initial upload

cookbook/custom_ratio/custom_ratio.bat

@@ -0,0 +1,10 @@
+:: Calculate effect of the model at a low height using difference distance-size
+:: ratios for the recursive division of tesseroids.
+:: WARNING: This is only an example. You should not use the -t option in
+::          practice
+
+tessgrd -r-3/3/-3/3 -b50/50 -z4e03 | ^
+tessgzz model.txt -t0.0001 -lratio1.log | ^
+tessgzz model.txt -t0.5 -lratio2.log | ^
+tessgzz model.txt -t1 -lratio3.log | ^
+tessgzz model.txt -v -lratio-default.log > output.txt

cookbook/custom_ratio/custom_ratio.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Calculate effect of the model at a low height using difference distance-size
+# ratios for the recursive division of tesseroids.
+# WARNING: This is only an example. You should not use the -t option in practice
+
+tessgrd -r-3/3/-3/3 -b50/50 -z4e03 | \
+tessgzz model.txt -t0.0001 -lratio1.log | \
+tessgzz model.txt -t0.5 -lratio2.log | \
+tessgzz model.txt -t1 -lratio3.log | \
+tessgzz model.txt -v -lratio-default.log > output.txt

cookbook/custom_ratio/model.txt

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+# Test tesseroid model file
+-1.5 1.5 -1.5 1.5 0 -5000 200

cookbook/custom_ratio/plot.py

@@ -0,0 +1,18 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+from matplotlib import pyplot as plt
+import numpy as np
+
+data = np.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = np.reshape(data[0], shape)
+lat = np.reshape(data[1], shape)
+for i, value in enumerate(data[3:]):
+    value = np.reshape(value, shape)
+    plt.figure(figsize=(4, 3))
+    plt.title("Column %d" % (i + 4))
+    plt.contourf(lon, lat, value, 50)
+    plt.colorbar()
+    plt.savefig('column%d.png' % (i + 4))

cookbook/dem_brasil/dem_brasil.bat

@@ -0,0 +1,22 @@
+
+
+:: First, insert the density information into
+:: the DEM file using the Python script.
+python dem_density.py dem.xyz > dem-dens.txt
+
+:: Next, use the modified DEM with tessmodgen
+:: to create a tesseroid model
+tessmodgen -s0.166667/0.166667 -z0 -v < dem-dens.txt ^
+> dem-tess.txt
+
+:: Calculate the GGT on a regular grid at 250km
+:: use the -l option to log the processes to files
+:: (usefull to diagnose when things go wrong)
+:: The output is dumped to dem-ggt.txt
+tessgrd -r-60/-45/-30/-15 -b50/50 -z250e03 | ^
+tessgxx dem-tess.txt -lgxx.log | ^
+tessgxy dem-tess.txt -lgxy.log | ^
+tessgxz dem-tess.txt -lgxz.log | ^
+tessgyy dem-tess.txt -lgyy.log | ^
+tessgyz dem-tess.txt -lgyz.log | ^
+tessgzz dem-tess.txt -lgzz.log -v > dem-ggt.txt

cookbook/dem_brasil/dem_brasil.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+
+# First, insert the density information into
+# the DEM file using the Python script.
+python dem_density.py dem.xyz > dem-dens.txt
+
+# Next, use the modified DEM with tessmodgen
+# to create a tesseroid model
+tessmodgen -s0.166667/0.166667 -z0 -v < dem-dens.txt \
+> dem-tess.txt
+
+# Calculate the GGT on a regular grid at 250km
+# use the -l option to log the processes to files
+# (usefull to diagnose when things go wrong)
+# The output is dumped to dem-ggt.txt
+tessgrd -r-60/-45/-30/-15 -b50/50 -z250e03 | \
+tessgxx dem-tess.txt -lgxx.log | \
+tessgxy dem-tess.txt -lgxy.log | \
+tessgxz dem-tess.txt -lgxz.log | \
+tessgyy dem-tess.txt -lgyy.log | \
+tessgyz dem-tess.txt -lgyz.log | \
+tessgzz dem-tess.txt -lgzz.log -v > dem-ggt.txt

