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4_sectorize.py
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4_sectorize.py
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#!/usr/bin/python3
import argparse
import os
import re
import png
import math
import csv
import numpy as np
from scipy.signal import hilbert
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Hilbertizing sensor data')
parser.add_argument(
'files',
metavar='FILES',
nargs='+',
help='List of CSV files with solo sensor data'
)
parser.add_argument(
'-o',
'--output',
required=True,
metavar='OUTPUT',
help='Output file name'
)
def interpolate(v1, v2, factor):
return v1*(1-factor) + v2*factor
def interpolate3(v0, v1, v2, v3, factor):
#d = v0
#c = -12.33*v0 + 15.0*v2 - 3.0*v2 + 0.33*v2
#b = -1.5*c - 3.5*v0 + 4.0*v1 - 0.5*v2
#a = v1 - v0 - b - c
d = v1
b = 0.5*v0 - v1 + 0.5*v2
a = (-v0 + 3.0*v2 - 3.0*v2 + v3)/6.0
c = -0.5*v0 + 0.5*v1 - a
return a*factor*factor*factor + b*factor*factor + c*factor + d
args = parser.parse_args()
for one_file in args.files:
with open(one_file) as ff:
data = [[abs(float(x)) for x in l.split()] for l in ff.readlines()]
h = len(data)*1.0
w = len(data[0])*1.0
angle_rad = 90*math.pi/180
r = h/angle_rad/2.0
R = r + w
q = r*math.cos(angle_rad/2.0)
nh = math.ceil(R - q)
nw = math.ceil(2*R*math.sin(angle_rad/2))
new_data = [[0 for x in range(nw+1)] for y in range(nh+1)]
cx = nw/2.0
cy = q
for i in range(nh):
for j in range(nw):
x = j + 0.5
y = i + 0.5
dx = x - cx
dy = cy + y
d = (dx**2 + dy**2)**0.5 - r
d1 = math.floor(d)
d2 = math.ceil(d)
a = (0.5 + math.atan2(dx, dy)/angle_rad)*h
#rayw = 4
#rayC = math.floor(a)
#rayB = rayC - math.floor(rayw/2)
#rayT = rayC + math.floor(rayw/2) + 1
#if all([0 <= d1 < w, 0 <= d2 < w, 0 < rayB, rayT < h]):
# rmax = 0
# ri = 6
# for ray in range(rayw):
# rv = interpolate(data[rayB + ray][d1], data[rayB + ray][d2], math.modf(d)[0])
# if (rv > rmax):
# rmax = rv
# ri = ray
# if (ri == math.floor(rayw/2)):
# new_data[i][j] = rmax*rmax
# else:
# new_data[i][j] = 0
# rCv = interpolate(data[rayC][d1], data[rayC][d2], math.modf(d)[0])
# new_data[i][j] += rCv*rCv
ray1 = math.floor(a)
ray2 = math.ceil(a)
ray0 = ray1 - 1
ray3 = ray2 + 1
if all([0 <= d1 < w, 0 <= d2 < w, 0 <= ray1 < h, 0 <= ray2 < h, 0 <= ray0 < h, 0 <= ray3 < h]):
r1v = interpolate(data[ray1][d1], data[ray1][d2], math.modf(d)[0])
r2v = interpolate(data[ray2][d1], data[ray2][d2], math.modf(d)[0])
r0v = interpolate(data[ray0][d1], data[ray0][d2], math.modf(d)[0])
r3v = interpolate(data[ray3][d1], data[ray3][d2], math.modf(d)[0])
# v = abs(interpolate3(r0v, r1v, r2v, r3v, math.modf(a)[0]))
v = interpolate(r1v, r2v, math.modf(a)[0])
#x = v*v
#z = math.log2(1 + x)
z = v*v*v#math.sqrt(v)
#v = interpolate(r1v, r2v, math.modf(a)[0])
new_data[i][j] = z
else:
new_data[i][j] = -1
max_data = max(map(max, new_data))
for i in range(nh):
for j in range(nw):
if (new_data[i][j] == -1):
new_data[i][j] = max_data
min_data = min(map(min, new_data))
data_color = [[int(255*(x-min_data)/(max_data-min_data)) for x in l] for l in new_data]
with open(one_file+"_"+args.output+"_sectorized.png", 'wb') as fff:
w = png.Writer(nw+1, nh+1, greyscale=True)
w.write(fff, data_color)