Job splitter consolidate

docs/source/user_guide/user_guide_chemistry.rst

@@ -536,7 +536,7 @@ This combines the main pieces in one small script:
     source.position.translation = [0, 0, 0]
     source.direction.type = "momentum"
     source.direction.momentum = [0, 0, 1]
-    source.n = 1
+    source.number_of_primaries = 1
 
     chem_actor = sim.add_actor("ChemicalCountingActor", "chem_actor")
     chem_actor.attached_to = target

docs/source/user_guide/user_guide_dynamic_parametrisations.rst

@@ -171,7 +171,7 @@ Example:
    source.image = "activity_0.mhd"
    source.direction.type = "iso"
    source.energy.mono = 1 * MeV
-   source.n = [2000, 2000, 2000]
+   source.number_of_primaries = [2000, 2000, 2000]
 
    source.add_dynamic_parametrisation(
        image=[

docs/source/user_guide/user_guide_how_to_convert_example_1.rst

@@ -209,7 +209,7 @@ GATE 10
     source.position.translation = [1060 * mm, 0 * mm, 0 * mm]
     source.direction.type = "focused"
     source.direction.focus_point = [1000 * mm, 0 * mm, 0 * mm]
-    source.n = 720000 / sim.number_of_threads
+    source.number_of_primaries = 720000
     source.visualization.count = 2000
     source.visualization.color = "red"
     source.visualization.size = 5

docs/source/user_guide/user_guide_multijobs.rst

@@ -7,7 +7,7 @@ their output again. This is useful in two main situations:
 - local execution on a workstation, where several jobs can run in parallel on
   the same machine
 - server execution, where the split jobs are submitted to a scheduler such as
-  Slurm or HTCondor
+  Slurm, HTCondor or PBS.
 
 This section focuses on the current user-facing workflow in GATE 10.
 
@@ -186,8 +186,8 @@ Server-based execution
 Server-based multi-job execution is intended to be file-based:
 
 1. prepare a campaign folder containing ``simulation.json`` and the required
-   input files, probably locally
-2. transfer the campaign folder to the server
+   input files, probably locally. To create the ```simulation.json`` file, you can use ``sim.to_json_file()`` in your script instead of ``sim.run(...)``.
+2. transfer the campaign folder to the server. Be aware of the paths used in the .json file for material database or other input files. They should be valid on the server.
 3. split the simulation on the server
 4. submit the child jobs through a scheduler
 5. inspect job status
@@ -246,6 +246,9 @@ As a convenience, the command also looks for a default file named
 ``jobs_backend_options.json`` inside the campaign folder when no
 ``--backend-options-json`` or ``--backend`` argument is provided.
 
+You can find examples you can adapt in the ``contrib/cluster`` folder for slurm and pbs backends.
+The backend options are usually site-specific and should be adapted to the target server.
+
 
 .. Example: Slurm
 .. ~~~~~~~~~~~~~~

docs/source/user_guide/user_guide_optical_photons.rst

@@ -16,7 +16,7 @@ Optical photon polarization is not yet implemented. A random polarization is the
     source.particle = 'opticalphoton'
     source.position.type = 'point'
     source.position.translation = [0, 0, 0]
-    source.n = 100
+    source.number_of_primaries = 100
     source.energy.mono = 1.9*eV
     source.mother = 'world
     source.direction.type = "iso"

docs/source/user_guide/user_guide_reference_sources_common_params.rst

@@ -11,13 +11,13 @@ a source description to the simulation, you do:
 .. code:: python
 
    source1 = sim.add_source('GenericSource', 'source1')
-   source1.n = 100
+   source1.number_of_primaries = 100
 
    source2 = sim.add_source('VoxelSource', 'source2')
    source2.activity = 10 * g4_units.Bq
 
