data_types

Author: boriskaus

docs/src/man/Tutorial_AlpineData.md

@@ -126,10 +126,10 @@ units = unique(tag) #get different units
 We will use these units later to save the Moho data separately for each tectonic unit.
 
 ### 2.2 Converting the data to a `GMG` dataset
-To convert this data to a `GMG` dataset, we now have to interpolate it to a regular grid. You can generate the respective grid with the `GMG` function `lonlatdepthGrid`
+To convert this data to a `GMG` dataset, we now have to interpolate it to a regular grid. You can generate the respective grid with the `GMG` function `lonlatdepth_grid`
 
 ```julia
-Lon,Lat,Depth = lonlatdepthGrid(9.9:0.02:15.1,45.0:.02:49.0,0km);
+Lon,Lat,Depth = lonlatdepth_grid(9.9:0.02:15.1,45.0:.02:49.0,0km);
 nothing #hide
 ```
 

docs/src/man/Tutorial_Basic.md

@@ -156,7 +156,7 @@ search: cross_section cross_section_volume cross_section_points cross_section_su
   Example:
   ≡≡≡≡≡≡≡≡
 
-  julia> Lon,Lat,Depth   =   lonlatdepthGrid(10:20,30:40,(-300:25:0)km);
+  julia> Lon,Lat,Depth   =   lonlatdepth_grid(10:20,30:40,(-300:25:0)km);
   julia> Data            =   Depth*2;                # some data
   julia> Vx,Vy,Vz        =   ustrip(Data*3),ustrip(Data*4),ustrip(Data*5);
   julia> Data_set3D      =   GeoData(Lon,Lat,Depth,(Depthdata=Data,LonData=Lon, Velocity=(Vx,Vy,Vz)));
@@ -204,10 +204,10 @@ In creating this image, I used the `Clip` tool of Paraview to only show topograp
 ### 4. Cartesian data
 As you can see, the curvature or the Earth is taken into account here. Yet, for many applications it is more convenient to work in Cartesian coordinates (kilometers) rather then in geographic coordinates.
 `GeophysicalModelGenerator` has a number of tools for this.
-First we need do define a `projectionPoint`  around which we project the data
+First we need do define a `ProjectionPoint`  around which we project the data
 
 ```julia
-proj = projectionPoint(Lon=12.0,Lat =43)
+proj = ProjectionPoint(Lon=12.0,Lat =43)
 
 Topo_cart = convert2CartData(Topo_Alps, proj)
 ```
@@ -259,7 +259,7 @@ Yet, because of the curvature of the Earth, the resulting 3D model is not strict
 This can be achieved in a relatively straightforward manner, by creating a new 3D dataset that is slightly within the curved boundaries of the projected data set:
 
 ```julia
-Tomo_rect = CartData(xyzGrid(-550.0:10:600, -500.0:10:700, -600.0:5:-17));
+Tomo_rect = CartData(xyz_grid(-550.0:10:600, -500.0:10:700, -600.0:5:-17));
 ```
 
 the routine `project_CartData` will then project the data from the geographic coordinates to the new rectilinear grid:
@@ -281,7 +281,7 @@ CartData
 we can do the same with topography:
 
 ```julia
-Topo_rect = CartData(xyzGrid(-550.0:1:600, -500.0:1:700, 0))
+Topo_rect = CartData(xyz_grid(-550.0:1:600, -500.0:1:700, 0))
 Topo_rect = project_CartData(Topo_rect, Topo_Alps, proj)
 ```
 

docs/src/man/Tutorial_FaultDensity.md

@@ -76,7 +76,7 @@ data   = faults.data[indlon,indlat]
 Create GeoData from restricted data
 
 ```julia
-Lon3D,Lat3D, Faults = lonlatdepthGrid(Lon,Lat,0);
+Lon3D,Lat3D, Faults = lonlatdepth_grid(Lon,Lat,0);
 Faults[:,:,1]       = data
 Data_Faults         = GeoData(Lon3D,Lat3D,Faults,(Faults=Faults,))
 ```

docs/src/man/Tutorial_Jura.md

@@ -100,10 +100,10 @@ Moreover, the resolution of the grids is different. Whereas the `TopoGeology` ha
 It is often useful to have them on exactly the same size grid
 
 We can do this in two steps:
-First, we define a `projectionPoint` along which we perform the projection
+First, we define a `ProjectionPoint` along which we perform the projection
 
 ```julia
-proj = projectionPoint(Lon=6, Lat=46.5)
+proj = ProjectionPoint(Lon=6, Lat=46.5)
 ```
 
