def plotsigsff(sig, sf, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
tot = N.ones(len(sf), 'f')
(sigbin, sfbin) = my.binitsumequal(sig, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sig, tot, nbin)
print sfbin
print totbin
(sff, sfferr) = my.ratioerror(sfbin, totbin)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -.05
ymax = 1.05
xmin = min(sig) - 10.
#xmax=max(sig)-200.
xmax = 350.
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0)
ppgplot.pglab("\gS\d5\u (gal/Mpc\u2\d)", "Fraction EW([OII])>4 \(2078)",
"")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
sig = N.array(sig, 'f')
sff = N.array(sff, 'f')
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 17)
my.errory(sigbin, sff, sfferr)
ppgplot.pgend()
def plotCircles(objectTable, margins):
margins = checkMargins(margins)
ppgplot.pgsci(3)
ppgplot.pgsfs(2)
index = 0
print ("Margins:", margins)
for obj in objectTable:
ra = obj["ra"]
dec = obj["dec"]
c = obj["class"]
if ra > margins[1][0] and ra < margins[0][0] and dec < margins[0][1] and dec > margins[1][1]:
# print index, ra, dec, x, y, c
index += 1
colour = 1
if c == -9:
colour = 2 # Red = Saturated
if c == 1:
colour = 4 # Blue = Galaxy
if c == -3:
colour = 5 # Cyan = Probable Galaxy
if c == -1:
colour = 3 # Green = Star
if c == -2:
colour = 8 # Orange = Probable Star
if c == 0:
colour = 2 # Red = Noise
ppgplot.pgsci(colour)
ppgplot.pgcirc(obj["x"], obj["y"], 5 + (5 * obj["pStar"]))
return index + 1
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
def undoSetColour(self):
try:
ci = self.previousColours.pop()
except IndexError:
ci = None
if not ci is None:
pgplot.pgsci(ci)
def setColour(self, colour):
if colour in self.colours.keys():
ci = self.colours[colour]['ci']
pgplot.pgsci(ci)
else:
try:
ci = colour
pgplot.pgsci(ci)
except:
raise ex.UnrecognizedChoiceObject(colour)
self.previousColours.append(ci)
def resetdefaults():
"""
resetdefaults():
Reset global plotting variables to default values.
"""
global ppgplot_font_, ppgplot_linestyle_, ppgplot_linewidth_, \
ppgplot_color_, ppgplot_font_size_
ppgplot.pgscf(ppgplot_font_)
ppgplot.pgsls(ppgplot_linestyle_)
ppgplot.pgslw(ppgplot_linewidth_)
ppgplot.pgsci(ppgplot_colors_[ppgplot_color_])
ppgplot.pgsch(ppgplot_font_size_)
def resetdefaults():
"""
resetdefaults():
Reset global plotting variables to default values.
"""
global ppgplot_font_, ppgplot_linestyle_, ppgplot_linewidth_, \
ppgplot_color_, ppgplot_font_size_
ppgplot.pgscf(ppgplot_font_)
ppgplot.pgsls(ppgplot_linestyle_)
ppgplot.pgslw(ppgplot_linewidth_)
ppgplot.pgsci(ppgplot_colors_[ppgplot_color_])
ppgplot.pgsch(ppgplot_font_size_)
def terminatePlot(self):
if not self.plotDeviceIsOpened:
raise ValueError("You have not yet opened a PGPLOT device.")
if self._drawBox:
pgplot.pgsci(1)
pgplot.pgbox(self._xAxisOptions, 0.0, 0, self._yAxisOptions, 0.0,
0)
pgplot.pglab(self.xLabel, self.yLabel, self.title)
pgplot.pgend()
self.plotDeviceIsOpened = False
def resetdefaults():
"""
resetdefaults():
Reset global plotting variables to default values.
"""
global ppgplot_font_, ppgplot_linestyle_, ppgplot_linewidth_, \
ppgplot_color_, ppgplot_font_size_
ppgplot.pgscf(ppgplot_font_)
ppgplot.pgsls(ppgplot_linestyle_)
ppgplot.pgslw(ppgplot_linewidth_)
ppgplot.pgsci(ppgplot_colors_[ppgplot_color_])
# My little add-on to switch the background to white
reset_colors()
ppgplot.pgsch(ppgplot_font_size_)
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 plot_selection(id, x0, y0, dt=2.0, w=10.0):
dx, dy = id.x1 - id.x0, id.y1 - id.y0
ang = np.arctan2(dy, dx)
r = np.sqrt(dx**2 + dy**2)
drdt = r / (id.t1 - id.t0)
sa, ca = np.sin(ang), np.cos(ang)
dx = np.array([-dt, -dt, dt, dt, -dt]) * drdt
dy = np.array([w, -w, -w, w, w])
x = ca * dx - sa * dy + x0
y = sa * dx + ca * dy + y0
ppg.pgsci(7)
ppg.pgline(x, y)
return
def makeplot():
psplotinit("noise.ps")
DATAMIN = 0.
DATAMAX = 15.
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
#print "making graph, ncl = ",ncl
path = os.getcwd()
f = path.split('/')
#print path
#print f
prefix = f[4]
title = prefix
ymin = -.05
ymax = max(aveaperr) + .1
#ymax=10.
ppgplot.pgenv(DATAMIN, DATAMAX, ymin, ymax, 0)
ppgplot.pglab("linear size N of aperture (pixel)", "rms in Sky (ADU/s)",
title)
ppgplot.pgsci(2) #red
ppgplot.pgslw(4) #line width
x = N.sqrt(avearea)
y = aveaperr
ppgplot.pgpt(x, y, 7)
#errory(x,y,erry)
ppgplot.pgsci(1) #black
#ppgplot.pgpt(isoarea,fluxerriso,3)
#x1=N.sqrt(contsubisoarea)
#y1=contsuberr
#x1=N.sqrt(isoarea)
#y1=fluxerriso
#y=n*y1
#ppgplot.pgpt(x1,y1,1)
#ppgplot.pgsci(4)#blue
#ppgplot.pgpt(x1,y,1)
#ppgplot.pgsci(1)#black
x = N.arange(0, 50, 1)
y = x * (a + b * a * x)
#y=N.sqrt(x)*.02
ppgplot.pgline(x, y)
#errory(x,y,erry)
ppgplot.pgend()
def pgplot(slot, plotterHandle):
print "We are about to plot: %s"%(slot)
lightcurveView = ppgplot.pgopen('/xs')
xValues = slot.photometry.times
xValues = numpy.arange(0, len(xValues))
yValues = slot.getColumn("Counts")
ppgplot.pgenv(min(xValues), max(xValues), min(yValues), max(yValues), 0, 0)
ppgplot.pgask(False)
ppgplot.pgsci(2)
ppgplot.pgpt(xValues, yValues, 1)
ppgplot.pgclos()
return
def plot_horizontal_grid(self):
sch, sls, sci = ppgplot.pgqch(), ppgplot.pgqls(), ppgplot.pgqci()
ppgplot.pgsci(15)
ppgplot.pgsls(2)
# Altitudes
for alt in np.arange(0.0, 90.0, 20.0):
az = np.arange(0.0, 360.0)
rx, ry = forward(self.az, self.alt, az, alt * np.ones_like(az))
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
# Azimuths
for az in np.arange(0.0, 360.0, 30.0):
alt = np.arange(0.0, 80.0)
rx, ry = forward(self.az, self.alt, az * np.ones_like(alt), alt)
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
ppgplot.pgsci(1)
ppgplot.pgsls(1)
# Horizon
az = np.arange(0.0, 360.0)
rx, ry = forward(self.az, self.alt, az, np.zeros_like(az))
for i in range(len(rx)):
if i == 0:
ppgplot.pgmove(rx[i], ry[i])
if (np.abs(rx[i]) <= 1.5 * self.w) & (np.abs(ry[i]) <= self.w):
ppgplot.pgdraw(rx[i], ry[i])
else:
ppgplot.pgmove(rx[i], ry[i])
def compdiff(x, y, xlabel, ylabel):
xmax = max(x)
xmin = min(x)
ymax = max(y)
ymin = min(y)
ave = N.average(N.compress((x < 21) & (im1.sn > 3), y))
std = scipy.stats.std(N.compress((x < 21) & (im1.sn > 3), y))
ppgplot.pgbox("", 0.0, 0, "", 0.0, 0)
ppgplot.pgenv(xmin, xmax, -.8, .8, 0)
ppgplot.pglab(xlabel, ylabel, "")
ppgplot.pgpt(x, y, 3)
x = N.arange((int(xmin) - 1), (int(xmax) + 2), 1)
y = ave * x / x
ppgplot.pgsci(2)
ppgplot.pgline(x, y)
ppgplot.pgsci(1)
y1 = y - std
ppgplot.pgline(x, y1)
y1 = y + std
ppgplot.pgline(x, y1)
def plot_header(fname, ff, iod_line):
# ppgplot arrays
heat_l = np.array([0.0, 0.2, 0.4, 0.6, 1.0])
heat_r = np.array([0.0, 0.5, 1.0, 1.0, 1.0])
heat_g = np.array([0.0, 0.0, 0.5, 1.0, 1.0])
heat_b = np.array([0.0, 0.0, 0.0, 0.3, 1.0])
# Plot
ppg.pgopen(fname)
ppg.pgpap(0.0, 1.0)
ppg.pgsvp(0.1, 0.95, 0.1, 0.8)
ppg.pgsch(0.8)
ppg.pgmtxt("T", 6.0, 0.0, 0.0,
"UT Date: %.23s COSPAR ID: %04d" % (ff.nfd, ff.site_id))
if is_calibrated(ff):
ppg.pgsci(1)
else:
ppg.pgsci(2)
ppg.pgmtxt(
"T", 4.8, 0.0, 0.0, "R.A.: %10.5f (%4.1f'') Decl.: %10.5f (%4.1f'')" %
(ff.crval[0], 3600.0 * ff.crres[0], ff.crval[1], 3600.0 * ff.crres[1]))
ppg.pgsci(1)
ppg.pgmtxt("T", 3.6, 0.0, 0.0, ("FoV: %.2f\\(2218)x%.2f\\(2218) "
"Scale: %.2f''x%.2f'' pix\\u-1\\d") %
(ff.wx, ff.wy, 3600.0 * ff.sx, 3600.0 * ff.sy))
ppg.pgmtxt(
"T", 2.4, 0.0, 0.0, "Stat: %5.1f+-%.1f (%.1f-%.1f)" %
(np.mean(ff.zmax), np.std(ff.zmax), ff.zmaxmin, ff.zmaxmax))
ppg.pgmtxt("T", 0.3, 0.0, 0.0, iod_line)
ppg.pgsch(1.0)
ppg.pgwnad(0.0, ff.nx, 0.0, ff.ny)
ppg.pglab("x (pix)", "y (pix)", " ")
ppg.pgctab(heat_l, heat_r, heat_g, heat_b, 5, 1.0, 0.5)
def plotsigo2(sig, o2, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -10.
ymax = 2.
xmin = min(sig) - 10.
#xmax=max(sig)-200.
xmax = 350.
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0)
ppgplot.pglab("\gS\d5\u (gal/Mpc\u2\d)", "EW([OII]) (\(2078))", "")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
sig = N.array(sig, 'f')
o2 = N.array(o2, 'f')
ppgplot.pgpt(sig, o2, 1)
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
def drawLegend(self,
listOfStyles,
listOfColours,
listOfTexts,
fracHeight=0.25,
isLeft=True,
isUp=True):
# if style is None just draw a line segment.
numTexts = len(listOfTexts)
x0 = self.xFracToWorld(0.02)
x1 = self.xFracToWorld(0.025)
x2 = self.xFracToWorld(0.03)
for i in range(numTexts):
yFrac = 1.0 - fracHeight * ((numTexts - i - 0.5) / float(numTexts))
y = self.yFracToWorld(yFrac)
self.setColour(listOfColours[i])
if listOfStyles[i] is None:
self.drawLine(x0, y, x1, y)
else:
self.drawPoint(0.5 * (x0 + x1), y, listOfStyles[i])
pgplot.pgsci(1)
pgplot.pgptxt(x2, y, 0.0, 0.0, listOfTexts[i])
def plotCircles(objectTable, margins):
margins = checkMargins(margins)
ppgplot.pgsci(3)
ppgplot.pgsfs(2)
index = 0
print("Margins:", margins)
for obj in objectTable:
ra = obj['ra']
dec = obj['dec']
c = obj['class']
if ra > margins[1][0] and ra < margins[0][0] and dec < margins[0][
1] and dec > margins[1][1]:
# print index, ra, dec, x, y, c
index += 1
colour = 1
if c == -9: colour = 2 # Red = Saturated
if c == 1: colour = 4 # Blue = Galaxy
if c == -3: colour = 5 # Cyan = Probable Galaxy
if c == -1: colour = 3 # Green = Star
if c == -2: colour = 8 # Orange = Probable Star
if c == 0: colour = 2 # Red = Noise
ppgplot.pgsci(colour)
ppgplot.pgcirc(obj['x'], obj['y'], 5 + (5 * obj['pStar']))
return (index + 1)
def plotsig10sffall(sigspec, sigphot, sf, file, nbin):
psplot = file + ".ps"
psplotinit(psplot)
ppgplot.pgbox("", 0.0, 0, "L", 0.0, 0)
ymin = -.01
ymax = 1.01
#xmin=min(sigspec)-10.
#xmax=max(sig)-200.