cookbook/dem_brasil/dem_density.py

@@ -0,0 +1,13 @@
+"""
+Assign density values for the DEM points.
+"""
+import sys
+import numpy
+
+lons, lats, heights = numpy.loadtxt(sys.argv[1], unpack=True)
+
+for i in xrange(len(heights)):
+    if heights[i] >=0:
+        print "%lf %lf %lf %lf" % (lons[i], lats[i], heights[i], 2670.0)
+    else:
+        print "%lf %lf %lf %lf" % (lons[i], lats[i], heights[i], 1670.0)

cookbook/dem_brasil/plot.py

@@ -0,0 +1,117 @@
+# Make some nice plots of the DEM, the densities used and the calculated GGT
+import numpy
+from matplotlib import pyplot as plt
+from mpl_toolkits.basemap import Basemap
+
+# Plot the DEM and density maps
+################################################################################
+lons, lats, heights, dens = numpy.loadtxt('dem-dens.txt', unpack=True)
+nlons = 151  # Number of points in the longitude direction
+nlats = len(lats)/nlons
+
+# Convert the lists to 2D grids
+glons = numpy.reshape(lons, (nlats, nlons))
+glats = numpy.reshape(lats, (nlats, nlons))
+gheights = numpy.reshape(heights, (nlats, nlons))
+gdens = numpy.reshape(dens, (nlats, nlons))
+
+# Set up a Mercator projection
+bm = Basemap(projection='merc',
+             llcrnrlon=lons[0], llcrnrlat=lats[-1],
+             urcrnrlon=lons[-1], urcrnrlat=lats[0],
+             lon_0=lons[nlons//2], lat_0=lats[len(lats)//2],
+             resolution='l',
+             area_thresh=10000)
+glons, glats = bm(glons, glats)
+
+# Plot the DEM first
+print "Plotting DEM"
+plt.figure()
+bm.drawmeridians(numpy.arange(lons[0]+5., lons[-1], 5.),
+                 labels=[0,0,0,1], fontsize=12, linewidth=0.5)
+bm.drawparallels(numpy.arange(lats[-1]+5., lats[0], 5.),
+                 labels=[1,0,0,0], fontsize=12, linewidth=0.5)
+bm.drawcoastlines(linewidth=1)
+bm.drawmapboundary()
+bm.drawcountries(linewidth=0.8)
+# Do the pseudocolor plot
+cf = bm.pcolor(glons, glats, gheights, cmap=plt.cm.gist_earth, \
+               vmin=-1000, vmax=1000)
+cb = plt.colorbar()
+cb.set_label("Height [m]")
+# Plot the calculation area used later
+w = -60
+e = -45
+s = -30
+n = -15
+areax, areay = bm([w, w, e, e, w], \
+                  [n, s, s, n, n])
+bm.plot(areax, areay, '-r', label="Computation grid", linewidth=1.8)
+plt.legend(shadow=True, loc='lower right', prop={'size':10})
+# Save a png figure
+plt.savefig('dem.png')
+
+# Now plot the densities
+print "Plotting density model"
+plt.figure()
