class PulserParameterModel(CategoryTreeModel):
expression = Expression()
def __init__(self, parameterList, parent=None):
super(PulserParameterModel, self).__init__(parameterList, parent)
self.headerLookup.update({
(QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole, 0): 'Name',
(QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole, 1): 'Value'
})
self.dataLookup.update({
(QtCore.Qt.DisplayRole, 0): lambda node: node.content.name,
(QtCore.Qt.DisplayRole, 1): lambda node: str(node.content.value),
(QtCore.Qt.EditRole, 1): lambda node: node.content.string,
(QtCore.Qt.BackgroundRole, 1): self.dependencyBgFunction,
(QtCore.Qt.ToolTipRole, 1): self.toolTipFunction
})
self.setDataLookup.update({
(QtCore.Qt.EditRole, 1): lambda index, value: self.setValue(index, value),
(QtCore.Qt.UserRole, 1): lambda index, value: self.setStrValue(index, value)
})
self.flagsLookup.update({
0: QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable,
1: QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsEditable | QtCore.Qt.ItemIsSelectable
})
self.numColumns = 2
def setValue(self, index, value):
node = self.nodeFromIndex(index)
node.content.value = value
return True
def setStrValue(self, index, strValue):
node = self.nodeFromIndex(index)
node.content.string = strValue
return True
class MeanEvaluation(EvaluationBase):
name = 'Mean'
tooltip = "Mean of observed counts"
errorBarTypes = ['shotnoise','statistical','min max']
expression = Expression()
def __init__(self, globalDict=None, settings=None):
EvaluationBase.__init__(self, globalDict, settings)
self.errorBarTypeLookup = {'shotnoise': self.evaluateShotnoise,
'statistical': self.evaluateStatistical,
'min max': self.evaluateMinMax}
def setDefault(self):
self.settings.setdefault('errorBarType', 'shotnoise')
self.settings.setdefault('transformation', "")
if type(self.settings['errorBarType']) in (int, float):
self.settings['errorBarType'] = self.errorBarTypes[self.settings['errorBarType']]
def evaluateShotnoise(self, countarray ):
summe = numpy.sum( countarray )
l = float(len(countarray))
mean = summe/l
stderror = math.sqrt( max(summe,1) )/l
return mean, (stderror/2. if summe>0 else 0, stderror/2. ), summe
def evaluateStatistical(self, countarray):
mean = numpy.mean( countarray )
stderr = numpy.std( countarray, ddof=1 ) / math.sqrt( max( len(countarray)-1, 1) )
return mean, (stderr/2.,stderr/2.), numpy.sum( countarray )
def evaluateMinMax(self, countarray):
mean = numpy.mean( countarray )
return mean, (mean-numpy.min(countarray), numpy.max(countarray)-mean), numpy.sum(countarray)
def evaluate(self, data, evaluation, expected=None, ppDict=None, globalDict=None):
countarray = evaluation.getChannelData(data)
if not countarray:
return 0, (0,0), 0
mean, (minus, plus), raw = self.errorBarTypeLookup[self.settings['errorBarType']](countarray)
if self.settings['transformation']!="":
mydict = { 'y': mean }
if ppDict:
mydict.update( ppDict )
mean = float(self.expression.evaluate(self.settings['transformation'], mydict))
mydict['y'] = mean+plus
plus = float(self.expression.evaluate(self.settings['transformation'], mydict))
mydict['y'] = mean-minus
minus = float(self.expression.evaluate(self.settings['transformation'], mydict))
return mean, (mean-minus, plus-mean), raw
return mean, (minus, plus), raw
def parameters(self):
parameterDict = super(MeanEvaluation, self).parameters()
if isinstance(self.settings['errorBarType'], int): #preserve backwards compatibility -- previously errorBarType was an int, now it's a string, so we map it over
self.settings['errorBarType'] = self.errorBarTypes[self.settings['errorBarType']]
parameterDict['errorBarType'] = Parameter(name='errorBarType', dataType='select',
choices=self.errorBarTypes, value=self.settings['errorBarType'])
parameterDict['transformation'] = Parameter(name='transformation', dataType='str',
value=self.settings['transformation'], tooltip="use y for the result in a mathematical expression")
return parameterDict
def evaluate(self, globalDict):
for parameter in list(self.parameterDict.values()):
if parameter.text is not None:
value = Expression().evaluateAsMagnitude(
parameter.text, globalDict)
parameter.value = value # set saved value to match new parameter text
modelIndex = self.createIndex(
self.parameterDict.index(parameter.name),
self.column.value)
self.dataChanged.emit(modelIndex, modelIndex)
self.valueChanged.emit(parameter)
class VarAsOutputChannel(object):
"""This is a class that makes the AWG parameters work as an external parameter output channel in a parameter scan.
The AWG variables are not external parameter output channels, but the external parameter scan method needs the scan
parameter to have several specific methods and attributes (as OutputChannel does). This class provides those attributes."""
expression = Expression()
def __init__(self, awgUi, name, globalDict):
self.awgUi = awgUi
self.name = name
self.useExternalValue = False
self.savedValue = None
self.globalDict = globalDict
@property
def value(self):
return self.awgUi.settings.varDict[self.name]['value']
@property
def strValue(self):
return self.awgUi.settings.varDict[self.name]['text']
@property
def device(self):
return self.awgUi.device
def saveValue(self, overwrite=True):
"""save current value"""
if self.savedValue is None or overwrite:
self.savedValue = self.value
return self.savedValue
def setValue(self, targetValue):
"""set the variable to targetValue"""
if targetValue is not None:
self.awgUi.settings.varDict[self.name]['value'] = targetValue
modelIndex = self.awgUi.tableModel.createIndex(
self.awgUi.settings.varDict.index(self.name),
self.awgUi.tableModel.column.value)
self.awgUi.tableModel.dataChanged.emit(modelIndex, modelIndex)
for channelUi in self.awgUi.awgChannelUiList:
channelUi.replot()
self.device.program()
return True
def restoreValue(self):
"""restore the value saved previously, if any, then clear the saved value."""
