462 lines
17 KiB
Python
462 lines
17 KiB
Python
import random
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import ch.psi.pshell.device.Readable.ReadableArray as ReadableArray
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import ch.psi.pshell.device.Readable.ReadableCalibratedArray as ReadableCalibratedArray
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import ch.psi.pshell.device.ArrayCalibration as ArrayCalibration
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import ch.psi.utils.Str
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from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian
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import java.awt.Color as Color
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Scienta = get_device("Scienta") #Scienta class is imported in startup.py, shadowing Scienta device name
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#Synchrronized Scienta counts
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for stat in Scienta.stats:
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add_device(stat, True)
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beam_ok = True
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class SimulatedOutput(Writable):
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def write(self, value):
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pass
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class SimulatedInput(Readable):
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def __init__(self):
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self.x = 0.0
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def read(self):
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self.x = self.x + 0.2
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noise = (random.random() - 0.5) / 20.0
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return math.sin(self.x) + noise
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sout = SimulatedOutput()
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sinp = SimulatedInput()
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def integrate_image(vertical = True):
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data = Scienta.dataArray.read()
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#Integrate and plot
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(width,height) = Scienta.getImageSize().tolist()
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integration = []
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if vertical:
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for i in range(width):
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p=0.0
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for j in range(height):
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p=p+data[j*width+i]
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integration.append(p)
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else:
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for j in range(height):
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p=0.0
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for i in range(width):
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p=p+data[j*width+i]
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integration.append(p)
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return integration
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class ImageEnergyDistribution(ReadableCalibratedArray):
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def getSize(self):
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(width,height) = Scienta.getImageSize().tolist()
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return width
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def read(self):
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return to_array(integrate_image(),'d')
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def getCalibration(self):
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c=Scienta.readImageDescriptor().calibration
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if c is None:
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return None
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return ArrayCalibration(c.scaleX, c.offsetX)
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EnergyDistribution = ImageEnergyDistribution()
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class ImageAngleDistribution(ReadableCalibratedArray):
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def getSize(self):
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(width,height) = Scienta.getImageSize().tolist()
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return height
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def read(self):
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return to_array(integrate_image(False),'d')
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def getCalibration(self):
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c=Scienta.readImageDescriptor().calibration
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if c is None:
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return None
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return ArrayCalibration(c.scaleY, c.offsetY)
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AngleDistribution = ImageAngleDistribution()
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class ImageCounts(Readable):
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def read(self):
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data = Scienta.dataArray.read()
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counts = sum(data)
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return counts
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Counts = ImageCounts()
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def init_scienta():
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"""
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turn on the analyser and start a mock measurement so that we get the correct array size.
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start a scienta acquisition and abort after 4 seconds.
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"""
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if Scienta.isSimulated():
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time.sleep(0.1)
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else:
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image_id = Scienta.currentImageCount
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Scienta.start()
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Scienta.waitReady(4000)
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Scienta.stop()
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Scienta.waitNewImage(500, image_id)
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def trig_scienta():
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if Scienta.isSimulated():
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time.sleep(0.1)
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else:
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image_id = Scienta.currentImageCount
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Scienta.start()
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Scienta.waitReady(-1)
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Scienta.waitNewImage(3000, image_id)
