Merge pull request #31 from ZenanH/main

🎉 Release new version v0.1.24

Author: ZenanH

CondaPkg.toml

@@ -1,32 +1,28 @@
 name = "MaterialPointGenerator"
 uuid = "18deba1c-5063-41ee-a388-01f264c5a914"
 authors = ["ZenanH <zenan.huo@outlook.com>"]
-version = "0.1.23"
+version = "0.1.24"
 
 [deps]
-CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-LibGEOS = "a90b1aa1-3769-5649-ba7e-abc5a9d163eb"
+FastPointQuery = "b325ad68-3eea-4afa-b43e-4b8e378fd76d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-CondaPkg = "0.2"
 DelimitedFiles = "1"
-LibGEOS = "0.9"
+FastPointQuery = "0.1"
 LiveServer = "1.5"
 NearestNeighbors = "0.4"
 PrecompileTools = "1.2"
-PythonCall = "0.9"
 julia = "1.11"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["Test"]

Project.toml

@@ -3,7 +3,7 @@
 [![CI](https://github.com/LandslideSIM/MaterialPointGenerator.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/LandslideSIM/MaterialPointGenerator.jl/actions/workflows/ci.yml) 
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg?logo=quicklook)](https://LandslideSIM.github.io/MaterialPointGenerator.jl/stable)
 [![Dev](https://img.shields.io/badge/docs-dev-red.svg?logo=quicklook)](https://LandslideSIM.github.io/MaterialPointGenerator.jl/dev)
-[![Version](https://img.shields.io/badge/version-v0.1.23-pink)]()
+[![Version](https://img.shields.io/badge/version-v0.1.24-pink)]()
 
 During the EGU2023 conference, when I presented a high-performance MPM  (Material Point Method) solver, I was asked, 
 "How do you discretize the computational model for the MPM?" I didn't have a clear answer (I didn't even consider it a problem) because the models were relatively simple and could be generated directly using some straightforward functions. However, as computational models gradually became more complex and diverse, I began to realize that this was indeed a very good question. The preprocessing for MPM should not be a computationally intensive task; it should be fast enough. Yet, I couldn't find a "plug-and-play" generalized code for this purpose. Some literatures have contributed to this issue, and I built upon their work to create a comprehensive and refined julia package. 

README.md

@@ -11,88 +11,26 @@
 
 module MaterialPointGenerator
 
-using CondaPkg, DelimitedFiles, LibGEOS, NearestNeighbors, Printf, PythonCall
-using LinearAlgebra: mul!, eigen, Symmetric, normalize
-using Statistics: mean
+using DelimitedFiles, Logging, FastPointQuery, NearestNeighbors, Printf
 using LiveServer: serve
 using PrecompileTools: @setup_workload, @compile_workload
 
-import LinearAlgebra.LAPACK: syev!
-
-const trimesh      = PythonCall.pynew()
-const np           = PythonCall.pynew()
-const rtree        = PythonCall.pynew()
-const shapely      = PythonCall.pynew()
-const MultiPolygon = PythonCall.pynew()
-const Polygon      = PythonCall.pynew()
-const Point        = PythonCall.pynew()
-const mapping      = PythonCall.pynew()
-const unary_union  = PythonCall.pynew()
-const rotate       = PythonCall.pynew()
-const rasterio     = PythonCall.pynew()
-const rasterize    = PythonCall.pynew()
-const pygmsh       = PythonCall.pynew()
-const pyKDTree     = PythonCall.pynew()
-const pytime       = PythonCall.pynew()
-const embreex      = PythonCall.pynew()
-
-function __init__()
-    @info "checking environment..."
-    # import Python modules
-    PythonCall.pycopy!(trimesh , pyimport("trimesh" ))
-    PythonCall.pycopy!(np      , pyimport("numpy"   ))
-    PythonCall.pycopy!(rtree   , pyimport("rtree"   ))
-    PythonCall.pycopy!(shapely , pyimport("shapely" ))
-    PythonCall.pycopy!(rasterio, pyimport("rasterio"))
-    PythonCall.pycopy!(pygmsh  , pyimport("gmsh"    ))
-    PythonCall.pycopy!(pytime  , pyimport("time"    ))
-    # import their submudules
-    PythonCall.pycopy!(Polygon     , pyimport("shapely.geometry"  ).Polygon     )
-    PythonCall.pycopy!(Point       , pyimport("shapely.geometry"  ).Point       )
-    PythonCall.pycopy!(mapping     , pyimport("shapely.geometry"  ).mapping     )
-    PythonCall.pycopy!(unary_union , pyimport("shapely.ops"       ).unary_union )
-    PythonCall.pycopy!(rotate      , pyimport("shapely.affinity"  ).rotate      )
-    PythonCall.pycopy!(rasterize   , pyimport("rasterio.features" ).rasterize   )
-    PythonCall.pycopy!(pyKDTree    , pyimport("scipy.spatial"     ).cKDTree     )
-    PythonCall.pycopy!(MultiPolygon, pyimport("shapely.geometry"  ).MultiPolygon)
-    if !Sys.isapple()
-        try 
-            if !haskey(CondaPkg.current_pip_packages(), "embreex")
-                CondaPkg.add_pip("embreex")
-                CondaPkg.resolve()
-            end
-            PythonCall.pycopy!(embreex, pyimport("embreex"))
-            @info "embreex loaded"
-        catch e
-            @warn """embreeX - Python Wrapper for Embree
-            1) cannot find compatible version
-            2) some features will fall back to native Python code
-            """
-        end
-    else 
-        @warn """embreeX - Python Wrapper for Embree
-        1) not supported on MacOS arm and x86
-        2) some features will fall back to native Python code
-        """
-    end
-end
-
-include(joinpath(@__DIR__, "meshgenerator.jl"))
-include(joinpath(@__DIR__, "polygon.jl"      ))
-include(joinpath(@__DIR__, "polyhedron.jl"   ))
-include(joinpath(@__DIR__, "dem.jl"          ))
+include(joinpath(@__DIR__, "reexport.jl"  ))
+include(joinpath(@__DIR__, "polygon.jl"   ))
+include(joinpath(@__DIR__, "polyhedron.jl"))
+include(joinpath(@__DIR__, "utils.jl"     ))
+include(joinpath(@__DIR__, "dem.jl"))
 include(joinpath(@__DIR__, "slbl/slbl.jl"    ))
 include(joinpath(@__DIR__, "slbl/slbl_gui.jl"))
-include(joinpath(@__DIR__, "utils.jl"        ))
 
-@setup_workload begin
-    points = rand(20, 3)
-    @compile_workload begin
-        # functions in utils.jl
-        getnormals(points)
-        # function in dem.jl
-        getpolygon(rand(10, 2))
+quiet(f) = redirect_stdout(devnull) do
+    redirect_stderr(devnull) do
+        with_logger(NullLogger()) do
+            f()
+        end
     end
 end
 
+include(joinpath(@__DIR__, "precompile.jl"))
+
 end