value = self.savedValue if self.strValue is None else self.expression.evaluateAsMagnitude(
self.strValue, self.globalDict)
if value is not None:
self.setValue(value)
self.savedValue = None
return True
class DACChannelSetting(object):
expression = Expression()
def __init__(self, globalDict=None):
self._globalDict = None
self._voltage = ExpressionValue(None, self._globalDict)
self.enabled = False
self.name = ""
self.resetAfterPP = False
def __setstate__(self, state):
self.__dict__ = state
self.__dict__.setdefault('resetAfterPP', False)
self.__dict__.setdefault('_globalDict', dict())
def __getstate__(self):
dictcopy = dict(self.__dict__)
dictcopy.pop('_globalDict', None)
return dictcopy
@property
def outputVoltage(self):
return self._voltage.value if self.enabled else Q(0, 'V')
@property
def globalDict(self):
return self._globalDict
@globalDict.setter
def globalDict(self, globalDict):
self._globalDict = globalDict
self._voltage.globalDict = globalDict
@property
def voltage(self):
return self._voltage.value
@voltage.setter
def voltage(self, v):
self._voltage.value = v
@property
def voltageText(self):
return self._voltage.string
@voltageText.setter
def voltageText(self, s):
self._voltage.string = s
@SetterProperty
def onChange(self, onChange):
self._voltage.valueChanged.connect(onChange)
class AWGSegment(AWGSegmentNode):
expression = Expression()
def __init__(self, parent, *args, **kwds):
super(AWGSegment, self).__init__(parent)
self.nodeType = nodeTypes.segment
self.stack = None
self.equation = kwds.get('equation', 'V0')
self.duration = kwds.get('duration', 'T0')
def __setstate__(self, state):
self.__dict__ = state
#self.__dict__['expression'] = Expression()
self.__dict__['stack'] = None
class FreerunningGenerator:
expression = Expression()
def __init__(self, scan):
self.scan = scan
def prepare(self, pulseProgramUi, maxUpdatesToWrite=None):
if self.scan.gateSequenceUi.settings.enabled:
_, data, self.gateSequenceSettings = self.scan.gateSequenceUi.gateSequenceScanData(
)
else:
data = []
return ([], data) # write 5 points to the fifo queue at start,
# this prevents the Step in Place from stopping in case the computer lags behind evaluating by up to 5 points
def restartCode(self, currentIndex):
return []
def dataNextCode(self, experiment):
return None
def xValue(self, index, data):
return self.expression.evaluate(
self.scan.xExpression,
{'x': data.scanvalue if data.scanvalue else 0
}) if self.scan.xExpression else data.scanvalue
def xRange(self):
return []
def appendData(self, traceList, x, evaluated, timeinterval):
if evaluated and traceList:
traceList[0].x = numpy.append(traceList[0].x, x)
traceList[0].timeintervalAppend(timeinterval)
for trace, (y, error, raw) in zip(traceList, evaluated):
trace.y = numpy.append(trace.y, y)
trace.raw = numpy.append(trace.raw, raw)
if error is not None:
trace.bottom = numpy.append(trace.bottom, error[0])
trace.top = numpy.append(trace.top, error[1])
def dataOnFinal(self, experiment, currentState):
experiment.onStop()
def expected(self, index):
return None
class DDSChannelSettings(object):
expression = Expression()
def __init__(self):
self.frequency = Q(0, 'MHz')
self.phase = Q(0)
self.amplitude = Q(0)
self.frequencyText = None
self.phaseText = None
self.amplitudeText = None
self.enabled = False
self.name = ""
self.squareEnabled = False
self.shutter = None
self.channel = None
def __setstate__(self, state):
self.__dict__ = state
self.__dict__.setdefault('name', '')
self.__dict__.setdefault('squareEnabled', False)
self.__dict__.setdefault('shutter', None)
self.__dict__.setdefault('channel', None)
def evaluateFrequency(self, globalDict):
if self.frequencyText:
oldfreq = self.frequency
self.frequency = self.expression.evaluateAsMagnitude(
self.frequencyText, globalDict)
return self.frequency != oldfreq
return False
def evaluateAmplitude(self, globalDict):
if self.amplitudeText:
oldamp = self.amplitude
self.amplitude = self.expression.evaluateAsMagnitude(
self.amplitudeText, globalDict)
return oldamp != self.amplitude
return False
def evaluatePhase(self, globalDict):
if self.phaseText:
oldphase = self.phase
self.phase = self.expression.evaluateAsMagnitude(
self.phaseText, globalDict)
return oldphase != self.phase
return False
class GateSequenceCompiler(object):
expression = Expression()
def __init__(self, pulseProgram):
self.pulseProgram = pulseProgram
self.compiledGates = dict()
"""Compile all gate sequences into binary representation
returns tuple of start address list and bytearray data"""
def gateSequencesCompile(self, gatesets):
logger = logging.getLogger(__name__)
logger.info("compiling {0} gateSequences.".format(
len(gatesets.GateSequenceDict)))
self.gateCompile(gatesets.gateDefinition)
addresses = list()
data = list()
index = 0
for gateset in list(gatesets.GateSequenceDict.values()):
gatesetdata = self.gateSequenceCompile(gateset)
addresses.append(index)
data.extend(gatesetdata)
index += len(gatesetdata) * 8
return addresses, data
"""Compile one gateset into its binary representation"""
def gateSequenceCompile(self, gateset):
data = list()
length = 0
for gate in gateset:
thisCompiledGate = self.compiledGates[gate]
data.extend(thisCompiledGate)
length += len(thisCompiledGate) // self.pulseListLength
return [length] + data
"""Compile each gate definition into its binary representation"""
def gateCompile(self, gateDefinition):
logger = logging.getLogger(__name__)
variables = self.pulseProgram.variables()
pulseList = list(gateDefinition.PulseDefinition.values())
self.pulseListLength = len(pulseList)
for gatename, gate in gateDefinition.Gates.items(
): # for all defined gates
data = list()
gateLength = 0
for name, strvalue in gate.pulsedict:
result = self.expression.evaluate(strvalue, variables)
if name != pulseList[gateLength % self.pulseListLength].name:
raise GateSequenceCompilerException(
"In gate {0} entry {1} found '{2}' expected '{3}'".
format(gatename, gateLength, name,
pulseList[gateLength % self.pulseListLength]))
encoding = gateDefinition.PulseDefinition[name].encoding
data.append(
self.pulseProgram.convertParameter(result, encoding))
gateLength += 1
if gateLength % self.pulseListLength != 0:
raise GateSequenceCompilerException(
"In gate {0} number of entries ({1}) is not a multiple of the pulse definition length ({2})"
.format(gatename, gateLength, self.pulseListLength))
self.compiledGates[gatename] = data
logger.info("compiled {0} to {1}".format(gatename, data))
# *****************************************************************
# IonControl: Copyright 2016 Sandia Corporation
# This Software is released under the GPL license detailed
# in the file "license.txt" in the top-level IonControl directory
# *****************************************************************
import unittest
from modules.Expression import Expression
import math
from modules.quantity import Q
ExprEval = Expression()
def e(expr, vars=dict(), useFloat=False):
return ExprEval.evaluate(expr, vars, useFloat=useFloat)
class TestExpression(unittest.TestCase):
def test_literals(self):
self.assertEqual(e("9"), 9)
self.assertEqual(e('-9'), -9)
self.assertEqual(e('--9'), 9)
self.assertEqual(e('-E'), -math.e)
self.assertEqual(e('9 + 3 + 6 + 25'), 9 + 3 + 6 + 25)
self.assertEqual(e("9 + 3 / 11"), 9 + 3 / 11)
self.assertEqual(e('9 * (7 + 28) / 12'), 9 * (7 + 28) / 12)
self.assertEqual(e("9 - 12 - 6"), 9 - 12 - 6)
self.assertEqual(e("9 - (12 - 6)"), 9 - (12 - 6))
self.assertEqual(e("2 * 3.14159"), 2 * 3.14159)
self.assertEqual(e("3.1415926535 * 3.1415926535 / 10"),
3.1415926535 * 3.1415926535 / 10)
self.assertEqual(e("PI * PI / 10"), math.pi * math.pi / 10)
class CounterSumMeanEvaluation(EvaluationBase):
"""Evaluate the mean of a sum of counters"""
name = 'Counter Sum Mean'
tooltip = "Mean of sum of observed counts"
hasChannel = False
errorBarTypes = ['shotnoise', 'statistical', 'min max']
expression = Expression()
def __init__(self, globalDict=None, settings=None):
EvaluationBase.__init__(self, globalDict, settings)
self.errorBarTypeLookup = {'shotnoise': self.evaluateShotnoise,
'statistical': self.evaluateStatistical,