+bm.drawmeridians(numpy.arange(lons[0]+5., lons[-1], 5.),
+                 labels=[0,0,0,1], fontsize=12, linewidth=0.5)
+bm.drawparallels(numpy.arange(lats[-1]+5., lats[0], 5.),
+                 labels=[1,0,0,0], fontsize=12, linewidth=0.5)
+bm.drawcoastlines(linewidth=1)
+bm.drawmapboundary()
+bm.drawcountries(linewidth=0.8)
+# Do the pseudocolor plot
+cf = bm.pcolor(glons, glats, gdens, cmap=plt.cm.jet)
+cb = plt.colorbar()
+cb.set_label(r"Density [$g.cm^{-3}$]")
+# Save a png figure
+plt.savefig('dem-dens.png')
+
+# Plot the GGT
+################################################################################
+print "Plotting GGT"
+data = numpy.loadtxt('dem-ggt.txt')
+lons, lats, heights, gxx, gxy, gxz, gyy, gyz, gzz = data.T
+nlons = 50  # Number of points in the longitude direction
+nlats = len(lats)/nlons
+
+# Convert the lists to 2D grids
+glons = numpy.reshape(lons, (nlats, nlons))
+glats = numpy.reshape(lats, (nlats, nlons))
+
+# Set up a Mercator projection
+bm = Basemap(projection='merc', \
+             llcrnrlon=lons[0], llcrnrlat=lats[0], \
+             urcrnrlon=lons[-1], urcrnrlat=lats[-1], \
+             lon_0=lons[nlons//2], lat_0=lats[len(lats)//2],
+             resolution='l', area_thresh=10000)
+glons, glats = bm(glons, glats)
+
+# Plot each component
+fig = plt.figure(figsize=(14,9))
+plt.subplots_adjust(wspace=0.35)
+titles = [r"$g_{xx}$", r"$g_{xy}$", r"$g_{xz}$", r"$g_{yy}$", r"$g_{yz}$",
+          r"$g_{zz}$"]
+fields = [gxx, gxy, gxz, gyy, gyz, gzz]
+for i, args in enumerate(zip(fields, titles)):
+    field, title = args
+    ax = plt.subplot(2, 3, i + 1, aspect='equal')
+    plt.title(title, fontsize=18)
+    # Make it a 2D grid
+    gfield = numpy.reshape(field, (nlats, nlons))
+    # Plot the coastlines and etc
+    mer = bm.drawmeridians(numpy.arange(lons[0]+3, lons[-1]-3, 3),
+                           labels=[0,0,0,1], fontsize=9, linewidth=0.5)
+    bm.drawparallels(numpy.arange(lats[0]+3, lats[-1]-3, 3),
+                     labels=[1,0,0,0], fontsize=9, linewidth=0.5)
+    bm.drawcoastlines(linewidth=1)
+    bm.drawmapboundary()
+    bm.drawcountries(linewidth=1)
+    bm.drawstates(linewidth=0.2)
+    # Make a pseudocolor plot
+    cf = bm.pcolor(glons, glats, gfield, cmap=plt.cm.jet)
+    cb = plt.colorbar(orientation='vertical', format='%.2f', shrink=0.8)
+    cb.set_label(r"$E\"otv\"os$")
+# Save a png figure
+plt.savefig('dem-ggt.png')