 We can simply transfer the TopoGeology map to Cartesian values with:
@@ -127,7 +127,7 @@ It is therefore better to use the `project_CartData` to project the `GeoData` st
 Let's first create this structure by using `x`,`y` coordinates that are slightly within the ranges given above:
 
 ```julia
-TopoGeology_cart = CartData(xyzGrid(range(-70,150,length=3500), range(-105,130,length=2500), 0.0))
+TopoGeology_cart = CartData(xyz_grid(range(-70,150,length=3500), range(-105,130,length=2500), 0.0))
 ```
 
 ```julia
@@ -219,8 +219,8 @@ We create both a surface and a 3D block
 ```julia
 nx, ny, nz = 1024, 1024, 128
 x,y,z = range(-100,180,nx), range(-50,70,ny), range(-8,4,nz)
-ComputationalSurf  =  CartData(xyzGrid(x,y,0))
-ComputationalGrid  =  CartData(xyzGrid(x,y,z))
+ComputationalSurf  =  CartData(xyz_grid(x,y,0))
+ComputationalGrid  =  CartData(xyz_grid(x,y,z))
 ```
 
 Re-interpolate the rotated to the new grid:

docs/src/man/Tutorial_LaPalma.md

@@ -65,7 +65,7 @@ In order to create model setups, it is helpful to first transfer the data to Car
 This requires us to first determine a *projection point*, that is fixed. Often, it is helpful to use the center of the topography for this. In the present example, we will center the model around La Palma itself:
 
 ```julia
-proj = projectionPoint(Lon=-17.84, Lat=28.56)
+proj = ProjectionPoint(Lon=-17.84, Lat=28.56)
 ```
 
 Once this is done you can convert the topographic data to the cartesian reference frame
@@ -80,7 +80,7 @@ In other cases, however, this is quite substantial (e.g., India-Asia collision z
 LaMEM needs an orthogonal grid of topography, which we can create with:
 
 ```julia
-Topo_model = CartData(xyzGrid(-35:.1:30,-15:.2:45,0));
+Topo_model = CartData(xyz_grid(-35:.1:30,-15:.2:45,0));
 nothing #hide
 ```
 
@@ -102,7 +102,7 @@ It is useful to plot the earthquake density in 3D, which indicates where most ac
 For this, we first create a 3D grid of the region:
 
 ```julia
-Grid_3D = CartData(xyzGrid(-35:.3:30,-15:.25:45,-50:.5:5))
+Grid_3D = CartData(xyz_grid(-35:.3:30,-15:.25:45,-50:.5:5))
 ```
 
 Next we check how many earthquakes are around the grid points:

docs/src/man/Tutorial_MohoTopo_Spada.md

@@ -108,7 +108,7 @@ data_Moho_combined = GeoData(lon, lat, depth, (MohoDepth=depth*km,))
 Next, we define a regular lon/lat grid
 
 ```julia
-Lon, Lat, Depth  = lonlatdepthGrid(4.1:0.1:11.9,42.5:.1:49,-30km)
+Lon, Lat, Depth  = lonlatdepth_grid(4.1:0.1:11.9,42.5:.1:49,-30km)
 ```
 
 We will use a nearest neighbor interpolation method to fit a surface through the data, which has the advantage that it will take the discontinuities into account.

docs/src/man/Tutorial_NumericalModel_2D.md

@@ -17,7 +17,7 @@ using GeophysicalModelGenerator
 nx,nz = 512,128
 x = range(-1000,1000, nx);
 z = range(-660,0,    nz);
-Grid2D = CartData(xyzGrid(x,0,z))
+Grid2D = CartData(xyz_grid(x,0,z))
 ```
 
 ````
@@ -233,7 +233,7 @@ We start with the horizontal part:
 nx,nz = 512,128
 x = range(-1000,1000, nx);
 z = range(-660,0,    nz);
-Grid2D = CartData(xyzGrid(x,0,z))
+Grid2D = CartData(xyz_grid(x,0,z))
 Phases = zeros(Int64, nx, 1, nz);
 Temp = fill(1350.0, nx, 1, nz);
 addBox!(Phases, Temp, Grid2D; xlim=(-800,0.0), zlim=(-80.0, 0.0), phase = ConstantPhase(1));
@@ -271,7 +271,7 @@ Our starting basis is the example above with ridge and overriding slab
 nx,nz = 512,128
 x = range(-1000,1000, nx);
 z = range(-660,0,    nz);
-Grid2D = CartData(xyzGrid(x,0,z))
+Grid2D = CartData(xyz_grid(x,0,z))
 Phases = zeros(Int64, nx, 1, nz);
 Temp = fill(1350.0, nx, 1, nz);
 lith = LithosphericPhases(Layers=[15 20 55], Phases=[3 4 5], Tlab=1250)

docs/src/man/Tutorial_NumericalModel_3D.md

@@ -18,7 +18,7 @@ nx,ny,nz = 512,512,128
 x = range(-1000,1000, nx);
 y = range(-1000,1000, ny);
 z = range(-660,0,    nz);
-Grid = CartData(xyzGrid(x,y,z));
+Grid = CartData(xyz_grid(x,y,z));
 ```
 
