MESHTAL file#
The complete API can be found at f4enix.output.meshtal.Meshtal
It is possible to parse and MCNP meshtal file and obtain a pyvista object for each of the fmeshes.
# import the related module and parse a meshtal file
from f4enix.output.meshtal import Meshtal
# file = 'mode0_plasma_prod.msht'
file = 'meshtal'
meshtal = Meshtal(file)
# By default all meshes are parsed, but if speed up is needed, only a subset
# of them can be selected
meshtal.readMesh()
meshtal
/home/docs/checkouts/readthedocs.org/user_builds/f4enix/envs/developing/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
<f4enix.output.meshtal.meshtal.Meshtal at 0x778e79e09210>
import tempfile # To have a scratch directory for the example
outpath = tempfile.gettempdir()
# fmeshes can be dumped all together
meshtal.write_all(outpath, out_format='vtk')
# Or specifically with higher control
meshtal.mesh[1004].write(outpath, list_array_names=['Value - Total'],
out_format='ip_fluent', outfile='custom name')
Writing x: 0%| | 0/1000 [00:00<?, ? x points/s]
Writing x: 100%|██████████| 1000/1000 [00:00<00:00, 100868.26 x points/s]
Writing y: 0%| | 0/1000 [00:00<?, ? y points/s]
Writing y: 100%|██████████| 1000/1000 [00:00<00:00, 94440.78 y points/s]
Writing z: 0%| | 0/1000 [00:00<?, ? z points/s]
Writing z: 100%|██████████| 1000/1000 [00:00<00:00, 103414.96 z points/s]
Writing values: 0%| | 0/1000 [00:00<?, ? values/s]
Writing values: 100%|██████████| 1000/1000 [00:00<00:00, 1027260.35 values/s]
instead of writing them all separate it could be useful to collapse all fmeshes into a single pyvista grid. This is possible only if the fmeshes have the same geometry and have no binning.
# this dictionary provides the names for the value and error in each fmesh
dict_names = {1004: ['neutron flux', 'neutron flux err'],
1024: ['photon flux', 'photon flux err']}
collapsed_grid = meshtal.collapse_grids(dict_names)
collapsed_grid
RectilinearGrid (0x778e3c32bca0) N Cells: 1000 N Points: 1331 X Bounds: -1.300e+03, 1.300e+03 Y Bounds: -1.300e+03, 1.300e+03 Z Bounds: -9.000e+02, 9.000e+02 Dimensions: 11, 11, 11 N Arrays: 4
# select a specific fmesh.
n_flux_fmesh = meshtal.mesh[1004]
# Access the related pyvista object and all its powerful methods
# The units from MCNP apply if no modification is made by the user
n_flux = n_flux_fmesh.grid
n_flux
RectilinearGrid (0x778e3c32ba00) N Cells: 1000 N Points: 1331 X Bounds: -1.300e+03, 1.300e+03 Y Bounds: -1.300e+03, 1.300e+03 Z Bounds: -9.000e+02, 9.000e+02 Dimensions: 11, 11, 11 N Arrays: 2
import pyvista as pv
try:
pv.start_xvfb()
except (OSError, AttributeError):
# this is needed only on Linux headless servers
pass
# quick plot to check that the meshes are not empty
clip = n_flux.clip(normal='z') # native pyvista clip
clip.plot(scalars='Value - Total', jupyter_backend='static')
2026-05-19 11:11:27.336 ( 0.944s) [ 778E8E0BFB80]vtkXOpenGLRenderWindow.:1460 WARN| bad X server connection. DISPLAY=
import pyvista as pv
# Load an .stl file to be used for plots and plot them with the fmesh to check
# that the units match. stl units will depend by export settings of the user
stl = pv.read('iter1D.stl').scale(10) # scale the stl to have same units
# Set up and show a pyvista plotter
plotter = pv.Plotter()
plotter.add_mesh(clip)
plotter.add_mesh(stl, opacity=0.4)
plotter.show(jupyter_backend='static')
# Load additional modules related to Atlas production
from f4enix.output.plotter import MeshPlotter, Atlas
from copy import deepcopy
# Get a basic PyVista mesh where to store all other data
global_mesh = deepcopy(meshtal.mesh[1004].grid)
# On the same grid, load all the different fmeshes results. In this case there
# are only two
for tally_num, fmesh in meshtal.mesh.items():
# get the FC card comment for the fmesh to be used as name of the scalar
name = fmesh.comments.strip()
# get the scalar values
data = fmesh.grid['Value - Total']
# Adding the array to the global mesh
global_mesh[name] = data
# Clean the original results from the template mesh
global_mesh.cell_data.remove('Value - Total')
global_mesh.cell_data.remove('Error - Total')
global_mesh
RectilinearGrid (0x778e36797880) N Cells: 1000 N Points: 1331 X Bounds: -1.300e+03, 1.300e+03 Y Bounds: -1.300e+03, 1.300e+03 Z Bounds: -9.000e+02, 9.000e+02 Dimensions: 11, 11, 11 N Arrays: 2
Another handy method from pyvista is the voxelization of stl file and subsequent mapping of pyvista grid onto these voxelized stl
import pyvista as pv
divisions = 50 # increase this will lead to better detail (i.e. smaller size of voxels)
voxelized = stl.voxelize(spacing=stl.length/divisions)
voxelized.clip(normal='y').plot(jupyter_backend='static')
mapped = voxelized.sample(meshtal.mesh[1004].grid)
plotter = pv.Plotter()
plotter.add_mesh(mapped.clip(normal='y'), scalars='Value - Total')
plotter.show(jupyter_backend='static')
Slicing#
There are different slicing methods that have been defined in F4Enix. All of them produces slices that can be automatically plotted in order to build an atlas.
