 | if 'odmr_2d_low_power' in self._statusVariables: |
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| 276 | self.odmr_2d_low_power = self._statusVariables['odmr_2d_low_power'] |
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| 277 | else: |
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| 278 | self.odmr_2d_low_power = 4 |
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| 279 | |||
| 280 | if 'odmr_2d_low_runtime' in self._statusVariables: |
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| 281 | self.odmr_2d_low_runtime = self._statusVariables['odmr_2d_low_runtime'] |
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| 282 | else: |
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| 283 | self.odmr_2d_low_runtime = 40 |
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| 284 | |||
| 285 | self.odmr_2d_low_fitfunction_list = self._odmr_logic.get_fit_functions() |
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| 286 | |||
| 287 | if 'odmr_2d_low_fitfunction' in self._statusVariables: |
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| 288 | self.odmr_2d_low_fitfunction = self._statusVariables['odmr_2d_low_fitfunction'] |
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| 289 | else: |
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| 290 | self.odmr_2d_low_fitfunction = self.odmr_2d_low_fitfunction_list[1] |
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| 291 | |||
| 292 | |||
| 293 | |||
| 294 | if 'odmr_2d_high_center_freq' in self._statusVariables: |
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| 295 | self.odmr_2d_high_center_freq = self._statusVariables['odmr_2d_high_center_freq'] |
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| 296 | else: |
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| 297 | self.odmr_2d_high_center_freq = 16768e6 |
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| 298 | |||
| 299 | if 'odmr_2d_high_step_freq' in self._statusVariables: |
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| 300 | self.odmr_2d_high_step_freq = self._statusVariables['odmr_2d_high_step_freq'] |
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| 301 | else: |
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| 302 | self.odmr_2d_high_step_freq = 0.15e6 |
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| 303 | |||
| 304 | if 'odmr_2d_high_range_freq' in self._statusVariables: |
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| 305 | self.odmr_2d_high_range_freq = self._statusVariables['odmr_2d_high_range_freq'] |
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| 306 | else: |
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| 307 | self.odmr_2d_high_range_freq = 25e6 |
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| 308 | |||
| 309 | if 'odmr_2d_high_power' in self._statusVariables: |
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| 310 | self.odmr_2d_high_power = self._statusVariables['odmr_2d_high_power'] |
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| 311 | else: |
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| 312 | self.odmr_2d_high_power = 2 |
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| 313 | |||
| 314 | if 'odmr_2d_high_runtime' in self._statusVariables: |
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| 315 | self.odmr_2d_high_runtime = self._statusVariables['odmr_2d_high_runtime'] |
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| 316 | else: |
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| 317 | self.odmr_2d_high_runtime = 40 |
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| 318 | |||
| 319 | self.odmr_2d_high_fitfunction_list = self._odmr_logic.get_fit_functions() |
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| 320 | |||
| 321 | if 'odmr_2d_high_fitfunction' in self._statusVariables: |
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| 322 | self.odmr_2d_high_fitfunction = self._statusVariables['odmr_2d_high_fitfunction'] |
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| 323 | else: |
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| 324 | self.odmr_2d_high_fitfunction = self.odmr_2d_high_fitfunction_list[1] |
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| 325 | |||
| 326 | if 'odmr_2d_save_after_measure' in self._statusVariables: |
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| 327 | self.odmr_2d_save_after_measure = self._statusVariables['odmr_2d_save_after_measure'] |
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| 328 | else: |
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| 329 | self.odmr_2d_save_after_measure = True |
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| 330 | |||
| 331 | if 'odmr_2d_peak_axis0_move_ratio' in self._statusVariables: |
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| 332 | self.odmr_2d_peak_axis0_move_ratio = self._statusVariables['odmr_2d_peak_axis0_move_ratio'] |
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| 333 | else: |
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| 334 | self.odmr_2d_peak_axis0_move_ratio = 0 # -13e6/ 0.01e-3 # in Hz/m |
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| 335 | |||
| 336 | if 'odmr_2d_peak_axis1_move_ratio' in self._statusVariables: |
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| 337 | self.odmr_2d_peak_axis1_move_ratio = self._statusVariables['odmr_2d_peak_axis1_move_ratio'] |
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| 338 | else: |
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| 339 | self.odmr_2d_peak_axis1_move_ratio = 0 # -6e6/0.05e-3 # in Hz/m |
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| 340 | |||
| 341 | # that is just a normalization value, which is needed for the ODMR |
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| 342 | # alignment, since the colorbar cannot display values greater (2**32)/2. |
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| 343 | # A solution has to found for that! |
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| 344 | self.norm = 1000 |
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| 345 | |||
| 346 | self.odmr_2d_single_trans = False # use that if only one ODMR |
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| 347 | # transition is available. |
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| 348 | |||
| 349 | # single shot alignment on nuclear spin settings (ALL IN SI!!!): |
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| 350 | if 'nuclear_2d_rabi_periode' in self._statusVariables: |
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| 351 | self.nuclear_2d_rabi_periode = self._statusVariables['nuclear_2d_rabi_periode'] |
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| 352 | else: |
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| 353 | self.nuclear_2d_rabi_periode = 1000e-9 |
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| 354 | |||
| 355 | if 'nuclear_2d_mw_freq' in self._statusVariables: |
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| 356 | self.nuclear_2d_mw_freq = self._statusVariables['nuclear_2d_mw_freq'] |
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| 357 | else: |
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| 358 | self.nuclear_2d_mw_freq = 100e6 |
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| 359 | |||
| 360 | if 'nuclear_2d_mw_channel' in self._statusVariables: |
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| 361 | self.nuclear_2d_mw_channel = self._statusVariables['nuclear_2d_mw_channel'] |
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| 362 | else: |
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| 363 | self.nuclear_2d_mw_channel = -1 |
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| 364 | |||
| 365 | if 'nuclear_2d_mw_power' in self._statusVariables: |
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| 366 | self.nuclear_2d_mw_power = self._statusVariables['nuclear_2d_mw_power'] |
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| 367 | else: |
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| 368 | self.nuclear_2d_mw_power = -30 |
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| 369 | |||
| 370 | if 'nuclear_2d_laser_time' in self._statusVariables: |
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| 371 | self.nuclear_2d_laser_time = self._statusVariables['nuclear_2d_laser_time'] |
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| 372 | else: |
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| 373 | self.nuclear_2d_laser_time = 900e-9 |
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| 374 | |||
| 375 | if 'nuclear_2d_laser_channel' in self._statusVariables: |
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| 376 | self.nuclear_2d_laser_channel = self._statusVariables['nuclear_2d_laser_channel'] |
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| 377 | else: |
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| 378 | self.nuclear_2d_laser_channel = 2 |
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| 379 | |||
| 380 | if 'nuclear_2d_detect_channel' in self._statusVariables: |
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| 381 | self.nuclear_2d_detect_channel = self._statusVariables['nuclear_2d_detect_channel'] |
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| 382 | else: |
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| 383 | self.nuclear_2d_detect_channel = 1 |
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| 384 | |||
| 385 | if 'nuclear_2d_idle_time' in self._statusVariables: |
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| 386 | self.nuclear_2d_idle_time = self._statusVariables['nuclear_2d_idle_time'] |
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| 387 | else: |
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| 388 | self.nuclear_2d_idle_time = 1500e-9 |
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| 389 | |||
| 390 | if 'nuclear_2d_reps_within_ssr' in self._statusVariables: |
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| 391 | self.nuclear_2d_reps_within_ssr = self._statusVariables['nuclear_2d_reps_within_ssr'] |
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| 392 | else: |
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| 393 | self.nuclear_2d_reps_within_ssr = 1000 |
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| 394 | |||
| 395 | if 'nuclear_2d_num_ssr' in self._statusVariables: |
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| 396 | self.nuclear_2d_num_ssr = self._statusVariables['nuclear_2d_num_ssr'] |
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| 397 | else: |
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| 398 | self.nuclear_2d_num_ssr = 3000 |
