utils.jl

Author: boriskaus

docs/src/man/Tutorial_AlpineData.md

@@ -363,10 +363,10 @@ nothing #hide
 At this stage we have the 3D velocity components on a grid. Yet, we don't have information yet about the elevation of the stations (as the provided data set did not give this).
 We could ignore that and set the elevation to zero, which would allow saving the data directly.
 Yet, a better way is to load the topographic map of the area and interpolate the elevation to the velocity grid.
-As we have already the loaded the topographic map in section 1 of this tutorial, we can simply reuse it. To interpolate, we will use the function `InterpolateDataFields2D`
+As we have already the loaded the topographic map in section 1 of this tutorial, we can simply reuse it. To interpolate, we will use the function `interpolateDataFields2D`
 
 ```julia
-topo_v, fields_v = InterpolateDataFields2D(Topo, Lon, Lat)
+topo_v, fields_v = interpolateDataFields2D(Topo, Lon, Lat)
 ```
 
 The variable we are interested in is the variable `topo_v`. `fields_v` contains the interpolation of all the fields in `Topo` to the new grid and we only keep it here for completeness.

docs/src/man/Tutorial_Basic.md

@@ -76,10 +76,10 @@ Note that I use the `Oleron` scientific colormap for the tomography which you ca
 
 ### 2. Extract subset of data
 As you can see the tomographic data covers a much larger area than the Alps itself, and in most of that area there is no data.
-It is thus advantageous to cut out a piece of the dataset that we are interested in which can be done with `ExtractSubVolume`:
+It is thus advantageous to cut out a piece of the dataset that we are interested in which can be done with `extractSubvolume`:
 
 ```julia
-Tomo_Alps = ExtractSubvolume(Tomo_Alps_full,Lon_level=(4,20),Lat_level=(36,50), Depth_level=(-600,-10))
+Tomo_Alps = extractSubvolume(Tomo_Alps_full,Lon_level=(4,20),Lat_level=(36,50), Depth_level=(-600,-10))
 
 write_Paraview(Tomo_Alps,"Tomo_Alps");
 ```
@@ -95,10 +95,10 @@ Which looks like:
 ### 3. Create cross sections
 Paraview has the option to `Slice` through the data but it is not very intuitive to do this in 3D. Another limitation of Paraview is that it does not have native support for spherical coordinates, and therefore the data is translated to cartesian (`x`,`y`,`z`) coordinates (with the center of the Earth at `(0,0,0)`).
 That makes this a bit cumbersome to make a cross-section at a particular location.
-If you are interested in this you can use the `CrossSection` function:
+If you are interested in this you can use the `crossSection` function:
 
 ```julia
-data_200km = CrossSection(Tomo_Alps, Depth_level=-200)
+data_200km = crossSection(Tomo_Alps, Depth_level=-200)
 ```
 
 ````
@@ -114,7 +114,7 @@ GeoData
 As you see, this is not exactly at 200 km depth, but at the closest `z`-level in the data sets. If you want to be exactly at 200 km, use the `Interpolate` option:
 
 ```julia
-data_200km_exact = CrossSection(Tomo_Alps, Depth_level=-200, Interpolate=true)
+data_200km_exact = crossSection(Tomo_Alps, Depth_level=-200, Interpolate=true)
 ```
 
 ````
@@ -129,10 +129,10 @@ GeoData
 
 In general, you can get help info for all functions with `?`:
 ```julia
-help?> CrossSection
-search: CrossSection CrossSectionVolume CrossSectionPoints CrossSectionSurface FlattenCrossSection
+help?> crossSection
+search: crossSection crossSectionVolume crossSectionPoints crossSectionSurface flattenCrossSection
 
-  CrossSection(DataSet::AbstractGeneralGrid; dims=(100,100), Interpolate=false, Depth_level=nothing, Lat_level=nothing, Lon_level=nothing, Start=nothing, End=nothing, Depth_extent=nothing, section_width=50km)
+  crossSection(DataSet::AbstractGeneralGrid; dims=(100,100), Interpolate=false, Depth_level=nothing, Lat_level=nothing, Lon_level=nothing, Start=nothing, End=nothing, Depth_extent=nothing, section_width=50km)
 
   Creates a cross-section through a GeoData object.
 
@@ -160,7 +160,7 @@ search: CrossSection CrossSectionVolume CrossSectionPoints CrossSectionSurface F
   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)));
-  julia> Data_cross      =   CrossSection(Data_set3D, Depth_level=-100km)
+  julia> Data_cross      =   crossSection(Data_set3D, Depth_level=-100km)
   GeoData
     size  : (11, 11, 1)
     lon   ϵ [ 10.0 : 20.0]
@@ -172,7 +172,7 @@ search: CrossSection CrossSectionVolume CrossSectionPoints CrossSectionSurface F
 Let's use this to make a vertical cross-section as well:
 
