Merge branch 'master' into oblate_coord_rot

.github/workflows/draft-pdf.yml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

.gitignore

@@ -19,11 +19,13 @@ swiftest_driver.sh
 !README.swifter 
 dump*
 !**/.gitignore
-!.github/workflows/build_wheels.yml
+!.github/workflows/*.yml
 !setup.py
 !examples/**
 !swiftest/**
 !tests/**
+!.zenodo.json
+!CITATION.cff
 *ipynb_checkpoints
 **/.DS_Store
 !version.txt

.zenodo.json

@@ -0,0 +1,58 @@
+{
+    "description": "Official release for the Journal of Open Source Software.", 
+    "license" : "GPLv3+", 
+    "title": "profminton/swiftest: v2023.10.2", 
+    "version": "v2023.10.2", 
+    "upload_type": "software", 
+    "publication_date": "2023-10-07", 
+    "creators": [
+      {
+        "name": "Carlisle Wishard",
+        "affiliation": [
+            "Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, USA",
+            "Evansville Museum of Arts, History & Science, USA"
+          ],
+        "orcid": "0009-0001-0733-3268"
+      },
+      {
+        "name": "Jennifer Pouplin",
+        "affiliation": [
+          "Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, USA",
+          "School of Aeronautics and Astronautics, Purdue University, USA"
+        ]
+      },
+      {
+        "name": "Jacob Elliott",
+        "affiliation": "Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, USA"
+      },
+      {
+        "name": "Dana Singh",
+        "affiliation": "SAIC, Princeton, NJ, USA"
+      },
+      {
+        "name": "Kaustub Anand",
+        "affiliation": [
+          "Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, USA",
+          "Department of Physics and Astronomy, Purdue University, USA"
+        ]
+      },
+      {
+        "name": "David Minton",
+        "affiliation": "Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, USA",
+        "orcid": "0000-0003-1656-9704"
+      }
+    ],
+    "access_right": "open",
+    "related_identifiers": [
+        {
+            "scheme": "url", 
+            "identifier": "https://github.com/profminton/swiftest/tree/v2023.10.2", 
+            "relation": "isSupplementTo"
+        }, 
+        {
+            "scheme": "doi", 
+            "identifier": "10.5281/zenodo.8417147", 
+            "relation": "isVersionOf"
+        }
+    ]
+}

CITATION.cff

@@ -0,0 +1,22 @@
+cff-version: 1.1.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: Wishard
+    given-names: Carlislse
+    orcid: https://orcid.org/0009-0001-0733-3268
+  - family-names: Pouplin
+    given-names: Jennifer 
+  - family-names: Elliott
+    given-names: Jacob
+  - family-names: Dana
+    given-names: Singh
+  - family-names: Anand
+    given-names: Kaustub
+  - family-names: Minton
+    given-names: David
+    affiliation: "1"
+    orcid: https://orcid.org/0000-0003-1656-9704
+title: profminton/swiftest: v2023.10.2
+version: v2023.10.2
+date-released: 2023-10-07
+

cmake/Modules/SetSwiftestFlags.cmake

@@ -563,8 +563,10 @@ IF (CMAKE_BUILD_TYPE STREQUAL "RELEASE" OR CMAKE_BUILD_TYPE STREQUAL "PROFILE")
             )
             # Tells the compiler to link to certain libraries in the Intel oneAPI Math Kernel Library (oneMKL). 
             SET_COMPILE_FLAG(CMAKE_Fortran_FLAGS_RELEASE "${CMAKE_Fortran_FLAGS_RELEASE}"
-                Fortran "-qmkl=cluster" # Intel
-                        "-qmkl"     # Intel
+                Fortran "-mkl=cluster" 
+                        "-mkl"
+                        "-qmkl=cluster"
+                        "-qmkl"     
             ) 
             # Enables additional interprocedural optimizations for a single file compilation
             SET_COMPILE_FLAG(CMAKE_Fortran_FLAGS_RELEASE "${CMAKE_Fortran_FLAGS_RELEASE}"

examples/Fragmentation/Fragmentation_Movie.py

@@ -14,22 +14,24 @@
 Generates and runs a set of Swiftest input files from initial conditions with the SyMBA integrator. All simulation 
 outputs are stored in the subdirectory named after their collisional regime. 
 
+**NOTE: You must have ffmpeg installed on your system before running this script. For instance, on MacOS:
+
+```brew install ffmpeg```
+
+on Ubuntu:
+
+```sudo apt-get install ffmpeg```
+
+
 Inputs
 _______
-None.
+User selects which collisional movie to generate
 
 Output
 ------
-collisions.log   : An ASCII file containing the information of any collisional events that occured.
-collisions.nc    : A NetCDF file containing the collision output.
-data.nc          : A NetCDF file containing the simulation output.
-encounters.nc    : A NetCDF file containing the encounter output.
-init_cond.nc     : A NetCDF file containing the initial conditions for the simulation.
-param.00...0.in  : A series of parameter input files containing the parameters for the simulation at every output stage.
-param.in         : An ASCII file containing the inital parameters for the simulation.
-param.restart.in : An ASCII file containing the parameters for the simulation at the last output. 
-swiftest.log     : An ASCII file containing the information on the status of the simulation as it runs.
-collision.mp4    : A movie file named after the collisional regime depicting the collision.
+
+Files are stored in directories for each collision type name requested
+[COLLISION_TYPE].mp4    : A movie file named after the collisional regime depicting the collision.
 