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| 399 | |||
| 400 | |||
| 401 | def on_deactivate(self, e): |
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| 402 | """ Deactivate the module properly. |
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| 403 | |||
| 404 | @param object e: Fysom.event object from Fysom class. A more detailed |
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| 405 | explanation can be found in the method activation. |
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| 406 | """ |
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| 407 | self._statusVariables['optimize_pos'] = self._optimize_pos |
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| 408 | self._statusVariables['fluorescence_integration_time'] = self.fluorescence_integration_time |
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| 409 | |||
| 410 | self._statusVariables['odmr_2d_low_center_freq'] = self.odmr_2d_low_center_freq |
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| 411 | self._statusVariables['odmr_2d_low_step_freq'] = self.odmr_2d_low_step_freq |
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| 412 | self._statusVariables['odmr_2d_low_range_freq'] = self.odmr_2d_low_range_freq |
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| 413 | self._statusVariables['odmr_2d_low_power'] = self.odmr_2d_low_power |
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| 414 | self._statusVariables['odmr_2d_low_runtime'] = self.odmr_2d_low_runtime |
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| 415 | self._statusVariables['odmr_2d_low_fitfunction'] = self.odmr_2d_low_fitfunction |
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| 416 | |||
| 417 | self._statusVariables['odmr_2d_high_center_freq'] = self.odmr_2d_high_center_freq |
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| 418 | self._statusVariables['odmr_2d_high_step_freq'] = self.odmr_2d_high_step_freq |
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| 419 | self._statusVariables['odmr_2d_high_range_freq'] = self.odmr_2d_high_range_freq |
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| 420 | self._statusVariables['odmr_2d_high_power'] = self.odmr_2d_high_power |
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| 421 | self._statusVariables['odmr_2d_high_runtime'] = self.odmr_2d_high_runtime |
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| 422 | self._statusVariables['odmr_2d_high_fitfunction'] = self.odmr_2d_high_fitfunction |
||
| 423 | self._statusVariables['odmr_2d_save_after_measure'] = self.odmr_2d_save_after_measure |
||
| 424 | self._statusVariables['odmr_2d_peak_axis0_move_ratio'] = self.odmr_2d_peak_axis0_move_ratio |
||
| 425 | self._statusVariables['odmr_2d_peak_axis1_move_ratio'] = self.odmr_2d_peak_axis1_move_ratio |
||
| 426 | |||
| 427 | self._statusVariables['nuclear_2d_rabi_periode'] = self.nuclear_2d_rabi_periode |
||
| 428 | self._statusVariables['nuclear_2d_mw_freq'] = self.nuclear_2d_mw_freq |
||
| 429 | self._statusVariables['nuclear_2d_mw_channel'] = self.nuclear_2d_mw_channel |
||
| 430 | self._statusVariables['nuclear_2d_mw_power'] = self.nuclear_2d_mw_power |
||
| 431 | self._statusVariables['nuclear_2d_laser_time'] = self.nuclear_2d_laser_time |
||
| 432 | self._statusVariables['nuclear_2d_laser_channel'] = self.nuclear_2d_laser_channel |
||
| 433 | self._statusVariables['nuclear_2d_detect_channel'] = self.nuclear_2d_detect_channel |
||
| 434 | self._statusVariables['nuclear_2d_idle_time'] = self.nuclear_2d_idle_time |
||
| 435 | self._statusVariables['nuclear_2d_reps_within_ssr'] = self.nuclear_2d_reps_within_ssr |
||
| 436 | self._statusVariables['nuclear_2d_num_ssr'] = self.nuclear_2d_num_ssr |
||
| 437 | |||
| 438 | def get_hardware_constraints(self): |
||
| 439 | """ Retrieve the hardware constraints. |
||
| 440 | |||
| 441 | @return dict: dict with constraints for the magnet hardware. The keys |
||
| 442 | are the labels for the axis and the items are again dicts |
||
| 443 | which contain all the limiting parameters. |
||
| 444 | """ |
||
| 445 | |||
| 446 | return self._magnet_device.get_constraints() |
||
| 447 | |||
| 448 | def move_rel(self, param_dict): |
||
| 449 | """ Move the specified axis in the param_dict relative with an assigned |
||
| 450 | value. |
||
| 451 | |||
| 452 | @param dict param_dict: dictionary, which passes all the relevant |
||
| 453 | parameters. E.g., for a movement of an axis |
||
| 454 | labeled with 'x' by 23 the dict should have the |
||
| 455 | form: |
||
| 456 | param_dict = { 'x' : 23 } |
||
| 457 | """ |
||
| 458 | |||
| 459 | # self._magnet_device.move_rel(param_dict) |
||
| 460 | # start_pos = self.get_pos(list(param_dict)) |
||
| 461 | # end_pos = dict() |
||
| 462 | # |
||
| 463 | # for axis_name in param_dict: |
||
| 464 | # end_pos[axis_name] = start_pos[axis_name] + param_dict[axis_name] |
||
| 465 | |||
| 466 | # if the magnet is moving, then the move_rel command will be neglected. |
||
| 467 | status_dict = self.get_status(list(param_dict)) |
||
| 468 | for axis_name in status_dict: |
||
| 469 | if status_dict[axis_name][0] != 0: |
||
| 470 | return |
||
| 471 | |||
| 472 | self.sigMoveRel.emit(param_dict) |
||
| 473 | # self._check_position_reached_loop(start_pos, end_pos) |
||
| 474 | self.sigPosChanged.emit(param_dict) |
||
| 475 | |||
| 476 | |||
| 477 | def get_pos(self, param_list=None): |
||
| 478 | """ Gets current position of the stage. |
||
| 479 | |||
| 480 | @param list param_list: optional, if a specific position of an axis |
||
| 481 | is desired, then the labels of the needed |
||
| 482 | axis should be passed as the param_list. |
||
| 483 | If nothing is passed, then from each axis the |
||
| 484 | position is asked. |
||
| 485 | |||
| 486 | @return dict: with keys being the axis labels and item the current |
||
| 487 | position. |
||
| 488 | """ |
||
| 489 | |||
| 490 | pos_dict = self._magnet_device.get_pos(param_list) |
||
| 491 | return pos_dict |
||
| 492 | |||
| 493 | def get_status(self, param_list=None): |
||
| 494 | """ Get the status of the position |
||
| 495 | |||
| 496 | @param list param_list: optional, if a specific status of an axis |
||
| 497 | is desired, then the labels of the needed |
||
| 498 | axis should be passed in the param_list. |
||
| 499 | If nothing is passed, then from each axis the |
||
| 500 | status is asked. |
||
| 501 | |||
| 502 | @return dict: with the axis label as key and a tuple of a status |
||
| 503 | number and a status dict as the item. |
||
| 504 | """ |
||
| 505 | status = self._magnet_device.get_status(param_list) |
||
| 506 | return status |
||
| 507 | |||
| 508 | def move_abs(self, param_dict): |
||
| 509 | """ Moves stage to absolute position (absolute movement) |
||
| 510 | |||
| 511 | @param dict param_dict: dictionary, which passes all the relevant |
||
| 512 | parameters, which should be changed. Usage: |
||
| 513 | {'axis_label': <a-value>}. |
||
| 514 | 'axis_label' must correspond to a label given |
||
| 515 | to one of the axis. |
||
| 516 | """ |
||
| 517 | # self._magnet_device.move_abs(param_dict) |
||
| 518 | start_pos = self.get_pos(list(param_dict)) |
||
| 519 | self.sigMoveAbs.emit(param_dict) |
||
| 520 | |||
| 521 | # self._check_position_reached_loop(start_pos, param_dict) |
||
| 522 | |||
| 523 | self.sigPosChanged.emit(param_dict) |
||
| 524 | |||
| 525 | def stop_movement(self): |
||
| 526 | """ Stops movement of the stage. """ |
||
| 527 | self._stop_measure = True |
||
| 528 | self.sigAbort.emit() |
||
| 529 | # self._magnet_device.abort() |
||
| 530 | |||
| 531 | |||
| 532 | def set_velocity(self, param_dict=None): |
||
| 533 | """ Write new value for velocity. |
||
| 534 | |||
| 535 | @param dict param_dict: dictionary, which passes all the relevant |
||
| 536 | parameters, which should be changed. Usage: |
||
| 537 | {'axis_label': <the-velocity-value>}. |
||
| 538 | 'axis_label' must correspond to a label given |
||
| 539 | to one of the axis. |
||
| 540 | """ |
||
| 541 | self._magnet_device.set_velocity(param_dict) |
||
| 542 | |||
| 543 | |||
| 544 | |||
| 545 | def _create_1d_pathway(self, axis_name, axis_range, axis_step, axis_vel): |
||
| 546 | """ Create a path along with the magnet should move with one axis |
||
| 547 | |||
| 548 | @param str axis_name: |
||
| 549 | @param float axis_range: |
||
| 550 | @param float axis_step: |
||
| 551 | |||
| 552 | @return: |
||
| 553 | |||
| 554 | Here you can also create fancy 1D pathways, not only linear but also |
||
| 555 | in any kind on nonlinear fashion. |
||
| 556 | """ |
||
| 557 | pass |
||
| 558 | |||
| 559 | def _create_2d_pathway(self, axis0_name, axis0_range, axis0_step, |
||
| 560 | axis1_name, axis1_range, axis1_step, init_pos, |
||
| 561 | axis0_vel=None, axis1_vel=None): |
||
| 562 | """ Create a path along with the magnet should move. |
||
| 563 | |||
| 564 | @param str axis0_name: |
||
| 565 | @param float axis0_range: |
||
| 566 | @param float axis0_step: |
||
| 567 | @param str axis1_name: |
||
| 568 | @param float axis1_range: |
||
| 569 | @param float axis1_step: |
||
| 570 | |||
| 571 | @return array: 1D np.array, which has dictionary as entries. In this |
||
| 572 | dictionary, it will be specified, how the magnet is going |
||
| 573 | from the present point to the next. |
||
| 574 | |||
| 575 | That should be quite a general function, which maps from a given matrix |
||
| 576 | and axes information a 2D array into a 1D path with steps being the |
||
| 577 | relative movements. |
||
| 578 | |||
| 579 | All kind of standard and fancy pathways through the array should be |
||
| 580 | implemented here! |
||
| 581 | The movement is not restricted to relative movements! |
||
| 582 | The entry dicts have the following structure: |
||
| 583 | |||
| 584 | pathway = [ dict1, dict2, dict3, ...] |
||
| 585 | |||
| 586 | whereas the dictionary can only have one or two key entries: |
||
| 587 | dict1[axis0_name] = {'move_rel': 123, 'move_vel': 3 } |
||
| 588 | dict1[axis1_name] = {'move_abs': 29.5} |
||
| 589 | |||
| 590 | Note that the entries may either have a relative OR an absolute movement! |
||
| 591 | Never both! Absolute movement will be taken always before relative |
||
| 592 | movement. Moreover you can specify in each movement step the velocity |
||
| 593 | and the acceleration of the movement. |
||
| 594 | E.g. if no velocity is specified, then nothing will be changed in terms |
||
| 595 | of speed during the move. |
||
| 596 | """ |
||
| 597 | |||
| 598 | # calculate number of steps (those are NOT the number of points!) |
||
| 599 | axis0_num_of_steps = int(axis0_range//axis0_step) |
||
| 600 | axis1_num_of_steps = int(axis1_range//axis1_step) |
