Merge pull request #248 from CEMeNT-PSAAP/dev

version 0.11.0

Author: jpmorgan98

.github/workflows/numba_reg.yml renamed to .github/workflows/regression_test-numba_cpu.yml

@@ -22,14 +22,12 @@ jobs:
     - uses: mpi4py/setup-mpi@v1
     - name: Install dependencies
       run: |
-        #sudo apt-get install libopenmpi-dev
-        #pip install numpy numba h5py matplotlib scipy pytest colorama mpi4py ngsolve distinctipy
-        pip list 
         pip install -e .
+        pip list 
     - name: Patch Numba
       run : |
         bash .github/workflows/patch.sh
     - name: Regression Test - Numba
       run: |
           cd test/regression
-          python run.py --mode=numba
+          python run.py --mode=numba

.github/workflows/mpi_numba_reg.yml renamed to .github/workflows/regression_test-numba_cpu_mpi.yml

@@ -8,7 +8,7 @@ jobs:
     strategy:
         fail-fast: false
         matrix:
-            os: ["ubuntu-latest"] #, "macos-latest"
+            os: ["ubuntu-latest"]
     steps:
     - uses: actions/checkout@v3
     - name: Set up python 3.11
@@ -22,10 +22,8 @@ jobs:
     - uses: mpi4py/setup-mpi@v1
     - name: Install dependencies
       run: |
-        #sudo apt-get install libopenmpi-dev
-        #pip install numpy numba h5py matplotlib scipy pytest colorama mpi4py ngsolve distinctipy
-        pip list 
         pip install -e .
+        pip list 
     - name: Patch Numba
       run : |
         bash .github/workflows/patch.sh

.github/workflows/numba_gpu.yml renamed to .github/workflows/regression_test-numba_gpu.yml

@@ -24,14 +24,12 @@ jobs:
     - uses: mpi4py/setup-mpi@v1
     - name: Install dependencies
       run: |
-        #sudo apt-get install libopenmpi-dev
-        #pip install numpy numba h5py matplotlib scipy pytest colorama mpi4py ngsolve distinctipy
-        pip list 
         pip install -e .
+        pip list 
     - name: Patch Numba
       run : |
         bash .github/workflows/patch.sh
     - name: Regression Test - Numba
       run: |
           cd test/regression
-          python run.py --mode=numba
+          python run.py --mode=numba --target=gpu

.github/workflows/python_reg.yml renamed to .github/workflows/regression_test-python.yml

@@ -22,14 +22,12 @@ jobs:
     - uses: mpi4py/setup-mpi@v1
     - name: Install dependencies
       run: |
-        #sudo apt-get install libopenmpi-dev
-        #pip install numpy numba h5py matplotlib scipy pytest colorama mpi4py ngsolve distinctipy
-        pip list 
         pip install -e .
+        pip list 
     - name: Patch Numba
       run : |
         bash .github/workflows/patch.sh
     - name: Regression Test - Python
       run: |
           cd test/regression
-          python run.py
+          python run.py

.github/workflows/mpi_reg.yml renamed to .github/workflows/regression_test-python_mpi.yml

@@ -22,10 +22,8 @@ jobs:
     - uses: mpi4py/setup-mpi@v1
     - name: Install dependencies
       run: |
-        #sudo apt-get install libopenmpi-dev
-        #pip install numpy numba h5py matplotlib scipy pytest colorama mpi4py ngsolve distinctipy
-        pip list 
         pip install -e .
+        pip list 
     - name: Patch Numba
       run : |
         bash .github/workflows/patch.sh

.github/workflows/unit_tests.yml renamed to .github/workflows/unit_test.yml

@@ -15,6 +15,7 @@ __pycache__
 
 # GPU cache
 __ptxcache__
+__harmonize_cache__
 
 # Editor
 .spyproject
@@ -55,3 +56,7 @@ docs/build
 docs/source/pythonapi/generated/
 
 *.csv
+
+# Regression tests
+dummy_nuclide.h5
+source_particles.h5

.gitignore

@@ -0,0 +1,11 @@
+repos:
+  # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 24.8.0
+    hooks:
+      - id: black
+        # It is recommended to specify the latest version of Python
+        # supported by your project here, or alternatively use
+        # pre-commit's default_language_version, see
+        # https://pre-commit.com/#top_level-default_language_version
+        language_version: python3.11

.pre-commit-config.yaml

@@ -17,35 +17,28 @@ Our documentation on installation, contribution, and a brief user guide is on [R
 