 ```julia
-Cross_vert = CrossSection(Tomo_Alps, Start=(5,47), End=(15,44))
+Cross_vert = crossSection(Tomo_Alps, Start=(5,47), End=(15,44))
 ```
 
 ````

docs/src/man/Tutorial_Jura.md

@@ -186,7 +186,7 @@ below = belowSurface(CrossSection_1_cart, TopoGeology_cart)
 We can add that to the cross-section with:
 
 ```julia
-CrossSection_1_cart = AddField(CrossSection_1_cart,"rocks",Int64.(below))
+CrossSection_1_cart = addField(CrossSection_1_cart,"rocks",Int64.(below))
 ```
 
 Note that we transfer the boolean to an integer
@@ -204,13 +204,13 @@ The result looks like:
 
 ## 3. Geological block model
 Yet, if you want to perform a numerical simulation of the Jura, it is more convenient to rotate the maps such that we can perform a simulation perpendicular to the strike of the mountain belt.
-This can be done with `RotateTranslateScale`:
+This can be done with `rotateTranslateScale`:
 
 ```julia
 RotationAngle = -43
-TopoGeology_cart_rot    = RotateTranslateScale(TopoGeology_cart, Rotate=RotationAngle)
-Basement_cart_rot       = RotateTranslateScale(Basement_cart, Rotate=RotationAngle)
-CrossSection_1_cart_rot = RotateTranslateScale(CrossSection_1_cart, Rotate=RotationAngle)
+TopoGeology_cart_rot    = rotateTranslateScale(TopoGeology_cart, Rotate=RotationAngle)
+Basement_cart_rot       = rotateTranslateScale(Basement_cart, Rotate=RotationAngle)
+CrossSection_1_cart_rot = rotateTranslateScale(CrossSection_1_cart, Rotate=RotationAngle)
 ```
 
 Next, we can create a new computational grid that is more conveniently oriented:
@@ -226,8 +226,8 @@ ComputationalGrid  =  CartData(xyzGrid(x,y,z))
 Re-interpolate the rotated to the new grid:
 
 ```julia
-GeologyTopo_comp_surf = InterpolateDataFields2D(TopoGeology_cart_rot, ComputationalSurf, Rotate=RotationAngle)
-Basement_comp_surf    = InterpolateDataFields2D(Basement_cart_rot,    ComputationalSurf, Rotate=RotationAngle)
+GeologyTopo_comp_surf = interpolateDataFields2D(TopoGeology_cart_rot, ComputationalSurf, Rotate=RotationAngle)
+Basement_comp_surf    = interpolateDataFields2D(Basement_cart_rot,    ComputationalSurf, Rotate=RotationAngle)
 ```
 
 Next we can use the surfaces to create a 3D block model.
@@ -254,8 +254,8 @@ Phases[id] .= 2
 Add to the computational grid:
 
 ```julia
-ComputationalGrid = AddField(ComputationalGrid,"Phases", Phases)
-ComputationalGrid = RemoveField(ComputationalGrid,"Z")
+ComputationalGrid = addField(ComputationalGrid,"Phases", Phases)
+ComputationalGrid = removeField(ComputationalGrid,"Z")
 ```
 
 Save the surfaces, cross-section and the grid:

docs/src/man/Tutorial_LaPalma.md

@@ -140,7 +140,7 @@ Phases[ind] .= 3 #Magma
 Add rocktypes to the grid:
 
 ```julia
-Grid_3D = AddField(Grid_3D,"Phases",Phases)
+Grid_3D = addField(Grid_3D,"Phases",Phases)
 ```
 
 We can save this to paraview format

docs/src/man/Tutorial_Votemaps.md

@@ -44,7 +44,7 @@ PSwave_Koulakov
   fields: (:dVp_percentage, :dVs_percentage)
 ```
 
-#### 2. Creating a Votemap
+#### 2. Creating a voteMap
 The idea of `Votemaps` is rather simple:
 - assume that a certain perturbation describes a feature (say, P wave anomalies >3% are interpreted to be slabs in the model of Paffrath)
 - Everything that fulfills this criteria gets the number 1, everything else 0.
@@ -59,7 +59,7 @@ So how do we create Votemaps?
 Doing this is rather simple:
 
