# Path: crayimage/simulation/geant.py
# class SimulationStream(object):
#   @classmethod
#   def from_run(cls, run, spectrum=(1170.0, 1330.0), *args, **kwargs):
#     runs = [
#       run[run.meta_info['energy'] == energy_line]
#       for energy_line in spectrum
#       ]
# 
#     track_len = np.max([max_track_len(r) for r in runs])
# 
#     tracks_xs, tracks_ys, tracks_vals = map(np.vstack,
#       zip(*[ read_sparse_run(r, track_len=track_len) for r in runs ])
#     )
# 
#     return cls(tracks_xs, tracks_xs, tracks_vals, *args, **kwargs)
# 
#   def __init__(self, tracks_xs, tracks_ys, tracks_vals,
#                sizes = (8, 32, 16, 8, 4, 2, 1), img_shape=(128, 128),
#                cache = 1):
#     if cache is None or cache <= 0:
#       def stream():
#         while True:
#           yield simulation(tracks_xs, tracks_ys, tracks_vals, batch_sizes=sizes, img_shape=img_shape)
# 
#       self.stream = stream()
#     else:
#       queue = Queue(maxsize=cache)
# 
#       worker = threading.Thread(
#         target=simulation_worker,
#         kwargs=dict(
#           output_queue=queue,
#           xs=tracks_xs, ys=tracks_ys, vals=tracks_vals,
#           batch_sizes=sizes,
#           img_shape=img_shape
#         )
#       )
# 
#       worker.daemon = True
#       worker.start()
# 
#       self.stream = queue_stream(queue)
# 
#   def __iter__(self):
#     return self
# 
#   def next(self):
#     return self.stream.next()


import numpy as np
  import time
  import matplotlib.pyplot as plt
from crayimage.simulation.geant import SimulationStream
from builtins import range
    delta_main * 1000.0 / n_iters
  ))

  stream = SimulationStream(xs, ys, vals, sizes=sizes, cache=-1)

  start = time.time()
  for i in range(n_iters):
    np.random.poisson(size=(main_job_n, 128, 128))
    stream.next()

  end = time.time()
  delta = end - start

  print('Both, %.3f millisec / batch, %.3f microsec per example' % (
    delta * 1000.0 / n_iters, delta * 10.0 ** 6 / n_iters / sum(sizes)
  ))

  start = time.time()
  for i in range(n_iters):
    arr = stream.next()

  end = time.time()
  delta = end - start

  print('Stream alone, %.3f millisec / batch, %.3f microsec per example' % (
    delta * 1000.0 / n_iters, delta * 10.0 ** 6 / n_iters / sum(sizes)
  ))


  for j, i in enumerate([0] + np.cumsum(sizes)[:-1].tolist()):

# Path: crayimage/simulation/geant.py
# class SimulationStream(object):
#   @classmethod
#   def from_run(cls, run, spectrum=(1170.0, 1330.0), *args, **kwargs):
#     runs = [
#       run[run.meta_info['energy'] == energy_line]
#       for energy_line in spectrum
#       ]
# 
#     track_len = np.max([max_track_len(r) for r in runs])
# 
#     tracks_xs, tracks_ys, tracks_vals = map(np.vstack,
#       zip(*[ read_sparse_run(r, track_len=track_len) for r in runs ])
#     )
# 
#     return cls(tracks_xs, tracks_xs, tracks_vals, *args, **kwargs)
# 
#   def __init__(self, tracks_xs, tracks_ys, tracks_vals,
#                sizes = (8, 32, 16, 8, 4, 2, 1), img_shape=(128, 128),
#                cache = 1):
#     if cache is None or cache <= 0:
#       def stream():
#         while True:
#           yield simulation(tracks_xs, tracks_ys, tracks_vals, batch_sizes=sizes, img_shape=img_shape)
# 
#       self.stream = stream()
#     else:
#       queue = Queue(maxsize=cache)
# 
#       worker = threading.Thread(
#         target=simulation_worker,
#         kwargs=dict(
#           output_queue=queue,
#           xs=tracks_xs, ys=tracks_ys, vals=tracks_vals,
#           batch_sizes=sizes,
#           img_shape=img_shape
#         )
#       )
# 
#       worker.daemon = True
#       worker.start()
# 
#       self.stream = queue_stream(queue)
# 
#   def __iter__(self):
#     return self
# 
#   def next(self):
#     return self.stream.next()


# Path: examples/geant.py
import numpy as np
  import time
  import matplotlib.pyplot as plt
from crayimage.simulation.geant import SimulationStream
from builtins import range

    delta_main * 1000.0 / n_iters
  ))

  stream = SimulationStream(xs, ys, vals, sizes=sizes, cache=-1)

  start = time.time()
  for i in range(n_iters):
    np.random.poisson(size=(main_job_n, 128, 128))
    stream.next()

  end = time.time()
  delta = end - start

  print('Both, %.3f millisec / batch, %.3f microsec per example' % (
    delta * 1000.0 / n_iters, delta * 10.0 ** 6 / n_iters / sum(sizes)
  ))

  start = time.time()
  for i in range(n_iters):
    arr = stream.next()

  end = time.time()
  delta = end - start

  print('Stream alone, %.3f millisec / batch, %.3f microsec per example' % (
    delta * 1000.0 / n_iters, delta * 10.0 ** 6 / n_iters / sum(sizes)
  ))


  for j, i in enumerate([0] + np.cumsum(sizes)[:-1].tolist()):

    plt.figure()