157 lines
4.4 KiB
Python
157 lines
4.4 KiB
Python
#!/usr/bin/python
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import os
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import sys
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import pickle
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import argparse
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from libs.util import touch
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from libs.util import convert_mat_to_vector
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from libs.util import convert_vector_to_mat
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from libs.fitting import modelfit_log_gamma
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from libs.plotting import plotRSS
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def loadData(filepath):
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try:
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return pickle.load(open(filepath, "rb"))
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except BaseException as e:
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print("err: {}".format(e))
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pass
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return None
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def fittingSingle(filepath, args):
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'''
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'''
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# load data from file
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data = loadData(filepath)
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if data is None:
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print("err: failed to load file {}".format(filepath))
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return
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# convert data matrix to vectors
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rx_locs, rx_rsses = convert_mat_to_vector(data, block_size=0.1)
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# start fitting
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min_pmse = float('inf')
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best_tx_loc = None
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best_tx_pwr = None
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best_env_gamma = None
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best_rsses = None
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for i in range(100):
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result = modelfit_log_gamma(
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rx_locs,
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rx_rsses,
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bounds_pwr=(-60, 0),
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bounds_gamma=(2, 6),
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bounds_loc_x=(0, 6.2),
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bounds_loc_y=(0, 6.2),
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monte_carlo_sampling=False,
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monte_carlo_sampling_rate=0.8
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)
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# unpack
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pmse, est_tx_loc, est_tx_pwr, est_env_gamma, est_rsses = result
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if pmse < min_pmse:
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print("|----- found better:", pmse, est_tx_loc, est_tx_pwr, est_env_gamma)
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min_pmse = pmse
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best_tx_loc = est_tx_loc
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best_tx_pwr = est_tx_pwr
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best_env_gamma = est_env_gamma
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best_rsses = est_rsses
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print("|- final:")
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print("|--- tx loc: {:.3f}, {:.3f}".format(best_tx_loc[0], best_tx_loc[1]))
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print("|--- tx pwr: {:.2f}".format(best_tx_pwr))
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print("|--- env gamma: {:.2f}".format(best_env_gamma))
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print("|--- fitting mse: {:.6f}".format(min_pmse))
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data_best_est = convert_vector_to_mat(rx_locs, best_rsses, data.shape, block_size=0.1)
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if args.outputfile:
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filename = os.path.basename(args.outputfile)
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with open(args.outputfile, 'a') as f:
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f.write(
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"{},".format(filename) +
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"{:.3f},{:.3f},".format(best_tx_loc[0], best_tx_loc[1]) +
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"{:.2f},{:.2f}".format(best_tx_pwr, best_env_gamma) +
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"{:.6f}".format(min_pmse) +
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"\n"
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)
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if args.visualize:
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plotRSS(
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data,
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data_best_est,
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est_tx_loc=best_tx_loc / 0.1
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)
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def main(args):
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# finding files
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filepaths = []
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if args.filepath:
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filepaths.append(args.filepath)
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if args.folderpath:
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files = os.listdir(args.folderpath)
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filepaths.extend(["{}/{}".format(args.folderpath, file) for file in files if '.pickle' in file])
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# prepare
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if args.outputfile:
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with open(args.outputfile, 'w') as f:
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f.write("filename,tx_x,tx_y,tx_pwr,env_gamma,fit_mse\n")
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# loop through and fit
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for i in range(len(filepaths)):
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print("- model fitting on file {}".format(filepaths[i]))
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fittingSingle(filepaths[i], args)
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if __name__ == '__main__':
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p = argparse.ArgumentParser(description='Traditional Model Fitting')
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p.add_argument(
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'--filepath', '-f',
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dest='filepath',
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default=None,
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help='input filepath for a pickle'
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)
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p.add_argument(
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'--folderpath', '-fd',
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dest='folderpath',
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default=None,
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help='input folderpath for many pickles'
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)
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p.add_argument(
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'--visualize', '-v',
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action='store_true',
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default=False,
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help='enable visualize'
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)
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p.add_argument(
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'--output', '-o',
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dest='outputfile',
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default=None,
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help='output results to filepath'
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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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if args.filepath is None and args.folderpath is None:
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print("at least specify file `-f` or folder `-fd`")
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sys.exit()
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elif args.filepath is None and not os.path.isdir(args.folderpath):
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print("folder {} not exit".format(args.folderpath))
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sys.exit()
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elif args.folderpath is None and not os.path.isfile(args.filepath):
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print("file {} not exit".format(args.filepath))
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sys.exit()
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if args.outputfile and not touch(args.outputfile):
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print("cannot create file {}".format(args.outputfile))
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sys.exit()
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main(args)
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