#xmax=400.
xmin = -1.
xmax = 2.7
ppgplot.pgenv(xmin, xmax, ymin, ymax, 0, 10)
ppgplot.pglab("\gS\d10\u (gal/Mpc\u2\d)", "Fraction EW([OII])>4 \(2078)",
"")
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
tot = N.ones(len(sf), 'f')
(sigbin, sfbin) = my.binitsumequal(sigspec, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sigspec, tot, nbin)
(sff, sfferr) = my.ratioerror(sfbin, totbin)
#sig=N.array(sig,'f')
#sff=N.array(sff,'f')
ppgplot.pgsci(2)
sigbin = N.log10(sigbin)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 17)
my.errory(sigbin, sff, sfferr)
(sigbin, sfbin) = my.binitsumequal(sigphot, sf, nbin)
(sigbin, totbin) = my.binitsumequal(sigphot, tot, nbin)
(sff, sfferr) = my.ratioerror(sfbin, totbin)
#sig=N.array(sig,'f')
#sff=N.array(sff,'f')
ppgplot.pgslw(4) #line width
ppgplot.pgsci(4)
sigbin = N.log10(sigbin)
ppgplot.pgline(sigbin, sff)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, sff, 21)
#my.errory(sigbin,sff,sfferr)
ppgplot.pgend()
def plotsighaall(sig, psig, o2b, file, nbin):
o2b = N.array(o2b, 'f')
sig = N.array(sig, 'f')
psig = N.array(psig, 'f')
#o2b=o2b+4.
o2 = N.compress(o2b > -500., o2b)
sig = N.compress(o2b > -500., sig)
psig = N.compress(o2b > -500., psig)
psplot = file + ".ps"
psplotinit(psplot)
#ppgplot.pgsch(0.7)
ppgplot.pgslw(7)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgswin(-2., 2., -5., 20.)
ppgplot.pgbox('blcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gS\d10\u (gal/Mpc\u2\d)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'EW(H\ga) (\(2078))')
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgpt(sigbin, o2bin, 17)
ppgplot.pgpt(N.log10(sig), o2, 1)
#my.errory(sigbin,o2bin,yerr)
#print 'dude2', sigbin, o2bin
ppgplot.pgsci(2)
ppgplot.pgline(sigbin, o2bin)
(sigbin, o2bin) = my.binit(psig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgsci(1)
ppgplot.pgpt(sigbin, o2bin, 21)
#my.errory(sigbin,o2bin,yerr)
ppgplot.pgsci(4)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
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 plotsigo2all(sig, psig, o2b, file, nbin):
#o2=N.zeros(len(o2b),'f')
#for i in range(len(o2b)):
#print i, sig[i], psig[i], o2b[i]
# if o2b[i] < 0:
# o2[i]=-1*o2b[i]
#print "hey", o2[i]
o2 = o2b
psplot = file + ".ps"
psplotinit(psplot)
ppgplot.pgsch(0.7)
(sigbin, o2bin) = my.binit(sig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgswin(-1., 3., -.5, 10.)
ppgplot.pgbox('bcnst', 0.0, 0.0, 'bcvnst', 0.0,
0.0) #tickmarks and labeling
ppgplot.pgsch(1.0)
ppgplot.pgmtxt('b', 2.5, 0.5, 0.5, "\gS\d10\u (gal/Mpc\u2\d)") #xlabel
ppgplot.pgsch(1.2)
ppgplot.pgmtxt('l', 2.6, 0.5, 0.5, 'EW([OII]) (\(2078))')
ppgplot.pgsls(1) #dotted
ppgplot.pgslw(4) #line width
ppgplot.pgsci(2)
ppgplot.pgpt(sigbin, o2bin, 17)
#print 'dude2', sigbin, o2bin
ppgplot.pgline(sigbin, o2bin)
(sigbin, o2bin) = my.binit(psig, o2, nbin)
#print 'dude', sigbin, o2bin
sigbin = N.log10(sigbin)
ppgplot.pgsci(4)
ppgplot.pgpt(sigbin, o2bin, 21)
ppgplot.pgline(sigbin, o2bin)
ppgplot.pgsci(1)
ppgplot.pgend()
def main(options):
global keepPlotting
keepPlotting = True
debug = options.debug
inputMS = options.inms
if inputMS == "":
print "Error: You must specify a MS name."
print ' Use "uvplot.py -h" to get help.'
return
if inputMS.endswith("/"):
inputMS = inputMS[:-1]
inputMSbasename = inputMS.split("/")[-1]
if inputMSbasename == "":
# The user has not specified the full path of the MS
inputMSbasename = inputMS
device = options.device
if device == "?":
ppgplot.pgldev()
return
xaxis = options.xaxis
yaxis = options.yaxis
column = options.column
nx, ny = options.nxy.split(",")
axlimits = options.axlimits.split(",")
if len(axlimits) == 4:
xmin, xmax, ymin, ymax = axlimits
else:
print "Error: You must specify four axis limits"
return
showFlags = options.flag
flagCol = options.colflag
showAutocorr = options.autocorr
showStats = options.statistics
timeslots = options.timeslots.split(",")
if len(timeslots) != 2:
print "Error: Timeslots format is start,end"
return
for i in range(len(timeslots)):
timeslots[i] = int(timeslots[i])
antToPlotSpl = options.antennas.split(",")
antToPlot = []
for i in range(len(antToPlotSpl)):
tmpspl = antToPlotSpl[i].split("..")
if len(tmpspl) == 1:
antToPlot.append(int(antToPlotSpl[i]))
elif len(tmpspl) == 2:
for j in range(int(tmpspl[0]), int(tmpspl[1]) + 1):
antToPlot.append(j)
else:
print "Error: Could not understand antenna list."
return
polarizations = options.polar.split(",")
for i in range(len(polarizations)):
polarizations[i] = int(polarizations[i])
convertStokes = options.stokes
operation = options.operation
if operation != "":
operation = int(operation)
if convertStokes:
print "Error: Stokes conversion is not compatible with special operations"
return
channels = options.channels.split(",")
if len(channels) != 2:
print "Error: Channels format is start,end"
return
for i in range(len(channels)):
channels[i] = int(channels[i])
if channels[1] == -1:
channels[1] = None # last element even if there is only one
else:
channels[1] += 1
queryMode = options.query
doUnwrap = options.wrap
if not queryMode:
# open the graphics device, use the right number of panels
ppgplot.pgbeg(device, int(nx), int(ny))
# set the font size
ppgplot.pgsch(1.5)
ppgplot.pgvstd()
# open the main table and print some info about the MS
t = pt.table(inputMS, readonly=True, ack=False)
firstTime = t.getcell("TIME", 0)
lastTime = t.getcell("TIME", t.nrows() - 1)
intTime = t.getcell("INTERVAL", 0)
print "Integration time:\t%f sec" % (intTime)
nTimeslots = (lastTime - firstTime) / intTime
if timeslots[1] == -1:
timeslots[1] = nTimeslots
else:
timeslots[1] += 1
print "Number of timeslots:\t%d" % (nTimeslots)
# open the antenna and spectral window subtables
tant = pt.table(t.getkeyword("ANTENNA"), readonly=True, ack=False)
tsp = pt.table(t.getkeyword("SPECTRAL_WINDOW"), readonly=True, ack=False)
numChannels = len(tsp.getcell("CHAN_FREQ", 0))
print "Number of channels:\t%d" % (numChannels)
print "Reference frequency:\t%5.2f MHz" % (tsp.getcell("REF_FREQUENCY", 0) / 1.0e6)
# Station names
antList = tant.getcol("NAME")
if len(antToPlot) == 1 and antToPlot[0] == -1:
antToPlot = range(len(antList))
print "Station list (only starred stations will be plotted):"
for i in range(len(antList)):
star = " "
if i in antToPlot:
star = "*"
print "%s %2d\t%s" % (star, i, antList[i])
# Bail if we're in query mode
if queryMode:
return
# select by time from the beginning, and only use specified antennas
tsel = t.query(
"TIME >= %f AND TIME <= %f AND ANTENNA1 IN %s AND ANTENNA2 IN %s"
% (firstTime + timeslots[0] * intTime, firstTime + timeslots[1] * intTime, str(antToPlot), str(antToPlot))
)
# values to use for each polarization
plotColors = [1, 2, 3, 4]
labXPositions = [0.35, 0.45, 0.55, 0.65]
labYPositions = [1.0, 1.0, 1.0, 1.0]
if convertStokes:
polLabels = ["I", "Q", "U", "V"]
else:
polLabels = ["XX", "XY", "YX", "YY"]
# define nicely written axis labels
axisLabels = {
"time": "Time",
"chan": "Channel",
"freq": "Frequency [MHz]",
"amp": "Visibility amplitude",
"real": "Real part of visibility",
"imag": "Imaginary part of visibility",
"phase": "Visibility phase [radians]",
}
# Now we loop through the baselines
ppgplot.pgpage()
for tpart in tsel.iter(["ANTENNA1", "ANTENNA2"]):
if not keepPlotting:
return
ant1 = tpart.getcell("ANTENNA1", 0)
ant2 = tpart.getcell("ANTENNA2", 0)
if ant1 not in antToPlot or ant2 not in antToPlot:
continue
if ant1 == ant2:
if not showAutocorr:
continue
# Get the values to plot, strategy depends on axis type
if xaxis == "time":
xaxisvals = getXAxisVals(tpart, xaxis, channels)
yaxisvals = getYAxisVals(
tpart, yaxis, column, operation, showFlags, flagCol, channels, doUnwrap, convertStokes
)
else:
xaxisvals = getXAxisVals(tsp, xaxis, channels)
yaxisvals = getYAxisVals(
tpart, yaxis, column, operation, showFlags, flagCol, channels, doUnwrap, convertStokes, xaxistype=1
)
if xaxisvals == None: # This baseline must be empty, go to next one
print "No good data on baseline %s - %s" % (antList[ant1], antList[ant2])
continue
if debug:
print xaxisvals.shape
print yaxisvals.shape
for r in range(len(xaxisvals)):
print "%s" % yaxisvals[r]
if len(xaxisvals) != len(yaxisvals): # something is wrong
print "Error: X and Y axis types incompatible"
return
# Plot the data, each polarization in a different color
ppgplot.pgsci(1)
if xmin == "":
minx = xaxisvals.min()
else:
minx = float(xmin)
if xmax == "":
maxx = xaxisvals.max()
else:
maxx = float(xmax)
if ymin == "":
miny = yaxisvals.min()
if numpy.ma.getmaskarray(yaxisvals.min()):
print "All data flagged on baseline %s - %s" % (antList[ant1], antList[ant2])
continue
else:
miny = float(ymin)
if ymax == "":
maxy = yaxisvals.max()
else:
maxy = float(ymax)
if minx == maxx:
minx -= 1.0
maxx += 1.0
else:
diffx = maxx - minx
minx -= 0.02 * diffx
maxx += 0.02 * diffx
if miny == maxy:
miny -= 1.0
maxy += 1.0
else:
diffy = maxy - miny
miny -= 0.02 * diffy
maxy += 0.02 * diffy
# ppgplot.pgpage()
ppgplot.pgswin(minx, maxx, miny, maxy)
if xaxis == "time":
ppgplot.pgtbox("ZHOBCNST", 0.0, 0, "BCNST", 0.0, 0)
else:
ppgplot.pgbox("BCNST", 0.0, 0, "BCNST", 0.0, 0)
# ppgplot.pglab(axisLabels[xaxis], axisLabels[yaxis], '%s - %s'%(antList[ant1],antList[ant2]))
# ppgplot.pgmtxt('T', 3.0, 0.5, 0.5, inputMSbasename)
ppgplot.pglab(
axisLabels[xaxis],
axisLabels[yaxis],
inputMSbasename + "(" + getDataDescription(column) + "): %s - %s" % (antList[ant1], antList[ant2]),
)
if operation != 0:
# some operations is defined
if operation == 1:
label = "XX-YY"
elif operation == 2:
label = "XY.YX*"
else:
print "Special operation not defined"
return
ppgplot.pgsci(plotColors[0])
tmpvals = yaxisvals
print "Baseline", antList[ant1], "-", antList[ant2], ": Plotting", len(
tmpvals[~tmpvals.mask]
), "points of " + label
ppgplot.pgpt(xaxisvals[~tmpvals.mask], tmpvals[~tmpvals.mask], 1)
addInfo(showStats, tmpvals[~tmpvals.mask], label, labXPositions[1], labYPositions[1])
else:
for j in polarizations:
ppgplot.pgsci(plotColors[j])
tmpvals = yaxisvals[:, j]
if j == polarizations[0]:
print "Baseline", antList[ant1], "-", antList[ant2], ": Plotting", len(
tmpvals[~tmpvals.mask]
), "points per polarization"
ppgplot.pgpt(xaxisvals[~tmpvals.mask], tmpvals[~tmpvals.mask], 1)
addInfo(showStats, tmpvals[~tmpvals.mask], polLabels[j], labXPositions[j], labYPositions[j])
ppgplot.pgpage()
# Close the PGPLOT device
ppgplot.pgclos()
positionString = generalUtils.toSexagesimal((ra, dec)) print "RA, DEC of image centre is: ", positionString, ra, dec margins = wcsSolution.all_pix2world([[0, 0], [width, height]], 1) margins = checkMargins(margins) print "ra, dec limits:", margins xlimits = (0, width) ylimits = (0, height) # try: x=width/2 y=height/2 newWidth = width newHeight = height ppgplot.pgsci(3) keyPressed = None while keyPressed != 'q': ch = ppgplot.pgcurs(x, y) x=ch[0] y=ch[1] keyPressed = ch[2] # print "Key pressed:", ch[2] if keyPressed== 'w': imageMinMax = (imageMinMax[1], imageMinMax[0]) if invertedColours: invertedColours= False else: invertedColours=True redraw() if keyPressed=='i':
def plotxy(y, x=None, title=None, rangex=None, rangey=None, \
labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', symbol=ppgplot_symbol_, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, font=ppgplot_font_, logx=0, logy=0, \
logx2=0, logy2=0, errx=None, erry=None, id=0, noscale=0, \
aspect=0.7727, fontsize=ppgplot_font_size_, ticks='in', \
panels=[1,1], device=ppgplot_device_, setup=1):
"""
plotxy(y, ...)
An interface to make various XY style plots using PGPLOT.
'y' is the 1D sequence object to plot.