 There are several source types, each one with different parameter. In
-this example, ``source1.n`` indicates that this source will generate 100
+this example, ``source1.number_of_primaries`` indicates that this source will generate 100
 Events. The second source manages the time and will generate 10 Events
 per second, so according to the simulation run timing, a different
 number of Events will be generated.
@@ -59,11 +59,11 @@ A minimal working example would be:
 .. code:: python
 
    source = sim.add_source("GenericSource", "mySource")
-   source.n = [10000, 150000, 36]
+   source.number_of_primaries = [10000, 150000, 36]
 
    sim.run_timing_intervals = [[0, 1 * sec], [2 * sec, 2.5 * sec], [4 * sec, 5 * sec]]
 
-Please note that sim.run_timing_intervals must have the same length as source.n.
+Please note that ``sim.run_timing_intervals`` must have the same length as ``source.number_of_primaries``.
 By default, all particles of a given run will be emitted at a timestamp equal to the minimum time of the corresponding timing interval.
 
 Coordinate system

docs/source/user_guide/user_guide_reference_sources_generic_source.rst

@@ -34,7 +34,7 @@ All parameters are stored into a dict-like structure (a Box). Particle
 can be ‘gamma’, ‘e+’, ‘e-’, ‘proton’ (all Geant4 names). The number of
 particles that will be generated by the source can be described by an
 activity ``source.activity = 10 * MBq`` or by a number of particle
-``source.n = 100``.
+``source.number_of_primaries = 100``.
 
 The positions from where the particles will be generated are defined by a
 shape (‘box’, ‘sphere’, ‘point’, ‘disc’), defined by several parameters

docs/source/user_guide/user_guide_reference_sources_ion_pencil_beam_source.rst

@@ -41,7 +41,7 @@ a 120 MeV/n carbon ion beam.
    source.energy.mono = 1440 * MeV
    source.particle = "ion 6 12"  # carbon
    source.position.translation = [100 * mm, 0 * mm, 0 * cm]
-   source.n = 20000
+   source.number_of_primaries = 20000
    source.direction.partPhSp_x = [
        2.3335754 * mm,
        2.3335754 * mrad,

docs/source/user_guide/user_guide_reference_sources_treatment_plan_pencil_beam_source.rst

@@ -85,7 +85,7 @@ simulation:
        # tps
        n_sim = 80000  # particles to simulate per beam
        tps = sim.add_source("TreatmentPlanPBSource", "TPSource")
-       tps.n = n_sim
+       tps.number_of_primaries = n_sim
        tps.beam_model = beamline
        tps.plan_path = ref_path / "TreatmentPlan2Spots.txt"
        tps.beam_nr = 1

docs/source/user_guide/user_guide_sources.rst

@@ -49,7 +49,7 @@ Alternatively, you can specify the fixed number of primary particles to be gener
 
 .. code-block:: python
 
-    source1.n = 100
+    source1.number_of_primaries = 100
 
 Note, however, that certain functionality regarding simulations with multiple runs is currently not supported when sources specify the fixed number of particles as above.
 

opengate/actors/biasingactors.py

@@ -418,11 +418,20 @@ class ScatterSplittingFreeFlightActor(
             False,
             {"doc": "Print debug information during the stepping action of the actor."},
         ),
+        "particles": (
+            [
+                "gamma",
+            ],
+            {
+                "doc": "For scatter splitting free flight, only gamma is supported",
+                "override": True,
+                "read_only": True,
+            },
+        ),
     }
 
     # Do NOT work with GammaGeneralProc
     processes = ["compt", "phot", "conv", "Rayl"]
-    particles = ["gamma"]
 
     user_output_config = {
         "info": {

opengate/actors/digitizers.py

@@ -466,12 +466,6 @@ class DigitizerBlurringActor(DigitizerWithRootOutput, g4.GateDigitizerBlurringAc
                 "doc": "FIXME",
             },
         ),
-        "blur_fwhm": (
-            None,
-            {
-                "doc": "FIXME",
-            },
-        ),
         "blur_reference_value": (
             0,
             {
@@ -503,6 +497,14 @@ def __initcpp__(self):
         g4.GateDigitizerBlurringActor.__init__(self, self.user_info)
         self.AddActions({"StartSimulationAction", "EndSimulationAction"})
 