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def otf(mode="ENERGY", e1=None, e2=None, beta1=None, beta2=None, theta1=None, theta2=None, \
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time=1.0, modulo=1, turn_off_beam=False):
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"""
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mode: "ENERGY" or "AMNGLE"
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"""
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run("otf", {
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"MODE":mode, \
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"E1":float(e1) if e1 is not None else None, \
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"E2":float(e2) if e2 is not None else None, \
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"BETA1":float(beta1) if beta1 is not None else None, \
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"BETA2":float(beta2) if beta2 is not None else None, \
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"THETA1":float(theta1) if theta1 is not None else None, \
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"THETA2":float(theta2) if theta2 is not None else None, \
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"TIME":float(time), \
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"MODULO":int(modulo), \
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"ENDSCAN":turn_off_beam, \
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})
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diag_channels = []
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#Manipulator Settings
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diag_channels.append(ManipulatorX.readback)
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diag_channels.append(ManipulatorY.readback)
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diag_channels.append(ManipulatorZ.readback)
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diag_channels.append(ManipulatorTheta.readback)
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diag_channels.append(ManipulatorTilt.readback)
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diag_channels.append(ManipulatorPhi.readback)
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# Beamline Settings
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diag_channels.append(MachineBumpXOffset)
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diag_channels.append(MachineBumpXAngle)
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diag_channels.append(MachineBumpYOffset)
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diag_channels.append(MachineBumpYAngle)
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diag_channels.append(DynamicBumpYOffset)
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diag_channels.append(DynamicBumpYAngle)
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diag_channels.append(FrontendVCenter)
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diag_channels.append(FrontendVSize)
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diag_channels.append(FrontendHCenter)
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diag_channels.append(FrontendHSize)
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diag_channels.append(MonoVCenter)
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diag_channels.append(MonoVSize)
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diag_channels.append(MonoBladeDown)
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diag_channels.append(MonoBladeUp)
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diag_channels.append(MonoHCenter)
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diag_channels.append(MonoHSize)
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diag_channels.append(MonoApertureMode)
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diag_channels.append(RefocusVCenter)
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diag_channels.append(RefocusVSize)
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diag_channels.append(RefocusHCenter)
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diag_channels.append(RefocusHSize)
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diag_channels.append(FocusYTrans)
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diag_channels.append(FocusZTrans)
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diag_channels.append(FocusXRot)
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diag_channels.append(FocusYRot)
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diag_channels.append(FocusZRot)
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diag_channels.append(RefocusYTrans)
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diag_channels.append(RefocusZTrans)
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diag_channels.append(RefocusXRot)
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diag_channels.append(RefocusYRot)
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diag_channels.append(RefocusZRot)
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diag_channels.append(MonoEnergy)
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diag_channels.append(MonoCff)
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diag_channels.append(MonoBeta)
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diag_channels.append(MonoTheta)
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diag_channels.append(MonoGrating)
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diag_channels.append(ExitSlit)
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# Auxiliary Measurements
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diag_channels.append(MachineCurrent)
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diag_channels.append(FocusWaterTemp)
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diag_channels.append(SampleCurrent)
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diag_channels.append(RefCurrent)
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#diag_channels.append(AuxCurrent)
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#diag_channels.append(AuxVoltage)
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diag_channels.append(ChamberPressure)
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diag_channels.append(BeamlinePressure)
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diag_channels.append(ManipulatorTempA)
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diag_channels.append(ManipulatorTempB)
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if get_device("ManipulatorCoolFlow"):
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diag_channels.append(ManipulatorCoolFlow)
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if get_device("ManipulatorCoolFlowSet"):
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diag_channels.append(ManipulatorCoolFlowSet)
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diag_channels_no_Scienta = list(diag_channels)
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# Scienta
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diag_channels.append(Scienta.channelBegin) #diag_channels.append(ChannelDouble("ChannelBegin", "X03DA-SCIENTA:cam1:CHANNEL_BEGIN_RBV"))
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diag_channels.append(Scienta.channelEnd) #diag_channels.append(ChannelDouble("ChannelEnd", "X03DA-SCIENTA:cam1:CHANNEL_END_RBV"))
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diag_channels.append(Scienta.sliceBegin) # diag_channels.append(ChannelDouble("SliceBegin", "X03DA-SCIENTA:cam1:SLICE_BEGIN_RBV"))
