221 lines
6.4 KiB
Python
221 lines
6.4 KiB
Python
#!/usr/bin/python
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import os
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import sys
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import time
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import pickle
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import argparse
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import numpy as np
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from multiprocessing import Process
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from multiprocessing import Manager
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from libs.util import convert_vector_to_mat
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from libs.consts import RANDOM_SEED_TEST
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from libs.consts import RANDOM_SEED_TRAIN
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from libs.models import log_gamma_floorplan
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from libs.spacemap import SpaceMap
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from libs.spacemap import SpaceBlock
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from libs.plotting import plotSpace
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def getRandomTXLocs(
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num: int,
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width: float,
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length: float,
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offset_w: float = 0.0,
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offset_l: float = 0.0
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):
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'''
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'''
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xys = np.random.rand(num, 2)
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xys[:, 0] = xys[:, 0] * width + offset_w
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xys[:, 1] = xys[:, 1] * length + offset_l
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return xys
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def getLocs(loc_x_range, loc_y_range, step_size: float = 0.3):
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loc_xs = np.arange(loc_x_range[0], loc_x_range[1] + step_size, step_size)
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loc_ys = np.arange(loc_y_range[0], loc_y_range[1] + step_size, step_size)
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return np.stack(np.meshgrid(loc_xs, loc_ys), -1).reshape(-1, 2)
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def getPowers(power_range, step_size: float = 2):
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return np.arange(power_range[0], power_range[1] + step_size, step_size)
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def log_gamma_floorplan_multi(floormap, rx_loc, tx_loc, power, gaussian_noise, results, kk):
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rss, __ = log_gamma_floorplan(
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floormap,
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rx_loc,
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tx_loc,
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power,
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gaussian_noise=gaussian_noise
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)
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results[kk] = rss
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def generateData(floormap, tx_locs, args):
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'''
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'''
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if args.train:
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tag = "input_synthetic_train"
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elif args.train_real:
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tag = "input_real_emu_train"
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elif args.test:
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tag = "testing_real_emu"
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tag = "{0}".format(tag)
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if args.withnoise:
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tag = "{}_noise_10dBvar".format(tag)
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# create folder
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folderp = "{}/{}".format(args.outfolder, tag)
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if not os.path.isdir(folderp):
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try:
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os.makedirs(folderp)
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except BaseException as e:
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print("err: {}".format(e))
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sys.exit(-1)
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# generate
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rx_locs = getLocs([0, 6.3], [0, 6.3], step_size=0.1)
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powers = getPowers([-40, 30])
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for i in range(tx_locs.shape[0]):
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fp_base = "{0}/img_{1:.2f}_{2:.2f}_2.0".format(folderp, tx_locs[i, 0] - 3.2, tx_locs[i, 1] - 3.2)
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for power in powers:
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rss_vec = []
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starttime = int(time.time())
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tx_loc = SpaceBlock(0, 0)
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floormap.traceRays(power, tx_loc)
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for j in range(0, rx_locs.shape[0], args.procnum):
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procs = []
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results = Manager().dict()
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for kk in range(args.procnum):
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rx_loc = SpaceBlock(rx_locs[j, 0], rx_locs[j, 1])
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proc = Process(
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target=log_gamma_floorplan_multi,
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args=(floormap, rx_loc, tx_loc, power, args.withnoise, results, kk)
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)
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proc.start()
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procs.append(proc)
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for proc in procs:
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proc.join()
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rss_vec.extend([results[kk] for kk in range(args.procnum)])
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# rx_loc = SpaceBlock(rx_locs[j, 0], rx_locs[j, 1])
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# rss, multipaths = log_gamma_floorplan(
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# floormap,
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# rx_loc,
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# tx_loc,
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# power,
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# gaussian_noise=args.withnoise
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# )
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# if args.visualize:
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# paths = []
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# for path in multipaths:
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# paths.extend(list(path))
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# print(rss, rx_loc, tx_loc)
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# plotSpace(floormap, space_rays=paths, cminmax=(-50, 0.0))
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# rss_vec.append(rss)
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duration = (int(time.time()) - starttime)
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print("duration: {}s ({:.2f}%) - len {}".format(duration, j/rx_locs.shape[0]*100.0, len(rss_vec)))
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# print("results: {}".format(rss_vec))
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rss_map = convert_vector_to_mat(rx_locs, np.array(rss_vec), (64, 64))
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with open("{}_{}.pickle".format(fp_base, int(power)), 'wb') as f:
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pickle.dump(rss_map, f, pickle.HIGHEST_PROTOCOL)
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sys.exit()
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def getFloormap(args):
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'''
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'''
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floormap = SpaceMap(width=6.4, length=6.4, block_size=0.1)
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penetrations, reflections, orientations = pickle.load(open(args.floormap, "rb"))
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floormap.setLosses(penetrations, reflections)
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floormap.setOrientations(orientations)
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return floormap
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def main(args):
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tx_locs = None
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if args.train:
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np.random.seed(RANDOM_SEED_TRAIN)
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tx_locs = getLocs([0.2, 6.2], [0.2, 6.2], step_size=0.3)
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elif args.train_real:
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np.random.seed(RANDOM_SEED_TRAIN)
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tx_locs = getRandomTXLocs(400, 6.4, 6.4, offset_w=0.0, offset_l=0.0)
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elif args.test:
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np.random.seed(RANDOM_SEED_TEST)
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tx_locs = getRandomTXLocs(400, 6.4, 6.4, offset_w=0.0, offset_l=0.0)
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else:
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print("nothing specified")
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return
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generateData(getFloormap(args), tx_locs, args)
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if __name__ == '__main__':
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p = argparse.ArgumentParser(description='Data Generator with Log Gamma Distance Model')
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p.add_argument(
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'outfolder',
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help='output generated data to folder'
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)
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p.add_argument(
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'--training',
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dest='train',
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action='store_true',
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default=False,
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help='generate training data (synthetic part)'
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)
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p.add_argument(
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'--training-real',
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dest='train_real',
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action='store_true',
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default=False,
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help='emulating training data (real data part)'
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)
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p.add_argument(
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'--testing',
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dest='test',
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action='store_true',
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default=False,
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help='emulating testing data (real data part)'
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)
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p.add_argument(
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'--visualize', '-v',
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dest='visualize',
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action='store_true',
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default=False,
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help='visualize'
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)
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p.add_argument(
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'--floormap', '-f',
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dest='floormap',
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default=None,
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help='floormap data'
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)
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p.add_argument(
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'--with-noise',
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dest='withnoise',
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action='store_true',
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default=False,
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help='add 10dB noise to generated data'
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)
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p.add_argument(
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'--proc', '-p',
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dest='procnum',
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type=int,
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default=1,
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help='number of parallel process'
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)
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try:
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args = p.parse_args()
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except BaseException as e:
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print(e)
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sys.exit()
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main(args)
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