merge relayout branch

1 files changed, 0 insertions, 149 deletions

diff --git a/sim.py b/sim.py
deleted file mode 100755
index 2e8f2f9..0000000
--- a/sim.py
+++ /dev/null
@@ -1,149 +0,0 @@
-#!/usr/bin/env python
-import time
-import os
-import numpy as np
-
-from chroma import generator
-from chroma import gpu
-from chroma import event
-from chroma import itertoolset
-
-def pick_seed():
-    """Returns a seed for a random number generator selected using
-    a mixture of the current time and the current process ID."""
-    return int(time.time()) ^ (os.getpid() << 16)
-
-class Simulation(object):
-    def __init__(self, detector, seed=None, cuda_device=None,
-                 geant4_processes=4, nthreads_per_block=64, max_blocks=1024):
-        self.detector = detector
-
-        self.nthreads_per_block = nthreads_per_block
-        self.max_blocks = max_blocks
-
-        if seed is None:
-            self.seed = pick_seed()
-        else:
-            self.seed = seed
-
-        # We have three generators to seed: numpy.random, GEANT4, and CURAND.
-        # The latter two are done below.
-        np.random.seed(self.seed)
-
-        if geant4_processes > 0:
-            self.photon_generator = generator.photon.G4ParallelGenerator(geant4_processes, detector.detector_material, base_seed=self.seed)
-        else:
-            self.photon_generator = None
-
-        self.context = gpu.create_cuda_context(cuda_device)
-
-        if not hasattr(detector, 'mesh'):
-            # need to build geometry
-            detector.build()
-
-        self.gpu_geometry = gpu.GPUGeometry(detector)
-        self.gpu_daq = gpu.GPUDaq(self.gpu_geometry, max(self.detector.pmtids))
-        self.gpu_pdf = gpu.GPUPDF()
-
-        self.rng_states = gpu.get_rng_states(self.nthreads_per_block*self.max_blocks, seed=self.seed)
-
-        self.pdf_config = None
-
-    def simulate(self, iterable, keep_photons_beg=False,
-                 keep_photons_end=False, run_daq=True, max_steps=10):
-        try:
-            first_element, iterable = itertoolset.peek(iterable)
-        except TypeError:
-            first_element, iterable = iterable, [iterable]
-
-        if isinstance(first_element, event.Event):
-            iterable = self.photon_generator.generate_events(iterable)
-        elif isinstance(first_element, event.Photons):
-            iterable = (event.Event(photons_beg=x) for x in iterable)
-        elif isinstance(first_element, event.Vertex):
-            iterable = (event.Event(primary_vertex=vertex, vertices=[vertex]) for vertex in iterable)
-            iterable = self.photon_generator.generate_events(iterable)
-
-        for ev in iterable:
-            gpu_photons = gpu.GPUPhotons(ev.photons_beg)
-
-            gpu_photons.propagate(self.gpu_geometry, self.rng_states,
-                                  nthreads_per_block=self.nthreads_per_block,
-                                  max_blocks=self.max_blocks,
-                                  max_steps=max_steps)
-
-            ev.nphotons = len(ev.photons_beg.pos)
-
-            if not keep_photons_beg:
-                ev.photons_beg = None
-
-            if keep_photons_end:
-                ev.photons_end = gpu_photons.get()
-
-            if run_daq:
-                gpu_channels = self.gpu_daq.acquire(gpu_photons, self.rng_states, nthreads_per_block=self.nthreads_per_block, max_blocks=self.max_blocks)
-                ev.channels = gpu_channels.get()
-
-            yield ev
-
-    def create_pdf(self, iterable, tbins, trange, qbins, qrange, nreps=1):
-        """Returns tuple: 1D array of channel hit counts, 3D array of
-        (channel, time, charge) pdfs."""
-        first_element, iterable = itertoolset.peek(iterable)
-
-        if isinstance(first_element, event.Event):
-            iterable = self.photon_generator.generate_events(iterable)
-
-        pdf_config = (tbins, trange, qbins, qrange)
-        if pdf_config != self.pdf_config:
-            self.pdf_config = pdf_config
-            self.gpu_pdf.setup_pdf(max(self.detector.pmtids), tbins, trange,
-                                   qbins, qrange)
-        else:
-            self.gpu_pdf.clear_pdf()
-
-        if nreps > 1:
-            iterable = itertoolset.repeating_iterator(iterable, nreps)
-
-        for ev in iterable:
-            gpu_photons = gpu.GPUPhotons(ev.photons_beg)
-            gpu_photons.propagate(self.gpu_geometry, self.rng_states,
-                                  nthreads_per_block=self.nthreads_per_block,
-                                  max_blocks=self.max_blocks)
-            gpu_channels = self.gpu_daq.acquire(gpu_photons, self.rng_states, nthreads_per_block=self.nthreads_per_block, max_blocks=self.max_blocks)
-            self.gpu_pdf.add_hits_to_pdf(gpu_channels)
-        
-        return self.gpu_pdf.get_pdfs()
-
-    def eval_pdf(self, event_channels, iterable, min_twidth, trange, min_qwidth, qrange, min_bin_content=20, nreps=1, ndaq=1, time_only=True):
-        """Returns tuple: 1D array of channel hit counts, 1D array of PDF
-        probability densities."""
-        self.gpu_pdf.setup_pdf_eval(event_channels.hit,
-                                    event_channels.t,
-                                    event_channels.q,
-                                    min_twidth,
-                                    trange,
-                                    min_qwidth,
-                                    qrange,
-                                    min_bin_content=min_bin_content,
-                                    time_only=True)
-
-        first_element, iterable = itertoolset.peek(iterable)
-
-        if isinstance(first_element, event.Event):
-            iterable = self.photon_generator.generate_events(iterable)
-
-        for ev in iterable:
-            gpu_photons = gpu.GPUPhotons(ev.photons_beg, ncopies=nreps)
-            gpu_photons.propagate(self.gpu_geometry, self.rng_states,
-                                  nthreads_per_block=self.nthreads_per_block,
-                                  max_blocks=self.max_blocks)
-            for gpu_photon_slice in gpu_photons.iterate_copies():
-                for idaq in xrange(ndaq):
-                    gpu_channels = self.gpu_daq.acquire(gpu_photon_slice, self.rng_states, nthreads_per_block=self.nthreads_per_block, max_blocks=self.max_blocks)
-                    self.gpu_pdf.accumulate_pdf_eval(gpu_channels)
-        
-        return self.gpu_pdf.get_pdf_eval()
-
-    def __del__(self):
-        self.context.pop()