from __future__ import annotations
import base64
import json
from dataclasses import asdict, dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Annotated, Dict, List, Tuple
import numpy as np
import xarray as xa
from openlifu.bf import Pulse, Sequence
from openlifu.bf.focal_patterns import FocalPattern
from openlifu.geo import Point
from openlifu.plan.param_constraint import ParameterConstraint
from openlifu.plan.solution_analysis import (
SolutionAnalysis,
SolutionAnalysisOptions,
find_centroid,
get_beamwidth,
get_mask,
)
from openlifu.util.annotations import OpenLIFUFieldData
from openlifu.util.json import PYFUSEncoder
from openlifu.util.units import getunitconversion, rescale_coords, rescale_data_arr
from openlifu.xdc import Transducer
def _construct_nc_filepath_from_json_filepath(json_filepath:Path) -> Path:
"""Construct a default filepath to netCDF file given filepath to associated solution json file."""
nc_filename = json_filepath.name.split(".")[0] + ".nc"
nc_filepath = json_filepath.parent / nc_filename
return nc_filepath
[docs]
@dataclass
class Solution:
"""
A sonication solution resulting from beamforming and running a simulation.
"""
id: Annotated[str, OpenLIFUFieldData("Solution ID", "ID of this solution")] = "solution" # the *solution* id, a concept that did not exist in the matlab software
"""ID of this solution"""
name: Annotated[str, OpenLIFUFieldData("Solution name", "Name of this solution")] = "Solution"
"""Name of this solution"""
protocol_id: Annotated[str | None, OpenLIFUFieldData("Protocol ID", "ID of the protocol that was used when generating this solution")] = None # this used to be called plan_id in the matlab code
"""ID of the protocol that was used when generating this solution"""
transducer_id: Annotated[str | None, OpenLIFUFieldData("Transducer ID", "ID of the transducer that was used when generating this solution")] = None
"""ID of the transducer that was used when generating this solution"""
date_created: Annotated[datetime, OpenLIFUFieldData("Creation date", "Solution creation time")] = field(default_factory=datetime.now)
"""Solution creation time"""
description: Annotated[str, OpenLIFUFieldData("Description", "Description of this solution")] = ""
"""Description of this solution"""
delays: Annotated[np.ndarray | None, OpenLIFUFieldData("Delays", "Vectors of time delays to steer the beam. Shape is (number of foci, number of transducer elements).")] = None
"""Vectors of time delays to steer the beam. Shape is (number of foci, number of transducer elements)."""
apodizations: Annotated[np.ndarray | None, OpenLIFUFieldData("Apodizations", "Vectors of apodizations to steer the beam. Shape is (number of foci, number of transducer elements).")] = None
"""Vectors of apodizations to steer the beam. Shape is (number of foci, number of transducer elements)."""
pulse: Annotated[Pulse, OpenLIFUFieldData("Pulse", "Pulse to send to the transducer when running sonication")] = field(default_factory=Pulse)
"""Pulse to send to the transducer when running sonication"""
voltage: Annotated[float, OpenLIFUFieldData("Voltage", "Voltage to use when running sonication. This is the voltage that will be set on the HV power supply.")] = 1.0
"""Voltage to use when running sonication. This is the voltage that will be set on the HV power supply."""
sequence: Annotated[Sequence, OpenLIFUFieldData("Pulse sequence", "Pulse sequence to use when running sonication")] = field(default_factory=Sequence)
"""Pulse sequence to use when running sonication"""
foci: Annotated[List[Point], OpenLIFUFieldData("Foci", "Points that are focused on in this Solution due to the focal pattern around the target. Each item in this list is a unique point from the focal pattern, and the pulse sequence is what determines how many times each point will be used.")] = field(default_factory=list)
"""Points that are focused on in this Solution due to the focal pattern around the target.
Each item in this list is a unique point from the focal pattern, and the pulse sequence is
what determines how many times each point will be used.