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diag_channels.append(Scienta.sliceEnd) #diag_channels.append(ChannelDouble("StepTime", "X03DA-SCIENTA:cam1:SLICE_END_RBV"))
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diag_channels.append(Scienta.numSlices) # diag_channels.append(ChannelDouble("NumSlices", "X03DA-SCIENTA:cam1:SLICES_RBV"))
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#diag_channels.append(Scienta.frames) # diag_channels.append(ChannelDouble("NumFrames", "X03DA-SCIENTA:cam1:FRAMES"))
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diag_channels.append(Scienta.numChannels) #diag_channels.append(ChannelDouble("NumChannels", "X03DA-SCIENTA:cam1:NUM_CHANNELS_RBV"))
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diag_channels.append(Scienta.lowEnergy) #diag_channels.append(ChannelDouble("LowEnergy", "X03DA-SCIENTA:cam1:LOW_ENERGY_RBV"))
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diag_channels.append(Scienta.centerEnergy) #diag_channels.append(ChannelDouble("CenterEnergy", "X03DA-SCIENTA:cam1:CENTRE_ENERGY_RBV"))
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diag_channels.append(Scienta.highEnergy) #diag_channels.append(ChannelDouble("HighEnergy", "X03DA-SCIENTA:cam1:HIGH_ENERGY_RBV"))
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diag_channels.append(ScientaDwellTime)
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diag_channels.append(AcquisitionMode) #diag_attrs.append(ChannelString("AcquisitionMode", "X03DA-SCIENTA:cam1:ACQ_MODE_RBV"))
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diag_channels.append(EnergyMode) #diag_attrs.append(ChannelString("EnergyMode", "X03DA-SCIENTA:cam1:ENERGY_MODE_RBV"))
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diag_channels.append(LensMode) #diag_attrs.append(ChannelString("LensMode", "X03DA-SCIENTA:cam1:LENS_MODE_RBV"))
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diag_channels.append(DetectorMode) #diag_attrs.append(ChannelString("DetectorMode", "X03DA-SCIENTA:cam1:DETECTOR_MODE_RBV"))
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diag_channels.append(PassEnergy) #diag_attrs.append(ChannelString("PassEnergy", "X03DA-SCIENTA:cam1:PASS_ENERGY_RBV"))
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diag_channels.append(ElementSet) #diag_attrs.append(ChannelString("ElementSet", "X03DA-SCIENTA:cam1:ELEMENT_SET_RBV"))
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diag_channels.append(ExcitationEnergy) #diag_channels.append(ChannelDouble("ExcitationEnergy", "X03DA-SCIENTA:cam1:EXCITATION_ENERGY_RBV"))
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diag_channels.append(StepSize) #diag_channels.append(ChannelDouble("StepSize", "X03DA-SCIENTA:cam1:STEP_SIZE_RBV"))
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diag_channels.append(NumIterations) #diag_channels.append(ChannelDouble("NumIterations", "X03DA-SCIENTA:cam1:NumExposures_RBV"))
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diag_channels.append(AnalyserSlit) #diag_attrs.append(ChannelString("ElemeAnalyserSlitntSet", "X03DA-SCIENTA:cam1:ANALYSER_SLIT_RBV"))
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snap_channels = []
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snap_channels.append(SampleCurrentGain)
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snap_channels.append(RefCurrentGain)
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#snap_channels.append(AuxCurrentGain)
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snap_channels.append(SampleCurrentAveraging)
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snap_channels.append(RefCurrentAveraging)
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#snap_channels.append(AuxCurrentAveraging)
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#snap_channels.append(AuxVoltageAveraging)
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snap_channels.append(SampleCurrentSampling)
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snap_channels.append(RefCurrentSampling)
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#snap_channels.append(AuxCurrentSampling)
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#snap_channels.append(AuxVoltageSampling)
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diag_channels = sorted(diag_channels, key=lambda channel: channel.name)
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snap_channels = sorted(snap_channels, key=lambda channel: channel.name)
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def get_diag_name(diag):
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return ch.psi.utils.Str.toTitleCase(diag.getName()).replace(" ", "").replace("Readback", "")
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def print_diag():
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for f in diag_channels:
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print "%-25s %s" % (get_diag_name(f) , str(f.read()))
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def create_diag_datasets(parent = None):
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if parent is None:
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parent = get_exec_pars().group
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group = parent + "attrs/"
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for f in diag_channels:
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create_dataset(group+get_diag_name(f) , 's' if (type(f) is ch.psi.pshell.epics.ChannelString) else 'd')
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def append_diag_datasets(parent = None):
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if parent is None:
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parent = get_exec_pars().group
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group = parent + "attrs/"
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for f in diag_channels:
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try:
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x = f.read()
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if x is None:
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x = '' if (type(f) is ch.psi.pshell.epics.ChannelString) else float('nan')
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append_dataset(group+get_diag_name(f), x)
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except:
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log("Error sampling " + str(get_diag_name(f)) + ": " + str(sys.exc_info()[1]))
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def create_metadata_datasets(parent = None):
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if parent is None:
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parent = "/"
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group = parent + "general/"
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for name in ["proposer", "proposal", "pgroup", "sample"]:
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setting = get_setting(name)
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save_dataset(group+name, setting if setting is not None else "", 's')
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setting = get_setting("authors")
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save_dataset(group+"authors", setting.split("|") if setting is not None else [""], '[s')
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def wait_beam():
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if not beam_ok:
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print "Waiting for beam..."
