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)))