"""
# there was "target_id" in the matlab software, but here we do not have the concept of a target ID.
# I believe this was only needed in the matlab software because solutions were organized by target rather
# than having their own unique solution ID. We do have unique solution IDs so it's possible we don't need
# this target attribute at all here. Keeping it here for now just in case.
target: Annotated[Point | None, OpenLIFUFieldData("Target point", "The ultimate target of this sonication. This sonication solution is focused on one focal point in a pattern that is centered on this target.")] = None
"""The ultimate target of this sonication. This sonication solution is focused on one focal point
in a pattern that is centered on this target."""
# In the matlab code the simulation result was saved as a separate .mat file.
# Here we include it as an xarray dataset.
simulation_result: Annotated[xa.Dataset, OpenLIFUFieldData("Simulation result", "The xarray Dataset of simulation results")] = field(default_factory=xa.Dataset)
"""The xarray Dataset of simulation results"""
approved: Annotated[bool, OpenLIFUFieldData("Approved?", "Approval state of this solution as a sonication plan. `True` means the user has provided some kind of confirmation that the solution is safe and acceptable to be executed.")] = False
"""Approval state of this solution as a sonication plan. `True` means the user has provided some
kind of confirmation that the solution is safe and acceptable to be executed."""
def __post_init__(self):
if self.delays is not None:
self.delays = np.array(self.delays, ndmin=2)
if self.apodizations is not None:
self.apodizations = np.array(self.apodizations, ndmin=2)
if self.pulse.frequency <= 0:
raise ValueError("Pulse frequency must be positive")
if self.voltage <= 0:
raise ValueError("Voltage must be positive")
if self.sequence.pulse_interval <= 0:
raise ValueError("Pulse interval must be positive")
if self.sequence.pulse_count <= 0:
raise ValueError("Pulse count must be positive")
if self.sequence.pulse_train_interval < 0:
raise ValueError("Pulse train interval must be non-negative")
elif (self.sequence.pulse_train_interval > 0) and (self.sequence.pulse_train_interval < (self.sequence.pulse_interval * self.sequence.pulse_count)):
raise ValueError("Pulse train interval must be greater than or equal to the total pulse interval")
if self.sequence.pulse_train_count <= 0:
raise ValueError("Pulse train count must be positive")
if len(self.foci)>0 and self.delays is not None and self.delays.shape[0] != len(self.foci):
raise ValueError(f"Delays number of foci ({self.delays.shape[0]}) does not match number of foci ({len(self.foci)})")
if len(self.foci)>0 and self.apodizations is not None and self.apodizations.shape[0] != len(self.foci):
raise ValueError(f"Apodizations number of foci ({self.apodizations.shape[0]}) does not match number of foci ({len(self.foci)})")
if self.delays is not None and self.apodizations is not None:
if self.apodizations.shape[0] != self.delays.shape[0]:
raise ValueError(f"Apodizations number of foci ({self.apodizations.shape[0]}) does not match delays number of foci ({self.delays.shape[0]})")
if self.apodizations.shape[1] != self.delays.shape[1]:
raise ValueError(f"Apodizations number of elements {self.apodizations.shape[1]} does not match delays shape ({self.delays.shape[1]})")
[docs]
def num_foci(self) -> int:
"""Get the number of foci"""
return len(self.foci)
[docs]
def analyze(self,
transducer: Transducer,
options: SolutionAnalysisOptions = SolutionAnalysisOptions(),
param_constraints: Dict[str,ParameterConstraint] | None = None) -> SolutionAnalysis:
"""Analyzes the treatment solution.
Args:
transducer: A Transducer item.
options: A struct for solution analysis options.
param_constraints: A dictionary of parameter constraints to apply to the analysis.
The keys are the parameter names and the values are the ParameterConstraint objects.
Returns: A struct containing the results of the analysis.
