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eval_atl_ant_tests_loop_fade.py
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'''
Created on May 30, 2016
@author: Peter Hillyard
'''
# This script is used to evaluate the performance of the three antenna types
# used during our experimentation in Atlanta.
import sys
import numpy as np
sys.path.insert(0, '../my_span_package') # Adds higher directory to python modules path.
import rti_class as rti
# node_loc_fname = 'data/node_loc_data/neals_old_house/node_loc_2012_11_01.txt'
# rss_fname = 'data/rss_data/neals_old_house/rss_2012_11_01_all.txt'
# node_loc_fname = 'data/node_loc_data/airbnb_atl/node_loc_2016_05_17.txt'
# rss_fname = 'data/rss_data/airbnb_atl/rss_2016_05_17_omni_peter_2_all.txt'
# pivot_coords_fname = 'data/true_loc_data/airbnb_atl/pivot_coords_2016_05_17.txt'
# path_ind_fname = 'data/true_loc_data/airbnb_atl/pivot_idx_2016_05_17.txt'
loc = 'gregs_house'
node_loc_date = '2016_05_19'
rss_date_and_name = '2016_05_19_lcom_peter_walk_2_all'
pivot_coord_date = '2016_05_19'
pivot_idx_date = '2016_05_19'
# loc = 'airbnb_atl'
# node_loc_date = '2016_05_17'
# rss_date_and_name = '2016_05_17_omni_peter_walk_2_all'
# pivot_coord_date = '2016_05_17'
# pivot_idx_date = '2016_05_17'
# loc = 'pizza_house'
# node_loc_date = '2016_05_16'
# rss_date_and_name = '2016_05_18_epatch_qi_2_all'
# pivot_coord_date ='2016_05_16'
# pivot_idx_date = '2016_05_16'
node_loc_fname = 'data/node_loc_data/' + loc + '/node_loc_' + node_loc_date + '.txt'
rss_fname = 'data/rss_data/' + loc + '/rss_' + rss_date_and_name + '.txt'
pivot_coords_fname = 'data/true_loc_data/' + loc + '/pivot_coords_' + pivot_coord_date + '.txt'
path_ind_fname = 'data/true_loc_data/' + loc + '/pivot_idx_' + pivot_idx_date + '.txt'
results_fname = 'data/results/' + loc + '/performance_' + rss_date_and_name + '.txt'
print rss_date_and_name
num_ch = 4
rti_T = 0.0
skip_time=16.0
cal_time=16.0
path_start_time = skip_time+cal_time
speed = 1.0 / 2.0
minRMSE = 100000.
# Loop through parameters
for delta_p in [1.0]:
for sigmax2 in [0.1,0.05,0.025,0.01]:
for delta in [7.5,10.0,12.0,15.0,20.0]:
for epl in [0.25,0.5,0.75,1.0]:
# Initialize rti stuff
# rti_obj = rti.ab_rti(node_loc_fname,num_ch,delta_p,sigmax2,delta,epl,rti_T,skip_time,cal_time)
# rti_obj.set_true_coord_params(path_start_time,speed,pivot_coords_fname,path_ind_fname)
# Initialize moving average rti stuff
# ltb_len = 20
# stb_len = 5
#
# rti_obj = rti.ma_rti(node_loc_fname,num_ch,delta_p,sigmax2,delta,epl,rti_T,skip_time,cal_time)
# rti_obj.set_mabd_params(ltb_len,stb_len)
# rti_obj.set_true_coord_params(path_start_time,speed,pivot_coords_fname,path_ind_fname)
#
# Initialize moving average, top M links rti stuff
ltb_len = 20
stb_len = 5
for M in [2,3]:
fade_type = 'lse'
rti_obj = rti.fade_level_rti(node_loc_fname,num_ch,delta_p,sigmax2,delta,epl,rti_T,skip_time,cal_time)
rti_obj.set_extra_params(ltb_len,stb_len,M,fade_type)
rti_obj.set_true_coord_params(path_start_time,speed,pivot_coords_fname,path_ind_fname)
#
# Initialize moving average, top M links rti stuff
# ktype = 'gaus' # type of kernel
# R = np.array(range(-110, 0)) # Range of RSS values
# sigma_G2 = 30.0
# beta_p = 0.99 # short term
# beta_q = 0.70 # long term
#
# rti_obj = rti.krti(node_loc_fname,num_ch,delta_p,sigmax2,delta,epl,rti_T,skip_time,cal_time)
# rti_obj.set_extra_params(R,ktype,sigma_G2,beta_p,beta_q)
# rti_obj.set_true_coord_params(path_start_time,speed,pivot_coords_fname,path_ind_fname)
est_coord_vec = []
true_coord_vec = []
# loop through all lines in the RSS .txt file
with open(rss_fname,'r') as f:
for line in f:
rti_obj.observe(line)
#rti_obj.plot_current_image(pause_time=0.05)
true_coord_vec.append(rti_obj.get_true_coord())
est_coord_vec.append(rti_obj.get_est_coord())
true_coord_vec = np.array(true_coord_vec)
est_coord_vec = np.array(est_coord_vec)
rmse = rti_obj.compute_rmse(true_coord_vec,est_coord_vec,save_type=None,out_fname=results_fname)
if rmse < minRMSE:
str_out = 'M=' + str(M)
print str_out
out_str = 'dp=' + str(delta_p) + ', sx2=' + str(sigmax2)
out_str += ', delta=' + str(delta) + ', lambda=' + str(epl)
out_str += ', rmse=' + str(rmse)
print out_str
minRMSE = rmse
#sre,yvals = rti_obj.compute_cdf(true_coord_vec,est_coord_vec)
# plt.plot(sre,yvals)
# plt.grid()
# plt.ylabel('Percentile')
# plt.xlabel('Error (ft)')
# plt.show()