'min max': self.evaluateMinMax}
def setDefault(self):
self.settings.setdefault('errorBarType', 'shotnoise')
self.settings.setdefault('transformation', "")
self.settings.setdefault('counters', [])
self.settings.setdefault('id', 0)
def evaluateShotnoise(self, countarray):
summe = numpy.sum(countarray)
l = float(len(countarray))
mean = summe / l
stderror = math.sqrt(max(summe, 1)) / l
return mean, (stderror / 2. if summe > 0 else 0, stderror / 2.), summe
def evaluateStatistical(self, countarray):
mean = numpy.mean(countarray)
stderr = numpy.std(countarray, ddof=1) / math.sqrt(max(len(countarray) - 1, 1))
return mean, (stderr / 2., stderr / 2.), numpy.sum(countarray)
def evaluateMinMax(self, countarray):
mean = numpy.mean(countarray)
return mean, (mean - numpy.min(countarray), numpy.max(countarray) - mean), numpy.sum(countarray)
def getCountArray(self, data):
counters = [((self.settings['id']&0xff)<<8) | (int(counter) & 0xff) for counter in self.settings['counters']]
listOfCountArrays = [data.count[counter] for counter in counters if counter in data.count.keys()]
countarray = [sum(sublist) for sublist in zip(*listOfCountArrays)]
return countarray
def evaluate(self, data, evaluation, expected=None, ppDict=None, globalDict=None):
countarray = self.getCountArray(data)
if not countarray:
return 0, (0, 0), 0
mean, (minus, plus), raw = self.errorBarTypeLookup[self.settings['errorBarType']](countarray)
if self.settings['transformation'] != "":
mydict = {'y': mean}
if ppDict:
mydict.update(ppDict)
mean = float(self.expression.evaluate(self.settings['transformation'], mydict))
mydict['y'] = mean + plus
plus = float(self.expression.evaluate(self.settings['transformation'], mydict))
mydict['y'] = mean - minus
minus = float(self.expression.evaluate(self.settings['transformation'], mydict))
return mean, (mean - minus, plus - mean), raw
return mean, (minus, plus), raw
def histogram(self, data, evaluation, histogramBins=50 ):
countarray = self.getCountArray(data)
y, x = numpy.histogram( countarray, range=(0, histogramBins), bins=histogramBins)
return y, x, None # third parameter is optional function
def parameters(self):
parameterDict = super(CounterSumMeanEvaluation, self).parameters()
parameterDict['id'] = Parameter(name='id', dataType='magnitude', value=self.settings['id'],
text=self.settings.get( ('id', 'text') ), tooltip='id of counters to sum')
parameterDict['counters'] = Parameter(name='counters', dataType='multiselect',
value=self.settings['counters'], choices=list(map(str, range(16))),
tooltip='counters to sum')
parameterDict['errorBarType'] = Parameter(name='errorBarType', dataType='select',
choices=self.errorBarTypes, value=self.settings['errorBarType'])
parameterDict['transformation'] = Parameter(name='transformation', dataType='str',
value=self.settings['transformation'],
tooltip="use y for the result in a mathematical expression")
return parameterDict
class Adjust(object):
expression = Expression()
dataChangedObject = DataChangedS()
dataChanged = dataChangedObject.dataChanged
def __init__(self, globalDict=dict()):
self._globalDict = globalDict
self._line = ExpressionValue(name="line",
globalDict=globalDict,
value=Q(0.0))
self._lineValue = self._line.value
self._lineGain = ExpressionValue(name="lineGain",
globalDict=globalDict,
value=Q(1.0))
self._globalGain = ExpressionValue(name="globalGain",
globalDict=globalDict,
value=Q(1.0))
self._line.valueChanged.connect(self.onLineExpressionChanged)
self._lineGain.valueChanged.connect(self.onLineExpressionChanged)
self._globalGain.valueChanged.connect(self.onLineExpressionChanged)
@property
def globalDict(self):
return self._globalDict
@globalDict.setter
def globalDict(self, globalDict):
self._globalDict = globalDict
self._lineGain.globalDict = globalDict
self._globalGain.globalDict = globalDict
self._line.globalDict = globalDict
@property
def line(self):
return self._lineValue
@line.setter
def line(self, value):
self._lineValue = value
@property
def lineGain(self):
return self._lineGain.value
@lineGain.setter
def lineGain(self, value):
self._lineGain.value = value
@property
def globalGain(self):
return self._globalGain.value
@globalGain.setter
def globalGain(self, value):
self._globalGain.value = value
@property
def lineString(self):
return self._line.string
@lineString.setter
def lineString(self, s):
self._line.string = s
@property
def lineGainString(self):
return self._lineGain.string
@lineGainString.setter
def lineGainString(self, s):
self._lineGain.string = s
@property
def globalGainString(self):
return self._globalGain.value
@globalGainString.setter
def globalGainString(self, s):
self._globalGain.string = s
def __getstate__(self):
dictcopy = dict(self.__dict__)
dictcopy.pop('_globalDict', None)
return dictcopy
def __setstate__(self, state):
state.setdefault('_globalDict', dict())
state.pop('line', None)
self.__dict__ = state
if not isinstance(self._line, ExpressionValue):
self._line = ExpressionValue(name="line",
globalDict=self._globalDict,
value=Q(self._line))
if not isinstance(self._lineGain, ExpressionValue):
self._lineGain = ExpressionValue(name='lineGain',
globalDict=self._globalDict,
value=Q(self._lineGain))
if not isinstance(self._globalGain, ExpressionValue):
self._globalGain = ExpressionValue(name='globalGain',
globalDict=self._globalDict,
value=Q(self._globalGain))
self._lineValue = self._line.value
self._line.valueChanged.connect(self.onLineExpressionChanged)
self._lineGain.valueChanged.connect(self.onLineExpressionChanged)
self._globalGain.valueChanged.connect(self.onLineExpressionChanged)
def onLineExpressionChanged(self, name, value, string, origin):
if name == 'line':
self._lineValue = value
self.dataChanged.emit(self)
class ParameterScanGenerator:
expression = Expression()
def __init__(self, scan):
self.scan = scan
self.nextIndexToWrite = 0
self.numUpdatedVariables = 1
def prepare(self, pulseProgramUi, maxUpdatesToWrite=None):
self.maxUpdatesToWrite = maxUpdatesToWrite
if self.scan.gateSequenceUi.settings.enabled:
_, data, self.gateSequenceSettings = self.scan.gateSequenceUi.gateSequenceScanData(
)
else:
data = []
parameterName = self.scan.scanParameter if self.scan.scanTarget == 'Internal' else self.scan.parallelInternalScanParameter
if parameterName == "None":
self.scan.code, self.numVariablesPerUpdate = NoneScanCode * len(
self.scan.list), 1
else:
self.scan.code, self.numVariablesPerUpdate = pulseProgramUi.variableScanCode(
parameterName, self.scan.list, extendedReturn=True)
self.numUpdatedVariables = len(self.scan.code) // 2 // len(
self.scan.list)
maxWordsToWrite = MaxWordsInFifo if maxUpdatesToWrite is None else 2 * self.numUpdatedVariables * maxUpdatesToWrite
if len(self.scan.code) > maxWordsToWrite:
self.nextIndexToWrite = maxWordsToWrite
return self.scan.code[:maxWordsToWrite], data
self.nextIndexToWrite = len(self.scan.code)
return self.scan.code, data
def restartCode(self, currentIndex):
maxWordsToWrite = MaxWordsInFifo if self.maxUpdatesToWrite is None else 2 * self.numUpdatedVariables * self.maxUpdatesToWrite
currentWordCount = 2 * self.numUpdatedVariables * currentIndex
if len(self.scan.code) - currentWordCount > maxWordsToWrite:
self.nextIndexToWrite = maxWordsToWrite + currentWordCount
return (self.scan.code[currentWordCount:self.nextIndexToWrite])
self.nextIndexToWrite = len(self.scan.code)
return self.scan.code[currentWordCount:]
def xValue(self, index, data):
value = self.scan.list[index]
if self.scan.xExpression:
value = self.expression.evaluate(self.scan.xExpression,
{"x": value})
if not is_Q(value):
return value
if (not self.scan.xUnit
and not value.dimensionless) or not value.dimensionality == Q(
1, self.scan.xUnit).dimensionality:
self.scan.xUnit = value.to_compact()
return value.m_as(self.scan.xUnit)
def dataNextCode(self, experiment):
if self.nextIndexToWrite < len(self.scan.code):
start = self.nextIndexToWrite
self.nextIndexToWrite = min(
len(self.scan.code) + 1,
self.nextIndexToWrite + 2 * self.numUpdatedVariables)
return self.scan.code[start:self.nextIndexToWrite]
return []
def dataOnFinal(self, experiment, currentState):
experiment.onStop()