"""
if param_constraints is None:
param_constraints = {}
solution_analysis = SolutionAnalysis()
if transducer.id != self.transducer_id:
# self.logger.error(f"Provided transducer id {transducer.id} does not match Solution transducer id: {self.transducer_id}")
raise ValueError(f"Provided transducer id {transducer.id} does not match Solution transducer id: {self.transducer_id}")
dt = 1 / (self.pulse.frequency * 20)
t = self.pulse.calc_time(dt)
input_signal_V = self.pulse.calc_pulse(t) * self.voltage
pnp_MPa_all = rescale_data_arr(rescale_coords(self.simulation_result['p_min'], options.distance_units),"MPa")
ipa_Wcm2_all = rescale_data_arr(rescale_coords(self.simulation_result['intensity'], options.distance_units), "W/cm^2")
if options.sidelobe_radius is np.nan:
options.sidelobe_radius = options.mainlobe_radius
standoff_Z = options.standoff_density * 1500
c_tic = 40e-3 # W cm-1
A_cm = transducer.get_area("cm")
d_eq_cm = np.sqrt(4*A_cm / np.pi)
ele_sizes_cm2 = np.array([elem.get_area("cm") for elem in transducer.elements])
# xyz = np.stack(np.meshgrid(*coords, indexing="xy"), axis=-1) #TODO: if fus.Axis is defined, coords.ndgrid(dim="ax")
# z_mask = xyz[..., -1] >= options.sidelobe_zmin #TODO: probably wrong here, should be z{1}>=options.sidelobe_zmin;
solution_analysis.duty_cycle_pulse_train_pct = self.get_pulsetrain_dutycycle()*100
solution_analysis.duty_cycle_sequence_pct = self.get_sequence_dutycycle()*100
if self.sequence.pulse_train_interval == 0:
solution_analysis.sequence_duration_s = float(self.sequence.pulse_interval * self.sequence.pulse_count * self.sequence.pulse_train_count)
else:
solution_analysis.sequence_duration_s = float(self.sequence.pulse_train_interval * self.sequence.pulse_train_count)
ita_mWcm2 = rescale_coords(self.get_ita(units="mW/cm^2"), options.distance_units)
power_W = np.zeros(self.num_foci())
TIC = np.zeros(self.num_foci())
for focus_index in range(self.num_foci()):
pnp_MPa = pnp_MPa_all.isel(focal_point_index=focus_index)
ipa_Wcm2 = ipa_Wcm2_all.isel(focal_point_index=focus_index)
focus = self.foci[focus_index].get_position(units=options.distance_units)
focus_mm = self.foci[focus_index].get_position(units="mm")
solution_analysis.target_position_lat_mm += [focus_mm[0]]
solution_analysis.target_position_ele_mm += [focus_mm[1]]
solution_analysis.target_position_ax_mm += [focus_mm[2]] # TODO: this should be a list, not a single value
apodization = self.apodizations[focus_index]
origin = transducer.get_effective_origin(apodizations=apodization, units=options.distance_units)
output_signal_Pa = []
output_signal_Pa = np.zeros((transducer.numelements(), len(input_signal_V)))
for i in range(transducer.numelements()):
apod_signal_V = input_signal_V * self.apodizations[focus_index, i]
output_signal_Pa[i] = transducer.elements[i].calc_output(apod_signal_V, dt)
p0_Pa = np.max(output_signal_Pa, axis=1)
mainlobe_mask = get_mask(
pnp_MPa,
focus = focus,
origin = origin,
distance = options.mainlobe_radius,
operator = '<',
aspect_ratio = options.mainlobe_aspect_ratio
)
sidelobe_mask = get_mask(
pnp_MPa,
focus = focus,
origin = origin,
distance = options.sidelobe_radius,
operator = '>',
aspect_ratio=options.mainlobe_aspect_ratio
)
z_mask = pnp_MPa.coords['ax'] > options.sidelobe_zmin