# Initialize the custom plotter with the mesh and stl
plotter = MeshPlotter(global_mesh, stl=stl)
# There are many default settings that can be modified, e.g.:
# plotter.legend_args['vertical'] = False
# toroidal slicing. It can be done on all the 180 deg or specify the sector
# and origin
toroidal_slices = plotter.slice_toroidal(30) # 30 deg. increment
# Show the first slice
print(toroidal_slices[0]) # (name of the slice, mesh slice, stl slice)
# Use pyvista native plotter just as an example to show what is the output
pv_plotter = pv.Plotter()
for slices in toroidal_slices:
mesh_slice = slices[1]
pv_plotter.add_mesh(mesh_slice)
pv_plotter.show(jupyter_backend='static')
/home/docs/checkouts/readthedocs.org/user_builds/f4enix/envs/developing/lib/python3.10/site-packages/pyvista/core/utilities/helpers.py:394: RuntimeWarning: invalid value encountered in divide
normal_ = normal_ / np.linalg.norm(normal_)
('theta = 0.0 deg', PolyData (0x778e340f8760)
N Cells: 100
N Points: 121
N Strips: 0
X Bounds: 0.000e+00, 0.000e+00
Y Bounds: -1.300e+03, 1.300e+03
Z Bounds: -9.000e+02, 9.000e+02
N Arrays: 2, PolyData (0x778e340f8820)
N Cells: 190
N Points: 138
N Strips: 0
X Bounds: 4.000e-01, 4.000e-01
Y Bounds: -1.379e+03, 1.379e+03
Z Bounds: -9.000e+02, 9.000e+02
N Arrays: 0)
Normal slicing#
# perform slices normal to the selected axis
horizontal_slices = plotter.slice_on_axis('z', 3) # 3 slices from max to min
# Show the first slice
print(toroidal_slices[0]) # (name of the slice, mesh slice, stl slice)
# Use pyvista native plotter just as an example to show what is the output
pv_plotter = pv.Plotter()
for slices in horizontal_slices:
mesh_slice = slices[1]
pv_plotter.add_mesh(mesh_slice)
pv_plotter.show(jupyter_backend='static')
('theta = 0.0 deg', PolyData (0x778e340f8760)
N Cells: 100
N Points: 121
N Strips: 0
X Bounds: 0.000e+00, 0.000e+00
Y Bounds: -1.300e+03, 1.300e+03
Z Bounds: -9.000e+02, 9.000e+02
N Arrays: 2, PolyData (0x778e340f8820)
N Cells: 190
N Points: 138
N Strips: 0
X Bounds: 4.000e-01, 4.000e-01
Y Bounds: -1.379e+03, 1.379e+03
Z Bounds: -9.000e+02, 9.000e+02
N Arrays: 0)
General slicing#
# General slicing using origin and normals for each slice
# [name, x, y, z, ux, uy, uz]
slices_param = [['slice1', 0, 0, 0, 1, 0, 0],
['slice2', 500, 700, 300, 0.5, 0.5, 0]]
general_slices = plotter.slice(slices_param)
# Use pyvista native plotter just as an example to show what is the output
pv_plotter = pv.Plotter()
for slices in general_slices:
mesh_slice = slices[1]
stl_slice = slices[2]
pv_plotter.add_mesh(mesh_slice)
pv_plotter.add_mesh(stl_slice)
pv_plotter.show(jupyter_backend='static')
Build the Atlas#
pv.global_theme.allow_empty_mesh = True
# Time to plot the slices
meshtal = Meshtal("meshtal")
stl = pv.read("iter1D.stl")
meshtal.readMesh()
mesh = meshtal.mesh[1004].grid
plotter = MeshPlotter(mesh, stl.scale(10))
toroidal_slices = plotter.slice_toroidal(30) # 30 deg. increment
horizontal_slices = plotter.slice_on_axis("z", 3) # 3 slices from max to min
global_mesh = plotter.mesh
# Cycle on all the mesh quantities
sections = []
for array_name in global_mesh.array_names:
# Arbitrary logic can be inserted here
min_val = 1e6
max_val = 1e15
n_colors = 9
min_max = (min_val, max_val)
# Plot both the vertical and toroidal slice related to the quantity
tot_images = []
for slices in [toroidal_slices, horizontal_slices]:
# Plot the slices
images = plotter.plot_slices(
slices, array_name, n_colors=n_colors, min_max=min_max
)
tot_images.extend(images)
sections.append((array_name, tot_images))