The optional entries are:
x: x values (default = 0, 1, ...)
title: graph title (default = None)
rangex: ranges for the x-axis (default = automatic)
rangey: ranges for the y-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: ranges for 2nd x-axis (default = None)
rangey2: ranges for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
errx: symmetric x errors (default = None)
erry: symmetric y errors (default = None)
symbol: symbol for points (default = None)
line: line style (default = 1 (solid))
width: line width (default = 1 (thin))
color: line and/or symbol color (default = 'white')
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: aspect ratio (default = 0.7727 (rect))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
setup: Auto-setup the plot (default = 1)
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is an array
y = Num.asarray(y);
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, ppgplot_colors_
# Define the X axis limits if needed
if x is None: x=Num.arange(len(y), dtype='f')
else: x = Num.asarray(x)
# Determine the scaling to use for the first axis
if rangex is None: rangex=[x.min(), x.max()]
if rangey is None:
if noscale: rangey=[y.min(), y.max()]
else: rangey=scalerange(y)
# Prep the plotting device...
if (not ppgplot_dev_prep_ and setup):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, \
logx, logy, logx2, logy2, font, fontsize, \
id, aspect, ticks, panels, device=device)
# Choose the line color
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Plot symbols (and errors) if requested
if not symbol is None:
ppgplot.pgpt(x, y, symbol)
# Error bars
if errx is not None:
if not logx:
errx = Num.asarray(errx)
if errx.size == 1:
errx = errx.repeat(x.size)
#ppgplot.pgerrx(x+errx, x-errx, y, 1.0)
ppgplot.pgerrb(5, x, y, errx, 1.0)
else:
errx = 10.0**Num.asarray(errx)
if errx.size == 1:
errx = errx.repeat(x.size)
#ppgplot.pgerrx(Num.log10(10.0**x + errx),
# Num.log10(10.0**x - errx), y, 1.0)
ppgplot.pgerrb(5, x, y, Num.log10(errx), 1.0)
if erry is not None:
if not logy:
erry = Num.asarray(erry)
if erry.size == 1:
erry = erry.repeat(y.size)
#ppgplot.pgerry(x, y+erry, y-erry, 1.0)
ppgplot.pgerrb(6, x, y, erry, 1.0)
else:
erry = 10.0**Num.asarray(erry)
if erry.size == 1:
erry = erry.repeat(y.size)
#ppgplot.pgerry(x, Num.log10(10.0**y + erry),
# Num.log10(10.0**y - erry), 1.0)
ppgplot.pgerrb(6, x, y, Num.log10(erry), 1.0)
# Plot connecting lines if requested
if not line is None:
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
ppgplot.pgline(x, y)
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] # Draw the grayscale bitmap ppgplot.pggray(fullFrame, 0, cols-1 , 0, rows-1 , 0, 255, pgPlotTransform) # Draw the full reference aperture list ppgplot.pgsci(3) for s in sourceList.getSources(): (x, y) = s.abs_position ppgplot.pgcirc(x, y, 10) margins = 10 if arg.preview: ppgplot.pgslct(bitmapView) ppgplot.pgsci(2) plotColour = [1, 2, 3, 4, 5, 6] for index, s in enumerate(referenceApertures.getSources()): window = allWindows[s.windowIndex] center = s.latestPosition xcenterInt = int(center[0])
def setmask(command, data, cdata):
"""
Sets the mask on a dset and dumps a file containing the mask which can be applied
to other dsets using 'appmask'. This is an interactive routine which will request
input from the user and is better not used in batch processing. Repeated calls of
this routine can be used to build complex masks. The masks are always applied in
the original order so that you can mask then partially unmask for example. Note that
whatever slot you choose to define the mask will always end up masked; if you don't
want this you may want to make a copy.
Interactive usage:
setmask slot mfile append [device reset x1 x2 y1 y2] mask type
Arguments:
slot -- an example slot to plot.
mfile -- mask file
append -- append to an old mask file if possible
device -- plot device, e.g. '/xs'
reset -- rest plot limits or not
x1 -- left X plot limit
x2 -- right X plot limit
y1 -- bottom Y plot limit
y2 -- top Y plot limit
mask -- mask 'M', or unmask 'U' or quit 'Q'.
type -- type of mask: 'X' masks using ranges in X
Mask types:
X -- mask a range in X
Y -- mask a range in Y
I -- mask a range of pixel indices
P -- mask in 'phase', i.e. a range that repeats periodically.
"""
import trm.dnl.mask as mask
# generate arguments
inpt = inp.Input(DINT_ENV, DINT_DEF, inp.clist(command))
# register parameters
inpt.register('slot', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('mfile', inp.Input.GLOBAL, inp.Input.PROMPT)
inpt.register('append', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('device', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('reset', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('mask', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('type', inp.Input.LOCAL, inp.Input.PROMPT)
# get inputs
slots = inpt.get_value('slot', 'slot to plot for mask definition', '1')
slist = interp_slots(slots, True, data, nfind=1)
dset = data[slist[0]]
device = inpt.get_value('device', 'plot device', '/xs')
# mask file
mfile = inpt.get_value('mfile', 'mask file to save results to', subs.Fname('mask','.msk', subs.Fname.NEW))
append = inpt.get_value('append', 'add to an old mask file if possible', True)
if append and mfile.exists():
mptr = open(mfile,'rb')
gmask = pickle.load(mptr)
gmask.app_mask(dset)
mptr.close()
else:
gmask = mask.Gmask()
# other parameters
reset = inpt.get_value('reset', 'reset plot limits automatically?', True)
# compute default limits
(x1,x2,y1,y2) = dset.plimits()
if (x2 - x1) < (x1+x2)/2./100.:
xoff = x1
x1 = 0.
x2 -= xoff
else:
xoff = 0.
yoff = 0.
if reset:
inpt.set_default('x1', x1)
inpt.set_default('x2', x2)
inpt.set_default('y1', y1)
inpt.set_default('y2', y2)
x1 = inpt.get_value('x1', 'left-hand limit of plot', x1)
x2 = inpt.get_value('x2', 'right-hand limit of plot', x2)
y1 = inpt.get_value('y1', 'bottom limit of plot', y1)
y2 = inpt.get_value('y2', 'top limit of plot', y2)
m_or_u = inpt.get_value('mask', 'M(ask), U(nmask) or Q(uit)?', 'm', lvals=['m', 'M', 'u', 'U', 'q', 'Q'])
if m_or_u.upper() == 'M':
mtext = 'mask'
else:
mtext = 'unmask'
mask_type = inpt.get_value('type', 'X, Y, P(hase), I(ndex) or Q(uit)?', 'x',
lvals=['x', 'X', 'y', 'Y', 'p', 'P', 'i', 'I', 'q', 'Q'])
# initialise plot
try:
pg.pgopen(device)
pg.pgsch(1.5)
pg.pgscf(2)
pg.pgslw(2)
pg.pgsci(4)
pg.pgenv(x1,x2,y1,y2,0,0)
(xlabel,ylabel) = dset.plabel(xoff,yoff)
pg.pgsci(2)
pg.pglab(xlabel, ylabel, dset.title)
# plot the dset
dset.plot(xoff,yoff)
x = (x1+x2)/2.
y = (y1+y2)/2.
# now define masks
ch = 'X'
while ch.upper() != 'Q':
# go through mask options
if mask_type.upper() == 'X':
print('Set cursor at the one end of the X range, Q to quit')
(xm1,y,ch) = pg.pgband(7,0,x,y)
if ch.upper() != 'Q':
print('Set cursor at the other end of ' +
'the X range, Q to quit')
xm2,y,ch = pg.pgband(7,0,xm1,y)
if ch.upper() != 'Q':
if xm1 > xm2: xm1,xm2 = xm2,xm1
umask = mask.Xmask(xoff+xm1, xoff+xm2, m_or_u.upper() == 'M')
elif mask_type.upper() == 'I':
print('Place cursor near a point and click to ' +
mtext + ' it, Q to quit')
x,y,ch = pg.pgband(7,0,x,y)
if ch.upper() != 'Q':
xmm1,xmm2,ymm1,ymm2 = pg.pgqvp(2)
xscale = (xmm2-xmm1)/(x2-x1)
yscale = (ymm2-ymm1)/(y2-y1)
# only consider good data of opposite 'polarity' to the
# change we are making.
ok = (dset.good == True) & \
(dset.mask == (m_or_u.upper() == 'M'))
if len(dset.x.dat[ok == True]):
# compute physical squared distance of cursor from
# points
sqdist = npy.power(
xscale*(dset.x.dat[ok]-(xoff+x)),2) + \
npy.power(yscale*(dset.y.dat[ok]-(yoff+y)),2)
# select the index giving the minimum distance
indices = npy.arange(len(dset))[ok]
index = indices[sqdist.min() == sqdist][0]
umask = mask.Imask(index, m_or_u.upper() == 'M')
else:
print('There seem to be no data to ' + mtext +
'; data already ' + mtext + 'ed are ignored.')
umask = None
if ch.upper() != 'Q' and umask is not None:
gmask.append(umask)
umask.app_mask(dset)
print('overplotting data')
# over-plot the dset
dset.plot(xoff,yoff)
pg.pgclos()
except pg.ioerror, err:
raise DintError(str(err))
ppgplot.pgtext(7.2,0.88,"twib_hour")
ppgplot.pgtext(7.55,0.88,"twib_min")
t_lmstmidn = hour + min/60 + sec/3600
twie_dn = twie_hour - 24 + twie_min/60
twib_dn = twib_hour + twib_min/60
ss_dn = ss_hour - 24 + ss_min/60
sr_dn = sr_hour + sr_min/60
# SHADE Twilight zone
#####################################################################
ppgplot.pgsfs(1)
ppgplot.pgsci(6)
ppgplot.pgrect(t_min, twie_dn, a_min, a_max)
ppgplot.pgrect(twib_dn, t_max, a_min, a_max)
ppgplot.pgsci(12)
ppgplot.pgrect(t_min, ss_dn, a_min, a_max)
ppgplot.pgrect(sr_dn, t_max, a_min, a_max)
ppgplot.pgsci(1)
# Read (RA,DEC) FROM OBJECT LIST
############################################################
open(FILE, "filename") || die ("cannot open filename")
n=0
while(line=<FILE>) {
def plot2d(z, x=None, y=None, title=None, rangex=None, rangey=None, \
rangez=None, labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', image=ppgplot_palette_, contours=None, \
logx=0, logy=0, logx2=0, logy2=0, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, labels=ppgplot_labels_, \
labelint=ppgplot_labelint_, labelmin=ppgplot_labelmin_, \
font=ppgplot_font_, id=0, noscale=0, aspect=1, \
fontsize=ppgplot_font_size_, ticks='out', panels=[1,1], \
device=ppgplot_device_):
"""
plot2d(z, ...)
An interface to make various 2D plots using PGPLOT.
'z' is the 2D Numpy array to be plotted.
The optional entries are:
x: x values (default = 0, 1, ...)
y: y values (default = 0, 1, ...)
title: graph title (default = None)
rangex: range for the x-axis (default = automatic)
rangey: range for the y-axis (default = automatic)
rangez: range for the z-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: range for 2nd x-axis (default = None)
rangey2: range for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
image: color palette for image (default = 'rainbow')
contours: list of contour values (default = None)
line: contour line style (default = 1 (solid))
width: contour line width (default = 1 (thin))
color: contour line color (default = 'white')
labels: color of contour labels (default = None)
labelint: contour label spacing (default = 20)
labelmin: min contour label spacing (default = 20)
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'out')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is a 2D array
z = Num.asarray(z)
if not len(z.shape) == 2:
print 'Input data array must be 2 dimensional.'
return
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, pgpalette
# Define the X and Y axis limits if needed
if x is None: x = Num.arange(z.shape[1], dtype='f')
else: x = Num.asarray(x)
if y is None: y = Num.arange(z.shape[0], dtype='f')
else: y = Num.asarray(y)
# Determine the scaling to use for the axes
if rangex is None:
dx = x[-1] - x[-2]
rangex = [x[0], x[-1] + dx]
if rangey is None:
dy = y[-1] - y[-2]
rangey = [y[0], y[-1] + dy]
if rangez is None: rangez=[Num.minimum.reduce(Num.ravel(z)), \
Num.maximum.reduce(Num.ravel(z))]
# Prep the plotting device...
if (not ppgplot_dev_prep_):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, logx, logy, \
logx2, logy2, font, fontsize, id, aspect, \
ticks, panels, device=device)
if image is not None:
# Set the color indices and the color table
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l, pgpalette.r, pgpalette.g, pgpalette.b)
# Construct the image
ppgplot.pgimag_s(z, 0.0, 0.0, rangex[0], rangey[0], \
rangex[1], rangey[1])
reset_colors()
if contours is not None:
contours = Num.asarray(contours)
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
# Choose the line color for the contourlines
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Construct the contours
ppgplot.pgcont_s(z, len(contours), contours, rangex[0], \
rangey[0], rangex[1], rangey[1])
# Label the contours if requested
if labels is not None:
# Choose the line color for the contourlines
if type(labels) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[labels])
else:
ppgplot.pgsci(labels)
for i in range(len(contours)):
ppgplot.pgconl_s(z, contours[i], str(contours[i]), labelint,
labelmin)
helpItem = {'key': "i/o", 'text': "Zoom in/out."}
help.append(helpItem)
helpItem = {'key': "q", 'text': "Quit."}
help.append(helpItem)
helpItem = {'key': "f", 'text': "Find local Ha maxima."}
help.append(helpItem)
helpItem = {'key': "l", 'text': "Show location of cursor."}
help.append(helpItem)
# try:
x = width / 2
y = height / 2
newWidth = width
newHeight = height
# pixelGrid = makeGrid(width, height, 100)
originalImageData = numpy.copy(imageData)
ppgplot.pgsci(3)
keyPressed = None
while keyPressed != 'q':
ch = ppgplot.pgcurs(x, y)
x = ch[0]
y = ch[1]
keyPressed = ch[2]
# print "Key pressed:", ch[2]
if keyPressed == '?':
print("Help:")
for helpItem in help:
print("\t" + helpItem['key'] + " : " + helpItem['text'])
print("")
if keyPressed == 'p':
if plotSources == True:
plotSources = False
def plotold():
xmin=2.2
xmax=3.2
ymin=-2.5
ymax=-.5
psplotinit('fSsigma3Gyr.ps')
ppgplot.pgbox("",0.0,0,"",0.0,0)
ppgplot.pgenv(xmin,xmax,ymin,ymax,0,30)
ppgplot.pglab("\gs (km/s)",'fS(10\u11\d:10\u13\d)',"")
ppgplot.pgsci(1)
ppgplot.pgline(sigma,frac)
ppgplot.pgsls(2)
ppgplot.pgsci(2)
ppgplot.pgline(sigma08,frac08)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
ppgplot.pgend()
xmin=2.2
xmax=3.2
ymin=11.
ymax=14.2
psplotinit('maccretsigma3Gyr.ps')
ppgplot.pgbox("",0.0,0,"",0.0,0)
ppgplot.pgenv(xmin,xmax,ymin,ymax,0,30)
ppgplot.pglab("\gs (km/s)",'M\dacc\u (M\d\(2281)\u)',"")
ppgplot.pgsci(1)
ppgplot.pgline(sigma,maccret)
ppgplot.pgsls(2)
ppgplot.pgsci(2)
ppgplot.pgline(sigma08,maccret08)
ppgplot.pgsls(1)
ppgplot.pgsci(1)
mylines=N.arange(-20.,20.,.4)
mylineswidth=3
ppgplot.pgsls(4)
ppgplot.pgslw(mylineswidth)
x=N.arange(0.,5.,1.)
lines=mylines
for y0 in lines:
y=3*x +y0
ppgplot.pgline(x,y)
ppgplot.pgsls(1)
ppgplot.pgend()
os.system('cp maccretsigma.ps /Users/rfinn/SDSS/paper/.')
os.system('cp fSsigma.ps /Users/rfinn/SDSS/paper/.')
allWindows.append(window)
(xmin, ymin, xmax, ymax) = determineFullFrameSize(allWindows)
fullFramexsize = xmax - xmin
fullFrameysize = ymax - ymin
""" Set up the PGPLOT windows """
xyPositionPlot = {}
xyPositionPlot['pgplotHandle'] = ppgplot.pgopen('/xs')
xyPositionPlot['yLimit'] = 1.0
xyPositionPlot['numXYPanels'] = len(referenceApertures.sources)
ppgplot.pgpap(6.18, 1.618)
ppgplot.pgsubp(1, xyPositionPlot['numXYPanels'])
ppgplot.pgsci(5)
for panel in range(xyPositionPlot['numXYPanels']):
currentSize = ppgplot.pgqch()
ppgplot.pgsch(1)
yLimit = xyPositionPlot['yLimit']
ppgplot.pgenv(startFrame, startFrame + frameRange, -yLimit, yLimit, 0, -2)
ppgplot.pgbox('A', 0.0, 0, 'BCG', 0.0, 0)
ppgplot.pglab("", "%d"%panel, "")
ppgplot.pgsch(currentSize)
ppgplot.pgask(False)
ppgplot.pgsci(1)
if (arg.preview):
bitmapView = {}
def mratiopg():
ppgplot.pgbeg("maccratio.ps/vcps",1,1) #color port.
ppgplot.pgpap(8.,1.)
ppgplot.pgpage
ppgplot.pgsch(1.3) #font size
ppgplot.pgslw(7) #line width
# 1st panel with symbols w/ stddev errorbars
#ylabel="SFR (M\d\(2281) \u yr\u-1\d)"
ylabel="L(H\ga) (10\u41\d erg s\u-1\d)"
xlabel="M\dr\u "
x1=.15
x2=.5
x3=.5
x4=.85
y1=x1
y2=x2
y3=x3
y4=x4
emarker=18
smarker=23
xmin=N.log10(1.e14)
xmax=N.log10(2.5e15)
#ymin=-1.
