def signal_data(savefile, alpha=0, delta=0, model_type = 'theoretical'):
'''
GENERATES RAIN ATTENUATION SIGNAL for a given noise percent
*alpha* and quantization step *delta*
Inputs:
savefile - string. File path to save the RSL data.
alpha - scalar. Noise percentage.
delta - scalar. Quantization step.
model_type - string. Attenuation model type. 'theoretical' is set by default.
Avaiable models: 'theoretical', 'empirical'
Output:
None
'''
#******************* DATA *******************
link = pn.read_csv(str('data/mobilenetwork.csv'))
frequency = np.array([18, 23, 38])*1e9
# Network area
border = ((0.,40.),(0.,40.))
((x0, x1), (y0, y1)) = border
# Monitor area
box = border
# Rainfall types
rain_type = [ 'lightrain' ,'shower']
#***************** START SIMULATION *****************
# Set simulation framework for the links in "border" area
experiment = simulation.MonitorArea( link, frequency, border)
experiment.set_link()
for i in rain_type:
f_att_data = simulation.create_folder(savefile + '/RSL_025km/')
maps, names = raindata.read_rain(rain_file + '/'+i)
att_measurement = []
name = []
for eachmap, k in zip(maps, names):
eachmap = eachmap[4*y0:4*y1, 4*x0:4*x1]
k = k.split('_')
k.sort()
event_time = k[1] + '_' +k[0]
total_rsl = experiment.generate_rsl( eachmap, alpha, delta,
model = model_type)[0]
att_measurement.append(total_rsl)
name.append(event_time)
data = np.array(att_measurement).T
df05 = pn.DataFrame(data, columns = name)
df05.to_csv( f_att_data +i+ '.csv')
return None