 ```julia
-Data_VoteMap = VoteMap( [Pwave_Paffrath,       PSwave_Koulakov,    Pwave_Zhao],
+Data_VoteMap = voteMap( [Pwave_Paffrath,       PSwave_Koulakov,    Pwave_Zhao],
                         ["dVp_Percentage>3.0","dVp_percentage>2.0","dVp_Percentage>2.0"], dims=(100,100,100))
 ```
 
@@ -72,7 +72,7 @@ GeoData
   lon   ϵ [ 4.0 : 18.0]
   lat   ϵ [ 38.0 : 49.01197604790419]
   depth ϵ [ -606.0385 km : -10.0 km]
-  fields: (:VoteMap,)
+  fields: (:voteMap,)
 ```
 
 And from this, we can generate profiles, visualize 3D features in Paraview etc. etc.
@@ -88,7 +88,7 @@ You can ofcourse argue that newer tomographic models include more data & should
 A simple way to take that into account is to list the model twice:
 
 ```julia
-Data_VoteMap = VoteMap( [Pwave_Paffrath,       Pwave_Paffrath,       PSwave_Koulakov,    Pwave_Zhao],
+Data_VoteMap = voteMap( [Pwave_Paffrath,       Pwave_Paffrath,       PSwave_Koulakov,    Pwave_Zhao],
                         ["dVp_Percentage>3.0","dVp_Percentage>3.0", "dVp_percentage>2.0","dVp_Percentage>2.0"],
                         dims=(100,100,100))
 ```

docs/src/man/profile_processing.md

@@ -9,7 +9,7 @@ The routines provided here have the following functionality:
 ```@docs
 load_GMG
 save_GMG
-CrossSection
+crossSection
 ProfileData
 extractProfileData
 createProfileData

docs/src/man/tools.md

@@ -3,22 +3,22 @@
 We have a number of functions with which we can extract sub-data from a 2D or 3D `GeoData` structure.
 
 ```@docs
-CrossSection
-ExtractSubvolume
-InterpolateDataFields
-VoteMap
-SubtractHorizontalMean
+crossSection
+extractSubvolume
+interpolateDataFields
+voteMap
+subtractHorizontalMean
 aboveSurface
 belowSurface
 interpolateDataOnSurface
-InterpolateTopographyOnPlane
-ParseColumns_CSV_File
-RotateTranslateScale!
+interpolateTopographyOnPlane
+parseColumns_CSV_File
+rotateTranslateScale!
 pointData2NearestGrid
 convert2UTMzone
 convert2CartData
 projectCartData
 drape_on_topo
-LithostaticPressure!
+lithostaticPressure!
 countMap
 ```

docs/src/man/tutorial_GPS.md

@@ -49,7 +49,7 @@ We can load that in julia as:
 data_file               =   CSV.File("ALPS2017_DEF_VT.GRD",datarow=18,header=false,delim=' ')
 
 num_columns             =   4;
-data                    =   ParseColumns_CSV_File(data_file, num_columns);     #Read numerical data from the file
+data                    =   parseColumns_CSV_File(data_file, num_columns);     #Read numerical data from the file
 lon_Vz, lat_Vz, Vz_vec  =   data[:,1], data[:,2], data[:,3]
 ```
 
@@ -123,7 +123,7 @@ Next, we load the horizontal velocities which is available in the file `ALPS2017
 ```julia
 download_data("https://store.pangaea.de/Publications/Sanchez-etal_2018/ALPS2017_DEF_HZ.GRD","ALPS2017_DEF_HZ.GRD")
 data_file                       =   CSV.File("ALPS2017_DEF_HZ.GRD",datarow=18,header=false,delim=' ')
-data                            =   ParseColumns_CSV_File(data_file, 10)
+data                            =   parseColumns_CSV_File(data_file, 10)
 lon_Hz, lat_Hz, Ve_Hz, Vn_Hz    =   data[:,1], data[:,2], data[:,3], data[:,4]
 ```
 