||
| 601 | |||
| 602 | # make an array of movement steps |
||
| 603 | axis0_steparray = [axis0_step] * axis0_num_of_steps |
||
| 604 | axis1_steparray = [axis1_step] * axis1_num_of_steps |
||
| 605 | |||
| 606 | pathway = [] |
||
| 607 | |||
| 608 | #FIXME: create these path modes: |
||
| 609 | if self.curr_2d_pathway_mode == 'spiral-in': |
||
| 610 | self.log.error('The pathway creation method "{0}" through the ' |
||
| 611 | 'matrix is not implemented yet!\nReturn an empty ' |
||
| 612 | 'patharray.'.format(self.curr_2d_pathway_mode)) |
||
| 613 | return [], [] |
||
| 614 | |||
| 615 | elif self.curr_2d_pathway_mode == 'spiral-out': |
||
| 616 | self.log.error('The pathway creation method "{0}" through the ' |
||
| 617 | 'matrix is not implemented yet!\nReturn an empty ' |
||
| 618 | 'patharray.'.format(self.curr_2d_pathway_mode)) |
||
| 619 | return [], [] |
||
| 620 | |||
| 621 | elif self.curr_2d_pathway_mode == 'diagonal-snake-wise': |
||
| 622 | self.log.error('The pathway creation method "{0}" through the ' |
||
| 623 | 'matrix is not implemented yet!\nReturn an empty ' |
||
| 624 | 'patharray.'.format(self.current_2d_pathway_mode)) |
||
| 625 | return [], [] |
||
| 626 | |||
| 627 | elif self.curr_2d_pathway_mode == 'selected-points': |
||
| 628 | self.log.error('The pathway creation method "{0}" through the ' |
||
| 629 | 'matrix is not implemented yet!\nReturn an empty ' |
||
| 630 | 'patharray.'.format(self.current_2d_pathway_mode)) |
||
| 631 | return [], [] |
||
| 632 | |||
| 633 | # choose the snake-wise as default for now. |
||
| 634 | else: |
||
| 635 | |||
| 636 | # create a snake-wise stepping procedure through the matrix: |
||
| 637 | axis0_pos = round(init_pos[axis0_name] - axis0_range/2, 7) |
||
| 638 | axis1_pos = round(init_pos[axis1_name] - axis1_range/2, 7) |
||
| 639 | |||
| 640 | # append again so that the for loop later will run once again |
||
| 641 | # through the axis0 array but the last value of axis1_steparray will |
||
| 642 | # not be performed. |
||
| 643 | axis1_steparray.append(axis1_num_of_steps) |
||
| 644 | |||
| 645 | # step_config is the dict containing the commands for one pathway |
||
| 646 | # entry. Move at first to start position: |
||
| 647 | step_config = dict() |
||
| 648 | |||
| 649 | if axis0_vel is None: |
||
| 650 | step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 651 | else: |
||
| 652 | step_config[axis0_name] = {'move_abs': axis0_pos, 'move_vel': axis0_vel} |
||
| 653 | |||
| 654 | if axis1_vel is None: |
||
| 655 | step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 656 | else: |
||
| 657 | step_config[axis1_name] = {'move_abs': axis1_pos, 'move_vel': axis1_vel} |
||
| 658 | |||
| 659 | pathway.append(step_config) |
||
| 660 | |||
| 661 | path_index = 0 |
||
| 662 | |||
| 663 | # these indices should be used to facilitate the mapping to a 2D |
||
| 664 | # array, since the |
||
| 665 | axis0_index = 0 |
||
| 666 | axis1_index = 0 |
||
| 667 | |||
| 668 | # that is a map to transform a pathway index value back to an |
||
| 669 | # absolute position and index. That will be important for saving the |
||
| 670 | # data corresponding to a certain path_index value. |
||
| 671 | back_map = dict() |
||
| 672 | back_map[path_index] = {axis0_name: axis0_pos, |
||
| 673 | axis1_name: axis1_pos, |
||
| 674 | 'index': (axis0_index, axis1_index)} |
||
| 675 | |||
| 676 | path_index += 1 |
||
| 677 | # axis0_index += 1 |
||
| 678 | |||
| 679 | go_pos_dir = True |
||
| 680 | for step_in_axis1 in axis1_steparray: |
||
| 681 | |||
| 682 | if go_pos_dir: |
||
| 683 | go_pos_dir = False |
||
| 684 | direction = +1 |
||
| 685 | else: |
||
| 686 | go_pos_dir = True |
||
| 687 | direction = -1 |
||
| 688 | |||
| 689 | for step_in_axis0 in axis0_steparray: |
||
| 690 | |||
| 691 | axis0_index += direction |
||
| 692 | # make move along axis0: |
||
| 693 | step_config = dict() |
||
| 694 | |||
| 695 | # relative movement: |
||
| 696 | # step_config[axis0_name] = {'move_rel': direction*step_in_axis0} |
||
| 697 | |||
| 698 | # absolute movement: |
||
| 699 | axis0_pos =round(axis0_pos + direction*step_in_axis0, 7) |
||
| 700 | |||
| 701 | # if axis0_vel is None: |
||
| 702 | # step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 703 | # step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 704 | # else: |
||
| 705 | # step_config[axis0_name] = {'move_abs': axis0_pos, |
||
| 706 | # 'move_vel': axis0_vel} |
||
| 707 | View Code Duplication | if axis1_vel is None and axis0_vel is None: |
|
|
Duplication
introduced
by
|
|||
| 708 | step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 709 | step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 710 | else: |
||
| 711 | step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 712 | step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 713 | |||
| 714 | if axis0_vel is not None: |
||
| 715 | step_config[axis0_name] = {'move_abs': axis0_pos, 'move_vel': axis0_vel} |
||
| 716 | |||
| 717 | if axis1_vel is not None: |
||
| 718 | step_config[axis1_name] = {'move_abs': axis1_pos, 'move_vel': axis1_vel} |
||
| 719 | |||
| 720 | # append to the pathway |
||
| 721 | pathway.append(step_config) |
||
| 722 | back_map[path_index] = {axis0_name: axis0_pos, |
||
| 723 | axis1_name: axis1_pos, |
||
| 724 | 'index': (axis0_index, axis1_index)} |
||
| 725 | path_index += 1 |
||
| 726 | |||
| 727 | if (axis1_index+1) >= len(axis1_steparray): |
||
| 728 | break |
||
| 729 | |||
| 730 | # make a move along axis1: |
||
| 731 | step_config = dict() |
||
| 732 | |||
| 733 | # relative movement: |
||
| 734 | # step_config[axis1_name] = {'move_rel' : step_in_axis1} |
||
| 735 | |||
| 736 | # absolute movement: |
||
| 737 | axis1_pos = round(axis1_pos + step_in_axis1, 7) |
||
| 738 | |||
| 739 | View Code Duplication | if axis1_vel is None and axis0_vel is None: |
|
| 740 | step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 741 | step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 742 | else: |
||
| 743 | step_config[axis0_name] = {'move_abs': axis0_pos} |
||
| 744 | step_config[axis1_name] = {'move_abs': axis1_pos} |
||
| 745 | |||
| 746 | if axis0_vel is not None: |
||
| 747 | step_config[axis0_name] = {'move_abs': axis0_pos, 'move_vel': axis0_vel} |
||
| 748 | |||
| 749 | if axis1_vel is not None: |
||
| 750 | step_config[axis1_name] = {'move_abs': axis1_pos, 'move_vel': axis1_vel} |
||
| 751 | |||
| 752 | pathway.append(step_config) |
||
| 753 | axis1_index += 1 |
||
| 754 | back_map[path_index] = {axis0_name: axis0_pos, |
||
| 755 | axis1_name: axis1_pos, |
||
| 756 | 'index': (axis0_index, axis1_index)} |
||
| 757 | path_index += 1 |
||
| 758 | |||
| 759 | |||
| 760 | |||
| 761 | return pathway, back_map |
||
| 762 | |||
| 763 | |||
| 764 | def _create_2d_cont_pathway(self, pathway): |
||
| 765 | |||
| 766 | # go through the passed 1D path and reduce the whole movement just to |
||
| 767 | # corner points |
||
| 768 | |||
| 769 | pathway_cont = dict() |
||
| 770 | |||
| 771 | return pathway_cont |
||
| 772 | |||
| 773 | def _prepare_2d_graph(self, axis0_start, axis0_range, axis0_step, |
||
| 774 | axis1_start, axis1_range, axis1_step): |
||
| 775 | # set up a matrix where measurement points are save to |
||
| 776 | # general method to prepare 2d images, and their axes. |
||
| 777 | |||
| 778 | # that is for the matrix image. +1 because number of points and not |
||
| 779 | # number of steps are needed: |
||
| 780 | num_points_axis0 = (axis0_range//axis0_step) + 1 |
||
| 781 | num_points_axis1 = (axis1_range//axis1_step) + 1 |
||
| 782 | matrix = np.zeros((num_points_axis0, num_points_axis1)) |
||
| 783 | |||
| 784 | # data axis0: |
||
| 785 | |||
| 786 | data_axis0 = np.arange(axis0_start, axis0_start + ((axis0_range//axis0_step)+1)*axis0_step, axis0_step) |
||
| 787 | |||
| 788 | # data axis1: |
||
| 789 | data_axis1 = np.arange(axis1_start, axis1_start + ((axis1_range//axis1_step)+1)*axis1_step, axis1_step) |
||
| 790 | |||
| 791 | return matrix, data_axis0, data_axis1 |
||
| 792 | |||
| 793 | |||
| 794 | |||
| 795 | |||
| 796 | def _prepare_1d_graph(self, axis_range, axis_step): |
||
| 797 | pass |
||
| 798 | |||
| 799 | |||
| 800 | |||
| 801 | |||
| 802 | |||
| 803 | def start_1d_alignment(self, axis_name, axis_range, axis_step, axis_vel, |
||
| 804 | stepwise_meas=True, continue_meas=False): |
||
| 805 | |||
| 806 | |||
| 807 | # actual measurement routine, which is called to start the measurement |
||
| 808 | |||
| 809 | |||
| 810 | if not continue_meas: |
||
| 811 | |||
| 812 | # to perform the '_do_measure_after_stop' routine from the beginning |
||
| 813 | # (which means e.g. an optimize pos) |
||
| 814 | |||
| 815 | self._prepare_1d_graph() |
||
| 816 | |||
| 817 | self._pathway = self._create_1d_pathway() |
||
| 818 | |||
| 819 | if stepwise_meas: |
||
| 820 | # just make it to an empty dict |
||
| 821 | self._pathway_cont = dict() |
||
| 822 | |||
| 823 | else: |
||
| 824 | # create from the path_points the continoues points |
||
| 825 | self._pathway_cont = self._create_1d_cont_pathway(self._pathway) |
||
| 826 | |||
| 827 | else: |
||
| 828 | # tell all the connected instances that measurement is continuing: |
||
| 829 | self.sigMeasurementContinued.emit() |
||
| 830 | |||
| 831 | # run at first the _move_to_curr_pathway_index method to go to the |
||
| 832 | # index position: |
||
| 833 | self._sigInitializeMeasPos.emit(stepwise_meas) |
||
| 834 | |||
| 835 | |||
| 836 | |||
| 837 | def start_2d_alignment(self, axis0_name, axis0_range, axis0_step, |
||
| 838 | axis1_name, axis1_range, axis1_step, |
||
| 839 | axis0_vel=None, axis1_vel=None, |
||
| 840 | stepwise_meas=True, continue_meas=False): |
||
| 841 | |||
| 842 | # before starting the measurement you should convince yourself that the |
||
| 843 | # passed traveling range is possible. Otherwise the measurement will be |
||
| 844 | # aborted and an error is raised. |
||
| 845 | # |
||
| 846 | # actual measurement routine, which is called to start the measurement |
||
| 847 | |||
| 848 | self._start_measurement_time = datetime.datetime.now() |
||
| 849 | self._stop_measurement_time = None |
||
| 850 | |||
| 851 | self._stop_measure = False |
||
| 852 | |||
| 853 | self._axis0_name = axis0_name |
||
| 854 | self._axis1_name = axis1_name |
||
| 855 | |||
| 856 | # save only the position of the axis, which are going to be moved |
||
| 857 | # during alignment, the return will be a dict! |
||
| 858 | self._saved_pos_before_align = self.get_pos([axis0_name, axis1_name]) |
||
| 859 | |||
| 860 | |||
| 861 | if not continue_meas: |
||
| 862 | |||
| 863 | self.sigMeasurementStarted.emit() |
||
| 864 | |||
| 865 | # the index, which run through the _pathway list and selects the |