#ymax=3.
ymin=0.
ymax=25.
ppgplot.pgsvp(x1,x4,y1,y4) #sets viewport
ppgplot.pgswin(xmin,xmax,ymin,ymax) #axes limits
ppgplot.pgbox('blncst',1.,2,'bcvnst',2.,2) #tickmarks and labeling
for i in range(len(lz1lm.mass)):
m=lz1lm.mass[i]
l=lz1lm.maccret[i]
h=hz1lm.maccret[i]
r=h/l
print i,m,l,h,r
#print lz1lm.maccret
#print hz1lm.maccret
#print hz3lm.maccret
r3lm=(hz3lm.maccret)/(lz3lm.maccret)
r3hm=(hz3hm.maccret)/(lz3hm.maccret)
#for i in range(len(r3)):
# print i,lz3.sigma[i],hz3.sigma[i],lz3.mass[i],hz3.mass[i]
# print i,lz01.sigma[i],hz01.sigma[i],lz01.mass[i],hz01.mass[i]
r1lm=hz1lm.maccret/lz1lm.maccret
r1hm=hz1hm.maccret/lz1hm.maccret
#ra=N.array(hz01.maccret,'d')
#rb=N.array(lz01.maccret,'d')
#r01=ra/rb
#for i in range(len(r01)):
#print "ratio ",hz01.maccret[i],lz01.maccret[i],ra[i],rb[i],r01[i]
ppgplot.pgsci(14)
ppgplot.pgsls(1)
ppgplot.pgline(N.log10(lz3lm.mass),r3lm)
ppgplot.pgsls(2)
ppgplot.pgline(N.log10(lz3hm.mass),r3hm)
ppgplot.pgsci(1)
ppgplot.pgsls(1)
ppgplot.pgline(N.log10(lz1lm.mass),r1lm)
ppgplot.pgsls(2)
ppgplot.pgline(N.log10(lz1hm.mass),r1hm)
xlabel='M\dcl\u (M\d\(2281)\u)'
ylabel='M\dacc\u(z=0.75) / M\dacc\u(z=0.07)'
ppgplot.pgsch(1.8)
ppgplot.pgslw(7)
ppgplot.pgmtxt('b',2.2,0.5,0.5,ylabel) #xlabel
ppgplot.pgmtxt('l',2.5,0.5,0.5,xlabel)
ppgplot.pgend()
def main(options):
debug = options.debug
MSlist = []
device = options.device
if device == '?':
ppgplot.pgldev()
return
for inmspart in options.inms.split(','):
for msname in glob.iglob(inmspart):
MSlist.append(msname)
if len(MSlist) == 0:
print('Error: You must specify at least one MS name.')
print(' Use "uvplot.py -h" to get help.')
return
if len(MSlist) > 1:
print('WARNING: Antenna selection (other than all) may not work well')
print(' when plotting more than one MS. Carefully inspect the')
print(' listings of antenna numbers/names!')
if options.title == '':
plottitle = options.inms
else:
plottitle = options.title
axlimits = options.axlimits.split(',')
if len(axlimits) == 4:
xmin, xmax, ymin, ymax = axlimits
else:
print('Error: You must specify four axis limits')
return
timeslots = options.timeslots.split(',')
if len(timeslots) != 3:
print('Error: Timeslots format is start,skip,end')
return
for i in range(len(timeslots)):
timeslots[i] = int(timeslots[i])
if timeslots[i] < 0:
print('Error: timeslots values must not be negative')
return
doPlotColors = options.colors
antToPlotSpl = options.antennas.split(',')
antToPlot = []
for i in range(len(antToPlotSpl)):
tmpspl = antToPlotSpl[i].split('..')
if len(tmpspl) == 1:
antToPlot.append(int(antToPlotSpl[i]))
elif len(tmpspl) == 2:
for j in range(int(tmpspl[0]), int(tmpspl[1]) + 1):
antToPlot.append(j)
else:
print('Error: Could not understand antenna list.')
return
queryMode = options.query
plotLambda = options.kilolambda
badval = 0.0
xaxisvals0 = numpy.array([])
yaxisvals0 = numpy.array([])
xaxisvals1 = numpy.array([])
yaxisvals1 = numpy.array([])
xaxisvals2 = numpy.array([])
yaxisvals2 = numpy.array([])
xaxisvals3 = numpy.array([])
yaxisvals3 = numpy.array([])
xaxisvals4 = numpy.array([])
yaxisvals4 = numpy.array([])
xaxisvals5 = numpy.array([])
yaxisvals5 = numpy.array([])
savex0 = numpy.array([])
savey0 = numpy.array([])
savex1 = numpy.array([])
savey1 = numpy.array([])
savex2 = numpy.array([])
savey2 = numpy.array([])
savex3 = numpy.array([])
savey3 = numpy.array([])
savex4 = numpy.array([])
savey4 = numpy.array([])
savex5 = numpy.array([])
savey5 = numpy.array([])
numPlotted = 0
ptcolor = 0
for inputMS in MSlist:
# open the main table and print some info about the MS
print('Getting info for', inputMS)
t = pt.table(inputMS, readonly=True, ack=False)
tfreq = pt.table(t.getkeyword('SPECTRAL_WINDOW'),
readonly=True,
ack=False)
ref_freq = tfreq.getcol('REF_FREQUENCY', nrow=1)[0]
ch_freq = tfreq.getcol('CHAN_FREQ', nrow=1)[0]
print('Reference frequency:\t%f MHz' % (ref_freq / 1.e6))
if options.wideband:
ref_wavelength = 2.99792458e8 / ch_freq
else:
ref_wavelength = [2.99792458e8 / ref_freq]
print('Reference wavelength:\t%f m' % (ref_wavelength[0]))
if options.sameuv and numPlotted > 0:
print('Assuming same uvw as first MS!')
if plotLambda:
for w in ref_wavelength:
xaxisvals0 = numpy.append(
xaxisvals0, [savex0 / w / 1000., -savex0 / w / 1000.])
yaxisvals0 = numpy.append(
yaxisvals0, [savey0 / w / 1000., -savey0 / w / 1000.])
xaxisvals1 = numpy.append(
xaxisvals1, [savex1 / w / 1000., -savex1 / w / 1000.])
yaxisvals1 = numpy.append(
yaxisvals1, [savey1 / w / 1000., -savey1 / w / 1000.])
xaxisvals2 = numpy.append(
xaxisvals2, [savex2 / w / 1000., -savex2 / w / 1000.])
yaxisvals2 = numpy.append(
yaxisvals2, [savey2 / w / 1000., -savey2 / w / 1000.])
xaxisvals3 = numpy.append(
xaxisvals3, [savex3 / w / 1000., -savex3 / w / 1000.])
yaxisvals3 = numpy.append(
yaxisvals3, [savey3 / w / 1000., -savey3 / w / 1000.])
xaxisvals4 = numpy.append(
xaxisvals4, [savex4 / w / 1000., -savex4 / w / 1000.])
yaxisvals4 = numpy.append(
yaxisvals4, [savey4 / w / 1000., -savey4 / w / 1000.])
xaxisvals5 = numpy.append(
xaxisvals5, [savex5 / w / 1000., -savex5 / w / 1000.])
yaxisvals5 = numpy.append(
yaxisvals5, [savey5 / w / 1000., -savey5 / w / 1000.])
else:
print(
'Plotting more than one MS with same uv, all in meters... do you want -k?'
)
xaxisvals0 = numpy.append(xaxisvals0, [savex0, -savex0])
yaxisvals0 = numpy.append(yaxisvals0, [savey0, -savey0])
xaxisvals1 = numpy.append(xaxisvals1, [savex1, -savex1])
yaxisvals1 = numpy.append(yaxisvals1, [savey1, -savey1])
xaxisvals2 = numpy.append(xaxisvals2, [savex2, -savex2])
yaxisvals2 = numpy.append(yaxisvals2, [savey2, -savey2])
xaxisvals3 = numpy.append(xaxisvals3, [savex3, -savex3])
yaxisvals3 = numpy.append(yaxisvals3, [savey3, -savey3])
xaxisvals4 = numpy.append(xaxisvals4, [savex4, -savex4])
yaxisvals4 = numpy.append(yaxisvals4, [savey4, -savey4])
xaxisvals5 = numpy.append(xaxisvals5, [savex5, -savex5])
yaxisvals5 = numpy.append(yaxisvals5, [savey5, -savey5])
continue
firstTime = t.getcell("TIME", 0)
lastTime = t.getcell("TIME", t.nrows() - 1)
intTime = t.getcell("INTERVAL", 0)
print('Integration time:\t%f sec' % (intTime))
nTimeslots = (lastTime - firstTime) / intTime
print('Number of timeslots:\t%d' % (nTimeslots))
if timeslots[1] == 0:
if nTimeslots >= 100:
timeskip = int(nTimeslots / 100)
else:
timeskip = 1
else:
timeskip = int(timeslots[1])
print('For each baseline, plotting one point every %d samples' %
(timeskip))
if timeslots[2] == 0:
timeslots[2] = nTimeslots
# open the antenna subtable
tant = pt.table(t.getkeyword('ANTENNA'), readonly=True, ack=False)
# Station names
antList = tant.getcol('NAME')
if len(antToPlot) == 1 and antToPlot[0] == -1:
antToPlot = list(range(len(antList)))
print('Station list (only starred stations will be plotted):')
for i in range(len(antList)):
star = ' '
if i in antToPlot: star = '*'
print('%s %2d\t%s' % (star, i, antList[i]))
# Bail if we're in query mode
if queryMode:
return
# select by time from the beginning, and only use specified antennas
tsel = t.query(
'TIME >= %f AND TIME <= %f AND ANTENNA1 IN %s AND ANTENNA2 IN %s' %
(firstTime + timeslots[0] * intTime, firstTime +
timeslots[2] * intTime, str(antToPlot), str(antToPlot)),
columns='ANTENNA1,ANTENNA2,UVW')
# Now we loop through the baselines
i = 0
nb = (len(antToPlot) * (len(antToPlot) - 1)) / 2
sys.stdout.write('Reading uvw for %d baselines: %04d/%04d' %
(nb, i, nb))
sys.stdout.flush()
for tpart in tsel.iter(["ANTENNA1", "ANTENNA2"]):
ant1 = tpart.getcell("ANTENNA1", 0)
ant2 = tpart.getcell("ANTENNA2", 0)
if ant1 not in antToPlot or ant2 not in antToPlot: continue
if ant1 == ant2: continue
i += 1
sys.stdout.write('\b\b\b\b\b\b\b\b\b%04d/%04d' % (i, nb))
sys.stdout.flush()
if doPlotColors:
stNameStr = antList[ant1][0] + antList[ant2][0]
if stNameStr == 'CC': ptcolor = 0
elif stNameStr == 'RR': ptcolor = 1
elif 'C' in stNameStr and 'R' in stNameStr: ptcolor = 2
elif 'C' in stNameStr: ptcolor = 3
elif 'R' in stNameStr: ptcolor = 4
else: ptcolor = 5
# Get the values to plot
uvw = tpart.getcol('UVW', rowincr=timeskip)
if numPlotted == 0:
savex0 = numpy.append(savex0, [uvw[:, 0], -uvw[:, 0]])
savey0 = numpy.append(savey0, [uvw[:, 1], -uvw[:, 1]])
savex1 = numpy.append(savex1, [uvw[:, 0], -uvw[:, 0]])
savey1 = numpy.append(savey1, [uvw[:, 1], -uvw[:, 1]])
savex2 = numpy.append(savex2, [uvw[:, 0], -uvw[:, 0]])
savey2 = numpy.append(savey2, [uvw[:, 1], -uvw[:, 1]])
savex3 = numpy.append(savex3, [uvw[:, 0], -uvw[:, 0]])
savey3 = numpy.append(savey3, [uvw[:, 1], -uvw[:, 1]])
savex4 = numpy.append(savex4, [uvw[:, 0], -uvw[:, 0]])
savey4 = numpy.append(savey4, [uvw[:, 1], -uvw[:, 1]])
savex5 = numpy.append(savex5, [uvw[:, 0], -uvw[:, 0]])
savey5 = numpy.append(savey5, [uvw[:, 1], -uvw[:, 1]])
if plotLambda:
for w in ref_wavelength:
if ptcolor == 0:
xaxisvals0 = numpy.append(
xaxisvals0,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals0 = numpy.append(
yaxisvals0,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
elif ptcolor == 1:
xaxisvals1 = numpy.append(
xaxisvals1,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals1 = numpy.append(
yaxisvals1,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
elif ptcolor == 2:
xaxisvals2 = numpy.append(
xaxisvals2,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals2 = numpy.append(
yaxisvals2,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
elif ptcolor == 3:
xaxisvals3 = numpy.append(
xaxisvals3,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals3 = numpy.append(
yaxisvals3,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
elif ptcolor == 4:
xaxisvals4 = numpy.append(
xaxisvals4,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals4 = numpy.append(
yaxisvals4,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
elif ptcolor == 5:
xaxisvals5 = numpy.append(
xaxisvals5,
[uvw[:, 0] / w / 1000., -uvw[:, 0] / w / 1000.])
yaxisvals5 = numpy.append(
yaxisvals5,
[uvw[:, 1] / w / 1000., -uvw[:, 1] / w / 1000.])
else:
if ptcolor == 0:
xaxisvals0 = numpy.append(xaxisvals0,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals0 = numpy.append(yaxisvals0,
[uvw[:, 1], -uvw[:, 1]])
elif ptcolor == 1:
xaxisvals1 = numpy.append(xaxisvals1,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals1 = numpy.append(yaxisvals1,
[uvw[:, 1], -uvw[:, 1]])
elif ptcolor == 2:
xaxisvals2 = numpy.append(xaxisvals2,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals2 = numpy.append(yaxisvals2,
[uvw[:, 1], -uvw[:, 1]])
elif ptcolor == 3:
xaxisvals3 = numpy.append(xaxisvals3,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals3 = numpy.append(yaxisvals3,
[uvw[:, 1], -uvw[:, 1]])
elif ptcolor == 4:
xaxisvals4 = numpy.append(xaxisvals4,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals4 = numpy.append(yaxisvals4,
[uvw[:, 1], -uvw[:, 1]])
elif ptcolor == 5:
xaxisvals5 = numpy.append(xaxisvals5,
[uvw[:, 0], -uvw[:, 0]])
yaxisvals5 = numpy.append(yaxisvals5,
[uvw[:, 1], -uvw[:, 1]])
#if debug:
# print uvw.shape
# print xaxisvals.shape
# print yaxisvals.shape
#else:
# sys.stdout.write('.')
# sys.stdout.flush()
sys.stdout.write(' Done!\n')
numPlotted += 1
print('Plotting uv points ...')
