def plotxy_preview(cls, plot_window, beam, var_x, var_y, nolost=0, title='PLOTXY', xtitle=None, ytitle=None):
matplotlib.rcParams['axes.formatter.useoffset']='False'
col1 = beam.getshonecol(var_x, nolost=nolost)
col2 = beam.getshonecol(var_y, nolost=nolost)
factor1 = ShadowPlot.get_factor(var_x)
factor2 = ShadowPlot.get_factor(var_y)
if xtitle is None: xtitle=(stp.getLabel(var_x-1))[0]
if ytitle is None: ytitle=(stp.getLabel(var_y-1))[0]
plot_window.addCurve(col1*factor1, col2*factor2, title, symbol='.', color='blue', replace=True) #'+', '^', ','
if not xtitle is None: plot_window.setGraphXLabel(xtitle)
if not ytitle is None: plot_window.setGraphYLabel(ytitle)
if not title is None: plot_window.setGraphTitle(title)
plot_window.setDrawModeEnabled(True, 'rectangle')
plot_window.setZoomModeEnabled(True)
def plot_histo(self, beam, col, nolost, xrange, ref, title, xtitle, ytitle, nbins = 100, xum=""):
ticket = beam.histo1(col, nbins=nbins, xrange=xrange, nolost=nolost, ref=ref)
factor=ShadowPlot.get_factor(col)
if ref != 0 and not ytitle is None: ytitle = ytitle + ' % ' + (stp.getLabel(ref-1))[0]
histogram = ticket['histogram']
bins = ticket['bins']*factor
self.plot_canvas.addCurve(bins[1:], histogram, title, symbol=',', color='blue', replace=True) #'+', '^', ','
if not xtitle is None: self.plot_canvas.setGraphXLabel(xtitle)
if not ytitle is None: self.plot_canvas.setGraphYLabel(ytitle)
if not title is None: self.plot_canvas.setGraphTitle(title)
self.plot_canvas.setDrawModeEnabled(True, 'rectangle')
self.plot_canvas.setZoomModeEnabled(True)
if ticket['fwhm'] == None: ticket['fwhm'] = 0.0
n_patches = len(self.plot_canvas._plot.graph.ax.patches)
if (n_patches > 0): self.plot_canvas._plot.graph.ax.patches.remove(self.plot_canvas._plot.graph.ax.patches[n_patches-1])
if not ticket['fwhm'] == 0.0:
x_fwhm_i, x_fwhm_f = ticket['fwhm_coordinates']
x_fwhm_i, x_fwhm_f = x_fwhm_i*factor, x_fwhm_f*factor
y_fwhm = max(histogram)*0.5
self.plot_canvas._plot.graph.ax.add_patch(FancyArrowPatch([x_fwhm_i, y_fwhm],
[x_fwhm_f, y_fwhm],
arrowstyle=ArrowStyle.CurveAB(head_width=2, head_length=4),
color='b',
linewidth=1.5))
self.plot_canvas.setGraphYLimits(0, max(histogram))
self.plot_canvas.replot()
self.info_box.intensity.setText("{:4.3f}".format(ticket['intensity']))
self.info_box.total_rays.setText(str(ticket['nrays']))
self.info_box.total_good_rays.setText(str(ticket['good_rays']))
self.info_box.total_lost_rays.setText(str(ticket['nrays']-ticket['good_rays']))
self.info_box.fwhm_h.setText("{:5.4f}".format(ticket['fwhm']*factor))
self.info_box.label_h.setText("FWHM " + xum)
def plot_histo_preview(cls, plot_window, beam, col, nolost, ref, title, xtitle, ytitle):
matplotlib.rcParams['axes.formatter.useoffset']='False'
factor=ShadowPlot.get_factor(col)
ticket = beam.histo1(col, nbins=100, xrange=None, nolost=nolost, ref=ref)
if ref != 0 and not ytitle is None: ytitle = ytitle + ' % ' + (stp.getLabel(ref-1))[0]
histogram = ticket['histogram']
bins = ticket['bins']*factor
plot_window.addCurve(bins[1:], histogram, title, symbol=',', color='blue', replace=True) #'+', '^', ','
if not xtitle is None: plot_window.setGraphXLabel(xtitle)
if not ytitle is None: plot_window.setGraphYLabel(ytitle)
plot_window.setDrawModeEnabled(True, 'rectangle')
plot_window.setZoomModeEnabled(True)
plot_window.setGraphYLimits(0, max(histogram))
plot_window.replot()
def plotxy_old(beam,cols1,cols2,nbins=25,nbins_h=None,level=5,xrange=None,yrange=None,nolost=0,title='PLOTXY',xtitle=None,ytitle=None,noplot=0,calfwhm=0,contour=0):
'''
Draw the scatter or contour or pixel-like plot of two columns of a Shadow.Beam instance or of a given shadow file, along with histograms for the intensity on the top and right side.
