def redraw():
ppgplot.pgslct(imagePlot['pgplotHandle'])
ppgplot.pgslw(3)
ppgplot.pggray(boostedImage, xlimits[0], xlimits[1] - 1, ylimits[0],
ylimits[1] - 1, imageMinMax[0], imageMinMax[1],
imagePlot['pgPlotTransform'])
if plotSources: plotCircles(dr2Objects, margins)
if plotHa:
reduceddr2cat = []
for selected in extendedHaSources:
reduceddr2cat.append(dr2Objects[selected])
plotCircles(reduceddr2cat, margins)
if plotGrid:
print("Plotting grid")
ppgplot.pgsci(6)
xVals = [p[0] for p in pixelGrid]
yVals = [p[1] for p in pixelGrid]
ppgplot.pgpt(xVals, yVals, 2)
if plotPointings:
ppgplot.pgsfs(2)
ppgplot.pgslw(10)
for p in pointings:
if p['type'] == "Maximum": ppgplot.pgsci(2)
if p['type'] == "Minimum": ppgplot.pgsci(4)
ppgplot.pgcirc(p['x'], p['y'], 30)
ppgplot.pgslw(1)
if plotBrightStars:
ppgplot.pgsci(3)
ppgplot.pgsfs(2)
ppgplot.pgslw(10)
for b in brightStars:
ppgplot.pgcirc(b['x'], b['y'], 40)
def drawMask(mask):
print ("Drawing the mask.")
if "pgplotHandle" not in maskPlot.keys():
maskPlot["pgplotHandle"] = ppgplot.pgopen("/xs")
maskPlot["pgPlotTransform"] = [0, 1, 0, 0, 0, 1]
else:
ppgplot.pgslct(maskPlot["pgplotHandle"])
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
ppgplot.pggray(mask, 0, width - 1, 0, height - 1, 0, 255, maskPlot["pgPlotTransform"])
ppgplot.pgslct(imagePlot["pgplotHandle"])
def drawMask(mask):
print("Drawing the mask.")
if 'pgplotHandle' not in maskPlot.keys():
maskPlot['pgplotHandle'] = ppgplot.pgopen('/xs')
maskPlot['pgPlotTransform'] = [0, 1, 0, 0, 0, 1]
else:
ppgplot.pgslct(maskPlot['pgplotHandle'])
ppgplot.pgpap(paperSize, aspectRatio)
ppgplot.pgsvp(0.0, 1.0, 0.0, 1.0)
ppgplot.pgswin(0, width, 0, height)
ppgplot.pggray(mask, 0, width - 1, 0, height - 1, 0, 255,
maskPlot['pgPlotTransform'])
ppgplot.pgslct(imagePlot['pgplotHandle'])
def selectDev(devID=''):
"""
DES: select an open device
INP: (int) device ID
"""
global CurrentDev, DevList
if devID in DevList:
ppgplot.pgslct(devID) # select the deviceID
CurrentDev = ppgplot.pgqid() # store the actual pgplot deviceID
else:
__MessHand.error("this is not an open device: " + ` devID `)
__queryDev()
def closeDev(devID='current'):
"""
DES: close selected device
INP: (int) device ID, 'all','current' (default)
"""
global CurrentDev, DevList
if devID in ['current', 'curren', 'curre', 'curr', 'cur']:
__MessHand.longinfo("closing the current device")
ppgplot.pgclos()
DevList.remove(CurrentDev)
if not DevList == []:
ppgplot.pgslct(DevList[0])
CurrentDev = ppgplot.pgqid() # store the actual deviceID
else:
CurrentDev = 0
elif devID in ['all']:
__MessHand.longinfo("closing all devices")
for device in DevList:
ppgplot.pgslct(device)
ppgplot.pgclos()
DevList = []
CurrentDev = 0
elif devID in DevList:
__MessHand.longinfo("closing device " + ` devID `)
ppgplot.pgslct(devID)
ppgplot.pgclos()
DevList.remove(devID)
if devID == CurrentDev and DevList != []:
ppgplot.pgslct(DevList[0])
CurrentDev = ppgplot.pgqid() # store the actual deviceID
else:
ppgplot.pgslct(CurrentDev)
else:
__MessHand.error("this is not an open device: " + ` devID `)
__queryDev()
def redraw():
ppgplot.pgslct(imagePlot["pgplotHandle"])
ppgplot.pgslw(3)
ppgplot.pggray(
boostedImage,
xlimits[0],
xlimits[1] - 1,
ylimits[0],
ylimits[1] - 1,
imageMinMax[0],
imageMinMax[1],
imagePlot["pgPlotTransform"],
)
if plotSources:
plotCircles(dr2Objects, margins)
if plotHa:
reduceddr2cat = []
for selected in extendedHaSources:
reduceddr2cat.append(dr2Objects[selected])
plotCircles(reduceddr2cat, margins)
if plotGrid:
print ("Plotting grid")
ppgplot.pgsci(6)
xVals = [p[0] for p in pixelGrid]
yVals = [p[1] for p in pixelGrid]
ppgplot.pgpt(xVals, yVals, 2)
if plotPointings:
ppgplot.pgsfs(2)
ppgplot.pgslw(10)