# open the graphics device, using only one panel
ppgplot.pgbeg(device, 1, 1)
# set the font size
ppgplot.pgsch(1)
ppgplot.pgvstd()
xaxisvals = numpy.append(
xaxisvals0,
numpy.append(
xaxisvals1,
numpy.append(
xaxisvals2,
numpy.append(xaxisvals3, numpy.append(xaxisvals4,
xaxisvals5)))))
yaxisvals = numpy.append(
yaxisvals0,
numpy.append(
yaxisvals1,
numpy.append(
yaxisvals2,
numpy.append(yaxisvals3, numpy.append(yaxisvals4,
yaxisvals5)))))
tmpvals0 = numpy.sqrt(xaxisvals0**2 + yaxisvals0**2)
tmpvals1 = numpy.sqrt(xaxisvals1**2 + yaxisvals1**2)
tmpvals2 = numpy.sqrt(xaxisvals2**2 + yaxisvals2**2)
tmpvals3 = numpy.sqrt(xaxisvals3**2 + yaxisvals3**2)
tmpvals4 = numpy.sqrt(xaxisvals4**2 + yaxisvals4**2)
tmpvals5 = numpy.sqrt(xaxisvals5**2 + yaxisvals5**2)
# Plot the data
if debug:
print(xaxisvals0[tmpvals0 != badval])
print(yaxisvals0[tmpvals0 != badval])
ppgplot.pgsci(1)
uvmax = max(xaxisvals.max(), yaxisvals.max())
uvmin = min(xaxisvals.min(), yaxisvals.min())
uvuplim = 0.02 * (uvmax - uvmin) + uvmax
uvlolim = uvmin - 0.02 * (uvmax - uvmin)
if xmin == '':
minx = uvlolim
else:
minx = float(xmin)
if xmax == '':
maxx = uvuplim
else:
maxx = float(xmax)
if ymin == '':
miny = uvlolim
else:
miny = float(ymin)
if ymax == '':
maxy = uvuplim
else:
maxy = float(ymax)
if minx == maxx:
minx = -1.0
maxx = 1.0
if miny == maxy:
miny = -1.0
maxy = 1.0
ppgplot.pgpage()
ppgplot.pgswin(minx, maxx, miny, maxy)
ppgplot.pgbox('BCNST', 0.0, 0, 'BCNST', 0.0, 0)
if plotLambda:
ppgplot.pglab('u [k\gl]', 'v [k\gl]', '%s' % (plottitle))
else:
ppgplot.pglab('u [m]', 'v [m]', '%s' % (plottitle))
ppgplot.pgpt(xaxisvals0[tmpvals0 != badval],
yaxisvals0[tmpvals0 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.35, 0.5, 'C-C')
ppgplot.pgsci(2)
ppgplot.pgpt(xaxisvals1[tmpvals1 != badval],
yaxisvals1[tmpvals1 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.50, 0.5, 'R-R')
ppgplot.pgsci(4)
ppgplot.pgpt(xaxisvals2[tmpvals2 != badval],
yaxisvals2[tmpvals2 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.65, 0.5, 'C-R')
ppgplot.pgsci(3)
ppgplot.pgpt(xaxisvals3[tmpvals3 != badval],
yaxisvals3[tmpvals3 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.55, 0.5, 'C-I')
ppgplot.pgsci(5)
ppgplot.pgpt(xaxisvals4[tmpvals4 != badval],
yaxisvals4[tmpvals4 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.65, 0.5, 'R-I')
ppgplot.pgsci(6)
ppgplot.pgpt(xaxisvals5[tmpvals5 != badval],
yaxisvals5[tmpvals5 != badval], 1)
#if doPlotColors: ppgplot.pgmtxt('T', 1, 0.75, 0.5, 'I-I')
# Close the PGPLOT device
ppgplot.pgclos()
normalVal = s.integrate((arg.nl, arg.nu))
print "Normalisation value:", normalVal, normalConstant
spectra[index].divide(normalVal/normalConstant)
normalVal = s.integrate((arg.nl, arg.nu))
print "New Normalisation value:", normalVal, normalConstant
if not arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
ppgplot.pgask(True)
pgPlotTransform = [0, 1, 0, 0, 0, 1]
yUpper = 2.5
yLower = -0.5
for spectrum in spectra:
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)
if hasEphemeris:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%f]"%(spectrum.objectName, spectrum.phase))
else:
ppgplot.pglab("wavelength [%s]"%spectrum.wavelengthUnits, "flux [%s]"%spectrum.fluxUnits, "%s [%s]"%(spectrum.objectName, spectrum.loadedFromFilename))
if arg.stacked:
mainPGPlotWindow = ppgplot.pgopen(arg.device)
def plotxy(y, x=None, title=None, rangex=None, rangey=None, \
labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', symbol=ppgplot_symbol_, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, font=ppgplot_font_, logx=0, logy=0, \
logx2=0, logy2=0, errx=None, erry=None, id=0, noscale=0, \
aspect=0.7727, fontsize=ppgplot_font_size_, ticks='in', \
panels=[1,1], device=ppgplot_device_, setup=1):
"""
plotxy(y, ...)
An interface to make various XY style plots using PGPLOT.
'y' is the 1D sequence object to plot.
The optional entries are:
x: x values (default = 0, 1, ...)
title: graph title (default = None)
rangex: ranges for the x-axis (default = automatic)
rangey: ranges for the y-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: ranges for 2nd x-axis (default = None)
rangey2: ranges for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
errx: symmetric x errors (default = None)
erry: symmetric y errors (default = None)
symbol: symbol for points (default = None)
line: line style (default = 1 (solid))
width: line width (default = 1 (thin))
color: line and/or symbol color (default = 'white')
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: aspect ratio (default = 0.7727 (rect))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
setup: Auto-setup the plot (default = 1)
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is an array
y = Num.asarray(y)
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, ppgplot_colors_
# Define the X axis limits if needed
if x is None: x = Num.arange(len(y), dtype='f')
else: x = Num.asarray(x)
# Determine the scaling to use for the first axis
if rangex is None: rangex = [x.min(), x.max()]
if rangey is None:
if noscale: rangey = [y.min(), y.max()]
else: rangey = scalerange(y)
# Prep the plotting device...
if (not ppgplot_dev_prep_ and setup):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, \
logx, logy, logx2, logy2, font, fontsize, \
id, aspect, ticks, panels, device=device)
# Choose the line color
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Plot symbols (and errors) if requested
if not symbol is None:
ppgplot.pgpt(x, y, symbol)
# Error bars
if errx is not None:
if not logx:
errx = Num.asarray(errx)
ppgplot.pgerrx(x + errx, x - errx, y, 1.0)
else:
errx = 10.0**Num.asarray(errx)
ppgplot.pgerrx(Num.log10(10.0**x + errx),
Num.log10(10.0**x - errx), y, 1.0)
if erry is not None:
if not logy:
erry = Num.asarray(erry)
ppgplot.pgerry(x, y + erry, y - erry, 1.0)
else:
erry = 10.0**Num.asarray(erry)
ppgplot.pgerry(x, Num.log10(10.0**y + erry),
Num.log10(10.0**y - erry), 1.0)
# Plot connecting lines if requested
if not line is None:
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
ppgplot.pgline(x, y)
mainPGPlotWindow = ppgplot.pgopen(device) ppgplot.pgask(True) pgPlotTransform = [0, 1, 0, 0, 0, 1] if numSpectra1 != numSpectra2: print "We need an equal number of spectra in each list." print numSpectra1, numSpectra2 sys.exit() for s1, s2 in zip(spectra1, spectra2): combinedSpectrum = copy.deepcopy(s1) combinedSpectrum.appendNewData(s2) ppgplot.pgsci(1) lowerWavelength = min(combinedSpectrum.wavelengths) upperWavelength = max(combinedSpectrum.wavelengths) lowerFlux = min(combinedSpectrum.flux) upperFlux = max(combinedSpectrum.flux) lowerFlux = 0 upperFlux = 2.5 ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0) ppgplot.pgsci(2) ppgplot.pgline(s1.wavelengths, s1.flux) ppgplot.pgsci(3) ppgplot.pgline(s2.wavelengths, s2.flux) ppgplot.pgsci(1)
helpItem = {"key": "i/o", "text": "Zoom in/out."}
help.append(helpItem)
helpItem = {"key": "q", "text": "Quit."}
help.append(helpItem)
helpItem = {"key": "f", "text": "Find local Ha maxima."}
help.append(helpItem)
helpItem = {"key": "l", "text": "Show location of cursor."}
help.append(helpItem)
# try:
x = width / 2
y = height / 2
newWidth = width
newHeight = height
# pixelGrid = makeGrid(width, height, 100)
originalImageData = numpy.copy(imageData)
ppgplot.pgsci(3)
keyPressed = None
while keyPressed != "q":
ch = ppgplot.pgcurs(x, y)
x = ch[0]
y = ch[1]
keyPressed = ch[2]
# print "Key pressed:", ch[2]
if keyPressed == "?":
print ("Help:")
for helpItem in help:
print ("\t" + helpItem["key"] + " : " + helpItem["text"])
print ("")
if keyPressed == "p":
if plotSources == True:
plotSources = False
positionString = generalUtils.toSexagesimal((ra, dec))
print "RA, DEC of image centre is: ", positionString, ra, dec
margins = wcsSolution.all_pix2world([[0, 0], [width, height]], 1)
margins = checkMargins(margins)
print "ra, dec limits:", margins
xlimits = (0, width)
ylimits = (0, height)
# try:
x = width / 2
y = height / 2
newWidth = width
newHeight = height
ppgplot.pgsci(3)
keyPressed = None
while keyPressed != 'q':
ch = ppgplot.pgcurs(x, y)
x = ch[0]
y = ch[1]
keyPressed = ch[2]
# print "Key pressed:", ch[2]
if keyPressed == 'w':
imageMinMax = (imageMinMax[1], imageMinMax[0])
if invertedColours: invertedColours = False
else: invertedColours = True
redraw()
if keyPressed == 'i':
def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path,
results_path):
# Read four frame
ff = fourframe(fname)
# Skip saturated frames
if np.sum(ff.zavg > 240.0) / float(ff.nx * ff.ny) > 0.95:
return
# Read satelite IDs
try:
f = open(fname + ".id")
except OSError:
print("Cannot open", fname + ".id")
else:
lines = f.readlines()
f.close()
tr = np.array([-0.5, 1.0, 0.0, -0.5, 0.0, 1.0])
# Parse identifications
idents = [satid(line) for line in lines]
# Identify unknowns
for ident0 in idents:
if ident0.catalog == "unidentified":
for ident1 in idents:
if ident1.catalog == "unidentified":
continue
# Find matches
p1 = inside_selection(ident1, ident0.t0, ident0.x0, ident0.y0)
p2 = inside_selection(ident1, ident0.t1, ident0.x1, ident0.y1)
# Match found
if p1 and p2:
# Copy info
ident0.norad = ident1.norad
ident0.catalog = ident1.catalog
ident0.state = ident1.state
ident1.state = "remove"
break
# Loop over identifications
for ident in idents:
# Skip superseded unknowns
if ident.state == "remove":
continue
# Skip slow moving objects
drdt = np.sqrt(ident.dxdt**2 + ident.dydt**2)
if drdt < drdtmin:
continue
# Extract significant pixels along a track
x, y, t, sig = ff.significant_pixels_along_track(
trksig, ident.x0, ident.y0, ident.dxdt, ident.dydt, trkrmin)
# Fit tracks
if len(t) > ntrkmin:
# Get times
tmin = np.min(t)
tmax = np.max(t)
tmid = 0.5 * (tmax + tmin)
mjd = ff.mjd + tmid / 86400.0
# Skip if no variance in time
if np.std(t - tmid) == 0.0:
continue
# Very simple polynomial fit; no weighting, no cleaning
px = np.polyfit(t - tmid, x, 1)
py = np.polyfit(t - tmid, y, 1)
# Extract results
x0, y0 = px[1], py[1]
dxdt, dydt = px[0], py[0]
xmin = x0 + dxdt * (tmin - tmid)
ymin = y0 + dydt * (tmin - tmid)
xmax = x0 + dxdt * (tmax - tmid)
ymax = y0 + dydt * (tmax - tmid)
cospar = get_cospar(ident.norad, ff.nfd)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(ident.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
# Create diagnostic plot
plot_header(fname.replace(".fits", "_%05d.png/png" % ident.norad),
ff, iod_line)
ppg.pgimag(ff.zmax, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1,
ff.zmaxmax, ff.zmaxmin, tr)
ppg.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppg.pgstbg(1)
ppg.pgsci(0)
if ident.catalog.find("classfd.tle") > 0:
ppg.pgsci(4)
elif ident.catalog.find("inttles.tle") > 0:
ppg.pgsci(3)
ppg.pgpt(np.array([x0]), np.array([y0]), 4)
ppg.pgmove(xmin, ymin)
ppg.pgdraw(xmax, ymax)
ppg.pgsch(0.65)
ppg.pgtext(np.array([x0]), np.array([y0]), " %05d" % ident.norad)
ppg.pgsch(1.0)
ppg.pgsci(1)
ppg.pgend()
# Store results
store_results(ident, fname, results_path, iod_line)
elif ident.catalog.find("classfd.tle") > 0:
# Track and stack
t = np.linspace(0.0, ff.texp)
x, y = ident.x0 + ident.dxdt * t, ident.y0 + ident.dydt * t
c = (x > 0) & (x < ff.nx) & (y > 0) & (y < ff.ny)
# Skip if no points selected
if np.sum(c) == 0:
store_not_seen(ident, fname, results_path)
continue
# Compute track
tmid = np.mean(t[c])
mjd = ff.mjd + tmid / 86400.0
xmid = ident.x0 + ident.dxdt * tmid
ymid = ident.y0 + ident.dydt * tmid
ztrk = ndimage.gaussian_filter(
ff.track(ident.dxdt, ident.dydt, tmid), 1.0)
vmin = np.mean(ztrk) - 2.0 * np.std(ztrk)
vmax = np.mean(ztrk) + 6.0 * np.std(ztrk)
# Select region
xmin = int(xmid - 100)
xmax = int(xmid + 100)
ymin = int(ymid - 100)
ymax = int(ymid + 100)
if xmin < 0:
xmin = 0
if ymin < 0:
ymin = 0
if xmax > ff.nx:
xmax = ff.nx - 1
if ymax > ff.ny:
ymax = ff.ny - 1
# Find peak
x0, y0, w, sigma = peakfind(ztrk[ymin:ymax, xmin:xmax])
x0 += xmin
y0 += ymin
# Skip if peak is not significant
if sigma < trksig:
store_not_seen(ident, fname, results_path)
continue
# Skip if point is outside selection area
if inside_selection(ident, tmid, x0, y0) is False:
store_not_seen(ident, fname, results_path)
continue
# Format IOD line
cospar = get_cospar(ident.norad, ff.nfd)
obs = observation(ff, mjd, x0, y0)
iod_line = "%s" % format_iod_line(ident.norad, cospar, ff.site_id,
obs.nfd, obs.ra, obs.de)
# Create diagnostic plot
pngfile = fname.replace(".fits", "_%05d.png" % ident.norad)
plot_header(pngfile + "/png", ff, iod_line)
ppg.pgimag(ztrk, ff.nx, ff.ny, 0, ff.nx - 1, 0, ff.ny - 1, vmax,
vmin, tr)
ppg.pgbox("BCTSNI", 0., 0, "BCTSNI", 0., 0)
ppg.pgstbg(1)
plot_selection(ident, xmid, ymid)
ppg.pgsci(0)
if ident.catalog.find("classfd.tle") > 0:
ppg.pgsci(4)
elif ident.catalog.find("inttles.tle") > 0:
ppg.pgsci(3)
ppg.pgpt(np.array([ident.x0]), np.array([ident.y0]), 17)
ppg.pgmove(ident.x0, ident.y0)
ppg.pgdraw(ident.x1, ident.y1)
ppg.pgpt(np.array([x0]), np.array([y0]), 4)
ppg.pgsch(0.65)
ppg.pgtext(np.array([ident.x0]), np.array([ident.y0]),
" %05d" % ident.norad)
ppg.pgsch(1.0)
ppg.pgsci(1)
ppg.pgend()
# Store results
store_results(ident, fname, results_path, iod_line)
ppgplot.pgeras()
ppgplot.pgsvp(0.01, 0.99, 0.01, 0.99)
ppgplot.pgwnad(-1.5 * m.w, 1.5 * m.w, -m.w, m.w)
# Set background depending on solar altitude
if m.sunalt > 0.0:
ppgplot.pgscr(0, 0.0, 0.0, 0.4)
elif m.sunalt > -6.0:
ppgplot.pgscr(0, 0.0, 0.0, 0.3)
elif m.sunalt > -12.0:
ppgplot.pgscr(0, 0.0, 0.0, 0.2)
elif m.sunalt > -18.0:
ppgplot.pgscr(0, 0.0, 0.0, 0.1)
else:
ppgplot.pgscr(0, 0.0, 0.0, 0.0)
ppgplot.pgsci(0)
# Plot box
ppgplot.pgrect(-1.5 * m.w, 1.5 * m.w, -m.w, m.w)
ppgplot.pgsci(1)
ppgplot.pgsch(1.0)
ppgplot.pgbox("BC", 0., 0, "BC", 0., 0)
ppgplot.pgpt1(0., 0., 2)
# Plot field-of-view
ppgplot.pgsfs(2)
ppgplot.pgrect(-m.fw, m.fw, -m.fh, m.fh)
ppgplot.pgsfs(1)
# Top left string
ppgplot.pgsch(0.8)
def plot(command, data, group, cdata):
"""
Plots dsets from a dictionary called data.
