# -*- coding: utf-8 -*-
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# Copyright (C) Laboratory of Imaging technologies,
# Faculty of Electrical Engineering,
# University of Ljubljana.
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# This file is part of PyXOpto.
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import os
import os.path
import copy
import numpy as np
from xopto.mcml import mc
from xopto.mcml.mcutil.fiber import MultimodeFiber
from xopto.cl import clinfo
from xopto.util.animation.common import create_path_animation
DEFAULT_CONFIG = {
'rmax': 15.0e-3,
'batch_packets': 100000,
'sv_num_packets': 256,
'sample':{
'mua': 10.0e2,
'mus': 125.0e2,
'n': 1.33,
'g': 0.9,
'd': 1.0e-3
},
'fiber':{
'ncore': 1.452,
'dcore': 200e-6,
'dcladding': 220e-6,
'na': 0.22
},
'fiber_sds': 0.6e-3,
'trace_maxlen': 512,
'radial_detector': {'start': 0.0, 'stop': 1.0e-3, 'n': 100},
'sv':{
'xaxis': {'start': -0.75e-3, 'stop': 0.75e-3, 'n': 300},
'yaxis': {'start': -0.75e-3, 'stop': 0.75e-3, 'n': 300},
'zaxis': {'start': 0.0e-3, 'stop': 1.0e-3, 'n': 200}
}
}
[docs]def default_configuration() -> dict:
'''
Returns a copy of the default simulation configuration that can be
optionally customized.
'''
return copy.deepcopy(DEFAULT_CONFIG)
[docs]def process_config(config: dict) -> dict:
'''
Process the simulation configuration. This will create objects for
the Monte Carlo simulator and return the amendded configuration.
Parameters
----------
config: dict
Customized default configuration.
Returns
-------
config: dict
Configuration with the created Monte Carlo simulator objects.
'''
fp_eps = np.finfo(np.float32).eps
sample = config['sample']
fiber_cfg = config['fiber']
fiber_cosmin = (1.0 - (fiber_cfg['na']/fiber_cfg['ncore'])**2)**0.5
fiber = MultimodeFiber(**fiber_cfg)
config['layers'] = mc.mclayer.Layers([
mc.mclayer.Layer(d=np.inf, mua=0.0, mus=0.0,
n=fiber_cfg['ncore'], pf=mc.mcpf.Hg(0.0)),
mc.mclayer.Layer(d=sample['d'], mua=sample['mua'], mus=sample['mus'],
n=sample['n'], pf=mc.mcpf.Hg(sample['g'])),
mc.mclayer.Layer(d=np.inf, mua=0.0, mus=0.0, n=fiber_cfg['ncore'],
pf=mc.mcpf.Hg(0.0))
])
config['source'] = mc.mcsource.UniformFiber(
fiber,
direction=(0.0, 0.0, 1.0),
position=(-config['fiber_sds']*0.5, 0.0, 0.0)
)
config['detectors'] = mc.mcdetector.Detectors(
top = mc.mcdetector.Radial(
mc.mcdetector.Axis(**config['radial_detector'])
),
bottom = mc.mcdetector.Radial(
mc.mcdetector.Axis(**config['radial_detector'])
)
)
config['trace'] = mc.mctrace.Trace(
maxlen=config['trace_maxlen'],
options=mc.mctrace.Trace.TRACE_ALL,
filter=mc.mctrace.Filter(
z=(-fp_eps + sample['d'], fp_eps + sample['d']),
pz=(fiber_cosmin, 1.0),
r=(0.0, fiber_cfg['dcore']*0.5, (config['fiber_sds']*0.5, 0.0))
)
)
config['sv'] = mc.mcsv.SamplingVolume(
xaxis=mc.mcsv.Axis(**config['sv']['xaxis']),
yaxis=mc.mcsv.Axis(**config['sv']['yaxis']),
zaxis=mc.mcsv.Axis(**config['sv']['zaxis'])
)
return config
[docs]def sv_fiber_paths_transmittance(filename: str, config: dict = None,
cl_device: clinfo.cl.Device = None,
overwrite: bool = False,
verbose: bool = False):
'''
Generates data for visualization of the packet paths in transmittance
configuration. Only the paths of photon packets that reach the detector
are shown (packets that contribute to the sampling volume).
Parameters
----------
filename: str
Output file for the simulation results or None.
config: dict
Monte Carlo configuration as returned by
:py:func:`default_configuration` and optionally customized.
cl_device:
OpenCL device on which to run the simulations or None.
overwrite: bool
If False, existing output files will not be overwritten.
verbose:
If nonzero, progress reports are periodically printed to the console.
Returns
-------
results: dict
Simulation results with and simulation configuration data.
'''
if filename and os.path.isfile(filename) and not overwrite:
raise FileExistsError('The output file "{}" already exists!')
if cl_device is None:
cl_device = clinfo.gpu()
if config is None:
config = default_configuration()
config = process_config(config)
sv = config['sv']
mc_obj = mc.Mc(layers=config['layers'], source=config['source'],
detectors=config['detectors'], trace=config['trace'],
cl_devices=cl_device)
mc_obj.rmax = config['rmax']
ref_filter = config['trace'].filter
sds = config['fiber_sds']
if verbose:
print('Processing transmittance paths for SDS {:.1f} um'.format(sds*1e6))
mc_obj.trace.filter = mc.mctrace.Filter(
z=ref_filter.z,
pz=ref_filter.pz,
r=(ref_filter.r[0][0], ref_filter.r[0][1], (sds*0.5, 0))
)
mc_obj.source.position = (-sds*0.5, 0, 0)
num_packets = 0
trace_res = out = None
while num_packets < config['sv_num_packets']:
out = mc_obj.run(config['batch_packets'], out)
trace_res = out[0]
num_packets = len(trace_res)
if verbose:
print(' Collected {}/{} packets'.format(
num_packets, config['sv_num_packets']), end='\r')
result = {
'trace': mc_obj.trace.todict(),
'layers': mc_obj.layers.todict(),
'source': mc_obj.source.todict(),
'sv': sv.todict(),
'rmax': mc_obj.rmax,
'batch_packets': config['batch_packets'],
'num_packets': num_packets,
'fiber_sds': config['fiber_sds'],
'trace_data': trace_res.data,
'trace_n': trace_res.n,
}
if filename:
np.savez_compressed(filename, **result)
return result
if __name__ == '__main__':
from xopto.util.animation.sv.fiber import common
common.main(sv_fiber_paths_transmittance, common.create_path_animation_xz,
default_configuration())