+    @property
+    def blur_fwhm(self):
+        return self.blur_sigma / fwhm_to_sigma
+
+    @blur_fwhm.setter
+    def blur_fwhm(self, value):
+        self.blur_sigma = value * fwhm_to_sigma
+
     def resolve_and_validate_config(self, context=None):
         super().resolve_and_validate_config(context=context)
         self.initialize_blurring_parameters()
@@ -523,13 +525,6 @@ def initialize_blurring_parameters(self):
             self.set_param_linear()
 
     def set_param_gauss(self):
-        if self.blur_fwhm is not None and self.blur_sigma is not None:
-            fatal(
-                f"Error, use blur_sigma or blur_fwhm, not both "
-                f"(there are: {self.blur_sigma} and {self.blur_fwhm}"
-            )
-        if self.blur_fwhm is not None:
-            self.blur_sigma = self.blur_fwhm * fwhm_to_sigma
         if self.blur_sigma is None:
             fatal(f"Error, use blur_sigma or blur_fwhm")
         self.blur_reference_value = -1
@@ -547,7 +542,6 @@ def set_param_inverse_square(self):
                 f"Error, use positive blur_resolution "
                 f"(current value =  {self.blur_resolution}"
             )
-        self.blur_fwhm = -1
         self.blur_sigma = -1
         if self.blur_slope is None:
             self.blur_slope = 0
@@ -804,7 +798,6 @@ class DigitizerSpatialBlurringActor(
     skip_attributes: List
     clear_every: int
     blur_attribute: str
-    blur_fwhm: float
     blur_sigma: float
     keep_in_solid_limits: bool
     use_truncated_Gaussian: bool
@@ -840,12 +833,6 @@ class DigitizerSpatialBlurringActor(
                 "doc": "Which attribute to blur, e.g. PostPosition.",
             },
         ),
-        "blur_fwhm": (
-            None,
-            {
-                "doc": "FWHM for the blurring.",
-            },
-        ),
         "blur_sigma": (
             None,
             {
@@ -876,18 +863,19 @@ def __initcpp__(self):
         g4.GateDigitizerSpatialBlurringActor.__init__(self, self.user_info)
         self.AddActions({"StartSimulationAction", "EndSimulationAction"})
 
+    @property
+    def blur_fwhm(self):
+        return self.blur_sigma / fwhm_to_sigma
+
+    @blur_fwhm.setter
+    def blur_fwhm(self, value):
+        if not hasattr(value, "__len__"):
+            value = [value] * 3
+        self.blur_sigma = np.array(value) * fwhm_to_sigma
+
     def initialize_blurring_parameters(self):
-        if self.blur_fwhm is not None and self.blur_sigma is not None:
-            fatal(
-                f"Error, use blur_sigma or blur_fwhm, not both "
-                f"(there are: {self.blur_sigma} and {self.blur_fwhm}"
-            )
         if not hasattr(self.blur_sigma, "__len__"):
             self.blur_sigma = [self.blur_sigma] * 3
-        if not hasattr(self.blur_fwhm, "__len__"):
-            self.blur_fwhm = [self.blur_fwhm] * 3
-        if self.blur_fwhm is not None:
-            self.blur_sigma = np.array(self.blur_fwhm) * fwhm_to_sigma
         if self.blur_sigma is None:
             fatal(f"Error, use blur_sigma or blur_fwhm")
 

opengate/bin/opengate_jobs_run.py

@@ -60,6 +60,10 @@ def _resolve_backend_configuration(campaign_dir, backend, backend_options_json):
 @click.option(
     "--backend",
     default=None,
+    type=click.Choice(
+        ["local_pool", "local_sequential", "htcondor", "slurm", "pbs"],
+        case_sensitive=False,
+    ),
     help="Execution backend to use. If omitted, the command looks for jobs_backend_options.json in CAMPAIGN_DIR.",
 )
 @click.option(