Interactive usage:
plot slots [device x1 x2 y1 y2 xoff ysep=0 pmask]
Arguments:
slots -- range of slots as in '1-10', or just a single slot '9' to plot, or a group
name such as 'ippeg'.
device -- plot device (e.g. '/xs', '3/xs', 'hardcopy.ps/cps')
x1 -- left-hand plot limit
x2 -- right-hand plot limit. Set = x1 for automatic determination
y1 -- lower plot limit
y2 -- upper plot limit. Set = y1 for automatic determination
xoff -- offset to start X axis from, 0 for automatic determination.
ysep -- separation in y
pmask -- whether to plot masked data
"""
# generate arguments
inpt = inp.Input(DINT_ENV, DINT_DEF, inp.clist(command))
# register parameters
inpt.register('slots', inp.Input.LOCAL, inp.Input.PROMPT)
inpt.register('device', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('x2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y1', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('y2', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('xoff', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('ysep', inp.Input.LOCAL, inp.Input.HIDE)
inpt.register('pmask', inp.Input.LOCAL, inp.Input.HIDE)
# get inputs
slots = inpt.get_value('slots', 'slots to plot', '1')
slist = interp_slots(slots, True, data, group)
device = inpt.get_value('device', 'plot device', '/xs')
x1 = inpt.get_value('x1', 'left-hand plot limit', 0.0)
x2 = inpt.get_value('x2', 'right-hand plot limit', 0.0)
y1 = inpt.get_value('y1', 'lower plot limit', 0.0)
y2 = inpt.get_value('y2', 'upper plot limit', 0.0)
xoff = inpt.get_value('xoff', 'X offset', 0.0)
inpt.set_default('ysep', 0.0)
ysep = inpt.get_value('ysep', 'vertical separation between successive dsets', 0.0)
pmask = inpt.get_value('pmask', 'do you want to plot the masked data too?', True)
# Determine limits automatically if required
if xoff !=0. or x1 == x2 or y1 == y2:
xa1 = None
xa2 = None
ya1 = None
ya2 = None
yadd = 0.
for i in slist:
(xi1,xi2,yi1,yi2) = data[i].plimits()
if xa1 is None:
xa1 = xi1
else:
xa1 = min(xa1, xi1)
if xa2 is None:
xa2 = xi2
else:
xa2 = max(xa2, xi2)
if ya1 is None and yi1 is not None:
ya1 = yi1 + yadd
elif yi1 is not None:
ya1 = min(ya1, yi1 + yadd)
if ya2 is None and yi2 is not None:
ya2 = yi2 + yadd
elif yi2 is not None:
ya2 = max(ya2, yi2 + yadd)
yadd += ysep
if xa1 is None or xa2 is None or ya1 is None or ya2 is None:
raise DintError('plot: no automatic limits could be evaluated; possibly no good data to plot?')
if xoff == 0.0 and (xa2 - xa1) < (xa1+xa2)/2./100.:
xoff = xa1
xa1 -= xoff
xa2 -= xoff
if x1 == x2:
x1 = xa1
x2 = xa2
if y1 == y2:
y1 = ya1
y2 = ya2
try:
pg.pgopen(device)
pg.pgsci(4)
pg.pgenv(x1, x2, y1, y2, 0, 0)
pg.pgsci(2)
first = data[slist[0]]
xlabel = first.x.label if xoff == 0 else first.x.label + '-' + str(xoff)
xlabel += ' (' + first.x.units + ')'
ylabel = first.y.label + ' (' + first.y.units + ')'
pg.pglab(xlabel, ylabel, first.title)
yadd = 0.
for slot in slist:
data[slot].plot(xoff,yoff=-yadd,masked=pmask)
if not cdata.mute:
print('Plotted slot ' + str(slot))
yadd += ysep
pg.pgclos()
except pg.ioerror, err:
raise DintError(str(err))
def plot_rating_sheet(rating):
"""
Plot a fact sheet on the ratings in the database corresponding to 'rating'.
'rating' is a dictionary of information from the MySQL database (as returned
by 'get_all_rating_types()'.
"""
plot_utils.beginplot("rating_report%s.ps" % currdatetime.strftime('%y%m%d'), vertical=True)
ch0 = ppgplot.pgqch()
ppgplot.pgsch(0.5)
ch = ppgplot.pgqch()
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 1 of 2" % (rating["name"], currdatetime.strftime('%c')))
ppgplot.pgsch(ch)
# Plot Histograms
all_ratings = get_ratings(rating["rating_id"])
range = xmin,xmax = np.min(all_ratings), np.max(all_ratings)
ppgplot.pgsci(1)
ppgplot.pgslw(1)
#===== Total/Classified/Unclassified
# Get data
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
(tot_counts, tot_left_edges)=np.histogram(all_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(tot_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, tot_counts)
(clsfd_counts, clsfd_left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3,4,5,6,7)), bins=NUMBINS, range=range)
ppgplot.pgsci(3) # plot classified in green
ppgplot.pgbin(tot_left_edges, clsfd_counts)
unclsfd_counts = tot_counts-clsfd_counts
ppgplot.pgsci(2) # plot unclassified in red
ppgplot.pgbin(tot_left_edges, unclsfd_counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Counts","")
#===== Class 1/2/3
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
(counts, left_edges)=np.histogram(get_ratings(rating["rating_id"], human_classification=(1,2,3)), bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Class 1/2/3","")
#===== RFI
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
rfi_ratings = get_ratings(rating["rating_id"], human_classification=(4,))
(counts, left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI","")
#===== Noise
ppgplot.pgsvp(0.1, 0.9, 0.3, 0.45)
noise_ratings = get_ratings(rating["rating_id"], human_classification=(5,))
(counts, left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","Noise","")
#===== Known/Harmonic
ppgplot.pgsvp(0.1, 0.9, 0.15, 0.3)
known_ratings = get_ratings(rating["rating_id"], human_classification=(6,7))
(counts, left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range)
ppgplot.pgswin(xmin,xmax,0,np.max(counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgsci(1) # plot in black
ppgplot.pgbin(tot_left_edges, counts)
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known/Harmonic","")
#===== Second page for differential histograms
plot_utils.nextpage(vertical=True)
ppgplot.pgsch(0.75)
ppgplot.pgtext(0,1,"Rating Report for %s (%s) - page 2 of 2" % (rating["name"], currdatetime.strftime('%c')))
#===== RFI - Known
ppgplot.pgsvp(0.1, 0.9, 0.75, 0.9)
if rfi_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - known_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Known","")
#===== RFI - Noise
ppgplot.pgsvp(0.1, 0.9, 0.6, 0.75)
if noise_ratings.size==0 or rfi_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(rfi_counts, rfi_left_edges)=np.histogram(rfi_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = rfi_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgsch(0.75)
ppgplot.pglab("","RFI - Noise","")
#===== Known - Noise
ppgplot.pgsvp(0.1, 0.9, 0.45, 0.6)
if noise_ratings.size==0 or known_ratings.size==0:
ppgplot.pgswin(0,1,0,1)
ppgplot.pgbox("BC",0,0,"BC",0,0)
# Y-axis label is taken care of outside of if/else (below)
ppgplot.pgsch(1.0)
ppgplot.pgptxt(0.5, 0.5, 0.0, 0.5, "Not enough data")
else:
(noise_counts, noise_left_edges)=np.histogram(noise_ratings, bins=NUMBINS, range=range, normed=True)
(known_counts, known_left_edges)=np.histogram(known_ratings, bins=NUMBINS, range=range, normed=True)
diff_counts = known_counts - noise_counts
ppgplot.pgswin(xmin,xmax,np.min(diff_counts)*1.1,np.max(diff_counts)*1.1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("BCNTS",0,5,"BCNTS",0,5)
ppgplot.pgbin(tot_left_edges, diff_counts)
ppgplot.pgsci(2) # set colour to red
ppgplot.pgline(tot_left_edges, np.zeros_like(tot_left_edges))
ppgplot.pgsci(1) # reset colour to black
ppgplot.pgswin(xmin,xmax,0,1)
ppgplot.pgsch(0.5)
ppgplot.pgbox("NTS",0,5,"",0,0)
ppgplot.pgsch(0.75)
ppgplot.pglab(rating["name"],"Known - Noise","")
ppgplot.pgsch(ch0) # reset character height
def main(options):
global keepPlotting
keepPlotting = True
debug = options.debug
inputMS = glob.glob(options.inms)
if inputMS == '':
print 'Error: You must specify a MS name.'
print ' Use "uvplot.py -h" to get help.'
return
if options.inms.endswith('/'):
options.inms = options.inms[:-1]
inputMSbasename = options.inms.split('/')[-1]
if inputMSbasename == '':
# The user has not specified the full path of the MS
inputMSbasename = options.inms
device = options.device
if device=='?':
ppgplot.pgldev()
return
xaxis = options.xaxis
if xaxis == 'ha':
print 'Adding derived columns to allow plotting hour angle...'
try:
pt.addDerivedMSCal(inputMS)
except:
print 'Failed, trying to remove and add columns...'
try:
pt.removeDerivedMSCal(inputMS)
pt.addDerivedMSCal(inputMS)
except:
print 'That failed too... plotting HA seems to not be possible.'
return
yaxis = options.yaxis
column = options.column
nx, ny = options.nxy.split(',')
axlimits = options.axlimits.split(',')
if len(axlimits) == 4:
xmin,xmax,ymin,ymax = axlimits
else:
print 'Error: You must specify four axis limits'
return
showFlags = options.flag
flagCol = options.colflag
showAutocorr = options.autocorr
showStats = options.statistics
timeslots = options.timeslots.split(',')
if len(timeslots) != 2:
print 'Error: Timeslots format is start,end'
return
for i in range(len(timeslots)): timeslots[i] = int(timeslots[i])
antToPlotSpl = options.antennas.split(',')
antToPlot = []
for i in range(len(antToPlotSpl)):
tmpspl = antToPlotSpl[i].split('..')
if len(tmpspl) == 1:
antToPlot.append(int(antToPlotSpl[i]))
elif len(tmpspl) == 2:
for j in range(int(tmpspl[0]),int(tmpspl[1])+1):
antToPlot.append(j)
else:
print 'Error: Could not understand antenna list.'