cookbook/simple_prism/model.txt

@@ -0,0 +1,3 @@
+# Test prism model file
+2000 5000 2000 15000 0 5000 1000
+10000 18000 10000 18000 0 5000 -1000

cookbook/simple_prism/plot.py

@@ -0,0 +1,25 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+import pylab
+
+data = pylab.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = pylab.reshape(data[0], shape)
+lat = pylab.reshape(data[1], shape)
+xmin, xmax = lon.min(), lon.max()
+ymin, ymax = lat.min(), lat.max()
+for i, value in enumerate(data[3:]):
+    value = pylab.reshape(value, shape)
+    pylab.figure(figsize=(4, 3))
+    pylab.title("Column %d" % (i + 4))
+    pylab.axis('scaled')
+    pylab.pcolor(lon, lat, value)
+    pylab.colorbar()
+    pylab.contour(lon, lat, value, 12, color='k')
+    #pylab.xlabel("Longitude")
+    #pylab.ylabel("Latitude")
+    pylab.xlim(xmin, xmax)
+    pylab.ylim(ymin, ymax)
+    pylab.savefig('column%d.png' % (i + 4))

cookbook/simple_prism/simple_prism.bat

@@ -0,0 +1,11 @@
+
+
+:: Generate a regular grid, pipe it to all the computation programs,
+:: and write the result to output.txt
+
+tessgrd -r0/20000/0/20000 -b50/50 -z1000 | ^
+prismpot model.txt | ^
+prismgx model.txt | prismgy model.txt | prismgz model.txt | ^
+prismgxx model.txt  | prismgxy model.txt  | ^
+prismgxz model.txt  | prismgyy model.txt  | ^
+prismgyz model.txt  | prismgzz model.txt > output.txt

cookbook/simple_prism/simple_prism.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Generate a regular grid, pipe it to all the computation programs,
+# and write the result to output.txt
+
+tessgrd -r0/20000/0/20000 -b50/50 -z1000 | \
+prismpot model.txt | \
+prismgx model.txt | prismgy model.txt | prismgz model.txt | \
+prismgxx model.txt  | prismgxy model.txt  | \
+prismgxz model.txt  | prismgyy model.txt  | \
+prismgyz model.txt  | prismgzz model.txt > output.txt

cookbook/simple_tess/model.txt

@@ -0,0 +1,3 @@
+# Test tesseroid model file
+10 20 10 20 0 -50000 200
+-20 -10 -20 -10 0 -30000 -500

cookbook/simple_tess/plot.py

@@ -0,0 +1,29 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+from matplotlib import pyplot as plt
+from mpl_toolkits.basemap import Basemap
+import numpy as np
+
+# Set up a projection
+bm = Basemap(projection='ortho', lon_0=0, lat_0=0,
+             resolution='l', area_thresh=10000)
+
+# Load the data and make them into matrices
+data = np.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = data[0].reshape(shape)
+lat = data[1].reshape(shape)
+glon, glat = bm(lon, lat)
+
+plt.figure(figsize=(14, 12))
+for i, value in enumerate(data[3:]):
+    plt.subplot(3, 4, i + 1)
+    plt.title("Column %d" % (i + 4))
+    bm.drawcoastlines()
+    bm.drawmapboundary()
+    bm.contourf(glon, glat, value.reshape(shape), 15, cmap=plt.cm.RdBu_r)
+    plt.colorbar(orientation="horizontal", pad=0, aspect=30)
+plt.tight_layout()
+plt.savefig('output.png')

cookbook/simple_tess/simple_tess.bat

@@ -0,0 +1,12 @@
+:: Generate a regular grid, pipe it to all the computation programs,
+:: and write the result to output.txt
+
+tessgrd -r-45/45/-45/45 -b101/101 -z260e03 | ^
+tesspot model.txt | ^
+tessgx model.txt | tessgy model.txt | tessgz model.txt | ^
+tessgxx model.txt  | tessgxy model.txt  | ^
+tessgxz model.txt  | tessgyy model.txt  | ^
+tessgyz model.txt  | tessgzz model.txt -v -llog.txt > output.txt
+
+:: Make a plot with the columns of output.txt
+python plot.py output.txt 101 101

cookbook/simple_tess/simple_tess.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Generate a regular grid, pipe it to all the computation programs,
+# and write the result to output.txt
+
+tessgrd -r-45/45/-45/45 -b101/101 -z260e03 | \
+tesspot model.txt | \
+tessgx model.txt | tessgy model.txt | tessgz model.txt | \
+tessgxx model.txt  | tessgxy model.txt  | \
+tessgxz model.txt  | tessgyy model.txt  | \
+tessgyz model.txt  | tessgzz model.txt -v -llog.txt > output.txt
+
+# Make a plot with the columns of output.txt
+python plot.py output.txt 101 101