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while not beam_ok:
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time.sleep(0.1)
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print "Beam ok"
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def before_readout():
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sample_scienta = False
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for dev in ["Scienta.spectrum","EnergyDistribution", "AngleDistribution", "Scienta.dataMatrix", "Counts"]:
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if dev in SENSORS:
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sample_scienta = True
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break
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for dev in [Scienta.spectrum,EnergyDistribution, AngleDistribution, Scienta.dataMatrix, Counts]:
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if dev in SENSORS:
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sample_scienta = True
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break
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wait_beam()
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if sample_scienta:
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trig_scienta()
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else:
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time.sleep(SampleCurrentAveraging.read() / 10.0)
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def after_readout(rec, scan):
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if beam_ok:
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if get_exec_pars().save:
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if rec.index == 0:
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if scan.index == 1:
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create_metadata_datasets()
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create_diag_datasets()
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append_diag_datasets()
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else:
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rec.invalidate()
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def after_scan():
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"""
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Close shutter and turn off analyser
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"""
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caput("X03DA-PC:AFTER-SCAN.PROC", 1)
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caput("X03DA-OP-VG7:WT_SET", 0)
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#caput("X03DA-FE-AB1:CLOSE4BL", 0)
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def set_adc_averaging(dwelltime=0.0):
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if dwelltime < 0.001:
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dwelltime = Scienta.getStepTime().read()
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dwelltime = min(dwelltime, 20.0)
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dwelltime = max(dwelltime, 0.1)
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fixed = AcquisitionMode.read() == "Fixed"
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else:
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fixed = True
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#value = int(dwelltime * 10.0) #averaging count in 100ms
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#SampleCurrentAveraging.write(value)
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#RefCurrentAveraging.write(value)
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#AuxCurrentAveraging.write(value)
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#AuxVoltageAveraging.write(value)
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def adjust_sensors():
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#Updating ranges from Scienta
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Scienta.update()
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global SENSORS
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if SENSORS is not None:
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# Move integration to end
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#sample_scienta = False
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for dev in ["Scienta.spectrum","EnergyDistribution", "AngleDistribution", "Scienta.dataMatrix"]:
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if dev in SENSORS:
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#sample_scienta = True
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SENSORS=SENSORS+[SENSORS.pop(SENSORS.index(dev))]
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for dev in ["Counts"]:
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if dev in SENSORS:
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#sample_scienta = True
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SENSORS=[SENSORS.pop(SENSORS.index(dev))] + SENSORS
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if "Scienta.dataMatrix" in SENSORS or Scienta.dataMatrix in SENSORS:
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print "Not ACC"
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set_exec_pars(accumulate = False)
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#if sample_scienta:
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# init_scienta()
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#Device aliases for data files
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set_device_alias(Scienta.dataMatrix, "ScientaImage")
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set_device_alias(Scienta.spectrum, "ScientaSpectrum")
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set_device_alias(Scienta.channelBegin, get_diag_name(Scienta.channelBegin))
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set_device_alias(Scienta.channelEnd, get_diag_name(Scienta.channelEnd))
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set_device_alias(Scienta.sliceBegin, get_diag_name(Scienta.sliceBegin))
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set_device_alias(Scienta.sliceEnd, get_diag_name(Scienta.sliceEnd))
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set_device_alias(Scienta.numChannels, get_diag_name(Scienta.numChannels))