 ## Installation
 
-We recommend using [`conda`](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) or some other environment manager to manage the MC/DC installation.
+We recommend using [Python virtual environments (venv)](https://docs.python.org/3/library/venv.html) or some other environment manager (e.g. conda) to manage the MC/DC installation.
 This avoids the need for admin access when installing MC/DC's dependencies and allows greater configurability for developers.
-For most users working on a single machine of which they are administrators, MC/DC can be installed via pip:
+For most users working in a venv, MC/DC can be installed via pip:
 ```bash
 pip install mcdc
 ```
-For developers or users on HPC machines, we recommend that you *not* use the pip distribution and instead install MC/DC and its dependencies via the included [install script](https://mcdc.readthedocs.io/en/latest/install.html), which builds `mpi4py` from source and uses conda to manage your environment. *This is the most reliable way to install and configure MC/DC*. It also takes care of the [Numba patch]() and can configure the [continuous energy data library](), if you have access.
+For developers or users on HPC machines, mpi4py is often distributed as part of an HPC machines given venv.
 
 ### Common issues with `mpi4py`
 
 The `pip mpi4py` distribution commonly has errors when building due to incompatible local MPI dependencies it builds off of. While pip does have some remedy for this, we recommend the following:
 * **Mac users:** we recommend `openmpi` is [installed via homebrew](https://formulae.brew.sh/formula/open-mpi) (note that more reliable mpi4py distribution can also be [found on homebrew](https://formulae.brew.sh/formula/mpi4py)), alternatively you can use `conda` if you don't have admin privileges;
 * **Linux users:** we recommend `openmpi` is installed via a root package manager if possible (e.g. `sudo apt install openmpi`) or a conda distribution (e.g. `conda install openmpi`)
-* **HPC users and developers on any system:** On HPC systems in particular, `mpi4py` must be built using the system's existing `mpi` installation. Installing MC/DC using the [install script](https://mcdc.readthedocs.io/en/latest/install.html) we've included will handle that for you by installing dependencies using conda rather than pip. It also takes care of the [Numba patch]() and can configure the [continuous energy data library](), if you have access.
+* **HPC users and developers on any system:** On HPC systems that do not supply a suitable venv, `mpi4py` may need to be built using the system's existing `mpi` installation. Installing MC/DC using the [install script](https://mcdc.readthedocs.io/en/latest/install.html) we've included will handle that for you by installing dependencies using conda rather than pip. It also takes care of the [Numba patch](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/patch_numba.sh) and can configure the [continuous energy data library](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/config_cont_energy.sh), if you have access.
 
 ### Numba Config
 
 Running MC/DC performantly in [Numba mode](#numba-mode) requires a patch to a single Numba file. If you installed MC/DC with the [install script](https://mcdc.readthedocs.io/en/latest/install.html), this patch has already been taken care of. If you installed via pip, we have a patch script will make the necessary changes for you:
-1. Download the `patch.sh` file [here]() (If you've cloned MC/DC's GitHub repository, you already have this file in your MCDC/ directory).
+1. Download the `patch.sh` file [here](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/patch_numba.sh) (If you've cloned MC/DC's GitHub repository, you already have this file in your MCDC/ directory).
 2. In your active conda environment, run `bash patch_numba.sh`.
 *If you manage your environment with conda, you will not need admin privileges*.
 
-### `visualizer` Config
-
-MC/DC has a visualizer built from the [netgen package](https://ngsolve.org/). This is not included in the base install of MC/DC due to the size of the dependencies (~300MB). To install these dependencies use
-```bash
-pip install mcdc['viz']
-```
-
 ## Running
 
 MC/DC can be executed in different modes: via pure python or via a `jit` compiled version (Numba mode). 

README.md

@@ -8,7 +8,7 @@
 # =============================================================================
 
 # Load material data
-lib = h5py.File("2d_c5g7_xs.h5", "r")
+lib = h5py.File("../../MGXS-C5G7.h5", "r")
 
 
 # Materials
@@ -41,23 +41,23 @@ def set_mat(mat):
 cy = mcdc.surface("cylinder-z", center=[0.0, 0.0], radius=radius)
 
 # Cells
-uo2 = mcdc.cell([-cy], mat_uo2)
-mox4 = mcdc.cell([-cy], mat_mox43)
-mox7 = mcdc.cell([-cy], mat_mox7)
-mox8 = mcdc.cell([-cy], mat_mox87)
-gt = mcdc.cell([-cy], mat_gt)
-fc = mcdc.cell([-cy], mat_fc)
-mod = mcdc.cell([+cy], mat_mod)
-modi = mcdc.cell([-cy], mat_mod)  # For all-water lattice
+uo2 = mcdc.cell(-cy, mat_uo2)
+mox4 = mcdc.cell(-cy, mat_mox43)
+mox7 = mcdc.cell(-cy, mat_mox7)
+mox8 = mcdc.cell(-cy, mat_mox87)
+gt = mcdc.cell(-cy, mat_gt)
+fc = mcdc.cell(-cy, mat_fc)
+mod = mcdc.cell(+cy, mat_mod)
+modi = mcdc.cell(-cy, mat_mod)  # For all-water lattice
 