opengate/contrib/cluster/pbs/opengate_job.pbs

@@ -0,0 +1,40 @@
+#!/bin/bash -x
+#PBS -l walltime=10:00:00
+#PBS -N opengate
+#PBS -l nodes=1:ppn=1
+#PBS -l mem=10gb
+#PBS -o opengate.out
+#PBS -e opengate.err
+
+function error {
+    echo "ERROR: $1"
+    exit 1
+}
+
+function warning {
+    echo "WARNING: $1"
+}
+
+date
+test -f ${HOME}/.bashrc && echo "Sourcing bashrc" && source ${HOME}/.bashrc
+set -u
+
+echo "Checking"
+uname -a
+echo "JOBDIR=${JOBDIR:?"unknown JOBDIR"}"
+
+echo Using $(which python)
+test -d "${JOBDIR}"  || error "invalid macro"
+
+
+# Enforce one thread
+ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
+
+echo "Lauching macro"
+date
+cd ${JOBDIR}
+cd ..
+opengate_job_runner --backend "slurm" ${JOBDIR} || error "gate failed"
+date
+
+echo "Success!!!"

opengate/contrib/cluster/pbs/pbs.json

@@ -0,0 +1,10 @@
+{
+  "backend": "pbs",
+  "backend_options": {
+    "submit_binary": "qsub",
+    "submit_filename": "opengate_job.pbs",
+    "command_line_args": [
+      "-V"
+    ]
+  }
+}

opengate/contrib/cluster/slurm/opengate_job.slurm

@@ -0,0 +1,41 @@
+#!/bin/bash -x
+#SBATCH --mem=20000               # Memory in MB per default
+#SBATCH --time=10:00:00
+#SBATCH --ntasks=1
+#SBATCH --output=Seq%j.out       # nom du fichier de sortie
+#SBATCH --error=Seq%j.out        # nom du fichier d'erreur (ici en commun avec la sortie)
+
+
+
+function error {
+    echo "ERROR: $1"
+    exit 1
+}
+
+function warning {
+    echo "WARNING: $1"
+}
+
+date
+test -f ${HOME}/.bashrc && echo "Sourcing bashrc" && source ${HOME}/.bashrc
+set -u
+
+echo "Checking"
+uname -a
+echo "JOBDIR=${JOBDIR:?"unknown JOBDIR"}"
+
+echo Using $(which python)
+test -d "${JOBDIR}"  || error "invalid macro"
+
+
+# Enforce one thread
+ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
+
+echo "Lauching macro"
+date
+cd ${JOBDIR}
+cd ..
+opengate_job_runner --backend "slurm" ${JOBDIR} || error "gate failed"
+date
+
+echo "Success!!!"

opengate/contrib/cluster/slurm/slurm.json

@@ -0,0 +1,10 @@
+{
+  "backend": "slurm",
+  "backend_options": {
+    "submit_binary": "sbatch",
+    "submit_filename": "opengate_job.slurm",
+    "command_line_args": [
+      "--export=ALL"
+    ]
+  }
+}

opengate/contrib/linacs/elektaversa.py

@@ -258,7 +258,7 @@ def add_cone(sim, p):
     ff_base.color = yellow
     ff_base.material = "flattening_filter_material_stain_steel"
 
-    cons_1 = volumes.ConsVolume(name=f"{linac_name}_flattening_filter_cons_1")
+    cons_1 = volumes.ConsVolume(name=f"{linac_name}_flattening_filter_cons_1_bis")
     cons_1.rmin1 = 77 / 2 * mm
     cons_1.rmax1 = 93 / 2 * mm
     cons_1.rmin2 = 77 / 2 * mm

opengate/geometry/solids.py

@@ -132,10 +132,13 @@ def from_dictionary(self, d):
                 "The provided dictionary does not contain an entry 'creator_volumes'."
             )
         for i, cv in enumerate(creator_volumes):
-            try:
-                vol = self.volume_manager.volumes[cv["user_info"]["name"]]
-            except KeyError:
+            if self.volume_manager is None:
                 vol = create_gate_object_from_dict(cv)
+            else:
+                try:
+                    vol = self.volume_manager.volumes[cv["user_info"]["name"]]
+                except KeyError:
+                    vol = create_gate_object_from_dict(cv)
 
             self.creator_volumes[i] = vol
             self.creator_volumes[i].from_dictionary(cv)