Inumpy.ts:
beam : str instance with the name of the shadow file to be loaded, or a Shadow.Beam initialized instance.
cols1 : first column.
cols2 : second column.
Optional Inumpy.ts:
nbins : int for the size of the grid (nbins x nbins). It will affect the plot only if non scatter.
nbins_h : int for the number of bins for the histograms
level : int number of level to be drawn. It will affect the plot only if contour.
xrange : tuple or list of length 2 describing the interval of interest for x, the data read from the chosen column.
yrange : tuple or list of length 2 describing the interval of interest for y, counts or intensity depending on ref.
nolost :
0 All rays
1 Only good rays
2 Only lost rays
title : title of the figure, it will appear on top of the window.
xtitle : label for the x axis.
ytitle : label for the y axis.
noplot :
0 plot the histogram
1 don't plot the histogram
calfwhm :
0 don't compute the fwhm
1 compute the fwhm and draw it
2 in addition to calfwhm=1, it computes now the intensity in a
slit of FWHM_h x FWHM_v
contour :
0 scatter plot
1 contour, black & white, only counts (without intensity)
2 contour, black & white, with intensity.
3 contour, colored, only counts (without intensity)
4 contour, colored, with intensity.
5 pixelized, colored, only counts (without intensity)
6 pixelized, colored, with intensity.
Outputs:
ShadowTools.Histo1_Ticket instance.
Error:
if an error occurs an ArgsError is raised.
Possible choice for col are:
1 X spatial coordinate [user's unit]
2 Y spatial coordinate [user's unit]
3 Z spatial coordinate [user's unit]
4 X' direction or divergence [rads]
5 Y' direction or divergence [rads]
6 Z' direction or divergence [rads]
7 X component of the electromagnetic vector (s-polariz)
8 Y component of the electromagnetic vector (s-polariz)
9 Z component of the electromagnetic vector (s-polariz)
10 Lost ray flag
11 Energy [eV]
12 Ray index
13 Optical path length
14 Phase (s-polarization)
15 Phase (p-polarization)
16 X component of the electromagnetic vector (p-polariz)
17 Y component of the electromagnetic vector (p-polariz)
18 Z component of the electromagnetic vector (p-polariz)
19 Wavelength [A]
20 R= SQRT(X^2+Y^2+Z^2)
21 angle from Y axis
22 the magnituse of the Electromagnetic vector
23 |E|^2 (total intensity)
24 total intensity for s-polarization
25 total intensity for p-polarization
26 K = 2 pi / lambda [A^-1]
27 K = 2 pi / lambda * col4 [A^-1]
28 K = 2 pi / lambda * col5 [A^-1]
29 K = 2 pi / lambda * col6 [A^-1]
30 S0-stokes = |Es|^2 + |Ep|^2
31 S1-stokes = |Es|^2 - |Ep|^2
32 S2-stokes = 2 |Es| |Ep| cos(phase_s-phase_p)
33 S3-stokes = 2 |Es| |Ep| sin(phase_s-phase_p)
'''
if nbins_h==None: nbins_h=nbins+1
try:
stp.plotxy_CheckArg(beam,cols1,cols2,nbins,nbins_h,level,xrange,yrange,nolost,title,xtitle,ytitle,noplot,calfwhm,contour)
except stp.ArgsError as e:
raise e
#plot_nicc.ioff()
plt.ioff()
col1,col2,col3,col4 = getshcol(beam,(cols1,cols2,10,23,))
nbins=nbins+1
if xtitle==None: xtitle=(stp.getLabel(cols1-1))[0]