 # Universes
-u = mcdc.universe([uo2, mod])["ID"]
-l = mcdc.universe([mox4, mod])["ID"]
-m = mcdc.universe([mox7, mod])["ID"]
-n = mcdc.universe([mox8, mod])["ID"]
-g = mcdc.universe([gt, mod])["ID"]
-f = mcdc.universe([fc, mod])["ID"]
-w = mcdc.universe([modi, mod])["ID"]
+u = mcdc.universe([uo2, mod])
+l = mcdc.universe([mox4, mod])
+m = mcdc.universe([mox7, mod])
+n = mcdc.universe([mox8, mod])
+g = mcdc.universe([gt, mod])
+f = mcdc.universe([fc, mod])
+w = mcdc.universe([modi, mod])
 
 # =============================================================================
 # Assemblies
@@ -125,14 +125,14 @@ def set_mat(mat):
 y0 = mcdc.surface("plane-y", y=-pitch * 17 / 2)
 y1 = mcdc.surface("plane-y", y=pitch * 17 / 2)
 # Cells
-assembly_uo2 = mcdc.cell([+x0, -x1, +y0, -y1], lattice_uo2)
-assembly_mox = mcdc.cell([+x0, -x1, +y0, -y1], lattice_mox)
-assembly_mod = mcdc.cell([+x0, -x1, +y0, -y1], lattice_mod)
+assembly_uo2 = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_uo2)
+assembly_mox = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_mox)
+assembly_mod = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_mod)
 
 # Set assemblies in their respective universes
-u_ = mcdc.universe([assembly_uo2])["ID"]
-m_ = mcdc.universe([assembly_mox])["ID"]
-w_ = mcdc.universe([assembly_mod])["ID"]
+u_ = mcdc.universe([assembly_uo2])
+m_ = mcdc.universe([assembly_mox])
+w_ = mcdc.universe([assembly_mod])
 
 # =============================================================================
 # Root universe: core
@@ -153,9 +153,9 @@ def set_mat(mat):
 y1_ = mcdc.surface("plane-y", y=0.0, bc="reflective")
 # Cell
 core = mcdc.cell(
-    [+x0_, -x1_, +y0_, -y1_],
+    +x0_ & -x1_ & +y0_ & -y1_,
     lattice_core,
-    lattice_center=[pitch * 17 * 3 / 2, -pitch * 17 * 3 / 2, 0.0],
+    translation=[pitch * 17 * 3 / 2, -pitch * 17 * 3 / 2, 0.0],
 )
 
 # Root universe
@@ -165,27 +165,21 @@ def set_mat(mat):
 # iQMC Parameters
 # =============================================================================
 N = 1e4
-maxit = 10
-tol = 1e-3
 Nx = 17 * 3 * 2
 Ny = 17 * 3 * 2
 G = 7
 x_grid = np.linspace(0.0, pitch * 17 * 3, Nx + 1)
 y_grid = np.linspace(-pitch * 17 * 3, 0.0, Ny + 1)
-solver = "power_iteration"
 
 phi0 = np.ones((G, Nx, Ny))
-fixed_source = np.zeros_like(phi0)
 
 mcdc.iQMC(
     x=x_grid,
     y=y_grid,
     g=np.ones(G),
     phi0=phi0,
-    fixed_source=fixed_source,
-    eigenmode_solver=solver,
-    maxit=maxit,
-    tol=tol,
+    mode="batched",
+    scores=["source-x", "source-y"],
 )
 
 # =============================================================================
@@ -194,7 +188,7 @@ def set_mat(mat):
 
 # Setting
 mcdc.setting(N_particle=N)
-mcdc.eigenmode()
+mcdc.eigenmode(N_inactive=40, N_active=10)
 
 # Run
 mcdc.run()

examples/eigenvalue/2d_c5g7_iqmc/input.py renamed to examples/c5g7/2d/k-eigenvalue-iqmc/input.py

@@ -8,7 +8,7 @@
 # =============================================================================
 
 # Load material data
-lib = h5py.File("2d_c5g7_xs.h5", "r")
+lib = h5py.File("../../MGXS-C5G7.h5", "r")
 
 
 # Materials
@@ -41,23 +41,23 @@ def set_mat(mat):
 cy = mcdc.surface("cylinder-z", center=[0.0, 0.0], radius=radius)
 
 # Cells
-uo2 = mcdc.cell([-cy], mat_uo2)
-mox4 = mcdc.cell([-cy], mat_mox43)
-mox7 = mcdc.cell([-cy], mat_mox7)
-mox8 = mcdc.cell([-cy], mat_mox87)
-gt = mcdc.cell([-cy], mat_gt)
-fc = mcdc.cell([-cy], mat_fc)
-mod = mcdc.cell([+cy], mat_mod)
-modi = mcdc.cell([-cy], mat_mod)  # For all-water lattice
+uo2 = mcdc.cell(-cy, mat_uo2)
+mox4 = mcdc.cell(-cy, mat_mox43)
+mox7 = mcdc.cell(-cy, mat_mox7)
+mox8 = mcdc.cell(-cy, mat_mox87)
+gt = mcdc.cell(-cy, mat_gt)
+fc = mcdc.cell(-cy, mat_fc)
+mod = mcdc.cell(+cy, mat_mod)
+modi = mcdc.cell(-cy, mat_mod)  # For all-water lattice
 