cookbook/tess2prism/plot.py

@@ -0,0 +1,32 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+import pylab
+from mpl_toolkits.basemap import Basemap
+
+# Set up a projection
+bm = Basemap(projection='ortho', lon_0=-80, lat_0=-40,
+             resolution='l', area_thresh=10000)
+
+data = pylab.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = pylab.reshape(data[0], shape)
+lat = pylab.reshape(data[1], shape)
+glon, glat = bm(lon, lat)
+
+for i, value in enumerate(data[3:]):
+    value = pylab.reshape(value, shape)
+    pylab.figure(figsize=(4, 3))
+    pylab.title("Column %d" % (i + 4))
+    bm.drawcoastlines()
+    #bm.fillcontinents(color='coral',lake_color='aqua')
+    #bm.drawmapboundary(fill_color='aqua')
+    bm.drawmapboundary()
+    bm.drawparallels(pylab.arange(-90.,120.,30.))
+    bm.drawmeridians(pylab.arange(0.,420.,60.))
+    #bm.bluemarble()
+    bm.pcolor(glon, glat, value)
+    pylab.colorbar()
+    #bm.contour(glon, glat, value, 12, linewidth=3)
+    pylab.savefig('column%d.png' % (i + 4))

cookbook/tess2prism/result.svg

@@ -0,0 +1,134 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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+
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cookbook/tess2prism/sample-prism-model.txt

@@ -0,0 +1,11 @@
+# Prisms converted from tesseroid model with tess2prism 1.1dev
+#   local time: Wed May 16 14:34:47 2012
+#   tesseroids file: stdin
+#   conversion type: equal mass|spherical coordinates
+#   format: dx dy dz density lon lat r
+# Test tesseroid model file
+221766.31696055 169882.854778591 50000 499.977196258595 -76 -40 6378137
+221766.31696055 169882.854778591 50000 499.977196258595 -78 -40 6378137
+221766.31696055 169882.854778591 50000 499.977196258595 -80 -40 6378137
+221766.31696055 169882.854778591 50000 499.977196258595 -82 -40 6378137
+221766.31696055 169882.854778591 50000 499.977196258595 -84 -40 6378137

cookbook/tess2prism/tess-model.txt

@@ -0,0 +1,6 @@
+# Test tesseroid model file
+-77 -75 -41 -39 0 -50000 500
+-79 -77 -41 -39 0 -50000 500
+-81 -79 -41 -39 0 -50000 500
+-83 -81 -41 -39 0 -50000 500
+-85 -83 -41 -39 0 -50000 500

cookbook/tess2prism/tess2prism.bat

@@ -0,0 +1,21 @@
+
+
+:: Generate a prism model from a tesseroid model.
+:: Prisms will have the same mass as the tesseroids and
+:: associated spherical coordinates of the center of
+:: the top of the tesseroid.
+
+tess2prism.exe < tess-model.txt > prism-model.txt
+
+:: Generate a regular grid in spherical coordinates,
+:: pipe the grid to the computation programs,
+:: and dump the result on output.txt
+:: prismpots calculates the potential in spherical
+:: coordinates, prismgs calculates the full
+:: gravity vector, and prismggts calculates the full
+:: gravity gradient tensor.
+
+tessgrd -r-160/0/-80/0 -b100/100 -z250e03 | ^
+prismpots prism-model.txt | ^
+prismgs prism-model.txt | ^
+prismggts prism-model.txt -v > output.txt

cookbook/tess2prism/tess2prism.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Generate a prism model from a tesseroid model.
+# Prisms will have the same mass as the tesseroids and
+# associated spherical coordinates of the center of
+# the top of the tesseroid.
+
+tess2prism < tess-model.txt > prism-model.txt
+
+# Generate a regular grid in spherical coordinates,
+# pipe the grid to the computation programs,
+# and dump the result on output.txt
+# prismpots calculates the potential in spherical
+# coordinates, prismgs calculates the full
+# gravity vector, and prismggts calculates the full
+# gravity gradient tensor.
+
+tessgrd -r-160/0/-80/0 -b100/100 -z250e03 | \
+prismpots prism-model.txt | \
+prismgs prism-model.txt | \
+prismggts prism-model.txt -v > output.txt