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set_device_alias(Scienta.numSlices, get_diag_name(Scienta.numSlices))
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set_device_alias(Scienta.lowEnergy, get_diag_name(Scienta.lowEnergy))
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set_device_alias(Scienta.centerEnergy, get_diag_name(Scienta.centerEnergy))
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set_device_alias(Scienta.highEnergy, get_diag_name(Scienta.highEnergy))
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set_device_alias(ManipulatorX.readback, get_diag_name(ManipulatorX.readback))
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set_device_alias(ManipulatorY.readback, get_diag_name(ManipulatorY.readback))
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set_device_alias(ManipulatorZ.readback, get_diag_name(ManipulatorZ.readback))
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set_device_alias(ManipulatorTheta.readback, get_diag_name(ManipulatorTheta.readback))
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set_device_alias(ManipulatorTilt.readback, get_diag_name(ManipulatorTilt.readback))
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set_device_alias(ManipulatorPhi.readback, get_diag_name(ManipulatorPhi.readback))
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#Additional device configuration
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ManipulatorPhi.trustedWrite = False
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def fit(ydata, xdata = None):
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"""
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"""
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if xdata is None:
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xdata = frange(0, len(ydata), 1)
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max_y= max(ydata)
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index_max = ydata.index(max_y)
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max_x= xdata[index_max]
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print "Max index:" + str(index_max),
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print " x:" + str(max_x),
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print " y:" + str(max_y)
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gaussians = fit_gaussians(ydata, xdata, [index_max,])
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(norm, mean, sigma) = gaussians[0]
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p = plot([ydata],["data"],[xdata], title="Fit" )[0]
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fitted_gaussian_function = Gaussian(norm, mean, sigma)
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scale_x = [float(min(xdata)), float(max(xdata)) ]
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points = max((len(xdata)+1), 100)
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resolution = (scale_x[1]-scale_x[0]) / points
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fit_y = []
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fit_x = frange(scale_x[0],scale_x[1],resolution, True)
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for x in fit_x:
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fit_y.append(fitted_gaussian_function.value(x))
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p.addSeries(LinePlotSeries("fit"))
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p.getSeries(1).setData(fit_x, fit_y)
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if abs(mean - xdata[index_max]) < ((scale_x[0] + scale_x[1])/2):
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print "Mean -> " + str(mean)
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p.addMarker(mean, None, "Mean="+str(round(norm,2)), Color.MAGENTA.darker())
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return (norm, mean, sigma)
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else:
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p.addMarker(max_x, None, "Max="+str(round(max_x,2)), Color.GRAY)
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print "Invalid gaussian fit: " + str(mean)
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return (None, None, None)
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def elog(title, message, attachments = [], author = None, category = "Info", domain = "", logbook = "Experiments", encoding=1):
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"""
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Add entry to ELOG.
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"""
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if author is None:
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author = "pshell" #get_context().getUser().name
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typ = "pshell"
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entry = ""
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cmd = 'G_CS_ELOG_add -l "' + logbook+ '" '
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cmd = cmd + '-a "Author=' + author + '" '
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cmd = cmd + '-a "Type=' + typ + '" '
|
|
cmd = cmd + '-a "Entry=' + entry + '" '
|
|
cmd = cmd + '-a "Title=' + title + '" '
|
|
cmd = cmd + '-a "Category=' + category + '" '
|
|
cmd = cmd + '-a "Domain=' + domain + '" '
|
|
for attachment in attachments:
|
|
cmd = cmd + '-f "' + attachment + '" '
|
|
cmd = cmd + '-n ' + str(encoding)
|
|
cmd = cmd + ' "' + message + '"'
|
|
#print cmd
|
|
#os.system (cmd)
|
|
#print os.popen(cmd).read()
|
|
import subprocess
|
|
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
|
|
(out, err) = proc.communicate()
|
|
if (err is not None) and err!="":
|
|
raise Exception(err)
|
|
print out
|
|
|
|
def get_plot_snapshots(title = None, file_type = "jpg", temp_path = get_context().setup.getContextPath()):
|
|
"""
|
|
Returns list with file names of plots snapshots from a plotting context.
|
|
"""
|
|
sleep(0.02) #Give some time to plot to be finished - it is not sync with acquisition
|
|
ret = []
|
|
for p in get_plots(title):
|
|
file_name = os.path.abspath(temp_path + "/" + p.getTitle() + "." + file_type)
|
|
p.saveSnapshot(file_name , file_type)
|
|
ret.append(file_name)
|
|
return ret
|