 # Universes
-u = mcdc.universe([uo2, mod])["ID"]
-l = mcdc.universe([mox4, mod])["ID"]
-m = mcdc.universe([mox7, mod])["ID"]
-n = mcdc.universe([mox8, mod])["ID"]
-g = mcdc.universe([gt, mod])["ID"]
-f = mcdc.universe([fc, mod])["ID"]
-w = mcdc.universe([modi, mod])["ID"]
+u = mcdc.universe([uo2, mod])
+l = mcdc.universe([mox4, mod])
+m = mcdc.universe([mox7, mod])
+n = mcdc.universe([mox8, mod])
+g = mcdc.universe([gt, mod])
+f = mcdc.universe([fc, mod])
+w = mcdc.universe([modi, mod])
 
 # =============================================================================
 # Assemblies
@@ -125,14 +125,14 @@ def set_mat(mat):
 y0 = mcdc.surface("plane-y", y=-pitch * 17 / 2)
 y1 = mcdc.surface("plane-y", y=pitch * 17 / 2)
 # Cells
-assembly_uo2 = mcdc.cell([+x0, -x1, +y0, -y1], lattice_uo2)
-assembly_mox = mcdc.cell([+x0, -x1, +y0, -y1], lattice_mox)
-assembly_mod = mcdc.cell([+x0, -x1, +y0, -y1], lattice_mod)
+assembly_uo2 = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_uo2)
+assembly_mox = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_mox)
+assembly_mod = mcdc.cell(+x0 & -x1 & +y0 & -y1, lattice_mod)
 
 # Set assemblies in their respective universes
-u_ = mcdc.universe([assembly_uo2])["ID"]
-m_ = mcdc.universe([assembly_mox])["ID"]
-w_ = mcdc.universe([assembly_mod])["ID"]
+u_ = mcdc.universe([assembly_uo2])
+m_ = mcdc.universe([assembly_mox])
+w_ = mcdc.universe([assembly_mod])
 
 # =============================================================================
 # Root universe: core
@@ -153,9 +153,9 @@ def set_mat(mat):
 y1_ = mcdc.surface("plane-y", y=0.0, bc="reflective")
 # Cell
 core = mcdc.cell(
-    [+x0_, -x1_, +y0_, -y1_],
+    +x0_ & -x1_ & +y0_ & -y1_,
     lattice_core,
-    lattice_center=[pitch * 17 * 3 / 2, -pitch * 17 * 3 / 2, 0.0],
+    translation=[pitch * 17 * 3 / 2, -pitch * 17 * 3 / 2, 0.0],
 )
 
 # Root universe
@@ -177,7 +177,7 @@ def set_mat(mat):
 # Tally
 x_grid = np.linspace(0.0, pitch * 17 * 3, 17 * 3 + 1)
 y_grid = np.linspace(-pitch * 17 * 3, 0.0, 17 * 3 + 1)
-mcdc.tally(scores=["flux"], x=x_grid, y=y_grid, g="all")
+mcdc.tally.mesh_tally(scores=["flux"], x=x_grid, y=y_grid, g="all")
 
 # Setting
 mcdc.setting(N_particle=1e3)

examples/eigenvalue/2d_c5g7_iqmc/process.py renamed to examples/c5g7/2d/k-eigenvalue-iqmc/process.py

@@ -9,14 +9,14 @@
 
 # Results
 with h5py.File("output.h5", "r") as f:
-    phi_avg = f["tally/flux/mean"][:]
-    phi_sd = f["tally/flux/sdev"][:]
+    phi_avg = f["tallies/mesh_tally_0/flux/mean"][:]
+    phi_sd = f["tallies/mesh_tally_0/flux/sdev"][:]
     k = f["k_cycle"][:]
     k_avg = f["k_mean"][()]
     k_sd = f["k_sdev"][()]
     rg = f["gyration_radius"][:]
-    x = f["tally/grid/x"][:]
-    y = f["tally/grid/y"][:]
+    x = f["tallies/mesh_tally_0/grid/x"][:]
+    y = f["tallies/mesh_tally_0/grid/y"][:]
 
 dx = x[1] - x[0]
 x_mid = 0.5 * (x[1:] + x[:-1])