172 lines
5.1 KiB
Python
172 lines
5.1 KiB
Python
import math
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import sys, traceback
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from mathutils import fit_polynomial, PolynomialFunction
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from plotutils import plot_line, plot_function
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from ch.psi.pshell.swing.Shell import STDOUT_COLOR
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import org.apache.commons.math3.stat.correlation.PearsonsCorrelation as PearsonsCorrelation
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start_task("outupdate", 0.0, 0.0)
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if get_exec_pars().source == CommandSource.ui:
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#dx = "SINEG01-RLLE-REF10:SIG-PHASE-AVG"
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#dy = "SINEG01-RLLE-REF20:SIG-PHASE-AVG"
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#dx = "SINEG01-RGUN-PUP10:SIG-AMPLT-AVG 4"
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#dy = "SINEG01-RGUN-PUP20:SIG-AMPLT-AVG 4"
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#dx = "SINDI01-RKLY-DCP10:REF-AMPLT"
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#dy = "SINDI01-RKLY-DCP10:REF-PHASE"
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#dx = "SINDI01-RLLE-REF10:SIG-PHASE-AVG"
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#dy = "SINDI01-RLLE-REF20:SIG-PHASE-AVG"
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dx = "TESTIOC:TESTCALCOUT:Input"
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dx = "TESTIOC:TESTCALCOUT:Output"
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dy = "TESTIOC:TESTSINUS:SinCalc"
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#dx = "SINEG01-DICT215:B1_CHARGE"
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#dy = "SINEG01-DBPM314:Q1"
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#dx=gsx.getReadback()
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#dy=gsy.getReadback()
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interval = 0.1
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window = 40
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p = plot(None)[0]
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bs = False
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linear_fit = True
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quadratic_fit = True
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#print dx
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#print dy
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corr = None
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pars_lin = None
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pars_quad = None
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for s in p.getAllSeries():
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p.removeSeries(s)
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_stream = None
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instances = []
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def _get_device(d):
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global _stream
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egu = None
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if isinstance(d, basestring):
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name = d.strip()
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d = None
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try:
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d = get_device(name)
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if d is None:
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d = eval(name)
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#print name
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if d is not None:
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if not isinstance(r, Device):
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d = None
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else:
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try:
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egu = d.unit
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except:
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pass
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except:
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pass
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if d is None:
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offset = 0
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if " " in name:
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tokens = name.split(" ")
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name = tokens[0]
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offset = int(tokens[1])
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if bs == True:
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if _stream == None:
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_stream = Stream("corr_stream", dispatcher)
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instances.append(_stream)
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d = _stream.addScalar(name, name, int(interval*100), offset)
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else:
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d = Channel(name)
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instances.append(d)
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try:
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egu = caget(name+".EGU",'s')
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except:
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pass
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else:
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try:
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egu = d.unit
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except:
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pass
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return d, egu
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dx, egux = _get_device(dx)
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dy, eguy = _get_device(dy)
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p.getAxis(p.AxisId.X).setLabel(egux)
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p.getAxis(p.AxisId.Y).setLabel(eguy)
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try:
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if _stream != None:
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_stream.initialize()
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_stream.start(True)
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p.addSeries(LinePlotSeries("Data"))
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sd=p.getSeries(0)
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sd.setLinesVisible(False)
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sd.setPointSize(4)
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if get_exec_pars().source == CommandSource.ui:
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if globals().has_key("marker"):
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p.removeMarker(marker)
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marker=None
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while(True):
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#Sample and plot data
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if bs == True:
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_stream.waitValueNot(_stream.take(), 10000)
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(x,y) = _stream.take().values
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else:
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x=dx.read()
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y=dy.read()
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sd.appendData(x, y)
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if len(sd.x) > window:
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#Remove First Element
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sd.token.remove(0)
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ax = sd.x
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ay = sd.y
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if len(ax)>2:
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x1, x2 = min(ax), max(ax)
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res = (x2-x1)/100
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if x1!=x2:
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#Display correlation
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corr= PearsonsCorrelation().correlation(to_array(ax,'d'), to_array(ay,'d'))
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s = "Correlation=" + str(round(corr,4))
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#print s
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if get_exec_pars().source == CommandSource.ui:
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if marker is not None:
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p.removeMarker(marker)
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marker = p.addMarker(x2+res, p.AxisId.X, s, p.getBackground())
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marker.setLabelPaint(STDOUT_COLOR)
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if linear_fit:
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#Calculate, print and plot linear fit
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pars_lin = (a0,a1) = fit_polynomial(ay, ax, 1)
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#print "Fit lin a1:" , a1, " a0:",a0
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y1 = poly(x1, pars_lin)
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y2 = poly(x2, pars_lin)
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plot_line(p, x1, y1, x2, y2, width = 2, color = Color.BLUE, name = "Fit Linear")
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if quadratic_fit:
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#Calculate, print and plot quadratic fit
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pars_quad = (a0,a1,a2) = fit_polynomial(ay, ax, 2)
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#print "Fit quad a2:" , a2, "a1:" , a1, " a0:",a0
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fitted_quad_function = PolynomialFunction(pars_quad)
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ax = frange(x1, x2, res, True)
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plot_function(p, fitted_quad_function, "Fit Quadratic", ax, color=Color.GREEN)
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if bs != True:
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time.sleep(interval)
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finally:
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for dev in instances:
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dev.close()
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stop_task("outupdate")
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