 """
 
@@ -48,6 +50,22 @@
 movie_titles = dict(zip(available_movie_styles, movie_title_list))
 num_movie_frames = 1000
 
+
+# ----------------------------------------------------------------------------------------------------------------------
+# To increase the number of bodies generated in each collision type, decrease the value of the corresponding nfrag_reduction number 
+# ----------------------------------------------------------------------------------------------------------------------
+nfrag_reduction = {"disruption_headon" : 10.0,
+         "disruption_off_axis"         : 10.0,
+         "supercatastrophic_headon"    : 10.0,
+         "supercatastrophic_off_axis"  : 10.0,
+         "hitandrun_disrupt"           : 10.0,
+         "hitandrun_pure"              : 1.0,
+         "merge"                       : 1.0,
+         "merge_spinner"               : 1.0,
+         }
+
+
+
 # These initial conditions were generated by trial and error
 names = ["Target","Projectile"]
 pos_vectors = {"disruption_headon"         : [np.array([1.0, -5.0e-05, 0.0]),
@@ -124,16 +142,6 @@
          "merge_spinner"             : 5.0e-3,
          }
 
-nfrag_reduction = {"disruption_headon" : 1.0,
-         "disruption_off_axis"         : 1.0,
-         "supercatastrophic_headon"    : 1.0,
-         "supercatastrophic_off_axis"  : 10.0,
-         "hitandrun_disrupt"           : 1.0,
-         "hitandrun_pure"              : 1.0,
-         "merge"                       : 1.0,
-         "merge_spinner"               : 1.0,
-         }
-
 density = 3000 * swiftest.AU2M**3 / swiftest.MSun
 GU = swiftest.GMSun * swiftest.YR2S**2 / swiftest.AU2M**3
 body_radius = body_Gmass.copy()
@@ -358,7 +366,11 @@ def vec_props(self, c):
         # Set fragmentation parameters
         minimum_fragment_gmass = 0.01 * body_Gmass[style][1] 
         gmtiny = 0.50 * body_Gmass[style][1] 
-        sim.set_parameter(collision_model="fraggle", encounter_save="both", gmtiny=gmtiny, minimum_fragment_gmass=minimum_fragment_gmass, nfrag_reduction=nfrag_reduction[style])
+        sim.set_parameter(collision_model="fraggle", 
+                          encounter_save="both", 
+                          gmtiny=gmtiny, 
+                          minimum_fragment_gmass=minimum_fragment_gmass, 
+                          nfrag_reduction=nfrag_reduction[style])
         sim.run(dt=5e-4, tstop=tstop[style], istep_out=1, dump_cadence=0)
 
         print("Generating animation")

examples/Multibody_Fragmentation/Multibody_Movie.py

@@ -14,6 +14,15 @@
 Generates, runs, and processes a set of initial conditions for a multi-body super-catastrophic distruption collisional event. 
 All Swiftest output files are stored in the /supercatastrophic_multi subdirectory.
 
+**NOTE: You must have ffmpeg installed on your system before running this script. For instance, on MacOS:
+
+```brew install ffmpeg```
+
+on Ubuntu:
+
+```sudo apt-get install ffmpeg```
+
+
 Inputs
 _______
 None.
@@ -47,6 +56,16 @@
 movie_titles = dict(zip(available_movie_styles, movie_title_list))
 num_movie_frames = 1000
 
+
+# ----------------------------------------------------------------------------------------------------------------------
+# To increase the number of bodies generated in each collision type, decrease the value of the corresponding nfrag_reduction number 
+# ----------------------------------------------------------------------------------------------------------------------
+nfrag_reduction = {
+    "supercatastrophic_multi" : 5.0,
+    }
+
+
+
 # These initial conditions were generated by trial and error
 names = ["Body1","Body2","Body3","Body4"]
 
@@ -287,7 +306,12 @@ def vec_props(self, c):
         # Set fragmentation parameters
         minimum_fragment_gmass = 0.05 * body_Gmass[style][1] 
         gmtiny = 0.10 * body_Gmass[style][1] 
-        sim.set_parameter(collision_model="fraggle", encounter_save="both", gmtiny=gmtiny, minimum_fragment_gmass=minimum_fragment_gmass, verbose=False)
+        sim.set_parameter(collision_model="fraggle", 
+                          encounter_save="both", 
+                          gmtiny=gmtiny, 
+                          minimum_fragment_gmass=minimum_fragment_gmass,
+                          nfrag_reduction=nfrag_reduction[style],
+                          verbose=False)
         sim.run(dt=5e-4, tstop=tstop[style], istep_out=1, dump_cadence=0)
 
         print("Generating animation")