def xRange(self):
return self.scan.start.m_as(self.scan.xUnit), self.scan.stop.m_as(
self.scan.xUnit)
def appendData(self, traceList, x, evaluated, timeinterval):
if evaluated and traceList:
traceList[0].x = numpy.append(traceList[0].x, x)
traceList[0].timeintervalAppend(timeinterval)
for trace, (y, error, raw) in zip(traceList, evaluated):
trace.y = numpy.append(trace.y, y)
trace.raw = numpy.append(trace.raw, raw)
if error is not None:
trace.bottom = numpy.append(trace.bottom, error[0])
trace.top = numpy.append(trace.top, error[1])
def expected(self, index):
return None
class PushVariable(object):
expression = Expression()
XMLTagName = "PushVariable"
def __init__(self):
self.push = False
self.destinationName = None
self.variableName = None
self.definition = ""
self.value = None
self.minimum = ""
self.maximum = ""
self.strMinimum = None
self.strMaximum = None
self.valueValid = True
self.minValid = True
self.maxValid = True
def __setstate__(self, s):
self.__dict__ = s
self.__dict__.setdefault('destinationName', None)
self.__dict__.setdefault('variableName', None)
self.__dict__.setdefault('strMinimum', None)
self.__dict__.setdefault('strMaximum', None)
self.__dict__.setdefault('valueValid', True)
self.__dict__.setdefault('minValid', True)
self.__dict__.setdefault('maxValid', True)
stateFields = [
'push', 'definition', 'destinationName', 'variableName', 'value',
'minimum', 'maximum', 'strMinimum', 'strMaximum', 'valueValid',
'minValid', 'maxValid'
]
def exportXml(self, element):
myElement = ElementTree.SubElement(element, self.XMLTagName)
xmlEncodeAttributes(self.__dict__, myElement)
return myElement
@staticmethod
def fromXmlElement(element, flat=False):
myElement = element if flat else element.find(PushVariable.XMLTagName)
v = PushVariable()
v.__dict__.update(xmlParseAttributes(myElement))
return v
def __eq__(self, other):
return isinstance(other, self.__class__) and tuple(
getattr(self, field) for field in self.stateFields) == tuple(
getattr(other, field) for field in self.stateFields)
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash(tuple(getattr(self, field) for field in self.stateFields))
def evaluate(self, variables=dict(), useFloat=False):
if self.definition:
try:
self.value = self.expression.evaluate(self.definition,
variables,
useFloat=useFloat)
self.valueValid = True
except Exception as e:
logging.getLogger(__name__).warning(str(e))
self.valueValid = False
if self.strMinimum:
try:
self.minimum = self.expression.evaluate(self.strMinimum,
variables,
useFloat=useFloat)
self.minValid = True
except Exception as e:
logging.getLogger(__name__).warning(str(e))
self.minValid = False
if self.strMaximum:
try:
self.maximum = self.expression.evaluate(self.strMaximum,
variables,
useFloat=useFloat)
self.maxValid = True
except Exception as e:
logging.getLogger(__name__).warning(str(e))
self.maxValid = False
def pushRecord(self, variables=None):
if variables is not None:
self.evaluate(variables)
if (self.push and self.destinationName is not None
and self.destinationName != 'None'
and self.variableName is not None
and self.variableName != 'None' and self.value is not None):
if ((not self.minimum or self.value >= self.minimum)
and (not self.maximum or self.value <= self.maximum)):
return [(self.destinationName, self.variableName, self.value)
], []
else:
logging.getLogger(__name__).warning(
"Result out of range, Not pushing {0} to {1}: {2} <= {3} <= {4}"
.format(self.variableName, self.destinationName,
self.minimum, self.value, self.maximum))
return [], [(self.destinationName, self.variableName)]
else:
if (self.push):
logging.getLogger(__name__).warning(
"Not pushing {0} to {1}: {2} <= {3} <= {4}, push not fully specified"
.format(self.variableName, self.destinationName,
self.minimum, self.value, self.maximum))
return [], []
@property
def key(self):
return (self.destinationName, self.variableName)
@property
def hasStrMinimum(self):
return (
1 if self.minValid else -1) if self.strMinimum is not None else 0
@property
def hasStrMaximum(self):
return (
1 if self.maxValid else -1) if self.strMaximum is not None else 0
@property
def valueStatus(self):
return 1 if self.valueValid else -1
class ExternalParameterControlModel(CategoryTreeModel):
valueChanged = QtCore.pyqtSignal(str, object)
expression = Expression()
def __init__(self, controlUi, parameterList=[], parent=None):
super(ExternalParameterControlModel,
self).__init__(parameterList, parent, nodeNameAttr='displayName')
self.parameterList = parameterList
self.controlUi = controlUi
for parameter in parameterList:
parameter.categories = parameter.device.name
self.headerLookup.update({
(QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole, 0):
'Name',
(QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole, 1):
'Control',
(QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole, 2):
'External'
})
self.dataLookup.update({
(QtCore.Qt.DisplayRole, 0):
lambda node: node.content.displayName,
(QtCore.Qt.DisplayRole, 1):
lambda node: str(node.content.targetValue),
(QtCore.Qt.EditRole, 1):
lambda node: firstNotNone(node.content.string,
str(node.content.targetValue)),
(QtCore.Qt.UserRole, 1):
lambda node: node.content.dimension,
(QtCore.Qt.DisplayRole, 2):
lambda node: str(node.content.value),
(QtCore.Qt.BackgroundRole, 1):
self.dependencyBgFunction,
(QtCore.Qt.ToolTipRole, 1):
self.toolTipFunction
})
self.setDataLookup.update({
(QtCore.Qt.EditRole, 1):
lambda index, value: self.setValue(index, value),
(QtCore.Qt.UserRole, 1):
lambda index, value: self.setStrValue(index, value),
})
self.flagsLookup = {
1:
QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsEditable
| QtCore.Qt.ItemIsSelectable
}
self.numColumns = 3
self.allowReordering = True
self.signalConnections = list()
def setParameterList(self, outputChannelDict):
self.beginResetModel()
self.parameterList = list(outputChannelDict.values())
# first disconnect all old signals, they may point to the wrong index
for inst, valueSlot, targetValueSlot in self.signalConnections:
inst.valueChanged.disconnect(valueSlot)
inst.targetChanged.disconnect(targetValueSlot)
del self.signalConnections[:] # clear the list
# now we can add new connections
for listIndex, inst in enumerate(self.parameterList):
inst.multipleChannels = len(inst.device._outputChannels) > 1
inst.categories = inst.device.name if inst.multipleChannels else None
inst.displayName = inst.channelName if inst.multipleChannels else inst.device.name
inst.toolTip = None
valueSlot = functools.partial(self.showValue, listIndex)
targetValueSlot = functools.partial(self.onTargetChanged,
listIndex)
inst.valueChanged.connect(valueSlot)
inst.targetChanged.connect(targetValueSlot)
self.signalConnections.append((inst, valueSlot, targetValueSlot))
self.clear()
self.endResetModel()
self.addNodeList(self.parameterList)
def showValue(self, listIndex, value, tooltip=None):
inst = self.parameterList[listIndex]
inst.toolTip = tooltip
id = inst.name if inst.multipleChannels else inst.device.name
node = self.nodeDict[id]
modelIndex = self.indexFromNode(node, 2)
self.dataChanged.emit(modelIndex, modelIndex)
def onTargetChanged(self, listIndex, value):
inst = self.parameterList[listIndex]
id = inst.name if inst.multipleChannels else inst.device.name
node = self.nodeDict[id]
modelIndex = self.indexFromNode(node, 1)
self.dataChanged.emit(modelIndex, modelIndex)
def setValue(self, index, value):
node = self.nodeFromIndex(index)
self._setValue(node.content, value)
def _setValue(self, inst, value):
logger = logging.getLogger(__name__)
logger.debug("setValue {0}".format(value))
inst.targetValue = value
self.setValueFollowup(inst)
return True
def setStrValue(self, index, strValue):
node = self.nodeFromIndex(index)
node.content.string = strValue
return True
def setValueFollowup(self, inst):
try:
logger = logging.getLogger(__name__)
logger.debug("setValueFollowup {0}".format(inst.value))
if not inst.setValue(inst.targetValue):
delay = int(inst.settings.delay.m_as('ms'))
QtCore.QTimer.singleShot(
delay, functools.partial(self.setValueFollowup, inst))
except Exception as e:
logger.exception(e)
logger.warning(