for p in pointings:
if p["type"] == "Maximum":
ppgplot.pgsci(2)
if p["type"] == "Minimum":
ppgplot.pgsci(4)
ppgplot.pgcirc(p["x"], p["y"], 30)
ppgplot.pgslw(1)
if plotBrightStars:
ppgplot.pgsci(3)
ppgplot.pgsfs(2)
ppgplot.pgslw(10)
for b in brightStars:
ppgplot.pgcirc(b["x"], b["y"], 40)
def getChiSqByParameters(params, *args):
global iteration, mainPlotWindow, currentPlotWindow, colour, inclination, phase
print "Params:", params
beta = params[0]
log_lambda = params[1]
scale_factor = params[2]
linear_offset = params[3]
print "Args:", args
temperature = args[0]
field = args[1]
# cos(theta) = cos(i)cos(beta) - sin(i)sin(beta)cos(phi + pi/2)
cosTheta = math.cos(radians(inclination)) * math.cos(radians(beta)) - math.sin(radians(inclination)) * math.sin(radians(beta))*math.cos(phase + math.pi/2.)
angle = math.acos(cosTheta) / math.pi * 180
print "Angle: %f [deg], Field: %f [MG], Temperature:%f [keV], log_lambda: %f, scale: %f, offset: %f"%(angle, field, temperature, log_lambda, scale_factor, linear_offset)
model = getSampledModel(observedSpectrum.wavelengths, angle, field, temperature, log_lambda)
model = [m * scale_factor + linear_offset for m in model]
chi = computeChiSq(observedSpectrum, model)
allChiSqs.append(chi)
print "Chi-squared:", chi
startWavelength = min(observedSpectrum.wavelengths)
endWavelength = max(observedSpectrum.wavelengths)
# Draw the most recent iteration
ppgplot.pgslct(currentPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgenv(startWavelength, endWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(observedSpectrum.wavelengths, observedSpectrum.flux)
ppgplot.pgsci(4)
ppgplot.pgline(observedSpectrum.wavelengths, model)
ppgplot.pgsci(1)
ppgplot.pglab("wavelength", "flux", "Current fit: %d"%iteration)
# Overplot the iteration on the original diagram
print "overplotting"
ppgplot.pgslct(mainPlotWindow)
ppgplot.pgsci(colour)
ppgplot.pgline(observedSpectrum.wavelengths, model)
colour += 1
if colour>15: colour = 1
ppgplot.pgsci(1)
# Re-generate the Chi-Squared plot
ppgplot.pgslct(chiSqPlotWindow)
if iteration > 9:
ppgplot.pgenv(0, iteration+1, 0, max(allChiSqs), 0, 0)
else:
ppgplot.pgenv(0, 10, 0, max(allChiSqs), 0, 0)
iterations = range(iteration+1)
ppgplot.pgpt(iterations, allChiSqs, 2)
minCh = min(allChiSqs)
medCh = numpy.median(allChiSqs)
maxCh = max(allChiSqs)
ppgplot.pglab("Iteration [n]", "Chi-squared", "Chi-squared values [%.2f, %.2f, %.2f]"%(minCh, medCh, maxCh))
iteration += 1
return chi
for xStep in range(borderMask, width - borderMask,
superPixelSize):
x1 = xStep
x2 = xStep + superPixelSize
y1 = yStep
y2 = yStep + superPixelSize
xpts = [x1, x1, x2, x2]
ypts = [y1, y2, y2, y1]
bitmapX = (x1 - borderMask) / superPixelSize
bitmapY = (y1 - borderMask) / superPixelSize
ppgplot.pgsfs(2)
ppgplot.pgsci(4)
ppgplot.pgpoly(xpts, ypts)
superPixel = maskedImageCopy[y1:y2, x1:x2]
if previewSuperPixel:
ppgplot.pgslct(spPreview['pgplotHandle'])
boostedPreview = generalUtils.percentiles(
superPixel, 20, 99)
ppgplot.pggray(boostedPreview, 0, superPixelSize - 1,
0, superPixelSize - 1, 0, 255,
imagePlot['pgPlotTransform'])
ppgplot.pgslct(imagePlot['pgplotHandle'])
superPixelObject = {}
mean = float(numpy.ma.mean(superPixel))
if math.isnan(mean): continue
superPixelObject['mean'] = mean
superPixelObject['median'] = numpy.ma.median(superPixel)
superPixelObject['max'] = numpy.ma.min(superPixel)
superPixelObject['min'] = numpy.ma.max(superPixel)
superPixelObject['x1'] = x1
ppgplot.pgsci(1)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
ppgplot.pglab("wavelength", "flux", spectrum.objectName)
observedArea = spectrum.integrate()
print "Wavelength range of observations:", observedSpectrumRange
