# Path: crayimage/runutils/stream.py
# def queue_stream(queue):
#   while True:
#     yield queue.get(block=True)


import numpy as np
import threading
  from queue import Queue
  from Queue import Queue
from crayimage.runutils import queue_stream
from .generation import simulation_samples, center_tracks_mean, center_tracks_mass, center_tracks_box, center_tracks_source
from .io import IndexedSparseImages, root_to_sparse, border_crossing, filter_border_crossing
from .ordering import order_tracks, order_sparse_images
      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()


# Path: crayimage/runutils/stream.py
# def queue_stream(queue):
#   while True:
#     yield queue.get(block=True)


# Path: crayimage/simulation/geant.py
import numpy as np
import threading
  from queue import Queue
  from Queue import Queue
from crayimage.runutils import queue_stream
from .generation import simulation_samples, center_tracks_mean, center_tracks_mass, center_tracks_box, center_tracks_source
from .io import IndexedSparseImages, root_to_sparse, border_crossing, filter_border_crossing
from .ordering import order_tracks, order_sparse_images

      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)