docs/src/man/tutorial_ISC_data.md

@@ -15,9 +15,9 @@ The main data-file, `ISC1.dat`, has 23 lines of comments (indicated with `#`), a
 julia> using CSV, GeophysicalModelGenerator
 julia> data_file = CSV.File("ISC1.dat",datarow=24,header=false,delim=',')
 ```
-As this data contains a lot of information that we are not interested in at the moment and which is given in non-numeric formats (e.g. date, time etc.), we will use our helper function *ParseColumns_CSV_File* to only extract columns with numeric data.
+As this data contains a lot of information that we are not interested in at the moment and which is given in non-numeric formats (e.g. date, time etc.), we will use our helper function *parseColumns_CSV_File* to only extract columns with numeric data.
 ```julia-repl
-julia> data      = ParseColumns_CSV_File(data_file, 14)
+julia> data      = parseColumns_CSV_File(data_file, 14)
 julia> lon       = data[:,2];
 julia> lat       = data[:,1];
 julia> depth     = -1* data[:,3];

docs/src/man/tutorial_load3DSeismicData.md

@@ -173,10 +173,10 @@ Note that using geographic coordinates is slightly cumbersome in Paraview. If yo
 #### 5. Extract and plot cross-sections of the data
 In many cases you would like to create cross-sections through the 3D data sets as well, and visualize that in Paraview. That is in principle possible in Paraview as well (using the `Slice` tool, as described above). Yet, in many cases we want to have it at a specific depth, or through pre-defined `lon/lat` coordinates.
 
-There is a simple way to achieve this using the `CrossSection` function.
+There is a simple way to achieve this using the `crossSection` function.
 To make a cross-section at a given depth:
 ```julia
-julia> Data_cross  =   CrossSection(Data_set, Depth_level=-100km)  
+julia> Data_cross  =   crossSection(Data_set, Depth_level=-100km)  
 GeoData 
   size  : (121, 94, 1)
   lon   ϵ [ 0.0 : 18.0]
@@ -190,7 +190,7 @@ julia> write_Paraview(Data_cross, "Zhao_CrossSection_100km")
 
 Or at a specific longitude:
 ```julia
-julia> Data_cross  =   CrossSection(Data_set, Lon_level=10)
+julia> Data_cross  =   crossSection(Data_set, Lon_level=10)
 GeoData 
   size  : (1, 94, 101)
   lon   ϵ [ 10.05 : 10.05]
@@ -205,7 +205,7 @@ As you see, this cross-section is not taken at exactly 10 degrees longitude. Tha
 
 If you wish to interpolate the data, specify `Interpolate=true`:
 ```julia
-julia> Data_cross = CrossSection(Data_set, Lon_level=10, Interpolate=true)
+julia> Data_cross = crossSection(Data_set, Lon_level=10, Interpolate=true)
 GeoData 
   size  : (1, 100, 100)
   lon   ϵ [ 10.0 : 10.0]
@@ -218,7 +218,7 @@ as you see, this causes the data to be interpolated on a `(100,100)` grid (which
 
 We can also create a diagonal cut through the model:
 ```julia
-julia> Data_cross  =   CrossSection(Data_set, Start=(1.0,39), End=(18,50))
+julia> Data_cross  =   crossSection(Data_set, Start=(1.0,39), End=(18,50))
 GeoData 
   size  : (100, 100, 1)
   lon   ϵ [ 1.0 : 18.0]
@@ -232,10 +232,10 @@ Here an image that shows the resulting cross-sections:
 ![Paraview_7](../assets/img/Tutorial_Zhao_Paraview_7.png)
 
 #### 6. Extract a (3D) subset of the data
-Sometimes, the data set covers a large region (e.g., the whole Earth), and you are only interested in a subset of this data for your project. You can obviously cut your data to the correct size in Paraview. Yet, an even easier way is the routine `ExtractSubvolume`:
+Sometimes, the data set covers a large region (e.g., the whole Earth), and you are only interested in a subset of this data for your project. You can obviously cut your data to the correct size in Paraview. Yet, an even easier way is the routine `extractSubvolume`:
 
 ```julia
-julia> Data_subset     =   ExtractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45))
+julia> Data_subset     =   extractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45))
 GeoData 
   size  : (48, 35, 101)
   lon   ϵ [ 4.95 : 12.0]
@@ -250,7 +250,7 @@ This gives the resulting image. You can obviously use that new, smaller, data se
 By default, we extract the original data and do not interpolate it on a new grid.
 In some cases, you will want to interpolate the data on a different grid. Use the `Interpolate=true` option for that:
 ```julia
-julia> Data_subset_interp     =   ExtractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45), Interpolate=true)
+julia> Data_subset_interp     =   extractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45), Interpolate=true)
 GeoData 
   size  : (50, 50, 50)
   lon   ϵ [ 5.0 : 12.0]