return
polarizations = options.polar.split(',')
for i in range(len(polarizations)):
polarizations[i] = int(polarizations[i])
convertStokes = options.stokes
operation = options.operation
if operation != '':
operation = int(operation)
if convertStokes:
print 'Error: Stokes conversion is not compatible with special operations'
return
channels = options.channels.split(',')
if len(channels) != 2:
print 'Error: Channels format is start,end'
return
for i in range(len(channels)): channels[i] = int(channels[i])
if channels[1] == -1:
channels[1] = None # last element even if there is only one
else:
channels[1] += 1
queryMode = options.query
doUnwrap = options.wrap
if not queryMode:
# open the graphics device, use the right number of panels
ppgplot.pgbeg(device, int(nx), int(ny))
# set the font size
ppgplot.pgsch(1.5)
ppgplot.pgvstd()
# open the main table and print some info about the MS
t = pt.table(inputMS, readonly=True, ack=False)
firstTime = t.query(sortlist='TIME',columns='TIME',limit=1).getcell("TIME", 0)
lastTime = t.query(sortlist='TIME',columns='TIME',offset=t.nrows()-1).getcell("TIME", 0)
intTime = t.getcell("INTERVAL", 0)
print 'Integration time:\t%f sec' % (intTime)
nTimeslots = (lastTime - firstTime) / intTime
if timeslots[1] == -1:
timeslots[1] = nTimeslots
else:
timeslots[1] += 1
print 'Number of timeslots:\t%d' % (nTimeslots)
# open the antenna and spectral window subtables
tant = pt.table(t.getkeyword('ANTENNA'), readonly=True, ack=False)
tsp = pt.table(t.getkeyword('SPECTRAL_WINDOW'), readonly=True, ack=False)
numChannels = len(tsp.getcell('CHAN_FREQ',0))
print 'Number of channels:\t%d' % (numChannels)
print 'Reference frequency:\t%5.2f MHz' % (tsp.getcell('REF_FREQUENCY',0)/1.e6)
# Station names
antList = tant.getcol('NAME')
if len(antToPlot)==1 and antToPlot[0]==-1:
antToPlot = range(len(antList))
print 'Station list (only starred stations will be plotted):'
for i in range(len(antList)):
star = ' '
if i in antToPlot: star = '*'
print '%s %2d\t%s' % (star, i, antList[i])
# Bail if we're in query mode
if queryMode:
return
# select by time from the beginning, and only use specified antennas
tsel = t.query('TIME >= %f AND TIME <= %f AND ANTENNA1 IN %s AND ANTENNA2 IN %s' % (firstTime+timeslots[0]*intTime,firstTime+timeslots[1]*intTime,str(antToPlot),str(antToPlot)))
# values to use for each polarization
plotColors = [1,2,3,4]
labXPositions = [0.35,0.45,0.55,0.65]
labYPositions = [1.0,1.0,1.0,1.0]
if convertStokes:
polLabels = ['I','Q','U','V']
else:
polLabels = ['XX','XY','YX','YY']
# define nicely written axis labels
axisLabels = {'time': 'Time',
'ha': 'Hour angle',
'chan': 'Channel',
'freq': 'Frequency [MHz]',
'amp': 'Visibility amplitude',
'real': 'Real part of visibility',
'imag': 'Imaginary part of visibility',
'phase': 'Visibility phase [radians]'}
# Now we loop through the baselines
ppgplot.pgpage()
for tpart in tsel.iter(["ANTENNA1","ANTENNA2"]):
if not keepPlotting: return
ant1 = tpart.getcell("ANTENNA1", 0)
ant2 = tpart.getcell("ANTENNA2", 0)
if ant1 not in antToPlot or ant2 not in antToPlot: continue
if ant1 == ant2:
if not showAutocorr:
continue
# Get the values to plot, strategy depends on axis type
if xaxis == 'time' or xaxis == 'ha':
xaxisvals = getXAxisVals(tpart, xaxis, channels)
yaxisvals = getYAxisVals(tpart, yaxis, column, operation, showFlags, flagCol, channels, doUnwrap, convertStokes)
else:
xaxisvals = getXAxisVals(tsp, xaxis, channels)
yaxisvals = getYAxisVals(tpart, yaxis, column, operation, showFlags, flagCol, channels, doUnwrap, convertStokes, xaxistype=1)
if xaxisvals == None: # This baseline must be empty, go to next one
print 'No good data on baseline %s - %s' % (antList[ant1],antList[ant2])
continue
if debug:
print xaxisvals.shape
print yaxisvals.shape
for r in range(len(xaxisvals)):
print '%s'%yaxisvals[r]
if len(xaxisvals) != len(yaxisvals): # something is wrong
print 'Error: X and Y axis types incompatible'
return
# Plot the data, each polarization in a different color
ppgplot.pgsci(1)
if xmin == '':
minx = xaxisvals.min()
else:
minx = float(xmin)
if xmax == '':
maxx = xaxisvals.max()
else:
maxx = float(xmax)
if ymin == '':
miny = yaxisvals.min()
if numpy.ma.getmaskarray(yaxisvals.min()):
print 'All data flagged on baseline %s - %s' % (antList[ant1],antList[ant2])
continue
else:
miny = float(ymin)
if ymax == '':
maxy = yaxisvals.max()
else:
maxy = float(ymax)
if minx == maxx:
minx -= 1.0
maxx += 1.0
else:
diffx = maxx - minx
minx -= 0.02*diffx
maxx += 0.02*diffx
if miny == maxy:
miny -= 1.0
maxy += 1.0
else:
diffy = maxy - miny
miny -= 0.02*diffy
maxy += 0.02*diffy
#ppgplot.pgpage()
ppgplot.pgswin(minx,maxx,miny,maxy)
if xaxis == 'time' or xaxis == 'ha':
ppgplot.pgtbox('ZHOBCNST',0.0,0,'BCNST',0.0,0)
else:
ppgplot.pgbox('BCNST',0.0,0,'BCNST',0.0,0)
#ppgplot.pglab(axisLabels[xaxis], axisLabels[yaxis], '%s - %s'%(antList[ant1],antList[ant2]))
#ppgplot.pgmtxt('T', 3.0, 0.5, 0.5, inputMSbasename)
ppgplot.pglab(axisLabels[xaxis], axisLabels[yaxis], inputMSbasename + '(' + getDataDescription(column) + '): %s - %s'%(antList[ant1],antList[ant2]))
if operation != 0:
# some operations is defined
if operation == 1:
label = 'XX-YY'
elif operation == 2:
label = 'XY.YX*'
else:
print 'Special operation not defined'
return
ppgplot.pgsci(plotColors[0])
tmpvals = yaxisvals
print 'Baseline',antList[ant1],'-',antList[ant2],': Plotting',len(tmpvals[~tmpvals.mask]),'points of ' + label
ppgplot.pgpt(xaxisvals[~tmpvals.mask], tmpvals[~tmpvals.mask], 1)
addInfo(showStats, tmpvals[~tmpvals.mask], label, labXPositions[1], labYPositions[1])
else:
for j in polarizations:
ppgplot.pgsci(plotColors[j])
tmpvals = yaxisvals[:,j]
if j == polarizations[0]:
print 'Baseline',antList[ant1],'-',antList[ant2],': Plotting',len(tmpvals[~tmpvals.mask]),'points per polarization'
ppgplot.pgpt(xaxisvals[~tmpvals.mask], tmpvals[~tmpvals.mask], 1)
addInfo(showStats, tmpvals[~tmpvals.mask], polLabels[j], labXPositions[j], labYPositions[j])
ppgplot.pgpage()
# Close the PGPLOT device
ppgplot.pgclos()
if xaxis=='ha':
print 'Removing derived columns...'
pt.removeDerivedMSCal(inputMS)
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)
continuumSpectrum.snipWavelengthRange(lowerCut, upperCut)
ppgplot.pgsci(2)
def prepplot(rangex, rangey, title=None, labx=None, laby=None, \
rangex2=None, rangey2=None, labx2=None, laby2=None, \
logx=0, logy=0, logx2=0, logy2=0, font=ppgplot_font_, \
fontsize=ppgplot_font_size_, id=0, aspect=1, ticks='in', \
panels=[1,1], device=ppgplot_device_):
"""
prepplot(rangex, rangey, ...)
Open a PGPLOT device for plotting.
'rangex' and 'rangey' are sequence objects giving min and
max values for each axis.
The optional entries are:
title: graph title (default = None)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: ranges for 2nd x-axis (default = None)
rangey2: ranges for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: Show ID line on plot (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'in')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
global ppgplot_dev_open_, ppgplot_dev_prep_
# Check if we will use second X or Y axes
# Note: if using a 2nd X axis, the range should correspond
# to the minimum and maximum values of the 1st X axis. If
# using a 2nd Y axis, the range should correspond to the
# scalerange() values of the 1st Y axis.
if rangex2 is None:
rangex2=rangex
otherxaxis=0
else: otherxaxis=1
if rangey2 is None:
rangey2=rangey
otheryaxis=0
else: otheryaxis=1
# Open the plot device
if (not ppgplot_dev_open_):
ppgplot.pgopen(device)
# My little add-on to switch the background to white
if device == '/XWIN':
reset_colors()
if device == '/AQT':
ppgplot.pgsci(0)
# Let the routines know that we already have a device open
ppgplot_dev_open_ = 1
# Set the aspect ratio
ppgplot.pgpap(0.0, aspect)
if (panels != [1,1]):
# Set the number of panels
ppgplot.pgsubp(panels[0], panels[1])
ppgplot.pgpage()
# Choose the font
ppgplot.pgscf(font)
# Choose the font size
ppgplot.pgsch(fontsize)
# Choose the font size
ppgplot.pgslw(ppgplot_linewidth_)
# Plot the 2nd axis if needed first
if otherxaxis or otheryaxis:
ppgplot.pgvstd()
ppgplot.pgswin(rangex2[0], rangex2[1], rangey2[0], rangey2[1])
# Decide how the axes will be drawn
if ticks=='in': env = "CMST"
else: env = "CMSTI"
if logx2: lxenv='L'
else: lxenv=''
if logy2: lyenv='L'
else: lyenv=''
if otherxaxis and otheryaxis:
ppgplot.pgbox(env+lxenv, 0.0, 0, env+lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("", 0.0, 0, env+lyenv, 0.0, 0)
else:
ppgplot.pgbox(env+lxenv, 0.0, 0, "", 0.0, 0)
# Now setup the primary axis
ppgplot.pgvstd()
ppgplot.pgswin(rangex[0], rangex[1], rangey[0], rangey[1])
# Decide how the axes will be drawn
if ticks=='in': env = "ST"
else: env = "STI"
if logx: lxenv='L'
else: lxenv=''
if logy: lyenv='L'
else: lyenv=''
if otherxaxis and otheryaxis:
ppgplot.pgbox("BN"+env+lxenv, 0.0, 0, "BN"+env+lyenv, 0.0, 0)
elif otheryaxis:
ppgplot.pgbox("BCN"+env+lxenv, 0.0, 0, "BN"+env+lyenv, 0.0, 0)
elif otherxaxis:
ppgplot.pgbox("BN"+env+lxenv, 0.0, 0, "BCN"+env+lyenv, 0.0, 0)
else:
ppgplot.pgbox("BCN"+env+lxenv, 0.0, 0, "BCN"+env+lyenv, 0.0, 0)
# My little add-on to switch the background to white
if device == '/AQT' or device == '/XWIN':
reset_colors()
# Add labels
if not title is None: ppgplot.pgmtxt("T", 3.2, 0.5, 0.5, title)
ppgplot.pgmtxt("B", 3.0, 0.5, 0.5, labx)
ppgplot.pgmtxt("L", 2.6, 0.5, 0.5, laby)
if otherxaxis: ppgplot.pgmtxt("T", 2.0, 0.5, 0.5, labx2)
if otheryaxis: ppgplot.pgmtxt("R", 3.0, 0.5, 0.5, laby2)
# Add ID line if required
if (id==1): ppgplot.pgiden()
# Let the routines know that we have already prepped the device
ppgplot_dev_prep_ = 1
def joy_division_plot(pulses, timeseries, downfactor=1, hgt_mult=1):
"""Plot each pulse profile on the same plot separated
slightly on the vertical axis.
'timeseries' is the Datfile object dissected.
Downsample profiles by factor 'downfactor' before plotting.
hgt_mult is a factor to stretch the height of the paper.
"""
first = True
ppgplot.pgbeg("%s.joydiv.ps/CPS" % \
os.path.split(timeseries.basefn)[1], 1, 1)
ppgplot.pgpap(10.25, hgt_mult*8.5/11.0) # Letter landscape
# ppgplot.pgpap(7.5, 11.7/8.3) # A4 portrait, doesn't print properly
ppgplot.pgiden()
ppgplot.pgsci(1)
# Set up main plot
ppgplot.pgsvp(0.1, 0.9, 0.1, 0.8)
ppgplot.pglab("Profile bin", "Single pulse profiles", "")
to_plot = []
xmin = 0
xmax = None
ymin = None
ymax = None
for pulse in pulses:
vertical_offset = (pulse.number-1)*JOYDIV_SEP
copy_of_pulse = pulse.make_copy()
if downfactor > 1:
# Interpolate before downsampling
interp = ((copy_of_pulse.N/downfactor)+1)*downfactor
copy_of_pulse.interpolate(interp)
copy_of_pulse.downsample(downfactor)
# copy_of_pulse.scale()
if first:
summed_prof = copy_of_pulse.profile.copy()
first = False
else:
summed_prof += copy_of_pulse.profile
prof = copy_of_pulse.profile + vertical_offset
min = prof.min()
if ymin is None or min < ymin:
ymin = min
max = prof.max()
if ymax is None or max > ymax:
ymax = max
max = prof.size-1
if xmax is None or max > xmax:
xmax = max
to_plot.append(prof)
yspace = 0.1*ymax
ppgplot.pgswin(0, xmax, ymin-yspace, ymax+yspace)
for prof in to_plot:
ppgplot.pgline(np.arange(0,prof.size), prof)
ppgplot.pgbox("BNTS", 0, 0, "BC", 0, 0)
# Set up summed profile plot
ppgplot.pgsvp(0.1, 0.9, 0.8, 0.9)
ppgplot.pglab("", "Summed profile", "Pulses from %s" % timeseries.datfn)
summed_prof = summed_prof - summed_prof.mean()
ppgplot.pgswin(0, xmax, summed_prof.min(), summed_prof.max())
ppgplot.pgline(np.arange(0, summed_prof.size), summed_prof)
ppgplot.pgbox("C", 0, 0, "BC", 0, 0)
ppgplot.pgclos()
def plot2d(z, x=None, y=None, title=None, rangex=None, rangey=None, \
rangez=None, labx='', laby='', rangex2=None, rangey2=None, \
labx2='', laby2='', image=ppgplot_palette_, contours=None, \
logx=0, logy=0, logx2=0, logy2=0, \
line=ppgplot_linestyle_, width=ppgplot_linewidth_, \
color=ppgplot_color_, labels=ppgplot_labels_, \
labelint=ppgplot_labelint_, labelmin=ppgplot_labelmin_, \
font=ppgplot_font_, id=0, noscale=0, aspect=1, \
fontsize=ppgplot_font_size_, ticks='out', panels=[1,1], \
device=ppgplot_device_):
"""
plot2d(z, ...)