if ytitle==None: ytitle=(stp.getLabel(cols2-1))[0]
if nolost==0: t = numpy.where(col3!=-3299)
if nolost==1: t = numpy.where(col3==1.0)
if nolost==2: t = numpy.where(col3!=1.0)
if xrange==None: xrange = stp.setGoodRange(col1[t])
if yrange==None: yrange = stp.setGoodRange(col2[t])
#print xrange
#print yrange
tx = numpy.where((col1>xrange[0])&(col1<xrange[1]))
ty = numpy.where((col2>yrange[0])&(col2<yrange[1]))
tf = set(list(t[0])) & set(list(tx[0])) & set(list(ty[0]))
t = (numpy.array(sorted(list(tf))),)
if len(t[0])==0:
print ("no point selected")
return None
#figure = pylab.plt.figure(figsize=(12,8),dpi=96)
figure = plt.figure(figsize=(12,8),dpi=96)
ratio = 8.0/12.0
left, width = 0.1*ratio, 0.65*ratio
bottom, height = 0.1, 0.65
bottom_h = bottom+height+0.02
left_h = left+width+0.02*ratio
rect_scatter = [0.10*ratio, 0.10, 0.65*ratio, 0.65]
rect_histx = [0.10*ratio, 0.77, 0.65*ratio, 0.20]
rect_histy = [0.77*ratio, 0.10, 0.20*ratio, 0.65]
rect_text = [1.00*ratio, 0.10, 1.20*ratio, 0.65]
axScatter = figure.add_axes(rect_scatter)
axScatter.set_xlabel(xtitle)
axScatter.set_ylabel(ytitle)
if contour==0:
axScatter.scatter(col1[t],col2[t],s=0.5)
if contour>0 and contour<7:
if contour==1 or contour==3 or contour==5: w = numpy.ones( len(col1) )
if contour==2 or contour==4 or contour==6: w = col4
grid = numpy.zeros(nbins*nbins).reshape(nbins,nbins)
for i in t[0]:
indX = stp.findIndex(col1[i],nbins,xrange[0],xrange[1])
indY = stp.findIndex(col2[i],nbins,yrange[0],yrange[1])
try:
grid[indX][indY] = grid[indX][indY] + w[i]
except IndexError:
pass
X, Y = numpy.mgrid[xrange[0]:xrange[1]:nbins*1.0j,yrange[0]:yrange[1]:nbins*1.0j]
L = numpy.linspace(numpy.amin(grid),numpy.amax(grid),level)
if contour==1 or contour==2: axScatter.contour(X, Y, grid, colors='k', levels=L)
if contour==3 or contour==4: axScatter.contour(X, Y, grid, levels=L)
if contour==5 or contour==6: axScatter.pcolor(X, Y, grid)
#axScatter.set_xlim(xrange)
#axScatter.set_ylim(yrange)
#axScatter.axis(xmin=xrange[0],xmax=xrange[1])
#axScatter.axis(ymin=yrange[0],ymax=yrange[1])
for tt in axScatter.get_xticklabels():
tt.set_size('x-small')
for tt in axScatter.get_yticklabels():
tt.set_size('x-small')
#if ref==0: col4 = numpy.ones(len(col4),dtype=float)
axHistx = figure.add_axes(rect_histx, sharex=axScatter)
axHisty = figure.add_axes(rect_histy, sharey=axScatter)
binx = numpy.linspace(xrange[0],xrange[1],nbins_h)
biny = numpy.linspace(yrange[0],yrange[1],nbins_h)
if contour==0 or contour==1 or contour==3 or contour==5:
hx, binx, patchx = axHistx.hist(col1[t],bins=binx,range=xrange,histtype='step',color='k')
hy, biny, patchy = axHisty.hist(col2[t],bins=biny,range=yrange,orientation='horizontal',histtype='step',color='k')
if contour==2 or contour==4 or contour==6:
hx, binx, patchx = axHistx.hist(col1[t],bins=binx,range=xrange,weights=col4[t],histtype='step',color='b')
hy, biny, patchy = axHisty.hist(col2[t],bins=biny,range=yrange,weights=col4[t],orientation='horizontal',histtype='step',color='b')
