def main():
logging.basicConfig(format='%(relativeCreated)8d ms // %(message)s')
description = 'generate random points, save them in a numpy array'
parser = ArgumentParser(description=description,
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--output', metavar='FILE', default='/tmp/out.png')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('count', type=int)
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
if args.debug:
logging.getLogger().setLevel(logging.DEBUG)
logging.debug('python version %s' % str(sys.version))
config = setup_config(args.count)
matrix = hm.process_shapes(config)
matrix = matrix.finalized()
arr = matrix_to_numpy(config, matrix)
print('shape: ' + str(arr.shape))
print('max value: %f' % arr.max())
nonzero = np.count_nonzero(arr)
print('nonzero cells: %d / %d (%d%%)' % (nonzero, arr.size,
int(100.0 * nonzero / arr.size)))
def main():
logging.basicConfig(format='%(relativeCreated)8d ms // %(message)s')
description = 'generate random points, save them in a numpy array'
parser = ArgumentParser(description=description,
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--output', metavar='FILE', default='/tmp/out.png')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('count', type=int)
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
if args.debug:
logging.getLogger().setLevel(logging.DEBUG)
logging.debug('python version %s' % str(sys.version))
config = setup_config(args.count)
matrix = hm.process_shapes(config)
matrix = matrix.finalized()
arr = matrix_to_numpy(config, matrix)
print('shape: ' + str(arr.shape))
print('max value: %f' % arr.max())
nonzero = np.count_nonzero(arr)
print('nonzero cells: %d / %d (%d%%)' %
(nonzero, arr.size, int(100.0 * nonzero / arr.size)))
def generate_heatmap(self):
self.hm_config.shapes = self.map_iter()
if len([s for s in self.map_iter()]) == 0:
self.heatmap_image = PIL.Image.open(config["map_img"])
return
matrix = hm.process_shapes(self.hm_config)
matrix = matrix.finalized()
self.heatmap_image = hm.ImageMaker(self.hm_config).make_image(matrix)
def main():
print "Starting processing"
replay_file = '2.StormReplay'
replay = ReplayLib.Replay(replay_file)
deaths = replay.get_deaths()
print "Starting heatmap generation"
config = setup_config(deaths)
matrix = hm.process_shapes(config)
matrix = matrix.finalized()
image = hm.ImageMaker(config).make_image(matrix)
image.save('sample.png')
print "Complete"
def main():
logging.basicConfig(format='%(relativeCreated)8d ms // %(message)s')
description = 'plot random points'
parser = ArgumentParser(description=description,
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--output', metavar='FILE', default='/tmp/out.png')
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('count', type=int)
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
if args.debug:
logging.getLogger().setLevel(logging.DEBUG)
logging.debug('python version %s' % str(sys.version))
config = setup_config(args.count)
matrix = hm.process_shapes(config)
matrix = matrix.finalized()
image = hm.ImageMaker(config).make_image(matrix)
image.save(args.output)
def main():
logging.basicConfig(format="%(relativeCreated)8d ms // %(message)s")
description = "generate random points, save them in a numpy array"
parser = ArgumentParser(description=description, formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("--output", metavar="FILE", default="/tmp/out.png")
parser.add_argument("-v", "--verbose", action="store_true")
parser.add_argument("--debug", action="store_true")
parser.add_argument("count", type=int)
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
if args.debug:
logging.getLogger().setLevel(logging.DEBUG)
logging.debug("python version %s" % str(sys.version))
config = setup_config(args.count)
matrix = hm.process_shapes(config)
matrix = matrix.finalized()
arr = matrix_to_numpy(config, matrix)
print("shape: " + str(arr.shape))
print("max value: %f" % arr.max())
nonzero = np.count_nonzero(arr)
print("nonzero cells: %d / %d (%d%%)" % (nonzero, arr.size, int(100.0 * nonzero / arr.size)))