An interface to make various 2D plots using PGPLOT.
'z' is the 2D Numpy array to be plotted.
The optional entries are:
x: x values (default = 0, 1, ...)
y: y values (default = 0, 1, ...)
title: graph title (default = None)
rangex: range for the x-axis (default = automatic)
rangey: range for the y-axis (default = automatic)
rangez: range for the z-axis (default = automatic)
labx: label for the x-axis (default = None)
laby: label for the y-axis (default = None)
rangex2: range for 2nd x-axis (default = None)
rangey2: range for 2nd y-axis (default = None)
labx2: label for the 2nd x-axis (default = None)
laby2: label for the 2nd y-axis (default = None)
logx: make the 1st x-axis log (default = 0 (no))
logy: make the 1st y-axis log (default = 0 (no))
logx2: make the 2nd x-axis log (default = 0 (no))
logy2: make the 2nd y-axis log (default = 0 (no))
image: color palette for image (default = 'rainbow')
contours: list of contour values (default = None)
line: contour line style (default = 1 (solid))
width: contour line width (default = 1 (thin))
color: contour line color (default = 'white')
labels: color of contour labels (default = None)
labelint: contour label spacing (default = 20)
labelmin: min contour label spacing (default = 20)
font: PGPLOT font to use (default = 1 (normal))
fontsize: PGPLOT font size to use (default = 1.0 (normal))
id: show ID line on plot (default = 0 (no))
noscale: turn off auto scaling (default = 0 (no))
aspect: Aspect ratio (default = 1 (square))
ticks: Ticks point in or out (default = 'out')
panels: Number of subpanels [r,c] (default = [1,1])
device: PGPLOT device to use (default = '/XWIN')
Note: Many default values are defined in global variables
with names like ppgplot_font_ or ppgplot_device_.
"""
# Make sure the input data is a 2D array
z = Num.asarray(z);
if not len(z.shape)==2:
print 'Input data array must be 2 dimensional.'
return
# Announce the global variables we will be using
global ppgplot_dev_open_, ppgplot_dev_prep_, pgpalette
# Define the X and Y axis limits if needed
if x is None: x=Num.arange(z.shape[1], dtype='f')
else: x = Num.asarray(x)
if y is None: y=Num.arange(z.shape[0], dtype='f')
else: y = Num.asarray(y)
# Determine the scaling to use for the axes
if rangex is None: rangex=[Num.minimum.reduce(x), \
Num.maximum.reduce(x)]
if rangey is None: rangey=[Num.minimum.reduce(y), \
Num.maximum.reduce(y)]
if rangez is None: rangez=[Num.minimum.reduce(Num.ravel(z)), \
Num.maximum.reduce(Num.ravel(z))]
# Prep the plotting device...
if (not ppgplot_dev_prep_):
prepplot(rangex, rangey, title, labx, laby, \
rangex2, rangey2, labx2, laby2, logx, logy, \
logx2, logy2, font, fontsize, id, aspect, \
ticks, panels, device=device)
if image is not None:
# Set the color indices and the color table
lo_col_ind, hi_col_ind = ppgplot.pgqcol()
lo_col_ind = lo_col_ind + 2
ppgplot.pgscir(lo_col_ind, hi_col_ind)
pgpalette.setpalette(image)
ppgplot.pgctab(pgpalette.l,pgpalette.r,pgpalette.g,pgpalette.b)
# Construct the image
ppgplot.pgimag_s(z, 0.0, 0.0, rangex[0], rangey[0], \
rangex[1], rangey[1])
reset_colors()
if contours is not None:
contours = Num.asarray(contours)
# Choose the line style
ppgplot.pgsls(line)
# Choose the line width
ppgplot.pgslw(width)
# Choose the line color for the contourlines
if type(color) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[color])
else:
ppgplot.pgsci(color)
# Construct the contours
ppgplot.pgcont_s(z, len(contours), contours, rangex[0], \
rangey[0], rangex[1], rangey[1])
# Label the contours if requested
if labels is not None:
# Choose the line color for the contourlines
if type(labels) == types.StringType:
ppgplot.pgsci(ppgplot_colors_[labels])
else:
ppgplot.pgsci(labels)
for i in range(len(contours)):
ppgplot.pgconl_s(z, contours[i], str(contours[i]),
labelint, labelmin)
ppgplot.pglab("x", "y", "Initial 10 frame stacked image.")
# Display the image on the user's screen
image = matplotlib.pyplot.imshow(boostedFullFrame, cmap='gray_r')
for s in allSources:
x, y = s[0], s[1]
matplotlib.pyplot.gca().add_artist(matplotlib.pyplot.Circle((x,y), 10, color='green', fill=False, linewidth=1.0))
if applyShift:
for s in topSources:
x, y = s[0], s[1]
matplotlib.pyplot.gca().add_artist(matplotlib.pyplot.Circle((x,y), 10, color='blue', fill=False, linewidth=1.0))
rows, cols = numpy.shape(boostedFullFrame)
ppgplot.pggray(boostedFullFrame, 0, cols-1, 0, rows-1, 0, 255, pgPlotTransform)
ppgplot.pgsfs(2) # Set fill style to 'outline'
ppgplot.pgsci(3) # Set the colour to 'green'
for s in allSources:
x, y = s[0], s[1]
ppgplot.pgcirc(x,y, 10)
""" End of the prework """
rdat.set(1) # Reset back to the first frame
frameRange = maximumFrames - startFrame + 1
if arg.numframes!=None:
requestedNumFrames = arg.numframes
if requestedNumFrames<(frameRange):
frameRange = requestedNumFrames
def main(options):
debug = options.debug
MSlist = []
for inmspart in options.inms.split(','):
for msname in glob.iglob(inmspart):
MSlist.append(msname)
if len(MSlist) == 0:
print 'Error: You must specify at least one MS name.'
print ' Use "uvplot.py -h" to get help.'
return
if len(MSlist) > 1:
print 'WARNING: Antenna selection (other than all) may not work well'
print ' when plotting more than one MS. Carefully inspect the'
print ' listings of antenna numbers/names!'
device = options.device
if device=='?':
ppgplot.pgldev()
return
if options.title == '':
plottitle = options.inms
else:
plottitle = options.title
axlimits = options.axlimits.split(',')
if len(axlimits) == 4:
xmin,xmax,ymin,ymax = axlimits
else:
print 'Error: You must specify four axis limits'
return
timeslots = options.timeslots.split(',')
if len(timeslots) != 3:
print 'Error: Timeslots format is start,skip,end'
return
for i in range(len(timeslots)):
timeslots[i] = int(timeslots[i])
if timeslots[i] < 0:
print 'Error: timeslots values must not be negative'
return
antToPlotSpl = options.antennas.split(',')
antToPlot = []
for i in range(len(antToPlotSpl)):
tmpspl = antToPlotSpl[i].split('..')
if len(tmpspl) == 1:
antToPlot.append(int(antToPlotSpl[i]))
elif len(tmpspl) == 2:
for j in range(int(tmpspl[0]),int(tmpspl[1])+1):
antToPlot.append(j)
else:
print 'Error: Could not understand antenna list.'
return
queryMode = options.query
plotLambda = options.kilolambda
badval = 0.0
xaxisvals = numpy.array([])
yaxisvals = numpy.array([])
savex = numpy.array([])
savey = numpy.array([])
numPlotted = 0
for inputMS in MSlist:
# open the main table and print some info about the MS
print 'Getting info for', inputMS
t = pt.table(inputMS, readonly=True, ack=False)
tfreq = pt.table(t.getkeyword('SPECTRAL_WINDOW'),readonly=True,ack=False)
ref_freq = tfreq.getcol('REF_FREQUENCY',nrow=1)[0]
ch_freq = tfreq.getcol('CHAN_FREQ',nrow=1)[0]
print 'Reference frequency:\t%f MHz' % (ref_freq/1.e6)
if options.wideband:
ref_wavelength = 2.99792458e8/ch_freq
else:
ref_wavelength = [2.99792458e8/ref_freq]
print 'Reference wavelength:\t%f m' % (ref_wavelength[0])
if options.sameuv and numPlotted > 0:
print 'Assuming same uvw as first MS!'
if plotLambda:
for w in ref_wavelength:
xaxisvals = numpy.append(xaxisvals,[savex/w/1000.,-savex/w/1000.])
yaxisvals = numpy.append(yaxisvals,[savey/w/1000.,-savey/w/1000.])
else:
print 'Plotting more than one MS with same uv, all in meters... do you want -k?'
xaxisvals = numpy.append(xaxisvals,[savex,-savex])
yaxisvals = numpy.append(yaxisvals,[savey,-savey])
continue
firstTime = t.getcell("TIME", 0)
lastTime = t.getcell("TIME", t.nrows()-1)
intTime = t.getcell("INTERVAL", 0)
print 'Integration time:\t%f sec' % (intTime)
nTimeslots = (lastTime - firstTime) / intTime
print 'Number of timeslots:\t%d' % (nTimeslots)
if timeslots[1] == 0:
if nTimeslots >= 100:
timeskip = int(nTimeslots/100)
else:
timeskip = 1
else:
timeskip = int(timeslots[1])
print 'For each baseline, plotting one point every %d samples' % (timeskip)
if timeslots[2] == 0:
timeslots[2] = nTimeslots
# open the antenna subtable
tant = pt.table(t.getkeyword('ANTENNA'), readonly=True, ack=False)
# Station names
antList = tant.getcol('NAME')
if len(antToPlot)==1 and antToPlot[0]==-1:
antToPlot = range(len(antList))
print 'Station list (only starred stations will be plotted):'
for i in range(len(antList)):
star = ' '
if i in antToPlot: star = '*'
print '%s %2d\t%s' % (star, i, antList[i])
# Bail if we're in query mode
if queryMode:
return
# select by time from the beginning, and only use specified antennas
tsel = t.query('TIME >= %f AND TIME <= %f AND ANTENNA1 IN %s AND ANTENNA2 IN %s' % (firstTime+timeslots[0]*intTime,firstTime+timeslots[2]*intTime,str(antToPlot),str(antToPlot)), columns='ANTENNA1,ANTENNA2,UVW')
# Now we loop through the baselines
i = 0
nb = (len(antToPlot)*(len(antToPlot)-1))/2
sys.stdout.write('Reading uvw for %d baselines: %04d/%04d'%(nb,i,nb))
sys.stdout.flush()
for tpart in tsel.iter(["ANTENNA1","ANTENNA2"]):
ant1 = tpart.getcell("ANTENNA1", 0)
ant2 = tpart.getcell("ANTENNA2", 0)
if ant1 not in antToPlot or ant2 not in antToPlot: continue
if ant1 == ant2: continue
i += 1
sys.stdout.write('\b\b\b\b\b\b\b\b\b%04d/%04d'%(i,nb))
sys.stdout.flush()
# Get the values to plot
uvw = tpart.getcol('UVW', rowincr=timeskip)
if numPlotted == 0:
savex = numpy.append(savex,[uvw[:,0],-uvw[:,0]])
savey = numpy.append(savey,[uvw[:,1],-uvw[:,1]])
if plotLambda:
for w in ref_wavelength:
xaxisvals = numpy.append(xaxisvals,[uvw[:,0]/w/1000.,-uvw[:,0]/w/1000.])
yaxisvals = numpy.append(yaxisvals,[uvw[:,1]/w/1000.,-uvw[:,1]/w/1000.])
else:
xaxisvals = numpy.append(xaxisvals,[uvw[:,0],-uvw[:,0]])
yaxisvals = numpy.append(yaxisvals,[uvw[:,1],-uvw[:,1]])
#if debug:
# print uvw.shape
# print xaxisvals.shape
# print yaxisvals.shape
#else:
# sys.stdout.write('.')
# sys.stdout.flush()
sys.stdout.write(' Done!\n')
numPlotted += 1
print 'Plotting uv points ...'
# open the graphics device, using only one panel
ppgplot.pgbeg(device, 1, 1)
# set the font size
ppgplot.pgsch(1)
ppgplot.pgvstd()
# Plot the data
if debug:
print xaxisvals
xaxisvals = numpy.array(xaxisvals)
yaxisvals = numpy.array(yaxisvals)
tmpvals = numpy.sqrt(xaxisvals**2+yaxisvals**2)
ppgplot.pgsci(1)
uvmax = max(xaxisvals.max(),yaxisvals.max())
uvmin = min(xaxisvals.min(),yaxisvals.min())
uvuplim = 0.02*(uvmax-uvmin)+uvmax
uvlolim = uvmin-0.02*(uvmax-uvmin)
if xmin == '':
minx = uvlolim
else:
minx = float(xmin)
if xmax == '':
maxx = uvuplim
else:
maxx = float(xmax)
if ymin == '':
miny = uvlolim
else:
miny = float(ymin)
if ymax == '':
maxy = uvuplim
else:
maxy = float(ymax)
if minx == maxx:
minx = -1.0
maxx = 1.0
if miny == maxy:
miny = -1.0
maxy = 1.0
ppgplot.pgpage()
ppgplot.pgswin(minx,maxx,miny,maxy)
ppgplot.pgbox('BCNST',0.0,0,'BCNST',0.0,0)
if plotLambda:
ppgplot.pglab('u [k\gl]', 'v [k\gl]', '%s'%(plottitle))
else:
ppgplot.pglab('u [m]', 'v [m]', '%s'%(plottitle))
ppgplot.pgpt(xaxisvals[tmpvals!=badval], yaxisvals[tmpvals!=badval], 1)
# Close the PGPLOT device
ppgplot.pgclos()
lowerFlambda = min(modelSpectrum.flux)
upperFlambda = max(modelSpectrum.flux)
lowerFlambda = 0
lowerFlux = 0
upperFlux = 0
for s in modelSpectra:
fmax = max(s.flux)
if fmax>upperFlux:
upperFlux = fmax
ppgplot.pgenv(lowerWavelength, upperWavelength, lowerFlux, upperFlux, 0, 0)
colour = 1
for s in modelSpectra:
ppgplot.pgsci(colour)
ppgplot.pgline(s.wavelengths, s.flux)
plotx = 8500
ploty = s.getNearestFlux(plotx)
ppgplot.pgptxt(plotx, ploty, 0, 0, "%2.0f'"%(s.angle))
colour+= 1
if colour>15: colour = 1
ppgplot.pgsci(1)
label = "B=%.0f MG, T=%.1f keV"%(field, temperature)
ppgplot.pglab("wavelength", "i_0", label)
ppgplot.pgclos()
if arg.device!=None:
# Write to an additional output devide