for tl in axHistx.get_xticklabels(): tl.set_visible(False)
for tl in axHisty.get_yticklabels(): tl.set_visible(False)
for tt in axHisty.get_xticklabels():
tt.set_rotation(270)
tt.set_size('x-small')
for tt in axHistx.get_yticklabels():
tt.set_size('x-small')
intensityinslit = 0.0
if calfwhm>=1:
fwhmx,txf, txi = stp.calcFWHM(hx,binx[1]-binx[0])
fwhmy,tyf, tyi = stp.calcFWHM(hy,biny[1]-biny[0])
axHistx.plot([binx[txi],binx[txf+1]],[max(hx)*0.5,max(hx)*0.5],'x-')
axHisty.plot([max(hy)*0.5,max(hy)*0.5],[biny[tyi],biny[tyf+1]],'x-')
print ("fwhm horizontal: %g" % fwhmx)
print ("fwhm vertical: %g" % fwhmy)
if calfwhm>=2:
xx1 = binx[txi]
xx2 = binx[txf+1]
yy1 = biny[tyi]
yy2 = biny[tyf+1]
print ("limits horizontal: %g %g " % (binx[txi],binx[txf+1]))
print ("limits vertical: %g %g " % (biny[tyi],biny[tyf+1]))
axScatter.plot([xx1,xx2,xx2,xx1,xx1],[yy1,yy1,yy2,yy2,yy1])
#fwhmx,txf, txi = stp.calcFWHM(hx,binx[1]-binx[0])
#fwhmy,tyf, tyi = stp.calcFWHM(hy,biny[1]-biny[0])
#calculate intensity in slit
if nolost==0: tt = numpy.where(col3!=-3299)
if nolost==1: tt = numpy.where(col3==1.0)
if nolost==2: tt = numpy.where(col3!=1.0)
ttx = numpy.where((col1>=xx1)&(col1<=xx2))
tty = numpy.where((col2>=yy1)&(col2<=yy2))
ttf = set(list(tt[0])) & set(list(ttx[0])) & set(list(tty[0]))
tt = (numpy.array(sorted(list(ttf))),)
if len(tt[0])>0:
intensityinslit = col4[tt].sum()
print ("Intensity in slit: %g ",intensityinslit)
if title!=None:
axHistx.set_title(title)
axText = figure.add_axes(rect_text)
ntot = len(numpy.where(col3!=3299)[0])
ngood = len(numpy.where(col3==1)[0])
nbad = ntot - ngood
if nolost==0: axText.text(0.0,0.8,"ALL RAYS")
if nolost==1: axText.text(0.0,0.8,"GOOD RAYS")
if nolost==2: axText.text(0.0,0.8,"LOST RAYS")
tmps = "intensity: "+str(col4[t].sum())
if calfwhm == 2:
tmps=tmps+" (in slit:"+str(intensityinslit)+") "
axText.text(0.0,0.7,tmps)
axText.text(0.0,0.6,"total number of rays: "+str(ntot))
axText.text(0.0,0.5,"total good rays: "+str(ngood))
axText.text(0.0,0.4,"total lost rays: "+str(ntot-ngood))
if calfwhm>=1:
axText.text(0.0,0.3,"fwhm H: "+str(fwhmx))
axText.text(0.0,0.2,"fwhm V: "+str(fwhmy))
if isinstance(beam,str): axText.text(0.0,0.1,"FILE: "+beam)
if isinstance(beam,sd.Beam): axText.text(0.0,0.1,"from Shadow3 Beam instance")
axText.text(0.0,0.0,"DIR: "+os.getcwd())
axText.set_axis_off()
#pylab.plt.draw()
plt.draw()
if noplot==0: figure.show()
ticket = plotxy_Ticket()
ticket.figure = figure
ticket.xrange = xrange
ticket.yrange = yrange
ticket.xtitle = xtitle
ticket.ytitle = ytitle
ticket.title = title
if calfwhm>=1:
ticket.fwhmx = fwhmx
ticket.fwhmy = fwhmy
ticket.intensity = col4[t].sum()
ticket.averagex = numpy.average( col1[t] )
ticket.averagey = numpy.average( col2[t] )
ticket.intensityinslit = intensityinslit
return ticket
def histo1_old(beam,col,xrange=None,yrange=None,nbins=50,nolost=0,ref=0,write=0,title='HISTO1',xtitle=None,ytitle=None,calfwhm=0,noplot=0):
'''
Plot the histogram of a column, simply counting the rays, or weighting with the intensity.
