Models can be loaded from file, or constructed from scratch (a grid file is still required).
Load from file¶
from geotess import Model
model = Model.read('geotess/data/crust20.geotess')
print(model)
********************************************************************************
GeoTessModel:
OS: Undefined 64-bit mode
Input Model File: geotess/data/crust20.geotess
Input Grid File: *
Generated by: GeoModel 7.0.1 Wed April 18 15:21:51 2012
Model Load Time: 0.000 sec
Memory footprint: 18.5971 MB
Model Description:
The Crust 2.0 model of Bassin, Laske and Masters (2000).
Bassin, C., Laske, G. and Masters, G.,
The Current Limits of Resolution for Surface Wave
Tomography in North America, EOS Trans AGU, 81, F897, 2000.
http://igppweb.ucsd.edu/~gabi/crust2.html
The original model was defined on a regular latitude-longitude
grid with 2 degree by 2 degree spacing. It was cell-based,
meaning that the model attributes (p velocity, s velocity and
density) were attached to the cells, not the nodes at the
corners of the cells.
This representation uses a triangular tessellation with
1 degree edge lengths and it is node based, meaning that
the attributes are attached to the nodes at the corners
of the triangles, not the triangles. The translation
was accomplished by first defining a node at the center
of each 2 degree cell in the original model and then
interpolating attribute values at the nodes of the
new model using simple bi-linear interpolation. There
was certainly some interpolation error involved in this
process.
In this representation, the water layer has been omitted.
The top of the new model is the surface of the solid
Earth. While the thickness of the water layer in the
oceans can be deduced in the new model by assuming that
the surface of the oceans corresponds to depth zero, the
thickness of the water layer in large inland lakes and
seas has been lost.
In the new representation, layer boundaries are defined in
terms of radius, not depth. Depths in the original model
were converted to radius using the radius of the WGS84
ellipsoid.
Sandy Ballard
Sandia National Laboratories
sballar@sandia.gov
4/14/2012
<end description>
EarthShape: WGS84
DataType: FLOAT
Attributes:
0: vp (km/sec)
1: vs (km/sec)
2: density (g/cc)
Layers:
Index TessId Name
6 0 ice
5 0 soft_sediments
4 0 hard_sediments
3 0 upper_crust
2 0 middle_crust
1 0 lower_crust
0 0 mantle
Layer connected number profile profile profile profile profile profile
Index vertices of points npoints constant thin empty surface surfemtpy
----- -------- --------- ------- -------- ------ ------- ------- -------
6 40962 40962 0 1542 39420 0 0 0
5 40962 40962 0 39267 1695 0 0 0
4 40962 40962 0 13561 27401 0 0 0
3 40962 40962 0 40962 0 0 0 0
2 40962 40962 0 40962 0 0 0 0
1 40962 40962 0 40962 0 0 0 0
0 40962 40962 0 0 40962 0 0 0
----- -------- --------- ------- -------- ------ ------- ------- -------
Total 40962 286734 0 177256 109478 0 0 0
GeoTessGrid
gridID = 808785948EB2350DD44E6C29BDEA6CAE
memory : 34.2188 MB
input Grid File : geotess/data/crust10.geotess
generated by software version : GeoModel 7.0.1 Wed April 18 15:21:51 2012
nTessellations = 1
nLevels = 7
nVertices = 40962
nTriangles = 109220
Tess Level LevelID NTri First Last+1
0 0 0 20 0 20
0 1 1 80 20 100
0 2 2 320 100 420
0 3 3 1280 420 1700
0 4 4 5120 1700 6820
0 5 5 20480 6820 27300
0 6 6 81920 27300 109220
********************************************************************************
Each Model has a .grid, which provides access to that Model's Grid instance.
Just like with Grids, PyGeoTess is distribed with the same GeoTess model files, like Crust2.0, which can be directly imported.
from geotess.data import crust20
print(crust20)
********************************************************************************
GeoTessModel:
OS: Undefined 64-bit mode
Input Model File: geotess/data/crust20.geotess
Input Grid File: *
Generated by: GeoModel 7.0.1 Wed April 18 15:21:51 2012
Model Load Time: 1.000 sec
Memory footprint: 18.5971 MB
Model Description:
The Crust 2.0 model of Bassin, Laske and Masters (2000).
Bassin, C., Laske, G. and Masters, G.,
The Current Limits of Resolution for Surface Wave
Tomography in North America, EOS Trans AGU, 81, F897, 2000.
http://igppweb.ucsd.edu/~gabi/crust2.html
The original model was defined on a regular latitude-longitude
grid with 2 degree by 2 degree spacing. It was cell-based,
meaning that the model attributes (p velocity, s velocity and
density) were attached to the cells, not the nodes at the
corners of the cells.
This representation uses a triangular tessellation with
1 degree edge lengths and it is node based, meaning that
the attributes are attached to the nodes at the corners
of the triangles, not the triangles. The translation
was accomplished by first defining a node at the center
of each 2 degree cell in the original model and then
interpolating attribute values at the nodes of the
new model using simple bi-linear interpolation. There
was certainly some interpolation error involved in this
process.