 Now we create an integer array that will hold the `Phases` information (which usually refers to the material or rock type in the simulation)
@@ -94,7 +94,7 @@ nx,ny,nz = 512,512,128
 x = range(-1000,1000, nx);
 y = range(-1000,1000, ny);
 z = range(-660,0,    nz);
-Grid = CartData(xyzGrid(x,y,z));
+Grid = CartData(xyz_grid(x,y,z));
 
 Phases = fill(2,nx,ny,nz);
 Temp = fill(1350.0, nx,ny,nz);

docs/src/man/datastructures.md

@@ -1,15 +1,15 @@
 # Data structures
 
 The main data structure used in GeophysicalModelGenerator.jl is `GeoData`, which contains info about the `longitude`,`latitude`, and `depth` of a data set, as well as several data sets itself.
-We also provide a `UTMData`, which is essentially the same but with UTM coordinates, and a `CartData` structure, which has Cartesian coordinates in kilometers (as used in many geodynamic codes). If one wishes to transfer `GeoData` to `CartData`, one needs to provide a `projectionPoint`.
+We also provide a `UTMData`, which is essentially the same but with UTM coordinates, and a `CartData` structure, which has Cartesian coordinates in kilometers (as used in many geodynamic codes). If one wishes to transfer `GeoData` to `CartData`, one needs to provide a `ProjectionPoint`.
 For plotting, we transfer this into the `ParaviewData` structure, which has cartesian coordinates centered around the center of the Earth. We employ the `wgs84` reference ellipsoid as provided by the [Geodesy.jl](https://github.com/JuliaGeo/Geodesy.jl) package to perform this transformation. 
 
 ```@docs
 GeoData
 CartData
 UTMData
 ParaviewData
-lonlatdepthGrid
-xyzGrid
-projectionPoint
+lonlatdepth_grid
+xyz_grid
+ProjectionPoint
 ```

docs/src/man/projection.md

@@ -4,7 +4,7 @@ Typically, you load a dataset by reading it into julia and either generating a `
 
 If you write the data to `Paraview`, it is internally converted to a Paraview structure (which involves `x,y,z` Cartesian Earth-Centered-Earth-Fixed (ECEF) coordinates using the `wgs84` ellipsoid). 
 
-Yet, if you do geodynamic calculations the chances are that the geodynamic code does not operate in spherical coordinates, but rather use cartesian ones. In that case you should transfer your data to the `CartData` structure, which requires you to specify a `projectionPoint` that is a point on the map that will later have the coordinates `(0,0)` in the `CartData` structure.
+Yet, if you do geodynamic calculations the chances are that the geodynamic code does not operate in spherical coordinates, but rather use cartesian ones. In that case you should transfer your data to the `CartData` structure, which requires you to specify a `ProjectionPoint` that is a point on the map that will later have the coordinates `(0,0)` in the `CartData` structure.
 
 
 #### 1. Converting
@@ -41,8 +41,8 @@ As the area is large, it covers a range of `UTM` zones (and every point has a UT
 Yet, what we could do instead is show all data with respect to a single UTM zone. For this, we have to select a point around which we project (in this case more or less in the center):
 
 ```julia
-julia> p=projectionPoint(Lon=17.3, Lat=37.5)
-projectionPoint(37.5, 17.3, 703311.4380385976, 4.152826288024972e6, 33, true)
+julia> p=ProjectionPoint(Lon=17.3, Lat=37.5)
+ProjectionPoint(37.5, 17.3, 703311.4380385976, 4.152826288024972e6, 33, true)
 ```
 
 Projecting the `GeoData` set using this projection point is done with:
@@ -77,7 +77,7 @@ Whereas this is ok to look at and compare with a LaMEM model setup, we cannot us
 #### 2. Projecting data
 For use with LaMEM, you would need an orthogonal cartesian grid. From the last command above we get some idea on the area, so we can create this:
 ```julia
-julia> Topo_Cart_orth  = CartData(xyzGrid(-2000:20:2000,-1000:20:1000,0))
+julia> Topo_Cart_orth  = CartData(xyz_grid(-2000:20:2000,-1000:20:1000,0))
 CartData 
     size   : (201, 101, 1)
     x      ϵ [ -2000.0 km : 2000.0 km]