It returns a ShadowTools.Histo1_Ticket which contains the histogram data, and the figure.
Inumpy.ts:
beam : str instance with the name of the shadow file to be loaded, or a Shadow.Beam initialized instance.
col : int for the chosen column.
Optional Inumpy.ts:
xrange : tuple or list of length 2 describing the interval of interest for x, the data read from the chosen column.
yrange : tuple or list of length 2 describing the interval of interest for y, counts or intensity depending on ref.
nbins : number of bins of the histogram.
nolost :
0 All rays
1 Only good rays
2 Only lost rays
ref :
0 only count the rays
1 weight with intensity (look at 23 |E|^2 total intensity)
write :
0 don't write any file
1 write the histogram into the file 'HISTO1'.
title : title of the figure, it will appear on top of the window.
xtitle : label for the x axis.
ytitle : label for the y axis.
calfwhm :
0 don't compute the fwhm
1 compute the fwhm
noplot :
0 plot the histogram
1 don't plot the histogram
orientation :
'vertical' x axis for data, y for intensity
'horizontal' y axis for data, x for intensity
plotxy :
0 standalone version
1 to use within plotxy
Outputs:
ShadowTools.Histo1_Ticket instance.
Error:
if an error occurs an ArgsError is raised.
Possible choice for col are:
1 X spatial coordinate [user's unit]
2 Y spatial coordinate [user's unit]
3 Z spatial coordinate [user's unit]
4 X' direction or divergence [rads]
5 Y' direction or divergence [rads]
6 Z' direction or divergence [rads]
7 X component of the electromagnetic vector (s-polariz)
8 Y component of the electromagnetic vector (s-polariz)
9 Z component of the electromagnetic vector (s-polariz)
10 Lost ray flag
11 Energy [eV]
12 Ray index
13 Optical path length
14 Phase (s-polarization)
15 Phase (p-polarization)
16 X component of the electromagnetic vector (p-polariz)
17 Y component of the electromagnetic vector (p-polariz)
18 Z component of the electromagnetic vector (p-polariz)
19 Wavelength [A]
20 R= SQRT(X^2+Y^2+Z^2)
21 angle from Y axis
22 the magnituse of the Electromagnetic vector
23 |E|^2 (total intensity)
24 total intensity for s-polarization
25 total intensity for p-polarization
26 K = 2 pi / lambda [A^-1]
27 K = 2 pi / lambda * col4 [A^-1]
28 K = 2 pi / lambda * col5 [A^-1]
29 K = 2 pi / lambda * col6 [A^-1]
30 S0-stokes = |Es|^2 + |Ep|^2
31 S1-stokes = |Es|^2 - |Ep|^2
32 S2-stokes = 2 |Es| |Ep| cos(phase_s-phase_p)
33 S3-stokes = 2 |Es| |Ep| sin(phase_s-phase_p)
'''
try: stp.Histo1_CheckArg(beam,col,xrange,yrange,nbins,nolost,ref,write,title,xtitle,ytitle,calfwhm,noplot)
except stp.ArgsError as e: raise e
col=col-1
if ref==1: ref = 23
#plot_nicc.ioff()
plt.ioff()
figure = plt.figure()
axHist = figure.add_axes([0.1,0.1,0.8,0.8])
if ytitle!=None:
ytitlesave=ytitle
else:
ytitlesave=None
if ref==0:
x, a = getshcol(beam,(col+1,10))
w = numpy.ones(len(x))
else:
x, a, w = getshcol(beam,(col+1,10,ref))
if nolost==0:
t = numpy.where(a!=-3299)
ytitle = 'All rays'
if nolost==1:
t = numpy.where(a==1.0)
ytitle = 'Good rays'
if nolost==2:
t = numpy.where(a!=1.0)
ytitle = 'Lost rays'
if len(t[0])==0:
print ("no rays match the selection, the histogram will not be plotted")
return
if ref==0:
ytitle = 'counts ' + ytitle
h,bins,patches = axHist.hist(x[t],bins=nbins,range=xrange,histtype='step',alpha=0.5)
if yrange==None: yrange = [0.0, numpy.max(h)]
hw=h
if ref>=22:
ytitle = (stp.getLabel(ref-1))[0] + ' ' + ytitle
h,bins = numpy.histogram(x[t],range=xrange,bins=nbins)
hw,bins,patches = axHist.hist(x[t],range=xrange, bins=nbins,histtype='step',alpha=0.5,weights=w[t])
if yrange==None: yrange = [0.0, numpy.max(hw)]
fwhm = None
if calfwhm==1:
fwhm, tf, ti = stp.calcFWHM(hw,bins[1]-bins[0])
axHist.plot([bins[ti],bins[tf+1]],[max(h)*0.5,max(h)*0.5],'x-')
print ("fwhm = %g" % fwhm)
if write==1: stp.Histo1_write(title,bins,h,hw,col,beam,ref-1)
if xtitle==None: xtitle=(stp.getLabel(col))[0]
axHist.set_xlabel(xtitle)
if ytitlesave!=None:
axHist.set_ylabel(ytitlesave)
else:
axHist.set_ylabel(ytitle)
if title!=None: axHist.set_title(title)
if xrange!=None: axHist.set_xlim(xrange)
if yrange!=None: axHist.set_ylim(yrange)
if noplot==0:
plt.show()
ticket = Histo1_Ticket()
ticket.histogram = hw
ticket.bin_center = bins[:-1]+(bins[1]-bins[0])*0.5
ticket.bin_left = bins[:-1]
ticket.figure = figure
ticket.xrange = xrange
ticket.yrange = yrange
ticket.xtitle = xtitle
ticket.ytitle = ytitle
ticket.title = title
ticket.fwhm = fwhm
ticket.intensity = w[t].sum()
return ticket
def getshonecol(beam,col):
'''
Extract a column from a shadow file (eg. begin.dat) or a Shadow.Beam instance.
The column are numbered in the fortran convention, i.e. starting from 1.
It returns a numpy.array filled with the values of the chosen column.
Inumpy.ts:
beam : str instance with the name of the shadow file to be loaded. OR
Shadow.Beam initialized instance.
col : int for the chosen columns.
Outputs:
numpy.array 1-D with length numpy.INT.
Error:
if an error occurs an ArgsError is raised.
Possible choice for col are:
1 X spatial coordinate [user's unit]
2 Y spatial coordinate [user's unit]
3 Z spatial coordinate [user's unit]
4 Xp direction or divergence [rads]
5 Yp direction or divergence [rads]
6 Zp direction or divergence [rads]
7 X component of the electromagnetic vector (s-polariz)
8 Y component of the electromagnetic vector (s-polariz)
9 Z component of the electromagnetic vector (s-polariz)
10 Lost ray flag
11 Energy [eV]
12 Ray index
13 Optical path length
14 Phase (s-polarization)
15 Phase (p-polarization)
16 X component of the electromagnetic vector (p-polariz)
17 Y component of the electromagnetic vector (p-polariz)
18 Z component of the electromagnetic vector (p-polariz)
19 Wavelength [A]
20 R= SQRT(X^2+Y^2+Z^2)
21 angle from Y axis
22 the magnituse of the Electromagnetic vector
23 |E|^2 (total intensity)
24 total intensity for s-polarization
25 total intensity for p-polarization
26 K = 2 pi / lambda [A^-1]
27 K = 2 pi / lambda * col4 [A^-1]
28 K = 2 pi / lambda * col5 [A^-1]
29 K = 2 pi / lambda * col6 [A^-1]
30 S0-stokes = |Es|^2 + |Ep|^2
31 S1-stokes = |Es|^2 - |Ep|^2
32 S2-stokes = 2 |Es| |Ep| cos(phase_s-phase_p)
33 S3-stokes = 2 |Es| |Ep| sin(phase_s-phase_p)
'''
try: stp.getshonecol_CheckArg(beam,col)
except stp.ArgsError as e: raise e
col=col-1
if isinstance(beam,sd.Beam):
ray = beam.rays
else:
bm = sd.Beam()
bm.load(beam)
ray = bm.rays
if col>=0 and col<18 and col!=10: column = ray[:,col]
if col==10: column = ray[:,col]/A2EV
if col==18: column = 2*numpy.pi*1.0e8/ray[:,10]
if col==19: column = numpy.sqrt(ray[:,0]*ray[:,0]+ray[:,1]*ray[:,1]+ray[:,2]*ray[:,2])
if col==20: column = numpy.arccos(ray[:,4])
if col==21: column = numpy.sqrt(numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8,15,16,17] ]),axis=0))