cookbook/tess2prism_flatten/plot.py

@@ -0,0 +1,25 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+import pylab
+
+data = pylab.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = pylab.reshape(data[0], shape)*0.001
+lat = pylab.reshape(data[1], shape)*0.001
+xmin, xmax = lon.min(), lon.max()
+ymin, ymax = lat.min(), lat.max()
+for i, value in enumerate(data[3:]):
+    value = pylab.reshape(value, shape)
+    pylab.figure(figsize=(4, 3))
+    pylab.title("Column %d" % (i + 4))
+    pylab.axis('scaled')
+    pylab.pcolor(lon, lat, value)
+    pylab.colorbar()
+    pylab.contour(lon, lat, value, 12, color='k')
+    #pylab.xlabel("Longitude")
+    #pylab.ylabel("Latitude")
+    pylab.xlim(xmin, xmax)
+    pylab.ylim(ymin, ymax)
+    pylab.savefig('column%d.png' % (i + 4))

cookbook/tess2prism_flatten/sample-prism-model.txt

@@ -0,0 +1,9 @@
+# Prisms converted from tesseroid model with tess2prism 1.1dev
+#   local time: Tue May  8 14:55:02 2012
+#   tesseroids file: stdin
+#   conversion type: flatten
+#   format: x1 x2 y1 y2 z1 z2 density
+# Test tesseroid model file
+1111100 1666650 1111100 1666650 0 30000 487.534658568521
+-1111100 1111100 -1666650 -1111100 0 50000 198.175508383774
+-1777760 -1111100 -1666650 555550 0 30000 -291.9029748328

cookbook/tess2prism_flatten/tess-model.txt

@@ -0,0 +1,4 @@
+# Test tesseroid model file
+10 15 10 15 0 -30000 500
+-15 -10 -10 10 0 -50000 200
+-15 5 -16 -10 0 -30000 -300

cookbook/tess2prism_flatten/tess2prism_flatten.bat

@@ -0,0 +1,21 @@
+
+
+:: Generate a prism model from a tesseroid model by
+:: flattening the tesseroids (1 degree = 111.11 km).
+:: This way the converted prisms can be used
+:: with the prism* programs in Cartesian coordinates.
+
+tess2prism --flatten < tess-model.txt > prism-model.txt
+
+:: Generate a regular grid in Cartesian coordinates,
+:: pipe the grid to the computation programs,
+:: and dump the result on output.txt
+
+tessgrd -r-3e06/3e06/-3e06/3e06 -b50/50 -z250e03 | ^
+prismpot prism-model.txt | ^
+prismgx prism-model.txt | ^
+prismgy prism-model.txt | ^
+prismgz prism-model.txt | ^
+prismgxx prism-model.txt  | prismgxy prism-model.txt  | ^
+prismgxz prism-model.txt  | prismgyy prism-model.txt  | ^
+prismgyz prism-model.txt  | prismgzz prism-model.txt > output.txt

cookbook/tess2prism_flatten/tess2prism_flatten.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Generate a prism model from a tesseroid model by
+# flattening the tesseroids (1 degree = 111.11 km).
+# This way the converted prisms can be used
+# with the prism* programs in Cartesian coordinates.
+
+tess2prism --flatten < tess-model.txt > prism-model.txt
+
+# Generate a regular grid in Cartesian coordinates,
+# pipe the grid to the computation programs,
+# and dump the result on output.txt
+
+tessgrd -r-3e06/3e06/-3e06/3e06 -b50/50 -z250e03 | \
+prismpot prism-model.txt | \
+prismgx prism-model.txt | \
+prismgy prism-model.txt | \
+prismgz prism-model.txt | \
+prismgxx prism-model.txt  | prismgxy prism-model.txt  | \
+prismgxz prism-model.txt  | prismgyy prism-model.txt  | \
+prismgyz prism-model.txt  | prismgzz prism-model.txt > output.txt

