diff --git a/cheetah/accelerator.py b/cheetah/accelerator.py
index 0423b13a..7b74b235 100644
--- a/cheetah/accelerator.py
+++ b/cheetah/accelerator.py
@@ -984,15 +984,20 @@ class BPM(Element):
"""
Beam Position Monitor (BPM) in a particle accelerator.
+ :param is_active: If `True` the BPM is active and will record the beam's position.
+ If `False` the BPM is inactive and will not record the beam's position.
:param name: Unique identifier of the element.
:param device: Device to move the beam's particle array to. If set to `"auto"` a
CUDA GPU is selected if available. The CPU is used otherwise.
"""
- def __init__(self, name: Optional[str] = None, device: str = "auto") -> None:
+ def __init__(
+ self, is_active: bool = False, name: Optional[str] = None, device: str = "auto"
+ ) -> None:
super().__init__(name=name, device=device)
- self.reading = (None, None)
+ self.is_active = is_active
+ self.reading = None
@property
def is_skippable(self) -> bool:
@@ -1003,17 +1008,16 @@ def transfer_map(self, energy: torch.Tensor) -> torch.Tensor:
def track(self, incoming: Beam) -> Beam:
if incoming is Beam.empty:
- self.reading = (None, None)
- return Beam.empty
+ self.reading = None
elif isinstance(incoming, ParameterBeam):
- self.reading = (incoming.mu_x, incoming.mu_y)
- return ParameterBeam(incoming._mu, incoming._cov, incoming.energy)
+ self.reading = torch.stack([incoming._mu_x, incoming._mu_y])
elif isinstance(incoming, ParticleBeam):
- self.reading = (incoming.mu_x, incoming.mu_y)
- return ParticleBeam(incoming.particles, incoming.energy, device=self.device)
+ self.reading = torch.stack([incoming.mu_x, incoming.mu_y])
else:
raise TypeError(f"Parameter incoming is of invalid type {type(incoming)}")
+ return deepcopy(incoming)
+
def split(self, resolution: torch.Tensor) -> list[Element]:
return [self]
@@ -1731,9 +1735,11 @@ def plot_reference_particle_traces(
in the plot.
"""
reference_segment = deepcopy(self)
- splits = reference_segment.split(resolution)
+ splits = reference_segment.split(resolution=torch.tensor(resolution))
- split_lengths = [split.length for split in splits]
+ split_lengths = [
+ split.length if hasattr(split, "length") else 0.0 for split in splits
+ ]
ss = [0] + [sum(split_lengths[: i + 1]) for i, _ in enumerate(split_lengths)]
references = []
diff --git a/docs/examples/simple.ipynb b/docs/examples/simple.ipynb
index 239185c8..7944b98a 100644
--- a/docs/examples/simple.ipynb
+++ b/docs/examples/simple.ipynb
@@ -57,6 +57,26 @@
")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "segment.is_skippable"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -68,7 +88,138 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_segment = Segment(elements=[Drift(length=0.2), BPM(name=\"BPM1\")])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_segment.is_skippable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Drift(length=0.2).is_skippable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BPM(name=\"BPM1\").is_skippable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BPM(name=\"BPM1\").is_active"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "cheetah.accelerator.BPM"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BPM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from cheetah import Screen\n",
+ "\n",
+ "Screen(name=\"SCR1\").is_skippable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -84,7 +235,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -100,23 +251,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 13,
"metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'Segment' object has no attribute 'is_skippable'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/Users/jankaiser/Documents/DESY/cheetah/docs/examples/simple.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m outgoing_beam \u001b[39m=\u001b[39m segment\u001b[39m.\u001b[39;49mtrack(incoming_beam)\n",
- "File \u001b[0;32m~/Documents/DESY/cheetah/cheetah/accelerator.py:1671\u001b[0m, in \u001b[0;36mSegment.track\u001b[0;34m(self, incoming)\u001b[0m\n\u001b[1;32m 1670\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtrack\u001b[39m(\u001b[39mself\u001b[39m, incoming: Beam) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Beam:\n\u001b[0;32m-> 1671\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mis_skippable:\n\u001b[1;32m 1672\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mtrack(incoming)\n\u001b[1;32m 1673\u001b[0m \u001b[39melse\u001b[39;00m:\n",
- "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/cheetah-dev/lib/python3.9/site-packages/torch/nn/modules/module.py:1614\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m modules:\n\u001b[1;32m 1613\u001b[0m \u001b[39mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1614\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 1615\u001b[0m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, name))\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'Segment' object has no attribute 'is_skippable'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"outgoing_beam = segment.track(incoming_beam)"
]
@@ -130,9 +267,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"segment.plot_overview(beam=incoming_beam)"
]