||
| 866 | # current measurement point |
||
| 867 | self._pathway_index = 0 |
||
| 868 | |||
| 869 | self._pathway, self._backmap = self._create_2d_pathway(axis0_name, axis0_range, |
||
| 870 | axis0_step, axis1_name, axis1_range, |
||
| 871 | axis1_step, self._saved_pos_before_align, |
||
| 872 | axis0_vel, axis1_vel) |
||
| 873 | |||
| 874 | # determine the start point, either relative or absolute! |
||
| 875 | # Now the absolute position will be used: |
||
| 876 | axis0_start = self._backmap[0][axis0_name] |
||
| 877 | axis1_start = self._backmap[0][axis1_name] |
||
| 878 | |||
| 879 | self._2D_data_matrix, \ |
||
| 880 | self._2D_axis0_data,\ |
||
| 881 | self._2D_axis1_data = self._prepare_2d_graph(axis0_start, axis0_range, |
||
| 882 | axis0_step, axis1_start, |
||
| 883 | axis1_range, axis1_step) |
||
| 884 | |||
| 885 | self._2D_add_data_matrix = np.zeros(shape=np.shape(self._2D_data_matrix), dtype=object) |
||
| 886 | |||
| 887 | |||
| 888 | if stepwise_meas: |
||
| 889 | # just make it to an empty dict |
||
| 890 | self._pathway_cont = dict() |
||
| 891 | |||
| 892 | else: |
||
| 893 | # create from the path_points the continuous points |
||
| 894 | self._pathway_cont = self._create_2d_cont_pathway(self._pathway) |
||
| 895 | |||
| 896 | # TODO: include here another mode, where a new defined pathway can be |
||
| 897 | # created, along which the measurement should be repeated. |
||
| 898 | # You have to follow the procedure: |
||
| 899 | # - Create for continuing the measurement just a proper |
||
| 900 | # pathway and a proper back_map in self._create_2d_pathway, |
||
| 901 | # => Then the whole measurement can be just run with the new |
||
| 902 | # pathway and back_map, and you do not have to adjust other |
||
| 903 | # things. |
||
| 904 | |||
| 905 | else: |
||
| 906 | # tell all the connected instances that measurement is continuing: |
||
| 907 | self.sigMeasurementContinued.emit() |
||
| 908 | |||
| 909 | # run at first the _move_to_curr_pathway_index method to go to the |
||
| 910 | # index position: |
||
| 911 | self._sigInitializeMeasPos.emit(stepwise_meas) |
||
| 912 | |||
| 913 | |||
| 914 | def _move_to_curr_pathway_index(self, stepwise_meas): |
||
| 915 | |||
| 916 | # move to the passed pathway index in the list _pathway and start the |
||
| 917 | # proper loop for that: |
||
| 918 | |||
| 919 | # move absolute to the index position, which is currently given |
||
| 920 | |||
| 921 | move_dict_vel, \ |
||
| 922 | move_dict_abs, \ |
||
| 923 | move_dict_rel = self._move_to_index(self._pathway_index, self._pathway) |
||
| 924 | |||
| 925 | self.set_velocity(move_dict_vel) |
||
| 926 | self.move_abs(move_dict_abs) |
||
| 927 | # self.move_rel(move_dict_rel) |
||
| 928 | |||
| 929 | |||
| 930 | |||
| 931 | # this function will return to this function if position is reached: |
||
| 932 | start_pos = self._saved_pos_before_align |
||
| 933 | end_pos = dict() |
||
| 934 | for axis_name in self._saved_pos_before_align: |
||
| 935 | end_pos[axis_name] = self._backmap[self._pathway_index][axis_name] |
||
| 936 | |||
| 937 | while self._check_is_moving(): |
||
| 938 | time.sleep(self._checktime) |
||
| 939 | self.log.debug("Went into while loop in _move_to_curr_pathway_index") |
||
| 940 | |||
| 941 | self.log.debug("(first movement) magnet moving ? {0}".format(self._check_is_moving())) |
||
| 942 | |||
| 943 | |||
| 944 | if stepwise_meas: |
||
| 945 | # start the Stepwise alignment loop body self._stepwise_loop_body: |
||
| 946 | self._sigStepwiseAlignmentNext.emit() |
||
| 947 | else: |
||
| 948 | # start the continuous alignment loop body self._continuous_loop_body: |
||
| 949 | self._sigContinuousAlignmentNext.emit() |
||
| 950 | |||
| 951 | |||
| 952 | def _stepwise_loop_body(self): |
||
| 953 | """ Go one by one through the created path |
||
| 954 | @return: |
||
| 955 | The loop body goes through the 1D array |
||
| 956 | """ |
||
| 957 | |||
| 958 | if self._stop_measure: |
||
| 959 | return |
||
| 960 | |||
| 961 | self._do_premeasurement_proc() |
||
| 962 | pos = self._magnet_device.get_pos() |
||
| 963 | self.log.debug("Current magnetic field before alignment measurement rho:{0}".format(pos['rho']) |
||
| 964 | + "phi: {0}".format(pos['phi']) + "theta: {0}".format(pos['theta'])) |
||
| 965 | # perform here one of the chosen alignment measurements |
||
| 966 | meas_val, add_meas_val = self._do_alignment_measurement() |
||
| 967 | |||
| 968 | # set the measurement point to the proper array and the proper position: |
||
| 969 | # save also all additional measurement information, which have been |
||
| 970 | # done during the measurement in add_meas_val. |
||
| 971 | self._set_meas_point(meas_val, add_meas_val, self._pathway_index, self._backmap) |
||
| 972 | |||
| 973 | # increase the index |
||
| 974 | self._pathway_index += 1 |
||
| 975 | |||
| 976 | if (self._pathway_index) < len(self._pathway): |
||
| 977 | |||
| 978 | # |
||
| 979 | self._do_postmeasurement_proc() |
||
| 980 | move_dict_vel, \ |
||
| 981 | move_dict_abs, \ |
||
| 982 | move_dict_rel = self._move_to_index(self._pathway_index, self._pathway) |
||
| 983 | |||
| 984 | self.set_velocity(move_dict_vel) |
||
| 985 | self.move_abs(move_dict_abs) |
||
| 986 | |||
| 987 | # this function will return to this function if position is reached: |
||
| 988 | start_pos = dict() |
||
| 989 | end_pos = dict() |
||
| 990 | for axis_name in self._saved_pos_before_align: |
||
| 991 | start_pos[axis_name] = self._backmap[self._pathway_index - 1][axis_name] |
||
| 992 | end_pos[axis_name] = self._backmap[self._pathway_index][axis_name] |
||
| 993 | |||
| 994 | while self._check_is_moving(): |
||
| 995 | time.sleep(self._checktime) |
||
| 996 | self.log.debug("Went into while loop in stepwise_loop_body") |
||
| 997 | |||
| 998 | self.log.debug("stepwise_loop_body reports magnet moving ? {0}".format(self._check_is_moving())) |
||
| 999 | |||
| 1000 | # rerun this loop again |
||
| 1001 | self._sigStepwiseAlignmentNext.emit() |
||
| 1002 | |||
| 1003 | else: |
||
| 1004 | self._end_alignment_procedure() |
||
| 1005 | |||
| 1006 | |||
| 1007 | def _continuous_loop_body(self): |
||
| 1008 | """ Go as much as possible in one direction |
||
| 1009 | |||
| 1010 | @return: |
||
| 1011 | |||
| 1012 | The loop body goes through the 1D array |
||
| 1013 | """ |
||
| 1014 | pass |
||
| 1015 | |||
| 1016 | |||
| 1017 | |||
| 1018 | def stop_alignment(self): |
||
| 1019 | """ Stops any kind of ongoing alignment measurement by setting a flag. |
||
| 1020 | """ |
||
| 1021 | |||
| 1022 | self._stop_measure = True |
||
| 1023 | |||
| 1024 | # abort the movement or check whether immediate abortion of measurement |
||
| 1025 | # was needed. |
||
| 1026 | |||
| 1027 | # check whether an alignment measurement is currently going on and send |
||
| 1028 | # a signal to stop that. |
||
| 1029 | |||
| 1030 | def _end_alignment_procedure(self): |
||
| 1031 | |||
| 1032 | # 1 check if magnet is moving and stop it |
||
| 1033 | |||
| 1034 | # move back to the first position before the alignment has started: |
||
| 1035 | # |
||
| 1036 | constraints = self.get_hardware_constraints() |
||
| 1037 | |||
| 1038 | last_pos = dict() |
||
| 1039 | for axis_name in self._saved_pos_before_align: |
||
| 1040 | last_pos[axis_name] = self._backmap[self._pathway_index-1][axis_name] |
||
| 1041 | |||
| 1042 | self.move_abs(self._saved_pos_before_align) |
||
| 1043 | |||
| 1044 | while self._check_is_moving(): |
||
| 1045 | time.sleep(self._checktime) |
||
| 1046 | |||
| 1047 | self.sigMeasurementFinished.emit() |
||
| 1048 | |||
| 1049 | self._pathway_index = 0 |
||
| 1050 | self._stop_measurement_time = datetime.datetime.now() |
||
| 1051 | |||
| 1052 | self.log.info('Alignment Complete!') |
||
| 1053 | |||
| 1054 | pass |
||
| 1055 | |||
| 1056 | |||
| 1057 | def _check_position_reached_loop(self, start_pos_dict, end_pos_dict): |
||
| 1058 | """ Perform just a while loop, which checks everytime the conditions |
||
| 1059 | |||
| 1060 | @param dict start_pos_dict: the position in this dictionary must be |
||
| 1061 | absolute positions! |
||
| 1062 | @param dict end_pos_dict: |
||
| 1063 | @param float checktime: the checktime in seconds |
||
| 1064 | |||
| 1065 | @return: |
||
| 1066 | |||
| 1067 | Whenever the magnet has passed 95% of the way, the method will return. |
||
| 1068 | |||
| 1069 | Check also whether the difference in position increases again, and if so |
||
| 1070 | stop the measurement and raise an error, since either the velocity was |
||
| 1071 | too fast or the magnet does not move further. |
||
| 1072 | """ |
||
| 1073 | |||
| 1074 | |||
| 1075 | distance_init = 0.0 |
||
| 1076 | constraints = self.get_hardware_constraints() |
||
| 1077 | minimal_distance = 0.0 |
||
| 1078 | for axis_label in start_pos_dict: |
||
| 1079 | distance_init = (end_pos_dict[axis_label] - start_pos_dict[axis_label])**2 |
||
| 1080 | minimal_distance = minimal_distance + (constraints[axis_label]['pos_step'])**2 |
||
| 1081 | distance_init = np.sqrt(distance_init) |
||
| 1082 | minimal_distance = np.sqrt(minimal_distance) |
||
| 1083 | |||
| 1084 | # take 97% distance tolerance: |
||
| 1085 | distance_tolerance = 0.03 * distance_init |
||
| 1086 | |||
| 1087 | current_dist = 0.0 |
||
| 1088 | |||
| 1089 | while True: |
||
| 1090 | time.sleep(self._checktime) |
||
| 1091 | |||
| 1092 | curr_pos = self.get_pos(list(end_pos_dict)) |
||
| 1093 | |||
| 1094 | for axis_label in start_pos_dict: |
||
| 1095 | current_dist = (end_pos_dict[axis_label] - curr_pos[axis_label])**2 |
||
| 1096 | |||
| 1097 | current_dist = np.sqrt(current_dist) |
||
| 1098 | |||
| 1099 | self.sigPosChanged.emit(curr_pos) |
||
| 1100 | |||
| 1101 | if (current_dist <= distance_tolerance) or (current_dist <= minimal_distance) or self._stop_measure: |
||
| 1102 | self.sigPosReached.emit() |
||
| 1103 | |||
| 1104 | break |
||
| 1105 | |||
| 1106 | #return either pos reached signal of check position |
||
| 1107 | |||
| 1108 | def _check_is_moving(self): |
||
| 1109 | """ |
||
| 1110 | |||
| 1111 | @return bool: True indicates the magnet is moving, False the magnet stopped movement |
||
| 1112 | """ |
||
| 1113 | # get axis names |
||
| 1114 | axes = [i for i in self._magnet_device.get_constraints()] |
||
| 1115 | state = self._magnet_device.get_status() |
||
| 1116 | |||
| 1117 | return (state[axes[0]][0] or state[axes[1]][0] or state[axes[2]][0]) is (1 or -1) |
||
| 1118 | |||
| 1119 | |||
| 1120 | def _set_meas_point(self, meas_val, add_meas_val, pathway_index, back_map): |
||
| 1121 | |||
| 1122 | # is it point for 1d meas or 2d meas? |
||
| 1123 | |||
| 1124 | # map the point back to the position in the measurement array |
||
