type generalization 60%

examples/obstacle.jl

@@ -2,6 +2,8 @@ using ShockwaveDetection
 using ShockwaveProperties
 using Euler2D:Euler2D
 
+ENV["JULIA_NUM_THREADS"] = "4"
+
 flow_data = FlowData("examples/data/obstacle/funky_square.celltape")
 point_detect_algo = ImageProcessingShockDetectionAlgo(0.2, :prewitt)
 dbscan_algo = DBSCANAlgo(0.25, 50, 40)

src/cluster.jl

@@ -1,12 +1,12 @@
 using Clustering
 
 """
-    struct DBSCANAlgo
+    struct DBSCANAlgo{T}
 
 A structure that keeps all the necessary parameters for the DBSCAN clustering algorithm.
 
 # Fields
-- `radius::Float64`: The radius within which to search for neighboring points.
+- `radius::T`: The radius within which to search for neighboring points.
 - `min_neighbors::Int`: The minimum number of neighbors required to form a dense region.
 - `min_cluster_size::Int`: The minimum number of points required to form a cluster.
 
@@ -17,14 +17,14 @@ A structure that keeps all the necessary parameters for the DBSCAN clustering al
 # Example
 algo = DBSCANAlgo(radius=1.0, min_neighbors=5, min_cluster_size=15)
 """
-struct DBSCANAlgo
-    radius::Float64
+struct DBSCANAlgo{T}
+    radius::T
     min_neighbors::Int
     min_cluster_size::Int
 
-    # Inner constructor with default values
-    function DBSCANAlgo(radius::Float64 = 0.5, min_neighbors::Int=3, min_cluster_size::Int=10)
-        new(radius, min_neighbors, min_cluster_size)
+   # Inner constructor with default values
+    function DBSCANAlgo(radius::T = 0.5, min_neighbors::Int = 3, min_cluster_size::Int = 10) where T
+        new{T}(radius, min_neighbors, min_cluster_size)
     end
 end #struct DBSCANAlgo
 

src/fitting.jl

@@ -1,15 +1,13 @@
 using LsqFit
 using LinearAlgebra
 
-struct Fitting
+struct Fitting{T}
     model::Function
-    parameters::Array{Float64}
-    error::Float64
-    range::Tuple{Float64, Float64}
+    parameters::Array{T}
+    error::T
+    range::Tuple{T, T}
 end
 
-#TODO: hline_model
-
 # The angle of the end point and the starting point is needed to determine the proper shape
 # For other models, only range of x suffices
 function circle_model(xy, p)

src/pipeline.jl

@@ -47,10 +47,8 @@ This function detects shock points in 2D flow data using a specified shock detec
 function detect(flow_data::FlowData, shock_point_algo::Abstract2DShockDetectionAlgo, cluster_algo::DBSCANAlgo)
     to = TimerOutput()
 
-    has_obstacle = isnothing(flow_data.u)  # Check if the flow data has an obstacle
-
     @timeit to "Detect Shock Points(2D)" begin
-        shock_positions_over_time = detect_shock_points(flow_data, shock_point_algo, has_obstacle)
+        shock_positions_over_time = detect_shock_points(flow_data, shock_point_algo)
     end
 
     @timeit to "Cluster Shock Points" begin

src/shock_point_detectors/2D/image_processing.jl

@@ -26,12 +26,12 @@ function replace_nan_with_mean!(matrix)
 end
 
 """
-    struct ImageProcessingShockDetectionAlgo <: Abstract2DShockDetectionAlgo
+    struct ImageProcessingShockDetectionAlgo{T} <: Abstract2DShockDetectionAlgo
 
 A structure that represents the image processing shock detection algorithm.
 
 # Fields
-- `threshold::Float64`: The threshold value for detecting shocks.
+- `threshold::T`: The threshold value for detecting shocks.
 - `kernelname::Symbol`: The name of the kernel to be used for image processing (e.g., `:sobel`, `:prewitt`).
 
 # Description
@@ -40,8 +40,8 @@ This struct is used to configure the image processing shock detection algorithm.
 # Example
 algo = ImageProcessingShockDetectionAlgo(threshold=0.5, kernelname=:sobel)
 """
-struct ImageProcessingShockDetectionAlgo <: Abstract2DShockDetectionAlgo
-    threshold::Float64
+struct ImageProcessingShockDetectionAlgo{T} <: Abstract2DShockDetectionAlgo
+    threshold::T
     kernelname::Symbol
 end # ImageProcessingShockDetectionAlgo
 
@@ -98,7 +98,7 @@ function detect_discon_at_timestep(density_at_t, velocity_at_t, pressure_at_t, a
     return high_gradient_intersection
 end
 
-function detect_shock_points(flow_data::FlowData, alg::ImageProcessingShockDetectionAlgo, has_obstacle)
+function detect_shock_points(flow_data::FlowData, alg::ImageProcessingShockDetectionAlgo)
     # Unpack all the values from the detector
     nsteps = flow_data.nsteps
     density_field = flow_data.density_field