sidelobe_mask = sidelobe_mask.where(z_mask, False)
pnp_mainlobe = pnp_MPa.where(mainlobe_mask)
pk = float(pnp_mainlobe.max())
mainlobe_focus = find_centroid(pnp_mainlobe, pk*10**(-3/20), "mm")
solution_analysis.focal_centroid_lat_mm += [mainlobe_focus[0]]
solution_analysis.focal_centroid_ele_mm += [mainlobe_focus[1]]
solution_analysis.focal_centroid_ax_mm += [mainlobe_focus[2]]
solution_analysis.mainlobe_pnp_MPa += [pk]
for dim, scale in zip(pnp_MPa.dims, options.mainlobe_aspect_ratio):
for threshdB in [3, 6]:
attr_name = f'beamwidth_{dim}_{threshdB}dB_mm'
bw0 = getattr(solution_analysis, attr_name)
cutoff = pk*10**(-threshdB / 20)
bw = get_beamwidth(
pnp_MPa,
focus=focus,
dim=dim,
cutoff=cutoff,
origin=origin,
min_offset=-scale*options.beamwidth_radius,
max_offset=scale*options.beamwidth_radius)
bw = getunitconversion(options.distance_units, "mm") * bw
bw = [*bw0, bw]
setattr(solution_analysis, attr_name, bw)
solution_analysis.mainlobe_isppa_Wcm2 += [float(ipa_Wcm2.where(mainlobe_mask).max())]
solution_analysis.mainlobe_ispta_mWcm2 += [float(ita_mWcm2.where(mainlobe_mask).max())]
solution_analysis.sidelobe_pnp_MPa += [float(pnp_MPa.where(sidelobe_mask).max())]
solution_analysis.sidelobe_isppa_Wcm2 += [float(ipa_Wcm2.where(sidelobe_mask).max())]
solution_analysis.global_pnp_MPa += [float(pnp_MPa.where(z_mask).max())]
solution_analysis.global_isppa_Wcm2 += [float(ipa_Wcm2.where(z_mask).max())]
i0_Wcm2 = (p0_Pa**2 / (2*standoff_Z)) * 1e-4
i0ta_Wcm2 = i0_Wcm2 * solution_analysis.duty_cycle_sequence_pct/100
power_W[focus_index] = np.mean(np.sum(i0ta_Wcm2 * ele_sizes_cm2 * self.apodizations[focus_index, :]))
TIC[focus_index] = power_W[focus_index] / (d_eq_cm * c_tic)
solution_analysis.p0_MPa += [1e-6*np.max(p0_Pa)]
solution_analysis.global_ispta_mWcm2 = float((ita_mWcm2*z_mask).max())
solution_analysis.MI = (np.max(solution_analysis.mainlobe_pnp_MPa)/np.sqrt(self.pulse.frequency*1e-6))
solution_analysis.TIC = np.mean(TIC)
solution_analysis.voltage_V = self.voltage
solution_analysis.power_W = np.mean(power_W)
solution_analysis.param_constraints = param_constraints
return solution_analysis
[docs]
def compute_scaling_factors(
self,
focal_pattern: FocalPattern,
analysis: SolutionAnalysis
) -> Tuple[np.ndarray, float, float]:
"""
Compute the scaling factors used to re-scale the apodizations, simulation results and pulse amplitude.
Args:
focal_pattern: FocalPattern
analysis: SolutionAnalysis
Returns:
apod_factors: A np.ndarray apodization factors
v0: A float representing the original pulse amplitude
v1: A float representing the new pulse amplitude
"""
scaling_factors = np.zeros(self.num_foci())
for i in range(self.num_foci()):
focal_pattern_pressure_in_MPa = focal_pattern.target_pressure * getunitconversion(focal_pattern.units, "MPa")
scaling_factors[i] = focal_pattern_pressure_in_MPa / analysis.mainlobe_pnp_MPa[i]
max_scaling = np.max(scaling_factors)
v0 = self.voltage
v1 = v0 * max_scaling
apod_factors = scaling_factors / max_scaling
return apod_factors, v0, v1
[docs]
def scale(
self,
transducer: Transducer,
focal_pattern: FocalPattern,
analysis_options: SolutionAnalysisOptions = SolutionAnalysisOptions()
) -> None:
"""
Scale the solution in-place to match the target pressure.