| 1125 | index_array = back_map[pathway_index]['index'] |
||
| 1126 | |||
| 1127 | # then index_array is actually no array, but just a number. That is the |
||
| 1128 | # 1D case: |
||
| 1129 | if np.shape(index_array) == (): |
||
| 1130 | |||
| 1131 | #FIXME: Implement the 1D save |
||
| 1132 | |||
| 1133 | self.sig1DMatrixChanged.emit() |
||
| 1134 | |||
| 1135 | elif np.shape(index_array)[0] == 2: |
||
| 1136 | |||
| 1137 | self._2D_data_matrix[index_array] = meas_val |
||
| 1138 | self._2D_add_data_matrix[index_array] = add_meas_val |
||
| 1139 | |||
| 1140 | # self.log.debug('Data "{0}", saved at intex "{1}"'.format(meas_val, index_array)) |
||
| 1141 | |||
| 1142 | self.sig2DMatrixChanged.emit() |
||
| 1143 | |||
| 1144 | elif np.shape(index_array)[0] == 3: |
||
| 1145 | |||
| 1146 | |||
| 1147 | #FIXME: Implement the 3D save |
||
| 1148 | self.sig3DMatrixChanged.emit() |
||
| 1149 | else: |
||
| 1150 | self.log.error('The measurement point "{0}" could not be set in ' |
||
| 1151 | 'the _set_meas_point routine, since either a 1D, a 2D or ' |
||
| 1152 | 'a 3D index array was expected, but an index array "{1}" ' |
||
| 1153 | 'was given in the passed back_map. Correct the ' |
||
| 1154 | 'back_map creation in the routine ' |
||
| 1155 | '_create_2d_pathway!'.format(meas_val, index_array)) |
||
| 1156 | |||
| 1157 | |||
| 1158 | |||
| 1159 | |||
| 1160 | pass |
||
| 1161 | |||
| 1162 | def _do_premeasurement_proc(self): |
||
| 1163 | # do a selected pre measurement procedure, like e.g. optimize position. |
||
| 1164 | |||
| 1165 | |||
| 1166 | # first attempt of an optimizer usage: |
||
| 1167 | if self._optimize_pos: |
||
| 1168 | self._do_optimize_pos() |
||
| 1169 | |||
| 1170 | return |
||
| 1171 | |||
| 1172 | def _do_optimize_pos(self): |
||
| 1173 | |||
| 1174 | curr_pos = self._confocal_logic.get_position() |
||
| 1175 | |||
| 1176 | self._optimizer_logic.start_refocus(curr_pos, caller_tag='magnet_logic') |
||
| 1177 | |||
| 1178 | # check just the state of the optimizer |
||
| 1179 | while self._optimizer_logic.getState() != 'idle' and not self._stop_measure: |
||
| 1180 | time.sleep(0.5) |
||
| 1181 | |||
| 1182 | # use the position to move the scanner |
||
| 1183 | self._confocal_logic.set_position('magnet_logic', |
||
| 1184 | self._optimizer_logic.optim_pos_x, |
||
| 1185 | self._optimizer_logic.optim_pos_y, |
||
| 1186 | self._optimizer_logic.optim_pos_z) |
||
| 1187 | |||
| 1188 | def _do_alignment_measurement(self): |
||
| 1189 | """ That is the main method which contains all functions with measurement routines. |
||
| 1190 | |||
| 1191 | Each measurement routine has to output the measurement value, but can |
||
| 1192 | also provide a dictionary with additional measurement parameters, which |
||
| 1193 | have been measured either as a pre-requisition for the measurement or |
||
| 1194 | are results of the measurement. |
||
| 1195 | |||
| 1196 | Save each measured value as an item to a keyword string, i.e. |
||
| 1197 | {'ODMR frequency (MHz)': <the_parameter>, ...} |
||
| 1198 | The save routine will handle the additional information and save them |
||
| 1199 | properly. |
||
| 1200 | |||
| 1201 | |||
| 1202 | @return tuple(float, dict): the measured value is of type float and the |
||
| 1203 | additional parameters are saved in a |
||
| 1204 | dictionary form. |
||
| 1205 | """ |
||
| 1206 | |||
| 1207 | # perform here one of the selected alignment measurements and return to |
||
| 1208 | # the loop body the measured values. |
||
| 1209 | |||
| 1210 | |||
| 1211 | # self.alignment_methods = ['fluorescence_pointwise', |
||
| 1212 | # 'fluorescence_continuous', |
||
| 1213 | # 'odmr_splitting', |
||
| 1214 | # 'odmr_hyperfine_splitting', |
||
| 1215 | # 'nuclear_spin_measurement'] |
||
| 1216 | |||
| 1217 | if self.curr_alignment_method == '2d_fluorescence': |
||
| 1218 | data, add_data = self._perform_fluorescence_measure() |
||
| 1219 | |||
| 1220 | elif self.curr_alignment_method == '2d_odmr': |
||
| 1221 | if self.odmr_2d_single_trans: |
||
| 1222 | data, add_data = self._perform_single_trans_contrast_measure() |
||
| 1223 | else: |
||
| 1224 | data, add_data = self._perform_odmr_measure() |
||
| 1225 | |||
| 1226 | elif self.curr_alignment_method == '2d_nuclear': |
||
| 1227 | data, add_data = self._perform_nuclear_measure() |
||
| 1228 | # data, add_data = self._perform_odmr_measure(11100e6, 1e6, 11200e6, 5, 10, 'Lorentzian', False,'') |
||
| 1229 | |||
| 1230 | |||
| 1231 | return data, add_data |
||
| 1232 | |||
| 1233 | |||
| 1234 | def _perform_fluorescence_measure(self): |
||
| 1235 | |||
| 1236 | #FIXME: that should be run through the TaskRunner! Implement the call |
||
| 1237 | # by not using this connection! |
||
| 1238 | |||
| 1239 | if self._counter_logic.get_counting_mode != 'continuous': |
||
| 1240 | self._counter_logic.set_counting_mode(mode='continuous') |
||
| 1241 | |||
| 1242 | self._counter_logic.start_saving() |
||
| 1243 | time.sleep(self.fluorescence_integration_time) |
||
| 1244 | data_array, parameters = self._counter_logic.save_data(to_file=False) |
||
| 1245 | |||
| 1246 | data_array = np.array(data_array)[:, 1] |
||
| 1247 | |||
| 1248 | return data_array.mean(), parameters |
||
| 1249 | |||
| 1250 | def _perform_odmr_measure(self): |
||
| 1251 | """ Perform the odmr measurement. |
||
| 1252 | |||
| 1253 | @return: |
||
| 1254 | """ |
||
| 1255 | |||
| 1256 | store_dict = {} |
||
| 1257 | |||
| 1258 | # optimize at first the position: |
||
| 1259 | self._do_optimize_pos() |
||
| 1260 | |||
| 1261 | |||
| 1262 | # correct the ODMR alignment the shift of the ODMR lines due to movement |
||
| 1263 | # in axis0 and axis1, therefore find out how much you will move in each |
||
| 1264 | # distance: |
||
| 1265 | if self._pathway_index == 0: |
||
| 1266 | axis0_pos_start = self._saved_pos_before_align[self._axis0_name] |
||
| 1267 | axis0_pos_stop = self._backmap[self._pathway_index][self._axis0_name] |
||
| 1268 | |||
| 1269 | axis1_pos_start = self._saved_pos_before_align[self._axis1_name] |
||
| 1270 | axis1_pos_stop = self._backmap[self._pathway_index][self._axis1_name] |
||
| 1271 | else: |
||
| 1272 | axis0_pos_start = self._backmap[self._pathway_index-1][self._axis0_name] |
||
| 1273 | axis0_pos_stop = self._backmap[self._pathway_index][self._axis0_name] |
||
| 1274 | |||
| 1275 | axis1_pos_start = self._backmap[self._pathway_index-1][self._axis1_name] |
||
| 1276 | axis1_pos_stop = self._backmap[self._pathway_index][self._axis1_name] |
||
| 1277 | |||
| 1278 | # that is the current distance the magnet has moved: |
||
| 1279 | View Code Duplication | axis0_move = axis0_pos_stop - axis0_pos_start |
|
| 1280 | axis1_move = axis1_pos_stop - axis1_pos_start |
||
| 1281 | print('axis0_move', axis0_move, 'axis1_move', axis1_move) |
||
| 1282 | |||
| 1283 | # in essence, get the last measurement value for odmr freq and calculate |
||
| 1284 | # the odmr peak shift for axis0 and axis1 based on the already measured |
||
| 1285 | # peaks and update the values odmr_2d_peak_axis0_move_ratio and |
||
| 1286 | # odmr_2d_peak_axis1_move_ratio: |
||
| 1287 | if self._pathway_index > 1: |
||
| 1288 | # in essence, get the last measurement value for odmr freq: |
||
| 1289 | View Code Duplication | if self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('low_freq_Frequency') is not None: |
|
| 1290 | low_odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['low_freq_Frequency']['value']*1e6 |
||
| 1291 | low_odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['low_freq_Frequency']['value']*1e6 |
||
| 1292 | elif self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('low_freq_Freq. 1') is not None: |
||
| 1293 | low_odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['low_freq_Freq. 1']['value']*1e6 |
||
| 1294 | low_odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['low_freq_Freq. 1']['value']*1e6 |
||
| 1295 | else: |
||
| 1296 | self.log.error('No previous saved lower odmr freq found in ' |
||
| 1297 | 'ODMR alignment data! Cannot do the ODMR Alignment!') |
||
| 1298 | |||
| 1299 | if self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('high_freq_Frequency') is not None: |
||
| 1300 | high_odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['high_freq_Frequency']['value']*1e6 |
||
| 1301 | high_odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['high_freq_Frequency']['value']*1e6 |
||
| 1302 | elif self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('high_freq_Freq. 1') is not None: |
||
| 1303 | high_odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['high_freq_Freq. 1']['value']*1e6 |
||
| 1304 | high_odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['high_freq_Freq. 1']['value']*1e6 |
||
| 1305 | else: |
||
| 1306 | self.log.error('No previous saved higher odmr freq found in ' |
||
| 1307 | 'ODMR alignment data! Cannot do the ODMR Alignment!') |
||
| 1308 | |||
| 1309 | # only if there was a non zero movement, the if make sense to |
||
| 1310 | # calculate the shift for either the axis0 or axis1. |
||
| 1311 | # BE AWARE THAT FOR A MOVEMENT IN AXIS0 AND AXIS1 AT THE SAME TIME |
||
| 1312 | # NO PROPER CALCULATION OF THE OMDR LINES CAN BE PROVIDED! |
||
| 1313 | if not np.isclose(axis0_move, 0.0): |
||
| 1314 | # update the correction ratio: |
||
| 1315 | low_peak_axis0_move_ratio = (low_odmr_freq1 - low_odmr_freq2)/axis0_move |
||
| 1316 | high_peak_axis0_move_ratio = (high_odmr_freq1 - high_odmr_freq2)/axis0_move |
||
| 1317 | |||
| 1318 | # print('low_odmr_freq2', low_odmr_freq2, 'low_odmr_freq1', low_odmr_freq1) |
||
| 1319 | # print('high_odmr_freq2', high_odmr_freq2, 'high_odmr_freq1', high_odmr_freq1) |
||
| 1320 | |||
| 1321 | # calculate the average shift of the odmr lines for the lower |
||
| 1322 | # and the upper transition: |
||
| 1323 | self.odmr_2d_peak_axis0_move_ratio = (low_peak_axis0_move_ratio +high_peak_axis0_move_ratio)/2 |
||
| 1324 | |||
| 1325 | # print('new odmr_2d_peak_axis0_move_ratio', self.odmr_2d_peak_axis0_move_ratio/1e12) |
||
| 1326 | if not np.isclose(axis1_move, 0.0): |
||
| 1327 | # update the correction ratio: |
||
| 1328 | low_peak_axis1_move_ratio = (low_odmr_freq1 - low_odmr_freq2)/axis1_move |
||
| 1329 | high_peak_axis1_move_ratio = (high_odmr_freq1 - high_odmr_freq2)/axis1_move |
||
| 1330 | |||
| 1331 | # calculate the average shift of the odmr lines for the lower |
||
| 1332 | # and the upper transition: |
||
| 1333 | self.odmr_2d_peak_axis1_move_ratio = (low_peak_axis1_move_ratio + high_peak_axis1_move_ratio)/2 |
||
| 1334 | |||
| 1335 | # print('new odmr_2d_peak_axis1_move_ratio', self.odmr_2d_peak_axis1_move_ratio/1e12) |
||
| 1336 | |||
| 1337 | # Measurement of the lower transition: |
||
| 1338 | # ------------------------------------- |