"Exception during setValueFollowup, number of adjusting devices likely to be faulty"
)
def update(self, iterable):
for destination, name, value in iterable:
if destination == 'External':
for inst in self.parameterList:
if inst.name == name:
break
inst.savedValue = value # set saved value to make this new value the default
node = self.nodeFromContent(inst)
self.setValue(self.indexFromNode(node, 1), value)
inst.strValue = None
logging.info(
"Pushed to external parameter {0} value {1}".format(
name, value))
def evaluate(self, name):
for inst in self.parameterList:
if inst.hasDependency:
value = self.expression.evaluateAsMagnitude(
inst.string, self.controlUi.globalDict)
self._setValue(inst, value)
inst.savedValue = value # set saved value to make this new value the default
node = self.nodeFromContent(inst)
index = self.indexFromNode(node, 1)
self.dataChanged.emit(index, index)
class FitFunctionBase(object, metaclass=FitFunctionMeta):
expression = Expression()
name = 'None'
parameterNames = list()
def __init__(self):
numParameters = len(self.parameterNames)
self.epsfcn = 0.0
self.parameters = [0] * numParameters
self.startParameters = [1] * numParameters
self.startParameterExpressions = None # will be initialized by FitUiTableModel if values are available
self.parameterEnabled = [True] * numParameters
self.parametersConfidence = [None] * numParameters
self.units = None
self.results = SequenceDict({'RMSres': ResultRecord(name='RMSres')})
self.useSmartStartValues = False
self.hasSmartStart = not hasattr(self.smartStartValues, 'isNative')
self.parametersUpdated = Observable()
self.parameterBounds = [[None, None] for _ in range(numParameters)]
self.parameterBoundsExpressions = None
self.useErrorBars = True
def __setstate__(self, state):
state.pop('parameterNames', None)
state.pop('cov_x', None)
state.pop('infodict', None)
state.pop('laguerreCacheEta', None)
state.pop('laguerreTable', None)
state.pop('pnCacheBeta', None)
state.pop('pnTable', None)
self.__dict__ = state
self.__dict__.setdefault('useSmartStartValues', False)
self.__dict__.setdefault('startParameterExpressions', None)
self.__dict__.setdefault('parameterBounds',
[[None, None]
for _ in range(len(self.parameterNames))])
self.__dict__.setdefault('parameterBoundsExpressions', None)
self.__dict__.setdefault('useErrorBars', True)
self.hasSmartStart = not hasattr(self.smartStartValues, 'isNative')
def allFitParameters(self, p):
"""return a list where the disabled parameters are added to the enabled parameters given in p"""
pindex = 0
params = list()
for index, enabled in enumerate(self.parameterEnabled):
if enabled:
params.append(p[pindex])
pindex += 1
else:
params.append(float(self.startParameters[index]))
return params
@staticmethod
def coercedValue(val, bounds):
if bounds[1] is not None and val >= bounds[1]:
val = float(0.95 * bounds[1] + 0.05 *
bounds[0] if bounds[0] is not None else bounds[1] -
0.01)
if bounds[0] is not None and val <= bounds[0]:
val = float(0.95 * bounds[0] + 0.05 *
bounds[1] if bounds[1] is not None else bounds[0] +
0.01)
return val
def enabledStartParameters(self, parameters=None, bounded=False):
"""return a list of only the enabled start parameters"""
if parameters is None:
parameters = self.startParameters
params = list()
if bounded:
for enabled, param, bounds in zip(self.parameterEnabled,
parameters,
self.parameterBounds):
if enabled:
params.append(self.coercedValue(float(param), bounds))
else:
for enabled, param in zip(self.parameterEnabled, parameters):
if enabled:
params.append(float(param))
return params
def enabledFitParameters(self, parameters=None):
"""return a list of only the enabled fit parameters"""
if parameters is None:
parameters = self.parameters
params = list()
for enabled, param in zip(self.parameterEnabled, parameters):
if enabled:
params.append(float(param))
return params
def enabledParameterNames(self):
"""return a list of only the enabled fit parameters"""
params = list()
for enabled, param in zip(self.parameterEnabled, self.parameterNames):
if enabled:
params.append(param)
return params
def setEnabledFitParameters(self, parameters):
"""set the fitted parameters if enabled"""
pindex = 0
for index, enabled in enumerate(self.parameterEnabled):
if enabled:
self.parameters[index] = parameters[pindex]
pindex += 1
else:
self.parameters[index] = float(self.startParameters[index])
def setEnabledConfidenceParameters(self, confidence):
"""set the parameter confidence values for the enabled parameters"""
pindex = 0
for index, enabled in enumerate(self.parameterEnabled):
if enabled:
self.parametersConfidence[index] = confidence[pindex]
pindex += 1
else:
self.parametersConfidence[index] = None
@native
def smartStartValues(self, x, y, parameters, enabled):
return None
def enabledSmartStartValues(self, x, y, parameters):
smartParameters = self.smartStartValues(x, y, parameters,
self.parameterEnabled)
return [
smartparam if enabled else param for enabled, param, smartparam in
zip(self.parameterEnabled, parameters, smartParameters)
] if smartParameters is not None else None
def evaluate(self, globalDict):
myReplacementDict = self.replacementDict()
if globalDict is not None:
myReplacementDict.update(globalDict)
if self.startParameterExpressions is not None:
self.startParameters = [
param if expr is None else self.expression.evaluateAsMagnitude(
expr, myReplacementDict) for param, expr in zip(
self.startParameters, self.startParameterExpressions)
]
if self.parameterBoundsExpressions is not None:
self.parameterBounds = [[
bound[0]
if expr[0] is None else self.expression.evaluateAsMagnitude(
expr[0], myReplacementDict),
bound[1] if expr[1] is None else
self.expression.evaluateAsMagnitude(expr[0], myReplacementDict)
] for bound, expr in zip(self.parameterBounds,
self.parameterBoundsExpressions)]
def enabledBounds(self):
result = [[
float(bounds[0]) if bounds[0] is not None else None,
float(bounds[1]) if bounds[1] is not None else None
] for enabled, bounds in zip(self.parameterEnabled,
self.parameterBounds) if enabled]
enabled = any((any(bounds) for bounds in result))
return result if enabled else None
def leastsq(self, x, y, parameters=None, sigma=None):
logger = logging.getLogger(__name__)
# Ensure all values of sigma or non zero by replacing with the minimum nonzero value
if sigma is not None and self.useErrorBars:
nonzerosigma = sigma[sigma > 0]
sigma[sigma == 0] = numpy.min(
nonzerosigma) if len(nonzerosigma) > 0 else 1.0
else:
sigma = None
if parameters is None:
parameters = [float(param) for param in self.startParameters]
if self.useSmartStartValues:
smartParameters = self.smartStartValues(x, y, parameters,
self.parameterEnabled)
if smartParameters is not None:
parameters = [
smartparam if enabled else param
for enabled, param, smartparam in zip(
self.parameterEnabled, parameters, smartParameters)
]
myEnabledBounds = self.enabledBounds()
if myEnabledBounds:
enabledOnlyParameters, cov_x, infodict, self.mesg, self.ier = leastsqbound(
self.residuals,
self.enabledStartParameters(parameters, bounded=True),
args=(y, x, sigma),
epsfcn=self.epsfcn,
full_output=True,
bounds=myEnabledBounds)
else:
enabledOnlyParameters, cov_x, infodict, self.mesg, self.ier = leastsq(
self.residuals,
self.enabledStartParameters(parameters),
args=(y, x, sigma),
epsfcn=self.epsfcn,
full_output=True)
self.setEnabledFitParameters(enabledOnlyParameters)
self.update(self.parameters)
logger.info("chisq {0}".format(sum(infodict["fvec"] *
infodict["fvec"])))
# calculate final chi square
self.chisq = sum(infodict["fvec"] * infodict["fvec"])
self.dof = max(len(x) - len(parameters), 1)
RMSres = Q(sqrt(self.chisq / self.dof))
RMSres.significantDigits = 3
self.results['RMSres'].value = RMSres
# chisq, sqrt(chisq/dof) agrees with gnuplot
logger.info("success {0} {1}".format(self.ier, self.mesg))
logger.info("Converged with chi squared {0}".format(self.chisq))
logger.info("degrees of freedom, dof {0}".format(self.dof))
logger.info(
"RMS of residuals (i.e. sqrt(chisq/dof)) {0}".format(RMSres))
logger.info("Reduced chisq (i.e. variance of residuals) {0}".format(
self.chisq / self.dof))