cookbook/tesslayers/makelayers.py

@@ -0,0 +1,34 @@
+import numpy as np
+import fatiando as ft
+
+shape = (41, 41)
+x, y = ft.grd.regular((-10, 10, 30, 50), shape)
+height = 800 - 1000*ft.utils.gaussian2d(x, y, 3, 1, x0=0, y0=37)
+rel = -7000*ft.utils.gaussian2d(x, y, 3, 5, x0=0, y0=40)
+thick = height - rel
+dens = 1900*np.ones_like(thick)
+data = np.transpose([x, y, height, thick, dens])
+with open('layers.txt', 'w') as f:
+    f.write("# Synthetic layer model of sediments and topography\n")
+    f.write("# Columns are:\n")
+    f.write("#   lon   lat   height   thickness   density\n")
+    np.savetxt(f, data, fmt='%g')
+ft.vis.figure(figsize=(4, 3))
+ft.vis.title('Depth of sediments [m]')
+ft.vis.axis('scaled')
+ft.vis.pcolor(x, y, rel, shape)
+ft.vis.colorbar()
+ft.vis.savefig('depth.png')
+ft.vis.figure(figsize=(4, 3))
+ft.vis.title('Topography [m]')
+ft.vis.axis('scaled')
+ft.vis.pcolor(x, y, height, shape)
+ft.vis.colorbar()
+ft.vis.savefig('topography.png')
+ft.vis.figure(figsize=(4, 3))
+ft.vis.title('Thickness of sediment layer [m]')
+ft.vis.axis('scaled')
+ft.vis.pcolor(x, y, thick, shape)
+ft.vis.colorbar()
+ft.vis.savefig('thickness.png')
+ft.vis.show()

cookbook/tesslayers/plot.py

@@ -0,0 +1,20 @@
+"""
+Plot the columns of the output files
+"""
+import sys
+import pylab
+
+data = pylab.loadtxt(sys.argv[1], unpack=True)
+shape = (int(sys.argv[2]), int(sys.argv[3]))
+lon = pylab.reshape(data[0], shape)
+lat = pylab.reshape(data[1], shape)
+for i, value in enumerate(data[3:]):
+    value = pylab.reshape(value, shape)
+    pylab.figure(figsize=(4, 3))
+    pylab.axis('scaled')
+    pylab.title("Column %d" % (i + 4))
+    pylab.pcolor(lon, lat, value)
+    pylab.colorbar()
+    pylab.xlim(lon.min(), lon.max())
+    pylab.ylim(lat.min(), lat.max())
+    pylab.savefig('column%d.png' % (i + 4))

cookbook/tesslayers/tesslayers.bat

@@ -0,0 +1,13 @@
+
+
+:: Convert the layer grids in layers.txt to tesseroids.
+:: The grid spacing passed to -s is used as the size of the tesseroids,
+:: so be careful!
+tesslayers.exe -s0.5/0.5 -v < layers.txt > tessmodel.txt
+
+:: Now calculate the gz and tensor effect of this model at 100km height
+tessgrd -r-8/8/32/48 -b50/50 -z100000 | ^
+tessgz tessmodel.txt | ^
+tessgxx tessmodel.txt | tessgxy tessmodel.txt | ^
+tessgxz tessmodel.txt | tessgyy tessmodel.txt | ^
+tessgyz tessmodel.txt | tessgzz tessmodel.txt -v > output.txt

cookbook/tesslayers/tesslayers.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+
+# Convert the layer grids in layers.txt to tesseroids.
+# The grid spacing passed to -s is used as the size of the tesseroids,
+# so be careful!
+tesslayers -s0.5/0.5 -v < layers.txt > tessmodel.txt
+
+# Now calculate the gz and tensor effect of this model at 100km height
+tessgrd -r-8/8/32/48 -b50/50 -z100000 | \
+tessgz tessmodel.txt | \
+tessgxx tessmodel.txt | tessgxy tessmodel.txt | \
+tessgxz tessmodel.txt | tessgyy tessmodel.txt | \
+tessgyz tessmodel.txt | tessgzz tessmodel.txt -v > output.txt