||
| 1339 | |||
| 1340 | freq_shift_low_axis0 = axis0_move * self.odmr_2d_peak_axis0_move_ratio |
||
| 1341 | freq_shift_low_axis1 = axis1_move * self.odmr_2d_peak_axis1_move_ratio |
||
| 1342 | |||
| 1343 | # correct here the center freq with the estimated corrections: |
||
| 1344 | self.odmr_2d_low_center_freq += (freq_shift_low_axis0 + freq_shift_low_axis1) |
||
| 1345 | # print('self.odmr_2d_low_center_freq',self.odmr_2d_low_center_freq) |
||
| 1346 | |||
| 1347 | # create a unique nametag for the current measurement: |
||
| 1348 | name_tag = 'low_trans_index_'+str(self._backmap[self._pathway_index]['index'][0]) \ |
||
| 1349 | +'_'+ str(self._backmap[self._pathway_index]['index'][1]) |
||
| 1350 | |||
| 1351 | # of course the shift of the ODMR peak is not linear for a movement in |
||
| 1352 | # axis0 and axis1, but we need just an estimate how to set the boundary |
||
| 1353 | # conditions for the first scan, since the first scan will move to a |
||
| 1354 | # start position and then it need to know where to search for the ODMR |
||
| 1355 | # peak(s). |
||
| 1356 | |||
| 1357 | # calculate the parameters for the odmr scan: |
||
| 1358 | low_start_freq = self.odmr_2d_low_center_freq - self.odmr_2d_low_range_freq/2 |
||
| 1359 | low_step_freq = self.odmr_2d_low_step_freq |
||
| 1360 | low_stop_freq = self.odmr_2d_low_center_freq + self.odmr_2d_low_range_freq/2 |
||
| 1361 | |||
| 1362 | param = self._odmr_logic.perform_odmr_measurement(low_start_freq, |
||
| 1363 | low_step_freq, |
||
| 1364 | low_stop_freq, |
||
| 1365 | self.odmr_2d_low_power, |
||
| 1366 | self.odmr_2d_low_runtime, |
||
| 1367 | self.odmr_2d_low_fitfunction, |
||
| 1368 | self.odmr_2d_save_after_measure, |
||
| 1369 | name_tag) |
||
| 1370 | |||
| 1371 | # restructure the output parameters: |
||
| 1372 | for entry in param: |
||
| 1373 | store_dict['low_freq_'+str(entry)] = param[entry] |
||
| 1374 | |||
| 1375 | # extract the frequency meausure: |
||
| 1376 | if param.get('Frequency') is not None: |
||
| 1377 | odmr_low_freq_meas = param['Frequency']['value']*1e6 |
||
| 1378 | elif param.get('Freq. 1') is not None: |
||
| 1379 | odmr_low_freq_meas = param['Freq. 1']['value']*1e6 |
||
| 1380 | else: |
||
| 1381 | # a default value for testing and debugging: |
||
| 1382 | odmr_low_freq_meas = 1000e6 |
||
| 1383 | |||
| 1384 | self.odmr_2d_low_center_freq = odmr_low_freq_meas |
||
| 1385 | # Measurement of the higher transition: |
||
| 1386 | # ------------------------------------- |
||
| 1387 | |||
| 1388 | |||
| 1389 | freq_shift_high_axis0 = axis0_move * self.odmr_2d_peak_axis0_move_ratio |
||
| 1390 | freq_shift_high_axis1 = axis1_move * self.odmr_2d_peak_axis1_move_ratio |
||
| 1391 | |||
| 1392 | # correct here the center freq with the estimated corrections: |
||
| 1393 | self.odmr_2d_high_center_freq += (freq_shift_high_axis0 + freq_shift_high_axis1) |
||
| 1394 | |||
| 1395 | # create a unique nametag for the current measurement: |
||
| 1396 | name_tag = 'high_trans_index_'+str(self._backmap[self._pathway_index]['index'][0]) \ |
||
| 1397 | +'_'+ str(self._backmap[self._pathway_index]['index'][1]) |
||
| 1398 | |||
| 1399 | # of course the shift of the ODMR peak is not linear for a movement in |
||
| 1400 | # axis0 and axis1, but we need just an estimate how to set the boundary |
||
| 1401 | # conditions for the first scan, since the first scan will move to a |
||
| 1402 | # start position and then it need to know where to search for the ODMR |
||
| 1403 | # peak(s). |
||
| 1404 | |||
| 1405 | # calculate the parameters for the odmr scan: |
||
| 1406 | high_start_freq = self.odmr_2d_high_center_freq - self.odmr_2d_high_range_freq/2 |
||
| 1407 | high_step_freq = self.odmr_2d_high_step_freq |
||
| 1408 | high_stop_freq = self.odmr_2d_high_center_freq + self.odmr_2d_high_range_freq/2 |
||
| 1409 | |||
| 1410 | param = self._odmr_logic.perform_odmr_measurement(high_start_freq, |
||
| 1411 | high_step_freq, |
||
| 1412 | high_stop_freq, |
||
| 1413 | self.odmr_2d_high_power, |
||
| 1414 | self.odmr_2d_high_runtime, |
||
| 1415 | self.odmr_2d_high_fitfunction, |
||
| 1416 | self.odmr_2d_save_after_measure, |
||
| 1417 | name_tag) |
||
| 1418 | # restructure the output parameters: |
||
| 1419 | for entry in param: |
||
| 1420 | store_dict['high_freq_'+str(entry)] = param[entry] |
||
| 1421 | |||
| 1422 | # extract the frequency meausure: |
||
| 1423 | if param.get('Frequency') is not None: |
||
| 1424 | odmr_high_freq_meas = param['Frequency']['value']*1e6 |
||
| 1425 | elif param.get('Freq. 1') is not None: |
||
| 1426 | odmr_high_freq_meas = param['Freq. 1']['value']*1e6 |
||
| 1427 | else: |
||
| 1428 | # a default value for testing and debugging: |
||
| 1429 | odmr_high_freq_meas = 2000e6 |
||
| 1430 | |||
| 1431 | # correct the estimated center frequency by the actual measured one. |
||
| 1432 | self.odmr_2d_high_center_freq = odmr_high_freq_meas |
||
| 1433 | |||
| 1434 | #FIXME: the normalization is just done for the display to view the |
||
| 1435 | # value properly! There is right now a bug in the colorbad |
||
| 1436 | # display, which need to be solved. |
||
| 1437 | diff = (abs(odmr_high_freq_meas - odmr_low_freq_meas)/2)/self.norm |
||
| 1438 | |||
| 1439 | while self._odmr_logic.getState() != 'idle' and not self._stop_measure: |
||
| 1440 | time.sleep(0.5) |
||
| 1441 | |||
| 1442 | return diff, store_dict |
||
| 1443 | |||
| 1444 | def _perform_single_trans_contrast_measure(self): |
||
| 1445 | """ Make an ODMR measurement on one single transition and use the |
||
| 1446 | contrast as a measure. |
||
| 1447 | """ |
||
| 1448 | |||
| 1449 | store_dict = {} |
||
| 1450 | |||
| 1451 | # optimize at first the position: |
||
| 1452 | self._do_optimize_pos() |
||
| 1453 | |||
| 1454 | # correct the ODMR alignment the shift of the ODMR lines due to movement |
||
| 1455 | # in axis0 and axis1, therefore find out how much you will move in each |
||
| 1456 | # distance: |
||
| 1457 | if self._pathway_index == 0: |
||
| 1458 | axis0_pos_start = self._saved_pos_before_align[self._axis0_name] |
||
| 1459 | axis0_pos_stop = self._backmap[self._pathway_index][self._axis0_name] |
||
| 1460 | |||
| 1461 | axis1_pos_start = self._saved_pos_before_align[self._axis1_name] |
||
| 1462 | axis1_pos_stop = self._backmap[self._pathway_index][self._axis1_name] |
||
| 1463 | else: |
||
| 1464 | axis0_pos_start = self._backmap[self._pathway_index-1][self._axis0_name] |
||
| 1465 | axis0_pos_stop = self._backmap[self._pathway_index][self._axis0_name] |
||
| 1466 | |||
| 1467 | axis1_pos_start = self._backmap[self._pathway_index-1][self._axis1_name] |
||
| 1468 | axis1_pos_stop = self._backmap[self._pathway_index][self._axis1_name] |
||
| 1469 | |||
| 1470 | # that is the current distance the magnet has moved: |
||
| 1471 | View Code Duplication | axis0_move = axis0_pos_stop - axis0_pos_start |
|
| 1472 | axis1_move = axis1_pos_stop - axis1_pos_start |
||
| 1473 | # print('axis0_move', axis0_move, 'axis1_move', axis1_move) |
||
| 1474 | |||
| 1475 | # in essence, get the last measurement value for odmr freq and calculate |
||
| 1476 | # the odmr peak shift for axis0 and axis1 based on the already measured |
||
| 1477 | # peaks and update the values odmr_2d_peak_axis0_move_ratio and |
||
| 1478 | # odmr_2d_peak_axis1_move_ratio: |
||
| 1479 | if self._pathway_index > 1: |
||
| 1480 | # in essence, get the last measurement value for odmr freq: |
||
| 1481 | if self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('Frequency') is not None: |
||
| 1482 | odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['Frequency']['value']*1e6 |
||
| 1483 | odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['Frequency']['value']*1e6 |
||
| 1484 | elif self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']].get('Freq. 1') is not None: |
||
| 1485 | odmr_freq1 = self._2D_add_data_matrix[self._backmap[self._pathway_index-1]['index']]['Freq. 1']['value']*1e6 |
||
| 1486 | odmr_freq2 = self._2D_add_data_matrix[self._backmap[self._pathway_index-2]['index']]['Freq. 1']['value']*1e6 |
||
| 1487 | else: |
||
| 1488 | self.log.error('No previous saved lower odmr freq found in ' |
||
| 1489 | 'ODMR alignment data! Cannot do the ODMR ' |
||
| 1490 | 'Alignment!') |
||
| 1491 | |||
| 1492 | |||
| 1493 | # only if there was a non zero movement, the if make sense to |
||
| 1494 | # calculate the shift for either the axis0 or axis1. |
||
| 1495 | # BE AWARE THAT FOR A MOVEMENT IN AXIS0 AND AXIS1 AT THE SAME TIME |
||
| 1496 | # NO PROPER CALCULATION OF THE OMDR LINES CAN BE PROVIDED! |
||
| 1497 | if not np.isclose(axis0_move, 0.0): |
||
| 1498 | # update the correction ratio: |
||
| 1499 | peak_axis0_move_ratio = (odmr_freq1 - odmr_freq2)/axis0_move |
||
| 1500 | |||
| 1501 | # calculate the average shift of the odmr lines for the lower |
||
| 1502 | # and the upper transition: |
||
| 1503 | self.odmr_2d_peak_axis0_move_ratio = peak_axis0_move_ratio |
||
| 1504 | |||
| 1505 | print('new odmr_2d_peak_axis0_move_ratio', self.odmr_2d_peak_axis0_move_ratio/1e12) |
||
| 1506 | if not np.isclose(axis1_move, 0.0): |
||
| 1507 | # update the correction ratio: |
||
| 1508 | peak_axis1_move_ratio = (odmr_freq1 - odmr_freq2)/axis1_move |
||
| 1509 | |||
| 1510 | |||
| 1511 | # calculate the shift of the odmr lines for the transition: |
||
| 1512 | self.odmr_2d_peak_axis1_move_ratio = peak_axis1_move_ratio |
||
| 1513 | |||
| 1514 | # Measurement of one transition: |
||
| 1515 | # ------------------------------------- |
||
| 1516 | |||
| 1517 | freq_shift_axis0 = axis0_move * self.odmr_2d_peak_axis0_move_ratio |
||
| 1518 | freq_shift_axis1 = axis1_move * self.odmr_2d_peak_axis1_move_ratio |
||
| 1519 | |||
| 1520 | # correct here the center freq with the estimated corrections: |
||
| 1521 | self.odmr_2d_low_center_freq += (freq_shift_axis0 + freq_shift_axis1) |
||
| 1522 | # print('self.odmr_2d_low_center_freq',self.odmr_2d_low_center_freq) |
||
| 1523 | |||
| 1524 | # create a unique nametag for the current measurement: |
||
| 1525 | name_tag = 'trans_index_'+str(self._backmap[self._pathway_index]['index'][0]) \ |
||
| 1526 | +'_'+ str(self._backmap[self._pathway_index]['index'][1]) |
||
| 1527 | |||
| 1528 | # of course the shift of the ODMR peak is not linear for a movement in |
||
| 1529 | # axis0 and axis1, but we need just an estimate how to set the boundary |
||
| 1530 | # conditions for the first scan, since the first scan will move to a |
||
| 1531 | # start position and then it need to know where to search for the ODMR |
||
| 1532 | # peak(s). |
||
| 1533 | |||
| 1534 | # calculate the parameters for the odmr scan: |
||
| 1535 | start_freq = self.odmr_2d_low_center_freq - self.odmr_2d_low_range_freq/2 |