# uncertainties are calculated as per gnuplot, "fixing" the result
# for non unit values of the reduced chisq.
# values at min match gnuplot
enabledParameterNames = self.enabledParameterNames()
if cov_x is not None:
enabledOnlyParametersConfidence = numpy.sqrt(
numpy.diagonal(cov_x)) * sqrt(self.chisq / self.dof)
self.setEnabledConfidenceParameters(
enabledOnlyParametersConfidence)
logger.info("Fitted parameters at minimum, with 68% C.I.:")
for i, pmin in enumerate(enabledOnlyParameters):
logger.info(
"%2i %-10s %12f +/- %10f" %
(i, enabledParameterNames[i], pmin,
sqrt(max(cov_x[i, i], 0)) * sqrt(self.chisq / self.dof)))
logger.info("Correlation matrix")
# correlation matrix close to gnuplot
messagelist = [" "]
for i in range(len(enabledOnlyParameters)):
messagelist.append("%-10s" % (enabledParameterNames[i], ))
logger.info(" ".join(messagelist))
messagelist = []
for i in range(len(enabledOnlyParameters)):
messagelist.append("%10s" % enabledParameterNames[i])
for j in range(i + 1):
messagelist.append(
"%10f" %
(cov_x[i, j] / sqrt(abs(cov_x[i, i] * cov_x[j, j])), ))
logger.info(" ".join(messagelist))
#-----------------------------------------------
else:
self.parametersConfidence = [None] * len(self.parametersConfidence)
return self.parameters
def __str__(self):
return "; ".join([
", ".join([self.name, self.functionString] + [
"{0}={1}".format(name, value)
for name, value in zip(self.parameterNames, self.parameters)
])
])
def setConstant(self, name, value):
setattr(self, name, value)
def update(self, parameters=None):
self.parametersUpdated.fire(values=self.replacementDict())
def toXmlElement(self, parent):
myroot = ElementTree.SubElement(parent, 'FitFunction', {
'name': self.name,
'functionString': self.functionString
})
for name, value, confidence, enabled, startExpression, bounds, boundsexpression in zip_longest(
self.parameterNames, self.parameters,
self.parametersConfidence, self.parameterEnabled,
self.startParameterExpressions, self.parameterBounds,
self.parameterBoundsExpressions):
e = ElementTree.SubElement(
myroot, 'Parameter', {
'name': name,
'confidence': repr(confidence),
'enabled': str(enabled),
'startExpression': str(startExpression),
'bounds': ",".join(map(str, bounds)),
'boundsExpression': ",".join(map(str, boundsexpression))
})
e.text = str(value)
for result in list(self.results.values()):
e = ElementTree.SubElement(myroot, 'Result', {
'name': result.name,
'definition': str(result.definition)
})
e.text = str(result.value)
return myroot
def toHdf5(self, group):
fitfunction_group = group.require_group('fitfunction')
fitfunction_group.attrs['name'] = self.name
fitfunction_group.attrs['functionString'] = self.functionString
parameter_group = fitfunction_group.require_group('parameters')
for index, (name, value, confidence, enabled, startExpression, bounds,
boundsexpression) in enumerate(
zip_longest(self.parameterNames, self.parameters,
self.parametersConfidence,
self.parameterEnabled,
self.startParameterExpressions,
self.parameterBounds,
self.parameterBoundsExpressions)):
g = parameter_group.require_group(name)
g.attrs['confidence'] = confidence
g.attrs['enabled'] = enabled
g.attrs['startExpression'] = str(startExpression)
g.attrs['bounds'] = bounds
g.attrs['boundsExpression'] = ",".join(map(str, boundsexpression))
g.attrs['value'] = value
g.attrs['index'] = index
results_group = fitfunction_group.require_group('results')
for result in list(self.results.values()):
g = results_group.requie_group(result.name)
g.attrs['definition'] = str(result.definition)
g.attrs['value'] = repr(result.value)
def residuals(self, p, y, x, sigma):
p = self.allFitParameters(p)
if sigma is not None:
return (y - self.functionEval(x, *p)) / sigma
else:
return y - self.functionEval(x, *p)
def value(self, x, p=None):
p = self.parameters if p is None else p
return self.functionEval(x, *p)
def replacementDict(self):
replacement = dict(list(zip(self.parameterNames, self.parameters)))
replacement.update(
dict(((v.name, v.value) for v in list(self.results.values()))))
return replacement
class AWGWaveform(object):
"""waveform object for AWG channels. Responsible for parsing and evaluating waveforms.
Attributes:
settings (Settings): main settings
channel (int): which channel this waveform belongs to
dependencies (set): The variable names that this waveform depends on
waveformCache (OrderedDict): cache of evaluated waveforms. The key in the waveform cache is a waveform, with all
variables evaluated except 't' (e.g. 7*sin(3*t)+4). The value is a dict. The key to that dict is a range (min, max), and the
value is a numpy array of samples, e.g. {(0, 2): numpay.array([1,2,31]), (12, 15): numpy.array([6,16,23,5])}
"""
expression = Expression()
def __init__(self, channel, settings, waveformCache):
self.settings = settings
self.channel = channel
self.waveformCache = waveformCache
self.dependencies = set()
self.updateDependencies()
@property
def sampleRate(self):
return self.settings.deviceProperties['sampleRate']
@property
def minSamples(self):
return self.settings.deviceProperties['minSamples']
@property
def maxSamples(self):
return self.settings.deviceProperties['maxSamples']
@property
def sampleChunkSize(self):
return self.settings.deviceProperties['sampleChunkSize']
@property
def padValue(self):
return self.calibrateInvIfNecessary(
self.settings.deviceProperties['padValue'])
@property
def minAmplitude(self):
return self.calibrateInvIfNecessary(
self.settings.deviceProperties['minAmplitude'])
@property
def maxAmplitude(self):
return self.calibrateInvIfNecessary(
self.settings.deviceProperties['maxAmplitude'])
@property
def stepsize(self):
return 1 / self.sampleRate
@property
def segmentDataRoot(self):
return self.settings.channelSettingsList[
self.channel]['segmentDataRoot']
def calibrateInvIfNecessary(self, p):
"""Converts raw to volts if useCalibration is True"""
if not self.settings.deviceSettings.get('useCalibration'):
return p
else:
return self.settings.deviceProperties['calibrationInv'](p)
def updateDependencies(self):
"""Determine the set of variables that the waveform depends on"""
logger = logging.getLogger(__name__)
self.dependencies = set()
self.updateSegmentDependencies(self.segmentDataRoot.children)
self.dependencies.discard('t')
def updateSegmentDependencies(self, nodeList):
for node in nodeList:
if node.nodeType == nodeTypes.segment:
node.stack = node.expression._parse_expression(node.equation)
nodeDependencies = node.expression.findDependencies(node.stack)
self.dependencies.update(nodeDependencies)
if isIdentifier(node.duration):
self.dependencies.add(node.duration)
elif node.nodeType == nodeTypes.segmentSet:
if isIdentifier(node.repetitions):
self.dependencies.add(node.repetitions)
self.updateSegmentDependencies(node.children) #recursive
def evaluate(self):
"""evaluate the waveform"""
_, sampleList = self.evaluateSegments(self.segmentDataRoot.children)
return self.compliantSampleList(sampleList)
def evaluateSegments(self, nodeList, startStep=0):
"""Evaluate the list of nodes in nodeList.
Args:
nodeList: list of nodes to evaluate
startStep: the step number at which evaluation starts
Returns:
startStep, sampleList: The step at which the next waveform begins, together with a list of samples
"""
sampleList = numpy.array([])
for node in nodeList:
if node.enabled:
if node.nodeType == nodeTypes.segment:
duration = self.settings.varDict[
node.duration]['value'] if isIdentifier(
node.duration
) else self.expression.evaluateAsMagnitude(
node.duration)
startStep, newSamples = self.evaluateEquation(
node, duration, startStep)
sampleList = numpy.append(sampleList, newSamples)
elif node.nodeType == nodeTypes.segmentSet:
repMag = self.settings.varDict[
node.repetitions]['value'] if isIdentifier(
node.repetitions
) else self.expression.evaluateAsMagnitude(
node.repetitions)
repetitions = int(repMag.to_base_units().val
) #convert to float, then to integer
for n in range(repetitions):
startStep, newSamples = self.evaluateSegments(
node.children, startStep) #recursive
sampleList = numpy.append(sampleList, newSamples)
return startStep, sampleList
def evaluateEquation(self, node, duration, startStep):
"""Evaluate the waveform of the specified node's equation.
The waveform caching works as follows: if self.settings.cacheDepth is greater than zero, waveform values are saved
to self.waveformCache as they are calculated, to speed up future waveform computations. self.waveformCache is an OrderedDict,
and the length is constrained to self.settings.cacheDepth. A key to the OrderedDict is an equation string, i.e. "7*sin(5*t)+3".
The value is a dict. A key to that dict is a sample range (start, stop), and the value is the values that equation takes over
that range.
When a waveform is requested, this function first looks to see if that waveform has a cache entry. If it does, it iterates
through the dict associated with that waveform, looking for a (start, stop) range that overlaps with the requested range.