# Show one of the images that will build the atlas
print(sections[0])
print(sections[0][1])
print(sections[0][1][-1])
sections[0][1][-1][1]
('Value - Total', [('theta = 0.0 deg', <PIL.Image.Image image mode=RGB size=816x522 at 0x778E367BA7A0>), ('theta = 30.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C7CA0>), ('theta = 60.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E36A85AE0>), ('theta = 90.0 deg', <PIL.Image.Image image mode=RGB size=220x467 at 0x778E340C7370>), ('theta = 120.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C52A0>), ('theta = 150.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C79D0>), ('Pz = -882.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E36A87D00>), ('Pz = 0.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E3411BF10>), ('Pz = 882.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E242B4D00>)])
[('theta = 0.0 deg', <PIL.Image.Image image mode=RGB size=816x522 at 0x778E367BA7A0>), ('theta = 30.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C7CA0>), ('theta = 60.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E36A85AE0>), ('theta = 90.0 deg', <PIL.Image.Image image mode=RGB size=220x467 at 0x778E340C7370>), ('theta = 120.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C52A0>), ('theta = 150.0 deg', <PIL.Image.Image image mode=RGB size=829x502 at 0x778E340C79D0>), ('Pz = -882.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E36A87D00>), ('Pz = 0.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E3411BF10>), ('Pz = 882.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E242B4D00>)]
('Pz = 882.0', <PIL.Image.Image image mode=RGB size=774x574 at 0x778E242B4D00>)
import tempfile # To have a scratch directory for the example
# Finally build the atlas. This can be built either from folders containing
# images, where each subfolder is interpreted as a new section, or adding each
# section manually. This second option is recommended since it allows to avoid
# saving the images to disk
# initialize the atlas
atlas = Atlas('Atlas example')
# Decrease the default width of plots (by default they occupy the entire text
# length)
atlas.default_width = atlas.default_width*0.9
# Build one section for each quantity
for arrayname, images in sections:
atlas.add_section(arrayname, images)
outpath = tempfile.gettempdir()
print(outpath) # We are saving in the default temporary directory
atlas.save(outpath)
/tmp
Add volume sampling error to CuV Meshtal files#
We are going to calculate the volume with a Monte Carlo integral. A random sample of N points inside a voxel is done, checking for each point if it falls inside the studied cell.
\[V_r=1/N ∑_i b_i\]
Where \(b_i\) is a random variable that follows a binomial distribution (0 or 1) depending on if the point fell inside the studied cell. \(V_r\) is the partial cell volume over the voxel volume. To know the uncertainty of the estimator V_r we can apply the uncertainty propagation law, as each point is sampled independently.
\[ σ_{(V_r)}^2=∑_i((∂V_r)/(∂b_i ))^2⋅σ_{b_i}^2 =∑_i1/N^2 ⋅σ_{b_i}^2\]
All \(b_i\) variables follow the same binomial probability distribution, that is, each point has the same chance to fall in the cell. Therefore, \(σ_{(b_i)}^2=σ_b^2\) . Then we can make the sum so:
\[σ_{V_r}^2=(σ_b^2)/N\]
Where \(σ_b^2\) can be calculated via the variance numeric estimator.
\[σ_b^2=〈b^2 〉-〈b〉^2\]
Up to this point, we have seen the same procedure as in many Monte Carlo integrals. For example, that if MCNP as seen in page 2-109 of the MCNP5 Manual I.However, the volume we are calculating is a binomial (0 or 1).