Args:
transducer: xdc.Transducer
focal_pattern: FocalPattern
analysis_options: plan.solution.SolutionAnalysisOptions
Returns:
analysis_scaled: the resulting plan.solution.SolutionAnalysis from scaled solution
"""
analysis = self.analyze(transducer, options=analysis_options)
apod_factors, v0, v1 = self.compute_scaling_factors(focal_pattern, analysis)
for i in range(self.num_foci()):
scaling = v1/v0*apod_factors[i]
self.simulation_result['p_min'][i].data *= scaling
self.simulation_result['p_max'][i].data *= scaling
self.simulation_result['intensity'][i].data *= scaling**2
self.apodizations[i] = self.apodizations[i]*apod_factors[i]
self.voltage = v1
[docs]
def get_pulsetrain_dutycycle(self) -> float:
"""
Compute the pulse train dutycycle given a sequence.
Returns:
A float.
"""
pulsetrain_dutycycle = min(1., self.pulse.duration / self.sequence.pulse_interval)
return pulsetrain_dutycycle
[docs]
def get_sequence_dutycycle(self) -> float:
"""
Compute the overall sequence dutycycle given a sequence.
Returns:
A float.
"""
if self.sequence.pulse_train_interval == 0:
between_pulsetrain_duty_cycle = 1
else:
between_pulsetrain_duty_cycle = (self.sequence.pulse_count * self.sequence.pulse_interval) / self.sequence.pulse_train_interval
sequence_duty_cycle = self.get_pulsetrain_dutycycle() * between_pulsetrain_duty_cycle
return sequence_duty_cycle
[docs]
def get_ita(self, units: str = "mW/cm^2") -> xa.DataArray:
"""
Calculate the intensity-time-area product for a treatment solution.
Args:
output: A struct for simulation results from the treatment.
units: str
Target units. Default "mW/cm^2".
Returns:
A Solution instance with the calculated intensity value.
"""
intensity_scaled = rescale_data_arr(self.simulation_result['intensity'], units)
pulsetrain_dutycycle = self.get_pulsetrain_dutycycle()
treatment_dutycycle = self.get_sequence_dutycycle()
pulse_seq = (np.arange(self.sequence.pulse_count) - 1) % self.num_foci() + 1
counts = np.zeros((1, 1, 1, self.num_foci()))
for i in range(self.num_foci()):
counts[0, 0, 0, i] = np.sum(pulse_seq == (i+1))
intensity = intensity_scaled.copy(deep=True)
isppa_avg = np.sum(np.expand_dims(intensity.data, axis=-1) * counts, axis=-1) / np.sum(counts)
intensity.data = isppa_avg * pulsetrain_dutycycle * treatment_dutycycle
return intensity
[docs]
def to_dict(self, include_simulation_data: bool = False) -> dict:
"""Serialize a Solution to a dictionary
Args:
include_simulation_data: if enabled then large simulation data arrays are included in the dict,
otherwise they are excluded.
Returns: A dictionary representing the complete Solution object.
"""
solution_dict = asdict(self)
if not include_simulation_data:
solution_dict.pop('simulation_result')
return solution_dict
[docs]
def to_json(self, include_simulation_data: bool, compact: bool) -> str:
"""Serialize a Solution to a json string
Args:
include_array_data: if enabled then large simulation data arrays are serialized somehow into the json,
so that they can be recovered via `from_json` alone. otherwise they are excluded.
compact: if enabled then the string is compact (not pretty). Disable for pretty.