If it finds an overlap, it computes only the non-overlapping part, and uses the cache for the remainder. It then updates
the cache entry. If it does not find an overlap, it calculates the entire waveform, and then adds an entry to the waveform's
dict of saved values.
A cacheDepth value less than zero indicates an unbounded cache.
Args:
node: The node whose equation is to be evaluated
duration: the length of time for which to evaluate the equation
startStep: the step at which to start evaluation
Returns:
sampleList: list of values to program to the AWG.
"""
#calculate number of samples
numSamples = duration * self.sampleRate
numSamples = int(round(numSamples)) #convert to float, then to integer
numSamples = max(
0, min(numSamples, self.maxSamples - startStep)
) #cap at maxSamples-startStep, so that the last sample does not exceed maxSamples
stopStep = numSamples + startStep - 1
nextSegmentStartStep = stopStep + 1
# first test expression with dummy variable to see if units match up, so user is warned otherwise
try:
node.expression.variabledict = {
varName: varValueTextDict['value']
for varName, varValueTextDict in self.settings.varDict.items()
}
node.expression.variabledict.update({'t': Q(1, 'us')})
node.expression.evaluateWithStack(
node.stack[:]) # TODO: does not exist anymore
error = False
except ValueError:
logging.getLogger(__name__).warning("Must be dimensionless!")
error = True
nextSegmentStartStep = startStep
sampleList = numpy.array([])
if not error:
varValueDict = {
varName: varValueTextDict['value'].to_base_units().val
for varName, varValueTextDict in self.settings.varDict.items()
}
varValueDict['t'] = sympy.Symbol('t')
sympyExpr = parse_expr(node.equation,
varValueDict) #parse the equation
key = str(sympyExpr)
if self.settings.cacheDepth != 0: #meaning, use the cache
if key in self.waveformCache:
self.waveformCache[key] = self.waveformCache.pop(
key) #move key to the most recent position in cache
for (sampleStartStep, sampleStopStep
), samples in self.waveformCache[key].items():
if startStep >= sampleStartStep and stopStep <= sampleStopStep: #this means the required waveform is contained within the cached waveform
sliceStart = startStep - sampleStartStep
sliceStop = stopStep - sampleStartStep + 1
sampleList = samples[sliceStart:sliceStop]
break
elif max(startStep, sampleStartStep) > min(
stopStep, sampleStopStep
): #this means there is no overlap
continue
else: #This means there is some overlap, but not an exact match
if startStep < sampleStartStep: #compute the first part of the sampleList
sampleListStart = self.computeFunction(
sympyExpr, varValueDict['t'], startStep,
sampleStartStep - 1)
if stopStep <= sampleStopStep: #use the cached part for the rest
sliceStop = stopStep - sampleStartStep + 1
sampleList = numpy.append(
sampleListStart, samples[:sliceStop])
self.waveformCache[key].pop(
(sampleStartStep, sampleStopStep)
) #update cache entry with new samples
self.waveformCache[key][(
startStep,
sampleStopStep)] = numpy.append(
sampleListStart, samples)
else: #compute the end of the sampleList, then use the cached part for the middle
sampleListEnd = self.computeFunction(
sympyExpr, varValueDict['t'],
sampleStopStep + 1, stopStep)
sampleList = numpy.append(
numpy.append(sampleListStart, samples),
sampleListEnd)
self.waveformCache[key].pop(
(sampleStartStep, sampleStopStep))
self.waveformCache[key][(
startStep, stopStep)] = sampleList
else: #compute the end of the sampleList, and use the cached part for the beginning
sampleListEnd = self.computeFunction(
sympyExpr, varValueDict['t'],
sampleStopStep + 1, stopStep)
sliceStart = startStep - sampleStartStep
sampleList = numpy.append(
samples[sliceStart:], sampleListEnd)
self.waveformCache[key].pop(
(sampleStartStep, sampleStopStep))
self.waveformCache[key][(
sampleStartStep, stopStep)] = numpy.append(
samples, sampleListEnd)
break
else: #This is an else on the for loop, it executes if there is no break (i.e. if there are no computed samples with overlap)
sampleList = self.computeFunction(
sympyExpr, varValueDict['t'], startStep, stopStep)
self.waveformCache[key][(startStep,
stopStep)] = sampleList
else: #if the waveform is not in the cache
sampleList = self.computeFunction(sympyExpr,
varValueDict['t'],
startStep, stopStep)
self.waveformCache[key] = {
(startStep, stopStep): sampleList
}
if self.settings.cacheDepth > 0 and len(
self.waveformCache) > self.settings.cacheDepth:
self.waveformCache.popitem(
last=False
) #remove the least recently used cache item
else: #if we're not using the cache at all
sampleList = self.computeFunction(sympyExpr, varValueDict['t'],
startStep, stopStep)
return nextSegmentStartStep, sampleList
def computeFunction(self, sympyExpr, tVar, startStep, stopStep):
"""Compute the value of a function over a specified range.
Args:
sympyExpr (str): A string containing a function of 't' (e.g. '7*sin(3*t)')
tVar (Symbol): sympy symbol for 't'
startStep (int): where to start computation
stopStep (int): the last sample to compute
Returns:
sampleList (numpy.array): list of function values
"""
numSamples = stopStep - startStep + 1
if numSamples <= 0:
sampleList = numpy.array([])
else:
func = sympy.lambdify(tVar, sympyExpr,
"numpy") #turn string into a python function
clippedFunc = lambda t: max(self.minAmplitude,
min(self.maxAmplitude, func(t))
) #clip at min and max amplitude
vectorFunc = numpy.vectorize(clippedFunc,
otypes=[numpy.float64
]) #vectorize the function
step = self.stepsize.m_as('s')
sampleList = vectorFunc(
(numpy.arange(numSamples) + startStep) *
step) #apply the function to each time step value
return sampleList
def compliantSampleList(self, sampleList):
"""Make the sample list compliant with the capabilities of the AWG
Args:
sampleList (numpy array): calculated list of samples, might be impossible for AWG to output
Returns:
sampleList (numpy array): output list of samples, within AWG capabilities
"""
numSamples = len(sampleList)
if numSamples > self.maxSamples:
sampleList = sampleList[:self.maxSamples]
if numSamples < self.minSamples:
extraNumSamples = self.minSamples - numSamples #make sure there are at least minSamples
if self.minSamples % self.sampleChunkSize != 0: #This should always be False if minSamples and sampleChunkSize are internally consistent
extraNumSamples += self.sampleChunkSize - (
self.minSamples % self.sampleChunkSize)
elif numSamples % self.sampleChunkSize != 0:
extraNumSamples = self.sampleChunkSize - (numSamples %
self.sampleChunkSize)
else:
extraNumSamples = 0
sampleList = numpy.append(sampleList,
[self.padValue] * extraNumSamples)
return sampleList
class ExpressionValue(QtCore.QObject):
expression = Expression()
valueChanged = QtCore.pyqtSignal(object, object, object, object)
def __init__(self, name=None, globalDict=None, value=Q(0)):
super(ExpressionValue, self).__init__()
self._globalDict = globalDict
self.name = name
self._string = None
self._value = value
self._updateFloatValue()
self.registrations = list() # subscriptions to global variable values
def _updateFloatValue(self):
try:
self.floatValue = float(self._value) # cached value as float
except (DimensionalityError, TypeError):
self.floatValue = None
def __getstate__(self):
return self.name, self._string, self._value
def __setstate__(self, state):
self.__init__( state[0] )
self._string = state[1]
self._value = state[2]
self._updateFloatValue()
def __eq__(self, other):
return other is not None and isinstance(other, ExpressionValue) and (self.name, self._string, self._value) == (other.name, other._string, other._value)
def __ne__(self, other):
return not self.__eq__(other)
@property
def globalDict(self):
return self._globalDict
@globalDict.setter
def globalDict(self, d):
self._globalDict = d
self.string = self._string
@property
def value(self):
return self._value
@value.setter
def value(self, v):
if isinstance(v, ExpressionValue):
self._value = v._value
self.string = v._string
#raise ExpressionValueException('cannot assign ExpressionValue value to ExpressionValue')
#logging.getLogger(__name__).error('cannot assign ExpressionValue value to ExpressionValue')
#v = mg(0)
else:
self._value = v
self._updateFloatValue()
self.valueChanged.emit(self.name, self._value, self._string, 'value')
@property