if col==22: column = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8,15,16,17] ]),axis=0)
if col==23: column = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8] ]),axis=0)
if col==24: column = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [15,16,17] ]),axis=0)
if col==25: column = ray[:,10]*1.0e8
if col==26: column = ray[:,3]*ray[:,10]*1.0e8
if col==27: column = ray[:,4]*ray[:,10]*1.0e8
if col==28: column = ray[:,5]*ray[:,10]*1.0e8
if col==29:
E2s = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8] ]),axis=0)
E2p = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [15,16,17] ]),axis=0)
column = E2p+E2s
if col==30:
E2s = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8] ]),axis=0)
E2p = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [15,16,17] ]),axis=0)
column = E2p-E2s
if col==31:
E2s = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8] ]),axis=0)
E2p = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [15,16,17] ]),axis=0)
Cos = numpy.cos(ray[:,13]-ray[:,14])
column = 2*E2s*E2p*Cos
if col==32:
E2s = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [6,7,8] ]),axis=0)
E2p = numpy.sum(numpy.array([ ray[:,i]*ray[:,i] for i in [15,16,17] ]),axis=0)
Sin = numpy.sin(ray[:,13]-ray[:,14])
column = 2*E2s*E2p*Sin
return column
def getshcol(beam,col):
'''
Extract multiple columns from a shadow file (eg.'begin.dat') or a Shadow.Beam instance.
The column are numbered in the fortran convention, i.e. starting from 1.
It returns a numpy.array filled with the values of the chosen column.
Inumpy.ts:
beam : str instance with the name of the shadow file to be loaded. OR
Shadow.Beam initialized instance.
col : tuple or list instance of int with the number of columns chosen.
Outputs:
numpy.array 2-D with dimension R x numpy.INT. Where R is the total number of column chosen
Error:
if an error occurs an ArgsError is raised.
Possible choice for col are:
1 X spatial coordinate [user's unit]
2 Y spatial coordinate [user's unit]
3 Z spatial coordinate [user's unit]
4 X' direction or divergence [rads]
5 Y' direction or divergence [rads]
6 Z' direction or divergence [rads]
7 X component of the electromagnetic vector (s-polariz)
8 Y component of the electromagnetic vector (s-polariz)
9 Z component of the electromagnetic vector (s-polariz)
10 Lost ray flag
11 Energy [eV]
12 Ray index
13 Optical path length
14 Phase (s-polarization)
15 Phase (p-polarization)
16 X component of the electromagnetic vector (p-polariz)
17 Y component of the electromagnetic vector (p-polariz)
18 Z component of the electromagnetic vector (p-polariz)
19 Wavelength [A]
20 R= SQRT(X^2+Y^2+Z^2)
21 angle from Y axis
22 the magnituse of the Electromagnetic vector
23 |E|^2 (total intensity)
24 total intensity for s-polarization
25 total intensity for p-polarization
26 K = 2 pi / lambda [A^-1]
27 K = 2 pi / lambda * col4 [A^-1]
28 K = 2 pi / lambda * col5 [A^-1]
29 K = 2 pi / lambda * col6 [A^-1]
30 S0-stokes = |Es|^2 + |Ep|^2
31 S1-stokes = |Es|^2 - |Ep|^2
32 S2-stokes = 2 |Es| |Ep| cos(phase_s-phase_p)
33 S3-stokes = 2 |Es| |Ep| sin(phase_s-phase_p)
'''
try: stp.getshcol_CheckArg(beam,col)
except stp.ArgsError as e: raise e
if isinstance(beam,sd.Beam):
bm = beam
else:
bm = sd.Beam()
bm.load(beam)
ret = []
if isinstance(col, int): return getshonecol(bm,col)
for c in col:
ret.append(getshonecol(bm,c))
return tuple(ret)