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