'''Uses gmt to normalize a set of GRD files and then stack them - the aim is to see where they agree most'''

#Normalize the files

for grdfile in files:

os.system('gmt grdmath %s NORM = %s' %(grdfile,grdfile[:-3]+'NORM.grd'))

basefilename = files[0][:-5]

normedfiles = glob.glob(basefilename+'*'+'NORM.grd')

print normedfiles

#Start summing the normed files

os.system('gmt grdmath %s %s ADD = %s' %(normedfiles[0],normedfiles[1],basefilename+'SUM.nc'))

for normedfile in normedfiles[2:]:

print 'Adding %s to stack' %normedfile

os.system('gmt grdmath %s %s ADD = %s' %(basefilename+'SUM.nc',normedfile,basefilename+'SUM.nc'))

#plot a gmt figure of the stacked tomography

if plot == True:

os.system('./stackmap.sh %s' %basefilename+'SUM.nc')

os.system('rm output_slices/*.grd')

'''

Plotting command for the becker slice'''

if not axobj:

fig = plt.figure(facecolor='white',figsize=(10,6))

datatracker = {}

axobj = fig

infile = Dataset(infile, model='r')

depths = infile.variables['y'][:]

lengths = infile.variables['x'][:]

data = infile.variables['z'][:][:]

infile.close()

datatracker[infile] = {'data' : data}

subplotno = '11%s' %str(i)

if i==0:

ax = axobj.add_subplot(321)

elif i==1:

ax = axobj.add_subplot(322)

elif i==2:

ax = axobj.add_subplot(323)

elif i==3:

ax = axobj.add_subplot(324)

elif i==4:

ax = axobj.add_subplot(325)

frame1 = plt.gca()

image = ax.imshow(data, interpolation='nearest',aspect='auto',cmap=plt.cm.seismic_r)

#plot on the boundaries:

#This assumes that the bottom of the map corresponds to 2000km depth! Its possible to change this, so

#be careful!

thousandkm = int(len(depths)/2.0)

sixsixtykm = int(len(depths)*6.6/20.0)

ax.plot([0,len(lengths)],[thousandkm,thousandkm],'k--',label='1000km')

ax.plot([0,len(lengths)],[sixsixtykm,sixsixtykm],'k-',label='660km')

#Get earthquake information if available and scale to the dimensions of matrix

#This is working to some extent, but adds to the run time and may fail in certain cases

#In the end it may not be that useful - may be better to plot earthquakes on a different plot,

#with just the slab1.0 contours, for example

if quakes:

if (len(quakes[0]) > 1):

# quakepoints = np.array(quakes[0])

# quakedepths = -np.array(quakes[1])

# maxlen = float(quakes[2])

# depthinc = len(depths)/2000.0

# xinc = len(lengths)/maxlen

# print depthinc,xinc

# quakedepths = quakedepths*depthinc

# quakepoints = len(lengths) - quakepoints*xinc

# print quakedepths

# print quakepoints

# ax.plot(quakepoints,quakedepths,'g.',linewidth=0.4)

if i == 2:

ax.text(30, 130, 'Max earthquake depth: %g km' %-min(quakes[1]), style='italic',bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})

ax.set_title('%s' %model)

ax.set_ylim([len(depths),0])

ax.set_xlim([0,len(lengths)])

cbar_ax = axobj.add_axes([0.88, 0.03, 0.03, 0.25])

colors = image.set_clim(-1.5,1.5)

axobj.colorbar(image,cbar_ax)

#Remove the numbers on the x and y axes - these are distrating

frame1.axes.get_xaxis().set_visible(False)

frame1.axes.get_yaxis().set_visible(False)

'''

Takes a netcdf file containing a stacked, normed profiles and attempts to contour what might be a slab - can use this to estimate dip, profile etc

The values of depthinc and llinc are defaults from the Ritsema code, which creates slices over angles of 180 degrees

and with a depth increment of 5 km

This produces a map showing the slice and contours in stacked velocity perturbation at a chosen level (typically 0.5)

'''

Mantlebase = 2895

infile = Dataset(infile, model='r')

filevariables = infile.variables.keys()

#Get data from the netCDF file

depths = infile.variables['y'][:]

lengths = infile.variables['x'][:]

data = infile.variables['z'][:][:]

infile.close()

#print np.shape(data)

#print np.shape(lengths)

#print np.shape(depths)

#Use image processing suite to find contours

contours = measure.find_contours(data,cval)

#Various plotting commands to produce the figure

fig, ax = plt.subplots()

thousandkm = int((Mantlebase-1000)/depthinc)

sixsixtykm = int((Mantlebase-660)/depthinc)

plt.set_cmap('jet_r')

ax.imshow(data, interpolation='linear',aspect='auto')

ax.plot([0,len(lengths)],[thousandkm,thousandkm],'k--',label='1000km')

ax.plot([0,len(lengths)],[sixsixtykm,sixsixtykm],'k-',label='660km')

for n, contour in enumerate(contours):

if n == 0:

ax.plot(contour[:, 1], contour[:, 0], 'r-', linewidth=2,label='contour at %g' %cval)

else:

ax.plot(contour[:, 1], contour[:, 0], 'r-', linewidth=2)

ax.set_ylim([0,len(depths)])

ax.set_xlim([len(lengths),0])

ax.set_title('Stacked slab image from netCDF')

plt.xlabel('Cross section x increment')

plt.ylabel('Cross section depth increment')

plt.legend(loc='best')

#plt.gca().invert_yaxis()

#plt.gca().invert_xaxis()