||
| 1536 | step_freq = self.odmr_2d_low_step_freq |
||
| 1537 | stop_freq = self.odmr_2d_low_center_freq + self.odmr_2d_low_range_freq/2 |
||
| 1538 | |||
| 1539 | param = self._odmr_logic.perform_odmr_measurement(start_freq, |
||
| 1540 | step_freq, |
||
| 1541 | stop_freq, |
||
| 1542 | self.odmr_2d_low_power, |
||
| 1543 | self.odmr_2d_low_runtime, |
||
| 1544 | self.odmr_2d_low_fitfunction, |
||
| 1545 | self.odmr_2d_save_after_measure, |
||
| 1546 | name_tag) |
||
| 1547 | |||
| 1548 | param['ODMR peak/Magnet move ratio axis0'] = self.odmr_2d_peak_axis0_move_ratio |
||
| 1549 | param['ODMR peak/Magnet move ratio axis1'] = self.odmr_2d_peak_axis1_move_ratio |
||
| 1550 | |||
| 1551 | # extract the frequency meausure: |
||
| 1552 | if param.get('Frequency') is not None: |
||
| 1553 | odmr_freq_meas = param['Frequency']['value']*1e6 |
||
| 1554 | cont_meas = param['Contrast']['value'] |
||
| 1555 | elif param.get('Freq. 1') is not None: |
||
| 1556 | odmr_freq_meas = param['Freq. 1']['value']*1e6 |
||
| 1557 | cont_meas = param['Contrast 0']['value'] + param['Contrast 1']['value'] + param['Contrast 2']['value'] |
||
| 1558 | else: |
||
| 1559 | # a default value for testing and debugging: |
||
| 1560 | odmr_freq_meas = 1000e6 |
||
| 1561 | cont_meas = 0.0 |
||
| 1562 | |||
| 1563 | self.odmr_2d_low_center_freq = odmr_freq_meas |
||
| 1564 | |||
| 1565 | while self._odmr_logic.getState() != 'idle' and not self._stop_measure: |
||
| 1566 | time.sleep(0.5) |
||
| 1567 | |||
| 1568 | return cont_meas, param |
||
| 1569 | |||
| 1570 | def _perform_nuclear_measure(self): |
||
| 1571 | """ Make a single shot alignment. """ |
||
| 1572 | |||
| 1573 | # possible parameters for the nuclear measurement: |
||
| 1574 | # self.nuclear_2d_rabi_periode |
||
| 1575 | # self.nuclear_2d_mw_freq |
||
| 1576 | # self.nuclear_2d_mw_channel |
||
| 1577 | # self.nuclear_2d_mw_power |
||
| 1578 | # self.nuclear_2d_laser_time |
||
| 1579 | # self.nuclear_2d_laser_channel |
||
| 1580 | # self.nuclear_2d_detect_channel |
||
| 1581 | # self.nuclear_2d_idle_time |
||
| 1582 | # self.nuclear_2d_reps_within_ssr |
||
| 1583 | # self.nuclear_2d_num_ssr |
||
| 1584 | self._load_pulsed_odmr() |
||
| 1585 | self._pulser_on() |
||
| 1586 | |||
| 1587 | # self.odmr_2d_low_center_freq |
||
| 1588 | # self.odmr_2d_low_step_freq |
||
| 1589 | # self.odmr_2d_low_range_freq |
||
| 1590 | # |
||
| 1591 | # self.odmr_2d_low_power, |
||
| 1592 | # self.odmr_2d_low_runtime, |
||
| 1593 | # self.odmr_2d_low_fitfunction, |
||
| 1594 | # self.odmr_2d_save_after_measure, |
||
| 1595 | |||
| 1596 | # Use the parameters from the ODMR alignment! |
||
| 1597 | cont_meas, param = self._perform_single_trans_contrast_measure() |
||
| 1598 | |||
| 1599 | odmr_freq = param['Freq. ' + str(self.nuclear_2d_mw_on_peak-1)]['value']*1e6 |
||
| 1600 | |||
| 1601 | self._set_cw_mw(switch_on=True, freq=odmr_freq, power=self.nuclear_2d_mw_power) |
||
| 1602 | self._load_nuclear_spin_readout() |
||
| 1603 | self._pulser_on() |
||
| 1604 | |||
| 1605 | # Check whether proper mode is active and if not activated that: |
||
| 1606 | if self._gc_logic.get_counting_mode() != 'finite-gated': |
||
| 1607 | self._gc_logic.set_counting_mode(mode='finite-gated') |
||
| 1608 | |||
| 1609 | # Set the count length for the single shot and start counting: |
||
| 1610 | self._gc_logic.set_count_length(self.nuclear_2d_num_ssr) |
||
| 1611 | |||
| 1612 | self._run_gated_counter() |
||
| 1613 | |||
| 1614 | self._set_cw_mw(switch_on=False) |
||
| 1615 | |||
| 1616 | # try with single poissonian: |
||
| 1617 | |||
| 1618 | |||
| 1619 | num_bins = (self._gc_logic.countdata.max() - self._gc_logic.countdata.min()) |
||
| 1620 | self._ta_logic.set_num_bins_histogram(num_bins) |
||
| 1621 | |||
| 1622 | hist_fit_x, hist_fit_y, param_single_poisson = self._ta_logic.do_fit('Poisson') |
||
| 1623 | |||
| 1624 | |||
| 1625 | param['chi_sqr_single'] = param_single_poisson['chi_sqr']['value'] |
||
| 1626 | |||
| 1627 | |||
| 1628 | # try with normal double poissonian: |
||
| 1629 | |||
| 1630 | # better performance by starting with half of number of bins: |
||
| 1631 | num_bins = int((self._gc_logic.countdata.max() - self._gc_logic.countdata.min())/2) |
||
| 1632 | self._ta_logic.set_num_bins_histogram(num_bins) |
||
| 1633 | |||
| 1634 | flip_prob, param2 = self._ta_logic.analyze_flip_prob(self._gc_logic.countdata, num_bins) |
||
| 1635 | |||
| 1636 | # self._pulser_off() |
||
| 1637 | # |
||
| 1638 | # self._load_pulsed_odmr() |
||
| 1639 | # self._pulser_on() |
||
| 1640 | |||
| 1641 | out_of_range = (param2['\u03BB0']['value'] < self._gc_logic.countdata.min() or param2['\u03BB0']['value'] > self._gc_logic.countdata.max()) or \ |
||
| 1642 | (param2['\u03BB1']['value'] < self._gc_logic.countdata.min() or param2['\u03BB1']['value'] > self._gc_logic.countdata.max()) |
||
| 1643 | |||
| 1644 | while (np.isnan(param2['fidelity'] or out_of_range) and num_bins > 4): |
||
| 1645 | # Reduce the number of bins if the calculation yields an invalid |
||
| 1646 | # number |
||
| 1647 | num_bins = int(num_bins/2) |
||
| 1648 | self._ta_logic.set_num_bins_histogram(num_bins) |
||
| 1649 | flip_prob, param2 = self._ta_logic.analyze_flip_prob(self._gc_logic.countdata, num_bins) |
||
| 1650 | |||
| 1651 | |||
| 1652 | # reduce the number of bins by one, so that the fitting algorithm |
||
| 1653 | # work. Eventually, that has to go in the fit constaints of the |
||
| 1654 | # algorithm. |
||
| 1655 | |||
| 1656 | out_of_range = (param2['\u03BB0']['value'] < self._gc_logic.countdata.min() or param2['\u03BB0']['value'] > self._gc_logic.countdata.max()) or \ |
||
| 1657 | (param2['\u03BB1']['value'] < self._gc_logic.countdata.min() or param2['\u03BB1']['value'] > self._gc_logic.countdata.max()) |
||
| 1658 | |||
| 1659 | if out_of_range: |
||
| 1660 | num_bins = num_bins-1 |
||
| 1661 | self._ta_logic.set_num_bins_histogram(num_bins) |
||
| 1662 | self.log.warning('Fitted values {0},{1} are out of range [{2},{3}]! ' |
||
| 1663 | 'Change the histogram a ' |
||
| 1664 | 'bit.'.format(param2['\u03BB0']['value'], |
||
| 1665 | param2['\u03BB1']['value'], |
||
| 1666 | self._gc_logic.countdata.min(), |
||
| 1667 | self._gc_logic.countdata.max())) |
||
| 1668 | |||
| 1669 | flip_prob, param2 = self._ta_logic.analyze_flip_prob(self._gc_logic.countdata, num_bins) |
||
| 1670 | |||
| 1671 | # run the lifetime calculatiion: |
||
| 1672 | # In order to calculate the T1 time one needs the length of one SingleShot readout |
||
| 1673 | dt = (self.nuclear_2d_rabi_periode/2 + self.nuclear_2d_laser_time + self.nuclear_2d_idle_time) * self.nuclear_2d_reps_within_ssr |
||
| 1674 | # param_lifetime = self._ta_logic.analyze_lifetime(self._gc_logic.countdata, dt, self.nuclear_2d_estimated_lifetime) |
||
| 1675 | # param.update(param_lifetime) |
||
| 1676 | |||
| 1677 | |||
| 1678 | # If everything went wrong, then put at least a reasonable number: |
||
| 1679 | if np.isnan(param2['fidelity']): |
||
| 1680 | param2['fidelity'] = 0.5 # that fidelity means that |
||
| 1681 | |||
| 1682 | # add the flip probability as a parameter to the parameter dict and add |
||
| 1683 | # also all the other parameters to that dict: |
||
| 1684 | param['flip_probability'] = flip_prob |
||
| 1685 | param.update(param2) |
||
| 1686 | |||
| 1687 | if self.nuclear_2d_use_single_poisson: |
||
| 1688 | # print(param) |
||
| 1689 | # print(param['chi_sqr']) |
||
| 1690 | return param['chi_sqr_single'], param |
||
| 1691 | |||
| 1692 | else: |
||
| 1693 | return param['fidelity'], param |
||
| 1694 | |||
| 1695 | def _run_gated_counter(self): |
||
| 1696 | |||
| 1697 | self._gc_logic.startCount() |
||
| 1698 | time.sleep(2) |
||
| 1699 | |||
| 1700 | # wait until the gated counter is done |
||
| 1701 | while self._gc_logic.getState() != 'idle' and not self._stop_measure: |
||
| 1702 | # print('in SSR measure') |
||
| 1703 | time.sleep(1) |
||
| 1704 | |||
| 1705 | |||
| 1706 | def _set_cw_mw(self, switch_on, freq=2.87e9, power=-40): |
||
| 1707 | |||
| 1708 | if switch_on: |
||
| 1709 | self._odmr_logic.set_frequency(freq) |
||
| 1710 | self._odmr_logic.set_power(power) |
||
| 1711 | self._odmr_logic.MW_on() |
||
| 1712 | else: |
||
| 1713 | self._odmr_logic.MW_off() |
||
| 1714 | |||
| 1715 | def _load_pulsed_odmr(self): |
||
| 1716 | """ Load a pulsed ODMR asset. """ |
||
| 1717 | #FIXME: Move this creation routine to the tasks! |
||
| 1718 | |||
| 1719 | self._seq_gen_logic.load_asset(asset_name='PulsedODMR') |
||
| 1720 | |||
| 1721 | def _load_nuclear_spin_readout(self): |
||
| 1722 | """ Load a nuclear spin readout asset. """ |
||
| 1723 | #FIXME: Move this creation routine to the tasks! |
||
| 1724 | |||
| 1725 | self._seq_gen_logic.load_asset(asset_name='SSR') |
||
| 1726 | |||
| 1727 | def _pulser_on(self): |
||
| 1728 | """ Switch on the pulser output. """ |
||
| 1729 | View Code Duplication | ||
| 1730 | self._set_channel_activation(active=True, apply_to_device=True) |
||
| 1731 | self._seq_gen_logic.pulser_on() |
||
| 1732 | |||
| 1733 | def _pulser_off(self): |
||
| 1734 | """ Switch off the pulser output. """ |
||
| 1735 | |||
| 1736 | self._set_channel_activation(active=False, apply_to_device=False) |
||
| 1737 | self._seq_gen_logic.pulser_off() |
||
| 1738 | |||
| 1739 | def _set_channel_activation(self, active=True, apply_to_device=False): |
||
| 1740 | """ Set the channels according to the current activation config to be either active or not. |
||
| 1741 | |||
| 1742 | @param bool active: the activation according to the current activation |
||
| 1743 | config will be checked and if channel |
||
| 1744 | is not active and active=True, then channel will be |
||
| 1745 | activated. Otherwise if channel is active and |
||
| 1746 | active=False channel will be deactivated. |
||
| 1747 | All other channels, which are not in activation |
||
| 1748 | config will be deactivated if they are not already |
||
| 1749 | deactivated. |
||
| 1750 | @param bool apply_to_device: Apply the activation or deactivation of the |
||
| 1751 | current activation_config either to the |
||
| 1752 | device and the viewboxes, or just to the |
||
| 1753 | viewboxes. |
||
| 1754 | """ |
||
| 1755 | |||
| 1756 | pulser_const = self._seq_gen_logic.get_hardware_constraints() |
||
| 1757 | |||
| 1758 | curr_config_name = self._seq_gen_logic.current_activation_config_name |
||
| 1759 | activation_config = pulser_const['activation_config'][curr_config_name] |
||
| 1760 | |||
| 1761 | # here is the current activation pattern of the pulse device: |
||
| 1762 | active_ch = self._seq_gen_logic.get_active_channels() |
||
| 1763 | |||
| 1764 | ch_to_change = {} # create something like a_ch = {1:True, 2:True} to switch |