def string(self):
return self._string if self._string is not None else str(self._value)
@string.setter
def string(self, s):
if self._globalDict is None:
raise ExpressionValueException("Global dictionary is not set in {0}".format(self.name))
self._string = s
for name, reference in self.registrations:
self._globalDict.valueChanged(name).disconnect(reference)
self.registrations[:] = []
if self._string:
self._value, dependencies = self.expression.evaluateAsMagnitude(self._string, self._globalDict, listDependencies=True)
self._updateFloatValue()
for dep in dependencies:
reference = WeakMethod.ref(self.recalculate)
self._globalDict.valueChanged(dep).connect(reference)
self.registrations.append((dep, reference))
@property
def hasDependency(self):
#return self._string is not None
return len(self.registrations)>0
@property
def data(self):
return (self.name, self._value, self._string )
@data.setter
def data(self, val):
self.name, self.value, self.string = val
def recalculate(self, name=None, value=None, origin=None):
if self._globalDict is None:
raise ExpressionValueException("Global dictionary is not set in {0}".format(self.name))
if self._string:
newValue = self.expression.evaluateAsMagnitude(self._string, self._globalDict)
if newValue != self._value:
self._value = newValue
self._updateFloatValue()
self.valueChanged.emit(self.name, self._value, self._string, 'recalculate')
def __hash__(self):
return hash(self._value)
def __str__(self):
return str(self._value)
def __deepcopy__(self, memo):
result = ExpressionValue(globalDict=self.globalDict)
memo[id(self)] = result
result.name = deepcopy(self.name)
result._string = deepcopy(self._string)
result._value = deepcopy(self._value)
return result
class AWGTableModel(QtCore.QAbstractTableModel):
"""Table model for displaying AWG variables"""
headerDataLookup = ["Variable", "Value"]
valueChanged = QtCore.pyqtSignal( object, object )
expression = Expression()
def __init__(self, settings, globalDict, parent=None, *args):
QtCore.QAbstractTableModel.__init__(self, parent, *args)
self.settings = settings
self.globalDict = globalDict
self.defaultBG = QtGui.QColor(QtCore.Qt.white)
self.textBG = QtGui.QColor(QtCore.Qt.green).lighter(175)
self.column = enum('variable', 'value')
self.defaultFontName = "Segoe UI"
self.defaultFontSize = 9
self.normalFont = QtGui.QFont(self.defaultFontName, self.defaultFontSize, QtGui.QFont.Normal)
self.boldFont = QtGui.QFont(self.defaultFontName, self.defaultFontSize, QtGui.QFont.Bold)
self.dataLookup = {
(QtCore.Qt.DisplayRole, self.column.variable): lambda row: self.settings.varDict.keyAt(row),
(QtCore.Qt.DisplayRole, self.column.value): lambda row: str(self.settings.varDict.at(row)['value']),
(QtCore.Qt.FontRole, self.column.variable): lambda row: self.boldFont if self.settings.varDict.keyAt(row).startswith('Duration') else self.normalFont,
(QtCore.Qt.FontRole, self.column.value): lambda row: self.boldFont if self.settings.varDict.keyAt(row).startswith('Duration') else self.normalFont,
(QtCore.Qt.EditRole, self.column.value): lambda row: firstNotNone( self.settings.varDict.at(row)['text'], str(self.settings.varDict.at(row)['value'])),
(QtCore.Qt.BackgroundColorRole, self.column.value): lambda row: self.defaultBG if self.settings.varDict.at(row)['text'] is None else self.textBG,
(QtCore.Qt.ToolTipRole, self.column.value): lambda row: self.settings.varDict.at(row)['text'] if self.settings.varDict.at(row)['text'] else None
}
self.setDataLookup = {
(QtCore.Qt.EditRole, self.column.value): self.setValue,
(QtCore.Qt.UserRole, self.column.value): self.setText
}
def setValue(self, index, value):
row = index.row()
name = self.settings.varDict.keyAt(row)
var = self.settings.varDict.at(row)
var['value'] = value
self.valueChanged.emit(name, value)
return True
def setText(self, index, value):
row = index.row()
self.settings.varDict.at(row)['text'] = value
return True
def rowCount(self, parent=QtCore.QModelIndex()):
return len(self.settings.varDict)
def columnCount(self, parent=QtCore.QModelIndex()):
return 2
def data(self, index, role):
if index.isValid():
return self.dataLookup.get((role, index.column()), lambda row: None)(index.row())
return None
def setData(self, index, value, role):
return self.setDataLookup.get((role, index.column()), lambda index, value: False )(index, value)
def flags(self, index):
return QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled if index.column()==self.column.variable else QtCore.Qt.ItemIsSelectable | QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsEditable
def headerData(self, section, orientation, role):
if (role == QtCore.Qt.DisplayRole):
if (orientation == QtCore.Qt.Horizontal):
return self.headerDataLookup[section]
elif (orientation == QtCore.Qt.Vertical):
return str(section)
return None # QtCore.QVariant()
def evaluate(self, name):
for (varName, varValueTextDict) in list(self.settings.varDict.items()):
expr = varValueTextDict['text']
if expr is not None:
value = self.expression.evaluateAsMagnitude(expr, self.globalDict)
self.settings.varDict[varName]['value'] = value # set saved value to make this new value the default
modelIndex = self.createIndex(self.settings.varDict.index(varName), self.column.value)
self.dataChanged.emit(modelIndex, modelIndex)
self.valueChanged.emit(varName, value)
class AWGDeviceBase(object):
"""base class for AWG Devices"""
expression = Expression()
def __init__(self, settings):
self.settings = settings
self.settings.deviceSettings.setdefault('programOnScanStart', False)
self.settings.deviceSettings.setdefault('useCalibration', False)
self.waveforms = []
for channel in range(self.deviceProperties['numChannels']):
self.waveforms.append(None)
if channel >= len(self.settings.channelSettingsList
): #create new channels if it's necessary
self.settings.channelSettingsList.append({
'segmentDataRoot':
AWGSegmentNode(None, ''),
'segmentTreeState':
None,
'plotEnabled':
True,
'plotStyle':
self.settings.plotStyles.lines
})
self.project = getProject()
awgConfigDict = list(
self.project.hardware[self.displayName].values())[0]
sampleRateText = awgConfigDict['sampleRate']
sampleRate = self.expression.evaluateAsMagnitude(sampleRateText)
self.deviceProperties['sampleRate'] = sampleRate
sample = 1 / self.deviceProperties['sampleRate']
#new_mag('sample', sample) # TODO: check whether the added samples = count / second in modules/quantity_units.txt
#new_mag('samples', sample) # replaces this
if not self.project.isEnabled('hardware', self.displayName):
self.enabled = False
else:
self.open()
def parameters(self):
"""return the parameter definitions used by the parameterTable to show the gui"""
return SequenceDict([
('Use Calibration',
Parameter(name='Use Calibration',
dataType='bool',
value=self.settings.deviceSettings['useCalibration'],
key='useCalibration')),
('Program on scan start',
Parameter(
name='Program on scan start',
dataType='bool',
value=self.settings.deviceSettings['programOnScanStart'],
key='programOnScanStart')),
('Program now',
Parameter(name='Program now', dataType='action',
value='program')),
('Trigger now',
Parameter(name='Trigger now', dataType='action', value='trigger'))
])
def update(self, parameter):
"""update the parameter, called by the parameterTable"""
if parameter.dataType != 'action':
self.settings.deviceSettings[parameter.key] = parameter.value
self.settings.saveIfNecessary()
if parameter.key == 'useCalibration':
self.settings.replot()
else:
getattr(self, parameter.value)()
#functions and attributes that must be defined by inheritors
def open(self):
raise NotImplementedError(
"'open' method must be implemented by specific AWG device class")
def program(self):
raise NotImplementedError(
"'program' method must be implemented by specific AWG device class"
)
def trigger(self):
raise NotImplementedError(
"'trigger' method must be implemented by specific AWG device class"
)
def close(self):
raise NotImplementedError(
"'close' method must be implemented by specific AWG device class")
@property
def deviceProperties(self):
raise NotImplementedError(
"'deviceProperties' must be set by specific AWG device class")
@property
def displayName(self):
raise NotImplementedError(
"'displayName' must be set by specific AWG device class")