# modelPlotWindow = ppgplot.pgopen(arg.device)
# pgPlotTransform = [0, 1, 0, 0, 0, 1]
# ppgplot.pgask(False)
currentPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(currentPlotWindow)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgline(spectrum.wavelengths, spectrum.flux)
ppgplot.pglab("wavelength", "flux", "Current fit")
chiSqPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(chiSqPlotWindow)
ppgplot.pgenv(0, 10, 0, 100, 0, 0)
ppgplot.pglab("Iteration [n]", "Chi-squared", "Chi-squared values")
colour = 2
allChiSqs = []
objects.append(target)
print "%d targets loaded"%len(objects)
#for o in objects:
# o.filterData('mag', 10, 16.5)
if arg.ps: device = "lightcurves.ps/ps"
else: device = "/xs"
PGPlotWindow = ppgplot.pgopen(device)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(PGPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgask(False)
for index, o in enumerate(objects):
HJD = o.getColumn('HJD')
mag = o.getColumn('mag')
err = o.getColumn('err')
startDate = numpy.min(HJD)
dates = [ d - startDate for d in HJD]
endDate = numpy.max(HJD)
magMax = numpy.max(mag) + err[numpy.argmax(mag)]
magMin = numpy.min(mag) - err[numpy.argmin(mag)]
meanError = numpy.mean(err)
print "%s Start date: %f, End date: %f"%(o.id, startDate, endDate)
ppgplot.pgenv(0, numpy.max(dates), magMax + meanError*2, magMin - meanError*2, 0, 0)
ppgplot.pgpt(dates, mag)
ppgplot.pgsci(1) ppgplot.pgenv(0, width-1, 0, height-1, 0, 0) redReference = numpy.array(images[0].split()[0]) redTotal = numpy.zeros(numpy.shape(redReference)) greenTotal = numpy.zeros(numpy.shape(redReference)) blueTotal = numpy.zeros(numpy.shape(redReference)) for frameIndex, image in enumerate(images): redData, greenData, blueData = image.split() # redData.show() # print redData redData = numpy.array(redData) ppgplot.pgslct(previewWindow) ppgplot.pggray(redData, 0, width-1, 0, height-1, 0, 255, pgPlotTransform) # correlation = scipy.signal.correlate2d(redData, redReference, mode='same', boundary='symm') shift = phase_cor(redData, redReference) y, x = numpy.unravel_index(numpy.argmax(shift), shift.shape) if x > width/2: xd = width - x else: xd = -x if y > height/2: yd = height - y else: yd = -y print "Offset from first frame:", x, y, xd, yd
data['flux_err'] = float(fields[3])
print data
modelledData.appendData(data)
dataFile.close()
print "%d targets loaded"%len(objects)
observedData.convertFluxMagnitude()
modelledData.convertFluxMagnitude()
if arg.ps: device = arg.name + "_lcurve.ps/ps"
else: device = "/xs"
PGPlotWindow = ppgplot.pgopen(device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
ppgplot.pgslct(PGPlotWindow)
ppgplot.pgsci(1)
ppgplot.pgask(False)
phases = observedData.getColumn('phase')
flux = observedData.getColumn('flux')
flux_err = observedData.getColumn('flux_err')
flux_err = [f/10 for f in flux_err]
flux = [f * 3631E3 for f in flux]
flux_err = [f * 3631E3 for f in flux_err]
mag = observedData.getColumn('mag')
err = observedData.getColumn('err')
maxFlux = 0
for f, fe in zip(flux, flux_err):
if f + fe > maxFlux: maxFlux = f + fe
# Duplicate data out to phase 2.0
for yStep in range(borderMask, height - borderMask, superPixelSize):
for xStep in range(borderMask, width - borderMask, superPixelSize):
x1 = xStep
x2 = xStep + superPixelSize
y1 = yStep
y2 = yStep + superPixelSize
xpts = [x1, x1, x2, x2]
ypts = [y1, y2, y2, y1]
bitmapX = (x1 - borderMask) / superPixelSize
bitmapY = (y1 - borderMask) / superPixelSize
ppgplot.pgsfs(2)
ppgplot.pgsci(4)
ppgplot.pgpoly(xpts, ypts)
superPixel = maskedImageCopy[y1:y2, x1:x2]
if previewSuperPixel:
ppgplot.pgslct(spPreview["pgplotHandle"])
boostedPreview = generalUtils.percentiles(superPixel, 20, 99)