In this representation, the water layer has been omitted.
The top of the new model is the surface of the solid
Earth. While the thickness of the water layer in the
oceans can be deduced in the new model by assuming that
the surface of the oceans corresponds to depth zero, the
thickness of the water layer in large inland lakes and
seas has been lost.
In the new representation, layer boundaries are defined in
terms of radius, not depth. Depths in the original model
were converted to radius using the radius of the WGS84
ellipsoid.
Sandy Ballard
Sandia National Laboratories
sballar@sandia.gov
4/14/2012
<end description>
EarthShape: WGS84
DataType: FLOAT
Attributes:
0: vp (km/sec)
1: vs (km/sec)
2: density (g/cc)
Layers:
Index TessId Name
6 0 ice
5 0 soft_sediments
4 0 hard_sediments
3 0 upper_crust
2 0 middle_crust
1 0 lower_crust
0 0 mantle
Layer connected number profile profile profile profile profile profile
Index vertices of points npoints constant thin empty surface surfemtpy
----- -------- --------- ------- -------- ------ ------- ------- -------
6 40962 40962 0 1542 39420 0 0 0
5 40962 40962 0 39267 1695 0 0 0
4 40962 40962 0 13561 27401 0 0 0
3 40962 40962 0 40962 0 0 0 0
2 40962 40962 0 40962 0 0 0 0
1 40962 40962 0 40962 0 0 0 0
0 40962 40962 0 0 40962 0 0 0
----- -------- --------- ------- -------- ------ ------- ------- -------
Total 40962 286734 0 177256 109478 0 0 0
GeoTessGrid
gridID = 808785948EB2350DD44E6C29BDEA6CAE
memory : 34.2188 MB
input Grid File : geotess/data/crust10.geotess
generated by software version : GeoModel 7.0.1 Wed April 18 15:21:51 2012
nTessellations = 1
nLevels = 7
nVertices = 40962
nTriangles = 109220
Tess Level LevelID NTri First Last+1
0 0 0 20 0 20
0 1 1 80 20 100
0 2 2 320 100 420
0 3 3 1280 420 1700
0 4 4 5120 1700 6820
0 5 5 20480 6820 27300
0 6 6 81920 27300 109220
********************************************************************************
Create a Model from scratch¶
Creating a GeoTessModel from scratch requires a grid file, some layers, and some attributes. PyGeoTess describes layers with 2-tuples. The first entry is the layer name, the second is the correspoding tessellation id. Similarly, with names and units, for attributes. Convenience namedtuple are provided, for readability.
# look at the grid, so you can assign layers to tessellation ids.
from geotess.data import grid_16000
from geotess import Model, Layer, Attribute
print(grid_16000)
GeoTessGrid
gridID = 4FD3D72E55EFA8E13CA096B4C8795F03
memory : 0.11776 MB
input Grid File : geotess/data/geotess_grid_16000.geotess
generated by software version : GridBuilder 0.0.0 Fri May 25 11:34:59 MDT 2012
nTessellations = 1
nLevels = 3
nVertices = 162
nTriangles = 420
Tess Level LevelID NTri First Last+1
0 0 0 20 0 20
0 1 1 80 20 100
0 2 2 320 100 420
crust = Layer(name='crust', tess_id=0)
mantle = Layer('mantle', 0)
Vp = Attribute(name='Vp', unit='km/s')
Vs = Attribute('Vs', 'km/s')
desc = "My awesome model."
model = Model('geotess/data/geotess_grid_16000.geotess', layers=[crust, mantle], attributes=[Vp, Vs],
dtype='float', earth_shape='wgs84', rconst=False, description=desc)
print(model)
********************************************************************************
GeoTessModel:
OS: Undefined 64-bit mode
Input Model File: none
Input Grid File: geotess/data/geotess_grid_16000.geotess
Generated by: PyGeoTess v0.1.1 2015-11-18 15:35:20.303894
Model Load Time: -1.000 sec
Memory footprint: 0.000646591 MB
Model Description:
My awesome model.
<end description>
EarthShape: WGS84
DataType: FLOAT
Attributes:
0: Vp (km/s)
1: Vs (km/s)
Layers:
Index TessId Name
1 0 MANTLE
0 0 CRUST
GeoTessGrid
gridID = 4FD3D72E55EFA8E13CA096B4C8795F03
memory : 0.11776 MB
input Grid File : geotess/data/geotess_grid_16000.geotess
generated by software version : GridBuilder 0.0.0 Fri May 25 11:34:59 MDT 2012
nTessellations = 1
nLevels = 3
nVertices = 162
nTriangles = 420
Tess Level LevelID NTri First Last+1
0 0 0 20 0 20
0 1 1 80 20 100
0 2 2 320 100 420
********************************************************************************
Now, let's save it.
# binary version
model.write('myawesome.geotess')
# ascii version
model.write('myawesome.ascii')
Roadmap¶
Make PyGeoTess work with both the standard CPython interpretor, using the GeoTess c++ library and Cython, and the Jython Python interpeter, using the GeoTess Java library.