\[〈b〉≈1/N ∑_ib_i =V_r\]
\[〈b^2 〉≈1/N ∑_ib_i^2 =1/N ∑_ib_i =V_r\]
Therefore:
\[σ_{(V_r)}^2=(σ_b^2)/N=(〈b^2 〉-〈b〉^2)/N=(V_r-V_r^2)/N\]
import tempfile # To have a scratch directory for the example
import os
import pyvista as pv
from f4enix.output.meshtal import Meshtal
from f4enix.output.cuv_sampling_error import add_sampling_error_to_vtk
# Read a meshtal and create a VTK file
meshtal = Meshtal("meshtal_cuv", filetype="CUV")
meshtal.readMesh(norm='ctot')
# write to vtk
tmpdir = tempfile.gettempdir()
outname = 'outfile'
outpath = os.path.join(tmpdir, outname)
meshtal.mesh[44].write(tmpdir, outfile=outname)
# Read the VTK file
grid = pv.read(os.path.join(tmpdir, outname + '.vtr'))
# The amount of sampling points per voxel should be identified from the MCNP input file
# by the user. In this example, it is set to 1000 points per voxel.
grid_with_errors = add_sampling_error_to_vtk(
grid=grid, cuv_file_path="meshtal_cuv", voxel_sampling_points=1000
)
grid_with_errors
RectilinearGrid (0x778ddde8bac0) N Cells: 16 N Points: 50 X Bounds: -1.000e+01, 1.000e+01 Y Bounds: -1.000e+01, 1.000e+01 Z Bounds: -5.000e+00, 5.000e+00 Dimensions: 5, 5, 2 N Arrays: 3
Apply transformations to FMESH grids#
It is possible to apply transformations to the FMESH tally grids of the meshtal file, given a MCNP TR card. The most standard way is by applying a TR card to a FMESH grid:
from numjuggler import parser
from f4enix.output.meshtal import Meshtal
# Define a -20 degrees rotation around the z axis with MCNP syntax
tr_text = ["*TR1 0 0 0 20.0000 70.0000 90 110.0000 20.0000 90 90 90 0\n"]
## Generate the transformation card
mcnp_transformation = parser.Card(tr_text, 5, 0)
mcnp_transformation.get_values()
# Read the meshtal file
meshtal = Meshtal("meshtal_transform")
meshtal.readMesh()
# Select the mesh to be transformed
mesh_tally_2024 = meshtal.mesh[2024]
# Plot the original mesh
mesh_tally_2024.grid.plot(scalars="Value - Total", show_edges=True, jupyter_backend='static')
# Apply the transformation to the mesh
mesh_tally_2024.apply_transformation(mcnp_transformation)
# Plot the transformed mesh
mesh_tally_2024.grid.plot(scalars="Value - Total", show_edges=True, jupyter_backend='static')
You can also apply multiple transformations to multiple meshes, just by providing a dictionary.
from numjuggler import parser
from f4enix.output.meshtal import Meshtal
# Define a -20 degrees rotation around the z axis with MCNP syntax
tr_rotation_1_text = ["*TR1 0 0 0 20.0000 70.0000 90 110.0000 20.0000 90 90 90 0\n"]
# Define a -45 degrees rotation around the z axis with MCNP syntax, with translation along x
tr_rotation_2_text = ["*TR1 10 0 0 45.0000 45.0000 90 135.0000 45.0000 90 90 90 0\n"]
## Generate the transformation cards
mcnp_transformation_1 = parser.Card(tr_rotation_1_text, 5, 0)
mcnp_transformation_1.get_values()
mcnp_transformation_2 = parser.Card(tr_rotation_2_text, 5, 0)
mcnp_transformation_2.get_values()
# Read the meshtal file
meshtal = Meshtal("meshtal_transform")
meshtal.readMesh()
dict_transformation = {2024: mcnp_transformation_1,
2124: mcnp_transformation_2}
meshtal.transform_multiple_fmesh(dict_transformation)
# Select the mesh with the first transformation
mesh_tally_2024 = meshtal.mesh[2024]
# Plot the transformed mesh
mesh_tally_2024.grid.plot(scalars="Value - Total", show_edges=True, jupyter_backend='static')
# Select the mesh with the second transformation
mesh_tally_2124 = meshtal.mesh[2124]
# Plot the transformed mesh
mesh_tally_2124.grid.plot(scalars="Value - Total", show_edges=True, jupyter_backend='static')
Moreover, you can provide F4Enix with an Input object and let F4Enix transform all FMESH tallies according to the corresponding tr=… labels assigned to each FMESH card in the input
from f4enix.input.MCNPinput import Input
from f4enix.output.meshtal import Meshtal
# Read the meshtal file
meshtal = Meshtal("meshtal_transform")
meshtal.readMesh()
inp = Input.from_input("transforms.i")
meshtal.transform_fmesh(inp)