ppgplot.pggray(
boostedPreview,
0,
superPixelSize - 1,
0,
superPixelSize - 1,
0,
255,
imagePlot["pgPlotTransform"],
)
ppgplot.pgslct(imagePlot["pgplotHandle"])
superPixelObject = {}
mean = float(numpy.ma.mean(superPixel))
recordedData.loadFromFile(logFilename)
recordedData.sortByHJD()
# print recordedData
"""
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
fitPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(False)
for spectrum in spectra:
ppgplot.pgslct(mainPGPlotWindow)
ppgplot.pgsci(1)
lowerWavelength = min(spectrum.wavelengths)
upperWavelength = max(spectrum.wavelengths)
lowerFlux = min(spectrum.flux)
upperFlux = max(spectrum.flux)
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
ppgplot.pgbin(spectrum.wavelengths, spectrum.flux)
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
# Grab the continuum from either side of the spectrum
lowerCut = 6540
upperCut = 6585
continuumSpectrum = copy.deepcopy(spectrum)
allWindows[windowIndex].addToStack(image) if arg.preview: fullFrame = numpy.zeros((fullFrameysize, fullFramexsize)) for w in allWindows: if (arg.stack): boostedImage = ultracamutils.percentiles(w.stackedData, 20, 99) else: boostedImage = ultracamutils.percentiles(w.data, 20, 99) xll = w.xll/w.xbin - xmin xsize = w.nx yll = w.yll/w.ybin - ymin ysize = w.ny fullFrame[yll:yll+ysize, xll:xll+xsize] = fullFrame[yll:yll+ysize, xll:xll+xsize] + boostedImage ppgplot.pgslct(mainPreview) (rows, cols) = numpy.shape(fullFrame) ppgplot.pggray(fullFrame, 0, cols-1, 0, rows-1, 0, 255, pgPlotTransform) ppgplot.pgsci(3) ppgplot.pgsfs(2) for s in allSources: (x, y) = s ppgplot.pgcirc(x, y, 10) # Now also draw the zoomed in region around the first aperture if (applyShift): (x, y) = masterApertureList[0] croppedFrame = fullFrame[y-10:y+10, x-9:x+10] ppgplot.pgslct(zoomedAperture) rows, cols = numpy.shape(croppedFrame) ppgplot.pggray(croppedFrame, 0, cols-1, 0, rows-1, 0, 255, pgPlotTransform)
timeLeftString = str(hours).zfill(2) + ":" + str(mins).zfill(2) + ":" + str(secs).zfill(2) ccdFrame = rdat() statusString = "\r%s Frame: [%d/%d]"%(timeLeftString, trueFrameNumber, frameRange) sys.stdout.write(statusString) sys.stdout.flush() windows = ccdFrame[0] for windowIndex, w in enumerate(windows): image = w._data allWindows[windowIndex].setData(image) if arg.preview: ppgplot.pgslct(bitmapView['pgplotHandle']) ppgplot.pgbbuf() fullFrame = numpy.zeros((fullFrameysize, fullFramexsize)) for w in allWindows: if (arg.stack): boostedImage = ultracamutils.percentiles(w.stackedData, 20, 99) else: boostedImage = ultracamutils.percentiles(w.data, 20, 99) xll = w.xll/w.xbin - xmin xsize = w.nx yll = w.yll/w.ybin - ymin ysize = w.ny fullFrame[yll:yll+ysize, xll:xll+xsize] = fullFrame[yll:yll+ysize, xll:xll+xsize] + boostedImage rows, cols = numpy.shape(fullFrame)
timeLeftString = str(hours).zfill(2) + ":" + str(mins).zfill(2) + ":" + str(secs).zfill(2) ccdFrame = rdat() statusString = "\r%s Frame: [%d/%d]"%(timeLeftString, trueFrameNumber, frameRange) sys.stdout.write(statusString) sys.stdout.flush() windows = ccdFrame[0] for windowIndex, w in enumerate(windows): image = w._data allWindows[windowIndex].setData(image) if arg.preview: ppgplot.pgslct(bitmapView) fullFrame = numpy.zeros((fullFrameysize, fullFramexsize)) for w in allWindows: if (arg.stack): boostedImage = ultracamutils.percentiles(w.stackedData, 20, 99) else: boostedImage = ultracamutils.percentiles(w.data, 20, 99) xll = w.xll/w.xbin - xmin xsize = w.nx yll = w.yll/w.ybin - ymin ysize = w.ny fullFrame[yll:yll+ysize, xll:xll+xsize] = fullFrame[yll:yll+ysize, xll:xll+xsize] + boostedImage dimensions = numpy.shape(fullFrame) rows = dimensions[0] cols = dimensions[1]