Returns: A json string representing the complete Solution object.
"""
solution_dict = asdict(self)
if include_simulation_data:
# Serialize xarray dataset into a string
solution_dict['simulation_result'] = base64.b64encode(self.simulation_result.to_netcdf(engine='scipy')).decode('utf-8')
else:
solution_dict.pop('simulation_result')
if compact:
return json.dumps(solution_dict, separators=(',', ':'), cls=PYFUSEncoder)
else:
return json.dumps(solution_dict, indent=4, cls=PYFUSEncoder)
[docs]
@staticmethod
def from_json(json_string : str, simulation_result: xa.Dataset | None=None) -> Solution:
"""Load a Solution from a json string.
Args:
json_string: the json string defining the Solution
simulation_result: the simulation result arrays to use. If the json string has this then it will
be read from the json string and it should not be provided in this argument.
Returns: The new Solution object.
"""
solution_dict = json.loads(json_string)
solution_dict["date_created"] = datetime.fromisoformat(solution_dict["date_created"])
if solution_dict["delays"] is not None:
solution_dict["delays"] = np.array(solution_dict["delays"])
if solution_dict["apodizations"] is not None:
solution_dict["apodizations"] = np.array(solution_dict["apodizations"], ndmin=2)
solution_dict["apodizations"] = np.array(solution_dict["apodizations"], ndmin=2)
solution_dict["pulse"] = Pulse.from_dict(solution_dict["pulse"])
solution_dict["sequence"] = Sequence.from_dict(solution_dict["sequence"])
solution_dict["foci"] = [
Point.from_dict(focus_dict)
for focus_dict in solution_dict["foci"]
]
if solution_dict["target"] is not None:
solution_dict["target"] = Point.from_dict(solution_dict["target"])
if simulation_result is not None:
if "simulation_result" in solution_dict:
raise ValueError(
"A simulation result was provided while the json string already contains `simulation_result`. "
"Unclear which to use!"
)
solution_dict["simulation_result"] = simulation_result
elif "simulation_result" in solution_dict:
# Deserialize xarray dataset from string
solution_dict["simulation_result"] = xa.open_dataset(
base64.b64decode(
solution_dict["simulation_result"].encode('utf-8')
),
engine='scipy',
)
return Solution(**solution_dict)
[docs]
def to_files(self, json_filepath:Path, nc_filepath:Path | None=None) -> None:
"""Save the solution to json and netCDF files.
json_filepath: where to save the json file with all data except the simulation results dataset
nc_filepath: where to save the netCDF file containing the simulation results.
If None then it will be saved in the same directory as the json file, with the same name but with
the extension *.nc
"""
if nc_filepath is None:
nc_filepath = _construct_nc_filepath_from_json_filepath(json_filepath)
json_filepath.parent.mkdir(parents=True, exist_ok=True)
nc_filepath.parent.mkdir(parents=True, exist_ok=True)
with json_filepath.open("w") as json_file:
json_file.write(
self.to_json(include_simulation_data=False, compact=False)
)
self.simulation_result.to_netcdf(nc_filepath, engine='h5netcdf')
[docs]
@staticmethod
def from_files(json_filepath:Path, nc_filepath:Path | None=None):
"""Read solution from json and netCDF files.
json_filepath: solution json file location, containing all data except the simulation results dataset
nc_filepath: netCDF file location, containing the simulation results.
If None then it will be assumed to be saved in the same directory as the json file, with the same name but with
the extension *.nc. This is the default saving behavior of Solution.to_files.
"""
if nc_filepath is None:
nc_filepath = _construct_nc_filepath_from_json_filepath(json_filepath)
simulation_result = xa.open_dataset(nc_filepath, engine='h5netcdf').load()
simulation_result.close() # this is needed to release the lock on the file so that it can be written to again
return Solution.from_json(
json_string = json_filepath.read_text(),
simulation_result = simulation_result,
)