||
| 1765 | |||
| 1766 | # check whether the correct channels are already active, and if not |
||
| 1767 | # correct for that and activate and deactivate the appropriate ones: |
||
| 1768 | available_ch = self._get_available_ch() |
||
| 1769 | for ch_name in available_ch: |
||
| 1770 | |||
| 1771 | # if the channel is in the activation, check whether it is active: |
||
| 1772 | if ch_name in activation_config: |
||
| 1773 | |||
| 1774 | if apply_to_device: |
||
| 1775 | # if channel is not active but activation is needed (active=True), |
||
| 1776 | # then add that to ch_to_change to change the state of the channels: |
||
| 1777 | if not active_ch[ch_name] and active: |
||
| 1778 | ch_to_change[ch_name] = active |
||
| 1779 | |||
| 1780 | # if channel is active but deactivation is needed (active=False), |
||
| 1781 | # then add that to ch_to_change to change the state of the channels: |
||
| 1782 | if active_ch[ch_name] and not active: |
||
| 1783 | View Code Duplication | ch_to_change[ch_name] = active |
|
| 1784 | |||
| 1785 | |||
| 1786 | else: |
||
| 1787 | # all other channel which are active should be deactivated: |
||
| 1788 | if active_ch[ch_name]: |
||
| 1789 | ch_to_change[ch_name] = False |
||
| 1790 | |||
| 1791 | self._seq_gen_logic.set_active_channels(ch_to_change) |
||
| 1792 | |||
| 1793 | def _get_available_ch(self): |
||
| 1794 | """ Helper method to get a list of all available channels. |
||
| 1795 | |||
| 1796 | @return list: entries are the generic string names of the channels. |
||
| 1797 | """ |
||
| 1798 | config = self._seq_gen_logic.get_hardware_constraints()['activation_config'] |
||
| 1799 | |||
| 1800 | available_ch = [] |
||
| 1801 | all_a_ch = [] |
||
| 1802 | all_d_ch = [] |
||
| 1803 | for conf in config: |
||
| 1804 | |||
| 1805 | # extract all analog channels from the config |
||
| 1806 | curr_a_ch = [entry for entry in config[conf] if 'a_ch' in entry] |
||
| 1807 | curr_d_ch = [entry for entry in config[conf] if 'd_ch' in entry] |
||
| 1808 | |||
| 1809 | # append all new analog channels to a temporary array |
||
| 1810 | for a_ch in curr_a_ch: |
||
| 1811 | if a_ch not in all_a_ch: |
||
| 1812 | all_a_ch.append(a_ch) |
||
| 1813 | |||
| 1814 | # append all new digital channels to a temporary array |
||
| 1815 | for d_ch in curr_d_ch: |
||
| 1816 | if d_ch not in all_d_ch: |
||
| 1817 | all_d_ch.append(d_ch) |
||
| 1818 | |||
| 1819 | all_a_ch.sort() |
||
| 1820 | all_d_ch.sort() |
||
| 1821 | available_ch.extend(all_a_ch) |
||
| 1822 | available_ch.extend(all_d_ch) |
||
| 1823 | |||
| 1824 | return available_ch |
||
| 1825 | |||
| 1826 | def _do_postmeasurement_proc(self): |
||
| 1827 | |||
| 1828 | # do a selected post measurement procedure, |
||
| 1829 | |||
| 1830 | return |
||
| 1831 | |||
| 1832 | |||
| 1833 | def get_available_odmr_peaks(self): |
||
| 1834 | """ Retrieve the information on which odmr peak the microwave can be |
||
| 1835 | applied. |
||
| 1836 | |||
| 1837 | @return list: with string entries denoting the peak number |
||
| 1838 | """ |
||
| 1839 | return [1, 2, 3] |
||
| 1840 | |||
| 1841 | def save_1d_data(self): |
||
| 1842 | |||
| 1843 | |||
| 1844 | # save also all kinds of data, which are the results during the |
||
| 1845 | # alignment measurements |
||
| 1846 | |||
| 1847 | pass |
||
| 1848 | |||
| 1849 | |||
| 1850 | def save_2d_data(self, tag=None, timestamp=None): |
||
| 1851 | """ Save the data of the """ |
||
| 1852 | |||
| 1853 | filepath = self._save_logic.get_path_for_module(module_name='Magnet') |
||
| 1854 | |||
| 1855 | if timestamp is None: |
||
| 1856 | timestamp = datetime.datetime.now() |
||
| 1857 | |||
| 1858 | # if tag is not None and len(tag) > 0: |
||
| 1859 | # filelabel = tag + '_magnet_alignment_data' |
||
| 1860 | # filelabel2 = tag + '_magnet_alignment_add_data' |
||
| 1861 | # else: |
||
| 1862 | # filelabel = 'magnet_alignment_data' |
||
| 1863 | # filelabel2 = 'magnet_alignment_add_data' |
||
| 1864 | |||
| 1865 | if tag is not None and len(tag) > 0: |
||
| 1866 | filelabel = tag + '_magnet_alignment_data' |
||
| 1867 | filelabel2 = tag + '_magnet_alignment_add_data' |
||
| 1868 | filelabel3 = tag + '_magnet_alignment_data_table' |
||
| 1869 | else: |
||
| 1870 | filelabel = 'magnet_alignment_data' |
||
| 1871 | filelabel2 = 'magnet_alignment_add_data' |
||
| 1872 | filelabel3 = 'magnet_alignment_data_table' |
||
| 1873 | |||
| 1874 | # prepare the data in a dict or in an OrderedDict: |
||
| 1875 | |||
| 1876 | # here is the matrix saved |
||
| 1877 | matrix_data = OrderedDict() |
||
| 1878 | |||
| 1879 | # here are all the parameters, which are saved for a certain matrix |
||
| 1880 | # entry, mainly coming from all the other logic modules except the magnet logic: |
||
| 1881 | add_matrix_data = OrderedDict() |
||
| 1882 | |||
| 1883 | # here are all supplementary information about the measurement, mainly |
||
| 1884 | # from the magnet logic |
||
| 1885 | supplementary_data = OrderedDict() |
||
| 1886 | |||
| 1887 | axes_names = list(self._saved_pos_before_align) |
||
| 1888 | |||
| 1889 | |||
| 1890 | matrix_data['Alignment Matrix'] = self._2D_data_matrix |
||
| 1891 | |||
| 1892 | parameters = OrderedDict() |
||
| 1893 | parameters['Measurement start time'] = self._start_measurement_time |
||
| 1894 | if self._stop_measurement_time is not None: |
||
| 1895 | parameters['Measurement stop time'] = self._stop_measurement_time |
||
| 1896 | parameters['Time at Data save'] = timestamp |
||
| 1897 | parameters['Pathway of the magnet alignment'] = 'Snake-wise steps' |
||
| 1898 | |||
| 1899 | for index, entry in enumerate(self._pathway): |
||
| 1900 | parameters['index_'+str(index)] = entry |
||
| 1901 | |||
| 1902 | parameters['Backmap of the magnet alignment'] = 'Index wise display' |
||
| 1903 | |||
| 1904 | for entry in self._backmap: |
||
| 1905 | parameters['related_intex_'+str(entry)] = self._backmap[entry] |
||
| 1906 | |||
| 1907 | |||
| 1908 | |||
| 1909 | self._save_logic.save_data(matrix_data, filepath, parameters=parameters, |
||
| 1910 | filelabel=filelabel, timestamp=timestamp, |
||
| 1911 | as_text=True) |
||
| 1912 | |||
| 1913 | self.log.debug('Magnet 2D data saved to:\n{0}'.format(filepath)) |
||
| 1914 | |||
| 1915 | # prepare the data in a dict or in an OrderedDict: |
||
| 1916 | add_data = OrderedDict() |
||
| 1917 | axis0_data = np.zeros(len(self._backmap)) |
||
| 1918 | axis1_data = np.zeros(len(self._backmap)) |
||
| 1919 | param_data = np.zeros(len(self._backmap), dtype='object') |
||
| 1920 | |||
| 1921 | for backmap_index in self._backmap: |
||
| 1922 | axis0_data[backmap_index] = self._backmap[backmap_index][self._axis0_name] |
||
| 1923 | axis1_data[backmap_index] = self._backmap[backmap_index][self._axis1_name] |
||
| 1924 | param_data[backmap_index] = str(self._2D_add_data_matrix[self._backmap[backmap_index]['index']]) |
||
| 1925 | |||
| 1926 | constr = self.get_hardware_constraints() |
||
| 1927 | units_axis0 = constr[self._axis0_name]['unit'] |
||
| 1928 | units_axis1 = constr[self._axis1_name]['unit'] |
||
| 1929 | |||
| 1930 | add_data['{0} values ({1})'.format(self._axis0_name, units_axis0)] = axis0_data |
||
| 1931 | add_data['{0} values ({1})'.format(self._axis1_name, units_axis1)] = axis1_data |
||
| 1932 | add_data['all measured additional parameter'] = param_data |
||
| 1933 | |||
| 1934 | |||
| 1935 | |||
| 1936 | self._save_logic.save_data(add_data, filepath, |
||
| 1937 | filelabel=filelabel2, timestamp=timestamp, |
||
| 1938 | as_text=True) |
||
| 1939 | # save the data table |
||
| 1940 | |||
| 1941 | count_data = self._2D_data_matrix |
||
| 1942 | x_val = self._2D_axis0_data |
||
| 1943 | y_val = self._2D_axis1_data |
||
| 1944 | save_dict = OrderedDict() |
||
| 1945 | axis0_key = '{0} values ({1})'.format(self._axis0_name, units_axis0) |
||
| 1946 | axis1_key = '{0} values ({1})'.format(self._axis1_name, units_axis1) |
||
| 1947 | counts_key = 'counts (c/s)' |
||
| 1948 | save_dict[axis0_key] = [] |
||
| 1949 | save_dict[axis1_key] = [] |
||
| 1950 | save_dict[counts_key] = [] |
||
| 1951 | |||
| 1952 | for ii, columns in enumerate(count_data): |
||
| 1953 | for jj, col_counts in enumerate(columns): |
||
| 1954 | # x_list = [x_val[ii]] * len(countlist) |
||
| 1955 | save_dict[axis0_key].append(x_val[ii]) |
||
| 1956 | save_dict[axis1_key].append(y_val[jj]) |
||
| 1957 | save_dict[counts_key].append(col_counts) |
||
| 1958 | |||
| 1959 | self._save_logic.save_data(save_dict, filepath, |
||
| 1960 | filelabel=filelabel3, timestamp=timestamp, |
||
| 1961 | as_text=True) |
||
| 1962 | |||
| 1963 | |||
| 1964 | def _move_to_index(self, pathway_index, pathway): |
||
| 1965 | |||
| 1966 | # make here the move and set also for the move the velocity, if |
||
| 1967 | # specified! |
||
| 1968 | |||
| 1969 | move_commmands = pathway[pathway_index] |
||
| 1970 | |||
| 1971 | move_dict_abs = dict() |
||
| 1972 | move_dict_rel = dict() |
||
| 1973 | move_dict_vel = dict() |
||
| 1974 | |||
| 1975 | for axis_name in move_commmands: |
||
| 1976 | |||
| 1977 | if move_commmands[axis_name].get('vel') is not None: |
||
| 1978 | move_dict_vel[axis_name] = move_commmands[axis_name]['vel'] |
||
| 1979 | |||
| 1980 | if move_commmands[axis_name].get('move_abs') is not None: |
||
| 1981 | move_dict_abs[axis_name] = move_commmands[axis_name]['move_abs'] |
||
| 1982 | elif move_commmands[axis_name].get('move_rel') is not None: |
||
| 1983 | move_dict_rel[axis_name] = move_commmands[axis_name]['move_rel'] |
||
| 1984 | |||
| 1985 | return move_dict_vel, move_dict_abs, move_dict_rel |
||
| 1986 | |||
| 1987 | def set_pos_checktime(self, checktime): |
||
| 1988 | if not np.isclose(0, checktime) and checktime>0: |
||
| 1989 | self._checktime = checktime |
||
| 1990 | else: |
||
| 1991 | self.log.warning('Could not set a new value for checktime, since ' |
||
| 1992 | 'the passed value "{0}" is either zero or negative!\n' |
||
| 1993 | 'Choose a proper checktime value in seconds, the old ' |
||
| 1994 | 'value will be kept!') |
||
| 1995 | |||
| 1996 | |||
| 1997 | def get_2d_data_matrix(self): |
||
| 1998 | return self._2D_data_matrix |
||
| 1999 | |||
| 2000 | def get_2d_axis_arrays(self): |
||
| 2001 | return self._2D_axis0_data, self._2D_axis1_data |
||
| 2002 | |||
| 2003 | def set_optimize_pos(self, state=True): |
||
| 2004 | """ Activate the optimize position option. """ |
||
| 2005 | self._optimize_pos = state |
||
| 2006 | |||
| 2007 | def get_optimize_pos(self): |
||
| 2008 | """ Retrieve whether the optimize position is set. |
||
| 2009 | |||
| 2010 | @return bool: whether the optimize_pos is set or not. |
||
| 2011 | """ |
||
| 2012 | return self._optimize_pos |
||
| 2013 |
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